Search

Your search keyword '"Poline, Jean Baptiste"' showing total 795 results
Start Over Author "Poline, Jean Baptiste"
795 results on '"Poline, Jean Baptiste"'

1. Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study

Author

Germani, Elodie, Baghwat, Nikhil, Dugré, Mathieu, Gau, Rémi, Montillo, Albert, Nguyen, Kevin, Sokolowski, Andrzej, Sharp, Madeleine, Poline, Jean-Baptiste, and Glatard, Tristan

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability. In this context, an evaluation of the robustness of such biomarkers is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to reproduce (same data, same method) and replicate (different data or method) the models described in Nguyen et al., 2021 to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in Nguyen et al.,2021 and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. The success of the reproduction was assessed using different criteria. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. The challenges encountered while reproducing and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

Published
2024

2. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Author

Poldrack, Russell A., Markiewicz, Christopher J., Appelhoff, Stefan, Ashar, Yoni K., Auer, Tibor, Baillet, Sylvain, Bansal, Shashank, Beltrachini, Leandro, Benar, Christian G., Bertazzoli, Giacomo, Bhogawar, Suyash, Blair, Ross W., Bortoletto, Marta, Boudreau, Mathieu, Brooks, Teon L., Calhoun, Vince D., Castelli, Filippo Maria, Clement, Patricia, Cohen, Alexander L, Cohen-Adad, Julien, D'Ambrosio, Sasha, de Hollander, Gilles, de la iglesia-Vayá, María, de la Vega, Alejandro, Delorme, Arnaud, Devinsky, Orrin, Draschkow, Dejan, Duff, Eugene Paul, DuPre, Elizabeth, Earl, Eric, Esteban, Oscar, Feingold, Franklin W., Flandin, Guillaume, galassi, anthony, Gallitto, Giuseppe, Ganz, Melanie, Gau, Rémi, Gholam, James, Ghosh, Satrajit S., Giacomel, Alessio, Gillman, Ashley G, Gleeson, Padraig, Gramfort, Alexandre, Guay, Samuel, Guidali, Giacomo, Halchenko, Yaroslav O., Handwerker, Daniel A., Hardcastle, Nell, Herholz, Peer, Hermes, Dora, Honey, Christopher J., Innis, Robert B., Ioanas, Horea-Ioan, Jahn, Andrew, Karakuzu, Agah, Keator, David B., Kiar, Gregory, Kincses, Balint, Laird, Angela R., Lau, Jonathan C., Lazari, Alberto, Legarreta, Jon Haitz, Li, Adam, Li, Xiangrui, Love, Bradley C., Lu, Hanzhang, Maumet, Camille, Mazzamuto, Giacomo, Meisler, Steven L., Mikkelsen, Mark, Mutsaerts, Henk, Nichols, Thomas E., Nikolaidis, Aki, Nilsonne, Gustav, Niso, Guiomar, Norgaard, Martin, Okell, Thomas W, Oostenveld, Robert, Ort, Eduard, Park, Patrick J., Pawlik, Mateusz, Pernet, Cyril R., Pestilli, Franco, Petr, Jan, Phillips, Christophe, Poline, Jean-Baptiste, Pollonini, Luca, Raamana, Pradeep Reddy, Ritter, Petra, Rizzo, Gaia, Robbins, Kay A., Rockhill, Alexander P., Rogers, Christine, Rokem, Ariel, Rorden, Chris, Routier, Alexandre, Saborit-Torres, Jose Manuel, Salo, Taylor, Schirner, Michael, Smith, Robert E., Spisak, Tamas, Sprenger, Julia, Swann, Nicole C., Szinte, Martin, Takerkart, Sylvain, Thirion, Bertrand, Thomas, Adam G., Torabian, Sajjad, Varoquaux, Gael, Voytek, Bradley, Welzel, Julius, Wilson, Martin, Yarkoni, Tal, and Gorgolewski, Krzysztof J.

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Published
2023

3. NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query.

Author

Queder, Nazek, Tien, Vivian, Abraham, Sanu, Urchs, Sebastian, Helmer, Karl, Chaplin, Derek, Kennedy, David, Poline, Jean-Baptiste, Ghosh, Satrajit, Van Erp, Theodorus, Keator, David, and Grethe, Jeffrey

Subjects
NIDM, annotation, dataset, neuroimaging, query
Abstract

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

Published
2023

4. Benchmarking missing-values approaches for predictive models on health databases

Author

Perez-Lebel, Alexandre, Varoquaux, Gaël, Morvan, Marine Le, Josse, Julie, and Poline, Jean-Baptiste

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

BACKGROUND: As databases grow larger, it becomes harder to fully control their collection, and they frequently come with missing values: incomplete observations. These large databases are well suited to train machine-learning models, for instance for forecasting or to extract biomarkers in biomedical settings. Such predictive approaches can use discriminative -- rather than generative -- modeling, and thus open the door to new missing-values strategies. Yet existing empirical evaluations of strategies to handle missing values have focused on inferential statistics. RESULTS: Here we conduct a systematic benchmark of missing-values strategies in predictive models with a focus on large health databases: four electronic health record datasets, a population brain imaging one, a health survey and two intensive care ones. Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning. We investigate prediction accuracy and computational time. For prediction after imputation, we find that adding an indicator to express which values have been imputed is important, suggesting that the data are missing not at random. Elaborate missing values imputation can improve prediction compared to simple strategies but requires longer computational time on large data. Learning trees that model missing values-with missing incorporated attribute-leads to robust, fast, and well-performing predictive modeling. CONCLUSIONS: Native support for missing values in supervised machine learning predicts better than state-of-the-art imputation with much less computational cost. When using imputation, it is important to add indicator columns expressing which values have been imputed., Comment: GigaScience, Oxford Univ Press, In press

