Altered cardiac timing neural avalanche in major depressive disorder: new insights from magnetoencephalography

脑磁图 默认模式网络 心脏周期 重性抑郁障碍 舒张期 神经科学 收缩 节奏 静息状态功能磁共振成像 心脏病学 心理学 内科学 医学 脑电图 功能磁共振成像 血压 认知
作者
Qian Liao,Xue Li,Cong Pei,Junneng Shao,Lingling Hua,Hongliang Zhou,Zhijian Yao,Qing Lü
出处
期刊:Psychiatry and Clinical Neurosciences [Wiley]
卷期号:79 (11): 718-727
标识
DOI:10.1111/pcn.13878
摘要

Aim Previous studies have found that patients with major depressive disorder (MDD) exist in a subcritical state, impairing information processing. However, the mechanisms behind these abnormalities remain unclear, as biomolecular explanations offer only a partial explanation. The brain's efficiency depends not only on molecular networks but also on peripheral physiological rhythms, particularly cardiac signals. Given that MDD involves deficits in heart–brain interactions, this study aims to examine how resting‐state neural avalanche dynamics vary across cardiac cycles in MDD. Methods We collected 5‐min resting‐state magnetoencephalography and electrocardiogram data from 60 patients with MDD and 60 healthy control participants. We defined neural avalanches during different phases of the cardiac cycle (systole versus diastole). Avalanche criticality metrics were computed to measure individuals' deviations from criticality. A mixed‐design two‐way anova was conducted to examine the main effects and interaction effects of cardiac phase and depression on avalanche criticality metrics. Results The avalanche metrics move toward from the critical point during diastole compared with systole in both groups at the whole‐brain level. However, the ‐band avalanche metrics in patients with MDD move further from the critical point during diastole compared with systole in the default mode network. Conclusion Our findings suggest that, when the cardiac cycle fails to dynamically regulate default mode network excitability fluctuations through phase‐specific inputs, ‐band activity becomes decoupled from the physiological cardiac rhythm, leading to a persistent bias of the network toward a subcritical state.
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