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1. Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

Author

Miracle Ozzoude, Joel Ramirez, Pradeep Reddy Raamana, Melissa F. Holmes, Kirstin Walker, Christopher J. M. Scott, Fuqiang Gao, Maged Goubran, Donna Kwan, Maria C. Tartaglia, Derek Beaton, Gustavo Saposnik, Ayman Hassan, Jane Lawrence-Dewar, Dariush Dowlatshahi, Stephen C. Strother, Sean Symons, Robert Bartha, Richard H. Swartz, and Sandra E. Black

Subjects
cortical thickness, cerebrovascular disease, stroke, cerebral small vessel disease, FreeSurfer, ONDRI, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer’s (FS’s) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.MethodsIn 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.ResultsCorrected procedures increased “Acceptable” QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced “Fail” ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).ConclusionThese findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS’s segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

Published
2020
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2. An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report

Author

Jee Su Suh, Luciano Minuzzi, Pradeep Reddy Raamana, Andrew Davis, Geoffrey B. Hall, Jacqueline Harris, Stefanie Hassel, Mojdeh Zamyadi, Stephen R. Arnott, Gésine L. Alders, Roberto B. Sassi, Roumen Milev, Raymond W. Lam, Glenda M. MacQueen, Stephen C. Strother, Sidney H. Kennedy, and Benicio N. Frey

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract

Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10–20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2–10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=−29,9,54.5,−7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks. Keywords: Major depressive disorder, Cortical thickness, Structural neuroimaging, Antidepressant response, Clinical trial

Published
2020
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3. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.

Author

Krzysztof J Gorgolewski, Fidel Alfaro-Almagro, Tibor Auer, Pierre Bellec, Mihai Capotă, M Mallar Chakravarty, Nathan W Churchill, Alexander Li Cohen, R Cameron Craddock, Gabriel A Devenyi, Anders Eklund, Oscar Esteban, Guillaume Flandin, Satrajit S Ghosh, J Swaroop Guntupalli, Mark Jenkinson, Anisha Keshavan, Gregory Kiar, Franziskus Liem, Pradeep Reddy Raamana, David Raffelt, Christopher J Steele, Pierre-Olivier Quirion, Robert E Smith, Stephen C Strother, Gaël Varoquaux, Yida Wang, Tal Yarkoni, and Russell A Poldrack

Subjects
Biology (General), QH301-705.5
Abstract

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Published
2017
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10. Proceedings of the OHBM Brainhack 2021

Author

Aki Nikolaidis, Matteo Manchini, Tibor Auer, Katherine L. Bottenhorn, Eva Alonso-Ortiz, Gabriel Gonzalez-Escamilla, Sofie Valk, Tristan Glatard, Melvin Selim Atay, Johanna M.M. Bayer, Janine Bijsterbosch, Johannes Algermissen, Natacha Beck, Patrick Bermudez, Isil Poyraz Bilgin, Steffen Bollmann, Claire Bradley, Megan E.J. Campbell, Bryan Caron, Oren Civier, Luis Pedro Coelho, Shady El Damaty, Samir Das, Mathieu Dugré, Eric Earl, Stefanie Evas, Nastassja Lopes Fischer, De Fu Yap, Kelly G. Garner, Remi Gau, Giorgio Ganis, Dylan G. E. Gomes, Martin Grignard, Samuel Guay, Omer Faruk Gulban, Sarah Hamburg, Yaroslav O. Halchenko, Valerie Hayot-Sasson, Dawn Liu Holford, Laurentius Huber, Manuel Illanes, Tom Johnstone, Avinash Kalyani, Kinshuk Kashyap, Han Ke, Ibrahim Khormi, Gregory Kiar, Vanja Ković, Tristan Kuehn, Achintya Kumar, Xavier Lecours-Boucher, Michael Lührs, Robert Luke, Cecile Madjar, Sina Mansour L., Chris Markeweicz, Paula Andrea Martinez, Alexandra McCarroll, Léa Michel, Stefano Moia, Aswin Narayanan, Guiomar Niso, Emmet A. O’Brien, Kendra Oudyk, François Paugam, Yuri G. Pavlov, Jean-Baptiste Poline, Benedikt A. Poser, Céline Provins, Pradeep Reddy Raamana, Pierre Rioux, David Romero-Bascones, Ekansh Sareen, Antonio Schettino, Alec Shaw, Thomas Shaw, Cooper A. Smout, Anđdela Šoškié, Jessica Stone, Suzy J Styles, Ryan Sullivan, Naoyuki Sunami, Shamala Sundaray, Jasmine Wei Rou, Dao Thanh Thuy, Sebastien Tourbier, Sebastián Urch, Alejandro de la Vega, Niruhan Viswarupan, Adina Wagner, Lennart Walger, Hao-Ting Wang, Fei Ting Woon, David White, Christopher Wiggins, Will Woods, Yu-Fang Yang, Ksenia Zaytseva, Judy D. Zhu, and Marcel P. Zwiers

