What type of study is this?

This is a Cohort Study study (also classified as: Quantitative Study).

October 10, 2025Open Access

Association between white matter structural damage and cognitive impairment in patients with cerebral small vessel disease based on TBSS technology

Key Points

Multivariate logistic regression model effectively identifies cognitive impairment in CSVD patients, enhancing diagnosis.
The model achieved a mean training AUC of 0.940 and testing AUC of 0.809, indicating strong predictive capability.
Significant DTI metrics correlated with cognitive impairment include fractional anisotropy and mean diffusivity in key white matter tracts.
This study connects specific white matter tract damage to cognitive dysfunction, offering a practical assessment tool.

Abstract

Objective Cognitive impairment in patients with cerebral small vessel disease (CSVD) is closely associated with white matter injury. This study aims to evaluate whether diffusion tensor imaging (DTI) metrics can predict the risk of cognitive impairment in CSVD patients. Methods We retrospectively analyzed data from 54 CSVD patients, classified into a cognitive impairment group (CI, n = 25) and a non-cognitive impairment group (NCI, n = 29). Using tract-based spatial statistics (TBSS), we computed fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) across 48 major white matter tracts. Significant DTI metrics identified by univariate logistic regression were used to construct a multivariate logistic regression model. Model performance was evaluated via 5-fold cross-validation based on the area under the ROC curve (AUC), calibration curves, and decision curve analysis. Results Several DTI metrics showed significant correlations with cognitive impairment, including FA (fornix, left corticospinal tract, bilateral medial lemniscus/inferior cerebellar peduncle, left cerebral peduncle, right cingulum hippocampus), MD (right superior cerebellar peduncle, left cerebral peduncle), and RD (bilateral medial lemniscus, right inferior/superior cerebellar peduncle, left cerebral peduncle, right external capsule, cingulum hippocampus). The multivariate model constructed based on these metrics demonstrated the best predictive performance, with a mean training AUC of 0.940 and testing AUC of 0.809. The calibration curves showed good agreement between predicted and observed outcomes, and decision curve analysis confirmed the clinical utility of the model. Conclusion The multivariate logistic regression model incorporating DTI metrics can effectively identify cognitive impairment in CSVD patients. This study establishes a link between damage in specific white matter tracts and cognitive dysfunction, providing a practical tool for assessing the risk of cognitive impairment in clinical settings.

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Cite This Study

Zheng et al. (Thu,) studied this question.

synapsesocial.com/papers/68e962596210319498c300b3 https://doi.org/https://doi.org/10.3389/fneur.2025.1647129

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