Cerebellar-thalamic and -cortical triple network dysconnectivity across psychosis risk and onset
Poster No:
1408
Submission Type:
Abstract Submission
Authors:
Minji Ha1, Inkyung Park2, Hyungyou Park3, Taekwan Kim4, Wu Jeong Hwang2, Jiseon Jang1, Minah Kim1, Jun Soo Kwon5
Institutions:
1Seoul National University Hospital, Seoul, Seoul, 2Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Seoul, 3Seoul National University College of Natural Sciences, Seoul, Korea, Republic of, 4Korea Advanced Institute of Science and Technology, Daejeon, Daejeon, 5Hanyang University Hospital, Seoul, Seoul
First Author:
Co-Author(s):
Inkyung Park
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Seoul
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Seoul
Wu Jeong Hwang
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Seoul
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Seoul
Introduction:
Cerebellar dysconnectivity has been consistently linked to psychosis, along with concurrent disruptions in the thalamus and cortical regions. These disruptions are associated with the triple network model, which explains psychopathological phenomena as abnormal interactions within the salience network (SAL), default mode network (DMN), and executive central network (ECN). However, the pattern of cerebellar functional network interactions with the cortical and thalamic networks across the different stages of psychosis risk within the triple network model still remains largely unknown.
Methods:
Resting-state fMRI data from 241 participants, including 37 first-episode psychosis (FEP) patients, 63 clinical high risk (CHR) individuals, 41 unaffected relatives (URs) of schizophrenia patients who have high genetic loading, and 100 healthy controls (HCs), were used. Functional connectivity between cerebellar and cortical networks, and between thalamic and cerebellar networks, was calculated. Subsequently, these functional connectivity values were compared across the groups, and correlations between error processing performance and functional connectivity strength were computed within the FEP.
Results:
FEP patients exhibited increased cerebellar-cortical and decreased cerebellar-thalamic connectivity compared to HCs. CHR individuals showed predominantly increased connectivity with localized disruptions across cerebellar, thalamic, and cortical regions. URs demonstrated no significant group differences. In FEP, reduced cerebellar-thalamic SAL connectivity was negatively correlated with error processing performance.
·Between-network connectivity results and the correlational analysis results
Conclusions:
These findings suggest a progression from localized disruptions within cerebellar, thalamic, and cortical networks to large-scale cross-regional dysconnectivity across psychosis stages. The observed correlations point to a potential role for the cerebellum in predictive coding and salience mapping, mediated by interactions with thalamic and cortical networks. This study expands the triple network model to encompass large-scale cortico-thalamo-cerebellar connectivity, offering new insights into psychosis progression and network-level disruption.
·Expanded cortico-thalamo-cerebellar triple network model.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Reasoning and Problem Solving
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cerebellum
Cognition
FUNCTIONAL MRI
Meta-Cognition
Psychiatric
Schizophrenia
Thalamus
1|2Indicates the priority used for review
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Provide references using APA citation style.
Ito M. (2006). Cerebellar circuitry as a neuronal machine. Progress in neurobiology, 78(3-5), 272–303.
Menon, V., Palaniyappan, L., & Supekar, K. (2023). Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia. Biological psychiatry, 94(2), 108–120.