Understanding the functional organization of the thalamocortical connectivity in Chronic SCZ
Poster No:
569
Submission Type:
Abstract Submission
Authors:
Harin Oh1, Han Byul Cho2, Shinwon Park3, Sunah Choi1, Jungha Lee1, Minah Kim4,5, Seok-Jun Hong6,7, Jun Soo Kwon8,5,9
Institutions:
1Seoul National University, Seoul, Seoul, 2Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon-si, Gyeonggi-do, 3Child Mind Institute, New York, NY, 4Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Seoul, 5Department of Psychiatry Seoul National University College of Medicine, Seoul, Korea, Republic of, 6Sungkyunkwan University, Gyeonggi-do, Suwon-si, 7Department of Biomedical Engineering, Suwon, Korea, Republic of, 8Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Seoul, 9Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea, Republic of
First Author:
Co-Author(s):
Han Byul Cho
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon-si, Gyeonggi-do
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon-si, Gyeonggi-do
Minah Kim
Department of Neuropsychiatry, Seoul National University Hospital|Department of Psychiatry Seoul National University College of Medicine
Seoul, Seoul|Seoul, Korea, Republic of
Department of Neuropsychiatry, Seoul National University Hospital|Department of Psychiatry Seoul National University College of Medicine
Seoul, Seoul|Seoul, Korea, Republic of
Seok-Jun Hong
Sungkyunkwan University|Department of Biomedical Engineering
Gyeonggi-do, Suwon-si|Suwon, Korea, Republic of
Sungkyunkwan University|Department of Biomedical Engineering
Gyeonggi-do, Suwon-si|Suwon, Korea, Republic of
Jun Soo Kwon
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences|Department of Psychiatry Seoul National University College of Medicine|Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Seoul|Seoul, Korea, Republic of|Seoul, Korea, Republic of
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences|Department of Psychiatry Seoul National University College of Medicine|Department of Neuropsychiatry, Seoul National University Hospital
Seoul, Seoul|Seoul, Korea, Republic of|Seoul, Korea, Republic of
Introduction:
Schizophrenia is a chronic psychiatric disorder with diverse heterogeneity in symptoms and subtypes. Despite such heterogeneity, one consistent finding is the disruption of thalamocortical functional connectivity throughout the disease progression, particularly characterized by reduced connectivity between the thalamus and prefrontal cortex (PFC), and increased connectivity with somatosensory and motor cortex1,2,3. Prior research has shown that increased and decreased connectivities are inversely correlated. Patients with greater connectivity to somatosensory areas tend to have reduced connectivity to the PFC, indicating an interconnected relationship between these connectivities and potential dysfunction stemming from a shared mechanism4. However, the clinical significance of this dysconnectivity is not fully understood; for instance, their correlation with specific symptoms or their potential as a diagnostic indicator remains uncertain due to current analytical limitations enabling to look at two ROIs at a time. Thus, major focus has been given to identifying the potential cause between the reduced PFC-thalamus and increased somatosensory-thalamus dysconnectivity patterns. Understanding the relationship between two dysconnectivities is crucial, as prefrontal-thalamic connectivity is heavily involved in higher-order functions, while somatosensory-thalamic connectivity plays a critical role in sensory processing and motor coordination5,6. To clarify the association between higher order and sensory processing in chronic schizophrenia, exploring the functional hierarchy and organization of those changes could be advantageous. Hence, this study aims to investigate thalamic functional gradient to identify the association between thalamic dysconnectivity patterns in chronic schizophrenia.
Methods:
We acquired resting state fMRI images from 63 chronic schizophrenia patients (SCZ, mean age=33.4yrs, M/F=36/27) and 70 healthy individuals (HC, mean age=25.5yrs, M/F=39/31). Thalamic functional connectome method was adapted from Haak7 for analysis with thalamus as inner region of interest (ROI) and the prefrontal cortex and somatosensory cortex as outer ROIs. The thalamocortical connectivity matrix was extracted from individual subjects, followed by construction of similarity matrix and manifold learning. We extracted two thalamic gradient maps that are known to reflect the thalamic structure (gradient 1) and the hierarchy of behavioral characterization ranging from perception to cognition (gradient 2)8. Statistical differences were calculated, with age as covariate, between schizophrenia patients and healthy individuals. Further analysis was performed to examine the functional network disturbances associated to thalamic dysconnectivity in chronic schizophrenia.
Results:
Two thalamic gradients presented different patterns (Fig 1A), which showed a statistical (MANOVA) difference in the left thalamus from SCZ compared to HC. The group difference from statistically significant voxel regions showed a greater decrease in the 1st gradient and an increase in the 2nd gradient (Fig 1D). The ANOVA analysis of individual gradients indicated that the second gradient of the left thalamus exhibited a compression within SCZ in comparison to HC at a trend level. Moreover, functional network distribution within the 2nd gradient unveiled a significantly gradient decrease in the visual and default mode networks, while an increase in the frontoparietal network (Fig 2B). This indicates a segregation of visual network and frontoparietal network in patients.
Conclusions:
Our result aligns with previous studies demonstrating changes in the 2nd gradient, which is known to reflect the functional hierarchy of thalamocortical connectivity patterns8,9. Our findings offer insights into the issues related to sensory to cognition processing proposed in chronic schizophrenia at the level of neuroimaging-informed functional networks.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Schizophrenia
Other - functional hierarchy
1|2Indicates the priority used for review
Provide references using author date format
2. Anticevic, A. et al., 2015. Association of Thalamic Dysconnectivity and Conversion to Psychosis in Youth and Young Adults at Elevated Clinical Risk. JAMA Psychiatry, 72(9), pp. 882-891.
3. Xi, C. et al., 2020. Schizophrenia patients and their healthy siblings share decreased prefronto-thalamic connectivity but not increased sensorimotor-thalamic connectivity. Schizophrenia Research, Volume 222, pp. 354-361.
4. Ramsay, I. S., 2019. An Activation Likelihood Estimate Meta-analysis of Thalamocortical Dysconnectivity in Psychosis. Biological Psychiatry: Cognitive neuroscience and neuroimaging, 4(10), pp. 859-869.
5. Parnaudeau, S., Bolkan, S. S. & Kellendonk, C., 2018. The Mediodorsal Thalamus: An Essential Partner of the Prefrontal Cortex for Cognition. Biological Psychiatry, 82(8), pp. 648-656.
6. Kumar, V. J., Beckmann, C. F., Scheffler, K. & Grodd, W., 2022. Relay and higher-order thalamic nuclei show an intertwined functional association with cortical-networks. Communications Biology, Volume 5, p. 1187.
7. Haak, K. V., Marquand, A. F. & Beckmann, C. F., 2018. Connectopic mapping with resting-state fMRI. NeuroImage, Volume 170, pp. 83-94.
8. Yang, S. et al., 2020. The thalamic functional gradient and its relationship to structural basis and cognitive relevance. NeuroImage, Volume 218, p. 116960.
9. Fan, Y.-S.et al., 2023. Macroscale Thalamic Functional Organization Disturbances and Underlying Core Cytoarchitecture in Early-Onset Schizophrenia. Schizophrenia Bulletin, 49(5), p. 1375–1386.
10. Gordon, E. M. et al., 2016. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cerebral Cortex, 26(1), p. 288–303.