Published
2023
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11. VisualQC: software development kit for medical and neuroimaging quality control and assurance

Author

Pradeep Reddy Raamana

Abstract

VisualQC is a medical imaging software library aimed to enable and improve certain challenging aspects of neuroimaging quality control (niQC). VisualQC is purpose-built for rigorous niQC and aims to greatly reduce the tediousness of manual visual QC. It achieves this by seamlessly (1) presenting relevant composite visualizations while alerting the user of any outliers based on advanced machine learning algorithms, (2) offering an easy way to record the ratings and notes, and (3) making it easy to quickly navigate through a large number of subjects. VisualQC offers a modular and extensible framework, to allow for solving a wide diversity of visual niQC tasks along with some assistive automation. We demonstrate this by showing a few common but diverse QC use-cases targeting visual review and rating of (1) the raw image quality for structural and functional MRI scans, (2) accuracy of anatomical segmentations either via Freesurfer or a generic voxel-based segmentation algorithm, (3) accuracy of the alignment between two images (registration algorithms), and (4) accuracy of defacing algorithms to protect patient privacy. We believe this modular and extensible API/classes will encourage the community to customize it for their own needs and with their own visionary ideas and encourage them to share their implementation with the community to improve the quality of neuroimaging data and analyses.

Published
2023
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12. Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI

Author

Pradeep Reddy Raamana, Athena Theyers, Tharushan Selliah, Piali Bhati, Stephen R. Arnott, Stefanie Hassel, Nuwan D. Nanayakkara, Christopher J. M. Scott, Jacqueline Harris, Mojdeh Zamyadi, Raymond W. Lam, Roumen Milev, Daniel J. Müller, Susan Rotzinger, Benicio N. Frey, Sidney H. Kennedy, Sandra E. Black, Anthony Lang, Mario Masellis, Sean Symons, Robert Bartha, Glenda M. MacQueen, and Stephen C. Strother

Abstract

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open-source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Published
2022
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13. Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight

Author

Pradeep Reddy Raamana and Stephen C. Strother

Subjects
Adult, Male, Histology, Adolescent, Databases, Factual, Computer science, Machine learning, computer.software_genre, 050105 experimental psychology, Machine Learning, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Histogram, Humans, Cognitive Dysfunction, 0501 psychology and cognitive sciences, Autistic Disorder, Child, Aged, 030304 developmental biology, computer.programming_language, Aged, 80 and over, 0303 health sciences, business.industry, General Neuroscience, 05 social sciences, Brain, Graph theory, Python (programming language), Brain Cortical Thickness, Prognosis, Magnetic Resonance Imaging, Toolbox, Disease Progression, Spatial ecology, Predictive power, Female, Pairwise comparison, Artificial intelligence, Anatomy, business, computer, Algorithms, 030217 neurology & neurosurgery, Network analysis
Abstract

Network-level analysis based on anatomical, pairwise similarities (e.g., cortical thickness) has been gaining increasing attention recently. However, there has not been a systematic study of the impact of spatial scale and edge definitions on predictive performance. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this study, we present a histogram-based approach to construct subject-wise weighted networks that enable a principled comparison across different methods of network analysis. We design several weighted networks based on three large publicly available datasets and perform a robust evaluation of their predictive power under four levels of separability. An interesting insight generated is that changes in nodal size (spatial scale) have no significant impact on predictive power among the three classification experiments and two disease cohorts studied, i.e., mild cognitive impairment and Alzheimer’s disease from ADNI, and Autism from the ABIDE dataset. We also release an open source python package called graynet to enable others to implement the novel network feature extraction algorithm, which is applicable to other modalities as well (due to its domain- and feature-agnostic nature) in diverse applications of connectivity research. In addition, the findings from the ADNI dataset are replicated in the AIBL dataset using an open source machine learning tool called neuropredict.

