Brain age reveals heterogeneous lifespan development of functional networks in depression
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
1372
Submission Type:
Abstract Submission
Authors:
Chenxuan Pang1, Xiaoyi Sun1, Xiaoqin Wang2, Dongtao Wei2, Yuan Chen3, Bangshan Liu4, Chu-Chung Huang5, Yanting Zheng6, Yankun Wu7, Taolin Chen8, Yuqi Cheng9, Xiufeng Xu9, Qiyong Gong8, Tianmei Si7, Shijun Qiu6, Ching-Po Lin10, Jingliang Cheng3, Yanqing Tang11, Fei Wang11, Jiang Qiu2, Peng Xie12, Lingjiang Li4, Yong He1, Mingrui Xia1
Institutions:
1Beijing Normal University, Beijing, Beijing, 2Southwest University, Chongqing, Chongqing, 3The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 4The Second Xiangya Hospital of Central South University, Changsha, Hunan, 5East China Normal University, Shanghai, Shanghai, 6The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 7Peking University Sixth Hospital, Beijing, Beijing, 8West China Hospital of Sichuan University, Chengdu, Sichuan, 9First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, 10National Yang Ming Chiao Tung University, Taipei, Taiwan, 11The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 12The 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:
Major depressive disorder (MDD) is a heterogeneous disorder with onset spanning early adolescence to older adulthood (Malhi & Mann, 2018), closely linked to abnormal brain lifespan development (Schmaal et al., 2017). Previous studies have established brain age prediction models, showing that MDD patients generally have an older brain age than HCs (Dunlop et al., 2021; Han et al., 2021). However, heterogeneity in brain age deviations among MDD patients and its links to divergent developmental trajectories of functional networks, clinical profiles, and gene expression patterns remain unclear. Addressing these issues could provide deeper insights into MDD neurodevelopmental heterogeneity.
Methods:
We utilized a resting-state functional MRI (r-fMRI) dataset included 1,105 patients with MDD and 1,065 matched healthy controls (HCs) across nine centers (age range: 11-64 years). After a standard preprocessing pipeline (Xia et al., 2019), we delineated individual functional networks for each subject using a sparsity-regularized non-negative matrix factorization (NMF) method (Li et al., 2017) and obtained the functional topography maps. Based on topography features of the HCs, we established a brain age prediction model, which was then applied to predict brain age in MDD patients. For each patient, we calculated the brain age gap (BAG) by subtracting chronological age from predicted age, categorizing patients with BAG>0 as "BAG+" (older brain) and those with BAG<0 as "BAG-" (younger brain). To identify functional networks associated with BAG in each category, we calculated correlations between the absolute BAG and topography maps. Principal component analysis was used to extract their main component of the regions significantly related to BAG, and a generalized additive model for location, scale, and shape (GAMLSS) (Borghi et al., 2006) was applied to delineate its growth trajectories in each category. We then utilized a 10-fold cross-validation Partial Least Squares regression to examine relationships between whole-brain topography and 17 HDRS item scores in the two categories. Finally, we explored gene expression profiles associated with the BAG-related regions from BrainSpan (Miller et al., 2014) dataset.
Results:
The topography model predicted HC chronological age with high accuracy in a 20-fold cross-validation (Fig 1A). Overall, MDD patients had a significantly larger BAG than HCs (Fig 1B). In BAG+ patients (632 patients), positive BAG-topography correlations were observed in the bilateral dorsolateral prefrontal and ventrolateral prefrontal cortices (located in ventral attention network, VAN), while negative correlations were seen in the sensory motor cortex and right angular gyrus (Fig 1C), suggesting VAN expansion and sensory motor network (SMN)/dorsal attention network (DAN) contraction linked to a higher BAG (Fig 1E). Their growth trajectory was significantly differed from HCs between ages 13-50 (Fig 1G), indicating accelerated development. In BAG- patients (473 patients), positive correlations were identified in the medial orbitofrontal cortex (OFC), while negative correlations were found in medial occipital and supplementary motor areas (Fig 1D). This indicates that VAN's extension to OFC and visual network (VIS)/SMN contraction linked to a lower BAG (Fig 1F). Their trajectory was significantly lower than HCs between ages 12-21 and 54-65 (Fig 1H), indicating delayed development. Clinically, BAG+ patients showed a link between frontoparietal network (FPN)/default mode network (DMN) topography and mood-related symptoms (Fig 2A), while BAG- patients showed a link between VIS/DMN topography and insomnia issues (Fig 2B). These two categories were also associated with distinct gene expression profiles (Fig 2C-D).
Conclusions:
These findings provide novel insights into the neurodevelopment of functional network topography and distinct molecular mechanisms underlying heterogeneity among MDD patients, which may inspire future precision medicine approaches for MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Development
FUNCTIONAL MRI
MRI
Psychiatric Disorders
Other - brain age; lifespan trajectory; major depressive disorder
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dunlop, K., et al. (2021). Accelerated brain aging predicts impulsivity and symptom severity in depression. Neuropsychopharmacology, 46(5), 911-919.
Han, L. K. M., et al. (2021). Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry, 26(9), 5124-5139.
Li, H., et al. (2017). Large-scale sparse functional networks from resting state fMRI. NeuroImage, 156, 1-13.
Malhi, G. S., et al. (2018). Depression. The Lancet, 392(10161), 2299-2312.
Miller, J. A., et al. (2014). Transcriptional landscape of the prenatal human brain. Nature, 508(7495), 199-206.
Schmaal, L., et al. (2017). Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Molecular Psychiatry, 22(6), 900-909.
Xia, M., et al. (2019). Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals. NeuroImage, 189, 700-714.