Comparative evaluation of diffusion models for detecting gray matter microstructural alterations in post-stroke depression patients.

Background: Post-stroke depression (PSD) is often underdiagnosed because its gray matter microstructural abnormalities can be subtle. This study compared four diffusion models (DTI, DKI, MAP, and NODDI) to identify objective imaging biomarkers for the early detection of PSD. Methods: Sixty-four participants were enrolled, including 23 age- and sex-matched healthy controls (HC), 26 patients with acute cerebral infarction without PSD (non-PSD; HAMD-21 < 7), and 15 patients with PSD (HAMD-21 ≥ 7). Clinical assessments (HAMD-21, ADL, MoCA, and MMSE) and diffusion spectrum imaging were performed. Intergroup differences in gray matter microstructure, diagnostic performance based on the area under the curve (AUC), and partial correlations adjusted for age, sex, and years of education were analyzed. Infarct location was not included as a covariate. Results: Significant differences among the three groups were observed in age, years of education, and all clinical scale scores ( Conclusion: Multimodal diffusion MRI detects microstructural abnormalities within emotion- and cognition-related circuits, particularly the amygdala and cingulate cortex, across the three groups. Temporolimbic NODDI parameters showed relatively higher AUC values in distinguishing PSD from HC, whereas anterior cingulate DKI metrics showed relatively higher AUC values in differentiating non-PSD stroke patients from HC. Combining multiple diffusion models may aid in the identification of PSD; however, these findings require further validation because infarct location was not adjusted for in the analyses.