Published
2020
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17. Behavioral Markers for Deficits in Speed of Processing in Cerebrovascular Disease

Author

Yuelee Khoo, Malcolm A. Binns, Manuel Montero-Odasso, Stephen C. Strother, Julia Fraser, Jennifer Mandzia, Kelly M Sunderland, Ying Chen, Benjamin Cornish, Brian C. Coe, Frederico Pieruccini-Faria, Dariush Dowlatshahi, William E. McIlroy, Leanne K. Casaubon, Angela K. Troyer, Paula M. McLaughlin, Stephen R. Arnott, Brian Levine, Douglas P. Munoz, Angela Roberts, Ayman Hassan, Wendy Lou, Karen Van Ooteghem, Joel Ramirez, Pradeep Reddy Raamana, Alicia J. Peltsch, Gustavo Saposnik, Demetrios J. Sahlas, Derek Beaton, Donna Kwan, Donald C. Brien, Joseph B. Orange, and Richard H. Swartz

Subjects
medicine.medical_specialty, Visual perception, Physical medicine and rehabilitation, Cohort, Saccade, medicine, Neuropsychology, Disease, Association (psychology), Psychology, Canonical correlation, Gait
Abstract

ObjectiveTo assess overlap and uniqueness of established behavioral markers of speed of processing for different aspects of visual information within a cerebrovascular disease cohort, and to examine the link between these speed of processing markers and functional behavior, specifically walking.MethodsA cohort of 161 participants with cerebrovascular disease recruited to the Ontario Neurodegenerative Disease Research Initiative (ONDRI) were examined with three types of assessments: neuropsychology, saccadic eye movement and gait. Principal component analysis (PCA) and canonical correlation analysis (CCA) were performed on select variables from these assessments to reveal commonalities and discrepancies among the measures.ResultsPCA analysis revealed different variable patterns between neuropsychology and saccade assessments, with the first component characterized primarily by neuropsychology, and the second and third components more influenced by the saccade assessments. CCA analysis did not reveal association between different types of assessments with the exception of a modest, but significant, positive association between speed of processing measures from the neuropsychological assessments and gait speed.DiscussionNeuropsychological tests and the pro-saccade task can be used for assessment of speed of processing for two major features of visual information, visual perception vs. spatial location. Despite a general lack of association between different types of assessments, combining gait speed as an important contributor to the models reinforces the idea of the link between speed of processing and complex function such as walking, and provides support for the importance of attending to the potential consequences of changes in speed of processing after neurologic injury.

Published
2021
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18. Centering inclusivity in the design of online conferences - An OHBM - Open Science perspective

