2019 Fiscal Year Final Research Report
Machine-learning-based investigation of the effect of psychotropic agents on resting-state functional connectivity
| Project/Area Number |
16K10233
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Psychiatric science
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| Research Institution | National Institutes for Quantum and Radiological Science and Technology |
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Principal Investigator |
Yahata Noriaki 国立研究開発法人量子科学技術研究開発機構, 量子生命科学領域, グループリーダー(定常) (70409150)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Keywords | 脳・神経 / 精神疾患 / 安静時脳機能顔図 / 機能的結合 / 薬理学 / 向精神薬 |
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| Outline of Final Research Achievements |
The present study aimed at establishing a machine-learning-based model that could quantitatively evaluate the effect of psychotropic agents on neuroimaging-based metrics such as interregional functional connectivity. The main results included that the established machine-learning model could successfully classify a group of mice (C57Bl/6) that had received chronic administration of antidepressant (selective serotonin reuptake inhibitor; SSRI) for four weeks from a control group (AUC~0.9). For the reliable classification, the machine learning algorithm selected a set of functional connectivity formed by the nodes in both cortical and subcortical areas such as cingulate, striatum, and association area. The similar algorithm was applied to a human data set of mood disorder patients. The derived model could classify two groups of patients with and without administration of SSRI at a level of AUC~0.7, indicating utility of the established methodology in the future biomarker development.
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| Free Research Field |
脳神経科学
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| Academic Significance and Societal Importance of the Research Achievements |
精神疾患を脳部位間の機能的な連係異常と捉え、その時空間的特徴を元に疾患のバイオマーカーを確立し、診断や治療に供する可能性に関心が集まっている。特に近年、安静状態にある脳領域間の同期状態（機能的結合）に、正常／疾患を区別する指標を見出す試みが進められている。一方、患者が治療中に服用する薬物が安静時機能的結合に影響を及ぼすことも知られており、機能的結合解析において疾患と薬物の影響を適切に切り分ける必要性があった。本研究を通し、長期薬物投与を受けるマウスの機能的結合の経時的変容について理解が深まった。今後ヒト研究へのフィードバックを通し、画像ベースの疾患バイオマーカーの精度向上への寄与が期待される。
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