Integrating dimensional and categorical solutions to identify biological subtypes of depression
Poster No:
501
Submission Type:
Abstract Submission
Authors:
Zilin Zhou1, Yingxue Gao1, Weijie Bao1, Mengyue Tang1, Xinyue Hu1, Hailong Li1, Lianqing Zhang1, weihong Kuang2, Qiyong Gong1,3, Xiaoqi Huang1,3
Institutions:
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2Mental Health Center, Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, China, 3The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University, Xiamen, China
First Author:
Zilin Zhou
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Co-Author(s):
Yingxue Gao
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Weijie Bao
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Mengyue Tang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Xinyue Hu
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Hailong Li
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Lianqing Zhang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University
Chengdu, China
weihong Kuang
Mental Health Center, Department of Psychiatry, West China Hospital of Sichuan University
Chengdu, China
Mental Health Center, Department of Psychiatry, West China Hospital of Sichuan University
Chengdu, China
Qiyong Gong
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University|The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University|The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Xiaoqi Huang
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University|The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University|The Xiaman Key Lab of psychoradiology and neuromodulation, West China Xiamen Hospital of Sichuan University
Chengdu, China|Xiamen, China
Introduction:
Major depressive disorder (MDD) is a heterogeneous syndrome with diverse clinical, neurobiological, and treatment-response profiles (Marx et al., 2023). Linking brain functional organization to clinical manifestations (dimensional) and identifying discrete MDD subtypes (categorical) are essential for guiding personalized therapy. However, previous studies were limited by scanner-related artifacts, complex clinical features (e.g., medication history) and model overfitting (Buch & Liston, 2020; Lynch et al., 2020). This study aimed to use dimensional and categorical strategies, integrating intrinsic functional connectivity (FC) and clinical characteristics, to parse heterogeneity in first-episode drug-naïve MDD within a large single-site sample.
Methods:
A total of 238 unmedicated first-episode MDD patients and 150 healthy controls (HC) were enrolled, matched for age, sex and education. Resting-state functional MRI and T1 images were acquired on 3T Siemens MRI scanner with a standardized preprocessing pipeline in DPABI. The 246×246 functional connectivity (FC) matrices were constructed using the Brainnetome atlas (Fan et al., 2016) and normalized through Fisher's Z-transformation.
We first used Spearman's rank correlation to identify FC features correlated with severity scores for one or more items of depressive/anxious symptoms (P < 0.0001), measured by Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA-14). Sparse canonical correlation (sCCA) was then applied to derive low-dimensional representations of FC features associated with linear combinations of clinical symptoms (FDR corrected P<0.05, 5000 permutation) (Xia et al., 2018). Next, k-means clustering was conducted on the sCCA canonical variates, with optimal cluster number and solution validity determined by 26 criteria ("Nbclust" R package). Clustering stability was assessed using Jaccard coefficient with bootstrapping (n=1000). Clinical symptoms and FC differences across MDD subtypes and HC were analyzed using general line model, controlling for age, sex, education, and head motion.
We first used Spearman's rank correlation to identify FC features correlated with severity scores for one or more items of depressive/anxious symptoms (P < 0.0001), measured by Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA-14). Sparse canonical correlation (sCCA) was then applied to derive low-dimensional representations of FC features associated with linear combinations of clinical symptoms (FDR corrected P<0.05, 5000 permutation) (Xia et al., 2018). Next, k-means clustering was conducted on the sCCA canonical variates, with optimal cluster number and solution validity determined by 26 criteria ("Nbclust" R package). Clustering stability was assessed using Jaccard coefficient with bootstrapping (n=1000). Clinical symptoms and FC differences across MDD subtypes and HC were analyzed using general line model, controlling for age, sex, education, and head motion.
Results:
Demographic and clinical data was detailed in Table 1. Two reliable covariation patterns between FC and clinical features in MDD were identified via sCCA (Figure 1a). In first pattern, depressed mood, psychic/somatic anxiety, and suicide, was positively contributed to dysconnectivity of bilateral temporal gyrus (TG) with subcortex, posterior cingulate cortex (PCC), and superior parietal cortex (SPC), and hypoconnectivity of right medial prefrontal cortex (MPFC) and PCC. In second pattern, depressed mood, anhedonia, retardation, psychic/somatic anxiety, and suicide, was negatively contributed to hypoconnectivity of left TG and PCC and of left SPC and visual cortex, and hyperconnectivity of left MPFC and visual cortex (Figure 1b).
Based on these brain-behavior covariations, we classified MDD into two subgroups according to clustering quality metrics (Figure 2a-d). Subgroup 1 (n=125): milder depressive/anxious symptoms, insomnia, and lower suicidal risk; subgroup 2 (n=113): more severe symptoms and higher suicidal risk (Figure 2e). Compared to HC, subgroup 1 showed minimal FC changes, while subgroup 2 showed more pronounced FC abnormalities, particularly in the right TG, mainly manifested as altered FC with bilateral SPL and contralateral TG (Figure 2f).
Based on these brain-behavior covariations, we classified MDD into two subgroups according to clustering quality metrics (Figure 2a-d). Subgroup 1 (n=125): milder depressive/anxious symptoms, insomnia, and lower suicidal risk; subgroup 2 (n=113): more severe symptoms and higher suicidal risk (Figure 2e). Compared to HC, subgroup 1 showed minimal FC changes, while subgroup 2 showed more pronounced FC abnormalities, particularly in the right TG, mainly manifested as altered FC with bilateral SPL and contralateral TG (Figure 2f).
Conclusions:
This study linked FC patterns to depressive/anxiety symptoms and suicide risk in MDD and revealed two discrete subtypes based on dimensional features. Subgroup1 showed milder symptoms/lower suicide risk and minimal FC changes, while Subgroup2 showed more severe symptoms/higher suicide risk and more pronounced FC alterations primarily in the right TG. These findings implicated potential role of the right TG in more severe MDD subtype from perspective of bottom-up neuroimaging-based biosubtyping, shedding lights on combining dimensional and categorical approaches to unravel heterogeneity of first-episode drug-naïve MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
ADULTS
Computational Neuroscience
FUNCTIONAL MRI
Machine Learning
Psychiatric Disorders
Other - major depressive disorder; subtyping
1|2Indicates the priority used for review
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Lynch, C. J., Gunning, F. M., & Liston, C. (2020). Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes. Biological Psychiatry, 88(1), 83-94.
Marx, W., Penninx, B. W. J. H., Solmi, M., Furukawa, T. A., Firth, J., Carvalho, A. F., & Berk, M. (2023). Major depressive disorder. Nature Reviews Disease Primers, 9(1).
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