WEKO3
アイテム
Computational neuroscience approach to biomarkers and treatments for mental disorders
https://repo.qst.go.jp/records/48259
https://repo.qst.go.jp/records/482599a5f2344-c571-4480-8b97-8d6905b7430a
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-08-02 | |||||
| タイトル | ||||||
| タイトル | Computational neuroscience approach to biomarkers and treatments for mental disorders | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Yahata, Noriaki
× Yahata, Noriaki× Kasai, Kiyoto× Kawato, Mitsuo× 八幡 憲明 |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations in computational neuroscience that undertook either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features were used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing “theranostics” for the first time in clinical psychiatry. | |||||
| 書誌情報 |
Psychiatry and Clinical Neurosciences 巻 71, 号 4, p. 215-237, 発行日 2017-04 |
|||||
| 出版者 | ||||||
| 出版者 | Wiley-Blackwell (Wiley-JAPAN) | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1440-1819 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 28032396 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1111/pcn.12502 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://onlinelibrary.wiley.com/doi/10.1111/pcn.12502/abstract | |||||
| 関連名称 | http://onlinelibrary.wiley.com/doi/10.1111/pcn.12502/abstract | |||||
