Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder
Introduction: The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FP...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158224000135 |
| _version_ | 1827319172533583872 |
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| author | Takuya Ishida Shinichi Yamada Kasumi Yasuda Shinya Uenishi Atsushi Tamaki Michiyo Tabata Natsuko Ikeda Shun Takahashi Sohei Kimoto |
| author_facet | Takuya Ishida Shinichi Yamada Kasumi Yasuda Shinya Uenishi Atsushi Tamaki Michiyo Tabata Natsuko Ikeda Shun Takahashi Sohei Kimoto |
| author_sort | Takuya Ishida |
| collection | DOAJ |
| description | Introduction: The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. Methods: We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. Results: We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. Conclusions: Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders. |
| first_indexed | 2024-03-08T03:24:10Z |
| format | Article |
| id | doaj.art-1fb8605ec340441096577e283f67b074 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-04-25T00:15:31Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-1fb8605ec340441096577e283f67b0742024-03-13T04:45:39ZengElsevierNeuroImage: Clinical2213-15822024-01-0141103574Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorderTakuya Ishida0Shinichi Yamada1Kasumi Yasuda2Shinya Uenishi3Atsushi Tamaki4Michiyo Tabata5Natsuko Ikeda6Shun Takahashi7Sohei Kimoto8Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Corresponding author at: Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Kimiidera 811-1, Wakayama 641-8509, Japan.Department of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Hanwa Izumi Hospital, Osaka 594-1157, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Hidaka Hospital, Wakayama 644-0002, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Wakayama Prefectural Mental Health Care Center, Wakayama 643-0811, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Neuropsychiatry, Nokamikosei Hospital, Wakayama 640-1141, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, Japan; Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan; Clinical Research and Education Center, Asakayama General Hospital, Osaka 590-0018, Japan; Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka 583-8555, JapanDepartment of Neuropsychiatry, Graduate School of Wakayama Medical University, Wakayama 641-8509, JapanIntroduction: The dynamics of large-scale networks, which are known as distributed sets of functionally synchronized brain regions and include the visual network (VIN), somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network (FPN), and default mode network (DMN), play important roles in emotional and cognitive processes in humans. Although disruptions in these large-scale networks are considered critical for the pathophysiological mechanisms of psychiatric disorders, their role in psychiatric disorders remains unknown. We aimed to elucidate the aberrant dynamics across large-scale networks in patients with schizophrenia (SZ) and mood disorders. Methods: We performed energy-landscape analysis to investigate the aberrant brain dynamics of seven large-scale networks across 50 healthy controls (HCs), 36 patients with SZ, and 42 patients with major depressive disorder (MDD) recruited at Wakayama Medical University. We identified major patterns of brain activity using energy-landscape analysis and estimated their duration, occurrence, and ease of transition. Results: We identified four major brain activity patterns that were characterized by the activation patterns of the DMN and VIN (state 1, DMN (-) VIN (-); state 2, DMN (+) VIN (+); state 3, DMN (-) VIN (+); and state 4, DMN (+) VIN (-)). The duration of state 1 and the occurrence of states 1 and 2 were shorter in the SZ group than in HCs and the MDD group, and the duration of state 3 was longer in the SZ group. The ease of transition between states 3 and 4 was larger in the SZ group than in the HCs and the MDD group. The ease of transition from state 3 to state 4 was negatively associated with verbal fluency in patients with SZ. The current study showed that the brain dynamics was more disrupted in SZ than in MDD. Conclusions: Energy-landscape analysis revealed aberrant brain dynamics across large-scale networks between SZ and MDD and their associations with cognitive abilities in SZ, which cannot be captured by conventional functional connectivity analyses. These results provide new insights into the pathophysiological mechanisms underlying SZ and mood disorders.http://www.sciencedirect.com/science/article/pii/S2213158224000135Energy-landscape analysisResting-state fMRIDefault mode networkPsychiatric disorder |
| spellingShingle | Takuya Ishida Shinichi Yamada Kasumi Yasuda Shinya Uenishi Atsushi Tamaki Michiyo Tabata Natsuko Ikeda Shun Takahashi Sohei Kimoto Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder NeuroImage: Clinical Energy-landscape analysis Resting-state fMRI Default mode network Psychiatric disorder |
| title | Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder |
| title_full | Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder |
| title_fullStr | Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder |
| title_full_unstemmed | Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder |
| title_short | Aberrant brain dynamics of large-scale functional networks across schizophrenia and mood disorder |
| title_sort | aberrant brain dynamics of large scale functional networks across schizophrenia and mood disorder |
| topic | Energy-landscape analysis Resting-state fMRI Default mode network Psychiatric disorder |
| url | http://www.sciencedirect.com/science/article/pii/S2213158224000135 |
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