Published
2022

5. The neurophysiological brain-fingerprint of Parkinson’s disease

Author

Breitner, John, Poirier, Judes, Baillet, Sylvain, Bellec, Pierre, Bohbot, Véronique, Chakravarty, Mallar, Collins, Louis, Etienne, Pierre, Evans, Alan, Gauthier, Serge, Hoge, Rick, Ituria-Medina, Yasser, Multhaup, Gerhard, Münter, Lisa-Marie, Rajah, Natasha, Rosa-Neto, Pedro, Soucy, Jean-Paul, Vachon-Presseau, Etienne, Villeneuve, Sylvia, Amouyel, Philippe, Appleby, Melissa, Ashton, Nicholas, Auld, Daniel, Ayranci, Gülebru, Bedetti, Christophe, Beland, Marie-Lise, Blennow, Kaj, Westman, Ann Brinkmalm, Cuello, Claudio, Dadar, Mahsa, Daoust, Leslie-Ann, Das, Samir, Dauar-Tedeschi, Marina, De Beaumont, Louis, Dea, Doris, Descoteaux, Maxime, Dufour, Marianne, Farzin, Sarah, Ferdinand, Fabiola, Fonov, Vladimir, Gonneaud, Julie, Kat, Justin, Kazazian, Christina, Labonté, Anne, Lafaille-Magnan, Marie-Elyse, Lalancette, Marc, Lambert, Jean-Charles, Leoutsakos, Jeannie-Marie, Mahar, Laura, Mathieu, Axel, McSweeney, Melissa, Meyer, Pierre-François, Miron, Justin, Near, Jamie, NewboldFox, Holly, Nilsson, Nathalie, Orban, Pierre, Picard, Cynthia, Binette, Alexa Pichet, Poline, Jean-Baptiste, Rabipour, Sheida, Salaciak, Alyssa, Settimi, Matthew, Subramaniapillai, Sivaniya, Tam, Angela, Tardif, Christine, Théroux, Louise, Tremblay-Mercier, Jennifer, Tullo, Stephanie, Ulku, Irem, Vallée, Isabelle, Zetterberg, Henrik, Nair, Vasavan, Pruessner, Jens, Aisen, Paul, Anthal, Elena, Barkun, Alan, Beaudry, Thomas, Benbouhoud, Fatiha, Brandt, Jason, Carmo, Leopoldina, Carrier, Charles Edouard, Cheewakriengkrai, Laksanun, Courcot, Blandine, Couture, Doris, Craft, Suzanne, Dansereau, Christian, Debacker, Clément, Desautels, René, Dubuc, Sylvie, Duclair, Guerda, Eisenberg, Mark, El-Khoury, Rana, Faubert, Anne-Marie, Fontaine, David, Frappier, Josée, Frenette, Joanne, Gagné, Guylaine, Gervais, Valérie, Giles, Renuka, Gordon, Renee, Jack, Clifford, Jutras, Benoit, Khachaturian, Zaven, Knopman, David, Kostopoulos, Penelope, Lapalme, Félix, Lee, Tanya, Lepage, Claude, Leppert, Illana, Madjar, Cécile, Maillet, David, Maltais, Jean-Robert, Mathotaarachchi, Sulantha, Mayrand, Ginette, Michaud, Diane, Montine, Thomas, Morris, John, Pagé, Véronique, Pascoal, Tharick, Peillieux, Sandra, Petkova, Mirela, Pogossova, Galina, Rioux, Pierre, Sager, Mark, Saint-Fort, Eunice Farah, Savard, Mélissa, Sperling, Reisa, Tabrizi, Shirin, Tariot, Pierre, Teigner, Eduard, Thomas, Ronald, Toussaint, Paule-Joanne, Tuwaig, Miranda, Venugopalan, Vinod, Verfaillie, Sander, Vogel, Jacob, Wan, Karen, Wang, Seqian, Yu, Elsa, Beaulieu-Boire, Isabelle, Blanchet, Pierre, Bogard, Sarah, Bouchard, Manon, Chouinard, Sylvain, Cicchetti, Francesca, Cloutier, Martin, Dagher, Alain, Degroot, Clotilde, Desautels, Alex, Dion, Marie Hélène, Drouin-Ouellet, Janelle, Dufresne, Anne-Marie, Dupré, Nicolas, Duquette, Antoine, Durcan, Thomas, Fellows, Lesley K., Fon, Edward, Gagnon, Jean-François, Gan-Or, Ziv, Genge, Angela, Jodoin, Nicolas, Karamchandani, Jason, Lafontaine, Anne-Louise, Langlois, Mélanie, Leveille, Etienne, Lévesque, Martin, Melmed, Calvin, Monchi, Oury, Montplaisir, Jacques, Panisset, Michel, Parent, Martin, Pham-An, Minh-Thy, Postuma, Ronald, Pourcher, Emmanuelle, Rao, Trisha, Rivest, Jean, Rouleau, Guy, Sharp, Madeleine, Soland, Valérie, Sidel, Michael, Wing Sun, Sonia Lai, Thiel, Alexander, Vitali, Paolo, da Silva Castanheira, Jason, Wiesman, Alex I., Hansen, Justine Y., and Misic, Bratislav

Published
2024
Full Text
View/download PDF

6. Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data.

Author

Poline, Jean-Baptiste, Kennedy, David N, Sommer, Friedrich T, Ascoli, Giorgio A, Van Essen, David C, Ferguson, Adam R, Grethe, Jeffrey S, Hawrylycz, Michael J, Thompson, Paul M, Poldrack, Russell A, Ghosh, Satrajit S, Keator, David B, Athey, Thomas L, Vogelstein, Joshua T, Mayberg, Helen S, and Martone, Maryann E

Subjects
Data Collection, Neurosciences, International Neuroinformatics Coordinating Facility, Interoperability, Neuroscience, Standards, Networking and Information Technology R&D (NITRD), Standards, International Neuroinformatics Coordinating Facility, Biochemistry and Cell Biology, Neurology & Neurosurgery
Abstract

In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.

Published
2022

7. Preventing dataset shift from breaking machine-learning biomarkers

Author

Dockès, Jéroôme, Varoquaux, Gaël, and Poline, Jean-Baptiste

Subjects
Computer Science - Machine Learning, Mathematics - Statistics Theory, Quantitative Biology - Quantitative Methods
Abstract

Machine learning brings the hope of finding new biomarkers extracted from cohorts with rich biomedical measurements. A good biomarker is one that gives reliable detection of the corresponding condition. However, biomarkers are often extracted from a cohort that differs from the target population. Such a mismatch, known as a dataset shift, can undermine the application of the biomarker to new individuals. Dataset shifts are frequent in biomedical research, e.g. because of recruitment biases. When a dataset shift occurs, standard machine-learning techniques do not suffice to extract and validate biomarkers. This article provides an overview of when and how dataset shifts breaks machine-learning extracted biomarkers, as well as detection and correction strategies., Comment: GigaScience, BioMed Central, In press

Published
2021

9. Data and Tools Integration in the Canadian Open Neuroscience Platform

Author

Poline, Jean-Baptiste, Das, Samir, Glatard, Tristan, Madjar, Cécile, Dickie, Erin W., Lecours, Xavier, Beaudry, Thomas, Beck, Natacha, Behan, Brendan, Brown, Shawn T., Bujold, David, Beauvais, Michael, Caron, Bryan, Czech, Candice, Dharsee, Moyez, Dugré, Mathieu, Evans, Ken, Gee, Tom, Ippoliti, Giulia, Kiar, Gregory, Knoppers, Bartha Maria, Kuehn, Tristan, Le, Diana, Lo, Derek, Mazaheri, Mandana, MacFarlane, Dave, Muja, Naser, O’Brien, Emmet A., O’Callaghan, Liam, Paiva, Santiago, Park, Patrick, Quesnel, Darcy, Rabelais, Henri, Rioux, Pierre, Legault, Mélanie, Tremblay-Mercier, Jennifer, Rotenberg, David, Stone, Jessica, Strauss, Ted, Zaytseva, Ksenia, Zhou, Joey, Duchesne, Simon, Khan, Ali R., Hill, Sean, and Evans, Alan C.

