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A multivariate distance-based analytic framework for connectome-wide association studies.

Zarrar Shehzad¹, Clare Kelly², Philip T Reiss³

¹Department of Psychology, Yale University, New Haven, CT, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.

|March 4, 2014

Summary

This summary is machine-generated.

We developed a fast, data-driven method called connectome-wide association studies (CWAS) to map brain connectivity to behavior. This approach efficiently identifies brain regions linked to various phenotypes, accelerating neuroimaging discoveries.

Keywords:

Brain–behavior relationships Connectome Discovery Functional connectivity Phenotype Resting-state

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

Area of Science:

Neuroimaging
Connectomics
Computational Neuroscience

Background:

Discovering brain-phenotype relationships is crucial but limited by computationally intensive statistical methods.
Existing approaches often require prior hypotheses or extensive multiple comparison corrections.
A need exists for efficient, data-driven techniques to survey brain-behavior links.

Purpose of the Study:

To introduce a computationally efficient, data-driven method for connectome-wide association studies (CWAS).
To enable comprehensive voxel-wise surveys of brain-behavior relationships across the entire connectome.
To identify voxels with significant whole-brain connectivity pattern variations related to phenotypic variables.

Main Methods:

Developed a novel CWAS framework for analyzing high-dimensional brain connectivity data.
Utilized resting-state fMRI data to test the analytic framework.
Validated robustness against nuisance covariates and varying brain resolutions, and demonstrated utility in guiding seed-based analyses.

Main Results:

Successfully identified significant connectivity-phenotype relationships for full-scale IQ.
Demonstrated CWAS applicability across diverse phenotypes including neurotypical development, ADHD, and pharmacological manipulation.
Found distinct connectome-wide association profiles for each phenotype, not achievable with traditional methods.

Conclusions:

The proposed CWAS framework is computationally efficient, extensible, and scalable.
This method accelerates the discovery of brain-behavior relationships within the connectome.
CWAS offers a powerful new approach for exploring neuroimaging connectomics.