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GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features

Arunkumar Kannan1, Brian Caffo2, Archana Venkataraman3

  • 1Dept. of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.

Machine Learning in Clinical Neuroimaging : 7Th International Workshop, MLCN 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings. MLCN (Workshop) (7Th : 2024 : Marrakesh, Morocco)
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method linking brain structure to functional connectivity. The approach enhances understanding of brain variations and individual differences in brain connections.

Keywords:
DiscriminabilityExplainabilityFunctional ConnectivityGeneralized Additive ModelsStructural FeaturesfMRI

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Biology

Background:

  • Functional connectivity (FC) is vital for cognition, but its link to structural brain features is unclear.
  • Existing methods struggle to fully integrate diverse structural information to explain FC variations.

Purpose of the Study:

  • To develop and validate a novel analysis method integrating multiple structural factors to explain functional connectivity variation.
  • To assess the repeatability and subject discriminability of the proposed structure-function regression models.

Main Methods:

  • Utilized generalized additive models (GAMs) to integrate anatomical morphology, voxel intensity, diffusion data, and geographic distance.
  • Employed region-pair or vertex-pair analysis, accounting for individual subject differences.
  • Assessed model repeatability using subject discriminability metrics on Human Connectome Project data (twin and non-twin pairs).

Main Results:

  • Direct structure/function regression models significantly explain variations in functional connectivity.
  • The proposed GAM-based approach demonstrates high repeatability and subject discriminability.
  • Analysis of twin and non-twin pairs confirmed the robustness of the model-based connectivity patterns.

Conclusions:

  • Integrating structural information via GAMs provides a powerful approach to understanding functional connectivity.
  • The findings offer insights into the underlying mechanisms of brain connectivity and individual differences.
  • This method advances the study of brain structure-function relationships and their repeatability.