Classification of schizophrenia with spectro-temporo-spatial MEG patterns in working memory

Objective: To investigate whether temporo-spatial patterns of brain oscillations extracted from multichannel magnetoencephalogram (MEG) recordings in a working memory task can be used successfully as a biometric marker to discriminate between healthy control subjects and patients with schizophrenia. Methods: Five letters appearing sequentially on a screen had to be memorized. The letters constituted a word in one condition and a pronounceable non-word in the other. Power changes of 248 channel MEG data were extracted in frequency sub-bands and a two-step filter and search algorithm was used to select informative features that discriminated patients and controls. Results: The discrimination between patients and controls was greater in the word condition than in the non-word condition. Furthermore, in the word condition, the most discriminant patterns were extracted in delta (1-4 Hz), alpha (12-16 Hz) and beta (16-24 Hz) frequency bands. These features were located in the left dorso-frontal, occipital and left fronto-temporal, respectively. Conclusion: The analysis of the oscillatory patterns of MEG recordings in the working memory task provided a high level of correct classification of patients and controls. Significance: We show, using a newly developed algorithm, that the temporo-spatial patterns of brain oscillations can be used as biometric marker that discriminate schizophrenia patients and healthy controls.