The Neuro Bureau ADHD-200 Preprocessed Repository

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.

Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

“…The rs-fMRI data from the ADHD200 consortium was pre-processed using the ATHENA pipeline (Bellec et al 2017) in order to remove the variations due to the physiological noise, head motion and scanner drifts. The denoised rs-fMRI data without bandpass filtering provided by (Bellec et al 2017) was anatomized using the AAL template into 116 regions. The 26 regions corresponding to the cerebellum were excluded to minimize the effects of involuntary activities such as breathing and cardiac activity.…”

“…The 26 regions corresponding to the cerebellum were excluded to minimize the effects of involuntary activities such as breathing and cardiac activity. Finally, a manual quality control (QC) check was performed and samples with incomplete data and that failed the QC in (Bellec et al 2017) were removed and rs-fMRI data from the 90 AAL regions were obtained from 465 people. Further details on the preprocessing and QC measures can be obtained from (Bellec et al 2017).…”

A standardized protocol for efficient and reliable quality control of brain registration in functional MRI studies

“…We used a combination of two publicly available datasets, COBRE (Mayer et al, 2013) and ADHD-200 (Bellec et al, 2017), for both the beta and the full launch of the project. These datasets have been made available after anonymization by consortia of research team, each of which received ethics approval at their local institutional review board, as well as informed consent from all participants.…”

GPU-DFC: A GPU-Based Parallel Algorithm for Computing Dynamic-Functional Connectivity of Big fMRI Data

“…In this experiment we applied the proposed method on real voxel-based fMRI data. The fMRI data that we used is part of preprocessed samples provided by ADHD-200 initiative [29]. It contains 30697 voxels with time series of length 257.…”