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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Published on: September 12, 2011

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging

Rayus Kuplicki¹, James Touthang¹, Obada Al Zoubi¹

¹Laureate Institute for Brain Research, Tulsa, OK, United States.

Frontiers in Psychiatry

|July 5, 2021

Summary

This summary is machine-generated.

This study introduces a scalable data infrastructure for neuroscience research, enabling efficient preprocessing, analysis, and sharing of complex, multi-level datasets using the Brain Imaging Data Structure (BIDS) format.

Keywords:

bids format common data element data processing pipelines human brain large-scale studies multi-level assessment neuroimaging scalable analytics

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Area of Science:

Neuroscience
Bioinformatics
Data Science

Background:

Neuroscience research generates high-dimensional, multimodal data requiring significant bioinformatic expertise.
Current data management practices often lack scalability and standardization for complex datasets.
Reproducible analysis pipelines are crucial for robust scientific discovery.

Purpose of the Study:

To present a common data elements and scalable data management infrastructure for large-scale neuroscience data.
To facilitate preprocessing, analysis, and sharing of multi-level datasets.
To support diverse data types and promote flexible integration of new data sources.

Main Methods:

Implementation of a data management infrastructure utilizing the Brain Imaging Data Structure (BIDS) format.
Support for various data modalities including MRI, fMRI, EEG, clinical, and laboratory data.
Integration of additional datasets (e.g., Fitbit) and flexibility for custom data type integration.

Main Results:

Demonstrated utility of the infrastructure with exemplar results from over 200 participants.
Successful application across 11 different analytical pipelines.
Validation of the infrastructure's capability to handle large-scale, multi-level data.

Conclusions:

The developed infrastructure enhances bioinformatic support for neuroscience studies.
The BIDS-compliant system promotes data standardization, preprocessing, and analysis efficiency.
The scalable and flexible infrastructure facilitates reproducible research and data sharing in neuroscience.