Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer’s disease
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Date
2023-03-01
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Abstract
In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray
matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a
novel approach called multi-link joint independent component analysis (ml-jICA). The proposed
model offers several improvements over the existing joint independent component analysis (jICA)
model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing
for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model
and then apply this approach to study the differences in resting fMRI and sMRI data from patients
with Alzheimer’s disease (AD) versus controls. The results yield significant differences with large
effect sizes that include regions in overlapping portions of default mode network, and also
hippocampus and thalamus. Importantly, we identify two joint components with partially
overlapping regions which show opposite effects for Alzheimer’s disease versus controls, but were
able to be separated due to being linked to distinct functional and structural patterns. This
highlights the unique strength of our approach and multimodal fusion approaches generally in
revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal
approach. These results represent the first work linking multiple fMRI ICNs to gray matter
components within a multimodal data fusion m