Published
2023
Full Text
View/download PDF

10. An algorithm-based multiple detection influence measure for high dimensional regression using expectile

Author

Barry, Amadou, Bhagwat, Nikhil, Misic, Bratislav, Poline, Jean-Baptiste, and Greenwood, Celia M. T.

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Computation, Statistics - Machine Learning
Abstract

The identification of influential observations is an important part of data analysis that can prevent erroneous conclusions drawn from biased estimators. However, in high dimensional data, this identification is challenging. Classical and recently-developed methods often perform poorly when there are multiple influential observations in the same dataset. In particular, current methods can fail when there is masking several influential observations with similar characteristics, or swamping when the influential observations are near the boundary of the space spanned by well-behaved observations. Therefore, we propose an algorithm-based, multi-step, multiple detection procedure to identify influential observations that addresses current limitations. Our three-step algorithm to identify and capture undesirable variability in the data, $\asymMIP,$ is based on two complementary statistics, inspired by asymmetric correlations, and built on expectiles. Simulations demonstrate higher detection power than competing methods. Use of the resulting asymptotic distribution leads to detection of influential observations without the need for computationally demanding procedures such as the bootstrap. The application of our method to the Autism Brain Imaging Data Exchange neuroimaging dataset resulted in a more balanced and accurate prediction of brain maturity based on cortical thickness. See our GitHub for a free R package that implements our algorithm: \texttt{asymMIP} (\url{github.com/AmBarry/hidetify})., Comment: 38 pages, 11 figures

Published
2021

11. Software for Neuroimaging Data Analysis

Author

Poline, Jean Baptiste, Kennedy, David, Migliore, Michele, Section editor, Linster, Christiane, Section editor, Cavarretta, Francesco, Section editor, Jaeger, Dieter, editor, and Jung, Ranu, editor

Published
2022
Full Text
View/download PDF

13. Teaching computational reproducibility for neuroimaging

Author

Millman, K. Jarrod, Brett, Matthew, Barnowski, Ross, and Poline, Jean-Baptiste

Subjects
Statistics - Other Statistics
Abstract

We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neuroimaging software packages. Our experience suggests that this combination leaves the student with a superficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work, such as the Unix command line, structured code, version control, automated testing, and code review and (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MRI scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engaging students in working reproducibly on real scientific questions. We propose that a course on this model should be the foundation for future programs in neuroimaging. We believe it will also serve as a model for teaching efficient and reproducible research in other fields of computational science.

Published
2018
Full Text
View/download PDF

15. Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Author

Kennedy, David N, Abraham, Sanu A, Bates, Julianna F, Crowley, Albert, Ghosh, Satrajit, Gillespie, Tom, Goncalves, Mathias, Grethe, Jeffrey S, Halchenko, Yaroslav O, Hanke, Michael, Haselgrove, Christian, Hodge, Steven M, Jarecka, Dorota, Kaczmarzyk, Jakub, Keator, David B, Meyer, Kyle, Martone, Maryann E, Padhy, Smruti, Poline, Jean-Baptiste, Preuss, Nina, Sincomb, Troy, and Travers, Matt

Subjects
Neurosciences, Substance Misuse, Clinical Research, reproducibility, neuroimaging, data model, publication, re-executability, Cognitive Sciences
Abstract

There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the "last mile" implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

Published
2019

17. Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure.

Author

Puebla, Iratxe, Ascoli, Giorgio A., Blume, Jeffrey, Chodacki, John, Finnell, Joshua, Kennedy, David N., Mair, Bernard, Martone, Maryann E., Wittenberg, Jamie, and Poline, Jean-Baptiste

Subjects
WEB-based user interfaces, JOB applications, LITERATURE reviews, ADOPTION, SOFTWARE as a service
Abstract

This article provides guidelines for universities to incorporate data and software contributions into their hiring, promotion, and tenure processes. It emphasizes the importance of aligning evaluation processes with open science principles and valuing diverse scholarly contributions. The guidelines include recommendations for developing clear guidelines for committee members, selecting a suitable committee with expertise in open data and software, and articulating metrics for evaluation. The document acknowledges that data and software competency is just one factor to consider and encourages institutions to balance it with other relevant factors. [Extracted from the article]

Published
2024
Full Text
View/download PDF

18. Influences of Age, Sex, and Moderate Alcohol Drinking on the Intrinsic Functional Architecture of Adolescent Brains

Author

Müller-Oehring, Eva M, Kwon, Dongjin, Nagel, Bonnie J, Sullivan, Edith V, Chu, Weiwei, Rohlfing, Torsten, Prouty, Devin, Nichols, B Nolan, Poline, Jean-Baptiste, Tapert, Susan F, Brown, Sandra A, Cummins, Kevin, Brumback, Ty, Colrain, Ian M, Baker, Fiona C, De Bellis, Michael D, Voyvodic, James T, Clark, Duncan B, Pfefferbaum, Adolf, and Pohl, Kilian M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Paediatrics, Psychology, Behavioral and Social Science, Mental Health, Alcoholism, Alcohol Use and Health, Substance Misuse, Clinical Research, Pediatric Research Initiative, Basic Behavioral and Social Science, Neurosciences, Pediatric, Underage Drinking, Mental health, Stroke, Oral and gastrointestinal, Good Health and Well Being, Adolescent, Aging, Alcohol Drinking, Brain, Child, Executive Function, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Neuropsychological Tests, Oxygen, Sex Characteristics, Young Adult, alcohol, emotion, executive control, functional MRI, puberty, resting state connectivity, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The transition from adolescent to adult cognition and emotional control requires neurodevelopmental maturation likely involving intrinsic functional networks (IFNs). Normal neurodevelopment may be vulnerable to disruption from environmental insult such as alcohol consumption commonly initiated during adolescence. To test potential disruption to IFN maturation, we used resting-state functional magnetic resonance imaging (rs-fMRI) in 581 no-to-low alcohol-consuming and 117 moderate-to-high-drinking youth. Functional seed-to-voxel connectivity analysis assessed age, sex, and moderate alcohol drinking on default-mode, executive-control, salience, reward, and emotion networks and tested cognitive and motor coordination correlates of network connectivity. Among no-to-low alcohol-consuming adolescents, executive-control frontolimbicstriatal connectivity was stronger in older than younger adolescents, particularly boys, and predicted better ability in balance, memory, and impulse control. Connectivity patterns in moderate-to-high-drinking youth were tested mainly in late adolescence when drinking was initiated. Implicated was the emotion network with attenuated connectivity to default-mode network regions. Our cross-sectional rs-fMRI findings from this large cohort of adolescents show sexual dimorphism in connectivity and suggest neurodevelopmental rewiring toward stronger and spatially more distributed executive-control networking in older than younger adolescents. Functional network rewiring in moderate-to-high-drinking adolescents may impede maturation of affective and self-reflection systems and obscure maturation of complex social and emotional behaviors.

