The MNI data-sharing and processing ecosystem

AbstractNeuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing

The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset

Measurement(s) functional brain measurement Technology Type(s) electroencephalography (EEG) • magnetic resonance imaging (MRI) • neuropsychological testing Factor Type(s) age of participants • gender of participants • handedness of participants • educational level of participants Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Cuba Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.1327734

Protocol for SYNchronising Exercises, Remedies in GaIt and Cognition at Home (SYNERGIC@Home): feasibility of a home-based double-blind randomised controlled trial to improve gait and cognition in individuals at risk for dementia

IntroductionPhysical exercise and cognitive training have the potential to enhance cognitive function and mobility in older adults at risk of Alzheimer's disease and related dementia (ADRD), but little is known about the feasibility of delivering multidomain interventions in home settings of older adults at risk of ADRD. This study aims to assess the feasibility of home-based delivery of exercise and cognitive interventions, and to evaluate the relationship between participants' intervention preferences and their subsequent adherence. Secondary objectives include the effect of the interventions on ADRD risk factors, including frailty, mobility, sleep, diet and psychological health.Methods and analysisThe SYNchronising Exercises, Remedies in GaIt and Cognition at Home (SYNERGIC@Home) feasibility trial is a randomised control trial that follows a 2×2 factorial design, with a 16-week home-based intervention programme (3 sessions per week) of physical exercises and cognitive training. Participants will be randomised in blocks of four to one of the following four arms: (1) combined exercise (aerobic and resistance)+cognitive training (NEUROPEAK); (2) combined exercise+control cognitive training (web searching); (3) control exercise (balance and toning)+cognitive training; and (4) control exercise+control cognitive training. SYNERGIC@Home will be implemented through video conferencing. Baseline and post-intervention assessments at 4-month and 10-month follow-up will include measures of cognition, frailty, mobility, sleep, diet and psychological health. Primary feasibility outcome is adherence to the interventions. Primary analytic outcome is the relationship between pre-allocation preference for a given intervention and subsequent adherence to the allocated intervention. A series of secondary analytic outcomes examining the potential effect of the individual and combined interventions on cognitive, mobility and general well-being will be measured at baseline and follow-up.Ethics and disseminationEthics approval was granted by the relevant research ethics boards. Findings of the study will be presented to stakeholders and published in peer-reviewed journals and at provincial, national and international conferences.Trial registration numberNCT04997681, Pre-results

2015 Brainhack Proceedings

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 meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei Liew A17 A cortical surface-based geodesic distance package for Python Daniel S Margulies, Marcel Falkiewicz, Julia M Huntenburg A18 Sharing data in the cloud David O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron Craddock A19 Detecting task-based fMRI compliance using plan abandonment techniques Ramon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A20 Self-organization and brain function Jörg P. Pfannmöller, Rickson Mesquita, Luis C.T. Herrera, Daniela Dentico A21 The Neuroimaging Data Model (NIDM) API Vanessa Sochat, B Nolan Nichols A22 NeuroView: a customizable browser-base utility Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A23 DIPY: Brain tissue classification Julio E. Villalon-Reina, Eleftherios Garyfallidi