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计算精神病学研究图谱(CPSYMAP):一个用于可视化研究论文的新数据库。

Computational Psychiatry Research Map (CPSYMAP): A New Database for Visualizing Research Papers.

作者信息

Kato Ayaka, Kunisato Yoshihiko, Katahira Kentaro, Okimura Tsukasa, Yamashita Yuichi

机构信息

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.

Laboratory for Circuit Mechanisms of Sensory Perception, RIKEN Center for Brain Science, Wako, Japan.

出版信息

Front Psychiatry. 2020 Dec 4;11:578706. doi: 10.3389/fpsyt.2020.578706. eCollection 2020.

DOI:10.3389/fpsyt.2020.578706
PMID:33343418
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7746554/
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.

摘要

随着计算神经科学、机器学习的最新进展以及对精神疾病的累积科学认识,计算精神病学领域正日益突出。基于前沿技术和高维数据的计算方法有望通过整合心理学和神经科学的概念来增进对精神疾病的理解,并为临床实践做出贡献。然而,该领域的多学科性质似乎限制了计算精神病学研究的发展。计算精神病学结合了神经科学、精神病学和计算方面的知识;因此,迫切需要一个平台来整合和协调这些观点。在本研究中,我们开发了一个新的数据库,用于将研究论文可视化为二维“地图”,称为计算精神病学研究地图(CPSYMAP)。该地图展示了论文在神经科学、精神病学和计算维度上的分布情况,使任何人都能找到小众研究并加深对该领域的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/2cfafaf65ceb/fpsyt-11-578706-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/b950b72f80de/fpsyt-11-578706-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/1b9583414466/fpsyt-11-578706-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/b94c056ab444/fpsyt-11-578706-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/f2cfd2058723/fpsyt-11-578706-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/5cf83a35ea4c/fpsyt-11-578706-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/2cfafaf65ceb/fpsyt-11-578706-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/b950b72f80de/fpsyt-11-578706-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/1b9583414466/fpsyt-11-578706-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/b94c056ab444/fpsyt-11-578706-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/f2cfd2058723/fpsyt-11-578706-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/5cf83a35ea4c/fpsyt-11-578706-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4775/7746554/2cfafaf65ceb/fpsyt-11-578706-g0006.jpg

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