期刊
BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING
卷 5, 期 1, 页码 35-44出版社
ELSEVIER
DOI: 10.1016/j.bpsc.2019.07.006
关键词
Complexity; Early-course psychosis; EEG; Microstates; Resting-state networks; Schizophrenia
资金
- National Alliance for Research in Schizophrenia and Affective Disorders Young Investigator Grant from the Brain and Behavior Research Foundation [27017]
- Psychiatric Genetics and Neuroscience Fellowship from Stanley Center for Psychiatric Research at the Broad Institute of Harvard and Massachusetts Institute of Technology
- Center for Biomedical Imaging (CIBM) of Geneva and Lausanne
BACKGROUND: Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis. METHODS: We used microstate analysis to transform resting-state high-density electroencephalography data from 22 patients with early-course psychosis and 22 healthy control subjects into sequences of characteristic scalp topographies. Sample entropy was used to calculate the complexity of microstate sequences across a range of template lengths. RESULTS: Patients and control subjects produced similar sets of 4 microstates that agree with a widely reported canonical set (A, B, C, and D). Relative to control subjects, patients had decreased frequency of microstate A. In control subjects, sample entropy decreased as template length increased, suggesting that sequence of microstate transitions is self-similar across multiple transitions. In patients, sample entropy did not decrease, suggesting a lack of self-similarity in transition sequences. This finding was unrelated to data length or microstate topography. Entropy was elevated in unmedicated patients, and it decreased in patients who were administered medication. We identified patterns of transitions between microstates that were overrepresented in control data compared with representation in patient data. CONCLUSIONS: Our findings suggest that patients with early-course psychosis have abnormally chaotic transitions between brain networks. This chaos may reflect an underlying abnormality in allocating neural resources and effecting appropriate transitions between distinct activity states in psychosis.
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