Connectome-based Growth Models Reveal Neurophysiological Subtypes of Subthreshold Depression
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
460
Submission Type:
Abstract Submission
Authors:
Xiaoyi Sun1, Guanmao Chen2, Pan Chen2, Xuan Bu1, Zhangzhang Qi2, Shu Zhang2, Chao Chen2, Zixuan Guo2, Xinyue Tang2, Ruoyi Chen2, Xiaoqin Wang3, Dongtao Wei3, Yuan Chen4, Bangshan Liu5, Chu-Chung Huang6, Yanting Zheng7, Yankun Wu8, Taolin Chen9, Yuqi Cheng10, Xiufeng Xu10, Qiyong Gong9, Tianmei Si8, Shijun Qiu7, Ching-Po Lin11, Jingliang Cheng4, Yanqing Tang12, Fei Wang12, Jiang Qiu3, Peng Xie13, Lingjiang Li5, Yong He1, DIDA-MDD Working Group1, Qian Tao2, Mingrui Xia1, Ying Wang2
Institutions:
1Beijing Normal University, Beijing, Beijing, 2Jinan University, Guangzhou, Guangzhou, 3Southwest University, Chongqing, Chongqing, 4The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 5The Second Xiangya Hospital of Central South University, Changsha, Hunan, 6East China Normal Univerisity, Shanghai, Shanghai, 7The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 8Peking University Sixth Hospital, Beijing, Beijing, 9West China Hospital of Sichuan University, Chengdu, Sichuan, 10First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 11National Yang-Ming Chiao-Tung University, Taipei, Taiwan, 12The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 13The First Affiliated Hospital of Chongqing Medical University, Chongqing, Chongqing
First Author:
Co-Author(s):
Yanting Zheng
The First Affiliated Hospital of Guangzhou University of Chinese Medicine
Guangzhou, Guangdong
The First Affiliated Hospital of Guangzhou University of Chinese Medicine
Guangzhou, Guangdong
Shijun Qiu
The First Affiliated Hospital of Guangzhou University of Chinese Medicine
Guangzhou, Guangdong
The First Affiliated Hospital of Guangzhou University of Chinese Medicine
Guangzhou, Guangdong
Introduction:
Subthreshold depression (StD) poses a high risk for major depressive disorder (MDD) and is characterized by significant clinical heterogeneity among individuals (Cuijpers and Smit, 2004; Eaton, et al., 1995). However, the neurobiological substrates of this heterogeneity remain largely unknown, posing substantial challenges for early detection and effective intervention. Previous studies using resting-state functional MRI (r-fMRI) have documented disruptions in the functional connectome in StD participants (Gao, et al., 2016; Hwang, et al., 2016; Yin, et al., 2024; Yokoyama, et al., 2018; Zhang, et al., 2021), advancing our understanding of its neurobiological basis. However, these studies primarily focused on group-averaged alterations, largely overlooking individual differences among StD participants. Here, we aimed to characterize the age-related trajectory of the functional connectome in a large healthy dataset using normative models, identifying clinically significant neurobiological subtypes based on each participant's deviations from this model. This exploration would deepen our understanding of the distinct neurobiological mechanism underlying clinical heterogeneity in StD and inspire imaging-derived candidate phenotypes for the guidance of precise diagnosis and treatment.
Methods:
This study included r-fMRI data from 197 StD participants (aged 18-35) at Jinan University and 1,203 healthy participants (aged 13-81) across ten research centers. After a standard preprocessing pipeline, we constructed a whole-brain functional network for each subject and calculated the functional connectivity strength (FCS) for each region. For each region, we estimated a normative model of FCS as a function against age and sex using Gaussian process regression (Marquand, et al., 2016) in healthy participants. Individual deviations of StD participants were then estimated based on these models and the extreme deviations were obtained with a threshold of ±2.6. To assess intersubject heterogeneity, we computed the percentage of participants showing extreme deviations in each region. Finally, we used k-means clustering to explore StD subtypes with distinct deviation patterns and compared subtype differences in clinical variables, cognitive function, gene expression profiles, and treatment responses to bright light therapy (BLT).
Results:
In total, 69.04% of StD participants showed extreme deviations in at least one brain region (28.93% and 58.38% for positive and negative deviations, respectively, Fig 1a). Extreme positive deviations were mostly located in the default mode regions, whereas negative deviations were mainly concentrated in the lateral temporal, medial occipital, and medial sensorimotor cortex (Fig 1b). However, extreme deviations in any given region were observed in less than 3.55% of participants, indicating high intersubject heterogeneity (Fig 1b). Two StD subtypes were identified: subtype 1 (n=68) showed more severe deviations than subtype 2 (n=129), and the deviation patterns were conversed (Fig 1c-e). Subtype 1 exhibited more severe depressive symptoms and poorer cognitive performance (p<0.05, Fig 1f). Using the AHBA dataset (Hawrylycz, et al., 2012), we found gene associations with FCS deviations only in subtype 1 (Fig 2a-b). Among 47 individuals with BLT follow-up data (subtype 1/2: n=15/32), clinical symptoms and brain deviations improved in both subtypes, whereas the direction of brain remission differed (Fig 2c-d). Support vector regression revealed that baseline deviation patterns significantly predicted treatment response only for subtype 1 (p=0.018) but not for subtype 2 (p>0.05, one-tailed permutation test) (Fig 2e).
·Fig 1
·Fig 2
Conclusions:
The present study highlights the inter-subject heterogeneity of functional connectome disruptions in StD and suggests two different neurophysiological subtypes, which provide valuable insights into the understanding of heterogeneous subclinical forms of MDD and pave the way toward personalized diagnosis and treatment.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Association Studies
Modeling and Analysis Methods:
Bayesian Modeling
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Psychiatric Disorders
Other - subthreshold depression, functional connectivity, connectome, individual difference, heterogeneous, neurophysiological subtypes
1|2Indicates the priority used for review
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Provide references using APA citation style.
Eaton, W.W., et al. (1995). Prodromes and precursors: epidemiologic data for primary prevention of disorders with slow onset. American Journal of Psychiatry, 152(7), 967-72.
Gao, C., et al. (2016). Decreased subcortical and increased cortical degree centrality in a nonclinical college student sample with subclinical depressive symptoms: a resting-state fMRI study. Frontiers in Human Neuroscience, 10, 617.
Hawrylycz, M.J., et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391-399.
Hwang, J.W., et al. (2016). Enhanced default mode network connectivity with ventral striatum in subthreshold depression individuals. Journal of Psychiatric Research, 76, 111-20.
Marquand, A.F., et al. (2016). Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biological Psychiatry, 80(7), 552-61.
Yin, X., et al. (2024). Brain network hierarchy reorganization in subthreshold depression. NeuroImage: Clinical, 42, 103594.
Yokoyama, S., et al. (2018). Effects of behavioral activation on default mode network connectivity in subthreshold depression: A preliminary resting-state fMRI study. Journal of Affective Disorders, 227, 156-163.
Zhang, B., et al. (2021). Altered spontaneous neural activity in the precuneus, middle and superior frontal gyri, and hippocampus in college students with subclinical depression. BMC Psychiatry, 21(1), 280.