Dataset Information

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

ABSTRACT: As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

SUBMITTER: Charles AS

PROVIDER: S-EPMC9119703 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

Charles Adam S AS Falk Benjamin B Turner Nicholas N Pereira Talmo D TD Tward Daniel D Pedigo Benjamin D BD Chung Jaewon J Burns Randal R Ghosh Satrajit S SS Kebschull Justus M JM Silversmith William W Vogelstein Joshua T JT

Annual review of neuroscience 20200413

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspe ...[more]

PMID: 32283996

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Project description:Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Dataset Information

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

Publications

Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics.

Similar Datasets

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