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PLoS One. 2017 Oct 19;12(10):e0185852. doi: 10.1371/journal.pone.0185852. eCollection 2017.

Connectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes.

Author information

Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
EEG and Cognition Laboratory, University of A Coruña, A Coruña, Spain.
Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel.
Institute for Psychiatric Studies, Sha'ar Menashe Mental Health Center, Hadera, Israel.


This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.

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