Electrophysiological correlates of mindfulness in patients with major depressive disorder

Author:

Sarlon Jan,Brühl Annette B.,Lang Undine E.,Kordon Andreas

Abstract

ObjectivesMindfulness-based interventions (MBI) can reduce both stress and depressive symptoms. However, the impact of mindfulness on stress level in depressed subjects remains unclear. This study aims to assess electrophysiological correlates of mindfulness in patients with major depressive disorder (MDD) at baseline, under stress exposure, and in relaxation following stress exposure.MethodsPerceived mindfulness was assessed with the Freiburger Mindfulness Inventory (FMI) in 89 inpatients (mean age 51) with MDD [mean Beck Depression Inventory (BDI) 30]. Electrophysiological parameters [resting heart rate (RHR), heart rate variability (HRV), respiration rate, skin conductance, and skin temperature] were recorded at 5-min baseline, 1-min stress exposure, and 5-min self-induced relaxation.ResultsFreiburger Mindfulness Inventory was strongly inversely correlated with symptom severity measured by BDI (r = –0.53, p < 0.001). No correlations between FM score and electrophysiological parameters in any of the three conditions (baseline, stress exposure, relaxed state) could be found. The factor openness was associated with higher VLF (very low frequency of HRV) in the baseline condition. However, this correlation was no more significant after regression analysis when corrected for respiratory rate, age, and sex.ConclusionAutonomous nervous reactivity in depression was not associated with perceived mindfulness as measured by FMI score and presented electrophysiological parameters, despite the strong inverse correlation between state mindfulness and symptom severity.

Publisher

Frontiers Media SA

Subject

General Neuroscience

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Adjunctive use of mindfulness-based mobile application in depression: randomized controlled study;European Archives of Psychiatry and Clinical Neuroscience;2024-09-04

2. Unraveling the Mysteries of Mind-Wandering and Meditation: A Comprehensive Investigation using Deep Learning;2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET);2023-11-23

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