Microstructural Alterations in Superficial White Matter Associated With Anhedonia and Suicidal Ideation in Major Depressive Disorder

Abstract

Background

Major depressive disorder (MDD) is characterized by impaired emotional and cognitive functions. Previous studies have focused on the long-range white matter bundles within the deep white matter connecting distant cortices. Less is known about the superficial white matter (SWM), which consists of short bundles connecting adjacent and precise cortices. Therefore, we investigated the differences in SWM between patients with MDD and healthy control participants (HCs) and its relationship with core clinical depressive symptoms.

Methods

Probabilistic tractography was used to generate the SWM bundles in 62 antidepressant-free patients with MDD and 77 HCs. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were used to compare the microstructural differences of SWM between the 2 groups. Correlations were calculated between the diffusion metrics in the SWM showing significant between-group differences and core clinical depressive symptoms.

Results

Compared with HCs, patients with MDD showed DTI metric changes in the SWM bundles connecting frontal-parietal-temporal-occipital cortices. For the NODDI metrics, patients with MDD showed a lower neurite density index in the SWM bundles connecting frontal-parietal-temporal cortices. Here, the neurite density index in the SWM bundles connecting prefrontal-insula regions was significantly negatively correlated with anhedonia and suicidal ideation. Patients with MDD displayed a higher orientation dispersion index in the SWM bundles connecting parietal, occipital, and posterior cingulate cortices.

Conclusions

SWM plays a crucial role in the neuropathology of MDD. The decreased neurite density in the SWM connecting prefrontal-insula regions may underlie anhedonia and suicidal ideation. Furthermore, NODDI metrics may offer more specific detection of SWM microstructural abnormalities than DTI metrics.