Author

Elizabeth Levitis, Cassandra Doris Gould van Praag, Remi Gau, Stephan Heunis, Elizabeth DuPre, Greg Kiar, Katherine L Bottenhorn, Tristan Glatard, Aki Nikolaidis, Kirstie Jane Whitaker, Matteo Mancini, Guiomar Niso, Soroosh Afyouni, Eva Alonso Ortiz, Stefan Appelhoff, Aurina Arnatkeviciute, Melvin Selim ATAY, Tibor Auer, Giulia Baracchini, Johanna Margarete Marianne Bayer, Michael J. S. Beauvais, Janine Diane Bijsterbosch, Isil Poyraz Bilgin, Saskia Bollmann, Steffen Bollmann, Rotem Botvinik-Nezer, Molly G Bright, Vince D Calhoun, Xiao Chen, Sidhant Chopra, Hu Chuan-Peng, Thomas Close, Savannah Cookson, Cameron Craddock, Alejandro De La Vega, Benjamin De Leener, Damion Demeter, Paola Di Maio, Erin W Dickie, Simon B Eickhoff, Oscar Esteban, Karolina Finc, Matteo Frigo, Saampras Ganesan, Melanie Ganz, Kelly Garner, Eduardo A. Garza-Villarreal, Gabriel Gonzalez-Escamilla, Rohit Goswami, John David Griffiths, Tijl Grootswagers, Samuel Guay, Olivia Guest, Daniel A. Handwerker, Peer Herholz, Katja Heuer, Dorien Huijser, Vittorio Iacovella, Michael Joseph, Agah Karakuzu, David Keator, Xenia Kobeleva, Manoj Kumar, Angie Laird, Linda J. Larson-Prior, Alexandra Lautarescu, Alberto Lazari, Jon Haitz Legarreta Gorroño, Jeff jeffers, Jinglei Lv, Sina Mansour L., David Meunier, Dustin Moraczewski, Tulika Nandi, Samuel A. Nastase, Matthias Nau, Stephanie Noble, Martin Norgaard, Johnes Obungoloch, Robert Oostenveld, Edwina R Orchard, Ana Luísa Pinho, Russell Poldrack, Anqi Qiu, Pradeep Reddy Raamana, Ariel Rokem, Saige Rutherford, Malvika Sharan, Thomas Shaw, Warda T Syeda, Meghan M. Testerman, Roberto Toro, Sofie L. Valk, Sofie Van Den Bossche, Gael P. Varoquaux, Frantisek Vasa, Michele Veldsman, Jakub Vohryzek, Adina Svenja Wagner, Reubs J Walsh, Tonya White, null zuxfoucault, Xihe Xie, Chao-Gan Yan, Yu-Fang Yang, Yohan Yee, Gaston E Zanitti, Ana Van Gulick, Eugene Duff, Camille MAUMET, National Institute of Mental Health (NIMH), University College of London [London] (UCL), University of Oxford, Université Catholique de Louvain = Catholic University of Louvain (UCL), Eindhoven University of Technology [Eindhoven] (TU/e), McGill University = Université McGill [Montréal, Canada], Florida International University [Miami] (FIU), Concordia University [Montreal], Child Mind Institute, The Alan Turing Institute, University of Sussex, Cardiff University, École Polytechnique de Montréal (EPM), Indiana University [Bloomington], Indiana University System, Universidad Politécnica de Madrid (UPM), Max Planck Institute for Human Development, Max-Planck-Gesellschaft, Monash University [Melbourne], Middle East Technical University [Ankara] (METU), University of Surrey (UNIS), Montreal Neurological Institute and Hospital, University of Melbourne, Orygen Youth Health Research Centre [Melbourne], Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), University of Reading (UOR), University of Queensland [Brisbane], Dartmouth College [Hanover], Northwestern University [Evanston], Georgia State University, University System of Georgia (USG), Institute of Psychology [Beijing], Chinese Academy of Sciences [Changchun Branch] (CAS), University of Chinese Academy of Sciences [Beijing] (UCAS), Nanjing Normal University (NNU), The University of Sydney, University of California (UC), University of Texas at Austin [Austin], CHU Sainte Justine [Montréal], Institute for Globally Distributed Open Research and Education (IGDORE ), Centre for Addiction and Mental Health [Toronto] (CAMH), University of Toronto, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, Université de Lausanne = University of Lausanne (UNIL), Nicolaus Copernicus University [Toruń], Université Côte d'Azur (UCA), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Rigshospitalet [Copenhagen], Copenhagen University Hospital, University of Copenhagen = Københavns Universitet (UCPH), University of Birmingham [Birmingham], Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), University of Iceland [Reykjavik], Indian Institute of Technology Kanpur (IIT Kanpur), Western Sydney University, Université de Montréal (UdeM), Radboud University [Nijmegen], Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Erasmus University Rotterdam, Universiteit Leiden, University of Trento [Trento], Université du Québec à Montréal = University of Québec in Montréal (UQAM), University Hospital Bonn, German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Princeton University, University of Arkansas for Medical Sciences (UAMS), Arkansas Children's Research Institute, King‘s College London, Université de Sherbrooke (UdeS), Aarhus University [Aarhus], Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Yale University [New Haven], Stanford University, Mbarara University of Science and Technology [Mbarara] (MUST), Karolinska Institutet [Stockholm], Université Paris-Saclay, National University of Singapore (NUS), Johns Hopkins University (JHU), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Washington [Seattle], Radboud University Medical Center [Nijmegen], University of Michigan [Ann Arbor], University of Michigan System, Institut Pasteur [Paris] (IP), Universiteit Gent = Ghent University (UGENT), Vrije Universiteit Amsterdam [Amsterdam] (VU), Department of Public Health, Erasmus University Medical Centre, Rotterdam, Erasmus University Medical Centre, Rotterdam, Academia Sinica, Weill Medical College of Cornell University [New York], Chinese Academy of Sciences [Beijing] (CAS), University of Würzburg, The Hospital for sick children [Toronto] (SickKids), Carnegie Mellon University [Pittsburgh] (CMU), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), R25-DA051675, National Institutes of Health, 1631325, National Science Foundation, 5R21MH118556-02, National Institute of Mental Health, BB/S008314/1, Biotechnology and Biological Sciences Research Council, G036716N, Fonds Wetenschappelijk Onderzoek, University of Oxford [Oxford], University of California, University of Lausanne (UNIL), University of Copenhagen = Københavns Universitet (KU), Universidad Nacional Autónoma de México (UNAM), Johannes Gutenberg - Universität Mainz (JGU), Radboud university [Nijmegen], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Leiden University, Institut Pasteur [Paris], Universiteit Gent = Ghent University [Belgium] (UGENT), Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Maumet, Camille

