diff --git a/docs/labguide/mentoring/mentoring.html b/docs/labguide/mentoring/mentoring.html
index f5b38b5..67ff39e 100644
--- a/docs/labguide/mentoring/mentoring.html
+++ b/docs/labguide/mentoring/mentoring.html
@@ -494,7 +494,6 @@ <h2 class="anchored" data-anchor-id="postdoctoral-training-in-the-lab">Postdocto
 <h3 class="anchored" data-anchor-id="how-can-i-get-a-postdoc-position-in-your-lab">How can I get a postdoc position in your lab?</h3>
 <ul>
 <li>We do not generally advertise postdoctoral positions, so if you are interested in a position in the lab, contact Dr.&nbsp;Poldrack directly. Be sure to send your CV, and explain in brief your research interests and why you think you would be a good fit for the lab.</li>
-<li>We are particularly interested in hiring postdocs who bring diversity to the lab, either through their personal identity, research interests, or experience.</li>
 </ul>
 </section>
 </section>
diff --git a/docs/labguide/research/code_management.html b/docs/labguide/research/code_management.html
index 3be209e..2987fa3 100644
--- a/docs/labguide/research/code_management.html
+++ b/docs/labguide/research/code_management.html
@@ -449,7 +449,17 @@ <h2 class="anchored" data-anchor-id="code-management">Code management</h2>
 <section id="code-review" class="level2">
 <h2 class="anchored" data-anchor-id="code-review">Code review</h2>
 <ul>
-<li>All coding projects should be reviewed prior to paper submission.</li>
+<li>All coding projects should undergo a review before paper submission.</li>
+<li>When writing code, keep in mind that it will be shared with others. This mindset improves readability and encourages careful development practices.</li>
+<li>At the very least, your code should:
+<ul>
+<li>Be version-controlled and have a straightforward installation process for dependencies.</li>
+<li>Include files that allow for testing.</li>
+<li>Be well-documented (e.g., comments in code) and clearly written.</li>
+<li>Contain a README.md file that explains the structure of your codebase.</li>
+</ul></li>
+<li>Code review is an ongoing process rather than a final step, so there is no expectation for your code to be “perfect” at any stage.</li>
+<li>Here are some useful resources on code review: (Awesome Code Review)[https://github.com/joho/awesome-code-review], (Blog post)[https://www.djmannion.net/code_review/].</li>
 </ul>
 </section>
 <section id="code-sharing" class="level2">
diff --git a/docs/people.html b/docs/people.html
index 282ced3..ee7e5a1 100644
--- a/docs/people.html
+++ b/docs/people.html
@@ -540,11 +540,11 @@ <h2 class="anchored">Graduate Students</h2>
 <h2 class="anchored">Research Staff</h2>
 <ul>
   
-  <li>Kriti Achyutuni</li>
+  <li>Kriti Achyutuni <a style="color: inherit;" href="https://orcid.org/0009-0006-2308-9468"><img src="https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png" alt="ORCID"></a></li>
   
-  <li>Logan Bennett</li>
+  <li>Logan Bennett <a style="color: inherit;" href="https://orcid.org/0000-0001-5866-7682"><img src="https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png" alt="ORCID"></a></li>
   
-  <li>Margot Mitchell</li>
+  <li>Margot Mitchell <a style="color: inherit;" href="https://orcid.org/0009-0001-3048-2931"><img src="https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png" alt="ORCID"></a></li>
   
   <li>Joe Wexler</li>
   
diff --git a/docs/projects.html b/docs/projects.html
index 7d4dafb..de48fb8 100644
--- a/docs/projects.html
+++ b/docs/projects.html
@@ -406,6 +406,7 @@ <h2 id="toc-title">On this page</h2>
    
   <ul>
   <li><a href="#cognitive-neuroscience-projects" id="toc-cognitive-neuroscience-projects" class="nav-link active" data-scroll-target="#cognitive-neuroscience-projects">Cognitive neuroscience projects</a></li>
+  <li><a href="#womens-brain-health-projects" id="toc-womens-brain-health-projects" class="nav-link" data-scroll-target="#womens-brain-health-projects">Women’s Brain Health Projects</a></li>
   <li><a href="#informaticsanalysis-projects" id="toc-informaticsanalysis-projects" class="nav-link" data-scroll-target="#informaticsanalysis-projects">Informatics/analysis projects</a></li>
   </ul>
 <div class="toc-actions"><ul><li><a href="https://github.com/poldracklab/poldracklab.github.io/edit/main/projects.md" class="toc-action"><i class="bi bi-github"></i>Edit this page</a></li><li><a href="https://github.com/poldracklab/poldracklab.github.io/issues/new" class="toc-action"><i class="bi empty"></i>Report an issue</a></li></ul></div></nav>
@@ -475,6 +476,24 @@ <h4 class="anchored" data-anchor-id="challenging-existing-models-and-developing-
 </ul>
 </section>
 </section>
+<section id="womens-brain-health-projects" class="level2">
+<h2 class="anchored" data-anchor-id="womens-brain-health-projects">Women’s Brain Health Projects</h2>
+<p>Our lab is part of the <a href="https://wbhi.ucsb.edu/">Ann S. Bowers Women’s Brain Health Initiative</a>.</p>
+<section id="wbhi-data-coordinating-center" class="level4">
+<h4 class="anchored" data-anchor-id="wbhi-data-coordinating-center">WBHI Data Coordinating Center</h4>
+<ul>
+<li>A project to coordinate data from several University of California Imaging Centers in service of better understanding women’s brain health.</li>
+<li>Current funding: Noyce Foundation</li>
+</ul>
+</section>
+<section id="longitudinal-neurocognitive-and-hormonal-phenotyping" class="level4">
+<h4 class="anchored" data-anchor-id="longitudinal-neurocognitive-and-hormonal-phenotyping">Longitudinal neurocognitive and hormonal phenotyping</h4>
+<ul>
+<li>A project collecting imaging, hormone and reproductive health information to understand the neural mechanisms that drive changes in mood and cognition across the menstrual cycle.</li>
+<li>Noyce Foundation</li>
+</ul>
+</section>
+</section>
 <section id="informaticsanalysis-projects" class="level2">
 <h2 class="anchored" data-anchor-id="informaticsanalysis-projects">Informatics/analysis projects</h2>
 <section id="openneuro" class="level4">
@@ -526,7 +545,7 @@ <h4 class="anchored" data-anchor-id="experiment-factory"><a href="https://www.ex
 <h4 class="anchored" data-anchor-id="neurosynth"><a href="http://neurosynth.org">Neurosynth</a></h4>
 <ul>
 <li>A platform for coordinate-based meta-analysis on neuroimaging data</li>
-<li>Previous funding: <a href="https://reporter.nih.gov/project-details/10543105">5R01MH096906</a></li>
+<li>Current funding: <a href="https://reporter.nih.gov/project-details/10982264">5R01MH096906</a></li>
 <li>Representative publications:
 <ul>
 <li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146590/">Large-scale automated synthesis of human functional neuroimaging data</a></li>
@@ -544,13 +563,6 @@ <h4 class="anchored" data-anchor-id="fmriprep"><a href="http://fmriprep.org">fMR
 </ul></li>
 </ul>
 </section>
-<section id="womens-brain-health-initiative-data-coordinating-center" class="level4">
-<h4 class="anchored" data-anchor-id="womens-brain-health-initiative-data-coordinating-center"><a href="https://wbhi.ucsb.edu/">Women’s Brain Health Initiative</a> Data Coordinating Center</h4>
-<ul>
-<li>A project to coordinate data from several University of California Imaging Centers in service of better understanding women’s brain health.</li>
-<li>Current funding: Noyce Foundation</li>
-</ul>
-</section>
 <section id="cognitive-atlas" class="level4">
 <h4 class="anchored" data-anchor-id="cognitive-atlas"><a href="http://cognitiveatlas.org">Cognitive Atlas</a></h4>
 <ul>
diff --git a/docs/publications.html b/docs/publications.html
index cd6d2c4..8bcb133 100644
--- a/docs/publications.html
+++ b/docs/publications.html
@@ -425,7 +425,7 @@ <h1 class="title">Publications</h1>
 </header>
 
 
-<div id="196e26c1" class="cell" data-message="false" data-execution_count="1">
+<div id="8089dc9c" class="cell" data-message="false" data-execution_count="1">
 <section id="section" class="level3">
 <h3 class="anchored" data-anchor-id="section">2025</h3>
 <p>Quah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M (2025). A data-driven latent variable approach to validating the research domain criteria framework. <em>Nat Commun, 16</em>, 830. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11743195">OA</a> <a href="https://doi.org/10.1038/s41467-025-55831-z">DOI</a></p>
@@ -898,7 +898,7 @@ <h3 class="anchored" data-anchor-id="section-29">1996</h3>
 <h3 class="anchored" data-anchor-id="section-30">1994</h3>
 <p>Poldrack RA, Cohen NJ (1994). On the representational/computational properties of multiple memory systems. <em>Behavioral and Brain Sciences, 17</em>, 416-417. <a href="https://doi.org/10.1017/s0140525x00035275">DOI</a></p>
 <p>&nbsp; &nbsp;</p>
-<p><em>Updated: 2025-02-04 - Publication list created using <a href="https://github.com/poldrack/academicdb">autocv</a></em></p>
+<p><em>Updated: 2025-02-26 - Publication list created using <a href="https://github.com/poldrack/academicdb">autocv</a></em></p>
 </section>
 </div>
 
diff --git a/docs/search.json b/docs/search.json
index 10f4cd8..3608fd4 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -37,7 +37,7 @@
     "href": "labguide/mentoring/mentoring.html",
     "title": "Training and Mentorship",
     "section": "",
-    "text": "This section of the guide is a first-person perspective from Dr. Poldrack.\n\n\nWe are always looking for undergraduate students to get involved on a variety of projects! If you are a Stanford affiliate and in the area, feel free to fill out this form and we will reach out to you.\n\n\n\n\n\n\nI am affiliated with several graduate programs at Stanford:\n\nPsychology\nNeuroscience\nBioengineering\nBiomedical Data Science (BMI)\n\n\n\n\n\nIn choosing a program, there are several important differences:\n\nResearch: While most of these programs are fairly flexible, there are generally some expectations regarding the kind of research you will do, depending on the specific program. For example, if you are joining the BMI program then your work is expected to have at least some focus on novel data analysis or informatics methods, whereas if you are joining Psychology your work is expected to make some contact with psychological function. Having said that, most of what we do in our lab could be done by a student in any of these programs.\nCoursework: Perhaps the biggest difference between programs is the kind of courses you are required to take. Each program has a set of core requirements. In psychology, you will take a number of core courses in different areas of psychology (cognitive, neuroscience, social, affective, developmental). In the neuroscience program you will take a set of core modules spanning different areas of neuroscience (including one on cognitive neuroscience that Justin Gardner and I teach), whereas in BMI you take core courses around informatics-related topics. In each program you will also take elective courses (often outside the department) that establish complementary core knowledge that is important for your particular research; for example, you can take courses in our world-class statistics department regardless of which program you enroll in. One way to think about this is: What do you want to learn about that is outside of your specific content area? Take a look at the core courses in each program and see which ones interest you the most.\nFirst-year experience: In Psychology, students generally jump straight into a specific lab (or a collaboration between labs), and spend their first year doing a first-year project that they present to their area meeting at the end of the year. In Neuroscience and BMI, students do rotations in multiple labs in their first year, and are expected to pick a lab by the end of their first year.\nAdmissions: All of these programs are highly selective, but each differs in the nature of its admissions process. At one end of the spectrum is the Psychology admissions process, where initial decisions for who to interview are made by the combined faculty within each area of the department. At the other end is the Neuroscience program, where initial decisions are made by an admissions committee. As a generalization, I would say that the Psychology process is better for candidates whose interests and experience fit very closely with a specific PI or set of PIs, whereas the committee process caters towards candidates who may not have settled on a specific topic or PI.\nCareer positioning: I think that the specific department that one graduates from matters a lot less than people think it does. For example, I have been in psychology departments that have hired faculty with PhDs in physics, applied mathematics, and computer science. I think that the work that you do and the skills that you acquire ultimately matter a lot more than the name of the program that is listed on your diploma. Having said that, you ultimately do need to position yourself so that a particular department will feel that you “fit” with them; this is often a challenge for people working on interdisciplinary topics, where sometimes it can be difficult to find a department who feels that the person fits well.\n\n\n\n\nThere are always more qualified applicants than there are spots in our graduate programs, and there is no way to guarantee admission to any particular program. On the flipside, there are also no absolute requirements: we look at the whole picture, and other factors can sometimes outweigh a weaker academic record. There are a few factors that are particularly important for admission to my lab:\n\nResearch experience: It is very rare for someone to be accepted into any of the programs I am affiliated with at Stanford without significant research experience. Sometimes this can be obtained as an undergraduate, but more often successful applicants to our program have spent at least a year working as a research assistant in an active research laboratory. There are a couple of important reasons for this. First, we want you to understand what you are getting into; many people have rosy ideas of what it’s like to be a scientist, which can fall away pretty quickly in light of the actual experience of doing science. Spending some time in a lab helps you make sure that this is how you want to spend your life. In addition, it provides you with someone who can write a recommendation letter that speaks very directly to your potential as a researcher. Letters are a very important part of the admissions process, and the most effective letters are those that go into specific detail about your abilities, aptitude, and motivation.\nTechnical skills: The research that we do in our lab is highly technical, requiring knowledge of computing systems, programming, and math/statistics. I would say that decent programming ability is a pretty firm prerequisite for entering my lab; once you enter the lab I want you to be able to jump directly into doing science, and this just can’t happen if you have to spend a year teaching yourself how to program from scratch. More generally, we expect you to be able to pick up new technical topics easily; I don’t expect students to necessarily show up knowing how a reinforcement learning model works, but I expect them to be able to go and figure it out on their own by reading the relevant papers and then implement it on their own. The best way to demonstrate programming ability is to show a specific project that you have worked on. This could be an open source project that you have contributed to, or a project that you did on the side for fun (for example, mine your own social media feed, or program a cognitive task and measure how your own behavior changes from day to day). If you don’t currently know how to program, see my post on learning to program from scratch, and get going!\n\nRisk taking and resilience: If we are doing interesting science then things are going to fail, and we have to learn from those failures and move on.  I want to know that you are someone who is willing to go out on a limb to try something risky, and can handle the inevitable failures gracefully.  Rather than seeing a statement of purpose that only lists all of your successes, I find it very useful to also know about risks you have taken (be they physical, social, or emotional), challenges you have faced, failures you have experienced, and most importantly what you learned from all of these experiences.\n\n\n\n\n\nDifferent advisers have different philosophies, and it’s important to be sure that you pick an advisor whose style is right for you. I would say that the most important characteristic of my style is that I foster independent thinking in my trainees. Publishing papers is important, but not as important as developing one’s ability to conceive novel and interesting questions and ask them in a rigorous way. This means that beyond the first year project, I don’t generally hand my students problems to work on; rather, I expect them to come up with their own questions, and then we work together to devise the right experiments to test them. Another important thing to know is that I try to motivate by example, rather than by command. I rarely breathe down my trainees necks about getting their work done, because I work on the assumption that they will be self-motivated. On the other hand, I’m fairly hands-on in the sense that I still love to get deep in the weeds of experimental design and data analysis; for example, I will regularly code alongside lab members. I would also add that I am highly amenable to joint mentorship with other faculty.\n\n\n\nI have instituted a policy that I will no longer meet one-on-one with potential graduate students prior to the application process to discuss potential admission into my lab, as this has the potential to exacerbate existing disparities in graduate school admissions. I am willing to meet with individuals (particularly those from underrepresented groups) to discuss the graduate admissions process and other academic issues more generally, as time permits.\n\n\n\n\n\n\n\nWe do not generally advertise postdoctoral positions, so if you are interested in a position in the lab, contact Dr. Poldrack directly. Be sure to send your CV, and explain in brief your research interests and why you think you would be a good fit for the lab.\nWe are particularly interested in hiring postdocs who bring diversity to the lab, either through their personal identity, research interests, or experience.\n\n\n\n\n\n\n\nGraduate students in the lab are expected to keep suitable documentation for all research activities (i.e. a github log, a google doc, etc).\n\n\n\nDr. Poldrack provides all lab members with access to a scheduling system with which they can schedule meetings during any open times on his calendar. - Upon request, lab members can schedule a regular standing meeting.\n\n\n\n\nI view mentorship as a life-long relationship, and you can view working with me as having a “Lifetime Guarantee” in the sense that I will always be there to help you, even long after you have left the lab – from letters of recommendation to career mentorship to personal advice.",
+    "text": "This section of the guide is a first-person perspective from Dr. Poldrack.\n\n\nWe are always looking for undergraduate students to get involved on a variety of projects! If you are a Stanford affiliate and in the area, feel free to fill out this form and we will reach out to you.\n\n\n\n\n\n\nI am affiliated with several graduate programs at Stanford:\n\nPsychology\nNeuroscience\nBioengineering\nBiomedical Data Science (BMI)\n\n\n\n\n\nIn choosing a program, there are several important differences:\n\nResearch: While most of these programs are fairly flexible, there are generally some expectations regarding the kind of research you will do, depending on the specific program. For example, if you are joining the BMI program then your work is expected to have at least some focus on novel data analysis or informatics methods, whereas if you are joining Psychology your work is expected to make some contact with psychological function. Having said that, most of what we do in our lab could be done by a student in any of these programs.\nCoursework: Perhaps the biggest difference between programs is the kind of courses you are required to take. Each program has a set of core requirements. In psychology, you will take a number of core courses in different areas of psychology (cognitive, neuroscience, social, affective, developmental). In the neuroscience program you will take a set of core modules spanning different areas of neuroscience (including one on cognitive neuroscience that Justin Gardner and I teach), whereas in BMI you take core courses around informatics-related topics. In each program you will also take elective courses (often outside the department) that establish complementary core knowledge that is important for your particular research; for example, you can take courses in our world-class statistics department regardless of which program you enroll in. One way to think about this is: What do you want to learn about that is outside of your specific content area? Take a look at the core courses in each program and see which ones interest you the most.\nFirst-year experience: In Psychology, students generally jump straight into a specific lab (or a collaboration between labs), and spend their first year doing a first-year project that they present to their area meeting at the end of the year. In Neuroscience and BMI, students do rotations in multiple labs in their first year, and are expected to pick a lab by the end of their first year.\nAdmissions: All of these programs are highly selective, but each differs in the nature of its admissions process. At one end of the spectrum is the Psychology admissions process, where initial decisions for who to interview are made by the combined faculty within each area of the department. At the other end is the Neuroscience program, where initial decisions are made by an admissions committee. As a generalization, I would say that the Psychology process is better for candidates whose interests and experience fit very closely with a specific PI or set of PIs, whereas the committee process caters towards candidates who may not have settled on a specific topic or PI.\nCareer positioning: I think that the specific department that one graduates from matters a lot less than people think it does. For example, I have been in psychology departments that have hired faculty with PhDs in physics, applied mathematics, and computer science. I think that the work that you do and the skills that you acquire ultimately matter a lot more than the name of the program that is listed on your diploma. Having said that, you ultimately do need to position yourself so that a particular department will feel that you “fit” with them; this is often a challenge for people working on interdisciplinary topics, where sometimes it can be difficult to find a department who feels that the person fits well.