Aberrant dynamic functional architecture in major depressive disorder: Vertex-Wise large-sample fMRI analyses reveal network-specific alterations and symptom associations (pdf)

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Aberrant dynamic functional architecture in major depressive disorder: Vertex-Wise large-sample fMRI analyses reveal network-specific alterations and symptom associations

Translational Psychiatry ARTICLE www.nature.com/tp OPEN Aberrant dynamic functional architecture in major depressive disorder: Vertex-Wise large-sample fMRI analyses reveal network-speciﬁc alterations and symptom associations 1234567890();,: Xue-Ying Li 1,2,3, Bin Lu2,3,4, Xiao Chen 2,3,5,6, Zi-Han Wang2,3, Yu-Wei Wang7, Yi-Fan Liao2,3, Zheng-Jia-Yi Hu2,8,9, Chen-Nan Wu2,3, Han-Lin Wang2,8,9,10,11, Qing-Lin Gao2,3, Yan-Rong Chen5,6, Si-Cheng Chen5,6, Xin-Yu Wang5,6, Hai-Long Liu2,3, Ze-Di Lin1,12, Li-Ping Cao 13, Guan-Mao Chen14, Jian-Shan Chen13, Tao Chen 15, Tao-Lin Chen16,17, Yu-Qi Cheng18, Zhao-Song Chu19, Xi-Long Cui20, Qi-Yong Gong 16,17, Wen-Bin Guo20, Can-Can He21, Qian Huang22, Xin-Lei Ji20, Feng-Nan Jia23, Li Kuang22, Bao-Juan Li24, Feng Li25, Hui-Xian Li26, Tao Li18,27, Xiao-Yun Liu28, Yan-Song Liu23, Zhe-Ning Liu20, Yi-Cheng Long20, Jian-Ping Lu29, Jiang Qiu30, Xiao-Xiao Shan20, Tian-Mei Si 31, Peng-Feng Sun32, Chuan-Yue Wang 25, Hua-Ning Wang 24, Xiang Wang 20, Ying Wang 14, Xin-Ran Wu30, Yan-Kun Wu31, Chun-Ming Xie 21, Guang-Rong Xie20, Peng Xie 33,34,35, Xiu-Feng Xu19, Zhen-Peng Xue29, Hong Yang15, Jian Yang34, Hua Yu18,27, Yong-Qiang Yu 36,37,38, Min-Lan Yuan39, Yong-Gui Yuan 28, Ai-Xia Zhang40, ✉ Ke-Rang Zhang 40, Wei Zhang39, Jing-Ping Zhao20, Jia-Jia Zhu 36,37,38, Xi-Nian Zuo 41,42, Chao-Gan Yan 1,2,3,5,6,8,9 , 32 ✉ Xiao-Ping Wu and on behalf of the DIRECT Consortium* © The Author(s) 2026 Major depressive disorder (MDD) imposes signiﬁcant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconﬁgurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identiﬁed signiﬁcant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identiﬁed a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and reﬁne personalized therapeutic interventions targeting speciﬁc dynamic dysfunctions in MDD. Translational Psychiatry (2026)16:127 ; https://doi.org/10.1038/s41398-026-03812-1 INTRODUCTION Major depressive disorder (MDD) has emerged as a prevalent mental health issue worldwide for decades. The global incidence of depression has increased by approximately 50%, with no reduction in its prevalence or burden since it affected 172 million individuals in 1990 [1, 2]. MDD accounts for 49·4 million disabilityadjusted life-years (DALYs) in a single year [3], imposing signiﬁcant burdens and suffering on individual patients while also profoundly impacting their families and society as a whole. Despite extensive research efforts, the underlying causes of depression and the associated changes in brain function still remain elusive [4]. Increasing evidence suggests that depression could be viewed as involving multiple symptom dimensions or clusters, a perspective often referred to as multidimensionality [5–7]. These different symptom clusters appear to vary in their responsiveness to treatments [6, 8]. For instance, dysphoric symptoms and anxiety symptoms respond best to stimulation of distinct brain circuits through transcranial magnetic stimulation (TMS) in the treatment of depression [6, 9]. Depression is not a single, uniform disorder, but rather a constellation of symptoms that may require targeted, symptom-speciﬁc treatments based on the unique neurobiological mechanisms underlying different symptom clusters [10]. MDD may involve multiple dysfunctions in emotional, cognitive, and somatic domains, engaging various brain regions, and this complexity has been accompanied by diverse methodological and quantitative approaches in the research literature [11–15]. In a recent systematic review [13], we documented previous fMRI studies on MDD that avoided several common methodological pitfalls, in which almost all studies focused on static metrics. For A full list of author afﬁliations appears at the end of the paper. Received: 26 December 2024 Revised: 8 December 2025 Accepted: 20 January 2026 X.-Y. Li et al. 2 instance, static functional connectivity has been primarily used as a simple yet effective indicator for evaluating functional brain networks, which are generally assumed to be relatively stable over the observed timescales, although not necessarily corresponding to direct structural connectivity [16–21]. Despite their utility, such static measures may overlook the brain’s dynamic nature. Neural activity ﬂuctuates continuously, even during so-called “resting states” [22]. Clearly, functional metrics depends on how it is measured, and metrics measured over shorter timescales may be more informative about temporal ﬂuctuations of the brain [18]. In other word, dynamic metrics have the beneﬁt of characterizing both the spatial and temporal ﬂuctuations of functional brain signals [23–25]. Several metrics have emerged to characterize the dynamic functional patterns [24, 26]. DFC variance or standard deviation (SD) refers to the time-varying changes in the strength of speciﬁc functional connectivity between brain regions, which is often used to measure the variability in the strength of dFCs [27, 28]. However, some studies have reported that the reliability of dFC variance or SD may be relatively limited [29, 30]. On the other hand, to characterize the consistency of these dFCs, temporal stability provides a measure of how stable the brain’s functional architecture is during a given period [26]. Dynamic functional architecture is critical for the brain’s ability to sustain consistent information processing and integration across time, which is essential for maintaining conscious states [31, 32]. Previous works [26, 28] have mapped the wholebrain distribution pattern of functional stability in healthy individuals and thoroughly conﬁrmed its reproducibility across different computational parameters and scanning conditions. These works found that healthy individuals exhibit high level (...truncated)