Multimodal Connectomics Improves the Characterization of Alzheimer's Disease-Related Network Alterations
Abstract
Alzheimer's disease progressively disrupts large-scale brain networks. Although structural and functional connectivity both capture aspects of this disruption, they are typically analyzed separately rather than jointly. To address this gap, a multilayer graph representation was used to model multimodal connectivity, and this approach was compared with unimodal representations based on structural connectivity alone, functional connectivity alone, and their simple concatenation in a multi-site cohort of 397 participants across stages of the Alzheimer's disease continuum. Connectivity-derived graph features were evaluated in pairwise classification analyses and in regression models predicting cognitive impairment. Multimodal models generally matched or outperformed unimodal approaches, while region-level analyses of multimodal features identified anatomically plausible patterns and partial convergence between classification- and regression-derived importance profiles. These results support multimodal connectomics as a useful framework for characterizing Alzheimer's disease-related brain-network disruption across both categorical and continuous formulations of disease burden.
Related
- The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows
- Disease-specific intrinsic frequency alterations in electroencephalographic signals of Alzheimer’s disease, frontotemporal dementia, and Parkinson’s disease
- Depth-Dependent Alterations in Cerebral Blood and Tissue Oxygenation Associated with Tau in Alzheimer's Disease Models
- The balance of local and distributed excitation shapes brain stability and reflects aging- and Alzheimer's disease-related alterations
- Multimodal neural correlates of cognitive awareness in aging and Alzheimer’s disease
BibTeX
@misc{ferritto2026multimodal,
title = {{Multimodal Connectomics Improves the Characterization of Alzheimer's Disease-Related Network Alterations}},
author = {Carlo Ferritto and Giulia Lioi and Pierre-Yves Jonin and Julie Coloigner},
howpublished = {HAL},
year = {2026},
month = oct,
url = {https://hal.science/hal-05649878v2},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multimodal Connectomics Improves the Characterization of Alzheimer's Disease-Related Network Alterations
AU - Carlo Ferritto
AU - Giulia Lioi
AU - Pierre-Yves Jonin
AU - Julie Coloigner
PY - 2026
DA - 2026/10/13
PB - HAL
UR - https://hal.science/hal-05649878v2
AB - Alzheimer's disease progressively disrupts large-scale brain networks. Although structural and functional connectivity both capture aspects of this disruption, they are typically analyzed separately rather than jointly. To address this gap, a multilayer graph representation was used to model multimodal connectivity, and this approach was compared with unimodal representations based on structural connectivity alone, functional connectivity alone, and their simple concatenation in a multi-site cohort of 397 participants across stages of the Alzheimer's disease continuum. Connectivity-derived graph features were evaluated in pairwise classification analyses and in regression models predicting cognitive impairment. Multimodal models generally matched or outperformed unimodal approaches, while region-level analyses of multimodal features identified anatomically plausible patterns and partial convergence between classification- and regression-derived importance profiles. These results support multimodal connectomics as a useful framework for characterizing Alzheimer's disease-related brain-network disruption across both categorical and continuous formulations of disease burden.
ER -