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

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.

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.

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.

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.

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.