Subjects
SDG 16 - Peace, collaborative events, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, SDG 16 - Peace, Justice and Strong Institutions, Review, Justice and Strong Institutions, online conferences, diversity, World Wide Web, inclusivity, open science, ddc:610, Sociology
Abstract

As the global health crisis unfolded throughout the world, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers and effectively enabled many individuals who have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g. caregiving responsibilities. Yet, the mere existence of online conferences is unfortunately no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the setup of the conference can provide more visibility to well-established researchers and reduce opportunities for early career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design.Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to: attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Published
2021
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21. Visual QC Protocol for FreeSurfer Cortical Parcellations from Anatomical MRI

Author

Susan Rotzinger, Piali Bhati, Pradeep Reddy Raamana, Nuwan D. Nanayakkara, Robert Bartha, Athena E. Theyers, Glenda MacQueen, Jacqueline K. Harris, Raymond W. Lam, Daniel J. Müller, Mojdeh Zamyadi, Stephen C. Strother, Benicio N. Frey, Roumen Milev, Sean P. Symons, Stephen R. Arnott, Mario Masellis, Stefanie Hassel, Anthony E. Lang, Sandra E. Black, Christopher J.M. Scott, Tharushan Selliah, and Sidney H. Kennedy

Subjects
Visual inspection, Protocol (science), Neuroimaging, business.industry, Computer science, media_common.quotation_subject, Pattern recognition, Quality (business), Artificial intelligence, business, Reliability (statistics), media_common
Abstract

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets, and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Published
2020
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22. Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions

Author

Donna Kwan, Joel Ramirez, Maria Carmela Tartaglia, Brian Tan, Kirstin Walker, Robert Bartha, Maged Goubran, Pradeep Reddy Raamana, Christopher J.M. Scott, Miracle Ozzoude, Stephen C. Strother, Sean P. Symons, Sandra E. Black, Richard H. Swartz, Melissa F. Holmes, and Derek Beaton

Subjects
medicine.medical_specialty, business.industry, Focal atrophy, Montreal Cognitive Assessment, Disease, Neurovascular bundle, medicine.disease, White matter, Atrophy, medicine.anatomical_structure, Neuroimaging, medicine, Radiology, business, Stroke
Abstract

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases can be estimated using specialised neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer’s cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and cerebrovascular disease, with varying degrees of neurovascular lesions and brain atrophy.MethodsIn 155 cerebrovascular disease patients enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FreeSurfer outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.ResultsCorrected procedures increased ‘Acceptable’ QC ratings from 18% to 76% for the cortical ribbon and from 38% to 92% for tissue segmentation. Corrected procedures reduced ‘Fail’ ratings from 11% to 0% for the cortical ribbon and 62% to 8% for tissue segmentation. FreeSurfer-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8mL vs. 15.9mL, pConclusionsThese findings suggest that correction procedures that account for brain atrophy and neurovascular lesions can significantly improve FreeSurfer’s segmentation results, reduce failure rates, and potentially increase sensitivity to examine brain-behaviour relationships. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and neurodegenerative disease in the ONDRI study.