\n\n\n\n\nThere are always more qualified applicants than there are spots in our graduate programs, and there is no way to guarantee admission to any particular program. On the flipside, there are also no absolute requirements: we look at the whole picture, and other factors can sometimes outweigh a weaker academic record. There are a few factors that are particularly important for admission to my lab:\n\nResearch experience: It is very rare for someone to be accepted into any of the programs I am affiliated with at Stanford without significant research experience. Sometimes this can be obtained as an undergraduate, but more often successful applicants to our program have spent at least a year working as a research assistant in an active research laboratory. There are a couple of important reasons for this. First, we want you to understand what you are getting into; many people have rosy ideas of what it’s like to be a scientist, which can fall away pretty quickly in light of the actual experience of doing science. Spending some time in a lab helps you make sure that this is how you want to spend your life. In addition, it provides you with someone who can write a recommendation letter that speaks very directly to your potential as a researcher. Letters are a very important part of the admissions process, and the most effective letters are those that go into specific detail about your abilities, aptitude, and motivation.\nTechnical skills: The research that we do in our lab is highly technical, requiring knowledge of computing systems, programming, and math/statistics. I would say that decent programming ability is a pretty firm prerequisite for entering my lab; once you enter the lab I want you to be able to jump directly into doing science, and this just can’t happen if you have to spend a year teaching yourself how to program from scratch. More generally, we expect you to be able to pick up new technical topics easily; I don’t expect students to necessarily show up knowing how a reinforcement learning model works, but I expect them to be able to go and figure it out on their own by reading the relevant papers and then implement it on their own. The best way to demonstrate programming ability is to show a specific project that you have worked on. This could be an open source project that you have contributed to, or a project that you did on the side for fun (for example, mine your own social media feed, or program a cognitive task and measure how your own behavior changes from day to day). If you don’t currently know how to program, see my post on learning to program from scratch, and get going!\n\nRisk taking and resilience: If we are doing interesting science then things are going to fail, and we have to learn from those failures and move on.  I want to know that you are someone who is willing to go out on a limb to try something risky, and can handle the inevitable failures gracefully.  Rather than seeing a statement of purpose that only lists all of your successes, I find it very useful to also know about risks you have taken (be they physical, social, or emotional), challenges you have faced, failures you have experienced, and most importantly what you learned from all of these experiences.\n\n\n\n\n\nDifferent advisers have different philosophies, and it’s important to be sure that you pick an advisor whose style is right for you. I would say that the most important characteristic of my style is that I foster independent thinking in my trainees. Publishing papers is important, but not as important as developing one’s ability to conceive novel and interesting questions and ask them in a rigorous way. This means that beyond the first year project, I don’t generally hand my students problems to work on; rather, I expect them to come up with their own questions, and then we work together to devise the right experiments to test them. Another important thing to know is that I try to motivate by example, rather than by command. I rarely breathe down my trainees necks about getting their work done, because I work on the assumption that they will be self-motivated. On the other hand, I’m fairly hands-on in the sense that I still love to get deep in the weeds of experimental design and data analysis; for example, I will regularly code alongside lab members. I would also add that I am highly amenable to joint mentorship with other faculty.\n\n\n\nI have instituted a policy that I will no longer meet one-on-one with potential graduate students prior to the application process to discuss potential admission into my lab, as this has the potential to exacerbate existing disparities in graduate school admissions. I am willing to meet with individuals (particularly those from underrepresented groups) to discuss the graduate admissions process and other academic issues more generally, as time permits.\n\n\n\n\n\n\n\nWe do not generally advertise postdoctoral positions, so if you are interested in a position in the lab, contact Dr. Poldrack directly. Be sure to send your CV, and explain in brief your research interests and why you think you would be a good fit for the lab.\n\n\n\n\n\n\n\nGraduate students in the lab are expected to keep suitable documentation for all research activities (i.e. a github log, a google doc, etc).\n\n\n\nDr. Poldrack provides all lab members with access to a scheduling system with which they can schedule meetings during any open times on his calendar. - Upon request, lab members can schedule a regular standing meeting.\n\n\n\n\nI view mentorship as a life-long relationship, and you can view working with me as having a “Lifetime Guarantee” in the sense that I will always be there to help you, even long after you have left the lab – from letters of recommendation to career mentorship to personal advice.",
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+    "text": "We do not generally advertise postdoctoral positions, so if you are interested in a position in the lab, contact Dr. Poldrack directly. Be sure to send your CV, and explain in brief your research interests and why you think you would be a good fit for the lab.",
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     "title": "Research subjects",
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-    "text": "Please note, these project are active as of August 2023. If you take our screener, we’ll be sure to contact you as soon as possible if you’re eligible.\n\n\nWe currently have the following opportunities available for research participants:\n\n\nJoin us as a participant!\n\nThe Poldrack Lab is recruiting volunteers for studies of how learning works in the brain. These studies involve 1-10 sessions, lasting up to 2-3 hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tests.\nEligible participants must be between 18 and 40 years old, native speakers of English, have uncorrected vision (or corrected vision with contacts), free of neurological illness, and have no metallic implants in or on their body.\nEligible participants will be compensated $20/hour for participation, and a $250 completion bonus for completing all 10 scanning and behavioral task sessions.\nFor general information about participants rights, contact 1-866-680-2906.\nIf you are interested, please fill out the recruitment form here: https://redcap.link/fiveweekscanstudy.\n\nJoin us as a participant!\n\n\nThe Poldrack Lab is recruiting volunteers for studies of how learning, decision making, and executive function work in the brain.  These studies involve 12 sessions lasting from 1.5 - 2 hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tests.\n\n\nEligible participants must be between 18 and 50 years old, speak English fluently, be right-handed, free of psychiatric and neurological illness, have no metallic implants in or on their body, have no history of eating disorder, and have no food allergies.\n\n\nYou may receive a total of $820 as payment for your participation, which is $20/hr for all sessions, and a $250 completion bonus for completing all 12 scanning sessions and post-scan tasks, and $200 for completing the behavioral tasks.\n\n\nFor general information about participants rights, contact 1-866-680-2906.\n\n\nIf you are interested, please fill out the recruitment form here https://redcap.stanford.edu/surveys/?s=WYFLTRCFA8.",
+    "text": "Please note, these project are active as of August 2023. If you take our screener, we’ll be sure to contact you as soon as possible if you’re eligible.\n\n\nWe currently have the following opportunities available for research participants:\n\n\nJoin us as a participant!\n\nThe Poldrack Lab is recruiting volunteers for studies of how cognitive control, memory, and decision-making work in the brain. These studies involve up to 20 experimental sessions lasting up to three hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tasks and questionnaires. You may also be asked to respond to daily prompts on an app regarding your menstrual cycle and your sleep schedule. If you do not have a menstrual cycle, you may respond to only the prompts about your sleep schedule. After the scanning session, you may be asked to provide a saliva sample. You may be excluded for irregular menstrual cycles.\nEligible participants must have a minimum of an 8th grade education, speak English fluently, be right-handed, and have no history of significant medical illnesses including cardiovascular disease, cancer, immunodeficiency disorders (such as HIV infection), diabetes, unstable endocrine disorders, neurological disorders, neuromuscular disorders, or blood dyscrasias; additionally, participants must not have experienced head trauma with loss of consciousness, cerebrovascular accident, seizures, or neurosurgical interventions, and should have no history of major psychotic disorders (including schizophrenia and bipolar disorder), substance dependence, the use of medication for psychiatric reasons, or a clinical diagnosis of major depression.\nEligible participants will be compensated up to $20/hour for participation, and may have the opportunity to earn bonuses up to $400. If asked to respond to our cell-phone based app prompts on your sleep and menstrual cycles, participants will be paid $1 per day for answering the 3 daily questions. Participants may receive an additional bonus up to $100 for completing a high proportion of these daily questions. - All participants who are US citizens must provide a social security number to receive direct payment. - Participants who are not US citizens, but have a social security number, and a Green Card (Alien Registration Card) can be paid directly. Photocopy of Green Card is required. - Non-US citizens, without a Green Card (Alien Registration Card), must have a F-1, J-1 or H-1B Stanford sponsored visa, an I-94 card, a passport, and must also complete a LA-6 form (provided at time of participation). Copies of all paperwork will be submitted to Stanford University and the participant will receive payment from the University after appropriate taxes are withheld. NON-US CITIZENS WITHOUT A GREEN CARD CANNOT BE PAID AT THE TIME OF THE STUDY.\nFor complaints, concerns, or participant’s right questions, please contact the Stanford Institutional Review Board (IRB) at (650)-723-2480.\nIf you are interested, please fill out the recruitment form: https://redcap.stanford.edu/surveys/?s=LKFF39MRM7HRK4PC.",
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+    "section": "Women’s Brain Health Projects",
+    "text": "Women’s Brain Health Projects\nOur lab is part of the Ann S. Bowers Women’s Brain Health Initiative.\n\nWBHI Data Coordinating Center\n\nA project to coordinate data from several University of California Imaging Centers in service of better understanding women’s brain health.\nCurrent funding: Noyce Foundation\n\n\n\nLongitudinal neurocognitive and hormonal phenotyping\n\nA project collecting imaging, hormone and reproductive health information to understand the neural mechanisms that drive changes in mood and cognition across the menstrual cycle.\nNoyce Foundation",
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-    "text": "Informatics/analysis projects\n\nOpenNeuro\n\nA data sharing archive for neuroscience data\nCurrent funding: R24MH117179\nRepresentative publications:\n\nThe OpenNeuro resource for sharing of neuroscience data\n\n\n\n\nThe impact of AI on Neuroimaging Data Privacy\n\nA project investigating the effectiveness of deidentification in the context of AI face recognition systems\nPrevious funding: Stanford Human-Centered Artificial Intelligence Institute\nRepresentative publications:\n\nDemystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis\n\n\n\n\nBrain Imaging Data Structure (BIDS)\n\nA framework for organization of neuroimaging data and metadata\nPrevious funding: R01MH126699, R24MH114705\nRepresentative publications:\n\nThe brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments\nThe past, present, and future of the brain imaging data structure (BIDS)\n\n\n\n\nExperiment Factory\n\nA platform for online behavioral experimentation\nCurrent funding: Columbia University Science of Behavior Change Resource and Coordinating Center\nRepresentative publications:\n\nThe Experiment Factory: Standardizing Behavioral Experiments\n\n\n\n\nNeurosynth\n\nA platform for coordinate-based meta-analysis on neuroimaging data\nPrevious funding: 5R01MH096906\nRepresentative publications:\n\nLarge-scale automated synthesis of human functional neuroimaging data\n\n\n\n\nfMRIPrep\n\nA robust and reproducible preprocessing workflow for fMRI data\nCurrent funding RF1MH121867\nRepresentative publications:\n\nfMRIPrep: a robust preprocessing pipeline for functional MRI\n\n\n\n\nWomen’s Brain Health Initiative Data Coordinating Center\n\nA project to coordinate data from several University of California Imaging Centers in service of better understanding women’s brain health.\nCurrent funding: Noyce Foundation\n\n\n\nCognitive Atlas\n\nAn ontology for cognitive science\nPrevious funding: 5R01MH082795\nRepresentative publications:\n\nThe cognitive atlas: toward a knowledge foundation for cognitive neuroscience\n\n\n\n\nNeurovault\n\nA data sharing archive for summary data from neuroimaging studies\nRepresentative publications:\n\nNeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain\n\n\n\n\nMRIQC\n\nA quality control tool for structural and functional MRI data\nRepresentative publications:\n\nMRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites\n\n\n\n\nTemplateflow\n\nA FAIR sharing archive and toolkit for neuroimaging templates and atlases\nRepresentative publications:\n\nTemplateFlow: FAIR-sharing of multi-scale, multi-species brain models",
+    "text": "Informatics/analysis projects\n\nOpenNeuro\n\nA data sharing archive for neuroscience data\nCurrent funding: R24MH117179\nRepresentative publications:\n\nThe OpenNeuro resource for sharing of neuroscience data\n\n\n\n\nThe impact of AI on Neuroimaging Data Privacy\n\nA project investigating the effectiveness of deidentification in the context of AI face recognition systems\nPrevious funding: Stanford Human-Centered Artificial Intelligence Institute\nRepresentative publications:\n\nDemystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis\n\n\n\n\nBrain Imaging Data Structure (BIDS)\n\nA framework for organization of neuroimaging data and metadata\nPrevious funding: R01MH126699, R24MH114705\nRepresentative publications:\n\nThe brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments\nThe past, present, and future of the brain imaging data structure (BIDS)\n\n\n\n\nExperiment Factory\n\nA platform for online behavioral experimentation\nCurrent funding: Columbia University Science of Behavior Change Resource and Coordinating Center\nRepresentative publications:\n\nThe Experiment Factory: Standardizing Behavioral Experiments\n\n\n\n\nNeurosynth\n\nA platform for coordinate-based meta-analysis on neuroimaging data\nCurrent funding: 5R01MH096906\nRepresentative publications:\n\nLarge-scale automated synthesis of human functional neuroimaging data\n\n\n\n\nfMRIPrep\n\nA robust and reproducible preprocessing workflow for fMRI data\nCurrent funding RF1MH121867\nRepresentative publications:\n\nfMRIPrep: a robust preprocessing pipeline for functional MRI\n\n\n\n\nCognitive Atlas\n\nAn ontology for cognitive science\nPrevious funding: 5R01MH082795\nRepresentative publications:\n\nThe cognitive atlas: toward a knowledge foundation for cognitive neuroscience\n\n\n\n\nNeurovault\n\nA data sharing archive for summary data from neuroimaging studies\nRepresentative publications:\n\nNeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain\n\n\n\n\nMRIQC\n\nA quality control tool for structural and functional MRI data\nRepresentative publications:\n\nMRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites\n\n\n\n\nTemplateflow\n\nA FAIR sharing archive and toolkit for neuroimaging templates and atlases\nRepresentative publications:\n\nTemplateFlow: FAIR-sharing of multi-scale, multi-species brain models",
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-    "text": "PI\n\n  \n  Russ Poldrack  \n  \n\n\nResearch Scientists\n\n  \n  Patrick Bissett \n  \n  Joshua Buckholtz \n  \n  Michael Demidenko  \n  \n  Chris Markiewicz  \n  \n  Jeanette Mumford  \n  \n\n\nPostdoctoral Scholars\n\n  \n  Eric Bridgeford \n  \n  Anita Jwa \n  \n\n\nGraduate Students\n\n  \n  Lynde Folsom \n  \n  Nastasia Klevak \n  \n  Jocelyn Ricard \n  \n  Gustavo Santiago-Reyes \n  \n  Anna Xu \n  \n\n\nResearch Staff\n\n  \n  Kriti Achyutuni\n  \n  Logan Bennett\n  \n  Margot Mitchell\n  \n  Joe Wexler\n  \n\n\nSoftware Developers\n\n  \n  Ross Blair\n  \n  Mathias Goncalves  \n  \n  Nell Hardcastle  \n  \n  Greg Noack\n  \n\n\nLab Alumni\n\n  \n  Adam Aron\n  \n  Akram Bakkour\n  \n  Austin Brotman\n  \n  Mei-Yen Chen\n  \n  Naomi Cherne\n  \n  Rastko Ciric\n  \n  Jessica Cohen\n  \n  Eliza Congdon\n  \n  Tyler Davis\n  \n  Elizabeth DuPre\n  \n  Joke Durnez\n  \n  Ian Eisenberg\n  \n  Ayse Zeynep Enkavi\n  \n  Oscar Esteban\n  \n  Karin Foerde\n  \n  Adriana Galvan\n  \n  Marta Garrido\n  \n  Dara Ghahremani\n  \n  Chris Gorgolewski\n  \n  Grace Huckins\n  \n  Paul Jaffe\n  \n  Koji Jimura\n  \n  Don Kalar\n  \n  Sanmi Koyejo\n  \n  Agatha Lenartowicz\n  \n  Romy Lorenz\n  \n  Nick Malecek\n  \n  Dana Mastrovito\n  \n  Craig Moodie\n  \n  Martin Norgaard  \n  \n  Rajeev Raizada\n  \n  Jaime Rios\n  \n  Angela Rizk-Jackson\n  \n  Ajay Satpute\n  \n  Tom Schonberg\n  \n  Sunjae Shim\n  \n  Mac Shine\n  \n  Vanessa Sochat\n  \n  Elena Stover\n  \n  Armin Thomas\n  \n  William Thompson\n  \n  Christopher Trepel\n  \n  Matilde Vaghi\n  \n  Jonathan Walters\n  \n  Corey White\n  \n  Eliott Wimmer\n  \n  Alara Wright\n  \n  Gui Xue\n  \n\n\n\nNo matching items",
+    "text": "PI\n\n  \n  Russ Poldrack  \n  \n\n\nResearch Scientists\n\n  \n  Patrick Bissett \n  \n  Joshua Buckholtz \n  \n  Michael Demidenko  \n  \n  Chris Markiewicz  \n  \n  Jeanette Mumford  \n  \n\n\nPostdoctoral Scholars\n\n  \n  Eric Bridgeford \n  \n  Anita Jwa \n  \n\n\nGraduate Students\n\n  \n  Lynde Folsom \n  \n  Nastasia Klevak \n  \n  Jocelyn Ricard \n  \n  Gustavo Santiago-Reyes \n  \n  Anna Xu \n  \n\n\nResearch Staff\n\n  \n  Kriti Achyutuni \n  \n  Logan Bennett \n  \n  Margot Mitchell \n  \n  Joe Wexler\n  \n\n\nSoftware Developers\n\n  \n  Ross Blair\n  \n  Mathias Goncalves  \n  \n  Nell Hardcastle  \n  \n  Greg Noack\n  \n\n\nLab Alumni\n\n  \n  Adam Aron\n  \n  Akram Bakkour\n  \n  Austin Brotman\n  \n  Mei-Yen Chen\n  \n  Naomi Cherne\n  \n  Rastko Ciric\n  \n  Jessica Cohen\n  \n  Eliza Congdon\n  \n  Tyler Davis\n  \n  Elizabeth DuPre\n  \n  Joke Durnez\n  \n  Ian Eisenberg\n  \n  Ayse Zeynep Enkavi\n  \n  Oscar Esteban\n  \n  Karin Foerde\n  \n  Adriana Galvan\n  \n  Marta Garrido\n  \n  Dara Ghahremani\n  \n  Chris Gorgolewski\n  \n  Grace Huckins\n  \n  Paul Jaffe\n  \n  Koji Jimura\n  \n  Don Kalar\n  \n  Sanmi Koyejo\n  \n  Agatha Lenartowicz\n  \n  Romy Lorenz\n  \n  Nick Malecek\n  \n  Dana Mastrovito\n  \n  Craig Moodie\n  \n  Martin Norgaard  \n  \n  Rajeev Raizada\n  \n  Jaime Rios\n  \n  Angela Rizk-Jackson\n  \n  Ajay Satpute\n  \n  Tom Schonberg\n  \n  Sunjae Shim\n  \n  Mac Shine\n  \n  Vanessa Sochat\n  \n  Elena Stover\n  \n  Armin Thomas\n  \n  William Thompson\n  \n  Christopher Trepel\n  \n  Matilde Vaghi\n  \n  Jonathan Walters\n  \n  Corey White\n  \n  Eliott Wimmer\n  \n  Alara Wright\n  \n  Gui Xue\n  \n\n\n\nNo matching items",
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     "href": "publications.html",
     "title": "Publications",
