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<p>The clinical manifestations of mood and psychotic disorders encompass phasic and transdiagnostic features. The complexity of these symptoms may hamper the development of biomarkers for these diagnostic categories. In the present study, machine learning was employed to cluster their transdiagnostic clinical manifestations as a means of developing state-dependent biomarkers with electroencephalography (EEG). This data-driven clustering classified the items of multiple symptom scales into three symptom domains, which subsequently stratified patients with major depressive, bipolar, and schizophrenia spectrum disorders into distinct symptom state groups. The specific brain activity profiles of each stratum were then characterized by resting-state and auditory steady-state EEG paradigms. The resting-state readings of patients in manic states revealed significantly increased gamma and beta oscillations, whereas 40-Hz and 20-Hz auditory steady-state responses (ASSRs) showed no significant differences. These findings suggest that manic states are associated with heightened high-frequency oscillatory activity, represented by gamma and beta oscillations, without a concomitant increase in sensory-evoked information processing precision. Further investigations of excessive gamma and beta oscillations may facilitate the development of state-dependent biomarkers for the assessment, diagnosis, and treatment of manic state symptomatology in clinical psychiatry.</p>

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Excessive gamma and beta oscillations in manic states across mood and psychotic disorders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月3日

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  • Masaya Yanagi,
  • Tsuyoshi Iwasaki,
  • Yoshihiro Iwamura,
  • Osamu Ichikawa,
  • Shizuka Ishida,
  • Osamu Shirakawa,
  • Mamoru Hashimoto,
  • Kazuhito Ikeda.

摘要

The clinical manifestations of mood and psychotic disorders encompass phasic and transdiagnostic features. The complexity of these symptoms may hamper the development of biomarkers for these diagnostic categories. In the present study, machine learning was employed to cluster their transdiagnostic clinical manifestations as a means of developing state-dependent biomarkers with electroencephalography (EEG). This data-driven clustering classified the items of multiple symptom scales into three symptom domains, which subsequently stratified patients with major depressive, bipolar, and schizophrenia spectrum disorders into distinct symptom state groups. The specific brain activity profiles of each stratum were then characterized by resting-state and auditory steady-state EEG paradigms. The resting-state readings of patients in manic states revealed significantly increased gamma and beta oscillations, whereas 40-Hz and 20-Hz auditory steady-state responses (ASSRs) showed no significant differences. These findings suggest that manic states are associated with heightened high-frequency oscillatory activity, represented by gamma and beta oscillations, without a concomitant increase in sensory-evoked information processing precision. Further investigations of excessive gamma and beta oscillations may facilitate the development of state-dependent biomarkers for the assessment, diagnosis, and treatment of manic state symptomatology in clinical psychiatry.