Useless! What you should be doing is creating EXACT 100% recovery protocols, thus preventing depression the correct way! No need to treat this secondary problem if you were to solve the primary problem of 100% recovery! Don't understand that? Get the hell out of stroke! A hell of a lot of mentors and senior researchers need to be removed!
Comparative evaluation of diffusion models for detecting gray matter microstructural alterations in post-stroke depression patients
Abstract
Post-stroke depression (PSD) is often underdiagnosed because its gray matter microstructural abnormalities can be subtle.(This is incredibly easy! One question! Are you fully recovered? Y/N? No would likely mean is depressed! You are totally overthinking this.) 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 (p < 0.05). Altered gray matter microstructure was primarily identified in the middle frontal gyrus, posterior cingulate gyrus, paracingulate gyrus and amygdala. For group discrimination, the left anterior cingulate DKI MD_p10 demonstrated the best performance for distinguishing HC from non-PSD (AUC = 0.916). The left putamen NODDI ODI_p10 showed the highest AUC (0.916) for distinguishing HC from PSD, whereas the left putamen NODDI ODI_p50 yielded the highest AUC (0.856) for distinguishing non-PSD from PSD. After FDR correction, multiple diffusion metrics were significantly correlated with MMSE and MoCA scores, whereas no imaging parameter showed a significant correlation with HAMD-21.
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.
