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Updated: May 26, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

DEEP-LEARNING CORTICAL REGISTRATION GUIDED BY STRUCTURAL AND DIFFUSION MRI AND CONNECTIVITY.

Zhen Zhou¹, Jian Li¹, Jonathan Williams¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|May 25, 2026

Summary

This summary is machine-generated.

This study introduces a new deep learning method, JOSAConn, for brain imaging analysis. JOSAConn improves functional alignment in neuroimaging by integrating white matter connectivity, outperforming existing methods.

Keywords:

Cortical surface registration functional alignment heat diffusion smoothing semi-supervised learning

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Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning

Background:

Accurate cortical surface registration is vital for group-level neuroimaging studies.
Current geometry-based methods struggle with inter-individual variability, leading to suboptimal functional alignment.
Bridging the gap between structural and functional brain organization remains a challenge.

Purpose of the Study:

To develop a novel deep-learning approach for improved cortical surface registration.
To integrate white matter structural connectivity into the Joint Surface-based Registration and Atlas Construction (JOSA) framework.
To enhance functional alignment in neuroimaging analyses by leveraging multimodal data.

Main Methods:

A deep-learning method (JOSAConn) was developed, incorporating white matter structural connectivity from diffusion MRI (dMRI) tractography.
Vertex-wise connectivity maps were generated using streamline-surface intersections and heat diffusion smoothing.
JOSAConn integrated connectivity features with scalar diffusion metrics and structural features.

Main Results:

JOSAConn significantly outperformed FreeSurfer in functional alignment across 15 task contrasts in HCP-YA subjects.
The method achieved Bonferroni-corrected p < 0.05 for 12 of 15 contrasts.
Structural connectivity effectively linked cortical geometry and functional organization.

Conclusions:

The multimodal JOSAConn approach enhances neuroimaging analysis by integrating structural connectivity.
This method improves functional alignment, addressing limitations of geometry-based registration.
The approach maintains clinical applicability while advancing the understanding of brain organization.