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-    "text": "2025\nQuah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M (2025). A data-driven latent variable approach to validating the research domain criteria framework. Nat Commun, 16, 830. OA DOI\n\n\n2024\nBissett PG, Eisenberg IW, Shim S et al. (2024). Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. Scientific Data, 11. OA DOI Data Code OSF\nBrumback AC, Ngiam WXQ, Lapato DM et al. (2024). Catalyzing communities of research rigour champions. Brain Communications, 6. DOI\nCiric R, Xu A, Poldrack RA (2024). hyve, a compositional visualisation engine for brain imaging data. bioRxiv. OA DOI\nDean DC, Tisdall MD, Wisnowski JL et al. (2024). Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Dev Cogn Neurosci, 70, 101452. OA DOI\nDemidenko MI, Mumford JA, Poldrack RA (2024). Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. bioRxiv. OA DOI\nDemidenko MI, Mumford JA, Ram N, Poldrack RA (2024). A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents. Developmental Cognitive Neuroscience, 65, 101337. OA DOI\nEkhtiari H, Zare-Bidoky M, Sangchooli A et al. (2024). Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology, 50, 67-84. OA DOI\nHagen MP, Provins C, MacNicol E et al. (2024). Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC. bioRxiv. OA DOI\nHayashi S, Caron BA, Heinsfeld AS et al. (2024). brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nature Methods, 21, 809-813. OA DOI\nHuckins G, Poldrack RA (2024). Generative dynamical models for classification of rsfMRI data. Netw Neurosci, 8, 1613-1633. OA DOI\nKapoor S, Cantrell EM, Peng K et al. (2024). REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science Advances, 10. OA DOI\nKovacs M, Varga MA, Dianovics D, Poldrack RA, Aczel B (2024). Opening the black box of article retractions: exploring the causes and consequences of data management errors. R Soc Open Sci, 11, 240844. OA DOI\nLi X, Bianchini Esper N, Ai L et al. (2024). Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nature Human Behaviour, 8, 2003-2017. DOI\nMadsen SJ, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Kuceyeski A, Saggar M (2024). Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning. bioRxiv. OA DOI\nMumford JA, Bissett PG, Jones HM, Shim S, Rios JAH, Poldrack RA (2024). The response time paradox in functional magnetic resonance imaging analyses. Nature Human Behaviour, 8, 349-360. DOI Data Code\nPoldrack RA, Markiewicz CJ, Appelhoff S et al. (2024). The past, present, and future of the brain imaging data structure (BIDS). Imaging Neurosci (Camb), 2, 1-19. OA DOI\nPoldrack RA, Markiewicz CJ, Appelhoff S et al. (2024). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv. OA\nQuah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M (2024). A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework. bioRxiv. OA DOI\nWannan CMJ, Nelson B, Addington J et al. (2024). Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophrenia Bulletin, 50, 496-512. OA DOI\n\n\n2023\nBirk JL, Otto MW, Cornelius T, Poldrack RA, Edmondson D (2023). Improving the Rigor of Mechanistic Behavioral Science: The Introduction of the Checklist for Investigating Mechanisms in Behavior-Change Research (CLIMBR). Behavior Therapy, 54, 708-713. OA DOI\nBissett PG, Eisenberg IW, Shim S et al. (2023). Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. bioRxiv. OA DOI\nBissett PG, Jones HM, Hagen MP, Bui TT, Li JK, Rios JAH, Mumford JA, Shine JM, Poldrack RA (2023). A dual-task approach to inform the taxonomy of inhibition-related processes. Journal of Experimental Psychology: Human Perception and Performance, 49, 277-289. OA DOI Data Code OSF\nFinn ES, Poldrack RA, Shine JM (2023). Functional neuroimaging as a catalyst for integrated neuroscience. Nature, 623, 263-273. DOI\nGanz M, Poldrack RA (2023). Data sharing in neuroimaging: experiences from the BIDS project. Nature Reviews Neuroscience, 24, 729-730. DOI\nJaffe PI, Poldrack RA, Schafer RJ, Bissett PG (2023). Modelling human behaviour in cognitive tasks with latent dynamical systems. Nature Human Behaviour, 7, 986-1000. DOI Data Code\nKiar G, Clucas J, Feczko E et al. (2023). Align with the NMIND consortium for better neuroimaging. Nature Human Behaviour, 7, 1027-1028. OA DOI\nPernet C, Svarer C, Blair R, Van Horn JD, Poldrack RA (2023). On the Long-term Archiving of Research Data. Neuroinformatics, 21, 243-246. DOI\nPoldrack RA (2023). Statistical Thinking: Analyzing Data in an Uncertain World. Princeton, NJ: Princeton University Press.\nSubash P, Gray A, Boswell M et al. (2023). A comparison of neuroelectrophysiology databases. Scientific Data, 10. OA DOI\nThomas AW, Ré C, Poldrack RA (2023). Benchmarking explanation methods for mental state decoding with deep learning models. NeuroImage, 273, 120109. OA DOI Code\nUddin LQ, Betzel RF, Cohen JR et al. (2023). Controversies and progress on standardization of large-scale brain network nomenclature. Network Neuroscience, 7, 864-905. DOI\n\n\n2022\nAdebimpe A, Bertolero M, Dolui S et al. (2022). ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nature Methods, 19, 683-686. OA DOI\nBissett PG, Poldrack RA (2022). Estimating the Time to Do Nothing: Toward Next-Generation Models of Response Inhibition. Current Directions in Psychological Science, 31, 556-563. DOI\nCiric R, Thompson WH, Lorenz R et al. (2022). TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models. Nature Methods, 19, 1568-1571. OA DOI\nCiric R, Thomas AW, Esteban O, Poldrack RA (2022). Differentiable programming for functional connectomics. Proceedings of the 2nd Machine Learning for Health symposium, PMLR , 193.0.\nDelorme A, Truong D, Youn C, Sivagnanam S, Stirm C, Yoshimoto K, Poldrack RA, Majumdar A, Makeig S (2022). NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data. Database, 2022. OA DOI\nJwa AS, Poldrack RA (2022). The spectrum of data sharing policies in neuroimaging data repositories. Human Brain Mapping, 43, 2707-2721. OA DOI\nParet C, Unverhau N, Feingold F, Poldrack RA, Stirner M, Schmahl C, Sicorello M (2022). Survey on Open Science Practices in Functional Neuroimaging. NeuroImage, 257, 119306. DOI\nPoline J, Kennedy DN, Sommer FT et al. (2022). Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics, 20, 507-512. OA DOI\nScherer EA, Metcalf SA, Whicker CL et al. (2022). Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder. Frontiers in Digital Health, 4. DOI\nScholz C, Chan H, Poldrack RA, de Ridder DTD, Smidts A, van der Laan LN (2022). Can we have a second helping? A preregistered direct replication study on the neurobiological mechanisms underlying self‐control. Human Brain Mapping, 43, 4995-5016. OA DOI\nThomas AW, Ré C, Poldrack RA (2022). Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences, 26, 972-986. DOI\nThomas AW, Ré C, Poldrack RA (2022). Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).\nVaghi MM, Hagen MP, Jones HM, Mumford JA, Bissett PG, Poldrack RA (2022). Relating psychiatric symptoms and self-regulation during the COVID-19 crisis. Translational Psychiatry, 12. OA DOI\nWalters J, King M, Bissett PG, Ivry RB, Diedrichsen J, Poldrack RA (2022). Predicting brain activation maps for arbitrary tasks with cognitive encoding models. NeuroImage, 263, 119610. OA DOI\nWimmer GE, Poldrack RA (2022). Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period. Memory &amp; Cognition, 50, 312-324. OA DOI\nde la Vega A, Rocca R, Blair RW, Markiewicz CJ, Mentch J, Kent JD, Herholz P, Ghosh SS, Poldrack RA, Yarkoni T (2022). Neuroscout, a unified platform for generalizable and reproducible fMRI research. eLife, 11. OA DOI\n\n\n2021\nAczel B, Szaszi B, Nilsonne G et al. (2021). Consensus-based guidance for conducting and reporting multi-analyst studies. eLife, 10. OA DOI\nBeam E, Potts C, Poldrack RA, Etkin A (2021). A data-driven framework for mapping domains of human neurobiology. Nature Neuroscience, 24, 1733-1744. OA DOI Data Code\nBissett PG, Hagen MP, Jones HM, Poldrack RA (2021). Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development (ABCD) study. eLife, 10. OA DOI Code\nBissett PG, Jones HM, Poldrack RA, Logan GD (2021). Severe violations of independence in response inhibition tasks. Science Advances, 7. OA DOI Data Code\nDelorme A, Truong D, Martinez-Cancino R, Pernet C, Sivagnanam S, Yoshimoto K, Poldrack R, Majumdar A, Makeig S (2021). Tools for Importing and Evaluating BIDS-EEG Formatted Data. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 210-213. DOI\nEnkavi AZ, Poldrack RA (2021). Implications of the Lacking Relationship Between Cognitive Task and Self-report Measures for Psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6, 670-672. DOI\nFox AS, Holley D, Klink PC et al. (2021). Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault. NeuroImage, 225, 117518. OA DOI\nKoenis MMG, Durnez J, Rodrigue AL et al. (2021). Associations of cannabis use disorder with cognition, brain structure, and brain function inAfrican Americans. Human Brain Mapping, 42, 1727-1741. OA DOI\nLam M, Chen C, Ge T et al. (2021). Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics. Neuropsychopharmacology, 46, 1788-1801. OA DOI\nLevitis E, van Praag CDG, Gau R et al. (2021). Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective. GigaScience, 10. OA DOI\nMacNicol E, Ciric R, Kim E, Censo DD, Cash D, Poldrack RA, Esteban O (2021). Atlas-Based Brain Extraction Is Robust Across RAT MRI Studies. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 312-315. DOI\nManapat PD, Edwards MC, MacKinnon DP, Poldrack RA, Marsch LA (2021). A Psychometric Analysis of the Brief Self-Control Scale. Assessment, 28, 395-412. OA DOI\nMarkiewicz CJ, Gorgolewski KJ, Feingold F et al. (2021). The OpenNeuro resource for sharing of neuroscience data. eLife, 10. OA DOI\nMazza GL, Smyth HL, Bissett PG et al. (2021). Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation. Journal of Personality Assessment, 103, 238-245. OA DOI Data\nMcNeish D, Mackinnon DP, Marsch LA, Poldrack RA (2021). Measurement in Intensive Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 28, 807-822. DOI\nPoldrack RA (2021). The physics of representation. Synthese, 199, 1307-1325. DOI\nPoldrack RA (2021). Diving into the deep end: a personal reflection on the MyConnectome study. Current Opinion in Behavioral Sciences, 40, 1-4. DOI\nPoldrack RA (2021). Hard To Break: Why Our Brains Make Habits Stick. Princeton, NJ: Princeton University Press.\nRodrigue AL, Mastrovito D, Esteban O et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6, 1135-1144. OA DOI\nZmigrod L, Eisenberg IW, Bissett PG, Robbins TW, Poldrack RA (2021). The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 376, 20200424. OA DOI\n\n\n2020\nAron AR, Ivry RB, Jeffery KJ, Poldrack RA, Schmidt R, Summerfield C, Urai AE (2020). How Can Neuroscientists Respond to the Climate Emergency?. Neuron, 106, 17-20. DOI\nBassett DS, Cullen KE, Eickhoff SB et al. (2020). Reflections on the past two decades of neuroscience. Nature Reviews Neuroscience, 21, 524-534. DOI\nBielczyk NZ, Ando A, Badhwar A et al. (2020). Effective Self-Management for Early Career Researchers in the Natural and Life Sciences. Neuron, 106, 212-217. OA DOI OSF\nBotvinik-Nezer R, Holzmeister F, Camerer CF et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84-88. OA DOI\nColenbier N, Van de Steen F, Uddin LQ, Poldrack RA, Calhoun VD, Marinazzo D (2020). Disambiguating the role of blood flow and global signal with partial information decomposition. NeuroImage, 213, 116699. DOI\nDockès J, Poldrack RA, Primet R, Gözükan H, Yarkoni T, Suchanek F, Thirion B, Varoquaux G (2020). NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife, 9. OA DOI\nEsteban O, Ciric R, Finc K et al. (2020). Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature Protocols, 15, 2186-2202. OA DOI\nEsteban O, Goncalves M, Markiewicz CJ, Ghosh SS, Poldrack RA (2020). Software Tool to Read, Represent, Manipulate, and Apply N-Dimensional Spatial Transforms. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 709-712. DOI\nLurie DJ, Kessler D, Bassett DS et al. (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4, 30-69. DOI OSF\nMollon J, Mathias SR, Knowles EEM et al. (2020). Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders. Psychological Medicine, 50, 48-57. OA DOI\nPoldrack RA, Huckins G, Varoquaux G (2020). Establishment of Best Practices for Evidence for Prediction. JAMA Psychiatry, 77, 534. OA DOI\nPoldrack RA, Whitaker K, Kennedy D (2020). Introduction to the special issue on reproducibility in neuroimaging. NeuroImage, 218, 116357. DOI\nThompson WH, Kastrati G, Finc K, Wright J, Shine JM, Poldrack RA (2020). Time‐varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Human Brain Mapping, 41, 2347-2356. OA DOI\nThompson WH, Nair R, Oya H, Esteban O, Shine JM, Petkov CI, Poldrack RA, Howard M, Adolphs R (2020). A data resource from concurrent intracranial stimulation and functional MRI of the human brain. Scientific Data, 7. OA DOI\nThompson WH, Wright J, Bissett PG, Poldrack RA (2020). Dataset decay and the problem of sequential analyses on open datasets. eLife, 9. OA DOI\nTozzi L, Staveland B, Holt-Gosselin B et al. (2020). The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. NeuroImage, 214, 116715. OA DOI\n\n\n2019\nAridan N, Malecek NJ, Poldrack RA, Schonberg T (2019). Neural correlates of effort-based valuation with prospective choices. NeuroImage, 185, 446-454. OA DOI Data\nBotvinik-Nezer R, Iwanir R, Holzmeister F, Huber J, Johannesson M, Kirchler M, Dreber A, Camerer CF, Poldrack RA, Schonberg T (2019). fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study. Scientific Data, 6. OA DOI Data\nEisenberg IW, Bissett PG, Zeynep Enkavi A, Li J, MacKinnon DP, Marsch LA, Poldrack RA (2019). Uncovering the structure of self-regulation through data-driven ontology discovery. Nature Communications, 10. OA DOI Data Code OSF\nEnkavi AZ, Eisenberg IW, Bissett PG, Mazza GL, MacKinnon DP, Marsch LA, Poldrack RA (2019). Reply to Friedman and Banich: Right measures for the research question. Proceedings of the National Academy of Sciences, 116, 24398-24399. OA DOI\nEnkavi AZ, Eisenberg IW, Bissett PG, Mazza GL, MacKinnon DP, Marsch LA, Poldrack RA (2019). Large-scale analysis of test–retest reliabilities of self-regulation measures. Proceedings of the National Academy of Sciences, 116, 5472-5477. OA DOI Data OSF\nEsteban O, Blair RW, Nielson DM, Varada JC, Marrett S, Thomas AG, Poldrack RA, Gorgolewski KJ (2019). Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Scientific Data, 6. OA DOI\nEsteban O, Markiewicz CJ, Blair RW et al. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16, 111-116. OA DOI\nKebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, Kong R, Poldrack RA, Yeo BT (2019). Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biological Psychiatry, 86, 779-791. DOI\nKing M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J (2019). Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature Neuroscience, 22, 1371-1378. OA DOI Data\nLam M, Hill WD, Trampush JW et al. (2019). Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways. The American Journal of Human Genetics, 105, 334-350. OA DOI\nLi M, Han Y, Aburn MJ, Breakspear M, Poldrack RA, Shine JM, Lizier JT (2019). Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain. PLOS Computational Biology, 15, e1006957. OA DOI\nPoldrack RA (2019). The Costs of Reproducibility. Neuron, 101, 11-14. DOI\nPoldrack RA, Feingold F, Frank MJ et al. (2019). The Importance of Standards for Sharing of Computational Models and Data. Computational Brain &amp; Behavior, 2, 229-232. DOI\nPoldrack RA, Gorgolewski KJ, Varoquaux G (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging. Annual Review of Biomedical Data Science, 2.0. DOI\nReid AT, Headley DB, Mill RD et al. (2019). Advancing functional connectivity research from association to causation. Nature Neuroscience, 22, 1751-1760. OA DOI\nShine JM, Bell PT, Matar E, Poldrack RA, Lewis SJG, Halliday GM, O’Callaghan C (2019). Dopamine depletion alters macroscopic network dynamics in Parkinson’s disease. Brain, 142, 1024-1034. OA DOI\nShine JM, Breakspear M, Bell PT, Ehgoetz Martens KA, Shine R, Koyejo O, Sporns O, Poldrack RA (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22, 289-296. DOI\nShine JM, Hearne LJ, Breakspear M, Hwang K, Müller EJ, Sporns O, Poldrack RA, Mattingley JB, Cocchi L (2019). The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei. Neuron, 104, 849-855.e3. DOI\nSmeets PA, Dagher A, Hare TA, Kullmann S, van der Laan LN, Poldrack RA, Preissl H, Small D, Stice E, Veldhuizen MG (2019). Good practice in food-related neuroimaging. The American Journal of Clinical Nutrition, 109, 491-503. OA DOI\nVaroquaux G, Poldrack RA (2019). Predictive models avoid excessive reductionism in cognitive neuroimaging. Current Opinion in Neurobiology, 55, 1-6. DOI\nVerbruggen F, Aron AR, Band GP et al. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife, 8. OA DOI\nYarkoni T, Markiewicz CJ, de la Vega A et al. (2019). PyBIDS: Python tools for BIDS datasets. J Open Source Softw, 4. OA DOI\nZuo X, Biswal BB, Poldrack RA (2019). Editorial: Reliability and Reproducibility in Functional Connectomics. Frontiers in Neuroscience, 13. DOI\n\n\n2018\nBakkour A, Botvinik-Nezer R, Cohen N, Hover AM, Poldrack RA, Schonberg T (2018). Spacing of cue-approach training leads to better maintenance of behavioral change. PLOS ONE, 13, e0201580. OA DOI OSF\nDavies G, Lam M, Harris SE et al. (2018). Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications, 9. OA DOI\nDockès J, Wassermann D, Poldrack R, Suchanek F, Thirion B, Varoquaux G (2018). Text to Brain: Predicting the Spatial Distribution of Neuroimaging Observations from Text Reports. In Lecture Notes in Computer Science (p. 584-592). Springer International Publishing. DOI\nEisenberg IW, Bissett PG, Canning JR et al. (2018). Applying novel technologies and methods to inform the ontology of self-regulation. Behaviour Research and Therapy, 101, 46-57. OA DOI OSF\nEsteban O, Poldrack RA, Gorgolewski KJ (2018). Improving Out-of-Sample Prediction of Quality of MRIQC. In Lecture Notes in Computer Science (p. 190-199). Springer International Publishing. DOI\nGorgolewski KJ, Nichols T, Kennedy DN, Poline J, Poldrack RA (2018). Making replication prestigious. Behavioral and Brain Sciences, 41. DOI\nLam M, Trampush JW, Yu J et al. (2018). Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018). Twin Research and Human Genetics, 21, 394-397. DOI OSF\nMathias SR, Knowles EE, Barrett J, Beetham T, Leach O, Buccheri S, Aberizk K, Blangero J, Poldrack RA, Glahn DC (2018). Deficits in visual working-memory capacity and general cognition in African Americans with psychosis. Schizophrenia Research, 193, 100-106. OA DOI\nNaselaris T, Bassett DS, Fletcher AK, Kording K, Kriegeskorte N, Nienborg H, Poldrack RA, Shohamy D, Kay K (2018). Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline. Trends in Cognitive Sciences, 22, 365-367. OA DOI\nPoldrack RA (2018). The New Mind Readers: What Neuroimaging Can and Cannot Reveal about our Thoughts. Princeton, NJ: Princeton University Press.\nPoldrack RA, Monahan J, Imrey PB, Reyna V, Raichle ME, Faigman D, Buckholtz JW (2018). Predicting Violent Behavior: What Can Neuroscience Add?. Trends in Cognitive Sciences, 22, 111-123. OA DOI OSF\nPoldrack RA, Sandak R (2018). Introduction to This Special Issue: The Cognitive Neuroscience of Reading. In The Cognitive Neuroscience of Reading (p. 199-202). Routledge. DOI\nSavage JE, Jansen PR, Stringer S et al. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50, 912-919. OA DOI\nShine JM, Aburn MJ, Breakspear M, Poldrack RA (2018). The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife, 7. OA DOI\nShine JM, Poldrack RA (2018). Principles of dynamic network reconfiguration across diverse brain states. NeuroImage, 180, 396-405. DOI\nShine JM, van den Brink RL, Hernaus D, Nieuwenhuis S, Poldrack RA (2018). Catecholaminergic manipulation alters dynamic network topology across cognitive states. Network Neuroscience, 2, 381-396. DOI\nTansey W, Koyejo O, Poldrack RA, Scott JG (2018). False Discovery Rate Smoothing. Journal of the American Statistical Association, 113, 1156-1171. DOI\nVaroquaux G, Schwartz Y, Poldrack RA, Gauthier B, Bzdok D, Poline J, Thirion B (2018). Atlases of cognition with large-scale human brain mapping. PLOS Computational Biology, 14, e1006565. OA DOI\nWhite C, Poldrack RA (2018). Methods for fMRI analysis. In The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition (Volume 5) (EJ Wagenmakers, Ed.) Wiley. DOI\nWimmer GE, Li JK, Gorgolewski KJ, Poldrack RA (2018). Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain. The Journal of Neuroscience, 38, 7649-7666. OA DOI OSF\n\n\n2017\nAcikalin MY, Gorgolewski KJ, Poldrack RA (2017). A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks. Frontiers in Neuroscience, 11. DOI\nBakkour A, Lewis-Peacock JA, Poldrack RA, Schonberg T (2017). Neural mechanisms of cue-approach training. NeuroImage, 151, 92-104. OA DOI\nEckert MA, Vaden KI, Maxwell AB et al. (2017). Common Brain Structure Findings Across Children with Varied Reading Disability Profiles. Scientific Reports, 7. OA DOI\nEsteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE, 12, e0184661. OA DOI OSF\nGilron R, Rosenblatt J, Koyejo O, Poldrack RA, Mukamel R (2017). What’s in a pattern? Examining the type of signal multivariate analysis uncovers at the group level. NeuroImage, 146, 113-120. DOI\nGorgolewski KJ, Alfaro-Almagro F, Auer T et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, 13, e1005209. OA DOI\nGorgolewski KJ, Durnez J, Poldrack RA (2017). Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research, 6, 1262. DOI\nKhanna R, Ghosh J, Poldrack RA, Koyejo O (2017). A Deflation Method for Structured Probabilistic PCA. Proceedings of the 2017 SIAM International Conference on Data Mining. DOI\nKiar G, Gorgolewski KJ, Kleissas D et al. (2017). Science in the cloud (SIC): A use case in MRI connectomics. Giga Science, 6. OA DOI\nLam M, Trampush JW, Yu J et al. (2017). Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets. Cell Reports, 21, 2597-2613. OA DOI\nLenartowicz A, Poldrack R (2017). Brain Imaging. In Encyclopedia of Behavioral Neuroscience, 2nd edition (p. 77-83). Elsevier. DOI\nMathias SR, Knowles EEM, Barrett J, Leach O, Buccheri S, Beetham T, Blangero J, Poldrack RA, Glahn DC (2017). The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging. Schizophrenia Bulletin, sbw168. OA DOI\nNichols TE, Das S, Eickhoff SB et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20, 299-303. OA DOI\nPoldrack R (2017). Neuroscience: The risks of reading the brain. Nature, 541, 156-156. DOI\nPoldrack RA (2017). Precision Neuroscience: Dense Sampling of Individual Brains. Neuron, 95, 727-729. DOI\nPoldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, Nichols TE, Poline J, Vul E, Yarkoni T (2017). Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18, 115-126. OA DOI OSF\nPoldrack RA, Gorgolewski KJ (2017). OpenfMRI: Open sharing of task fMRI data. NeuroImage, 144, 259-261. OA DOI\nRubin TN, Koyejo O, Gorgolewski KJ, Jones MN, Poldrack RA, Yarkoni T (2017). Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLOS Computational Biology, 13, e1005649. OA DOI\nShine JM, Kucyi A, Foster BL, Bickel S, Wang D, Liu H, Poldrack RA, Hsieh L, Hsiang JC, Parvizi J (2017). Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain. The Journal of Neuroscience, 37, 9667-9674. OA DOI\nTrampush JW, Yang MLZ, Yu J et al. (2017). GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Molecular Psychiatry, 22, 336-345. OA DOI\nXiao X, Dong Q, Gao J, Men W, Poldrack RA, Xue G (2017). Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval. The Journal of Neuroscience, 37, 2986-2998. OA DOI\n\n\n2016\nBakkour A, Leuker C, Hover AM, Giles N, Poldrack RA, Schonberg T (2016). Mechanisms of Choice Behavior Shift Using Cue-approach Training. Frontiers in Psychology, 7. DOI\nEckert MA, Berninger VW, Hoeft F, Vaden KI (2016). A case of Bilateral Perisylvian Syndrome with reading disability. Cortex, 76, 121-124. OA DOI\nEisenberg I, Poldrack RA (2016). Task-set Selection in Probabilistic Environments: a Model of Task-set Inference. Proceedings of Cognitive Science Society.\nGorgolewski KJ, Auer T, Calhoun VD et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3. OA DOI\nGorgolewski KJ, Poldrack RA (2016). A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLOS Biology, 14, e1002506. OA DOI\nGorgolewski KJ, Varoquaux G, Rivera G et al. (2016). NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. NeuroImage, 124, 1242-1244. OA DOI\nHastings J, Frishkoff G, Smith B et al. (2016). Interdyscyplinarne perspektywy rozwoju, integracji i zastosowań ontologii poznawczych. AVANT. The Journal of the Philosophical-Interdisciplinary Vanguard, VII, 101-117. DOI\nHodgson K, Poldrack RA, Curran JE et al. (2016). Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index. Cerebral Cortex. OA DOI\nPatterson TK, Lenartowicz A, Berkman ET, Ji D, Poldrack RA, Knowlton BJ (2016). Putting the brakes on the brakes: negative emotion disrupts cognitive control network functioning and alters subsequent stopping ability. Experimental Brain Research, 234, 3107-3118. OA DOI\nPoldrack R, Congdon E, Triplett W et al. (2016). A phenome-wide examination of neural and cognitive function. Scientific Data, 3. OA DOI Data\nPoldrack RA, Yarkoni T (2016). From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annual Review of Psychology, 67, 587-612. OA DOI\nShine J, Bissett P, Bell P, Koyejo O, Balsters J, Gorgolewski K, Moodie C, Poldrack R (2016). The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron, 92, 544-554. OA DOI\nShine JM, Eisenberg I, Poldrack RA (2016). Computational specificity in the human brain. Behavioral and Brain Sciences, 39. DOI\nShine JM, Koyejo O, Poldrack RA (2016). Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proceedings of the National Academy of Sciences, 113, 9888-9891. OA DOI\nSochat VV, Eisenberg IW, Enkavi AZ, Li J, Bissett PG, Poldrack RA (2016). The Experiment Factory: Standardizing Behavioral Experiments. Frontiers in Psychology, 7. DOI\nWager TD, Atlas LY, Botvinick MM, Chang LJ, Coghill RC, Davis KD, Iannetti GD, Poldrack RA, Shackman AJ, Yarkoni T (2016). Pain in the ACC?. Proceedings of the National Academy of Sciences, 113. OA DOI\nWiener M, Sommer F, Ives Z, Poldrack R, Litt B (2016). Enabling an Open Data Ecosystem for the Neurosciences. Neuron, 92, 617-621. DOI\nWorthy DA, Davis T, Gorlick MA, Cooper JA, Bakkour A, Mumford JA, Poldrack RA, Todd Maddox W (2016). Neural correlates of state-based decision-making in younger and older adults. NeuroImage, 130, 13-23. OA DOI\n\n\n2015\nChen M, Jimura K, White CN, Maddox WT, Poldrack RA (2015). Multiple brain networks contribute to the acquisition of bias in perceptual decision-making. Frontiers in Neuroscience, 9. DOI\nGorgolewski KJ, Varoquaux G, Rivera G et al. (2015). NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in Neuroinformatics, 9. DOI\nHelfinstein SM, Mumford JA, Poldrack RA (2015). If all your friends jumped off a bridge: The effect of others’ actions on engagement in and recommendation of risky behaviors. Journal of Experimental Psychology: General, 144, 12-17. DOI\nKhanna R, Ghosh J, Poldrack RA, Koyejo O (2015). Sparse submodular probabilistic PCA. Proceedings of the 18th International conference on Artificial Intelligence and Statistics (AISTATS) .\nLaumann T, Gordon E, Adeyemo B et al. (2015). Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Neuron, 87, 657-670. OA DOI Data\nMumford JA, Poline J, Poldrack RA (2015). Orthogonalization of Regressors in fMRI Models. PLOS ONE, 10, e0126255. OA DOI\nPoldrack R (2015). Reverse Inference. In Brain Mapping (p. 647-650). Elsevier. DOI\nPoldrack R (2015). Introduction to Cognitive Neuroscience. In Brain Mapping (p. 259-260). Elsevier. DOI\nPoldrack RA (2015). Is “efficiency” a useful concept in cognitive neuroscience?. Developmental Cognitive Neuroscience, 11, 12-17. OA DOI Code\nPoldrack RA, Farah MJ (2015). Progress and challenges in probing the human brain. Nature, 526, 371-379. DOI\nPoldrack RA, Laumann TO, Koyejo O et al. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6. OA DOI Data Code\nPoldrack RA, Poline J (2015). The publication and reproducibility challenges of shared data. Trends in Cognitive Sciences, 19, 59-61. DOI\nShine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA (2015). Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage, 122, 399-407. DOI\nSochat VV, Gorgolewski KJ, Koyejo O, Durnez J, Poldrack RA (2015). Effects of thresholding on correlation-based image similarity metrics. Frontiers in Neuroscience, 9. DOI\n\n\n2014\nAron AR, Robbins TW, Poldrack RA (2014). Right inferior frontal cortex: addressing the rebuttals. Frontiers in Human Neuroscience, 8. DOI\nAron AR, Robbins TW, Poldrack RA (2014). Inhibition and the right inferior frontal cortex: one decade on. Trends in Cognitive Sciences, 18, 177-185. DOI\nCongdon E, Altshuler LL, Mumford JA et al. (2014). Neural activation during response inhibition in adult attention-deficit/hyperactivity disorder: Preliminary findings on the effects of medication and symptom severity. Psychiatry Research: Neuroimaging, 222, 17-28. OA DOI\nDavis T, LaRocque KF, Mumford JA, Norman KA, Wagner AD, Poldrack RA (2014). What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. NeuroImage, 97, 271-283. OA DOI Code\nDavis T, Poldrack RA (2014). Quantifying the Internal Structure of Categories Using a Neural Typicality Measure. Cerebral Cortex, 24, 1720-1737. DOI\nDavis T, Xue G, Love BC, Preston AR, Poldrack RA (2014). Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory. The Journal of Neuroscience, 34, 7472-7484. OA DOI\nHastings J, Frishkoff GA, Smith B, Jensen M, Poldrack RA, Lomax J, Bandrowski A, Imam F, Turner JA, Martone ME (2014). Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies. Frontiers in Neuroinformatics, 8. DOI\nHelfinstein SM, Schonberg T, Congdon E, Karlsgodt KH, Mumford JA, Sabb FW, Cannon TD, London ED, Bilder RM, Poldrack RA (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 2470-2475. OA DOI Data\nJimura K, Cazalis F, Stover ERS, Poldrack RA (2014). The neural basis of task switching changes with skill acquisition. Frontiers in Human Neuroscience, 8. DOI\nKoyejo O, Khanna R, Ghosh J, Poldrack RA (2014). On Prior Distributions and Approximate Inference for Structured Variables. Advances in Neural Information Processing Systems, 27.0.\nMumford JA, Davis T, Poldrack RA (2014). The impact of study design on pattern estimation for single-trial multivariate pattern analysis. NeuroImage, 103, 130-138. DOI\nPoldrack RA, Gorgolewski KJ (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17, 1510-1517. DOI\nSchonberg T, Bakkour A, Hover AM, Mumford JA, Nagar L, Perez J, Poldrack RA (2014). Changing value through cued approach: an automatic mechanism of behavior change. Nature Neuroscience, 17, 625-630. OA DOI\nSchonberg T, Bakkour A, Hover AM, Mumford JA, Poldrack RA (2014). Influencing Food Choices by Training: Evidence for Modulation of Frontoparietal Control Signals. Journal of Cognitive Neuroscience, 26, 247-268. OA DOI\nThakkar KN, Congdon E, Poldrack RA, Sabb FW, London ED, Cannon TD, Bilder RM (2014). Women are more sensitive than men to prior trial events on the Stop‐signal task. British Journal of Psychology, 105, 254-272. OA DOI\nWagshal D, Knowlton BJ, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Asarnow RF (2014). Impaired automatization of a cognitive skill in first-degree relatives of patients with schizophrenia. Psychiatry Research, 215, 294-299. OA DOI\nWagshal D, Knowlton BJ, Suthana NA, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Asarnow RF (2014). Evidence for Corticostriatal Dysfunction During Cognitive Skill Learning in Adolescent Siblings of Patients With Childhood-Onset Schizophrenia. Schizophrenia Bulletin, 40, 1030-1039. OA DOI\nWhite CN, Congdon E, Mumford JA, Karlsgodt KH, Sabb FW, Freimer NB, London ED, Cannon TD, Bilder RM, Poldrack RA (2014). Decomposing Decision Components in the Stop-signal Task: A Model-based Approach to Individual Differences in Inhibitory Control. Journal of Cognitive Neuroscience, 26, 1601-1614. OA DOI Data\nWhite CN, Poldrack RA (2014). Decomposing bias in different types of simple decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 385-398. DOI Data Code\n\n\n2013\nBarch DM, Burgess GC, Harms MP et al. (2013). Function in the human connectome: Task-fMRI and individual differences in behavior. NeuroImage, 80, 169-189. OA DOI\nBrakewood B, Poldrack RA (2013). The ethics of secondary data analysis: Considering the application of Belmont principles to the sharing of neuroimaging data. NeuroImage, 82, 671-676. DOI\nCongdon E, Bato AA, Schonberg T, Mumford JA, Karlsgodt KH, Sabb FW, London ED, Cannon TD, Bilder RM, Poldrack RA (2013). Differences in neural activation as a function of risk-taking task parameters. Frontiers in Neuroscience, 7. DOI\nDavis T, Poldrack RA (2013). Measuring neural representations with fMRI: practices and pitfalls. Annals of the New York Academy of Sciences, 1296, 108-134. DOI\nFox CR, Poldrack RA (2013). Prospect theory and the brain. In Neuroeconomics: Decision Making and the Brain (2nd Edition) (P Glimcher et al. , Ed.) Elsevier.\nGalván A, Schonberg T, Mumford J, Kohno M, Poldrack RA, London ED (2013). Greater risk sensitivity of dorsolateral prefrontal cortex in young smokers than in nonsmokers. Psychopharmacology, 229, 345-355. OA DOI\nHsieh CJ, Sustik MA, Dhillon IS, Ravikumar R, Poldrack RA (2013). BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. Advances in Neural Information Processing Systems .\nKoyejo O, Patel P, Ghosh J, Poldrack RA (2013). Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models. 2013 International Workshop on Pattern Recognition in Neuroimaging, 9-12. DOI\nMalecek NJ, Poldrack RA (2013). Beyond dopamine: The noradrenergic system and mental effort. Behavioral and Brain Sciences, 36, 698-699. DOI\nPark M, Koyejo O, Ghosh J, Poldrack RA, Pillow JW (2013). Bayesian structure learning for functional neuroimaging. Sixteenth International Conference on Artificial Intelligence and Statistics (AIST ATS). .\nPoldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD, Devlin JT, Cumba C, Koyejo O, Milham MP (2013). Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in Neuroinformatics, 7. DOI\nPoline J, Poldrack R (2013). Introduction to the special issue: Toward a new era of databasing and data sharing for neuroimaging. NeuroImage, 82, 645-646. DOI\nVan Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62-79. OA DOI\nWhite CN, Poldrack RA (2013). Using fMRI to Constrain Theories of Cognition. Perspectives on Psychological Science, 8, 79-83. DOI\nXue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA (2013). Complementary Role of Frontoparietal Activity and Cortical Pattern Similarity in Successful Episodic Memory Encoding. Cerebral Cortex, 23, 1562-1571. OA DOI\n\n\n2012\nCongdon E, Mumford JA, Cohen JR, Galvan A, Canli T, Poldrack RA (2012). Measurement and Reliability of Response Inhibition. Frontiers in Psychology, 3. DOI\nCourtney KE, Arellano R, Barkley‐Levenson E, Gálvan A, Poldrack RA, MacKillop J, David Jentsch J, Ray LA (2012). The Relationship Between Measures of Impulsivity and Alcohol Misuse: An Integrative Structural Equation Modeling Approach. Alcoholism: Clinical and Experimental Research, 36, 923-931. OA DOI\nGhahremani DG, Lee B, Robertson CL et al. (2012). Striatal Dopamine D2/D3Receptors Mediate Response Inhibition and Related Activity in Frontostriatal Neural Circuitry in Humans. The Journal of Neuroscience, 32, 7316-7324. OA DOI\nHelfinstein SM, Poldrack RA (2012). The young and the reckless. Nature Neuroscience, 15, 803-805. DOI\nJimura K, Poldrack RA (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50, 544-552. DOI\nMumford JA, Turner BO, Ashby FG, Poldrack RA (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59, 2636-2643. OA DOI\nPoldrack RA (2012). The future of fMRI in cognitive neuroscience. NeuroImage, 62, 1216-1220. OA DOI\nPoldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T (2012). Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping. PLoS Computational Biology, 8, e1002707. OA DOI Code\nPoline J, Breeze JL, Ghosh S et al. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6. DOI\nPoline J, Poldrack RA (2012). Frontiers in Brain Imaging Methods Grand Challenge. Frontiers in Neuroscience, 6. DOI\nSatpute AB, Mumford JA, Naliboff BD, Poldrack RA (2012). Human anterior and posterior hippocampus respond distinctly to state and trait anxiety. Emotion, 12, 58-68. DOI\nSchonberg T, Fox CR, Mumford JA, Congdon E, Trepel C, Poldrack RA (2012). Decreasing Ventromedial Prefrontal Cortex Activity During Sequential Risk-Taking: An fMRI Investigation of the Balloon Analog Risk Task. Frontiers in Neuroscience, 6. DOI Data\nTurner BO, Mumford JA, Poldrack RA, Ashby FG (2012). Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. NeuroImage, 62, 1429-1438. OA DOI\nWagshal D, Knowlton BJ, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Fernandez VG, Asarnow RF (2012). Deficits in probabilistic classification learning and liability for schizophrenia. Psychiatry Research, 200, 167-172. OA DOI\nWhite CN, Mumford JA, Poldrack RA (2012). Perceptual Criteria in the Human Brain. The Journal of Neuroscience, 32, 16716-16724. OA DOI\n\n\n2011\nCohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA (2011). Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications. Frontiers in Neuroscience, 5. DOI\nGalván A, Poldrack RA, Baker CM, McGlennen KM, London ED (2011). Neural Correlates of Response Inhibition and Cigarette Smoking in Late Adolescence. Neuropsychopharmacology, 36, 970-978. OA DOI\nGhahremani DG, Tabibnia G, Monterosso J, Hellemann G, Poldrack RA, London ED (2011). Effect of Modafinil on Learning and Task-Related Brain Activity in Methamphetamine-Dependent and Healthy Individuals. Neuropsychopharmacology, 36, 950-959. OA DOI\nHübner NO, Assadian O, Poldrack R et al. (2011). Endowashers: an overlooked risk for possible post-endoscopic infections. GMS Krankenhhyg Interdiszip, 6, Doc13. OA DOI\nLenartowicz A, Verbruggen F, Logan GD, Poldrack RA (2011). Inhibition-related Activation in the Right Inferior Frontal Gyrus in the Absence of Inhibitory Cues. Journal of Cognitive Neuroscience, 23, 3388-3399. DOI\nPoldrack R (2011). Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding. Neuron, 72, 692-697. OA DOI\nPoldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW, Bilder RM (2011). The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience. Frontiers in Neuroinformatics, 5. DOI\nPoldrack RA, Mumford JA, Nichols TE (2011). Handbook of Functional MRI Data Analysis. Cambridge: Cambridge University Press.\nRizk-Jackson A, Stoffers D, Sheldon S, Kuperman J, Dale A, Goldstein J, Corey-Bloom J, Poldrack RA, Aron AR (2011). Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington’s disease using machine learning techniques. NeuroImage, 56, 788-796. DOI\nSchonberg T, Fox CR, Poldrack RA (2011). Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Cognitive Sciences, 15, 11-19. OA DOI\nStern JM, Caporro M, Haneef Z, Yeh HJ, Buttinelli C, Lenartowicz A, Mumford JA, Parvizi J, Poldrack RA (2011). Functional imaging of sleep vertex sharp transients. Clinical Neurophysiology, 122, 1382-1386. OA DOI\nTabibnia G, Monterosso JR, Baicy K, Aron AR, Poldrack RA, Chakrapani S, Lee B, London ED (2011). Different Forms of Self-Control Share a Neurocognitive Substrate. The Journal of Neuroscience, 31, 4805-4810. OA DOI\nXue G, Mei L, Chen C, Lu Z, Poldrack R, Dong Q (2011). Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression. Journal of Cognitive Neuroscience, 23, 1624-1633. OA DOI\nYarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8, 665-670. OA DOI\n\n\n2010\nCho S, Moody TD, Fernandino L, Mumford JA, Poldrack RA, Cannon TD, Knowlton BJ, Holyoak KJ (2010). Common and Dissociable Prefrontal Loci Associated with Component Mechanisms of Analogical Reasoning. Cerebral Cortex, 20, 524-533. DOI\nCohen J (2010). Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals. Frontiers in Human Neuroscience. DOI\nCohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA (2010). A unique adolescent response to reward prediction errors. Nature Neuroscience, 13, 669-671. OA DOI\nCongdon E, Mumford JA, Cohen JR, Galvan A, Aron AR, Xue G, Miller E, Poldrack RA (2010). Engagement of large-scale networks is related to individual differences in inhibitory control. NeuroImage, 53, 653-663. OA DOI\nCongdon E, Poldrack RA, Freimer NB (2010). Neurocognitive Phenotypes and Genetic Dissection of Disorders of Brain and Behavior. Neuron, 68, 218-230. OA DOI\nCrone EA, Poldrack RA, Durston S (2010). Challenges and methods in developmental neuroimaging. Human Brain Mapping, 31, 835-837. OA DOI\nGhahremani DG, Monterosso J, Jentsch JD, Bilder RM, Poldrack RA (2010). Neural Components Underlying Behavioral Flexibility in Human Reversal Learning. Cerebral Cortex, 20, 1843-1852. OA DOI\nKenner NM, Mumford JA, Hommer RE, Skup M, Leibenluft E, Poldrack RA (2010). Inhibitory Motor Control in Response Stopping and Response Switching. Journal of Neuroscience, 30, 8512-8518. OA DOI\nKriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E (2010). Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask. Journal of Cerebral Blood Flow &amp; Metabolism, 30, 1551-1557. OA DOI\nLenartowicz A, Kalar DJ, Congdon E, Poldrack RA (2010). Towards an Ontology of Cognitive Control. Topics in Cognitive Science, 2, 678-692. DOI\nLenartowicz A, Poldrack R (2010). Brain Imaging. In Encyclopedia of Behavioral Neuroscience (p. 187-193). Elsevier. DOI\nMumford JA, Horvath S, Oldham MC, Langfelder P, Geschwind DH, Poldrack RA (2010). Detecting network modules in fMRI time series: A weighted network analysis approach. NeuroImage, 52, 1465-1476. OA DOI\nPoldrack RA (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?. Perspectives on Psychological Science, 5, 753-761. DOI\nPoldrack RA (2010). Interpreting developmental changes in neuroimaging signals. Human Brain Mapping, 31, 872-878. OA DOI\nPoldrack RA, Carr V, Foerde K (2010). Flexibility and generalization in memory systems. In Generalization of Knowledge: Multidisciplinary perspectives  (M Banich, D Caccamise , Ed.) New York, NY: Psychology Press..\nPoldrack RA, Mumford JA (2010). On the proper role of non-independent ROI analysis: A commentary on Vul and Kanwisher. In Foundational Issues in Human Brain Mapping (SJ Hanson, M Bunzl, Ed.) Cambridge, MA: MIT Press. DOI\nRamsey J, Hanson S, Hanson C, Halchenko Y, Poldrack R, Glymour C (2010). Six problems for causal inference from fMRI. NeuroImage, 49, 1545-1558. DOI\nScott-Van Zeeland AA, Abrahams BS, Alvarez-Retuerto AI et al. (2010). Altered Functional Connectivity in Frontal Lobe Circuits Is Associated with Variation in the Autism Risk Gene CNTNAP2. Science Translational Medicine, 2. OA DOI\nScott‐Van Zeeland AA, Dapretto M, Ghahremani DG, Poldrack RA, Bookheimer SY (2010). Reward processing in autism. Autism Research, 3, 53-67. OA DOI\nXue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA (2010). Greater Neural Pattern Similarity Across Repetitions Is Associated with Better Memory. Science, 330, 97-101. OA DOI\nXue G, Mei L, Chen C, Lu Z, Poldrack RA, Dong Q (2010). Facilitating Memory for Novel Characters by Reducing Neural Repetition Suppression in the Left Fusiform Cortex. PLoS ONE, 5, e13204. OA DOI\nYarkoni T, Poldrack RA, Van Essen DC, Wager TD (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends in Cognitive Sciences, 14, 489-496. OA DOI\n\n\n2009\nAron A, Poldrack R, Wise S (2009). Cognition: Basal Ganglia Role. In Encyclopedia of Neuroscience (p. 1069-1077). Elsevier. DOI\nAron AR, Wise SP, Poldrack RA (2009). Role of the basal ganglia in cognition. In The New Encyclopedia of Neuroscience (Volume 2) (LR Squire, Ed.) Oxford, UK: Academic Press.\nBarch DM, Braver TS, Carter CS, Poldrack RA, Robbins TW (2009). CNTRICS Final Task Selection: Executive Control. Schizophrenia Bulletin, 35, 115-135. OA DOI\nBilder R, Sabb F, Cannon T, London E, Jentsch J, Parker DS, Poldrack R, Evans C, Freimer N (2009). Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience, 164, 30-42. OA DOI\nBilder RM, Poldrack RA, Parker DS, Reise SP, Jentsch JD, Cannon T, London E, Sabb FW, Foland L, Rizk-Jackson A, Kalar D, Brown N, Carstensen A, Freimer N (2009). Cognitive phenomics. In Handbook of Neuropsychology of Mental Disorders  (S Wood, N Allen, C Pantelis , Ed.) Cambridge: Cambridge University Press.\nBilder RM, Poldrack RA, Parker DS, Reise SP, Jentsch JD, Cannon T, London E, Sabb FW, Foland-Ross L, Rizk-Jackson A, Kalar D, Brown N, Carstensen A, Freimer N (2009). Cognitive phenomics. In The Neuropsychology of Mental Illness (p. 271-282). Cambridge University Press. DOI\nBilder RM, Sabb FW, Parker DS, Kalar D, Chu WW, Fox J, Freimer NB, Poldrack RA (2009). Cognitive ontologies for neuropsychiatric phenomics research. Cognitive Neuropsychiatry, 14, 419-450. OA DOI\nFoerde K, Poldrack R (2009). Procedural Learning in Humans. In Encyclopedia of Neuroscience (p. 1083-1091). Elsevier. DOI\nFox CR, Poldrack RA (2009). Prospect Theory and the Brain. In Neuroeconomics (p. 145-173). Elsevier. DOI\nGhahremani DG, Poldrack RA (2009). Neuroimaging and interactive memory systems. In Neuroimaging of Human MemoryLinking cognitive processes to neural systems (p. 77-88). Oxford University Press. DOI\nGlimcher P, Fehr E, Camerer C, Poldrack RA (Eds.) (2009). Neuroeconomics. San Diego: Academic Press. DOI\nGlimcher PW, Camerer CF, Fehr E, Poldrack RA (2009). Introduction. In Neuroeconomics (p. 1-12). Elsevier. DOI\nLee B, London ED, Poldrack RA et al. (2009). Striatal Dopamine D2/D3Receptor Availability Is Reduced in Methamphetamine Dependence and Is Linked to Impulsivity. The Journal of Neuroscience, 29, 14734-14740. OA DOI\nPoldrack RA, Halchenko YO, Hanson SJ (2009). Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals. Psychological Science, 20, 1364-1372. OA DOI\nPoldrack RA, Mumford JA (2009). Independence in ROI analysis: where is the voodoo?. Social Cognitive and Affective Neuroscience, 4, 208-213. OA DOI\nSabb F, Burggren A, Higier R et al. (2009). Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence. Neuroscience, 164, 88-107. OA DOI\nThompson PM, Miller MI, Poldrack RA, Nichols TE, Taylor JE, Worsley KJ, Ratnanather JT (2009). Special Issue on Mathematics in Brain Imaging. NeuroImage, 45, S1-S2. DOI\nVANHORN J (2009). Functional MRI at the crossroads. International Journal of Psychophysiology, 73, 3-9. OA DOI\n\n\n2008\nCOHEN JR, POLDRACK RA (2008). Automaticity in motor sequence learning does not impair response inhibition. Psychonomic Bulletin &amp; Review, 15, 108-115. DOI\nFoerde K, Poldrack RA, Knowlton BJ et al. (2008). Selective corticostriatal dysfunction in schizophrenia: Examination of motor and cognitive skill learning. Neuropsychology, 22, 100-109. DOI\nGluck MA, Poldrack RA, Kéri S (2008). The cognitive neuroscience of category learning. Neuroscience &amp; Biobehavioral Reviews, 32, 193-196. DOI\nKarlsgodt KH, van Erp TG, Poldrack RA, Bearden CE, Nuechterlein KH, Cannon TD (2008). Diffusion Tensor Imaging of the Superior Longitudinal Fasciculus and Working Memory in Recent-Onset Schizophrenia. Biological Psychiatry, 63, 512-518. DOI\nPoldrack RA (2008). The role of fMRI in Cognitive Neuroscience: where do we stand?. Current Opinion in Neurobiology, 18, 223-227. DOI\nPoldrack RA, Fletcher PC, Henson RN, Worsley KJ, Brett M, Nichols TE (2008). Guidelines for reporting an fMRI study. NeuroImage, 40, 409-414. OA DOI\nPoldrack RA, Foerde K (2008). Category learning and the memory systems debate. Neuroscience &amp; Biobehavioral Reviews, 32, 197-205. DOI\nPoldrack RA, Wagner AD (2008). The Interface Between Neuroscience and Psychological Science. Current Directions in Psychological Science, 17, 61-61. DOI\nRaizada RDS (2008). Challenge-driven attention: Interacting frontal and brainstem systems. Frontiers in Human Neuroscience, 1. DOI\nShattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008). Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage, 39, 1064-1080. OA DOI\nTohka J, Foerde K, Aron AR, Tom SM, Toga AW, Poldrack RA (2008). Automatic independent component labeling for artifact removal in fMRI. NeuroImage, 39, 1227-1245. OA DOI\nXue G, Aron AR, Poldrack RA (2008). Common Neural Substrates for Inhibition of Spoken and Manual Responses. Cerebral Cortex, 18, 1923-1932. DOI Data\nXue G, Ghahremani DG, Poldrack RA (2008). Neural Substrates for Reversing Stimulus–Outcome and Stimulus–Response Associations. The Journal of Neuroscience, 28, 11196-11204. OA DOI\n\n\n2007\nAron AR, Behrens TE, Smith S, Frank MJ, Poldrack RA (2007). Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI. The Journal of Neuroscience, 27, 3743-3752. OA DOI\nDevlin JT, Poldrack RA (2007). In praise of tedious anatomy. NeuroImage, 37, 1033-1041. OA DOI\nFoerde K, Poldrack RA, Knowlton BJ (2007). Secondary-task effects on classification learning. Memory &amp; Cognition, 35, 864-874. DOI\nMumford JA, Poldrack RA (2007). Modeling group fMRI data. Social Cognitive and Affective Neuroscience, 2, 251-257. OA DOI\nPoldrack RA (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2, 67-70. OA DOI\nPoldrack RA, Devlin JT (2007). On the fundamental role of anatomy in functional imaging: Reply to commentaries on “In praise of tedious anatomy”. NeuroImage, 37, 1066-1068. DOI\nRaizada RD, Poldrack RA (2007). Selective Amplification of Stimulus Differences during Categorical Processing of Speech. Neuron, 56, 726-740. DOI\nThermenos HW, Seidman LJ, Poldrack RA, Peace NK, Koch JK, Faraone SV, Tsuang MT (2007). Elaborative Verbal Encoding and Altered Anterior Parahippocampal Activation in Adolescents and Young Adults at Genetic Risk for Schizophrenia Using fMRI. Biological Psychiatry, 61, 564-574. DOI\nTom SM, Fox CR, Trepel C, Poldrack RA (2007). The Neural Basis of Loss Aversion in Decision-Making Under Risk. Science, 315, 515-518. DOI Data\nXue G, Poldrack RA (2007). The Neural Substrates of Visual Perceptual Learning of Words: Implications for the Visual Word Form Area Hypothesis. Journal of Cognitive Neuroscience, 19, 1643-1655. DOI\n\n\n2006\nAron AR, Gluck MA, Poldrack RA (2006). Long-term test–retest reliability of functional MRI in a classification learning task. NeuroImage, 29, 1000-1006. OA DOI Data\nAron AR, Poldrack RA (2006). Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus. The Journal of Neuroscience, 26, 2424-2433. OA DOI\nFoerde K, Knowlton BJ, Poldrack RA (2006). Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences, 103, 11778-11783. OA DOI Data\nPOLDRACK R (2006). Can cognitive processes be inferred from neuroimaging data?. Trends in Cognitive Sciences, 10, 59-63. DOI\nPoldrack RA, Willingham DB (2006). Functional neuroimaging of skill learning. In Handbook of Neuroimaging of Cognition, 2nd Edition  (R Cabeza, A Kingstone, Ed.) Cambridge, MA: MIT Press.\nRodriguez PF, Aron A, Poldrack R (2006). Ventral–striatal/nucleus–accumbens sensitivity to prediction errors during classification learning. Human Brain Mapping, 27, 306-313. OA DOI\nSEIDMAN L, THERMENOS H, POLDRACK R, PEACE N, KOCH J, FARAONE S, TSUANG M (2006). Altered brain activation in dorsolateral prefrontal cortex in adolescents and young adults at genetic risk for schizophrenia: An fMRI study of working memory. Schizophrenia Research, 85, 58-72. DOI\nSegonne F, Liu A, Poldrack R, Golland P, Fischl B (2006). Shape-based Discrimination and Classification of Cortical Surfaces. 18th International Conference on Pattern Recognition (ICPR’06), 445-448. DOI\nThermenos HW, Seidman LJ, Poldrack RA, Peace NK, Koch JK, Faraone SV, Tsuang MT (2006). RETRACTED: Elaborative Verbal Encoding and Altered Anterior Parahippocampal Activation in Adolescents and Young Adults at Genetic Risk for Schizophrenia Using fMRI. Biol Psychiatry. DOI\n\n\n2005\nAron AR, Poldrack RA (2005). The Cognitive Neuroscience of Response Inhibition: Relevance for Genetic Research in Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 57, 1285-1292. DOI\nBadre D, Poldrack RA, Paré-Blagoev EJ, Insler RZ, Wagner AD (2005). Dissociable Controlled Retrieval and Generalized Selection Mechanisms in Ventrolateral Prefrontal Cortex. Neuron, 47, 907-918. DOI\nGoldstein JM, Jerram M, Poldrack R, Ahern T, Kennedy DN, Seidman LJ, Makris N (2005). Hormonal Cycle Modulates Arousal Circuitry in Women Using Functional Magnetic Resonance Imaging. The Journal of Neuroscience, 25, 9309-9316. OA DOI\nGoldstein JM, Jerram M, Poldrack R, Anagnoson R, Breiter HC, Makris N, Goodman JM, Tsuang MT, Seidman LJ (2005). Sex differences in prefrontal cortical brain activity during fMRI of auditory verbal working memory. Neuropsychology, 19, 509-519. DOI\nKatzir T, Misra M, Poldrack RA (2005). Imaging phonology without print: Assessing the neural correlates of phonemic awareness using fMRI. NeuroImage, 27, 106-115. DOI\nPoldrack RA, Sabb FW, Foerde K, Tom SM, Asarnow RF, Bookheimer SY, Knowlton BJ (2005). The Neural Correlates of Motor Skill Automaticity. The Journal of Neuroscience, 25, 5356-5364. OA DOI\nStone WS, Thermenos HW, Tarbox SI, Poldrack RA, Seidman LJ (2005). Medial temporal and prefrontal lobe activation during verbal encoding following glucose ingestion in schizophrenia: A pilot fMRI study. Neurobiology of Learning and Memory, 83, 54-64. DOI\nThermenos HW, Goldstein JM, Buka SL, Poldrack RA, Koch JK, Tsuang MT, Seidman LJ (2005). The effect of working memory performance on functional MRI in schizophrenia. Schizophrenia Research, 74, 179-194. DOI\nTrepel C, Fox CR, Poldrack RA (2005). Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Cognitive Brain Research, 23, 34-50. DOI\nValera EM, Faraone SV, Biederman J, Poldrack RA, Seidman LJ (2005). Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 439-447. DOI\n\n\n2004\nAron AR, Robbins TW, Poldrack RA (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8, 170-177. DOI\nAron AR, Shohamy D, Clark J, Myers C, Gluck MA, Poldrack RA (2004). Human Midbrain Sensitivity to Cognitive Feedback and Uncertainty During Classification Learning. Journal of Neurophysiology, 92, 1144-1152. DOI\nMisra M, Katzir T, Wolf M, Poldrack RA (2004). Neural Systems for Rapid Automatized Naming in Skilled Readers: Unraveling the RAN-Reading Relationship. Scientific Studies of Reading, 8, 241-256. DOI\nPoldrack RA, Rodriguez P (2004). How do memory systems interact? Evidence from human classification learning. Neurobiology of Learning and Memory, 82, 324-332. DOI\nPoldrack RA, Sandak R (2004). Introduction to This Special Issue: The Cognitive Neuroscience of Reading. Scientific Studies of Reading, 8, 199-202. DOI\nPoldrack RA, Wagner AD (2004). What Can Neuroimaging Tell Us About the Mind?. Current Directions in Psychological Science, 13, 177-181. DOI\nShohamy D (2004). Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology. Brain, 127, 851-859. DOI\nThermenos HW, Seidman LJ, Breiter H, Goldstein JM, Goodman JM, Poldrack R, Faraone SV, Tsuang MT (2004). Functional magnetic resonance imaging during auditory verbal working memory in nonpsychotic relatives of persons with schizophrenia: a pilot study. Biological Psychiatry, 55, 490-500. DOI\nThompson PM, Miller MI, Ratnanather JT, Poldrack RA, Nichols TE (2004). Preface to the special issue. NeuroImage, 23, S1. DOI\n\n\n2003\nBoas DA, Strangman G, Culver JP, Hoge RD, Jasdzewski G, Poldrack RA, Rosen BR, Mandeville JB (2003). Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy?. Physics in Medicine and Biology, 48, 2405-2418. DOI\nJasdzewski G, Strangman G, Wagner J, Kwong K, Poldrack R, Boas D (2003). Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy. NeuroImage, 20, 479-488. DOI\nMisra M, Katzir T, Wolf M, Poldrack RA (2003). Neural systems for rapid automatized naming identified using fMRI. Scientific Studies of Reading, 8.0.\nPoldrack RA, Packard MG (2003). Competition among multiple memory systems: converging evidence from animal and human brain studies. Neuropsychologia, 41, 245-251. DOI\nPoldrack RA, Rodriguez P (2003). Sequence Learning. Neuron, 37, 891-893. DOI\nPoldrack RA, Sandak R (2003). Introduction to special issue: The cognitive neuroscience of reading. Scientific Studies of Reading, 8.0.\nSperling R, Chua E, Cocchiarella A, Rand-Giovannetti E, Poldrack R, Schacter DL, Albert M (2003). Putting names to faces:. NeuroImage, 20, 1400-1410. OA DOI\nTemple E, Deutsch GK, Poldrack RA, Miller SL, Tallal P, Merzenich MM, Gabrieli JDE (2003). Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 100, 2860-2865. OA DOI\n\n\n2002\nGolby A, Poldrack R, Illes J, Chen D, Desmond J, Gabrieli J (2002). Memory Lateralization in Medial Temporal Lobe Epilepsy Assessed by Functional MRI. Epilepsia, 43, 855-863. DOI\nPoldrack RA (2002). Neural Systems for Perceptual Skill Learning. Behavioral and Cognitive Neuroscience Reviews, 1, 76-83. DOI\nPoldrack RA, Paré-Blagoev EJ, Grant PE (2002). Pediatric Functional Magnetic Resonance Imaging: Progress and Challenges. Topics in Magnetic Resonance Imaging, 13, 61-70. DOI\n\n\n2001\nGolby AJ (2001). Material-specific lateralization in the medial temporal lobe and prefrontal cortex during memory encoding. Brain, 124, 1841-1854. DOI\nPoldrack RA (2001). A Structural Basis for Developmental Dyslexia:Evidence from Diffusion Tensor Imaging. In Dyslexia, Fluency and the Brain (M Wolf, Ed.) York Press: Timonium, MD.\nPoldrack RA, Clark J, Paré-Blagoev EJ, Shohamy D, Creso Moyano J, Myers C, Gluck MA (2001). Interactive memory systems in the human brain. Nature, 414, 546-550. DOI Data\nPoldrack RA, Gabrieli JDE (2001). Characterizing the neural mechanisms of skill learning and repetition priming. Brain, 124, 67-82. DOI\nPoldrack RA, Temple E, Protopapas A, Nagarajan S, Tallal P, Merzenich M, Gabrieli JDE (2001). Relations between the Neural Bases of Dynamic Auditory Processing and Phonological Processing: Evidence from fMRI. Journal of Cognitive Neuroscience, 13, 687-697. DOI\nTemple E, Poldrack RA, Salidis J, Deutsch GK, Tallal P, Merzenich MM, Gabrieli JDE (2001). Disrupted neural responses to phonological and orthographic processing in dyslexic children: an fMRI study. Neuroreport, 12, 299-307. DOI\nVuilleumier P, Sagiv N, Hazeltine E, Poldrack RA, Swick D, Rafal RD, Gabrieli JDE (2001). Neural fate of seen and unseen faces in visuospatial neglect: A combined event-related functional MRI and event-related potential study. Proceedings of the National Academy of Sciences, 98, 3495-3500. OA DOI\nWagner AD, Paré-Blagoev E, Clark J, Poldrack RA (2001). Recovering Meaning. Neuron, 31, 329-338. DOI\n\n\n2000\nHazeltine E, Poldrack R, Gabrieli JDE (2000). Neural Activation During Response Competition. Journal of Cognitive Neuroscience, 12, 118-129. DOI\nKlingberg T, Hedehus M, Temple E, Salz T, Gabrieli JD, Moseley ME, Poldrack RA (2000). Microstructure of Temporo-Parietal White Matter as a Basis for Reading Ability. Neuron, 25, 493-500. DOI\nPoldrack RA (2000). Imaging Brain Plasticity: Conceptual and Methodological Issues— A Theoretical Review. NeuroImage, 12, 1-13. DOI\nSeger CA, Poldrack RA, Prabhakaran V, Zhao M, Glover GH, Gabrieli JD (2000). Hemispheric asymmetries and individual differences in visual concept learning as measured by functional MRI. Neuropsychologia, 38, 1316-1324. DOI\nSeger CA, Prabhakaran V, Poldrack RA, Gabrieli JDE (2000). Neural activity differs between explicit and implicit learning of artificial grammar strings: An fMRI study. Psychobiology, 28, 283-292. DOI\nTemple E, Poldrack RA, Protopapas A, Nagarajan S, Salz T, Tallal P, Merzenich MM, Gabrieli JDE (2000). Disruption of the neural response to rapid acoustic stimuli in dyslexia: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 97, 13907-13912. OA DOI\n\n\n1999\nDemb JB, Poldrack RA, Gabrieli JDE (1999). Functional neuroimaging of word processing in normal and dyslexic readers. In Converging Methods for Understanding Reading and Dyslexia  (R Klein, P. McMullen, Ed.) Cambridge, MA: MIT Press.\nIlles J, Francis WS, Desmond JE, Gabrieli JD, Glover GH, Poldrack R, Lee CJ, Wagner AD (1999). Convergent Cortical Representation of Semantic Processing in Bilinguals. Brain and Language, 70, 347-363. DOI\nPoldrack RA, Prabhakaran V, Seger CA, Gabrieli JD (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564-74. DOI\nPoldrack RA, Prabhakaran V, Seger CA, Gabrieli JDE (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564-574. DOI\nPoldrack RA, Selco SL, Field JE, Cohen NJ (1999). The relationship between skill learning and repetition priming: Experimental and computational analyses. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 208-235. DOI\nPoldrack RA, Selco SL, Field JE, Cohen NJ (1999). The relationship between skill learning and repetition priming: experimental and computational analyses. J Exp Psychol Learn Mem Cogn, 25, 208-35. DOI\nPoldrack RA, Wagner AD, Prull MW, Desmond JE, Glover GH, Gabrieli JD (1999). Functional Specialization for Semantic and Phonological Processing in the Left Inferior Prefrontal Cortex. NeuroImage, 10, 15-35. DOI\n\n\n1998\nGabrieli JD, Brewer JB, Poldrack RA (1998). Images of Medial Temporal Lobe Functions in Human Learning and Memory. Neurobiology of Learning and Memory, 70, 275-283. DOI\nGabrieli JDE, Poldrack RA, Desmond JE (1998). The role of left prefrontal cortex in language and memory. Proceedings of the National Academy of Sciences, 95, 906-913. OA DOI\nPoldrack R (1998). The neural basis of visual skill learning: an fMRI study of mirror reading. Cerebral Cortex, 8, 1-10. DOI\nPoldrack RA, Gabrieli JD (1998). Memory and the Brain: What’s Right and What’s Left?. Cell, 93, 1091-1093. DOI\nPoldrack RA, Logan GD (1998). What is the mechanism for fluency in successive recognition?. Acta Psychologica, 98, 167-181. DOI\nWagner AD, Poldrack RA, Eldridge LL, Desmond JE, Glover GH, E. Gabrieli JD (1998). Material-specific lateralization of prefrontal activation during episodic encoding and retrieval. NeuroReport, 9, 3711-3717. DOI\n\n\n1997\nCohen NJ, Poldrack RA, Eichenbaum H (1997). Memory for Items and Memory for Relations in the Procedural/Declarative Memory Framework. Memory, 5, 131-178. DOI\nGabrieli JD, Keane MM, Zarella MM, Poldrack RA (1997). Preservation of Implicit Memory for New Associations in Global Amnesia. Psychological Science, 8, 326-329. DOI\nPoldrack RA, Cohen NJ (1997). Priming of new associations in reading time: What is learned?. Psychonomic Bulletin &amp; Review, 4, 398-402. DOI\nPoldrack RA, Gabrieli JDE (1997). Functional Anatomy of Long-Term Memory. Journal of Clinical Neurophysiology, 14, 294-310. DOI\nPoldrack RA, Logan GD (1997). Fluency and response speed in recognition judgments. Memory &amp; Cognition, 25, 1-10. DOI\n\n\n1996\nPoldrack RA (1996). On testing for stochastic dissociations. Psychonomic Bulletin &amp; Review, 3, 434-448. DOI\n\n\n1994\nPoldrack RA, Cohen NJ (1994). On the representational/computational properties of multiple memory systems. Behavioral and Brain Sciences, 17, 416-417. DOI\n   \nUpdated: 2025-02-04 - Publication list created using autocv",
+    "text": "2025\nQuah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M (2025). A data-driven latent variable approach to validating the research domain criteria framework. Nat Commun, 16, 830. OA DOI\n\n\n2024\nBissett PG, Eisenberg IW, Shim S et al. (2024). Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. Scientific Data, 11. OA DOI Data Code OSF\nBrumback AC, Ngiam WXQ, Lapato DM et al. (2024). Catalyzing communities of research rigour champions. Brain Communications, 6. DOI\nCiric R, Xu A, Poldrack RA (2024). hyve, a compositional visualisation engine for brain imaging data. bioRxiv. OA DOI\nDean DC, Tisdall MD, Wisnowski JL et al. (2024). Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol. Dev Cogn Neurosci, 70, 101452. OA DOI\nDemidenko MI, Mumford JA, Poldrack RA (2024). Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task. bioRxiv. OA DOI\nDemidenko MI, Mumford JA, Ram N, Poldrack RA (2024). A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents. Developmental Cognitive Neuroscience, 65, 101337. OA DOI\nEkhtiari H, Zare-Bidoky M, Sangchooli A et al. (2024). Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility. Neuropsychopharmacology, 50, 67-84. OA DOI\nHagen MP, Provins C, MacNicol E et al. (2024). Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC. bioRxiv. OA DOI\nHayashi S, Caron BA, Heinsfeld AS et al. (2024). brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nature Methods, 21, 809-813. OA DOI\nHuckins G, Poldrack RA (2024). Generative dynamical models for classification of rsfMRI data. Netw Neurosci, 8, 1613-1633. OA DOI\nKapoor S, Cantrell EM, Peng K et al. (2024). REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science Advances, 10. OA DOI\nKovacs M, Varga MA, Dianovics D, Poldrack RA, Aczel B (2024). Opening the black box of article retractions: exploring the causes and consequences of data management errors. R Soc Open Sci, 11, 240844. OA DOI\nLi X, Bianchini Esper N, Ai L et al. (2024). Moving beyond processing- and analysis-related variation in resting-state functional brain imaging. Nature Human Behaviour, 8, 2003-2017. DOI\nMadsen SJ, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Kuceyeski A, Saggar M (2024). Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning. bioRxiv. OA DOI\nMumford JA, Bissett PG, Jones HM, Shim S, Rios JAH, Poldrack RA (2024). The response time paradox in functional magnetic resonance imaging analyses. Nature Human Behaviour, 8, 349-360. DOI Data Code\nPoldrack RA, Markiewicz CJ, Appelhoff S et al. (2024). The past, present, and future of the brain imaging data structure (BIDS). Imaging Neurosci (Camb), 2, 1-19. OA DOI\nPoldrack RA, Markiewicz CJ, Appelhoff S et al. (2024). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). ArXiv. OA\nQuah SKL, Jo B, Geniesse C, Uddin LQ, Mumford JA, Barch DM, Fair DA, Gotlib IH, Poldrack RA, Saggar M (2024). A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework. bioRxiv. OA DOI\nWannan CMJ, Nelson B, Addington J et al. (2024). Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophrenia Bulletin, 50, 496-512. OA DOI\n\n\n2023\nBirk JL, Otto MW, Cornelius T, Poldrack RA, Edmondson D (2023). Improving the Rigor of Mechanistic Behavioral Science: The Introduction of the Checklist for Investigating Mechanisms in Behavior-Change Research (CLIMBR). Behavior Therapy, 54, 708-713. OA DOI\nBissett PG, Eisenberg IW, Shim S et al. (2023). Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. bioRxiv. OA DOI\nBissett PG, Jones HM, Hagen MP, Bui TT, Li JK, Rios JAH, Mumford JA, Shine JM, Poldrack RA (2023). A dual-task approach to inform the taxonomy of inhibition-related processes. Journal of Experimental Psychology: Human Perception and Performance, 49, 277-289. OA DOI Data Code OSF\nFinn ES, Poldrack RA, Shine JM (2023). Functional neuroimaging as a catalyst for integrated neuroscience. Nature, 623, 263-273. DOI\nGanz M, Poldrack RA (2023). Data sharing in neuroimaging: experiences from the BIDS project. Nature Reviews Neuroscience, 24, 729-730. DOI\nJaffe PI, Poldrack RA, Schafer RJ, Bissett PG (2023). Modelling human behaviour in cognitive tasks with latent dynamical systems. Nature Human Behaviour, 7, 986-1000. DOI Data Code\nKiar G, Clucas J, Feczko E et al. (2023). Align with the NMIND consortium for better neuroimaging. Nature Human Behaviour, 7, 1027-1028. OA DOI\nPernet C, Svarer C, Blair R, Van Horn JD, Poldrack RA (2023). On the Long-term Archiving of Research Data. Neuroinformatics, 21, 243-246. DOI\nPoldrack RA (2023). Statistical Thinking: Analyzing Data in an Uncertain World. Princeton, NJ: Princeton University Press.\nSubash P, Gray A, Boswell M et al. (2023). A comparison of neuroelectrophysiology databases. Scientific Data, 10. OA DOI\nThomas AW, Ré C, Poldrack RA (2023). Benchmarking explanation methods for mental state decoding with deep learning models. NeuroImage, 273, 120109. OA DOI Code\nUddin LQ, Betzel RF, Cohen JR et al. (2023). Controversies and progress on standardization of large-scale brain network nomenclature. Network Neuroscience, 7, 864-905. DOI\n\n\n2022\nAdebimpe A, Bertolero M, Dolui S et al. (2022). ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion. Nature Methods, 19, 683-686. OA DOI\nBissett PG, Poldrack RA (2022). Estimating the Time to Do Nothing: Toward Next-Generation Models of Response Inhibition. Current Directions in Psychological Science, 31, 556-563. DOI\nCiric R, Thompson WH, Lorenz R et al. (2022). TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models. Nature Methods, 19, 1568-1571. OA DOI\nCiric R, Thomas AW, Esteban O, Poldrack RA (2022). Differentiable programming for functional connectomics. Proceedings of the 2nd Machine Learning for Health symposium, PMLR , 193.0.\nDelorme A, Truong D, Youn C, Sivagnanam S, Stirm C, Yoshimoto K, Poldrack RA, Majumdar A, Makeig S (2022). NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data. Database, 2022. OA DOI\nJwa AS, Poldrack RA (2022). The spectrum of data sharing policies in neuroimaging data repositories. Human Brain Mapping, 43, 2707-2721. OA DOI\nParet C, Unverhau N, Feingold F, Poldrack RA, Stirner M, Schmahl C, Sicorello M (2022). Survey on Open Science Practices in Functional Neuroimaging. NeuroImage, 257, 119306. DOI\nPoline J, Kennedy DN, Sommer FT et al. (2022). Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data. Neuroinformatics, 20, 507-512. OA DOI\nScherer EA, Metcalf SA, Whicker CL et al. (2022). Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder. Frontiers in Digital Health, 4. DOI\nScholz C, Chan H, Poldrack RA, de Ridder DTD, Smidts A, van der Laan LN (2022). Can we have a second helping? A preregistered direct replication study on the neurobiological mechanisms underlying self‐control. Human Brain Mapping, 43, 4995-5016. OA DOI\nThomas AW, Ré C, Poldrack RA (2022). Interpreting mental state decoding with deep learning models. Trends in Cognitive Sciences, 26, 972-986. DOI\nThomas AW, Ré C, Poldrack RA (2022). Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data. Advances in Neural Information Processing Systems 35 (NeurIPS 2022).\nVaghi MM, Hagen MP, Jones HM, Mumford JA, Bissett PG, Poldrack RA (2022). Relating psychiatric symptoms and self-regulation during the COVID-19 crisis. Translational Psychiatry, 12. OA DOI\nWalters J, King M, Bissett PG, Ivry RB, Diedrichsen J, Poldrack RA (2022). Predicting brain activation maps for arbitrary tasks with cognitive encoding models. NeuroImage, 263, 119610. OA DOI\nWimmer GE, Poldrack RA (2022). Reward learning and working memory: Effects of massed versus spaced training and post-learning delay period. Memory &amp; Cognition, 50, 312-324. OA DOI\nde la Vega A, Rocca R, Blair RW, Markiewicz CJ, Mentch J, Kent JD, Herholz P, Ghosh SS, Poldrack RA, Yarkoni T (2022). Neuroscout, a unified platform for generalizable and reproducible fMRI research. eLife, 11. OA DOI\n\n\n2021\nAczel B, Szaszi B, Nilsonne G et al. (2021). Consensus-based guidance for conducting and reporting multi-analyst studies. eLife, 10. OA DOI\nBeam E, Potts C, Poldrack RA, Etkin A (2021). A data-driven framework for mapping domains of human neurobiology. Nature Neuroscience, 24, 1733-1744. OA DOI Data Code\nBissett PG, Hagen MP, Jones HM, Poldrack RA (2021). Design issues and solutions for stop-signal data from the Adolescent Brain Cognitive Development (ABCD) study. eLife, 10. OA DOI Code\nBissett PG, Jones HM, Poldrack RA, Logan GD (2021). Severe violations of independence in response inhibition tasks. Science Advances, 7. OA DOI Data Code\nDelorme A, Truong D, Martinez-Cancino R, Pernet C, Sivagnanam S, Yoshimoto K, Poldrack R, Majumdar A, Makeig S (2021). Tools for Importing and Evaluating BIDS-EEG Formatted Data. 