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16 results on '"Mao, ChengLu"'

4. Connectome gradient dysfunction contributes to white matter hyperintensity‐related cognitive decline.

Author

Yang, Dan, Tan, Yi, Zhou, ZhiXin, Ke, Zhihong, Huang, Lili, Mo, Yuting, Tang, Limoran, Mao, ChengLu, Hu, Zheqi, Cheng, Yue, Shao, Pengfei, Zhang, Bing, Zhu, Xiaolei, and Xu, Yun

Abstract

Background: Although white matter hyperintensity (WMH) is closely associated with cognitive decline, the precise neurobiological mechanisms underlying this relationship are not fully elucidated. Connectome studies have identified a primary‐to‐transmodal gradient in functional brain networks that support the spectrum from sensation to cognition. However, whether connectome gradient structure is altered as WMH progresses and how this alteration is associated with WMH‐related cognitive decline remain unknown. Methods: A total of 758 WMH individuals completed cognitive assessment and resting‐state functional MRI (rs‐fMRI). The functional connectome gradient was reconstructed based on rs‐fMRI by using a gradient decomposition framework. Interrelations among the spatial distribution of WMH, functional gradient measures, and specific cognitive domains were explored. Results: As the WMH volume increased, the executive function (r = −0.135, p = 0.001) and information‐processing speed (r = −0.224, p = 0.001) became poorer, the gradient range (r = −0.099, p = 0.006), and variance (r = −0.121, p < 0.001) of the primary‐to‐transmodal gradient reduced. A narrower gradient range (r = 0.131, p = 0.001) and a smaller gradient variance (r = 0.136, p = 0.001) corresponded to a poorer executive function. In particular, the relationship between the frontal/occipital WMH and executive function was partly mediated by gradient range/variance of the primary‐to‐transmodal gradient. Conclusions: These findings indicated that WMH volume, the primary‐to‐transmodal gradient, and cognition were interrelated. The detrimental effect of the frontal/occipital WMH on executive function was partly mediated by the decreased differentiation of the connectivity pattern between the primary and transmodal areas. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Glymphatic Dysfunction Mediates the Influence of White Matter Hyperintensities on Episodic Memory in Cerebral Small Vessel Disease

Author

Ke, Zhihong, primary, Mo, Yuting, additional, Li, Jiangnan, additional, Yang, Dan, additional, Huang, Lili, additional, Yang, Zhiyuan, additional, Qin, Ruomeng, additional, Mao, Chenglu, additional, Lv, Weiping, additional, Huang, Yanan, additional, Hu, Zheqi, additional, Zhang, Bing, additional, and Xu, Yun, additional

Published
2022
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16. An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy

Author

Yao, Zhelv, Mao, Chenglu, Ke, Zhihong, and Xu, Yun

Abstract

BackgroundThere is high variability in the clinical outcomes of patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT).Methods217 consecutive patients with anterior circulation large vessel occlusion who underwent MT between August 2018 and January 2022 were analysed. The primary outcome was functional independence defined as a modified Rankin Scale score of 0–2 at 3 months. In the derivation cohort (August 2018 to December 2020), 7 ensemble ML models were trained on 70% of patients and tested on the remaining 30%. The model’s performance was further validated on the temporal validation cohort (January 2021 to January 2022). The SHapley Additive exPlanations (SHAP) framework was applied to interpret the prediction model.ResultsDerivation analyses generated a 9-item score (PFCML-MT) comprising age, National Institutes of Health Stroke Scale score, collateral status, and postoperative laboratory indices (albumin-to-globulin ratio, estimated glomerular filtration rate, blood neutrophil count, C-reactive protein, albumin and serum glucose levels). The area under the curve was 0.87 for the test set and 0.84 for the temporal validation cohort. SHAP analysis further determined the thresholds for the top continuous features. This model has been translated into an online calculator that is freely available to the public (https://zhelvyao-123-60-sial5s.streamlitapp.com).ConclusionsUsing ML and readily available features, we developed an ML model that can potentially be used in clinical practice to generate real-time, accurate predictions of the outcome of patients with AIS treated with MT.

Published
2023
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