White matter hyperintensity (WMH) is a common finding in aging population and considered to be a contributor to cognitive decline. Our study aimed to characterize the spatial patterns of WMH in different severities and explore its impact on cognition and brain microstructure in non-demented elderly. Lesions were both qualitatively (Fazekas scale) and quantitatively assessed among 321 community-dwelled individuals with MRI scanning. Voxel- and atlas-based analyses of the whole-brain white matter microstructure were performed. The WMH of the same severities was found to occur uniformly with a specific pattern of lesions. The severity of WMH had a significant negative association with the performance of working and episodic memory, beginning to appear in Fazekas 3 and 4. The white matter tracts presented significant impairments in Fazekas 3, which showed brain-wide changes above Fazekas 4. Lower FA in the superior cerebellar peduncle and left posterior thalamic radiation was mainly associated with episodic memory, and the middle cerebellar peduncle was significantly associated with working memory. These results support that memory is the primary domain to be affected by WMH, and the effect may potentially be influenced by tract-specific WM abnormalities. Fazekas scale 3 might be the critical stage predicting a future decline in cognition.

Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient’s symptom phenotypes, computation‐based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of a patient’s clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it is very difficult to conduct effective representation for unrecorded symptom terms in an existing knowledge base. In this study, we proposed a subnetwork‐based symptom term mapping method (SSTM) and constructed a SSTM‐based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from a knowledge network to effectively represent the embedding features of clinical symptom terms (especially the unrecorded terms). The experimental results showed that our method performs better than state‐of‐the‐art methods. In addition, the comprehensive experiments of TCMPR with different hyperparameters (i.e., feature embedding, feature dimension, subnetwork filter threshold, and feature fusion) demonstrate that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.

Patients with type 2 diabetes mellitus (T2DM) have a considerably high risk of developing dementia, especially for those with mild cognitive impairment (MCI). The investigation of the microstructural change of white matter (WM) between T2DM with amnesic MCI (T2DM-aMCI) and T2DM with normal cognition (T2DM-NC) and their relationships to cognitive performances can help to understand the brain variations in T2DM-related amnesic cognitive impairment. In the current study, 36 T2DM-aMCI patients, 40 T2DM-NC patients, and 40 healthy control (HC) individuals underwent diffusion tensor image and T1-weighted MRI scans and comprehensive cognition assessments. All of these cognitive functions exhibited intergroup ranking differences in patients. The T2DM-NC patients and HC individuals did not reveal any significant differences in WM integrity. The T2DM-aMCI patients showed disrupted integrity in multiple WM tracts compared with HC and T2DM-NC. Specifically, the damaged WM integrity of the right inferior fronto-occipital fasciculus and the right inferior longitudinal fasciculus exhibited significant correlations with episodic memory and attention function impairment in T2DM patients. Furthermore, cognitive impairment-related WM microstructural damage was associated with the degeneration of cortex connected to the affected WM tract. These findings indicate that degeneration exists extensively in WM tracts in T2DM-aMCI, whereas no brain WM damage is evident in T2DM-NC.