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Large‐scale analysis of functional connectivity within intrinsic brain networks using functional magnetic resonance imaging (fMRI) data has been widely used for identifying biomarkers in various psychiatric disorders. While the emerging access to large neuroimaging datasets provides unprecedented opportunities for exploring brain functions, they also pose significant computational complexity challenges due to the large amount of inherent variability across individuals and the complexity of brain activity patterns. To address these challenges, this paper introduces two novel constrained ICA methods, arc‐EBM and minc‐EBM, designed to overcome the computational complexity issue by incorporating prior information into the analysis framework. The proposed methods preserve the subject variability by adaptively selecting the constrained parameters for different functional networks and individuals, while also allowing estimation flexibility for activities not covered by the prior information through the concept of free components. Our methods are shown to enhance the precision of functional network estimation and improve the capture of subject variability across different cohorts. We evaluate the proposed methods using both synthetic and real fMRI data. By applying the proposed methods to a resting‐state fMRI dataset including 179 subjects, both algorithms successfully reveal significant group differences in functional network connectivity between healthy controls and schizophrenia patients. The observed group differences, particularly the abnormal connectivity alterations in networks involving the thalamus, subthalamus/hypothalamus, and superior temporal gyrus, align with findings from previous clinical studies. Furthermore, our results demonstrate that the constraint parameters adaptively selected by arc‐EBM reveal more diverse resting‐state network structures in individuals with schizophrenia compared with healthy controls. This finding is consistent with prior studies and suggests that the selected constraint parameters could serve as potential biomarkers for mental disorder diagnosis.
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Brain network dynamics fingerprints are resilient to data heterogeneity
Abstract Context.Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions.Objective.We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models.Approach.Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (hidden Markov models—HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain’s spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions.Main Results.Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data.Significance.These results demonstrate that we can (i) use large scale dataset to train models that can be then used to interrogate subject-specific data, (ii) recover the unique trajectories of brain activity changes in each individual, but also (iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.
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- Award ID(s):
- 1926829
- PAR ID:
- 10304411
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Neural Engineering
- Volume:
- 18
- Issue:
- 2
- ISSN:
- 1741-2560
- Page Range / eLocation ID:
- Article No. 026004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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