統合失調症と双極性障害の神経的基盤を脳オルガノイドで解明(Neural basis of schizophrenia and bipolar disorder found in brain organoids)

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2025-09-22 ジョンズ・ホプキンス大学

ジョンズ・ホプキンス大学の研究チームは、血液や皮膚細胞から作製した脳オルガノイドを用い、統合失調症や双極性障害に特有の神経活動パターンを初めて同定しました。機械学習を用いて電気的インパルスを解析した結果、健康な脳と疾患脳の発火様式を識別でき、診断精度は83%、微弱電気刺激後は92%に向上しました。これらの特徴的な電気活動は疾患のバイオマーカーとなりうることが示され、従来の臨床判断に依存する診断法の限界を補う可能性があります。将来的には患者由来オルガノイドを利用し、薬剤候補や投与濃度を試験することで個別化治療にも応用できると期待されます。成果はAPL Bioengineeringに掲載されました。

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機械学習による統合失調症・双極性障害iPS細胞由来モデルにおける電気生理学的シグネチャの検出
Machine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder

Kai Cheng;Autumn Williams;Anannya Kshirsagar;Sai Kulkarni;Rakesh Karmacharya;Deok-Ho Kim;Sridevi V. Sarma;Annie Kathuria
APL Bioengineering  Published:September 22 2025
DOI:https://doi.org/10.1063/5.0250559

The workflow of the proposed analysis pipeline was designed from EEG analysis12 to uncover distinct electrophysiological signatures associated with schizophrenia (SCZ) and bipolar disorder (BPD). The process begins with the generation of iPSCs from patient cohorts, including both SCZ and BPD patients, as well as healthy controls. These iPSCs are differentiated into cerebral organoids (COs) and cortical interneurons, which serve as in vitro models for studying neuropsychiatric disorders. The COs and cortical interneurons in monolayer culture (2DNs) are then subjected to electrophysiological analysis using a MED64-Presto (Alpha MED Scientific Inc., Osaka, Japan) setup to capture dynamic neural activity. The raw MEA recordings from these models are processed through a series of preprocessing steps, including noise filtering, down-sampling, spike collation, and rate coding to identify specific patterns of neural firing and connectivity. The refined data are used to develop stimulus–response dynamic network models, visualized as heatmaps and connectivity sink matrices. A features map computed from the sink matrices is passed through the minimum redundancy maximum relevance (MRMR) selection algorithm.16 The selected features, considered as distinct electrophysiological signatures associated with SCZ and BPD, facilitate a machine learning-based classification of patient cohorts, enabling the identification of unique neural signatures and network dynamics that differentiate SCZ and BPD from each other and from healthy controls. This integrated approach leverages iPSC-derived in vitro models and advanced computational modeling to provide insights into the pathophysiology of these disorders.

Neuropsychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) remain challenging to diagnose due to the absence of objective biomarkers, with current assessments relying largely on subjective clinical evaluations. In this study, we present a computational analysis pipeline designed to identify disease-specific electrophysiological signatures from multi-electrode array (MEA) recordings of patient-derived cerebral organoids (COs) and two-dimensional cortical interneuron cultures (2DNs). Using a Support Vector Machine classifier optimized for high-dimensional data, we achieved 95.8% classification accuracy in distinguishing SCZ from control samples in 2DNs under both baseline and post-electrical-stimulation (PES) conditions with the extracted electrophysiological signatures. In COs, classification accuracy improved from 83.3% at baseline to 91.6% following PES, enabling robust separation of control, SCZ, and BPD cohorts. Key discriminative features included channel-specific measures of network activity, with PES significantly enhancing classification performance, particularly for BPD. These results underscore the potential of MEA-based functional phenotyping, coupled with machine learning, to uncover reliable, stimulation-sensitive electrophysiological biomarkers, offering a path toward more objective diagnosis and personalized treatment strategies for neuropsychiatric disorders.

医療・健康
APL Bioengineeringジョンズ・ホプキンス大学双極性障害機械学習統合失調症脳オルガノイド
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