title: Computational neuroscience approach to biomarkers and treatments for mental disorders.
abstract: 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 into computational neuroscience that have undertaken 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 have 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 have been 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.
authors.affiliation: Department of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.|Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.|ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan.
authors.forename: Noriaki
authors.initials: N
authors.lastname: Yahata
authors.affiliation: Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Kiyoto
authors.initials: K
authors.lastname: Kasai
authors.affiliation: ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan.
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawato
mesh_terms: D015415:Biomarkers; D019295:Computational Biology; D006801:Humans; D001523:Mental Disorders / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000628:therapy*; D058765:Neurofeedback / Q000379:methods; D059906:Neuroimaging; D009488:Neurosciences / Q000379:methods*
chemical_list: D015415:Biomarkers
keywords: biomarkers; computational psychiatry; machine learning; neuroimaging; resting-state functional connectivity