Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia

Tingting Xu, Massoud Stephane, Keshab K. Parhi

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the disease itself.

Original languageEnglish (US)
Pages (from-to)135-143
Number of pages9
JournalClinical EEG and Neuroscience
Issue number2
StatePublished - Apr 2013

Bibliographical note

Funding Information:
The authors thank Dr Arthur Leuthold (Department of Neuroscience, University of Minnesota) for collecting the magnetoencephalograhy data. Computational power was supported by Minnesota Super Computing Institute (MSI).

Copyright 2013 Elsevier B.V., All rights reserved.


  • Lexical processing
  • Machine intelligence
  • Magnetoencephalograhy
  • Neural oscillation
  • Schizophrenia


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