Hierarchical structurally informed states: a structure-functional constrained ICA connectivity model
Poster No:
1454
Submission Type:
Abstract Submission
Authors:
Mahshid Fouladivanda1, Armin Iraji2, Vince Calhoun3
Institutions:
1Georgia State University, Atlanta, GA, 2GSU, Atlanta, GA, 3GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
The human brain is a complex system arises from interactions of functional networks, defined by their time varying functional activity, and their connectome (Calhoun & Sui, 2016). Reoccurring patterns of interactions in brain, known as brain states (Allen et al., 2014) are measured by resting-state functional MRI (rs-fMRI), while the connectome (Seguin et al., 2023), whole brain structural connectivity, can be estimated from diffusion MRI (dMRI). However, little is known about to what degree brain dynamic interactions may be influenced by structural connectivity. To address this gap, we propose a novel structurally regularized dynamic functional connectivity model to identify structurally informed states (SISs). We demonstrate the existence of six SISs in brain, each characterized by unique patterns.
Methods:
FBIRN dataset (Keator et al., 2016) including rs-fMRI and dMRI with 311 participants, grouped into healthy control (age: 37.0 ± 11.0) and schizophrenia (age: 37.9 ± 11.5) was used in this study. The rs-fMRI images were preprocessed similar to (Qi et al., 2019) and the procedure in (Wu et al., 2015) was applied to preprocess the dMRI images. After preprocessing rs-fMRI, we determined subject-specific dynamic functional network connectivity (dFNC), indicating Pearson's correlation between different brain networks over time, using sliding window approach (Allen et al., 2014). To generate networks from the fMRI data, we used spatially constrained ICA via the NeuroMark_fMRI_1.0 template (Du et al., 2020) consisting of N = 53 replicable networks.
Subject-specific structural connectivity (SC) was constructed by performing deterministic tractography on preprocessed dMRI as in (Wu et al., 2015). Next, using the same networks as for the dFNC, SC matrices were computed which contains the number of fiber tracts between paired networks. Then, we applied group-level ICA (Calhoun et al., 2003) on SC matrices to obtain separate sets of group-level SC (gSC). The optimal number of the gSC, here is six, was chosen based on the elbow criteria.
Our proposed structurally regularized dynamic functional connectivity model integrates structural and dynamic functional connectivity through a multi-objective optimization process. The proposed model was implemented via constrained ICA (Du & Fan, 2013; Du et al., 2020), aiming to cluster the dFNCs into the distinct connectivity patterns, by maximizing the independence of each dFNC cluster as well as its similarity to the prior information (gSC). The multi-objective problem was converged to an optimal solution through an iterative gradient ascent method.
Subject-specific structural connectivity (SC) was constructed by performing deterministic tractography on preprocessed dMRI as in (Wu et al., 2015). Next, using the same networks as for the dFNC, SC matrices were computed which contains the number of fiber tracts between paired networks. Then, we applied group-level ICA (Calhoun et al., 2003) on SC matrices to obtain separate sets of group-level SC (gSC). The optimal number of the gSC, here is six, was chosen based on the elbow criteria.
Our proposed structurally regularized dynamic functional connectivity model integrates structural and dynamic functional connectivity through a multi-objective optimization process. The proposed model was implemented via constrained ICA (Du & Fan, 2013; Du et al., 2020), aiming to cluster the dFNCs into the distinct connectivity patterns, by maximizing the independence of each dFNC cluster as well as its similarity to the prior information (gSC). The multi-objective problem was converged to an optimal solution through an iterative gradient ascent method.
Results:
Results show there are six SISs in brain. Findings reveal there is a state (state 1), not reported in unimodal studies, which shows intra-domain/local hyperactivity. It also has higher mean dwell time compared to other remaining states. In addition, higher-order networks including cognitive control and default-mode, interactions were observed in two other separate states. Overall, it indicates existence of a state which is highly engaged throughout the experiment and may serves as a backbone for brain activity, while other higher-order networks show inter-activity. In addition, by performing statistical analysis, we found number of significantly different (FDR-corrected P-value<0.05) connections between healthy control and schizophrenia subjects at different states, mostly in the backbone state.
Conclusions:
Our focus was on developing a structural-functional constrained model to determine unique pattern reoccurring, we termed structurally informed states (SISs). Our findings, support this concept, providing evidence of a functional/structural connectivity backbone as well as hierarchical properties unpinning higher order domains during rest. In sum, it is suggested that using both structural and functional information allows us to more completely characterize brain dynamic interactions linked to structural connectivity.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling 1
Methods Development 2
Keywords:
Other - Multimodal brain states; connectome; dynamic functional connectivity; ICA; multi-objective
1|2Indicates the priority used for review
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