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We present a novel approach based on static and dynamic inter\u2010network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time\u2010varying connectivity strength across functional regions from each source network, compared to HC. C\u2010means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low\u2010scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large\u2010scale functional entropy correlation. K\u2010means clustering analysis on time\u2010indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions.<\/jats:p>","DOI":"10.1002\/hbm.70134","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T03:32:31Z","timestamp":1739244751000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Static and Dynamic Cross\u2010Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients"],"prefix":"10.1002","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-589X","authenticated-orcid":false,"given":"Natalia","family":"Maksymchuk","sequence":"first","affiliation":[{"name":"Tri\u2010Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4679-7567","authenticated-orcid":false,"given":"Robyn L.","family":"Miller","sequence":"additional","affiliation":[{"name":"Tri\u2010Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University  Atlanta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8730-8152","authenticated-orcid":false,"given":"Juan R.","family":"Bustillo","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences University of New Mexico  Albuquerque New Mexico USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6500-6548","authenticated-orcid":false,"given":"Judith 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