Evidence map›Paper›PMID 41776190›Full record

ArticleScientific reports2026

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

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

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Masaya Yanagi *Department of Neuropsychiatry, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osaka-Sayama, Osaka, 589-8511, Japan. yanagi@med.kindai.ac.jp.
Tsuyoshi Iwasaki *Research Division, Sumitomo Pharma. Co., Ltd., Suita, Osaka, Japan.
Yoshihiro IwamuraResearch Division, Sumitomo Pharma. Co., Ltd., Suita, Osaka, Japan.
Osamu IchikawaResearch Division, Sumitomo Pharma. Co., Ltd., Suita, Osaka, Japan.
Shizuka IshidaDepartment of Neuropsychiatry, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Osamu ShirakawaDepartment of Neuropsychiatry, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Mamoru HashimotoDepartment of Neuropsychiatry, Faculty of Medicine, Kindai University, 377-2 Ohnohigashi, Osaka-Sayama, Osaka, 589-8511, Japan.
Kazuhito IkedaResearch Division, Sumitomo Pharma. Co., Ltd., Suita, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Beta RhythmBipolar DisorderGamma RhythmManiaPsychotic DisordersAdultBrainElectroencephalographyEvoked Potentials, AuditoryFemaleHumansMajor Depressive DisorderMaleMiddle AgedSchizophreniaYoung AdultASSRBipolar disorderMajor depressionResting-state EEGSchizophreniaUnsupervised learning

Identifiers

PMID41776190
PMCPMC13065767

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.