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Article ; Online: Play the Pain

Najmeh Khalili-Mahani / Eileen Holowka / Sandra Woods / Rilla Khaled / Mathieu Roy / Myrna Lashley / Tristan Glatard / Janis Timm-Bottos / Albert Dahan / Marieke Niesters / Richard B. Hovey / Bart Simon / Laurence J. Kirmayer

Frontiers in Psychiatry, Vol

A Digital Strategy for Play-Oriented Research and Action

2021 Volume 12

Abstract: The value of understanding patients' illness experience and social contexts for advancing medicine and clinical care is widely acknowledged. However, methodologies for rigorous and inclusive data gathering and integrative analysis of biomedical, cultural, ...

Abstract	The value of understanding patients' illness experience and social contexts for advancing medicine and clinical care is widely acknowledged. However, methodologies for rigorous and inclusive data gathering and integrative analysis of biomedical, cultural, and social factors are limited. In this paper, we propose a digital strategy for large-scale qualitative health research, using play (as a state of being, a communication mode or context, and a set of imaginative, expressive, and game-like activities) as a research method for recursive learning and action planning. Our proposal builds on Gregory Bateson's cybernetic approach to knowledge production. Using chronic pain as an example, we show how pragmatic, structural and cultural constraints that define the relationship of patients to the healthcare system can give rise to conflicted messaging that impedes inclusive health research. We then review existing literature to illustrate how different types of play including games, chatbots, virtual worlds, and creative art making can contribute to research in chronic pain. Inspired by Frederick Steier's application of Bateson's theory to designing a science museum, we propose DiSPORA (Digital Strategy for Play-Oriented Research and Action), a virtual citizen science laboratory which provides a framework for delivering health information, tools for play-based experimentation, and data collection capacity, but is flexible in allowing participants to choose the mode and the extent of their interaction. Combined with other data management platforms used in epidemiological studies of neuropsychiatric illness, DiSPORA offers a tool for large-scale qualitative research, digital phenotyping, and advancing personalized medicine.
Keywords	chronic pain ; personalized medicine ; citizen labs ; stigma & discrimination ; digital health ; serious games (SGs) ; Psychiatry ; RC435-571
Subject code	360
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks

Gregory Kiar / Yohan Chatelain / Pablo de Oliveira Castro / Eric Petit / Ariel Rokem / Gaël Varoquaux / Bratislav Misic / Alan C. Evans / Tristan Glatard

PLoS ONE, Vol 16, Iss

2021 Volume 11

Abstract: The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead ... ...

Abstract	The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 − 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.

Gregory Kiar / Yohan Chatelain / Pablo de Oliveira Castro / Eric Petit / Ariel Rokem / Gaël Varoquaux / Bratislav Misic / Alan C Evans / Tristan Glatard

PLoS ONE, Vol 16, Iss 11, p e

2021 Volume 0250755

Abstract	The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Casey Paquola / Jessica Royer / Lindsay B Lewis / Claude Lepage / Tristan Glatard / Konrad Wagstyl / Jordan DeKraker / Paule-J Toussaint / Sofie L Valk / Louis Collins / Ali R Khan / Katrin Amunts / Alan C Evans / Timo Dickscheid / Boris Bernhardt

eLife, Vol

2021 Volume 10

Abstract: Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is ‘BigBrain’. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging ... ...

Abstract	Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is ‘BigBrain’. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, ’BigBrainWarp’, that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
Keywords	neuroimaging ; histology ; multi-modal ; anatomy ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Subject code	020
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem

Jorge Bosch-Bayard / Eduardo Aubert-Vazquez / Shawn T. Brown / Christine Rogers / Gregory Kiar / Tristan Glatard / Lalet Scaria / Lidice Galan-Garcia / Maria L. Bringas-Vega / Trinidad Virues-Alba / Armin Taheri / Samir Das / Cecile Madjar / Zia Mohaddes / Leigh MacIntyre / CHBMP / Alan C. Evans / Pedro A. Valdes-Sosa

Frontiers in Neuroinformatics, Vol

Normative SPM of EEG Source Spectra

2020 Volume 14

Abstract: The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age- ... ...

