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Shorter and inflexible intrinsic neural timescales of the self in schizophrenia
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DOI：10.1503/jpn.240093.png)
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Abstract
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Background: Schizophrenia is hypothesized to involve a disturbance in the temporal dynamics of self-processing, specifically within the interoceptive, exteroceptive, and cognitive layers of the self. This study aimed to investigate the intrinsic neural timescales (INTs) within these self-processing layers among people with schizophrenia. Methods: We conducted a functional magnetic resonance imaging (fMRI) study to investigate INTs, as measured by the autocorrelation window, among people with schizophrenia and healthy controls during both resting-state and task (memory encoding and retrieval) conditions. We obtained data from the UCLA Consortium for Neuropsychiatric Phenomics data set and preprocessed using fMRIPrep. Results: We included 45 people with schizophrenia and 65 healthy controls. Compared with controls, participants with schizophrenia exhibited significantly shorter INTs across all 3 self-processing layers during rest (p < 0.05). In addition, those with schizophrenia showed less INT shortening during task states, leading to reduced rest-task differences in INT across all self-processing layers (p < 0.05). We observed similar patterns of shortened INTs in primary sensory and motor regions. Limitations We included people with schizophrenia taking medication, which may influence INTs; our study was also limited by the relatively slow temporal resolution of the fMRI data and the higher variability of the autocorrelation function in the schizophrenia group, compared with the control group. Conclusion: Our findings suggest that schizophrenia is characterized by a global temporal disturbance of the self, manifesting as shorter and inflexible INTs across self-processing and sensorimotor regions. These results support the hypothesis that schizophrenia involves a fundamental disruption in the temporal integration of neural signals, contributing to the core self-disturbance observed in the disorder.
Keywords:
BRAIN
SEGMENTATION
EXPERIENCE
ACCURATE
DYNAMICS
ROBUST
STATES
NOISE
MODEL
BOLD
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