Wenhao Zhu1, Hao Huang1, Shiqi Yang2, Xiang Luo1, Wenzhen Zhu2, Shabei Xu1, Qi Meng1, Chengchao Zuo1, Kun Zhao3,4, Hesheng Liu5, Yong Liu3,6,7,8, Wei Wang1. 1. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 2. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 3. Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 4. School of Information Science and Engineering, Shandong Normal University, Ji'nan, China. 5. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 6. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 7. Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 8. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Abstract
BACKGROUND: White matter hyperintensities (WMH) are common in older adults and are associated with cognitive decline. However, little is known about the functional changes underlying cognitive decline in WMH subjects. OBJECTIVES: To investigate whole-brain functional connectivity (FC) underpinnings of cognitive decline in WMH subjects using univariate and multivariate analyses. METHODS: Twenty-three WMH subjects with mild cognitive impairment (WMH-MCI), 43 WMH subjects with no cognitive impairment (WMH-nCI), and 55 healthy controls underwent resting-state functional MRI scans. Whole-brain FC was calculated using the fine-grained human Brainnetome Atlas, followed by performance of between-group comparisons and FC-cognition correlation analysis. A multivariate analysis using support vector machine (SVM) was performed to classify WMH-MCI and WMH-nCI subjects based on FC. RESULTS: Both the WMH-MCI and WMH-nCI subjects exhibited characteristic impaired FC patterns. Markedly reduced FC involving subcortical nuclei and cortical hub regions of cognitive networks, especially the cingulate cortex, was identified in the WMH-MCI patients. In the WMH-MCI group, several connections involving the cingulate cortex were associated with cognitive decline. The exploratory mediation analyses indicated that FC alterations could partially explain the association between WMH and cognition. Furthermore, an SVM classifier based on FC distinguished WMH-MCI and WMH-nCI subjects with 78.8% accuracy. Connections that contributed most to the classification showed a similar distribution as the connections identified in the univariate analysis. CONCLUSIONS: This study provides a new window into the pathophysiology of cognitive impairment in WMH subjects and offer a novel and potential approach for early detection of the cognitive impairment in WMH subjects at the individual level.
BACKGROUND:White matter hyperintensities (WMH) are common in older adults and are associated with cognitive decline. However, little is known about the functional changes underlying cognitive decline in WMH subjects. OBJECTIVES: To investigate whole-brain functional connectivity (FC) underpinnings of cognitive decline in WMH subjects using univariate and multivariate analyses. METHODS: Twenty-three WMH subjects with mild cognitive impairment (WMH-MCI), 43 WMH subjects with no cognitive impairment (WMH-nCI), and 55 healthy controls underwent resting-state functional MRI scans. Whole-brain FC was calculated using the fine-grained human Brainnetome Atlas, followed by performance of between-group comparisons and FC-cognition correlation analysis. A multivariate analysis using support vector machine (SVM) was performed to classify WMH-MCI and WMH-nCI subjects based on FC. RESULTS: Both the WMH-MCI and WMH-nCI subjects exhibited characteristic impaired FC patterns. Markedly reduced FC involving subcortical nuclei and cortical hub regions of cognitive networks, especially the cingulate cortex, was identified in the WMH-MCI patients. In the WMH-MCI group, several connections involving the cingulate cortex were associated with cognitive decline. The exploratory mediation analyses indicated that FC alterations could partially explain the association between WMH and cognition. Furthermore, an SVM classifier based on FC distinguished WMH-MCI and WMH-nCI subjects with 78.8% accuracy. Connections that contributed most to the classification showed a similar distribution as the connections identified in the univariate analysis. CONCLUSIONS: This study provides a new window into the pathophysiology of cognitive impairment in WMH subjects and offer a novel and potential approach for early detection of the cognitive impairment in WMH subjects at the individual level.
Entities:
Keywords:
Functional connectivity; mild cognitive impairment; resting-state functional MRI; support vector machine; white matter hyperintensities