Published
2020
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23. Effective self-management for early career researchers in the natural and life sciences

Author

Courtney C. Walton, Meena M. Makary, Vince D. Calhoun, Veronika Cheplygina, Ricarda Braukmann, Sridar Narayanan, Leanna M. Hernandez, Mengxia Gao, Ali Khatibi, Russell A. Poldrack, Michele Veldsman, Amelie Haugg, Adriana Bankston, Karolina Finc, Christian La, Ayaka Ando, Katherine L. Bottenhorn, Ece Ercan, Natalia Z. Bielczyk, Daniel Kessler, Claire Godard-Sebillotte, Daniel J. Lurie, Phillip G. D. Ward, Pradeep Reddy Raamana, Taylor Salo, Xinqi Zhou, David M. A. Mehler, Chiara Caldinelli, AmanPreet Badhwar, Davide Valeriani, Chris Allen, Sofie L. Valk, Kaori L. Ito, Lucina Q. Uddin, Julio A. Yanes, Aki Nikolaidis, Heidi Foo, Catarina Costa Boffino, and Medical Image Analysis

Subjects
0301 basic medicine, Work, self-management, mentoring, networking, career development, Biological Science Disciplines, Article, 03 medical and health sciences, 0302 clinical medicine, Humans, Natural (music), Human brain mapping, early career researchers, Early career, ddc:610, Life Style, Brain Mapping, Medical education, Self-management, Career Choice, General Neuroscience, Mentors, Research Personnel, 030104 developmental biology, Natural Science Disciplines, ECRs, Psychology, 030217 neurology & neurosurgery, Career development
Abstract

This is a working paper for a project launched and managed by the members of the OHBM Student and Postdoc Special Interest Group (SP-SIG, www.ohbmtrainees.com), in collaboration with two guest early career researchers, Dan Kessler from University of Michigan and Daniel Lurie from University of Berkeley. We would like to further develop the manuscript before submitting this work for peer review. Therefore, to provide advice that can be generalizable to a wide variety of situations, demographics, and countries, we are seeking contributions from the OHBM community. We would like to welcome everyone willing to participate to join us and discuss this subject on the associated Google group: https://groups.google.com/forum/#!forum/effective-self-management-for-ecrs The deadline for contributions is February 28th, 2019. All the questions with respect to this project can be directed to: ohbmtrainees@gmail.com

Published
2020
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24. An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report

Author

Stefanie Hassel, Roumen Milev, Jacqueline K. Harris, Glenda MacQueen, Roberto B. Sassi, Luciano Minuzzi, Mojdeh Zamyadi, Geoffrey B. Hall, Stephen R. Arnott, Raymond W. Lam, Sidney H. Kennedy, Stephen C. Strother, Andrew D. Davis, Gésine L. Alders, Jee Su Suh, Benicio N. Frey, and Pradeep Reddy Raamana

Subjects
Male, Aripiprazole, Gastroenterology, lcsh:RC346-429, 0302 clinical medicine, Study report, Cortex (anatomy), Outcome Assessment, Health Care, Longitudinal Studies, Cerebral Cortex, Antidepressant response, 05 social sciences, Regular Article, Middle Aged, Magnetic Resonance Imaging, Antidepressive Agents, 3. Good health, Clinical trial, medicine.anatomical_structure, Neurology, Antidepressant, Major depressive disorder, lcsh:R858-859.7, Drug Therapy, Combination, Female, medicine.drug, Adult, medicine.medical_specialty, Depression scale, Cognitive Neuroscience, Neuroimaging, Citalopram, lcsh:Computer applications to medicine. Medical informatics, 050105 experimental psychology, Cortical thickness, 03 medical and health sciences, Internal medicine, mental disorders, medicine, Escitalopram, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, lcsh:Neurology. Diseases of the nervous system, Depressive Disorder, Major, business.industry, medicine.disease, Structural neuroimaging, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

Highlights • Patients with major depression underwent two-stage 16-week antidepressant trial. • Whole-brain cortical thickness (CT) was measured in patients and healthy controls. • Site (N = 6) differences were corrected at each vertex using ComBat. • Patients exhibited thinner cortex in left rostral middle frontal cortex at baseline. • There were no baseline CT features associated with antidepressant response., Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10–20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2–10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=−29,9,54.5,−7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.