Published
2018

21. Methods for decoding cortical gradients of functional connectivity

Author

Peraza, Julio A., primary, Salo, Taylor, additional, Riedel, Michael C., additional, Bottenhorn, Katherine L., additional, Poline, Jean-Baptiste, additional, Dockès, Jérôme, additional, Kent, James D., additional, Bartley, Jessica E., additional, Flannery, Jessica S., additional, Hill-Bowen, Lauren D., additional, Pintos Lobo, Rosario, additional, Poudel, Ranjita, additional, Ray, Kimberly L., additional, Robinson, Jennifer L., additional, Laird, Robert W., additional, Sutherland, Matthew T., additional, de la Vega, Alejandro, additional, and Laird, Angela R., additional

Published
2024
Full Text
View/download PDF

22. Deep Discriminative Learning for Autism Spectrum Disorder Classification

Author

Zhang, Mingli, Zhao, Xin, Zhang, Wenbin, Chaddad, Ahmad, Evans, Alan, Poline, Jean Baptiste, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hartmann, Sven, editor, Küng, Josef, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2020
Full Text
View/download PDF

23. Grand Challenges for Global Brain Sciences

Author

Vogelstein, Joshua T., Amunts, Katrin, Andreou, Andreas, Angelaki, Dora, Ascoli, Giorgio, Bargmann, Cori, Burns, Randal, Cali, Corrado, Chance, Frances, Chun, Miyoung, Church, George, Cline, Hollis, Coleman, Todd, de La Rochefoucauld, Stephanie, Denk, Winfried, Elgoyhen, Ana Belen, Cummings, Ralph Etienne, Evans, Alan, Harris, Kenneth, Hausser, Michael, Hill, Sean, Inverso, Samuel, Jackson, Chad, Jain, Viren, Kass, Rob, Kasthuri, Bobby, Kiar, Gregory, Kording, Konrad, Koushika, Sandhya, Krakauer, John, Landis, Story, Layton, Jeff, Luo, Qingming, Marblestone, Adam, Markowitz, David, McArthur, Justin, Mensh, Brett, Milham, Michael, Mitra, Partha, Neskovic, Pedja, Nicolelis, Miguel, O'Brien, Richard, Oliva, Aude, Orban, Gergo, Peng, Hanchuan, Picchini-Schaffer, Alyssa, Picciotto, Marina, Poline, Jean-Baptiste, Poo, Mu-ming, Pouget, Alex, Raghavachari, Sri, Roskams, Jane, Sejnowski, Terry, Sommer, Fritz, Spruston, Nelson, Swanson, Larry, Toga, Arthur, Vogelstein, R. Jacob, Yuste, Rafael, Zador, Anthony, Huganir, Richard, and Miller, Michael

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

The next grand challenges for society and science are in the brain sciences. A collection of 60+ scientists from around the world, together with 10+ observers from national, private, and foundations, spent two days together discussing the top challenges that we could solve as a global community in the next decade. We eventually settled on three challenges, spanning anatomy, physiology, and medicine. Addressing all three challenges requires novel computational infrastructure. The group proposed the advent of The International Brain Station (TIBS), to address these challenges, and launch brain sciences to the next level of understanding., Comment: 6 pages

Published
2016

24. Substance Use Initiation, Particularly Alcohol, in Drug-Naive Adolescents: Possible Predictors and Consequences From a Large Cohort Naturalistic Study

Author

Rapp, Michael, Artiges, Eric, Schneider, Sophia, Paus, Tomas, Barbot, Alexis, Barker, Gareth, Bokde, Arun, Vetter, Nora, Büchel, Christian, Cattrell, Anna, Constant, Patrick, Gowland, Penny, Crombag, Hans, Dalley, Jeffrey, Decideur, Benjamin, Spranger, Tade, Ripley, Tamzin, Heym, Nadja, Flor, Herta, Sommer, Wolfgang, Fuchs, Birgit, Gallinat, Jürgen, Garavan, Hugh, Spanagel, Rainer, Kaviani, Mehri, Heinrichs, Bert, Andreas Heinz, Subramaniam, Naresh, Jia, Tianye, Ihlenfeld, Albrecht, Ireland, James, Ittermann, Bernd, Conrod, Patricia, Banaschewski, Tobias, Jones, Jennifer, Klaassen, Arno, Lalanne, Christophe, Lanzerath, Dirk, Lawrence, Claire, Lemaitre, Hervé, Desrivieres, Sylvane, Mallik, Catherine, Mann, Karl, Mar, Adam, Martinez-Medina, Lourdes, Martinot, Jean-Luc, Mennigen, Eva, Mesquita de Carvahlo, Fabiana, Schwartz, Yannick, Bruehl, Ruediger, Müller, Kathrin, Nees, Frauke, Nymberg, Charlotte, Lathrop, Mark, Robbins, Trevor, Pausova, Zdenka, Pentilla, Jani, Biondo, Francesca, Poline, Jean-Baptiste, Poustka, Luise, Millenet, Sabina, Smolka, Michael, Fröhner, Juliane, Struve, Maren, Williams, Steve, Hübner, Thomas, Bromberg, Uli, Aydin, Semiha, Rogers, John, Romanowski, Alexander, Schmäl, Christine, Schmidt, Dirk, Ripke, Stephan, Arroyo, Mercedes, Schubert, Florian, Pena-Oliver, Yolanda, Fauth-Bühler, Mira, Mignon, Xavier, Whelan, Robert, Speiser, Claudia, Fadai, Tahmine, Stephens, Dai, Ströhle, Andreas, Paillere, Marie-Laure, Strache, Nicole, Theobald, David, Jurk, Sarah, Vulser, Helene, Miranda, Ruben, Yacubilin, Juliana, Frouin, Vincent, Genauck, Alexander, Parchetka, Caroline, Gemmeke, Isabel, Kruschwitz, Johann, WeiB, Katharina, Walter, Henrik, Feng, Jianfeng, Papadopoulos, Dimitri, Filippi, Irina, Ing, Alex, Ruggeri, Barbara, Xu, Bing, Macare, Christine, Chu, Congying, Hanratty, Eanna, Burke Quinlan, Erin, Robert, Gabriel, Schumann, Gunter, Yu, Tao, Ziesch, Veronika, Stedman, Alicia, Ivanov, Iliyan, Parvaz, Muhammad A., Velthorst, Eva, Shaik, Riaz B., Sandin, Sven, Gan, Gabriela, Spechler, Philip, Albaugh, Matthew D., Chaarani, Bader, Mackey, Scott, Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Grigis, Antoine, Heinz, Andreas, Paillère Martinot, Marie-Laure, Lemaitre, Herve, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Hohmann, Sarah, Fröhner, Juliane H., and Smolka, Michael N.

