Tokuda Tomoki, Yamashita Okito, Sakai Yuki, Yoshimoto Junichiro
Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.
Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
Front Psychiatry. 2021 Aug 18;12:683280. doi: 10.3389/fpsyt.2021.683280. eCollection 2021.
Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.
最近，维度方法备受关注，为理解不同精神障碍的连续体带来了范式转变。与这一新范式一致，我们以无监督的方式研究是否存在与各种精神障碍相关的共同功能连接，而无需明确使用诊断标签信息。为此，我们独特地将一种新开发的基于网络的多重聚类方法应用于静息态功能连接数据，这使我们能够识别相关脑子网对并据此确定受试者聚类解决方案。因此，我们识别出四个受试者聚类，分别被表征为重度抑郁症（MDD）、年轻健康对照（年轻HC）、精神分裂症（SCZ）/双相情感障碍（BD）和自闭症谱系障碍（ASD），相关脑子网由小脑 - 丘脑 - 苍白球 - 颞叶回路表示。聚类结果使用独立数据集进行了验证。本研究是基于数据驱动的脑子网在功能连接无监督学习框架下的首次跨障碍分析。
