Abstract
The field of computational psychiatry is growing in prominence along with recent advances in computational neuroscience, machine learning, and the cumulative scientific understanding of psychiatric disorders. Computational approaches based on cutting-edge technologies and high-dimensional data are expected to provide an understanding of psychiatric disorders with integrating the notions of psychology and neuroscience, and to contribute to clinical practices. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional “map” called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding ofthe field.
Highlights
The understanding of psychiatric disorders is based on multiple interacting levels, from genetics to cells, neural circuits, cognition, behavior, and the surrounding environment
In addition to efforts to accumulate large-scale biological and psychological evidence such as the ENIGMA [1] and COCORO [2] projects, recent advances in computational methodology have helped to handle high-dimensional data to understand psychiatric disorders [3, 4] Bringing mathematical methodologies and theoretical frameworks to psychiatric research is expected to have a central role in the development of treatments and preventive strategies; the use of such methods constitutes an area of research called computational psychiatry (CPSY), which has come to be treated as an important discipline of psychiatry
Before filtering by the DSM-5 category, the reinforcement learning crossed with modelfitting (58 papers) is the most common methodology (Figure 5); the cell with neural network models crossed with modeling computational processes of the brain and the cells with Bayes have a large number of the papers (13–14 papers) after filtering by schizophrenia (Figure 6)
Summary
The understanding of psychiatric disorders is based on multiple interacting levels, from genetics to cells, neural circuits, cognition, behavior, and the surrounding environment. In addition to efforts to accumulate large-scale biological and psychological evidence such as the ENIGMA [1] and COCORO [2] projects, recent advances in computational methodology have helped to handle high-dimensional data to understand psychiatric disorders [3, 4] Bringing mathematical methodologies and theoretical frameworks to psychiatric research is expected to have a central role in the development of treatments and preventive strategies; the use of such methods constitutes an area of research called computational psychiatry (CPSY), which has come to be treated as an important discipline of psychiatry The development of this field has been reviewed in many papers from the perspective of clinical and computational neuroscience [5,6,7,8,9,10]. To accelerate CPSY research, it is crucial to link each CPSY study to the knowledge derived from different fields, including accumulated evidence of neuroscience, traditional psychiatry, and computational techniques.
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