Published
2021
Full Text
View/download PDF

25. Reward Versus Nonreward Sensitivity of the Medial Versus Lateral Orbitofrontal Cortex Relates to the Severity of Depressive Symptoms

Author

Artiges, Eric, Aydin, Semiha, Banaschewski, Tobias, Barbot, Alexis, Barker, Gareth, Becker, Andreas, Bezivin-Frere, Pauline, Biondo, Francesca, Bokde, Arun, Büchel, Christian, Chu, Congying, Conrod, Patricia, Daedelow, Laura, Dalley, Jeffrey, Desrivieres, Sylvane, Dooley, Eoin, Filippi, Irina, Fillmer, Ariane, Flor, Herta, Fröhner, Juliane, Frouin, Vincent, Garavan, Hugh, Gowland, Penny, Grimmer, Yvonne, Heinz, Andreas, Hohmann, Sarah, Ihlenfeld, Albrecht, Ing, Alex, Isensee, Corinna, Ittermann, Bernd, Jia, Tianye, Lemaitre, Hervé, Lethbridge, Emma, Martinot, Jean-Luc, Millenet, Sabina, Miller, Sarah, Miranda, Ruben, Nees, Frauke, Paillere, Marie-Laure, Papadopoulos, Dimitri, Paus, Tomáš, Pausova, Zdenka, Pentilla, Jani, Poline, Jean-Baptiste, Poustka, Luise, Burke, Erin, Rapp, Michael, Robbins, Trevor, Robert, Gabriel, Rogers, John, Ruggeri, Barbara, Schumann, Gunter, Smolka, Michael, Stringaris, Argyris, van Noort, Betteke, Walter, Henrik, Whelan, Robert, Simon, Roux, Williams, Steve, Zhang, Yuning, Xie, Chao, Rolls, Edmund T., Robbins, Trevor W., Sahakian, Barbara J., Zhang, Jie, Liu, Zhaowen, Cheng, Wei, Luo, Qiang, Zac Lo, Chun-Yi, Wang, He, Barker, Gareth J., Bokde, Arun L.W., Quinlan, Erin Burke, Desrivières, Sylvane, Grigis, Antoine, Paillère Martinot, Marie-Laure, Orfanos, Dimitri Papadopoulos, Fröhner, Juliane H., Smolka, Michael N., and Feng, Jianfeng

Published
2021
Full Text
View/download PDF

26. A very simple, re-executable neuroimaging publication

Author

Ghosh, Satrajit S, Poline, Jean-Baptiste, Keator, David B, Halchenko, Yaroslav O, Thomas, Adam G, Kessler, Daniel A, and Kennedy, David N

Subjects
Information and Computing Sciences, Library and Information Studies, Neuroimaging analysis, re-executable publication, reproducibility, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

Reproducible research is a key element of the scientific process. Re-executability of neuroimaging workflows that lead to the conclusions arrived at in the literature has not yet been sufficiently addressed and adopted by the neuroimaging community. In this paper, we document a set of procedures, which include supplemental additions to a manuscript, that unambiguously define the data, workflow, execution environment and results of a neuroimaging analysis, in order to generate a verifiable re-executable publication. Re-executability provides a starting point for examination of the generalizability and reproducibility of a given finding.

Published
2017

27. Modeling and Analysis Brain Development via Discriminative Dictionary Learning

Author

Zhang, Mingli, Guo, Yuhong, Zhang, Caiming, Poline, Jean-Baptiste, Evans, Alan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Knoll, Florian, editor, Maier, Andreas, editor, Rueckert, Daniel, editor, and Ye, Jong Chul, editor

Published
2019
Full Text
View/download PDF

29. Cannabis-Associated Psychotic-like Experiences Are Mediated by Developmental Changes in the Parahippocampal Gyrus

Author

Rapp, Michael, Artiges, Eric, Schneider, Sophia, Bach, Christine, Paus, Tomas, Barbot, Alexis, Barker, Gareth, Bokde, Arun, Vetter, Nora, Büchel, Christian, Cattrell, Anna, Constant, Patrick, Gowland, Penny, Crombag, Hans, Czech, Katharina, Dalley, Jeffrey, Decideur, Benjamin, Spranger, Tade, Ripley, Tamzin, Heym, Nadja, Flor, Herta, Sommer, Wolfgang, Fuchs, Birgit, Gallinat, Jürgen, Garavan, Hugh, Spanagel, Rainer, Kaviani, Mehri, Heinrichs, Bert, Heinz, Andreas, Subramaniam, Naresh, Jia, Tianye, Ihlenfeld, Albrecht, Delosis, James Ireland, Ittermann, Bernd, Conrod, Patricia, Banaschewski, Tobias, Jones, Jennifer, Klaassen, Arno, Lalanne, Christophe, Lanzerath, Dirk, Lawrence, Claire, Lemaitre, Hervé, Desrivieres, Sylvane, Mallik, Catherine, Mann, Karl, Mar, Adam, Martinez-Medina, Lourdes, Martinot, Jean-Luc, Mennigen, Eva, de Carvahlo, Fabiana Mesquita, Schwartz, Yannick, Bruehl, Ruediger, Müller, Kathrin, Nees, Frauke, Nymberg, Charlotte, Lathrop, Mark, Robbins, Trevor, Pausova, Zdenka, Pentilla, Jani, Biondo, Francesca, Poline, Jean-Baptiste, Hohmann, Sarah, Poustka, Luise, Millenet, Sabina, Smolka, Michael, Fröhner, Juliane, Struve, Maren, Williams, Steve, Hübner, Thomas, Bromberg, Uli, Aydin, Semiha, Rogers, John, Romanowski, Alexander, Schmäl, Christine, Schmidt, Dirk, Ripke, Stephan, Arroyo, Mercedes, Schubert, Florian, Pena-Oliver, Yolanda, Fauth-Bühler, Mira, Mignon, Xavier, Whelan, Robert, Speiser, Claudia, Fadai, Tahmine, Stephens, Dai, Ströhle, Andreas, Paillere, Marie-Laure, Strache, Nicole, Theobald, David, Jurk, Sarah, Vulser, Helene, Miranda, Ruben, Yacubilin, Juliana, Frouin, Vincent, Genauck, Alexander, Parchetka, Caroline, Gemmeke, Isabel, Kruschwitz, Johann, WeiB, Katharina, Walter, Henrik, Feng, Jianfeng, Papadopoulos, Dimitri, Filippi, Irina, Ing, Alex, Ruggeri, Barbara, Xu, Bing, Macare, Christine, Chu, Congying, Hanratty, Eanna, Quinlan, Erin Burke, Robert, Gabriel, Schumann, Gunter, Yu, Tao, Ziesch, Veronika, Stedman, Alicia, Zhu, Liping, Desrivières, Sylvane, Bi, Yan, Bokde, Arun L.W., Liu, ChuanXin, Ji, Lei, Ren, Decheng, Du, Li, Hou, Binyin, Paillère Martinot, Marie-Laure, Orfanos, Dimitri Papadopoulos, Luo, Qiang, Smolka, Michael N., Vetter, Nora C., Lei, Cai, Fröhner, Juliane H., He, Guang, and He, Lin

