Open AccessConnectivity maps based analysis of EEG for the advanced diagnosis of schizophrenia attributes
Author(s) -
Zack Dvey-Aharon,
Noa Fogelson,
Alva Peled,
Nathan Intrator
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0185852
Subject(s) - false positive paradox , schizophrenia (object oriented programming) , electroencephalography , computer science , task (project management) , set (abstract data type) , diagnosis of schizophrenia , pattern recognition (psychology) , node (physics) , artificial intelligence , sensitivity (control systems) , machine learning , psychosis , medicine , psychiatry , management , structural engineering , electronic engineering , engineering , economics , programming language
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.