Abstract	The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age-corrected normative Statistical Parametric Maps of EEG log source spectra testing compliance to a normative database. This toolbox was developed at the Cuban Neuroscience Center as part of the first wave of the Cuban Human Brain Mapping Project (CHBMP) and has been validated and used in different health systems for several decades. Incorporation into the MNI ecosystem now provides CBRAIN registered users access to its full functionality and is accompanied by a public release of the source code on GitHub and Zenodo repositories. Among other features are the calculation of EEG scalp spectra, and the estimation of their source spectra using the Variable Resolution Electrical Tomography (VARETA) source imaging. Crucially, this is completed by the evaluation of z spectra by means of the built-in age regression equations obtained from the CHBMP database (ages 5–87) to provide normative Statistical Parametric Mapping of EEG log source spectra. Different scalp and source visualization tools are also provided for evaluation of individual subjects prior to further post-processing. Openly releasing this software in the CBRAIN platform will facilitate the use of standardized qEEGt methods in different research and clinical settings. An updated precis of the methods is provided in Appendix I as a reference for the toolbox. qEEGt/CBRAIN is the first installment of instruments developed by the neuroinformatic platform of the Cuba-Canada-China (CCC) project.
Keywords	Statistical Parametric Mapping ; qEEGt ; CBRAIN ; EEG tomography ; quantitative EEG ; open science ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
Subject code	020
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Data and Tools Integration in the Canadian Open Neuroscience Platform

Jean-Baptiste Poline / Samir Das / Tristan Glatard / Cécile Madjar / Erin W. Dickie / Xavier Lecours / Thomas Beaudry / Natacha Beck / Brendan Behan / Shawn T. Brown / David Bujold / Michael Beauvais / Bryan Caron / Candice Czech / Moyez Dharsee / Mathieu Dugré / Ken Evans / Tom Gee / Giulia Ippoliti /

Gregory Kiar / Bartha Maria Knoppers / Tristan Kuehn / Diana Le / Derek Lo / Mandana Mazaheri / Dave MacFarlane / Naser Muja / Emmet A. O’Brien / Liam O’Callaghan / Santiago Paiva / Patrick Park / Darcy Quesnel / Henri Rabelais / Pierre Rioux / Mélanie Legault / Jennifer Tremblay-Mercier / David Rotenberg / Jessica Stone / Ted Strauss / Ksenia Zaytseva / Joey Zhou / Simon Duchesne / Ali R. Khan / Sean Hill / Alan C. Evans

Scientific Data, Vol 10, Iss 1, Pp 1-

2023 Volume 11

Abstract: Measurement(s) Digital Data Repository Technology Type(s) Digital Data ... ...

Abstract	Measurement(s) Digital Data Repository Technology Type(s) Digital Data Repository
Keywords	Science ; Q
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Head-to-head comparison of two popular cortical thickness extraction algorithms

Alberto Redolfi / David Manset / Frederik Barkhof / Lars-Olof Wahlund / Tristan Glatard / Jean-François Mangin / Giovanni B Frisoni / neuGRID Consortium, for the Alzheimer’s Disease Neuroimaging Initiative

PLoS ONE, Vol 10, Iss 3, p e

a cross-sectional and longitudinal study.

2015 Volume 0117692

Abstract: The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer's. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0).Images from 185 ADNI1 cases (69 elderly controls (CTR), ...

