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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Constructing the resting state structural connectome.

Olusola Ajilore¹, Liang Zhan², Johnson Gadelkarim³

¹Department of Psychiatry, University of Illinois Chicago, IL, USA.

Frontiers in Neuroinformatics

|January 11, 2014

Summary

This summary is machine-generated.

This study introduces functional-by-structural hierarchical (FSH) mapping to integrate brain connectivity data. FSH reveals distinct self-referential processing network differences in depression by combining functional and structural connectome information.

Keywords:

connectivity fMRI major depression multimodal neuroimaging

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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

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Published on: November 13, 2016

Area of Science:

Neuroscience
Computational Biology
Medical Imaging

Background:

Functional and structural brain connectivity are often studied independently.
Their relationship and integration into a unified model remain underexplored.
Multimodal connectome data from resting state fMRI and tractography offer complementary insights.

Purpose of the Study:

To develop and apply a novel computational technique, functional-by-structural hierarchical (FSH) mapping.
To integrate resting state fMRI (rsfMRI) and whole-brain tractography-derived connectome data.
To investigate the relationship between functional and structural brain connectivity.

Main Methods:

FSH models rsfMRI correlation as an exponential decay function of structural connectivity graph distance.
White matter tract utilization during rsfMRI is incorporated via a binary utilization matrix (U).
rsSC is computed by combining tractography-derived structural connectivity with the utilization matrix; U is estimated using simulated annealing.

Main Results:

No significant group differences were found in isolated functional or structural connectome modular structures.
FSH revealed significantly different association patterns in the posterior cingulate cortex and right precuneus in depressed individuals.
Depressed subjects showed stronger associations in regions critical for self-referential processing.

Conclusions:

Integrating multimodal imaging data using FSH enhances sensitivity for detecting group differences in brain connectomes.
FSH is a novel computational technique with potential for increased power in neuroimaging studies.
The findings highlight the utility of FSH for uncovering subtle group differences in brain network organization.