Published
2020
Full Text
View/download PDF

32. Sharing brain mapping statistical results with the neuroimaging data model

Author

Maumet, Camille, Auer, Tibor, Bowring, Alexander, Chen, Gang, Das, Samir, Flandin, Guillaume, Ghosh, Satrajit, Glatard, Tristan, Gorgolewski, Krzysztof J, Helmer, Karl G, Jenkinson, Mark, Keator, David B, Nichols, B Nolan, Poline, Jean-Baptiste, Reynolds, Richard, Sochat, Vanessa, Turner, Jessica, and Nichols, Thomas E

Subjects
Biological Psychology, Biological Sciences, Psychology, Neurosciences, Bioengineering, Brain, Brain Mapping, Data Interpretation, Statistical, Humans, Information Dissemination, Information Storage and Retrieval, Linear Models, Magnetic Resonance Imaging, Meta-Analysis as Topic, Reproducibility of Results
Abstract

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

Published
2016

33. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

Author

Gorgolewski, Krzysztof J, Auer, Tibor, Calhoun, Vince D, Craddock, R Cameron, Das, Samir, Duff, Eugene P, Flandin, Guillaume, Ghosh, Satrajit S, Glatard, Tristan, Halchenko, Yaroslav O, Handwerker, Daniel A, Hanke, Michael, Keator, David, Li, Xiangrui, Michael, Zachary, Maumet, Camille, Nichols, B Nolan, Nichols, Thomas E, Pellman, John, Poline, Jean-Baptiste, Rokem, Ariel, Schaefer, Gunnar, Sochat, Vanessa, Triplett, William, Turner, Jessica A, Varoquaux, Gaël, and Poldrack, Russell A

Subjects
Information and Computing Sciences, Cognitive and Computational Psychology, Psychology, Neurosciences, Biomedical Imaging, Brain Disorders, Neurological, Data Collection, Datasets as Topic, Humans, Magnetic Resonance Imaging, Neuroimaging
Abstract

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Published
2016

34. Effects of Alzheimer’s disease genetic risk on brain morphometric development in three multiple‐ancestry pediatric datasets

Author

Vogel, Jacob W, primary, Schultz, Laura M, additional, Larsen, Bart, additional, Seidlitz, Jakob, additional, Al‐Sharif, Noor B, additional, Barzilay, Ran, additional, Cieslak, Matthew, additional, Covitz, Sydney, additional, Gur, Raquel E, additional, Gur, Ruben C, additional, Huguet, Guillaume, additional, La Joie, Renaud, additional, McMillan, Corey T, additional, Nilsson, Nathalie I.V., additional, Poline, Jean‐Baptiste, additional, Ruparel, Kosha, additional, Shinohara, Russell T., additional, Wisse, Laura EM, additional, Wolf, Daniel, additional, Wolk, David A., additional, Alexander‐Bloch, Aaron, additional, Almasy, Laura, additional, and Satterthwaite, Theodore, additional

