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The Neuro Bureau ADHD-200 Preprocessed repository.

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The ADHD-200 Global Competition provided preprocessed resting-state fMRI and s-MRI data to identify attention-deficit/hyperactivity disorder biomarkers. This resource facilitated machine learning analysis for researchers lacking specialized neuroimaging data preparation skills.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • The ADHD-200 Global Competition aimed to identify attention-deficit/hyperactivity disorder (ADHD) biomarkers using neuroimaging data.
  • Researchers faced challenges with the specialized data preparation required for resting-state functional magnetic resonance imaging (rs-fMRI).

Purpose of the Study:

  • To address data preparation barriers for the ADHD-200 competition by releasing preprocessed rs-fMRI and structural MRI (s-MRI) data.
  • To provide a comprehensive resource for machine learning analysis in ADHD research.

Main Methods:

  • Prospective collaboration with competition participants to preprocess data from 973 individuals.
  • Development and release of multiple analytical pipelines for rs-fMRI and s-MRI data.
  • Data processing included denoising, registration, regional time series extraction, intrinsic connectivity network mapping, and grey matter density calculation.

Main Results:

  • The "ADHD-200 Preprocessed" dataset was made publicly available via the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).
  • The dataset included various analytical derivatives, such as denoised fMRI volumes and intrinsic connectivity network maps.
  • Multiple research teams utilized the preprocessed data for the ADHD-200 competition, with a biostatistics team developing the winning entry.

Conclusions:

  • The ADHD-200 Preprocessed release was a pioneering large-scale public resource for preprocessed rs-fMRI and s-MRI data.
  • This resource facilitated broader participation in ADHD biomarker discovery by lowering the technical barrier for data analysis.
  • The unique inclusion of diverse processing paths offers a valuable platform for comparative neuroimaging research.