1. Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets
- Author
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Takuya Ishida, Yuko Nakamura, Saori C Tanaka, Yuki Mitsuyama, Satoshi Yokoyama, Hotaka Shinzato, Eri Itai, Go Okada, Yuko Kobayashi, Takahiko Kawashima, Jun Miyata, Yujiro Yoshihara, Hidehiko Takahashi, Susumu Morita, Shintaro Kawakami, Osamu Abe, Naohiro Okada, Akira Kunimatsu, Ayumu Yamashita, Okito Yamashita, Hiroshi Imamizu, Jun Morimoto, Yasumasa Okamoto, Toshiya Murai, Kiyoto Kasai, Mitsuo Kawato, and Shinsuke Koike
- Subjects
Psychiatry and Mental health - Abstract
Background and HypothesisDynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.Study DesignWe applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.Study ResultsDCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.ConclusionsDCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.
- Published
- 2023