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Updated: Jan 12, 2026

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
Published on: August 11, 2015
Neurovascular coupling abnormalities in first-episode drug-naïve major depressive disorder patients
Shuyang Cai1, Qinger Guo1, Xiumei Wu2,3
1Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310003, China.
Background:
Major depressive disorder (MDD) is currently recognized as the most common and debilitating psychiatric disorder worldwide, while the pathophysiological mechanisms underlying MDD remain unclear. This study aims to investigate neurovascular coupling (NVC) alteration in MDD patients and its correlation with disease severity and sex.
Methods:
Arterial spin labeling and blood-oxygen-level-dependent data were collected from 51 MDD patients and 51 healthy controls (HCs). Spatial and temporal correlations between the amplitude of low-frequency fluctuation (ALFF) and cerebral blood flow (CBF) were analyzed as NVC metrics. Differences between groups and subgroups, as well as correlations between ALFF-CBF coupling and clinical measurements, were explored.
Results:
First, spatial correlations found that, compared to HCs, MDD patients showed reduced whole-brain ALFF-CBF coupling, with subgroup analysis revealing significant reductions in severe and female MDD patients. Second, temporal correlation analysis showed that, compared to HCs, moderate MDD patients demonstrated increased ALFF-CBF coupling in the left insula, whereas severe MDD patients showed reduced coupling in the left anterior cingulate cortex and increased coupling in the right superior occipital gyrus. In subgroup analysis, male MDD patients presented lower ALFF-CBF coupling in the left superior frontal orbital gyrus. Third, the spatial correlation of ALFF-CBF coupling in female MDD patients was negatively correlated with anxiety symptom scores.
Conclusions:
NVC decoupling reflects a potential neuropathological mechanism in MDD, with heterogeneous spatial-temporal patterns based on disease severity and sex.
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