Distinctive Subcortical Connectivity Predicts Treatment Response in First-Episode Schizophrenia
Poster No:
504
Submission Type:
Abstract Submission
Authors:
huan huang1, Jingyu Zhou1, Sisi Jiang1, Jianfu Li1, Dezhong Yao1, Cheng Luo1,2
Institutions:
1School of life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, 2Corresponding author, Chengdu, China
First Author:
huan huang
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
Co-Author(s):
Jingyu Zhou
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
Sisi Jiang
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
Jianfu Li
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
Dezhong Yao
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
School of life Science and Technology, University of Electronic Science and Technology of China
Chengdu, Sichuan
Cheng Luo
School of life Science and Technology, University of Electronic Science and Technology of China|Corresponding author
Chengdu, Sichuan|Chengdu, China
School of life Science and Technology, University of Electronic Science and Technology of China|Corresponding author
Chengdu, Sichuan|Chengdu, China
Introduction:
Neuroimaging studies provide substantial evidence of brain functional and neurochemical alterations in schizophrenia, particularly in subcortical regions (Keshavan et al., 2020). Antipsychotics are believed to alleviate symptoms by antagonizing dopamine D2 receptors, which are predominantly located in subcortical regions (McCutcheon, Marques, & Howes, 2020). Abnormal subcortical functional connectivity (SFC) has been identified in schizophrenia and shows potential as a predictive biomarker for antipsychotic treatment response (Mehta et al., 2021). However, prior studies have largely focused on individual subcortical regions, neglecting the unique connectivity patterns between them. This study aimed to explore the relationship between SFC and treatment outcomes in schizophrenia using multivariate methods and machine learning algorithms.
Methods:
Resting-state functional MRI data were obtained from 119 medication-naive, first-episode schizophrenia patients and 133 matched healthy controls. MRI scans were conducted at baseline for all participants and after 12 weeks of antipsychotic medication (APM) for patients only. Clinical symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at both time points. The 30 PANSS items were used to construct a clinical symptom profile for each patient, represented as a 30 × 1 vector for analysis. Subcortical regions of interest included the amygdala, hippocampus, striatum, and thalamus, further parcellated into 36 subregions (Fan et al., 2016). Blood oxygen level-dependent signals were extracted from these 36 subregions, and Pearson's correlation coefficients (SFC values) were calculated between the mean time series of each region, generating a 36 × 36 SFC matrix for each participant. To identify SFC patterns associated with APM, partial least squares (PLS) analysis was applied to examine multivariate associations between SFC changes (∆SFC) and symptom changes (∆PANSS). The significance of each latent component (LC) was determined using 1000 permutation tests. Distinctive SFC patterns contributing most to a given LC were identified based on their correlation values (Dong et al., 2022). Finally, support vector regression (SVR) was employed to predict antipsychotic treatment outcomes based on the distinctive SFC patterns at baseline.
Results:
Only the first LC (LC1) was significant (permutation test, p = 0.018). LC1 showed a significant association between connectivity changes and clinical composite scores (r = 0.384, p < 0.001). The ∆SFC loadings for LC1 (Fig. 1A) revealed a distributed pattern dominated by the striatum, with additional involvement of the amygdala and thalamus. The PANSS loadings for LC1 (Fig. 1B) indicated that greater clinical scores were associated with overall increases in ∆PANSS, reflecting heightened symptom severity in schizophrenia patients. Furthermore, SVR analysis demonstrated that the predicted reduction in PANSS total scores, based on the baseline SFC pattern, was significantly positively correlated with the actual reduction after APM (r = 0.237, p = 0.009; Fig. 1C).
·The Distinctive Subcortical Functional Connectivity (SFC) Pattern
Conclusions:
In this study, we used multivariate approaches to identify a distinctive abnormal SFC pattern, primarily centered in the striatum with involvement of the amygdala and thalamus, that is associated with antipsychotic treatment outcomes in schizophrenia. These findings highlight the potential of SFC patterns as biomarkers, paving the way for personalized treatment strategies and improved prognosis through early diagnosis. This study was supported by the grant from Chengdu Science and Technology Bureau(2024-YF05-02056-SN).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Multivariate Approaches 2
Keywords:
FUNCTIONAL MRI
Schizophrenia
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Fan, L. Z., et al. (2016). The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 26(8), 3508-3526.
Keshavan, M. S., et al. (2020). Neuroimaging in Schizophrenia. Neuroimaging Clinics of North America, 30(1), 73-+.
McCutcheon, R. A., et al. (2020). Schizophrenia-An Overview. Jama Psychiatry, 77(2), 201-210.
Mehta, U. M., et al. (2021). Resting-state functional connectivity predictors of treatment response in schizophrenia-A systematic review and meta-analysis. Schizophrenia Research, 237, 153-165.