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 210-213. DOI\nEnkavi AZ, Poldrack RA (2021). Implications of the Lacking Relationship Between Cognitive Task and Self-report Measures for Psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6, 670-672. DOI\nFox AS, Holley D, Klink PC et al. (2021). Sharing voxelwise neuroimaging results from rhesus monkeys and other species with Neurovault. NeuroImage, 225, 117518. OA DOI\nKoenis MMG, Durnez J, Rodrigue AL et al. (2021). Associations of cannabis use disorder with cognition, brain structure, and brain function inAfrican Americans. Human Brain Mapping, 42, 1727-1741. OA DOI\nLam M, Chen C, Ge T et al. (2021). Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics. Neuropsychopharmacology, 46, 1788-1801. OA DOI\nLevitis E, van Praag CDG, Gau R et al. (2021). Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective. GigaScience, 10. OA DOI\nMacNicol E, Ciric R, Kim E, Censo DD, Cash D, Poldrack RA, Esteban O (2021). Atlas-Based Brain Extraction Is Robust Across RAT MRI Studies. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 312-315. DOI\nManapat PD, Edwards MC, MacKinnon DP, Poldrack RA, Marsch LA (2021). A Psychometric Analysis of the Brief Self-Control Scale. Assessment, 28, 395-412. OA DOI\nMarkiewicz CJ, Gorgolewski KJ, Feingold F et al. (2021). The OpenNeuro resource for sharing of neuroscience data. eLife, 10. OA DOI\nMazza GL, Smyth HL, Bissett PG et al. (2021). Correlation Database of 60 Cross-Disciplinary Surveys and Cognitive Tasks Assessing Self-Regulation. Journal of Personality Assessment, 103, 238-245. OA DOI Data\nMcNeish D, Mackinnon DP, Marsch LA, Poldrack RA (2021). Measurement in Intensive Longitudinal Data. Structural Equation Modeling: A Multidisciplinary Journal, 28, 807-822. DOI\nPoldrack RA (2021). The physics of representation. Synthese, 199, 1307-1325. DOI\nPoldrack RA (2021). Diving into the deep end: a personal reflection on the MyConnectome study. Current Opinion in Behavioral Sciences, 40, 1-4. DOI\nPoldrack RA (2021). Hard To Break: Why Our Brains Make Habits Stick. Princeton, NJ: Princeton University Press.\nRodrigue AL, Mastrovito D, Esteban O et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6, 1135-1144. OA DOI\nZmigrod L, Eisenberg IW, Bissett PG, Robbins TW, Poldrack RA (2021). The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Philosophical Transactions of the Royal Society B: Biological Sciences, 376, 20200424. OA DOI\n\n\n2020\nAron AR, Ivry RB, Jeffery KJ, Poldrack RA, Schmidt R, Summerfield C, Urai AE (2020). How Can Neuroscientists Respond to the Climate Emergency?. Neuron, 106, 17-20. DOI\nBassett DS, Cullen KE, Eickhoff SB et al. (2020). Reflections on the past two decades of neuroscience. Nature Reviews Neuroscience, 21, 524-534. DOI\nBielczyk NZ, Ando A, Badhwar A et al. (2020). Effective Self-Management for Early Career Researchers in the Natural and Life Sciences. Neuron, 106, 212-217. OA DOI OSF\nBotvinik-Nezer R, Holzmeister F, Camerer CF et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84-88. OA DOI\nColenbier N, Van de Steen F, Uddin LQ, Poldrack RA, Calhoun VD, Marinazzo D (2020). Disambiguating the role of blood flow and global signal with partial information decomposition. NeuroImage, 213, 116699. DOI\nDockès J, Poldrack RA, Primet R, Gözükan H, Yarkoni T, Suchanek F, Thirion B, Varoquaux G (2020). NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife, 9. OA DOI\nEsteban O, Ciric R, Finc K et al. (2020). Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nature Protocols, 15, 2186-2202. OA DOI\nEsteban O, Goncalves M, Markiewicz CJ, Ghosh SS, Poldrack RA (2020). Software Tool to Read, Represent, Manipulate, and Apply N-Dimensional Spatial Transforms. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 709-712. DOI\nLurie DJ, Kessler D, Bassett DS et al. (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4, 30-69. DOI OSF\nMollon J, Mathias SR, Knowles EEM et al. (2020). Cognitive impairment from early to middle adulthood in patients with affective and nonaffective psychotic disorders. Psychological Medicine, 50, 48-57. OA DOI\nPoldrack RA, Huckins G, Varoquaux G (2020). Establishment of Best Practices for Evidence for Prediction. JAMA Psychiatry, 77, 534. OA DOI\nPoldrack RA, Whitaker K, Kennedy D (2020). Introduction to the special issue on reproducibility in neuroimaging. NeuroImage, 218, 116357. DOI\nThompson WH, Kastrati G, Finc K, Wright J, Shine JM, Poldrack RA (2020). Time‐varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Human Brain Mapping, 41, 2347-2356. OA DOI\nThompson WH, Nair R, Oya H, Esteban O, Shine JM, Petkov CI, Poldrack RA, Howard M, Adolphs R (2020). A data resource from concurrent intracranial stimulation and functional MRI of the human brain. Scientific Data, 7. OA DOI\nThompson WH, Wright J, Bissett PG, Poldrack RA (2020). Dataset decay and the problem of sequential analyses on open datasets. eLife, 9. OA DOI\nTozzi L, Staveland B, Holt-Gosselin B et al. (2020). The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. NeuroImage, 214, 116715. OA DOI\n\n\n2019\nAridan N, Malecek NJ, Poldrack RA, Schonberg T (2019). Neural correlates of effort-based valuation with prospective choices. NeuroImage, 185, 446-454. OA DOI Data\nBotvinik-Nezer R, Iwanir R, Holzmeister F, Huber J, Johannesson M, Kirchler M, Dreber A, Camerer CF, Poldrack RA, Schonberg T (2019). fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study. Scientific Data, 6. OA DOI Data\nEisenberg IW, Bissett PG, Zeynep Enkavi A, Li J, MacKinnon DP, Marsch LA, Poldrack RA (2019). Uncovering the structure of self-regulation through data-driven ontology discovery. Nature Communications, 10. OA DOI Data Code OSF\nEnkavi AZ, Eisenberg IW, Bissett PG, Mazza GL, MacKinnon DP, Marsch LA, Poldrack RA (2019). Reply to Friedman and Banich: Right measures for the research question. Proceedings of the National Academy of Sciences, 116, 24398-24399. OA DOI\nEnkavi AZ, Eisenberg IW, Bissett PG, Mazza GL, MacKinnon DP, Marsch LA, Poldrack RA (2019). Large-scale analysis of test–retest reliabilities of self-regulation measures. Proceedings of the National Academy of Sciences, 116, 5472-5477. OA DOI Data OSF\nEsteban O, Blair RW, Nielson DM, Varada JC, Marrett S, Thomas AG, Poldrack RA, Gorgolewski KJ (2019). Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Scientific Data, 6. OA DOI\nEsteban O, Markiewicz CJ, Blair RW et al. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16, 111-116. OA DOI\nKebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, Kong R, Poldrack RA, Yeo BT (2019). Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biological Psychiatry, 86, 779-791. DOI\nKing M, Hernandez-Castillo CR, Poldrack RA, Ivry RB, Diedrichsen J (2019). Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature Neuroscience, 22, 1371-1378. OA DOI Data\nLam M, Hill WD, Trampush JW et al. (2019). Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways. The American Journal of Human Genetics, 105, 334-350. OA DOI\nLi M, Han Y, Aburn MJ, Breakspear M, Poldrack RA, Shine JM, Lizier JT (2019). Transitions in information processing dynamics at the whole-brain network level are driven by alterations in neural gain. PLOS Computational Biology, 15, e1006957. OA DOI\nPoldrack RA (2019). The Costs of Reproducibility. Neuron, 101, 11-14. DOI\nPoldrack RA, Feingold F, Frank MJ et al. (2019). The Importance of Standards for Sharing of Computational Models and Data. Computational Brain &amp; Behavior, 2, 229-232. DOI\nPoldrack RA, Gorgolewski KJ, Varoquaux G (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging. Annual Review of Biomedical Data Science, 2.0. DOI\nReid AT, Headley DB, Mill RD et al. (2019). Advancing functional connectivity research from association to causation. Nature Neuroscience, 22, 1751-1760. OA DOI\nShine JM, Bell PT, Matar E, Poldrack RA, Lewis SJG, Halliday GM, O’Callaghan C (2019). Dopamine depletion alters macroscopic network dynamics in Parkinson’s disease. Brain, 142, 1024-1034. OA DOI\nShine JM, Breakspear M, Bell PT, Ehgoetz Martens KA, Shine R, Koyejo O, Sporns O, Poldrack RA (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22, 289-296. DOI\nShine JM, Hearne LJ, Breakspear M, Hwang K, Müller EJ, Sporns O, Poldrack RA, Mattingley JB, Cocchi L (2019). The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei. Neuron, 104, 849-855.e3. DOI\nSmeets PA, Dagher A, Hare TA, Kullmann S, van der Laan LN, Poldrack RA, Preissl H, Small D, Stice E, Veldhuizen MG (2019). Good practice in food-related neuroimaging. The American Journal of Clinical Nutrition, 109, 491-503. OA DOI\nVaroquaux G, Poldrack RA (2019). Predictive models avoid excessive reductionism in cognitive neuroimaging. Current Opinion in Neurobiology, 55, 1-6. DOI\nVerbruggen F, Aron AR, Band GP et al. (2019). A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task. eLife, 8. OA DOI\nYarkoni T, Markiewicz CJ, de la Vega A et al. (2019). PyBIDS: Python tools for BIDS datasets. J Open Source Softw, 4. OA DOI\nZuo X, Biswal BB, Poldrack RA (2019). Editorial: Reliability and Reproducibility in Functional Connectomics. Frontiers in Neuroscience, 13. DOI\n\n\n2018\nBakkour A, Botvinik-Nezer R, Cohen N, Hover AM, Poldrack RA, Schonberg T (2018). Spacing of cue-approach training leads to better maintenance of behavioral change. PLOS ONE, 13, e0201580. OA DOI OSF\nDavies G, Lam M, Harris SE et al. (2018). Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications, 9. OA DOI\nDockès J, Wassermann D, Poldrack R, Suchanek F, Thirion B, Varoquaux G (2018). Text to Brain: Predicting the Spatial Distribution of Neuroimaging Observations from Text Reports. In Lecture Notes in Computer Science (p. 584-592). Springer International Publishing. DOI\nEisenberg IW, Bissett PG, Canning JR et al. (2018). Applying novel technologies and methods to inform the ontology of self-regulation. Behaviour Research and Therapy, 101, 46-57. OA DOI OSF\nEsteban O, Poldrack RA, Gorgolewski KJ (2018). Improving Out-of-Sample Prediction of Quality of MRIQC. In Lecture Notes in Computer Science (p. 190-199). Springer International Publishing. DOI\nGorgolewski KJ, Nichols T, Kennedy DN, Poline J, Poldrack RA (2018). Making replication prestigious. Behavioral and Brain Sciences, 41. DOI\nLam M, Trampush JW, Yu J et al. (2018). Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018). Twin Research and Human Genetics, 21, 394-397. DOI OSF\nMathias SR, Knowles EE, Barrett J, Beetham T, Leach O, Buccheri S, Aberizk K, Blangero J, Poldrack RA, Glahn DC (2018). Deficits in visual working-memory capacity and general cognition in African Americans with psychosis. Schizophrenia Research, 193, 100-106. OA DOI\nNaselaris T, Bassett DS, Fletcher AK, Kording K, Kriegeskorte N, Nienborg H, Poldrack RA, Shohamy D, Kay K (2018). Cognitive Computational Neuroscience: A New Conference for an Emerging Discipline. Trends in Cognitive Sciences, 22, 365-367. OA DOI\nPoldrack RA (2018). The New Mind Readers: What Neuroimaging Can and Cannot Reveal about our Thoughts. Princeton, NJ: Princeton University Press.\nPoldrack RA, Monahan J, Imrey PB, Reyna V, Raichle ME, Faigman D, Buckholtz JW (2018). Predicting Violent Behavior: What Can Neuroscience Add?. Trends in Cognitive Sciences, 22, 111-123. OA DOI OSF\nPoldrack RA, Sandak R (2018). Introduction to This Special Issue: The Cognitive Neuroscience of Reading. In The Cognitive Neuroscience of Reading (p. 199-202). Routledge. DOI\nSavage JE, Jansen PR, Stringer S et al. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50, 912-919. OA DOI\nShine JM, Aburn MJ, Breakspear M, Poldrack RA (2018). The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife, 7. OA DOI\nShine JM, Poldrack RA (2018). Principles of dynamic network reconfiguration across diverse brain states. NeuroImage, 180, 396-405. DOI\nShine JM, van den Brink RL, Hernaus D, Nieuwenhuis S, Poldrack RA (2018). Catecholaminergic manipulation alters dynamic network topology across cognitive states. Network Neuroscience, 2, 381-396. DOI\nTansey W, Koyejo O, Poldrack RA, Scott JG (2018). False Discovery Rate Smoothing. Journal of the American Statistical Association, 113, 1156-1171. DOI\nVaroquaux G, Schwartz Y, Poldrack RA, Gauthier B, Bzdok D, Poline J, Thirion B (2018). Atlases of cognition with large-scale human brain mapping. PLOS Computational Biology, 14, e1006565. OA DOI\nWhite C, Poldrack RA (2018). Methods for fMRI analysis. In The Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition (Volume 5) (EJ Wagenmakers, Ed.) Wiley. DOI\nWimmer GE, Li JK, Gorgolewski KJ, Poldrack RA (2018). Reward Learning over Weeks Versus Minutes Increases the Neural Representation of Value in the Human Brain. The Journal of Neuroscience, 38, 7649-7666. OA DOI OSF\n\n\n2017\nAcikalin MY, Gorgolewski KJ, Poldrack RA (2017). A Coordinate-Based Meta-Analysis of Overlaps in Regional Specialization and Functional Connectivity across Subjective Value and Default Mode Networks. Frontiers in Neuroscience, 11. DOI\nBakkour A, Lewis-Peacock JA, Poldrack RA, Schonberg T (2017). Neural mechanisms of cue-approach training. NeuroImage, 151, 92-104. OA DOI\nEckert MA, Vaden KI, Maxwell AB et al. (2017). Common Brain Structure Findings Across Children with Varied Reading Disability Profiles. Scientific Reports, 7. OA DOI\nEsteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLOS ONE, 12, e0184661. OA DOI OSF\nGilron R, Rosenblatt J, Koyejo O, Poldrack RA, Mukamel R (2017). What’s in a pattern? Examining the type of signal multivariate analysis uncovers at the group level. NeuroImage, 146, 113-120. DOI\nGorgolewski KJ, Alfaro-Almagro F, Auer T et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, 13, e1005209. OA DOI\nGorgolewski KJ, Durnez J, Poldrack RA (2017). Preprocessed Consortium for Neuropsychiatric Phenomics dataset. F1000Research, 6, 1262. DOI\nKhanna R, Ghosh J, Poldrack RA, Koyejo O (2017). A Deflation Method for Structured Probabilistic PCA. Proceedings of the 2017 SIAM International Conference on Data Mining. DOI\nKiar G, Gorgolewski KJ, Kleissas D et al. (2017). Science in the cloud (SIC): A use case in MRI connectomics. Giga Science, 6. OA DOI\nLam M, Trampush JW, Yu J et al. (2017). Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets. Cell Reports, 21, 2597-2613. OA DOI\nLenartowicz A, Poldrack R (2017). Brain Imaging. In Encyclopedia of Behavioral Neuroscience, 2nd edition (p. 77-83). Elsevier. DOI\nMathias SR, Knowles EEM, Barrett J, Leach O, Buccheri S, Beetham T, Blangero J, Poldrack RA, Glahn DC (2017). The Processing-Speed Impairment in Psychosis Is More Than Just Accelerated Aging. Schizophrenia Bulletin, sbw168. OA DOI\nNichols TE, Das S, Eickhoff SB et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20, 299-303. OA DOI\nPoldrack R (2017). Neuroscience: The risks of reading the brain. Nature, 541, 156-156. DOI\nPoldrack RA (2017). Precision Neuroscience: Dense Sampling of Individual Brains. Neuron, 95, 727-729. DOI\nPoldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, Munafò MR, Nichols TE, Poline J, Vul E, Yarkoni T (2017). Scanning the horizon: towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18, 115-126. OA DOI OSF\nPoldrack RA, Gorgolewski KJ (2017). OpenfMRI: Open sharing of task fMRI data. NeuroImage, 144, 259-261. OA DOI\nRubin TN, Koyejo O, Gorgolewski KJ, Jones MN, Poldrack RA, Yarkoni T (2017). Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLOS Computational Biology, 13, e1005649. OA DOI\nShine JM, Kucyi A, Foster BL, Bickel S, Wang D, Liu H, Poldrack RA, Hsieh L, Hsiang JC, Parvizi J (2017). Distinct Patterns of Temporal and Directional Connectivity among Intrinsic Networks in the Human Brain. The Journal of Neuroscience, 37, 9667-9674. OA DOI\nTrampush JW, Yang MLZ, Yu J et al. (2017). GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium. Molecular Psychiatry, 22, 336-345. OA DOI\nXiao X, Dong Q, Gao J, Men W, Poldrack RA, Xue G (2017). Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval. The Journal of Neuroscience, 37, 2986-2998. OA DOI\n\n\n2016\nBakkour A, Leuker C, Hover AM, Giles N, Poldrack RA, Schonberg T (2016). Mechanisms of Choice Behavior Shift Using Cue-approach Training. Frontiers in Psychology, 7. DOI\nEckert MA, Berninger VW, Hoeft F, Vaden KI (2016). A case of Bilateral Perisylvian Syndrome with reading disability. Cortex, 76, 121-124. OA DOI\nEisenberg I, Poldrack RA (2016). Task-set Selection in Probabilistic Environments: a Model of Task-set Inference. Proceedings of Cognitive Science Society.\nGorgolewski KJ, Auer T, Calhoun VD et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3. OA DOI\nGorgolewski KJ, Poldrack RA (2016). A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLOS Biology, 14, e1002506. OA DOI\nGorgolewski KJ, Varoquaux G, Rivera G et al. (2016). NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. NeuroImage, 124, 1242-1244. OA DOI\nHastings J, Frishkoff G, Smith B et al. (2016). Interdyscyplinarne perspektywy rozwoju, integracji i zastosowań ontologii poznawczych. AVANT. The Journal of the Philosophical-Interdisciplinary Vanguard, VII, 101-117. DOI\nHodgson K, Poldrack RA, Curran JE et al. (2016). Shared Genetic Factors Influence Head Motion During MRI and Body Mass Index. Cerebral Cortex. OA DOI\nPatterson TK, Lenartowicz A, Berkman ET, Ji D, Poldrack RA, Knowlton BJ (2016). Putting the brakes on the brakes: negative emotion disrupts cognitive control network functioning and alters subsequent stopping ability. Experimental Brain Research, 234, 3107-3118. OA DOI\nPoldrack R, Congdon E, Triplett W et al. (2016). A phenome-wide examination of neural and cognitive function. Scientific Data, 3. OA DOI Data\nPoldrack RA, Yarkoni T (2016). From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure. Annual Review of Psychology, 67, 587-612. OA DOI\nShine J, Bissett P, Bell P, Koyejo O, Balsters J, Gorgolewski K, Moodie C, Poldrack R (2016). The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron, 92, 544-554. OA DOI\nShine JM, Eisenberg I, Poldrack RA (2016). Computational specificity in the human brain. Behavioral and Brain Sciences, 39. DOI\nShine JM, Koyejo O, Poldrack RA (2016). Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention. Proceedings of the National Academy of Sciences, 113, 9888-9891. OA DOI\nSochat VV, Eisenberg IW, Enkavi AZ, Li J, Bissett PG, Poldrack RA (2016). The Experiment Factory: Standardizing Behavioral Experiments. Frontiers in Psychology, 7. DOI\nWager TD, Atlas LY, Botvinick MM, Chang LJ, Coghill RC, Davis KD, Iannetti GD, Poldrack RA, Shackman AJ, Yarkoni T (2016). Pain in the ACC?. Proceedings of the National Academy of Sciences, 113. OA DOI\nWiener M, Sommer F, Ives Z, Poldrack R, Litt B (2016). Enabling an Open Data Ecosystem for the Neurosciences. Neuron, 92, 617-621. DOI\nWorthy DA, Davis T, Gorlick MA, Cooper JA, Bakkour A, Mumford JA, Poldrack RA, Todd Maddox W (2016). Neural correlates of state-based decision-making in younger and older adults. NeuroImage, 130, 13-23. OA DOI\n\n\n2015\nChen M, Jimura K, White CN, Maddox WT, Poldrack RA (2015). Multiple brain networks contribute to the acquisition of bias in perceptual decision-making. Frontiers in Neuroscience, 9. DOI\nGorgolewski KJ, Varoquaux G, Rivera G et al. (2015). NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in Neuroinformatics, 9. DOI\nHelfinstein SM, Mumford JA, Poldrack RA (2015). If all your friends jumped off a bridge: The effect of others’ actions on engagement in and recommendation of risky behaviors. Journal of Experimental Psychology: General, 144, 12-17. DOI\nKhanna R, Ghosh J, Poldrack RA, Koyejo O (2015). Sparse submodular probabilistic PCA. Proceedings of the 18th International conference on Artificial Intelligence and Statistics (AISTATS) .\nLaumann T, Gordon E, Adeyemo B et al. (2015). Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Neuron, 87, 657-670. OA DOI Data\nMumford JA, Poline J, Poldrack RA (2015). Orthogonalization of Regressors in fMRI Models. PLOS ONE, 10, e0126255. OA DOI\nPoldrack R (2015). Reverse Inference. In Brain Mapping (p. 647-650). Elsevier. DOI\nPoldrack R (2015). Introduction to Cognitive Neuroscience. In Brain Mapping (p. 259-260). Elsevier. DOI\nPoldrack RA (2015). Is “efficiency” a useful concept in cognitive neuroscience?. Developmental Cognitive Neuroscience, 11, 12-17. OA DOI Code\nPoldrack RA, Farah MJ (2015). Progress and challenges in probing the human brain. Nature, 526, 371-379. DOI\nPoldrack RA, Laumann TO, Koyejo O et al. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6. OA DOI Data Code\nPoldrack RA, Poline J (2015). The publication and reproducibility challenges of shared data. Trends in Cognitive Sciences, 19, 59-61. DOI\nShine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA (2015). Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage, 122, 399-407. DOI\nSochat VV, Gorgolewski KJ, Koyejo O, Durnez J, Poldrack RA (2015). Effects of thresholding on correlation-based image similarity metrics. Frontiers in Neuroscience, 9. DOI\n\n\n2014\nAron AR, Robbins TW, Poldrack RA (2014). Right inferior frontal cortex: addressing the rebuttals. Frontiers in Human Neuroscience, 8. DOI\nAron AR, Robbins TW, Poldrack RA (2014). Inhibition and the right inferior frontal cortex: one decade on. Trends in Cognitive Sciences, 18, 177-185. DOI\nCongdon E, Altshuler LL, Mumford JA et al. (2014). Neural activation during response inhibition in adult attention-deficit/hyperactivity disorder: Preliminary findings on the effects of medication and symptom severity. Psychiatry Research: Neuroimaging, 222, 17-28. OA DOI\nDavis T, LaRocque KF, Mumford JA, Norman KA, Wagner AD, Poldrack RA (2014). What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. NeuroImage, 97, 271-283. OA DOI Code\nDavis T, Poldrack RA (2014). Quantifying the Internal Structure of Categories Using a Neural Typicality Measure. Cerebral Cortex, 24, 1720-1737. DOI\nDavis T, Xue G, Love BC, Preston AR, Poldrack RA (2014). Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory. The Journal of Neuroscience, 34, 7472-7484. OA DOI\nHastings J, Frishkoff GA, Smith B, Jensen M, Poldrack RA, Lomax J, Bandrowski A, Imam F, Turner JA, Martone ME (2014). Interdisciplinary perspectives on the development, integration, and application of cognitive ontologies. Frontiers in Neuroinformatics, 8. DOI\nHelfinstein SM, Schonberg T, Congdon E, Karlsgodt KH, Mumford JA, Sabb FW, Cannon TD, London ED, Bilder RM, Poldrack RA (2014). Predicting risky choices from brain activity patterns. Proceedings of the National Academy of Sciences, 111, 2470-2475. OA DOI Data\nJimura K, Cazalis F, Stover ERS, Poldrack RA (2014). The neural basis of task switching changes with skill acquisition. Frontiers in Human Neuroscience, 8. DOI\nKoyejo O, Khanna R, Ghosh J, Poldrack RA (2014). On Prior Distributions and Approximate Inference for Structured Variables. Advances in Neural Information Processing Systems, 27.0.