Literature DB >> 27423255

The Neuro Bureau ADHD-200 Preprocessed repository.

Pierre Bellec1, Carlton Chu2, François Chouinard-Decorte3, Yassine Benhajali4, Daniel S Margulies5, R Cameron Craddock6.   

Abstract

In 2011, the "ADHD-200 Global Competition" was held with the aim of identifying biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (s-MRI) data collected on 973 individuals. Statisticians and computer scientists were potentially the most qualified for the machine learning aspect of the competition, but generally lacked the specialized skills to implement the necessary steps of data preparation for rs-fMRI. Realizing this barrier to entry, the Neuro Bureau prospectively collaborated with all competitors by preprocessing the data and sharing these results at the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (http://www.nitrc.org/frs/?group_id=383). This "ADHD-200 Preprocessed" release included multiple analytical pipelines to cater to different philosophies of data analysis. The processed derivatives included denoised and registered 4D fMRI volumes, regional time series extracted from brain parcellations, maps of 10 intrinsic connectivity networks, fractional amplitude of low frequency fluctuation, and regional homogeneity, along with grey matter density maps. The data was used by several teams who competed in the ADHD-200 Global Competition, including the winning entry by a group of biostaticians. To the best of our knowledge, the ADHD-200 Preprocessed release was the first large public resource of preprocessed resting-state fMRI and structural MRI data, and remains to this day the only resource featuring a battery of alternative processing paths.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Data sharing; Neuroimaging competition; Preprocessed fMRI

Mesh:

Year:  2016        PMID: 27423255     DOI: 10.1016/j.neuroimage.2016.06.034

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  26 in total

1.  Functional Neuroimaging Evidence for Distinct Neurobiological Pathways in Attention-Deficit/Hyperactivity Disorder.

Authors:  Michael C Stevens; Godfrey D Pearlson; Vince D Calhoun; Katie L Bessette
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2017-09-23

2.  A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder Detection.

Authors:  Ming Chen; Hailong Li; Jinghua Wang; Jonathan R Dillman; Nehal A Parikh; Lili He
Journal:  Radiol Artif Intell       Date:  2019-12-11

3.  ConCeptCNN: A novel multi-filter convolutional neural network for the prediction of neurodevelopmental disorders using brain connectome.

Authors:  Ming Chen; Hailong Li; Howard Fan; Jonathan R Dillman; Hui Wang; Mekibib Altaye; Bin Zhang; Nehal A Parikh; Lili He
Journal:  Med Phys       Date:  2022-03-14       Impact factor: 4.506

4.  Brain connectivity alteration detection via matrix-variate differential network model.

Authors:  Jiadong Ji; Yong He; Lei Liu; Lei Xie
Journal:  Biometrics       Date:  2020-09-01       Impact factor: 2.571

5.  Abnormal hemispheric asymmetry of both brain function and structure in attention deficit/hyperactivity disorder: a meta-analysis of individual participant data.

Authors:  Ningning He; Lena Palaniyappan; Zeqiang Linli; Shuixia Guo
Journal:  Brain Imaging Behav       Date:  2021-05-21       Impact factor: 3.978

6.  Novel subgroups of attention-deficit/hyperactivity disorder identified by topological data analysis and their functional network modular organizations.

Authors:  Sunghyon Kyeong; Jae-Jin Kim; Eunjoo Kim
Journal:  PLoS One       Date:  2017-08-22       Impact factor: 3.240

7.  Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects: a resting-state functional MRI study.

Authors:  Jisu Hong; Bo-Yong Park; Hwan-Ho Cho; Hyunjin Park
Journal:  Neural Regen Res       Date:  2017-10       Impact factor: 5.135

8.  Advances in Studying Brain Morphology: The Benefits of Open-Access Data.

Authors:  Christopher R Madan
Journal:  Front Hum Neurosci       Date:  2017-08-04       Impact factor: 3.169

9.  Shared endo-phenotypes of default mode dsfunction in attention deficit/hyperactivity disorder and autism spectrum disorder.

Authors:  Julius M Kernbach; Theodore D Satterthwaite; Danielle S Bassett; Jonathan Smallwood; Daniel Margulies; Sarah Krall; Philip Shaw; Gaël Varoquaux; Bertrand Thirion; Kerstin Konrad; Danilo Bzdok
Journal:  Transl Psychiatry       Date:  2018-07-17       Impact factor: 6.222

10.  Preprocessed Consortium for Neuropsychiatric Phenomics dataset.

Authors:  Krzysztof J Gorgolewski; Joke Durnez; Russell A Poldrack
Journal:  F1000Res       Date:  2017-07-28
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