Published
2020

25. P3‐342: INFLUENCE OF NETWORK CONSTRUCTION METHODS ON PATH LENGTH VALUES IN ALZHEIMER'S DISEASE: A MULTI‐STUDY ANALYSIS OF MRI CONNECTIVITY STUDIES

Author

Ellen Dicks, Yong He, Faisal Beg, Geert Jan Biessels, Cristian Carmeli, Guangyu Chen, Koray Çiftçi, Zhengjia Dai, Edwin Van Dellen, Martin Dyrba, Eric J. Friedman, Willem de Haan, Mahdi Jalili, Jaeseung Jeong, Yong Jeong, Tianzi Jiang, Maria G. Knyazeva, Dong Young Lee, Shi-Jiang Li, Yong Liu, Paul McCarthy, Luis Peraza Rodriguez, David Phillips, Pradeep Reddy Raamana, Yael D. Reijmer, Eun Hyun Seo, Jorge Sepulcre, Anja Soldan, John Suckling, John-Paul Taylor, Gao-Jun Teng, Alle Meije Wink, Frederik Barkhof, Philip Scheltens, Wiesje M. Van der Flier, and Betty M. Tijms

Subjects
Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Epidemiology, Health Policy, Neurology (clinical), Geriatrics and Gerontology
Published
2018
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26. [IC‐P‐076]: MULTI‐CLASS DIFFERENTIAL DIAGNOSIS AMONG ALZHEIMER's, PARKINSON's, CORTICOBASAL SYNDROME AND PROGRESSIVE SUPRANUCLEAR PALSY

Author

Stephen C. Strother and Pradeep Reddy Raamana

Subjects
Class (set theory), Pediatrics, medicine.medical_specialty, Epidemiology, business.industry, Health Policy, medicine.disease, 030218 nuclear medicine & medical imaging, Progressive supranuclear palsy, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, medicine, Neurology (clinical), Geriatrics and Gerontology, Differential diagnosis, business, 030217 neurology & neurosurgery
Published
2017
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28. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Author

Gabriel A. Devenyi, Nathan W. Churchill, Pierre-Olivier Quirion, Tal Yarkoni, Anisha Keshavan, Gregory Kiar, Christopher J. Steele, Stephen C. Strother, Gaël Varoquaux, R. Cameron Craddock, Alexander L. Cohen, J. Swaroop Guntupalli, Mihai Capota, Guillaume Flandin, Robert E. Smith, Oscar Esteban, Pradeep Reddy Raamana, Krzysztof J. Gorgolewski, Tibor Auer, Franziskus Liem, Russell A. Poldrack, Mark Jenkinson, Fidel Alfaro-Almagro, M. Mallar Chakravarty, David Raffelt, Pierre Bellec, Satrajit S. Ghosh, Anders Eklund, Yida Wang, McGovern Institute for Brain Research at MIT, Ghosh, Satrajit S, University of Zurich, and Gorgolewski, Krzysztof J

Subjects
0301 basic medicine, Computer science, Functional magnetic resonance imaging, 2804 Cellular and Molecular Neuroscience, Diagnostic Radiology, User-Computer Interface, 0302 clinical medicine, Human–computer interaction, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Software tools, Biology (General), Operating systems, 0303 health sciences, Brain Mapping, Data Processing, Ecology, 10093 Institute of Psychology, Radiology and Imaging, Software Development, Software Engineering, Brain, Magnetic Resonance Imaging, Data processing, Radiology Information Systems, Computational Theory and Mathematics, Proof of concept, Modeling and Simulation, Data analysis, Engineering and Technology, Information Technology, Software versioning, Algorithms, Research Article, Computer and Information Sciences, QH301-705.5, Imaging Techniques, Operating Systems, Brain Morphometry, Medical Engineering, UFSP13-4 Dynamics of Healthy Aging, Neuroimaging, Research and Analysis Methods, 03 medical and health sciences, Cellular and Molecular Neuroscience, Software portability, Magnetic resonance imaging, 1311 Genetics, Diagnostic Medicine, Image Interpretation, Computer-Assisted, 1312 Molecular Biology, Genetics, Humans, Molecular Biology, Simulation, Ecology, Evolution, Behavior and Systematics, Medicinteknik, 030304 developmental biology, business.industry, Software Tools, Diffusion Weighted Imaging, Software development, Information technology, Biology and Life Sciences, Usability, Diffusion weighted imaging, Data structure, 1105 Ecology, Evolution, Behavior and Systematics, 030104 developmental biology, Container (abstract data type), 150 Psychology, business, 2303 Ecology, 030217 neurology & neurosurgery, Software, 2611 Modeling and Simulation, 1703 Computational Theory and Mathematics, Neuroscience
Abstract