Abstract	The measurement of cortical shrinkage is a candidate marker of disease progression in Alzheimer's. This study evaluated the performance of two pipelines: Civet-CLASP (v1.1.9) and Freesurfer (v5.3.0).Images from 185 ADNI1 cases (69 elderly controls (CTR), 37 stable MCI (sMCI), 27 progressive MCI (pMCI), and 52 Alzheimer (AD) patients) scanned at baseline, month 12, and month 24 were processed using the two pipelines and two interconnected e-infrastructures: neuGRID (https://neugrid4you.eu) and VIP (http://vip.creatis.insa-lyon.fr). The vertex-by-vertex cross-algorithm comparison was made possible applying the 3D gradient vector flow (GVF) and closest point search (CPS) techniques.The cortical thickness measured with Freesurfer was systematically lower by one third if compared to Civet's. Cross-sectionally, Freesurfer's effect size was significantly different in the posterior division of the temporal fusiform cortex. Both pipelines were weakly or mildly correlated with the Mini Mental State Examination score (MMSE) and the hippocampal volumetry. Civet differed significantly from Freesurfer in large frontal, parietal, temporal and occipital regions (p<0.05). In a discriminant analysis with cortical ROIs having effect size larger than 0.8, both pipelines gave no significant differences in area under the curve (AUC). Longitudinally, effect sizes were not significantly different in any of the 28 ROIs tested. Both pipelines weakly correlated with MMSE decay, showing no significant differences. Freesurfer mildly correlated with hippocampal thinning rate and differed in the supramarginal gyrus, temporal gyrus, and in the lateral occipital cortex compared to Civet (p<0.05). In a discriminant analysis with ROIs having effect size larger than 0.6, both pipelines yielded no significant differences in the AUC.Civet appears slightly more sensitive to the typical AD atrophic pattern at the MCI stage, but both pipelines can accurately characterize the topography of cortical thinning at the dementia stage.
Keywords	Medicine ; R ; Science ; Q
Subject code	150
Language	English
Publishing date	2015-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

Olivier Commowick / Audrey Istace / Michaël Kain / Baptiste Laurent / Florent Leray / Mathieu Simon / Sorina Camarasu Pop / Pascal Girard / Roxana Améli / Jean-Christophe Ferré / Anne Kerbrat / Thomas Tourdias / Frédéric Cervenansky / Tristan Glatard / Jérémy Beaumont / Senan Doyle / Florence Forbes / Jesse Knight / April Khademi /

Amirreza Mahbod / Chunliang Wang / Richard McKinley / Franca Wagner / John Muschelli / Elizabeth Sweeney / Eloy Roura / Xavier Lladó / Michel M. Santos / Wellington P. Santos / Abel G. Silva-Filho / Xavier Tomas-Fernandez / Hélène Urien / Isabelle Bloch / Sergi Valverde / Mariano Cabezas / Francisco Javier Vera-Olmos / Norberto Malpica / Charles Guttmann / Sandra Vukusic / Gilles Edan / Michel Dojat / Martin Styner / Simon K. Warfield / François Cotton / Christian Barillot

Scientific Reports, Vol 8, Iss 1, Pp 1-

2018 Volume 17

Abstract: Abstract We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent ... ...

Abstract	Abstract We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: 2015 Brainhack Proceedings

R. Cameron Craddock / Pierre Bellec / Daniel S. Margules / B. Nolan Nichols / Jörg P. Pfannmöller / AmanPreet Badhwar / David Kennedy / Jean-Baptiste Poline / Roberto Toro / Ben Cipollini / Ariel Rokem / Daniel Clark / Krzysztof J. Gorgolewski / Daniel J. Clark / Samir Das / Cécile Madjar / Ayan Sengupta / Zia Mohades / Sebastien Dery /

Weiran Deng / Eric Earl / Damion V. Demeter / Kate Mills / Glad Mihai / Luka Ruzic / Nick Ketz / Andrew Reineberg / Marianne C. Reddan / Anne-Lise Goddings / Javier Gonzalez-Castillo / Caroline Froehlich / Gil Dekel / Daniel S. Margulies / Ben D. Fulcher / Tristan Glatard / Reza Adalat / Natacha Beck / Rémi Bernard / Najmeh Khalili-Mahani / Pierre Rioux / Marc-Étienne Rousseau / Alan C. Evans / Yaroslav O. Halchenko / Matteo Visconti di Oleggio Castello / Raúl Hernández-Pérez

GigaScience, Vol 5, Iss S1, Pp 1-

2016 Volume 26

Abstract: ... D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck ...

Abstract	Table of contents I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johnson A16 Facilitating big data ...
Keywords	Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	004 ; 006
Language	English
Publishing date	2016-11-01T00:00:00Z
Publisher	Oxford University Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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