Poster No:
559
Submission Type:
Abstract Submission
Authors:
Minji Ha1, Inkyung Park1, Taekwan Kim2, Wu Jeong Hwang1, Sunghyun Park3, Minah Kim3, Jun Soo Kwon1
Institutions:
1Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Korea, Republic of, 2Department of Bio and Brain Engineering, Information & Electronics Research Institute, Korea Advance, Daejeon, Korea, Republic of, 3Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea, Republic of
First Author:
Minji Ha
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Korea, Republic of
Co-Author(s):
Inkyung Park
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Korea, Republic of
Taekwan Kim
Department of Bio and Brain Engineering, Information & Electronics Research Institute, Korea Advance
Daejeon, Korea, Republic of
Wu Jeong Hwang
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Korea, Republic of
Sunghyun Park
Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Korea, Republic of
Minah Kim
Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Korea, Republic of
Jun Soo Kwon
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences
Seoul, Korea, Republic of
Introduction:
Cerebellar dysconnectivity has been consistently reported in patients with psychosis, along with concurrent disruptions in the thalamus and associative cortical regions. These disruptions are associated with the triple network model for psychopathology, 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), 63 clinical high risk (CHR), 41 unaffected relatives (URs), and 100 healthy controls (HCs), were used. To capture the network properties of thalamus and cerebellum, we used the parcellated thalamic and cerebellar networks maps derived from FC to cortical networks using winner-take-all approach. Then, we computed and compared the functional connectivity (FC) between cerebellar and cortical triple networks, as well as between thalamic and cerebellar triple networks, across the groups.
Results:
Compared to the HCs, FEP showed more widespread dysconnectivity between networks across different regions, while CHR showed dysconnectivity primarily between the networks within the same region. URs, although not significantly different from HCs, displayed a unique FC patterns of triple networks compared to the other clinical groups. In our exploratory analysis aimed at investigating the association between error-based learning performance and the triple network FC in FEP, we found a statistically significant correlation between the FC of cerebellar SAL and thalamic SAL and the accuracy of the CANTAB Spatial Working Meory task.
Conclusions:
Our findings expand the classical triple network model to include the cerebellar and thalamic networks, potentially indicating the role of cerebellar network in predicting functional impairment and that of thalamic network in inappropriate information processing in psychosis. Distinct patterns of dysfunctional network connectivity across the psychosis risks suggest the necessity for further research to investigate a large-scale triple network model as a potential marker for early psychosis pathophysiology.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Cerebellum
FUNCTIONAL MRI
Thalamus
Other - Psychosis, Large-scale network
1|2Indicates the priority used for review
Provide references using author date format
1. Moberget, T., & Ivry, R. B. (2019). Prediction, Psychosis, and the Cerebellum. Biological psychiatry. Cognitive neuroscience and neuroimaging, 4(9), 820–831.
2. Ha, M. (2023). Aberrant cortico-thalamo-cerebellar network interactions and their association with impaired cognitive functioning in patients with schizophrenia. Schizophrenia, 9(1).
3. Dong, D. (2017). Dysfunction of large-scale brain networks in schizophrenia: A meta-analysis of resting-state functional connectivity. Schizophrenia Bulletin, 44(1), 168–181.
4. O’Neill, A. (2018). Dysconnectivity of large-scale functional networks in early psychosis: A meta-analysis. Schizophrenia Bulletin, 45(3), 579–590.
5. Kim, M. (2022). Large-scale thalamocortical triple network dysconnectivities in patients with first-episode psychosis and individuals at risk for psychosis. Schizophrenia Bulletin, 49(2), 375–384.
6. Sokolov, A. (2017). The Cerebellum: Adaptive Prediction for Movement and Cognition. Trends in cognitive sciences, 21(5), 313–332.
7. Hwang, K. (2017). The Human Thalamus Is an Integrative Hub for Functional Brain Networks. The Journal of neuroscience : the official journal of the Society for Neuroscience, 37(23), 5594–5607.