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. 2025 Feb;32(2):932-950.
doi: 10.1016/j.acra.2024.08.034. Epub 2024 Sep 17.

Integrative MR Imaging Interpretation in Cognitive Impairment with Alzheimer's Disease, Small Vessel Disease, and Glymphatic Function-Related MR Parameters

Sung-Hye You  1 , Byungjun Kim  2 , InSeong Kim  3 , Kyung-Sook Yang  4 , Kyung Min Kim  1 , Bo Kyu Kim  1 , Jae Ho Shin  1
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Free article

Integrative MR Imaging Interpretation in Cognitive Impairment with Alzheimer's Disease, Small Vessel Disease, and Glymphatic Function-Related MR Parameters

Sung-Hye You et al. Acad Radiol. 2025 Feb.
Free article

Abstract

Rationale and objectives: The role of MR imaging in patients with cognitive impairment is to evaluate each component of Alzheimer's disease (AD), small vessel disease (SVD), and glymphatic function. We want to validate the diagnostic performance of the comprehensive interpretation of these parameters to predict the cognitive impairment stage. MATERIALS AND METHODS: This retrospective single-center study included 359 patients with cognitive impairment who had undergone MRI (FLAIR, T2WI, 3D-T1WI, susceptibility-weighted imaging, and diffusion tensor imaging [DTI]) and a neuropsychological screening battery between January 2020 and July 2022. Each AD and SVD-related MR parameter was visually evaluated, and DTI analysis along the perivascular space (ALPS) index was calculated. Volumetry analysis was performed using Neurophet AQUA AI-based software. Using logistic regression analysis, four types of models were developed and compared by adding the components in the following order: (1) clinical factors and AD, (2) SVD, (3) glymphatic function-related MR parameters, and (4) volumetric data. Chi-square automatic interaction detection algorithm was used to develop diagnostic tree analysis (DTA) model to predict late-stage cognitive impairment.

Results: APOE4 status, years of education, medial temporal lobe atrophy score, Fazekas scale score, DTI-ALPS index, and white matter hyperintensity were significant predictors of late-stage cognitive impairment. The performance of the prediction model increased from Model 1 to Model 4 (AUC: 0.880, 0.899, 0.914, and 0.945, respectively). The overall accuracy of the DTA model was 87.47%.

Conclusion: Integrative brain MRI assessments in patients with cognitive impairment, AD, SVD, and glymphatic function-related MR parameters, improve the prediction of late-stage cognitive impairment.

Keywords: Alzheimer's disease; Diffusion tensor imaging; Glymphatics; Magnetic resonance imaging; Small vessel disease.

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Conflict of interest statement

Declaration of Competing Interest InSeong Kim is an employee. We acknowledge the potential influence of these interests on the outcomes of the study. It is important to note that InSeong Kim was not involved in the analysis or interpretation of the research results. Other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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