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Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.
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- Author(s): Ambrosen, Karen S. (AUTHOR); Fredriksson, Fanny (AUTHOR); Anhøj, Simon (AUTHOR); Bak, Nikolaj (AUTHOR); van Dellen, Edwin (AUTHOR); Dominicus, Livia (AUTHOR); Lemvigh, Cecilie K. (AUTHOR); Sørensen, Mikkel E. (AUTHOR); Nielsen, Mette Ø. (AUTHOR); Bojesen, Kirsten B. (AUTHOR); Fagerlund, Birgitte (AUTHOR); Glenthøj, Birte Y. (AUTHOR); Oranje, Bob (AUTHOR); Hansen, Lars K. (AUTHOR); Ebdrup, Bjørn H. (AUTHOR)
- Source:
European Archives of Psychiatry & Clinical Neuroscience. Dec2023, Vol. 273 Issue 8, p1785-1796. 12p. - Source:
- Additional Information
- Subject Terms:
- Abstract: Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4–8 Hz), and two clusters based on the beta band (12–30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of European Archives of Psychiatry & Clinical Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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