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Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls. | LitMetric

AI Article Synopsis

  • Quasi-stable electrical fields in EEG, known as microstates, provide insights into large-scale brain network dynamics, particularly for discerning differences between schizophrenia patients and healthy individuals.
  • Using machine learning and multivariate pattern analysis, the study developed a feature set to classify schizophrenia cases, achieving an accuracy of 82.7% with a high AUC of 0.84.
  • The research suggests that alterations in microstate measures could indicate disrupted brain network activity in patients with schizophrenia, offering potential avenues for diagnosis and understanding of the condition.

Article Abstract

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features to create a specified feature set to represent microstate characteristics. Machine learning approaches using these features for classification of patients with schizophrenia were compared with prior EEG based machine learning studies. Our microstate segmentation in both patients with schizophrenia and healthy controls yielded topographies that were similar to the normative database established earlier by Koenig et al. Our machine learning model was based on large sample size, low number of features and state-of-art K-fold cross-validation technique. The multivariate analysis revealed three patterns of correlated features, which yielded an AUC of 0.84 for the group separation (accuracy: 82.7%, sensitivity/specificity: 83.5%/85.3%). Microstate segmentation of resting state EEG results in informative features to discriminate patients with schizophrenia from healthy individuals. Moreover, alteration in microstate measures may represent disturbed activity of networks in patients with schizophrenia.

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Source
http://dx.doi.org/10.1016/j.psychres.2020.112938DOI Listing

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