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms., United States. National Institutes of Health (NIH-NIBIB R01 EB020740)

Published
2017

29. F107. Cortical Thickness Features Differentiate 16-Week Antidepressant Response Profiles in Major Depressive Disorder

Author

Roberto B. Sassi, Zahinoor Ismail, Claudio N. Soares, Faranak Farzan, Daniel J. Mueller, Roumen Milev, Jacqueline K. Harris, Gulshan B. Sharma, Glenda MacQueen, Stephen R. Arnott, Benicio N. Frey, Jane A. Foster, Luciano Minuzzi, Stefanie Hassel, Susan Rotzinger, Geoffrey B. Hall, Sidney H. Kennedy, Jee Su Suh, Raymond W. Lam, Kate L. Harkness, Jonathan Downar, Gésine L. Alders, Pierre Blier, Andrew D. Davis, Pradeep Reddy Raamana, Peter Giacobbe, Shane McInerney, Sagar V. Parikh, Rudolf Uher, Mojdeh Zamyadi, Gustavo Turecki, Francesco Leri, and Stephen C. Strother

Subjects
medicine.medical_specialty, Endocrinology, business.industry, Internal medicine, medicine, Major depressive disorder, Antidepressant, medicine.disease, business, Biological Psychiatry
Published
2019
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30. IC‐P‐044: Systematic Comparison of Cortical Thickness Based Networks

Author

Pradeep Reddy Raamana and Stephen C. Strother

Subjects
Epidemiology, Computer science, Health Policy, 05 social sciences, 050105 experimental psychology, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, 0501 psychology and cognitive sciences, Neurology (clinical), Geriatrics and Gerontology, 030217 neurology & neurosurgery
Published
2016
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31. P4‐187: Systematic Comparison of Cortical Thickness–Based Networks

Author

Stephen C. Strother and Pradeep Reddy Raamana

Subjects
Epidemiology, Computer science, Health Policy, 05 social sciences, 050105 experimental psychology, 03 medical and health sciences, Psychiatry and Mental health, Cellular and Molecular Neuroscience, 0302 clinical medicine, Developmental Neuroscience, 0501 psychology and cognitive sciences, Neurology (clinical), Geriatrics and Gerontology, 030217 neurology & neurosurgery
Published
2016
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32. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

Author

Andrés Hoyos-Idrobo, Bertrand Thirion, Yannick Schwartz, Denis A. Engemann, Gaël Varoquaux, Pradeep Reddy Raamana, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Rotman Research Institute at the Baycrest Centre (RRI), Université Pierre et Marie Curie - Paris 6 (UPMC), ANR-11-BINF-0004,NiConnect,Outils pour la Recherche Clinique par cartographie de la connectivité cérébrale fonctionnelle(2011), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
FOS: Computer and information sciences, 0301 basic medicine, model selection, decoding, Computer science, Cognitive Neuroscience, sparse, Neuroimaging, Machine Learning (stat.ML), computer.software_genre, cross-validation, Cross-validation, 03 medical and health sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, MVPA, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.4: Applications, Brain Diseases, Model selection, [SCCO.NEUR]Cognitive science/Neuroscience, fMRI, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, bagging, 030104 developmental biology, ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION/I.5.2: Design Methodology/I.5.2.0: Classifier design and evaluation, Neurology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Data mining, computer, 030217 neurology & neurosurgery, Decoding methods
Abstract

Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within-and across-subject predictions, on multiple datasets --anatomical and functional MRI and MEG-- and simulations. Theory and experiments outline that the popular " leave-one-out " strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be more favorable to use sane defaults, in particular for non-sparse decoders., Comment: NeuroImage, Elsevier, 2016

Published
2016
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33. O3‐09‐04: Network‐level analysis of PET metabolic features for detection of Alzheimer's disease

Author

Mirza Faisal Beg, Karteek Popuri, Lei Wang, Kathryn I. Alpert, and Pradeep Reddy Raamana

Subjects
Epidemiology, business.industry, Health Policy, Disease, Computational biology, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Network level, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, METABOLIC FEATURES
Published
2015
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