\nMumford JA, Davis T, Poldrack RA (2014). The impact of study design on pattern estimation for single-trial multivariate pattern analysis. NeuroImage, 103, 130-138. DOI\nPoldrack RA, Gorgolewski KJ (2014). Making big data open: data sharing in neuroimaging. Nature Neuroscience, 17, 1510-1517. DOI\nSchonberg T, Bakkour A, Hover AM, Mumford JA, Nagar L, Perez J, Poldrack RA (2014). Changing value through cued approach: an automatic mechanism of behavior change. Nature Neuroscience, 17, 625-630. OA DOI\nSchonberg T, Bakkour A, Hover AM, Mumford JA, Poldrack RA (2014). Influencing Food Choices by Training: Evidence for Modulation of Frontoparietal Control Signals. Journal of Cognitive Neuroscience, 26, 247-268. OA DOI\nThakkar KN, Congdon E, Poldrack RA, Sabb FW, London ED, Cannon TD, Bilder RM (2014). Women are more sensitive than men to prior trial events on the Stop‐signal task. British Journal of Psychology, 105, 254-272. OA DOI\nWagshal D, Knowlton BJ, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Asarnow RF (2014). Impaired automatization of a cognitive skill in first-degree relatives of patients with schizophrenia. Psychiatry Research, 215, 294-299. OA DOI\nWagshal D, Knowlton BJ, Suthana NA, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Asarnow RF (2014). Evidence for Corticostriatal Dysfunction During Cognitive Skill Learning in Adolescent Siblings of Patients With Childhood-Onset Schizophrenia. Schizophrenia Bulletin, 40, 1030-1039. OA DOI\nWhite CN, Congdon E, Mumford JA, Karlsgodt KH, Sabb FW, Freimer NB, London ED, Cannon TD, Bilder RM, Poldrack RA (2014). Decomposing Decision Components in the Stop-signal Task: A Model-based Approach to Individual Differences in Inhibitory Control. Journal of Cognitive Neuroscience, 26, 1601-1614. OA DOI Data\nWhite CN, Poldrack RA (2014). Decomposing bias in different types of simple decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40, 385-398. DOI Data Code\n\n\n2013\nBarch DM, Burgess GC, Harms MP et al. (2013). Function in the human connectome: Task-fMRI and individual differences in behavior. NeuroImage, 80, 169-189. OA DOI\nBrakewood B, Poldrack RA (2013). The ethics of secondary data analysis: Considering the application of Belmont principles to the sharing of neuroimaging data. NeuroImage, 82, 671-676. DOI\nCongdon E, Bato AA, Schonberg T, Mumford JA, Karlsgodt KH, Sabb FW, London ED, Cannon TD, Bilder RM, Poldrack RA (2013). Differences in neural activation as a function of risk-taking task parameters. Frontiers in Neuroscience, 7. DOI\nDavis T, Poldrack RA (2013). Measuring neural representations with fMRI: practices and pitfalls. Annals of the New York Academy of Sciences, 1296, 108-134. DOI\nFox CR, Poldrack RA (2013). Prospect theory and the brain. In Neuroeconomics: Decision Making and the Brain (2nd Edition) (P Glimcher et al. , Ed.) Elsevier.\nGalván A, Schonberg T, Mumford J, Kohno M, Poldrack RA, London ED (2013). Greater risk sensitivity of dorsolateral prefrontal cortex in young smokers than in nonsmokers. Psychopharmacology, 229, 345-355. OA DOI\nHsieh CJ, Sustik MA, Dhillon IS, Ravikumar R, Poldrack RA (2013). BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables. Advances in Neural Information Processing Systems .\nKoyejo O, Patel P, Ghosh J, Poldrack RA (2013). Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models. 2013 International Workshop on Pattern Recognition in Neuroimaging, 9-12. DOI\nMalecek NJ, Poldrack RA (2013). Beyond dopamine: The noradrenergic system and mental effort. Behavioral and Brain Sciences, 36, 698-699. DOI\nPark M, Koyejo O, Ghosh J, Poldrack RA, Pillow JW (2013). Bayesian structure learning for functional neuroimaging. Sixteenth International Conference on Artificial Intelligence and Statistics (AIST ATS). .\nPoldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD, Devlin JT, Cumba C, Koyejo O, Milham MP (2013). Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in Neuroinformatics, 7. DOI\nPoline J, Poldrack R (2013). Introduction to the special issue: Toward a new era of databasing and data sharing for neuroimaging. NeuroImage, 82, 645-646. DOI\nVan Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62-79. OA DOI\nWhite CN, Poldrack RA (2013). Using fMRI to Constrain Theories of Cognition. Perspectives on Psychological Science, 8, 79-83. DOI\nXue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA (2013). Complementary Role of Frontoparietal Activity and Cortical Pattern Similarity in Successful Episodic Memory Encoding. Cerebral Cortex, 23, 1562-1571. OA DOI\n\n\n2012\nCongdon E, Mumford JA, Cohen JR, Galvan A, Canli T, Poldrack RA (2012). Measurement and Reliability of Response Inhibition. Frontiers in Psychology, 3. DOI\nCourtney KE, Arellano R, Barkley‐Levenson E, Gálvan A, Poldrack RA, MacKillop J, David Jentsch J, Ray LA (2012). The Relationship Between Measures of Impulsivity and Alcohol Misuse: An Integrative Structural Equation Modeling Approach. Alcoholism: Clinical and Experimental Research, 36, 923-931. OA DOI\nGhahremani DG, Lee B, Robertson CL et al. (2012). Striatal Dopamine D2/D3Receptors Mediate Response Inhibition and Related Activity in Frontostriatal Neural Circuitry in Humans. The Journal of Neuroscience, 32, 7316-7324. OA DOI\nHelfinstein SM, Poldrack RA (2012). The young and the reckless. Nature Neuroscience, 15, 803-805. DOI\nJimura K, Poldrack RA (2012). Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia, 50, 544-552. DOI\nMumford JA, Turner BO, Ashby FG, Poldrack RA (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59, 2636-2643. OA DOI\nPoldrack RA (2012). The future of fMRI in cognitive neuroscience. NeuroImage, 62, 1216-1220. OA DOI\nPoldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T (2012). Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping. PLoS Computational Biology, 8, e1002707. OA DOI Code\nPoline J, Breeze JL, Ghosh S et al. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6. DOI\nPoline J, Poldrack RA (2012). Frontiers in Brain Imaging Methods Grand Challenge. Frontiers in Neuroscience, 6. DOI\nSatpute AB, Mumford JA, Naliboff BD, Poldrack RA (2012). Human anterior and posterior hippocampus respond distinctly to state and trait anxiety. Emotion, 12, 58-68. DOI\nSchonberg T, Fox CR, Mumford JA, Congdon E, Trepel C, Poldrack RA (2012). Decreasing Ventromedial Prefrontal Cortex Activity During Sequential Risk-Taking: An fMRI Investigation of the Balloon Analog Risk Task. Frontiers in Neuroscience, 6. DOI Data\nTurner BO, Mumford JA, Poldrack RA, Ashby FG (2012). Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. NeuroImage, 62, 1429-1438. OA DOI\nWagshal D, Knowlton BJ, Cohen JR, Poldrack RA, Bookheimer SY, Bilder RM, Fernandez VG, Asarnow RF (2012). Deficits in probabilistic classification learning and liability for schizophrenia. Psychiatry Research, 200, 167-172. OA DOI\nWhite CN, Mumford JA, Poldrack RA (2012). Perceptual Criteria in the Human Brain. The Journal of Neuroscience, 32, 16716-16724. OA DOI\n\n\n2011\nCohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA (2011). Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications. Frontiers in Neuroscience, 5. DOI\nGalván A, Poldrack RA, Baker CM, McGlennen KM, London ED (2011). Neural Correlates of Response Inhibition and Cigarette Smoking in Late Adolescence. Neuropsychopharmacology, 36, 970-978. OA DOI\nGhahremani DG, Tabibnia G, Monterosso J, Hellemann G, Poldrack RA, London ED (2011). Effect of Modafinil on Learning and Task-Related Brain Activity in Methamphetamine-Dependent and Healthy Individuals. Neuropsychopharmacology, 36, 950-959. OA DOI\nHübner NO, Assadian O, Poldrack R et al. (2011). Endowashers: an overlooked risk for possible post-endoscopic infections. GMS Krankenhhyg Interdiszip, 6, Doc13. OA DOI\nLenartowicz A, Verbruggen F, Logan GD, Poldrack RA (2011). Inhibition-related Activation in the Right Inferior Frontal Gyrus in the Absence of Inhibitory Cues. Journal of Cognitive Neuroscience, 23, 3388-3399. DOI\nPoldrack R (2011). Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding. Neuron, 72, 692-697. OA DOI\nPoldrack RA, Kittur A, Kalar D, Miller E, Seppa C, Gil Y, Parker DS, Sabb FW, Bilder RM (2011). The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience. Frontiers in Neuroinformatics, 5. DOI\nPoldrack RA, Mumford JA, Nichols TE (2011). Handbook of Functional MRI Data Analysis. Cambridge: Cambridge University Press.\nRizk-Jackson A, Stoffers D, Sheldon S, Kuperman J, Dale A, Goldstein J, Corey-Bloom J, Poldrack RA, Aron AR (2011). Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington’s disease using machine learning techniques. NeuroImage, 56, 788-796. DOI\nSchonberg T, Fox CR, Poldrack RA (2011). Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends in Cognitive Sciences, 15, 11-19. OA DOI\nStern JM, Caporro M, Haneef Z, Yeh HJ, Buttinelli C, Lenartowicz A, Mumford JA, Parvizi J, Poldrack RA (2011). Functional imaging of sleep vertex sharp transients. Clinical Neurophysiology, 122, 1382-1386. OA DOI\nTabibnia G, Monterosso JR, Baicy K, Aron AR, Poldrack RA, Chakrapani S, Lee B, London ED (2011). Different Forms of Self-Control Share a Neurocognitive Substrate. The Journal of Neuroscience, 31, 4805-4810. OA DOI\nXue G, Mei L, Chen C, Lu Z, Poldrack R, Dong Q (2011). Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression. Journal of Cognitive Neuroscience, 23, 1624-1633. OA DOI\nYarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8, 665-670. OA DOI\n\n\n2010\nCho S, Moody TD, Fernandino L, Mumford JA, Poldrack RA, Cannon TD, Knowlton BJ, Holyoak KJ (2010). Common and Dissociable Prefrontal Loci Associated with Component Mechanisms of Analogical Reasoning. Cerebral Cortex, 20, 524-533. DOI\nCohen J (2010). Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals. Frontiers in Human Neuroscience. DOI\nCohen JR, Asarnow RF, Sabb FW, Bilder RM, Bookheimer SY, Knowlton BJ, Poldrack RA (2010). A unique adolescent response to reward prediction errors. Nature Neuroscience, 13, 669-671. OA DOI\nCongdon E, Mumford JA, Cohen JR, Galvan A, Aron AR, Xue G, Miller E, Poldrack RA (2010). Engagement of large-scale networks is related to individual differences in inhibitory control. NeuroImage, 53, 653-663. OA DOI\nCongdon E, Poldrack RA, Freimer NB (2010). Neurocognitive Phenotypes and Genetic Dissection of Disorders of Brain and Behavior. Neuron, 68, 218-230. OA DOI\nCrone EA, Poldrack RA, Durston S (2010). Challenges and methods in developmental neuroimaging. Human Brain Mapping, 31, 835-837. OA DOI\nGhahremani DG, Monterosso J, Jentsch JD, Bilder RM, Poldrack RA (2010). Neural Components Underlying Behavioral Flexibility in Human Reversal Learning. Cerebral Cortex, 20, 1843-1852. OA DOI\nKenner NM, Mumford JA, Hommer RE, Skup M, Leibenluft E, Poldrack RA (2010). Inhibitory Motor Control in Response Stopping and Response Switching. Journal of Neuroscience, 30, 8512-8518. OA DOI\nKriegeskorte N, Lindquist MA, Nichols TE, Poldrack RA, Vul E (2010). Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask. Journal of Cerebral Blood Flow &amp; Metabolism, 30, 1551-1557. OA DOI\nLenartowicz A, Kalar DJ, Congdon E, Poldrack RA (2010). Towards an Ontology of Cognitive Control. Topics in Cognitive Science, 2, 678-692. DOI\nLenartowicz A, Poldrack R (2010). Brain Imaging. In Encyclopedia of Behavioral Neuroscience (p. 187-193). Elsevier. DOI\nMumford JA, Horvath S, Oldham MC, Langfelder P, Geschwind DH, Poldrack RA (2010). Detecting network modules in fMRI time series: A weighted network analysis approach. NeuroImage, 52, 1465-1476. OA DOI\nPoldrack RA (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?. Perspectives on Psychological Science, 5, 753-761. DOI\nPoldrack RA (2010). Interpreting developmental changes in neuroimaging signals. Human Brain Mapping, 31, 872-878. OA DOI\nPoldrack RA, Carr V, Foerde K (2010). Flexibility and generalization in memory systems. In Generalization of Knowledge: Multidisciplinary perspectives  (M Banich, D Caccamise , Ed.) New York, NY: Psychology Press..\nPoldrack RA, Mumford JA (2010). On the proper role of non-independent ROI analysis: A commentary on Vul and Kanwisher. In Foundational Issues in Human Brain Mapping (SJ Hanson, M Bunzl, Ed.) Cambridge, MA: MIT Press. DOI\nRamsey J, Hanson S, Hanson C, Halchenko Y, Poldrack R, Glymour C (2010). Six problems for causal inference from fMRI. NeuroImage, 49, 1545-1558. DOI\nScott-Van Zeeland AA, Abrahams BS, Alvarez-Retuerto AI et al. (2010). Altered Functional Connectivity in Frontal Lobe Circuits Is Associated with Variation in the Autism Risk Gene CNTNAP2. Science Translational Medicine, 2. OA DOI\nScott‐Van Zeeland AA, Dapretto M, Ghahremani DG, Poldrack RA, Bookheimer SY (2010). Reward processing in autism. Autism Research, 3, 53-67. OA DOI\nXue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA (2010). Greater Neural Pattern Similarity Across Repetitions Is Associated with Better Memory. Science, 330, 97-101. OA DOI\nXue G, Mei L, Chen C, Lu Z, Poldrack RA, Dong Q (2010). Facilitating Memory for Novel Characters by Reducing Neural Repetition Suppression in the Left Fusiform Cortex. PLoS ONE, 5, e13204. OA DOI\nYarkoni T, Poldrack RA, Van Essen DC, Wager TD (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends in Cognitive Sciences, 14, 489-496. OA DOI\n\n\n2009\nAron A, Poldrack R, Wise S (2009). Cognition: Basal Ganglia Role. In Encyclopedia of Neuroscience (p. 1069-1077). Elsevier. DOI\nAron AR, Wise SP, Poldrack RA (2009). Role of the basal ganglia in cognition. In The New Encyclopedia of Neuroscience (Volume 2) (LR Squire, Ed.) Oxford, UK: Academic Press.\nBarch DM, Braver TS, Carter CS, Poldrack RA, Robbins TW (2009). CNTRICS Final Task Selection: Executive Control. Schizophrenia Bulletin, 35, 115-135. OA DOI\nBilder R, Sabb F, Cannon T, London E, Jentsch J, Parker DS, Poldrack R, Evans C, Freimer N (2009). Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience, 164, 30-42. OA DOI\nBilder RM, Poldrack RA, Parker DS, Reise SP, Jentsch JD, Cannon T, London E, Sabb FW, Foland L, Rizk-Jackson A, Kalar D, Brown N, Carstensen A, Freimer N (2009). Cognitive phenomics. In Handbook of Neuropsychology of Mental Disorders  (S Wood, N Allen, C Pantelis , Ed.) Cambridge: Cambridge University Press.\nBilder RM, Poldrack RA, Parker DS, Reise SP, Jentsch JD, Cannon T, London E, Sabb FW, Foland-Ross L, Rizk-Jackson A, Kalar D, Brown N, Carstensen A, Freimer N (2009). Cognitive phenomics. In The Neuropsychology of Mental Illness (p. 271-282). Cambridge University Press. DOI\nBilder RM, Sabb FW, Parker DS, Kalar D, Chu WW, Fox J, Freimer NB, Poldrack RA (2009). Cognitive ontologies for neuropsychiatric phenomics research. Cognitive Neuropsychiatry, 14, 419-450. OA DOI\nFoerde K, Poldrack R (2009). Procedural Learning in Humans. In Encyclopedia of Neuroscience (p. 1083-1091). Elsevier. DOI\nFox CR, Poldrack RA (2009). Prospect Theory and the Brain. In Neuroeconomics (p. 145-173). Elsevier. DOI\nGhahremani DG, Poldrack RA (2009). Neuroimaging and interactive memory systems. In Neuroimaging of Human MemoryLinking cognitive processes to neural systems (p. 77-88). Oxford University Press. DOI\nGlimcher P, Fehr E, Camerer C, Poldrack RA (Eds.) (2009). Neuroeconomics. San Diego: Academic Press. DOI\nGlimcher PW, Camerer CF, Fehr E, Poldrack RA (2009). Introduction. In Neuroeconomics (p. 1-12). Elsevier. DOI\nLee B, London ED, Poldrack RA et al. (2009). Striatal Dopamine D2/D3Receptor Availability Is Reduced in Methamphetamine Dependence and Is Linked to Impulsivity. The Journal of Neuroscience, 29, 14734-14740. OA DOI\nPoldrack RA, Halchenko YO, Hanson SJ (2009). Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals. Psychological Science, 20, 1364-1372. OA DOI\nPoldrack RA, Mumford JA (2009). Independence in ROI analysis: where is the voodoo?. Social Cognitive and Affective Neuroscience, 4, 208-213. OA DOI\nSabb F, Burggren A, Higier R et al. (2009). Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence. Neuroscience, 164, 88-107. OA DOI\nThompson PM, Miller MI, Poldrack RA, Nichols TE, Taylor JE, Worsley KJ, Ratnanather JT (2009). Special Issue on Mathematics in Brain Imaging. NeuroImage, 45, S1-S2. DOI\nVANHORN J (2009). Functional MRI at the crossroads. International Journal of Psychophysiology, 73, 3-9. OA DOI\n\n\n2008\nCOHEN JR, POLDRACK RA (2008). Automaticity in motor sequence learning does not impair response inhibition. Psychonomic Bulletin &amp; Review, 15, 108-115. DOI\nFoerde K, Poldrack RA, Knowlton BJ et al. (2008). Selective corticostriatal dysfunction in schizophrenia: Examination of motor and cognitive skill learning. Neuropsychology, 22, 100-109. DOI\nGluck MA, Poldrack RA, Kéri S (2008). The cognitive neuroscience of category learning. Neuroscience &amp; Biobehavioral Reviews, 32, 193-196. DOI\nKarlsgodt KH, van Erp TG, Poldrack RA, Bearden CE, Nuechterlein KH, Cannon TD (2008). Diffusion Tensor Imaging of the Superior Longitudinal Fasciculus and Working Memory in Recent-Onset Schizophrenia. Biological Psychiatry, 63, 512-518. DOI\nPoldrack RA (2008). The role of fMRI in Cognitive Neuroscience: where do we stand?. Current Opinion in Neurobiology, 18, 223-227. DOI\nPoldrack RA, Fletcher PC, Henson RN, Worsley KJ, Brett M, Nichols TE (2008). Guidelines for reporting an fMRI study. NeuroImage, 40, 409-414. OA DOI\nPoldrack RA, Foerde K (2008). Category learning and the memory systems debate. Neuroscience &amp; Biobehavioral Reviews, 32, 197-205. DOI\nPoldrack RA, Wagner AD (2008). The Interface Between Neuroscience and Psychological Science. Current Directions in Psychological Science, 17, 61-61. DOI\nRaizada RDS (2008). Challenge-driven attention: Interacting frontal and brainstem systems. Frontiers in Human Neuroscience, 1. DOI\nShattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008). Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage, 39, 1064-1080. OA DOI\nTohka J, Foerde K, Aron AR, Tom SM, Toga AW, Poldrack RA (2008). Automatic independent component labeling for artifact removal in fMRI. NeuroImage, 39, 1227-1245. OA DOI\nXue G, Aron AR, Poldrack RA (2008). Common Neural Substrates for Inhibition of Spoken and Manual Responses. Cerebral Cortex, 18, 1923-1932. DOI Data\nXue G, Ghahremani DG, Poldrack RA (2008). Neural Substrates for Reversing Stimulus–Outcome and Stimulus–Response Associations. The Journal of Neuroscience, 28, 11196-11204. OA DOI\n\n\n2007\nAron AR, Behrens TE, Smith S, Frank MJ, Poldrack RA (2007). Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI. The Journal of Neuroscience, 27, 3743-3752. OA DOI\nDevlin JT, Poldrack RA (2007). In praise of tedious anatomy. NeuroImage, 37, 1033-1041. OA DOI\nFoerde K, Poldrack RA, Knowlton BJ (2007). Secondary-task effects on classification learning. Memory &amp; Cognition, 35, 864-874. DOI\nMumford JA, Poldrack RA (2007). Modeling group fMRI data. Social Cognitive and Affective Neuroscience, 2, 251-257. OA DOI\nPoldrack RA (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2, 67-70. OA DOI\nPoldrack RA, Devlin JT (2007). On the fundamental role of anatomy in functional imaging: Reply to commentaries on “In praise of tedious anatomy”. NeuroImage, 37, 1066-1068. DOI\nRaizada RD, Poldrack RA (2007). Selective Amplification of Stimulus Differences during Categorical Processing of Speech. Neuron, 56, 726-740. DOI\nThermenos HW, Seidman LJ, Poldrack RA, Peace NK, Koch JK, Faraone SV, Tsuang MT (2007). Elaborative Verbal Encoding and Altered Anterior Parahippocampal Activation in Adolescents and Young Adults at Genetic Risk for Schizophrenia Using fMRI. Biological Psychiatry, 61, 564-574. DOI\nTom SM, Fox CR, Trepel C, Poldrack RA (2007). The Neural Basis of Loss Aversion in Decision-Making Under Risk. Science, 315, 515-518. DOI Data\nXue G, Poldrack RA (2007). The Neural Substrates of Visual Perceptual Learning of Words: Implications for the Visual Word Form Area Hypothesis. Journal of Cognitive Neuroscience, 19, 1643-1655. DOI\n\n\n2006\nAron AR, Gluck MA, Poldrack RA (2006). Long-term test–retest reliability of functional MRI in a classification learning task. NeuroImage, 29, 1000-1006. OA DOI Data\nAron AR, Poldrack RA (2006). Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus. The Journal of Neuroscience, 26, 2424-2433. OA DOI\nFoerde K, Knowlton BJ, Poldrack RA (2006). Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences, 103, 11778-11783. OA DOI Data\nPOLDRACK R (2006). Can cognitive processes be inferred from neuroimaging data?. Trends in Cognitive Sciences, 10, 59-63. DOI\nPoldrack RA, Willingham DB (2006). Functional neuroimaging of skill learning. In Handbook of Neuroimaging of Cognition, 2nd Edition  (R Cabeza, A Kingstone, Ed.) Cambridge, MA: MIT Press.\nRodriguez PF, Aron A, Poldrack R (2006). Ventral–striatal/nucleus–accumbens sensitivity to prediction errors during classification learning. Human Brain Mapping, 27, 306-313. OA DOI\nSEIDMAN L, THERMENOS H, POLDRACK R, PEACE N, KOCH J, FARAONE S, TSUANG M (2006). Altered brain activation in dorsolateral prefrontal cortex in adolescents and young adults at genetic risk for schizophrenia: An fMRI study of working memory. Schizophrenia Research, 85, 58-72. DOI\nSegonne F, Liu A, Poldrack R, Golland P, Fischl B (2006). Shape-based Discrimination and Classification of Cortical Surfaces. 18th International Conference on Pattern Recognition (ICPR’06), 445-448. DOI\nThermenos HW, Seidman LJ, Poldrack RA, Peace NK, Koch JK, Faraone SV, Tsuang MT (2006). RETRACTED: Elaborative Verbal Encoding and Altered Anterior Parahippocampal Activation in Adolescents and Young Adults at Genetic Risk for Schizophrenia Using fMRI. Biol Psychiatry. DOI\n\n\n2005\nAron AR, Poldrack RA (2005). The Cognitive Neuroscience of Response Inhibition: Relevance for Genetic Research in Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 57, 1285-1292. DOI\nBadre D, Poldrack RA, Paré-Blagoev EJ, Insler RZ, Wagner AD (2005). Dissociable Controlled Retrieval and Generalized Selection Mechanisms in Ventrolateral Prefrontal Cortex. Neuron, 47, 907-918. DOI\nGoldstein JM, Jerram M, Poldrack R, Ahern T, Kennedy DN, Seidman LJ, Makris N (2005). Hormonal Cycle Modulates Arousal Circuitry in Women Using Functional Magnetic Resonance Imaging. The Journal of Neuroscience, 25, 9309-9316. OA DOI\nGoldstein JM, Jerram M, Poldrack R, Anagnoson R, Breiter HC, Makris N, Goodman JM, Tsuang MT, Seidman LJ (2005). Sex differences in prefrontal cortical brain activity during fMRI of auditory verbal working memory. Neuropsychology, 19, 509-519. DOI\nKatzir T, Misra M, Poldrack RA (2005). Imaging phonology without print: Assessing the neural correlates of phonemic awareness using fMRI. NeuroImage, 27, 106-115. DOI\nPoldrack RA, Sabb FW, Foerde K, Tom SM, Asarnow RF, Bookheimer SY, Knowlton BJ (2005). The Neural Correlates of Motor Skill Automaticity. The Journal of Neuroscience, 25, 5356-5364. OA DOI\nStone WS, Thermenos HW, Tarbox SI, Poldrack RA, Seidman LJ (2005). Medial temporal and prefrontal lobe activation during verbal encoding following glucose ingestion in schizophrenia: A pilot fMRI study. Neurobiology of Learning and Memory, 83, 54-64. DOI\nThermenos HW, Goldstein JM, Buka SL, Poldrack RA, Koch JK, Tsuang MT, Seidman LJ (2005). The effect of working memory performance on functional MRI in schizophrenia. Schizophrenia Research, 74, 179-194. DOI\nTrepel C, Fox CR, Poldrack RA (2005). Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Cognitive Brain Research, 23, 34-50. DOI\nValera EM, Faraone SV, Biederman J, Poldrack RA, Seidman LJ (2005). Functional neuroanatomy of working memory in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry, 57, 439-447. DOI\n\n\n2004\nAron AR, Robbins TW, Poldrack RA (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Sciences, 8, 170-177. DOI\nAron AR, Shohamy D, Clark J, Myers C, Gluck MA, Poldrack RA (2004). Human Midbrain Sensitivity to Cognitive Feedback and Uncertainty During Classification Learning. Journal of Neurophysiology, 92, 1144-1152. DOI\nMisra M, Katzir T, Wolf M, Poldrack RA (2004). Neural Systems for Rapid Automatized Naming in Skilled Readers: Unraveling the RAN-Reading Relationship. Scientific Studies of Reading, 8, 241-256. DOI\nPoldrack RA, Rodriguez P (2004). How do memory systems interact? Evidence from human classification learning. Neurobiology of Learning and Memory, 82, 324-332. DOI\nPoldrack RA, Sandak R (2004). Introduction to This Special Issue: The Cognitive Neuroscience of Reading. Scientific Studies of Reading, 8, 199-202. DOI\nPoldrack RA, Wagner AD (2004). What Can Neuroimaging Tell Us About the Mind?. Current Directions in Psychological Science, 13, 177-181. DOI\nShohamy D (2004). Cortico-striatal contributions to feedback-based learning: converging data from neuroimaging and neuropsychology. Brain, 127, 851-859. DOI\nThermenos HW, Seidman LJ, Breiter H, Goldstein JM, Goodman JM, Poldrack R, Faraone SV, Tsuang MT (2004). Functional magnetic resonance imaging during auditory verbal working memory in nonpsychotic relatives of persons with schizophrenia: a pilot study. Biological Psychiatry, 55, 490-500. DOI\nThompson PM, Miller MI, Ratnanather JT, Poldrack RA, Nichols TE (2004). Preface to the special issue. NeuroImage, 23, S1. DOI\n\n\n2003\nBoas DA, Strangman G, Culver JP, Hoge RD, Jasdzewski G, Poldrack RA, Rosen BR, Mandeville JB (2003). Can the cerebral metabolic rate of oxygen be estimated with near-infrared spectroscopy?. Physics in Medicine and Biology, 48, 2405-2418. DOI\nJasdzewski G, Strangman G, Wagner J, Kwong K, Poldrack R, Boas D (2003). Differences in the hemodynamic response to event-related motor and visual paradigms as measured by near-infrared spectroscopy. NeuroImage, 20, 479-488. DOI\nMisra M, Katzir T, Wolf M, Poldrack RA (2003). Neural systems for rapid automatized naming identified using fMRI. Scientific Studies of Reading, 8.0.\nPoldrack RA, Packard MG (2003). Competition among multiple memory systems: converging evidence from animal and human brain studies. Neuropsychologia, 41, 245-251. DOI\nPoldrack RA, Rodriguez P (2003). Sequence Learning. Neuron, 37, 891-893. DOI\nPoldrack RA, Sandak R (2003). Introduction to special issue: The cognitive neuroscience of reading. Scientific Studies of Reading, 8.0.\nSperling R, Chua E, Cocchiarella A, Rand-Giovannetti E, Poldrack R, Schacter DL, Albert M (2003). Putting names to faces:. NeuroImage, 20, 1400-1410. OA DOI\nTemple E, Deutsch GK, Poldrack RA, Miller SL, Tallal P, Merzenich MM, Gabrieli JDE (2003). Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 100, 2860-2865. OA DOI\n\n\n2002\nGolby A, Poldrack R, Illes J, Chen D, Desmond J, Gabrieli J (2002). Memory Lateralization in Medial Temporal Lobe Epilepsy Assessed by Functional MRI. Epilepsia, 43, 855-863. DOI\nPoldrack RA (2002). Neural Systems for Perceptual Skill Learning. Behavioral and Cognitive Neuroscience Reviews, 1, 76-83. DOI\nPoldrack RA, Paré-Blagoev EJ, Grant PE (2002). Pediatric Functional Magnetic Resonance Imaging: Progress and Challenges. Topics in Magnetic Resonance Imaging, 13, 61-70. DOI\n\n\n2001\nGolby AJ (2001). Material-specific lateralization in the medial temporal lobe and prefrontal cortex during memory encoding. Brain, 124, 1841-1854. DOI\nPoldrack RA (2001). A Structural Basis for Developmental Dyslexia:Evidence from Diffusion Tensor Imaging. In Dyslexia, Fluency and the Brain (M Wolf, Ed.) York Press: Timonium, MD.\nPoldrack RA, Clark J, Paré-Blagoev EJ, Shohamy D, Creso Moyano J, Myers C, Gluck MA (2001). Interactive memory systems in the human brain. Nature, 414, 546-550. DOI Data\nPoldrack RA, Gabrieli JDE (2001). Characterizing the neural mechanisms of skill learning and repetition priming. Brain, 124, 67-82. DOI\nPoldrack RA, Temple E, Protopapas A, Nagarajan S, Tallal P, Merzenich M, Gabrieli JDE (2001). Relations between the Neural Bases of Dynamic Auditory Processing and Phonological Processing: Evidence from fMRI. Journal of Cognitive Neuroscience, 13, 687-697. DOI\nTemple E, Poldrack RA, Salidis J, Deutsch GK, Tallal P, Merzenich MM, Gabrieli JDE (2001). Disrupted neural responses to phonological and orthographic processing in dyslexic children: an fMRI study. Neuroreport, 12, 299-307. DOI\nVuilleumier P, Sagiv N, Hazeltine E, Poldrack RA, Swick D, Rafal RD, Gabrieli JDE (2001). Neural fate of seen and unseen faces in visuospatial neglect: A combined event-related functional MRI and event-related potential study. Proceedings of the National Academy of Sciences, 98, 3495-3500. OA DOI\nWagner AD, Paré-Blagoev E, Clark J, Poldrack RA (2001). Recovering Meaning. Neuron, 31, 329-338. DOI\n\n\n2000\nHazeltine E, Poldrack R, Gabrieli JDE (2000). Neural Activation During Response Competition. Journal of Cognitive Neuroscience, 12, 118-129. DOI\nKlingberg T, Hedehus M, Temple E, Salz T, Gabrieli JD, Moseley ME, Poldrack RA (2000). Microstructure of Temporo-Parietal White Matter as a Basis for Reading Ability. Neuron, 25, 493-500. DOI\nPoldrack RA (2000). Imaging Brain Plasticity: Conceptual and Methodological Issues— A Theoretical Review. NeuroImage, 12, 1-13. DOI\nSeger CA, Poldrack RA, Prabhakaran V, Zhao M, Glover GH, Gabrieli JD (2000). Hemispheric asymmetries and individual differences in visual concept learning as measured by functional MRI. Neuropsychologia, 38, 1316-1324. DOI\nSeger CA, Prabhakaran V, Poldrack RA, Gabrieli JDE (2000). Neural activity differs between explicit and implicit learning of artificial grammar strings: An fMRI study. Psychobiology, 28, 283-292. DOI\nTemple E, Poldrack RA, Protopapas A, Nagarajan S, Salz T, Tallal P, Merzenich MM, Gabrieli JDE (2000). Disruption of the neural response to rapid acoustic stimuli in dyslexia: Evidence from functional MRI. Proceedings of the National Academy of Sciences, 97, 13907-13912. OA DOI\n\n\n1999\nDemb JB, Poldrack RA, Gabrieli JDE (1999). Functional neuroimaging of word processing in normal and dyslexic readers. In Converging Methods for Understanding Reading and Dyslexia  (R Klein, P. McMullen, Ed.) Cambridge, MA: MIT Press.\nIlles J, Francis WS, Desmond JE, Gabrieli JD, Glover GH, Poldrack R, Lee CJ, Wagner AD (1999). Convergent Cortical Representation of Semantic Processing in Bilinguals. Brain and Language, 70, 347-363. DOI\nPoldrack RA, Prabhakaran V, Seger CA, Gabrieli JD (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564-74. DOI\nPoldrack RA, Prabhakaran V, Seger CA, Gabrieli JDE (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13, 564-574. DOI\nPoldrack RA, Selco SL, Field JE, Cohen NJ (1999). The relationship between skill learning and repetition priming: Experimental and computational analyses. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 208-235. DOI\nPoldrack RA, Selco SL, Field JE, Cohen NJ (1999). The relationship between skill learning and repetition priming: experimental and computational analyses. J Exp Psychol Learn Mem Cogn, 25, 208-35. DOI\nPoldrack RA, Wagner AD, Prull MW, Desmond JE, Glover GH, Gabrieli JD (1999). Functional Specialization for Semantic and Phonological Processing in the Left Inferior Prefrontal Cortex. NeuroImage, 10, 15-35. DOI\n\n\n1998\nGabrieli JD, Brewer JB, Poldrack RA (1998). Images of Medial Temporal Lobe Functions in Human Learning and Memory. Neurobiology of Learning and Memory, 70, 275-283. DOI\nGabrieli JDE, Poldrack RA, Desmond JE (1998). The role of left prefrontal cortex in language and memory. Proceedings of the National Academy of Sciences, 95, 906-913. OA DOI\nPoldrack R (1998). The neural basis of visual skill learning: an fMRI study of mirror reading. Cerebral Cortex, 8, 1-10. DOI\nPoldrack RA, Gabrieli JD (1998). Memory and the Brain: What’s Right and What’s Left?. Cell, 93, 1091-1093. DOI\nPoldrack RA, Logan GD (1998). What is the mechanism for fluency in successive recognition?. Acta Psychologica, 98, 167-181. DOI\nWagner AD, Poldrack RA, Eldridge LL, Desmond JE, Glover GH, E. Gabrieli JD (1998). Material-specific lateralization of prefrontal activation during episodic encoding and retrieval. NeuroReport, 9, 3711-3717. DOI\n\n\n1997\nCohen NJ, Poldrack RA, Eichenbaum H (1997). Memory for Items and Memory for Relations in the Procedural/Declarative Memory Framework. Memory, 5, 131-178. DOI\nGabrieli JD, Keane MM, Zarella MM, Poldrack RA (1997). Preservation of Implicit Memory for New Associations in Global Amnesia. Psychological Science, 8, 326-329. DOI\nPoldrack RA, Cohen NJ (1997). Priming of new associations in reading time: What is learned?. Psychonomic Bulletin &amp; Review, 4, 398-402. DOI\nPoldrack RA, Gabrieli JDE (1997). Functional Anatomy of Long-Term Memory. Journal of Clinical Neurophysiology, 14, 294-310. DOI\nPoldrack RA, Logan GD (1997). Fluency and response speed in recognition judgments. Memory &amp; Cognition, 25, 1-10. DOI\n\n\n1996\nPoldrack RA (1996). On testing for stochastic dissociations. Psychonomic Bulletin &amp; Review, 3, 434-448. DOI\n\n\n1994\nPoldrack RA, Cohen NJ (1994). On the representational/computational properties of multiple memory systems. Behavioral and Brain Sciences, 17, 416-417. DOI\n   \nUpdated: 2025-02-26 - Publication list created using autocv",
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@@ -607,7 +617,7 @@
     "href": "labguide/research/code_management.html",
     "title": "Code management and sharing",
     "section": "",
-    "text": "All code should be managed using a version control system, preferably git.\n\nThere are many resources online for learning git. A good starting point is Git for beginners: The definitive practical guide.\nGit questions can be addressed to the #git channel on the Poldracklab Slack.\n\nCode should be regularly pushed to a remote server, preferably Github.\n\nOur default is that all repositories for research projects will be public, unless there is a strong reason to keep it private.\n\nAny substantive code copied or adapted from another source (e.g. stackoverflow) should be attributed to the source (including the URL of the source).\n\nReferences to the copied or adapted sources should be added to the script files where they are written (preferably on the line before the source).\nCreate a notices.md following this template to reference larger portions of code that were copied or adapted to your project.\n\nCare must be taken to avoid sharing identifiable data, private passwords, AWS credentials, and other confidential information when using Github.\n\nAny private information should be stored in a separate text file that is read in by the code, and those files should be added to the .gitignore file so that they will not be checked in.\nAny accidental breach should be dealt with and reported to Dr. Poldrack and other relevant parties immediately.\n\n\n\n\n\n\nAll coding projects should be reviewed prior to paper submission.\n\n\n\n\n\nAll code should be made available via Github, upon submission of the preprint at the latest.\nAll shared code should be accompanied by an open source license.\n\nOur general preference is for permissive licenses such as MIT or Apache, but other licenses may be used (e.g. in collaborative projects that use GPL-style licenses).\nIf code is used from other projects, the licensing requirements for that code should be obeyed.\n\nA Github release should be generated for code related to any paper submission, containing the exact code used to implement all of the included analyses for the submitted version.\n\nThe Github repository should be linked to Zenodo in order to generate a DOI for each release, and this DOI should be included in the manuscript (rather than the Github link).",
+    "text": "All code should be managed using a version control system, preferably git.\n\nThere are many resources online for learning git. A good starting point is Git for beginners: The definitive practical guide.\nGit questions can be addressed to the #git channel on the Poldracklab Slack.\n\nCode should be regularly pushed to a remote server, preferably Github.\n\nOur default is that all repositories for research projects will be public, unless there is a strong reason to keep it private.\n\nAny substantive code copied or adapted from another source (e.g. stackoverflow) should be attributed to the source (including the URL of the source).\n\nReferences to the copied or adapted sources should be added to the script files where they are written (preferably on the line before the source).\nCreate a notices.md following this template to reference larger portions of code that were copied or adapted to your project.\n\nCare must be taken to avoid sharing identifiable data, private passwords, AWS credentials, and other confidential information when using Github.\n\nAny private information should be stored in a separate text file that is read in by the code, and those files should be added to the .gitignore file so that they will not be checked in.\nAny accidental breach should be dealt with and reported to Dr. Poldrack and other relevant parties immediately.\n\n\n\n\n\n\nAll coding projects should undergo a review before paper submission.\nWhen writing code, keep in mind that it will be shared with others. This mindset improves readability and encourages careful development practices.\nAt the very least, your code should:\n\nBe version-controlled and have a straightforward installation process for dependencies.\nInclude files that allow for testing.\nBe well-documented (e.g., comments in code) and clearly written.\nContain a README.md file that explains the structure of your codebase.\n\nCode review is an ongoing process rather than a final step, so there is no expectation for your code to be “perfect” at any stage.\nHere are some useful resources on code review: (Awesome Code Review)[https://github.com/joho/awesome-code-review], (Blog post)[https://www.djmannion.net/code_review/].\n\n\n\n\n\nAll code should be made available via Github, upon submission of the preprint at the latest.\nAll shared code should be accompanied by an open source license.\n\nOur general preference is for permissive licenses such as MIT or Apache, but other licenses may be used (e.g. in collaborative projects that use GPL-style licenses).\nIf code is used from other projects, the licensing requirements for that code should be obeyed.\n\nA Github release should be generated for code related to any paper submission, containing the exact code used to implement all of the included analyses for the submitted version.\n\nThe Github repository should be linked to Zenodo in order to generate a DOI for each release, and this DOI should be included in the manuscript (rather than the Github link).",
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     "href": "labguide/research/code_management.html#code-review",
     "title": "Code management and sharing",
     "section": "",
-    "text": "All coding projects should be reviewed prior to paper submission.",
+    "text": "All coding projects should undergo a review before paper submission.\nWhen writing code, keep in mind that it will be shared with others. This mindset improves readability and encourages careful development practices.\nAt the very least, your code should:\n\nBe version-controlled and have a straightforward installation process for dependencies.\nInclude files that allow for testing.\nBe well-documented (e.g., comments in code) and clearly written.\nContain a README.md file that explains the structure of your codebase.\n\nCode review is an ongoing process rather than a final step, so there is no expectation for your code to be “perfect” at any stage.\nHere are some useful resources on code review: (Awesome Code Review)[https://github.com/joho/awesome-code-review], (Blog post)[https://www.djmannion.net/code_review/].",
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diff --git a/docs/software.html b/docs/software.html
index cbc17f3..3958c8b 100644
--- a/docs/software.html
+++ b/docs/software.html
@@ -481,7 +481,7 @@ <h2 class="anchored" data-anchor-id="task-software-used-in-consortium-for-neurop
 </section>
 <section id="datacodeosf-pages-for-published-papers" class="level2">
 <h2 class="anchored" data-anchor-id="datacodeosf-pages-for-published-papers">Data/code/OSF pages for published papers</h2>
-<div id="4630d756" class="cell" data-message="false" data-execution_count="1">
+<div id="87dafb11" class="cell" data-message="false" data-execution_count="1">
 <section id="section" class="level3">
 <h3 class="anchored" data-anchor-id="section">2024</h3>
 <p>Bissett PG, Eisenberg IW, Shim S et al.&nbsp;(2024). Cognitive tasks, anatomical MRI, and functional MRI data evaluating the construct of self-regulation. <em>Scientific Data, 11</em>. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/11271451">OA</a> <a href="https://doi.org/10.1038/s41597-024-03636-y">DOI</a> <a href="https://openneuro.org/datasets/ds004636/versions/1.0.4">Data</a> <a href="https://zenodo.org/records/11211816">Code</a> <a href="https://osf.io/sjvnw/">OSF</a></p>
diff --git a/docs/volunteer.html b/docs/volunteer.html
index 217ad06..ef75d80 100644
--- a/docs/volunteer.html
+++ b/docs/volunteer.html
@@ -434,29 +434,11 @@ <h1 class="title">Research subjects</h1>
 <h2 class="anchored">
 Join us as a participant!
 </h2>
-<p>The Poldrack Lab is recruiting volunteers for studies of how learning works in the brain. These studies involve 1-10 sessions, lasting up to 2-3 hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tests.</p>
-<p>Eligible participants must be between 18 and 40 years old, native speakers of English, have uncorrected vision (or corrected vision with contacts), free of neurological illness, and have no metallic implants in or on their body.</p>
-<p>Eligible participants will be compensated $20/hour for participation, and a $250 completion bonus for completing all 10 scanning and behavioral task sessions.</p>
-<p>For general information about participants rights, contact 1-866-680-2906.</p>
-<p>If you are interested, please fill out the recruitment form here: https://redcap.link/fiveweekscanstudy.</p>
-<h2 class="anchored">
-Join us as a participant!
-</h2>
-<p>
-The Poldrack Lab is recruiting volunteers for studies of how learning, decision making, and executive function work in the brain.&nbsp; These studies involve 12 sessions lasting from 1.5 - 2 hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tests.
-</p>
-<p>
-Eligible participants must be between 18 and 50 years old, speak English fluently, be right-handed, free of psychiatric and neurological illness, have no metallic implants in or on their body, have no history of eating disorder, and have no food allergies.
-</p>
-<p>
-You may receive a total of $820 as payment for your participation, which is $20/hr for all sessions, and a $250 completion bonus for completing all 12 scanning sessions and post-scan tasks, and $200 for completing the behavioral tasks.
-</p>
-<p>
-<span>For general information about participants rights, contact 1-866-680-2906.</span>
-</p>
-<p>
-If you are interested, please fill out the recruitment form here&nbsp;<a href="https://redcap.stanford.edu/surveys/?s=WYFLTRCFA8">https://redcap.stanford.edu/surveys/?s=WYFLTRCFA8</a>.
-</p>
+<p>The Poldrack Lab is recruiting volunteers for studies of how cognitive control, memory, and decision-making work in the brain. These studies involve up to 20 experimental sessions lasting up to three hours each. During each session, you will be scanned using functional magnetic resonance imaging and/or perform computerized or paper and pencil behavioral tasks and questionnaires. You may also be asked to respond to daily prompts on an app regarding your menstrual cycle and your sleep schedule. If you do not have a menstrual cycle, you may respond to only the prompts about your sleep schedule. After the scanning session, you may be asked to provide a saliva sample. You may be excluded for irregular menstrual cycles.</p>
+<p>Eligible participants must have a minimum of an 8th grade education, speak English fluently, be right-handed, and have no history of significant medical illnesses including cardiovascular disease, cancer, immunodeficiency disorders (such as HIV infection), diabetes, unstable endocrine disorders, neurological disorders, neuromuscular disorders, or blood dyscrasias; additionally, participants must not have experienced head trauma with loss of consciousness, cerebrovascular accident, seizures, or neurosurgical interventions, and should have no history of major psychotic disorders (including schizophrenia and bipolar disorder), substance dependence, the use of medication for psychiatric reasons, or a clinical diagnosis of major depression.</p>
+<p>Eligible participants will be compensated up to $20/hour for participation, and may have the opportunity to earn bonuses up to $400. If asked to respond to our cell-phone based app prompts on your sleep and menstrual cycles, participants will be paid $1 per day for answering the 3 daily questions. Participants may receive an additional bonus up to $100 for completing a high proportion of these daily questions. - All participants who are US citizens must provide a social security number to receive direct payment. - Participants who are not US citizens, but have a social security number, and a Green Card (Alien Registration Card) can be paid directly. Photocopy of Green Card is required. - Non-US citizens, without a Green Card (Alien Registration Card), must have a F-1, J-1 or H-1B Stanford sponsored visa, an I-94 card, a passport, and must also complete a LA-6 form (provided at time of participation). Copies of all paperwork will be submitted to Stanford University and the participant will receive payment from the University after appropriate taxes are withheld. NON-US CITIZENS WITHOUT A GREEN CARD CANNOT BE PAID AT THE TIME OF THE STUDY.</p>
+<p>For complaints, concerns, or participant’s right questions, please contact the Stanford Institutional Review Board (IRB) at (650)-723-2480.</p>
+<p>If you are interested, please fill out the recruitment form: https://redcap.stanford.edu/surveys/?s=LKFF39MRM7HRK4PC.</p>
 
 
 
diff --git a/labguide/mentoring/mentoring.md b/labguide/mentoring/mentoring.md
index a48f408..05c37ff 100644
--- a/labguide/mentoring/mentoring.md
+++ b/labguide/mentoring/mentoring.md
@@ -178,10 +178,7 @@ more generally, as time permits.
     the lab, contact Dr. Poldrack directly. Be sure to send your CV, and
     explain in brief your research interests and why you think you would
     be a good fit for the lab.
-- We are particularly
-    interested in hiring postdocs who bring diversity to the lab, either
-    through their personal identity, research interests, or experience.
-
+    
 ## Mentoring practices
 
 ### Weekly report