Abstract
We introduce an extension of independent component analysis (ICA), called multiscale ICA, and design an approach to capture dynamic functional source interactions within and between multiple spatial scales. Multiscale ICA estimates functional sources at multiple spatial scales without imposing direct constraints on the size of functional sources, overcomes the limitation of using fixed anatomical locations, and eliminates the need for model-order selection in ICA analysis. We leveraged this approach to study sex-specific and sex-common connectivity patterns in schizophrenia. Results show dynamic reconfiguration and interaction within and between multi-spatial scales. Sex-specific differences occur (a) within the subcortical domain, (b) between the somatomotor and cerebellum domains, and (c) between the temporal domain and several others, including the subcortical, visual, and default mode domains. Most of the sex-specific differences belong to between-spatial-scale functional interactions and are associated with a dynamic state with strong functional interactions between the visual, somatomotor, and temporal domains and their anticorrelation patterns with the rest of the brain. We observed significant correlations between multi-spatial-scale functional interactions and symptom scores, highlighting the importance of multiscale analyses to identify potential biomarkers for schizophrenia. As such, we recommend such analyses as an important option for future functional connectivity studies.
Highlights
Multispatial-Scale Dynamic InteractionsBrain function has been modeled as coordination and interaction between functional sources, which has been summarized via the principles of segregation and integration (Genon et al, 2018)
Multispatial-Scale Functional Segregation: Intrinsic Connectivity Networks We performed spatial independent component analysis (ICA) with 25, 50, 75, and 100 components on resting-state fMRI (rsfMRI) data from 827 subjects to functionally segregate the brain at different spatial scales
Based on the criteria explained in the Materials and Methods section, we identified 15, 28, 36, and 48 independent components as Intrinsic connectivity network (ICN) for model orders 25, 50, 75, and 100, respectively
Summary
Multispatial-Scale Dynamic InteractionsBrain function has been modeled as coordination and interaction between functional sources, which has been summarized via the principles of segregation and integration (Genon et al, 2018). Functional sources exist at different spatial scales, and dynamic functional interactions occur both within and between different spatial scales. In the case of using functional sources as nodes, information at different spatial scales captures functional integration among those sources at multiple resolutions. Functional interactions occur among functional sources across (within and between) different spatial scales (e.g., large networks interact with small networks), which convey important information about the brain as shown in this study. This relationship is effectively ignored if using a single spatial scale to analyze the data
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