PhD Scientific Days 2019

Budapest, April 25–26, 2019

Functional networks of EEG microstates in patient with schizophrenia: Graph theoretical analysis

Baradits, Máté

Dr. Máté Baradits, Brigitta Kakuszi, Sára Bálint, Prof. Dr. István Bitter, Dr. Pál Czobor
Semmelweis University, Department of Psychiatry and Psychotherapy, Budapest

Language of the presentation


Text of the abstract

Introduction: Lehmann et al. proposed that EEG comprises periods of quasi-stable spatial configurations that remain stable for 80–120 ms before rapidly transitioning to a different microstate. The functional connectivity within these “microstates” may contain additional information over traditional whole resting state EEG functional connectivity analysis. Functional connectivity of EEG signals may be assessed through connectivity matrices, which can be analyzed by graph theoretical approaches. Since alterations of EEG microstates were reported in patients with schizophrenia, there may be disturbed functional connectivity between brain regions within these microstates.
Aim: To compare functional connectivity of EEG microstates between patients with schizophrenia and healthy controls.
Methods: We investigated EEG recordings of 82 patients with schizophrenia and 75 healthy controls collected at rest with closed eyes. We used Modified k-mean clustering algorithm adapted for EEG microstate segmentaion. Phase Lag Index (PLI) in all microstates, in four frequency bands – theta (4-8Hz), alpha (8-13Hz), beta (13-30Hz) and gamma (30-50Hz) – was derived between all EEG channels, yielding 16 connectivity matrices (brain graphs). These brain graphs were used to derive graph theoretical features in multiple scales, including the clustering coefficient, shortest path length, global- and local efficiency, strength of connectivity and small-world property.
Results: Global efficiency was reduced in theta frequency bands in all microstate classes in patients with schizophrenia, whereas the strength of connectivity was larger in microstate class B in theta, beta and gamma frequency bands. Moreover, global efficiency correlated negatively with PANSS total score, negative and depression factors, and strength of connectivity correlated positively with PANSS total score and negative factor.
Conclusion: Patients with schizophrenia showed altered functional connectivity in EEG microstate, especially in microstate class B. Furthermore, these alterations were associated with psychopathology. Thus microstate functional connectivity analysis may help uncover the neurophysiological basis of the symptomatology of schizophrenia.

Data of the presenter

Doctoral School: Mental Health Sciences
Program: Psychiatry
Supervisor: Pál Czobor
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