Published
2023
Full Text
View/download PDF

36. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F., Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A., Adcock, R. Alison, Avesani, Paolo, Baczkowski, Blazej M., Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G., Berkers, Ruud M. W. J., Bhanji, Jamil P., Biswal, Bharat B., Bobadilla-Suarez, Sebastian, Bortolini, Tiago, Bottenhorn, Katherine L., Bowring, Alexander, Braem, Senne, Brooks, Hayley R., Brudner, Emily G., Calderon, Cristian B., Camilleri, Julia A., Castrellon, Jaime J., Cecchetti, Luca, Cieslik, Edna C., Cole, Zachary J., Collignon, Olivier, Cox, Robert W., Cunningham, William A., Czoschke, Stefan, Dadi, Kamalaker, Davis, Charles P., Luca, Alberto De, Delgado, Mauricio R., Demetriou, Lysia, Dennison, Jeffrey B., Di, Xin, Dickie, Erin W., Dobryakova, Ekaterina, Donnat, Claire L., Dukart, Juergen, Duncan, Niall W., Durnez, Joke, Eed, Amr, Eickhoff, Simon B., Erhart, Andrew, Fontanesi, Laura, Fricke, G. Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, Genon, Sarah, Glatard, Tristan, Glerean, Enrico, Goeman, Jelle J., Golowin, Sergej A. E., González-García, Carlos, Gorgolewski, Krzysztof J., Grady, Cheryl L., Green, Mikella A., Guassi Moreira, João F., Guest, Olivia, Hakimi, Shabnam, Hamilton, J. Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B., Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A., Hughes, Matthew E., Iacovella, Vittorio, Iordan, Alexandru D., Isager, Peder M., Isik, Ayse I., Jahn, Andrew, Johnson, Matthew R., Johnstone, Tom, Joseph, Michael J. E., Juliano, Anthony C., Kable, Joseph W., Kassinopoulos, Michalis, Koba, Cemal, Kong, Xiang-Zhen, Koscik, Timothy R., Kucukboyaci, Nuri Erkut, Kuhl, Brice A., Kupek, Sebastian, Laird, Angela R., Lamm, Claus, Langner, Robert, Lauharatanahirun, Nina, Lee, Hongmi, Lee, Sangil, Leemans, Alexander, Leo, Andrea, Lesage, Elise, Li, Flora, Li, Monica Y. C., Lim, Phui Cheng, Lintz, Evan N., Liphardt, Schuyler W., Losecaat Vermeer, Annabel B., Love, Bradley C., Mack, Michael L., Malpica, Norberto, Marins, Theo, Maumet, Camille, McDonald, Kelsey, McGuire, Joseph T., Melero, Helena, Méndez Leal, Adriana S., Meyer, Benjamin, Meyer, Kristin N., Mihai, Glad, Mitsis, Georgios D., Moll, Jorge, Nielson, Dylan M., Nilsonne, Gustav, Notter, Michael P., Olivetti, Emanuele, Onicas, Adrian I., Papale, Paolo, Patil, Kaustubh R., Peelle, Jonathan E., Pérez, Alexandre, Pischedda, Doris, Poline, Jean-Baptiste, Prystauka, Yanina, Ray, Shruti, Reuter-Lorenz, Patricia A., Reynolds, Richard C., Ricciardi, Emiliano, Rieck, Jenny R., Rodriguez-Thompson, Anais M., Romyn, Anthony, Salo, Taylor, Samanez-Larkin, Gregory R., Sanz-Morales, Emilio, Schlichting, Margaret L., Schultz, Douglas H., Shen, Qiang, Sheridan, Margaret A., Silvers, Jennifer A., Skagerlund, Kenny, Smith, Alec, Smith, David V., Sokol-Hessner, Peter, Steinkamp, Simon R., Tashjian, Sarah M., Thirion, Bertrand, Thorp, John N., Tinghög, Gustav, Tisdall, Loreen, Tompson, Steven H., Toro-Serey, Claudio, Torre Tresols, Juan Jesus, Tozzi, Leonardo, Truong, Vuong, Turella, Luca, van ‘t Veer, Anna E., Verguts, Tom, Vettel, Jean M., Vijayarajah, Sagana, Vo, Khoi, Wall, Matthew B., Weeda, Wouter D., Weis, Susanne, White, David J., Wisniewski, David, Xifra-Porxas, Alba, Yearling, Emily A., Yoon, Sangsuk, Yuan, Rui, Yuen, Kenneth S. L., Zhang, Lei, Zhang, Xu, Zosky, Joshua E., Nichols, Thomas E., Poldrack, Russell A., and Schonberg, Tom

Published
2020
Full Text
View/download PDF

37. Ongoing dynamics in large-scale functional connectivity predict perception

Author

Sadaghiani, Sepideh, Poline, Jean-Baptiste, Kleinschmidt, Andreas, and D’Esposito, Mark

Subjects
Biological Psychology, Physical Sciences, Psychology, Neurosciences, Behavioral and Social Science, Basic Behavioral and Social Science, Clinical Research, Neurological, Acoustic Stimulation, Algorithms, Auditory Perception, Brain, Brain Mapping, Humans, Magnetic Resonance Imaging, Models, Neurological, Nerve Net, Neural Pathways, Psychomotor Performance, Rest, Sound, functional connectivity, brain networks, dynamics, graph theory, classification
Abstract

Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22-40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.

Published
2015

38. PyXNAT: XNAT in Python

Author

Schwartz, Yannick, Barbot, Alexis, Thyreau, Benjamin, Frouin, Vincent, Varoquaux, Gaël, Siram, Aditya, Marcus, Daniel, and Poline, Jean-Baptiste

Subjects
Computer Science - Databases, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a backend library to build XNAT clients and as an alternative frontend from the command line.

Published
2013
Full Text
View/download PDF

39. Low Smoking Exposure, the Adolescent Brain, and the Modulating Role of CHRNA5 Polymorphisms

Author

Mann, Karl, Struve, Maren, Rietschel, Marcella, Spanagel, Rainer, Fauth-Bühler, Mira, Millenet, Sabina, Grimmer, Yvonne, Ivanov, Nikolay, Strache, Nicole, Rapp, Michael, Ströhle, Andreas, Reuter, Jan, Barbot, Alexis, Thyreau, Benjamin, Schwartz, Yannick, Lalanne, Christophe, Bricaud, Zuleima, Briand, Fanny Gollier, Lemaitre, Hervé, Massicotte, Jessica, Vulser, Helene, Pentillä, Jani, Galinowski, André, Jia, Tianye, Werts, Helen, Topper, Lauren, Reed, Laurence, Andrew, Chris, Mallik, Catherine, Ruggeri, Barbara, Nymberg, Charlotte, Smith, Lindsay, Loth, Eva, Havatzias, Stephanie, Stueber, Kerstin, Stringaris, Argyris, Constant, Patrick, Brühl, Ruediger, Ihlenfeld, Albrecht, Walaszek, Bernadeta, Hübner, Thomas, Müller, Kathrin, Ripke, Stephan, Rodehacke, Sarah, Mennigen, Eva, Schmidt, Dirk, Vetter, Nora, Ziesch, Veronika, Jones, Jennifer, Poline, Jean-Baptiste, Fadai, Tahmine, Yacubian, Juliana, Schneider, Sophia, Lawrence, Claire, Newman, Craig, Head, Kay, Heym, Nadja, Pausova, Zdenka, Tahmasebi, Amir, Chaarani, Bader, Kan, Kees-Jan, Mackey, Scott, Spechler, Philip A., Potter, Alexandra, Orr, Catherine, D’Alberto, Nicholas, Hudson, Kelsey E., Banaschewski, Tobias, Bokde, Arun L.W., Bromberg, Uli, Büchel, Christian, Cattrell, Anna, Conrod, Patricia J., Desrivières, Sylvane, Flor, Herta, Frouin, Vincent, Gallinat, Jürgen, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Nees, Frauke, Papadopoulos-Orfanos, Dimitri, Paus, Tomáš, Poustka, Luise, Smolka, Michael N., Walter, Henrik, Whelan, Robert, Higgins, Stephen T., Schumann, Gunter, Althoff, Robert R., Stein, Elliot A., and Garavan, Hugh

Published
2019
Full Text
View/download PDF

40. Improving accuracy and power with transfer learning using a meta-analytic database

Author

Schwartz, Yannick, Varoquaux, Gaël, Pallier, Christophe, Pinel, Philippe, Poline, Jean-Baptiste, and Thirion, Bertrand

Subjects
Statistics - Machine Learning
Abstract

Typical cohorts in brain imaging studies are not large enough for systematic testing of all the information contained in the images. To build testable working hypotheses, investigators thus rely on analysis of previous work, sometimes formalized in a so-called meta-analysis. In brain imaging, this approach underlies the specification of regions of interest (ROIs) that are usually selected on the basis of the coordinates of previously detected effects. In this paper, we propose to use a database of images, rather than coordinates, and frame the problem as transfer learning: learning a discriminant model on a reference task to apply it to a different but related new task. To facilitate statistical analysis of small cohorts, we use a sparse discriminant model that selects predictive voxels on the reference task and thus provides a principled procedure to define ROIs. The benefits of our approach are twofold. First it uses the reference database for prediction, i.e. to provide potential biomarkers in a clinical setting. Second it increases statistical power on the new task. We demonstrate on a set of 18 pairs of functional MRI experimental conditions that our approach gives good prediction. In addition, on a specific transfer situation involving different scanners at different locations, we show that voxel selection based on transfer learning leads to higher detection power on small cohorts., Comment: MICCAI, Nice : France (2012)

Published
2012

41. Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?

