Apr 23, 2020

Network communication models improve the behavioral and functional predictive utility of the human structural connectome

BioRxiv : the Preprint Server for Biology
Caio SeguinA. Zalesky

Abstract

The structure and function of the human connectome are coupled, but the correspondence is far from exact. We aimed to establish whether accounting for polysynaptic (multi-hop) paths in structural brain networks would improve prediction of interindividual variation in behavior as well as the strength of coupling with functional brain networks. Diffusion-weighted MRI and tractography were used to map structural connectomes for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic transmission, paths between unconnected pairs of regions were identified using each of 15 candidate models of brain network communication, giving rise to 15 communication matrices for each individual. Communication matrices were (i) used to perform predictions of five data-driven behavioral dimensions and (ii) correlated to interregional resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, network communication models improved the performance of structural connectivity. Communicability and navigation typically led to the most accurate behavioral predictions amongst the explored communication models. Accounting for polysynaptic communication in structural brain networks a...Continue Reading

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Mentioned in this Paper

Nucleic Acid Sequencing
Human DNA Sequencing
Sequencing
Sequence Determinations, DNA
Molecular Genetic Technique
Analysis
High-Throughput RNA Sequencing
Motivation
Metric
DNA Sequence

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