Abnormal Temporal Variability Organization Associated with Compromised Verbal Memory and Psychotic Symptoms in...

Schizophrenia (SCZ) is a highly disabling psychiatric disorder marked by compromised brain dynamic interactions. However, the underlying neuropathology of SCZ remains poorly elucidated in terms of its associated disruption of network-level and rhythm-specific dynamic fluctuations during the resting state. Herein, using a sample entropy-based temporal variability analysis framework, we investigate the complex fluctuation patterns of resting-state electroencephalogram networks as they transition over time for SCZ patients and their unaffected relatives (R-SCZ), as well as healthy controls (HC). Next, potential associations between variability networks and individual cognitive traits/clinical recordings were explored. Rhythm-specific abnormalities of baseline brain dynamics may disrupt the normal brain function of SCZ, particularly through the decoupling of frontal-temporal and temporal-parietal variability connectivity in the alpha and beta rhythms. The diminished variability differences observed between R-SCZ and HC suggest the possible existence of shared familial factors that influence specific traits related to network variability. Moreover, multidimensional representations of temporal variability networks can quantitatively characterize and even predict an individual's cognitive function (e.g. verbal memory) and clinical symptoms of SCZ patients. Current findings may offer new insights into comprehending the neuropathology and the potential role of familial susceptibility in SCZ, which may facilitate advancements in early diagnosis and intervention strategies.