Author

Varoquaux, Gaël, Gramfort, Alexandre, Poline, Jean Baptiste, and Thirion, Bertrand

Subjects
Statistics - Applications, Quantitative Biology - Neurons and Cognition
Abstract

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems.

Published
2012
Full Text
View/download PDF

42. Brain covariance selection: better individual functional connectivity models using population prior

Author

Varoquaux, Gaël, Gramfort, Alexandre, Poline, Jean Baptiste, and Thirion, Bertrand

Subjects
Statistics - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties of brain connectivity graphs. However, to date, there has been no demonstration that the limited and noisy data available in spontaneous activity observations could be used to learn full-brain probabilistic models that generalize to new data. Learning such models entails two main challenges: i) modeling full brain connectivity is a difficult estimation problem that faces the curse of dimensionality and ii) variability between subjects, coupled with the variability of functional signals between experimental runs, makes the use of multiple datasets challenging. We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population. We show that individual models learned from functional Magnetic Resonance Imaging (fMRI) data using this population prior generalize better to unseen data than models based on alternative regularization schemes. To our knowledge, this is the first report of a cross-validated model of spontaneous brain activity. Finally, we use the estimated graphical model to explore the large-scale characteristics of functional architecture and show for the first time that known cognitive networks appear as the integrated communities of functional connectivity graph., Comment: in Advances in Neural Information Processing Systems, Vancouver : Canada (2010)

Published
2010

43. ICA-based sparse feature recovery from fMRI datasets

Author

Varoquaux, Gaël, Keller, Merlin, Poline, Jean Baptiste, Ciuciu, Philippe, and Thirion, Bertrand

Subjects
Statistics - Applications
Abstract

Spatial Independent Components Analysis (ICA) is increasingly used in the context of functional Magnetic Resonance Imaging (fMRI) to study cognition and brain pathologies. Salient features present in some of the extracted Independent Components (ICs) can be interpreted as brain networks, but the segmentation of the corresponding regions from ICs is still ill-controlled. Here we propose a new ICA-based procedure for extraction of sparse features from fMRI datasets. Specifically, we introduce a new thresholding procedure that controls the deviation from isotropy in the ICA mixing model. Unlike current heuristics, our procedure guarantees an exact, possibly conservative, level of specificity in feature detection. We evaluate the sensitivity and specificity of the method on synthetic and fMRI data and show that it outperforms state-of-the-art approaches.

Published
2010

44. CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Author

Varoquaux, Gaël, Sadaghiani, Sepideh, Poline, Jean Baptiste, and Thirion, Bertrand

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications
Abstract

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible.

Published
2009

45. NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data

Author

Wang, Lei, primary, Ambite, José Luis, additional, Appaji, Abhishek, additional, Bijsterbosch, Janine, additional, Dockes, Jerome, additional, Herrick, Rick, additional, Kogan, Alex, additional, Lander, Howard, additional, Marcus, Daniel, additional, Moore, Stephen M., additional, Poline, Jean-Baptiste, additional, Rajasekar, Arcot, additional, Sahoo, Satya S., additional, Turner, Matthew D., additional, Wang, Xiaochen, additional, Wang, Yue, additional, and Turner, Jessica A., additional

Published
2023
Full Text
View/download PDF

46. NiMARE: Neuroimaging Meta-Analysis Research Environment

Author

Salo, Taylor, primary, Yarkoni, Tal, additional, Nichols, Thomas E., additional, Poline, Jean-Baptiste, additional, Bilgel, Murat, additional, Bottenhorn, Katherine L., additional, Jarecka, Dorota, additional, Kent, James D., additional, Kimbler, Adam, additional, Nielson, Dylan M., additional, Oudyk, Kendra M., additional, Peraza, Julio A., additional, Pérez, Alexandre, additional, Reeders, Puck C., additional, Yanes, Julio A., additional, and Laird, Angela R., additional

Published
2023
Full Text
View/download PDF

47. Meta-analytic decoding of the cortical gradient of functional connectivity

Author

Peraza, Julio A., primary, Salo, Taylor, additional, Riedel, Michael C., additional, Bottenhorn, Katherine L., additional, Poline, Jean-Baptiste, additional, Dockès, Jérôme, additional, Kent, James D., additional, Bartley, Jessica E., additional, Flannery, Jessica S., additional, Hill-Bowen, Lauren D., additional, Lobo, Rosario Pintos, additional, Poudel, Ranjita, additional, Ray, Kimberly L., additional, Robinson, Jennifer L., additional, Laird, Robert W., additional, Sutherland, Matthew T., additional, de la Vega, Alejandro, additional, and Laird, Angela R., additional

Published
2023
Full Text
View/download PDF

48. NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query

Author

Queder, Nazek, primary, Tien, Vivian B., additional, Abraham, Sanu Ann, additional, Urchs, Sebastian Georg Wenzel, additional, Helmer, Karl G., additional, Chaplin, Derek, additional, van Erp, Theo G. M., additional, Kennedy, David N., additional, Poline, Jean-Baptiste, additional, Grethe, Jeffrey S., additional, Ghosh, Satrajit S., additional, and Keator, David B., additional

Published
2023
Full Text
View/download PDF

50. Data sharing in neuroimaging research

Author

Poline, Jean-Baptiste, Breeze, Janis L., Ghosh, Satrajit, Gorgolewski, Krzysztof, Halchenko, Yaroslav O., Hanke, Michael, Haselgrove, Christian, Helmer, Karl G., Keator, David B., Marcus, Daniel S., Poldrack, Russell A., Schwartz, Yannick, Ashburner, John, and Kennedy, David N.

Abstract

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.

Published
2012

Catalog

Books, media, physical & digital resources
See catalog results