Journal Article10.1093/brain/awae145
Meso-cortical pathway damage in cognition, apathy and gait in cerebral small vessel disease.
Hao Li,Mina A. Jacob,Mengfei Cai,R P C Kessels,David G. Norris,Marco Duering,F-E de Leeuw,Anil M. Tuladhar +7 more
- 06 May 2024
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TL;DR: Meso-cortical pathway damage is associated with cognitive impairment, apathy and gait dysfunction in cerebral small vessel disease.
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Abstract: Cerebral small vessel disease (SVD) is known to contribute to cognitive impairment, apathy, and gait dysfunction. Although associations between cognitive impairment and either apathy or gait dysfunction have been shown in SVD, the inter-relations among these three clinical features and their potential common neural basis remains unexplored. The dopaminergic meso-cortical and meso-limbic pathways have been known as the important brain circuits for both cognitive control, emotion regulation and motor function. Here, we investigated the potential inter-relations between cognitive impairment, apathy, and gait dysfunction, with a specific focus on determining whether these clinical features are associated with damage to the meso-cortical and meso-limbic pathways in SVD. In this cross-sectional study, we included 213 participants with SVD in whom MRI scans and comprehensive neurobehavioral assessments were administered. These assessments comprised of six clinical measures: processing speed, executive function, memory, apathy (based on the Apathy Evaluation Scale), and gait function (based on the time and steps in Timed Up and Go test). We reconstructed five tracts connecting ventral tegmental area (VTA) and the dorsolateral prefrontal cortex (dlPFC), ventral lateral PFC (vlPFC), medial orbitofrontal cortex (mOFC), anterior cingulate cortex (ACC) and nucleus accumbens (NAc) within meso-cortical and meso-limbic pathways using diffusion weighted imaging. The damage along the five tracts was quantified using the free water (FW) and FW-corrected mean diffusivity (MD-t) indices. Furthermore, we explored the inter-correlations among the six clinical measures and identified their common components using principal component analysis (PCA). Linear regression analyses showed that higher FW values of tracts within meso-cortical pathways were related to these clinical measures in cognition, apathy, and gait (all P-corrected values < 0.05). PCA showed strong inter-associations among these clinical measures and identified a common component wherein all six clinical measures loaded on. Higher FW values of tracts within meso-cortical pathways were related to the PCA-derived common component (all P-corrected values < 0.05). Moreover, FW values of VTA-ACC tract showed the strongest contribution to the PCA-derived common component over all other neuroimaging features. In conclusion, our study showed that the three clinical features (cognitive impairment, apathy, and gait dysfunction) of SVD are strongly inter-related and that the damage in meso-cortical pathway could be the common neural basis underlying the three features in SVD. These findings advance our understanding of the mechanisms behind these clinical features of SVD and have the potential to inform novel management and intervention strategies for SVD.
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Citations
Cholinergic Disruption Contributes to Motoric Cognitive Dysfunction in Cerebral Small Vessel Disease
Mengfei Cai,Hao Li,Milan Nemy,Mina A. Jacob,David G. Norris,Marco Duering,Yuhu Zhang,Roy P.C. Kessels,Lenka Vyslouzilova,Stefan J. Teipel,Daniel Ferreira,Frank-Erik de Leeuw,Anil M. Tuladhar +12 more
TL;DR: This study investigates the relationship between cholinergic disruption and motoric cognitive dysfunction in cerebral small vessel disease, finding that disruption in cholinergic cortical pathways, particularly in the external capsule, contributes to cognitive and gait decline.
An Explainable Ensemble Learning -Based Auxiliary Diagnosis System for Cerebral Small Vessel Disease
Benben Wang,Baoqing Han,Huizhen Lu,Hao Wang,Chengliang Zhang,Zongqing Wang,Ke Zhang,Xue Han,Chuanliu Wang,Jianwei Xu,Congsi Wang +10 more
- 28 Oct 2025
Abstract: Abstract Cerebral small vessel disease (CSVD) poses major public health challenges, yet current MRI based diagnosis detects only established damage, and existing auxiliary methods, mostly based on conventional statistics, lack sufficient feature extraction capability and generalizability, thereby limiting early warning and precision management. Accordingly, we developed an intelligent auxiliary diagnostic system grounded in an interpretable ensemble learning framework, aiming to enable early detection and warning of CSVD. To support this development, a total of 597 sets of electronic medical record data from Quzhou Affiliated Hospital of Wenzhou Medical University were used as the study cohort. Firstly, a multidimensional feature evaluation and selection method was proposed, identifying 12 key predictive factors out of 23 relevant variables. Subsequently, the optimal algorithm was selected from Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost, and Multilayer Perceptron Classifier, based on Area Under the Curve (AUC) and Accuracy metrics, and a stacking ensemble learning strategy was then employed for model construction. The developed model demonstrated excellent discriminative performance, achieving an AUC of 0.924 while maintaining a low Brier score of 0.1065. By integrating the SHAP interpretability algorithm, the model provided intuitive visualizations of feature importance, thereby enhancing transparency and facilitating clinical adoption. Ultimately, this study achieved effective integration of early warning and auxiliary diagnostic functions for CSVD. These results indicate that the proposed system possesses high accuracy, interpretability, and deployability, underscoring its broad potential for early warning and personalized management of CSVD.
Artificial intelligence–assisted oculo‐gait measurements for cognitive impairment in cerebral small vessel disease
Huimin Chen,Hao Du,Fang Yi,Tingting Wang,Shuo Yang,Yuesong Pan,Hongyi Yan,Dandan Liu,Mengyuan Zhou,Yiyi Chen,Mengxi Zhao,Jingtao Pi,Yingying Yang,Xiangmin Fan,Xueli Cai,Ziyu Qiu,Jipeng Zhang,Yawei Liu,Wenping Gu,Yilong Wang +19 more
TL;DR: This study uses AI-assisted oculo-gait measurements to identify cognitive impairment in cerebral small vessel disease, finding associations between anti-saccade accuracy, stride velocity, and swing velocity with cognitive status, with moderate accuracy in distinguishing cognitive impairment.
Choroid Plexus Free-Water Correlates with Glymphatic function in Alzheimer Disease: The RJNB-D Study
Binyin Li,Xiaomeng Xu,Yukui Zhang,Junfang Zhang,Yi Wang,Magdy Selim,Yanan Zheng,Rongxi Shen,Qi Huang,Wenjing Wang,Wei Xu,Yihui Guan,Jun Liu,Yulei Deng,Fang Xie +14 more
- 05 Aug 2024
TL;DR: This study investigates the relationship between choroid plexus free-water fraction and Alzheimer's disease, finding elevated free-water fraction correlates with impaired glymphatic function, neurodegeneration, and cognitive decline in Alzheimer's patients.
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