A078 Biol Psychiat | 边缘中心视角揭示抑郁症奖赏回路过度多样化
来源：公众号 PSY-Brain_Frontier ｜ 发布：2025-09-15 ｜ 原文链接
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基本信息：
Title: Edge-centric Brain Connectome Representations Reveal Increased Brain Functional Diversity of Reward Circuit in Patients with Major Depressive Disorder
发表时间：2025.9.3
Journal: Biological Psychiatry
影响因子：9.0
Keywords：Depression; Magnetic resonance imaging; Edge-centric; Reward circuit; Gene expression; Neurotransmitter
研究背景
我们常把大脑比作一张交通网络图：不同区域是“城市”，连接它们的神经纤维是“道路”。过去大多数研究关注的是“城市”本身及其直接连接（即所谓的“节点中心”分析），却往往忽视了“道路与道路之间”的互动。但在真实的交通系统中，道路的交织才真正决定了城市的运转效率。
在以往的抑郁症研究中，已有大量证据表明，抑郁症并非单一区域损伤，而是大脑网络层面的“沟通失调”（即多个脑区构成的脑网络出现问题）。然而，传统方法在解析这种复杂的网络紊乱时仍有局限。于是，研究者开始尝试一种更新颖的“边缘中心（edge-centric）”方法，直接聚焦在大脑区域之间的互动关系，从而揭示更高阶的功能模式。
基于这一思路，本研究想要探索：重度抑郁症患者是否在关键的奖赏回路中表现出异常的“大脑功能多样性”？
省流总结
本研究首次采用“边缘中心”大脑连接组方法，从大规模多中心静息态 fMRI 数据（838 名抑郁症患者 vs 881 名健康对照）出发，捕捉到传统方法难以揭示的网络特征。结果发现，抑郁症患者在奖赏回路（前额叶、扣带回、纹状体、丘脑等）表现出功能多样性显著增加，即这些区域同时参与更多重叠网络，可能造成信息传递效率下降。进一步分析显示，这种异常与炎症相关基因表达以及5-HT1B 血清素受体分布紧密关联，提示炎症和神经递质系统或共同驱动抑郁症的奖赏功能障碍。更重要的是，基于该方法构建的机器学习模型在区分患者与健康人群时优于传统方法，展现出临床转化潜力。
这一发现不仅深化了我们对抑郁症病理机制的理解，也揭示了大脑功能障碍是“道路与道路之间的互动”失衡所致，而非单一区域问题。未来，针对炎症和血清素系统的精准干预，或许能为抑郁症患者带来更有效的治疗路径。
核心图片
Fig. 1 Overview of the experimental design and analysis pipeline
Fig. 2 Differences in normalized entropy between MDD patients and HC
Fig 3 Neurobiological profiles associated with case-control differences in normalized entropy
Fig. 4 Individual-level classification performance of eFC- and nFC-based models
Abstract
Background: Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previous brain connectome studies have primarily relied on node-centric approaches, neglecting critical edge-edge interactions that may capture essential features of network dysfunction.
Methods: This study included resting-state functional MRI data from 838 MDD patients and 881 healthy controls (HC) across 23 sites. We applied a novel edge-centric connectome model to estimate edge functional connectivity and identify overlapping network communities. Regional functional diversity was quantified via normalized entropy based on community overlap patterns. Neurobiological decoding was performed to map brain-wide relationships between functional diversity alterations and patterns of gene expression and neurotransmitter distribution. Comparative machine learning analyses further evaluated the diagnostic utility of edge-centric versus node-centric connectome representations.
Results: Compared with HC, MDD patients exhibited significantly increased functional diversity within the prefrontal-striatal-thalamic reward circuit. Neurobiological decoding analysis revealed that functional diversity alterations in MDD were spatially associated with transcriptional patterns enriched for inflammatory processes, as well as distribution of 5-HT1B receptors. Machine learning analyses demonstrated superior classification performance of edge-centric models over traditional node-centric approaches in distinguishing MDD patients from HC at the individual level.
Conclusions: Our findings highlighted that abnormal functional diversity within the reward processing system might underlie multi-level neurobiological mechanisms of MDD. The edge-centric connectome approach offers a valuable tool for identifying disease biomarkers, characterizing individual variation and advancing current understanding of complex network configuration in psychiatric disorders.
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本文解读基于：Edge-centric Brain Connectome Representations Reveal Increased Brain Functional Diversity of Reward Circuit in Patients with Major Depressive Disorder，Doi：10.1016/j.biopsych.2025.08.013。核心图表、方法细节、统计结果与讨论见原文及其拓展数据。
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