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Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2022) 6 (3): 634–664.
Published: 01 July 2022
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Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.
Includes: Supplementary data
Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2022) 6 (2): 357–381.
Published: 01 June 2022
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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. Author Summary Brain function can be modeled as the dynamic interactions between functional sources (e.g., intrinsic connectivity networks, ICNs) at different spatial scales. Each spatial scale contains its own functional sources with unique information. For example, the default mode (DM)-ICNs from lower order independent component analysis (ICA) are not a simple union of DM-ICNs from a higher order. Furthermore, dynamic functional interactions occur both within and between different spatial scales, which has been underrepresented. Here, we introduce multiscale ICA to capture functional sources and their interactions across multiple spatial scales. We leveraged this approach to study sex-specific changes in schizophrenia. Most sex-specific differences occur in between-model order, highlighting the benefit of multi-spatial-scale analysis. In sum, studying multi-spatial-scale functional sources provides us with a wealth of information to better characterize brain function.
Includes: Supplementary data