Jean de Dieu Uwisengeyimana1,2, Benedictor Alexander Nguchu1, Yaming Wang1, Du Zhang1, Yanpeng Liu1, Zhoufan Jiang1, Xiaoxiao Wang3, Bensheng Qiu4. 1. Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China. 2. Department of Electrical and Electronics Engineering, College of Science and Technology, University of Rwanda, Kigali, Rwanda. 3. Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China. wang506@ustc.edu.cn. 4. Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, Anhui, China. bqiu@ustc.edu.cn.
Abstract
BACKGROUND: Intervention against age-related neurodegenerative diseases may be difficult once extensive structural and functional deteriorations have already occurred in the brain. AIM: Investigating 6-year longitudinal changes and implications of regional brain atrophy and functional connectivity in the triple-network model as biomarkers of preclinical cognitive impairment in healthy aging. METHODS: We acquired longitudinal cognitive scores and magnetic resonance imaging (MRI) data from 74 healthy old adults. Resting-state functional MRI (rs-fMRI) analysis was conducted using FSL6.0.1 to examine functional connectivity changes and regional brain morphometries were quantified using FreeSurfer5.3. Finally, we cross-validated and compared two support vector machine (SVM) regression models to predict future 6-year cognition score from the baseline regional brain atrophy and resting-state functional connectivity (rs-FC) measures. RESULTS: After a 6-year follow-up, our results (P < 0.05-corrected) indicated significant connectivity reduction within all the three brain networks, significant differences in regional brain volumes and cortical thickness. We also observed significant improvement in episodic memory and significant decline in executive functions. Finally, comparing the two models, we observed that regional brain atrophy predictors were more efficient in approximating future 6-year cognitive scores (R = 0.756, P < 0.0001) than rs-FC predictors (R = 0.6, P < 0.0001). CONCLUSION: This study used longitudinal data to keep subject variability low and to increase the validity of the results. We demonstrated significant changes in structural and functional MRI over 6 years. Our findings present a potential neuroimaging-based biomarker to detect cognitive impairment and prevent risks of neurodegenerative diseases in healthy old adults.
BACKGROUND: Intervention against age-related neurodegenerative diseases may be difficult once extensive structural and functional deteriorations have already occurred in the brain. AIM: Investigating 6-year longitudinal changes and implications of regional brain atrophy and functional connectivity in the triple-network model as biomarkers of preclinical cognitive impairment in healthy aging. METHODS: We acquired longitudinal cognitive scores and magnetic resonance imaging (MRI) data from 74 healthy old adults. Resting-state functional MRI (rs-fMRI) analysis was conducted using FSL6.0.1 to examine functional connectivity changes and regional brain morphometries were quantified using FreeSurfer5.3. Finally, we cross-validated and compared two support vector machine (SVM) regression models to predict future 6-year cognition score from the baseline regional brain atrophy and resting-state functional connectivity (rs-FC) measures. RESULTS: After a 6-year follow-up, our results (P < 0.05-corrected) indicated significant connectivity reduction within all the three brain networks, significant differences in regional brain volumes and cortical thickness. We also observed significant improvement in episodic memory and significant decline in executive functions. Finally, comparing the two models, we observed that regional brain atrophy predictors were more efficient in approximating future 6-year cognitive scores (R = 0.756, P < 0.0001) than rs-FC predictors (R = 0.6, P < 0.0001). CONCLUSION: This study used longitudinal data to keep subject variability low and to increase the validity of the results. We demonstrated significant changes in structural and functional MRI over 6 years. Our findings present a potential neuroimaging-based biomarker to detect cognitive impairment and prevent risks of neurodegenerative diseases in healthy old adults.
Authors: Harini Eavani; Mohamad Habes; Theodore D Satterthwaite; Yang An; Meng-Kang Hsieh; Nicolas Honnorat; Guray Erus; Jimit Doshi; Luigi Ferrucci; Lori L Beason-Held; Susan M Resnick; Christos Davatzikos Journal: Neurobiol Aging Date: 2018-06-15 Impact factor: 4.673
Authors: David H Salat; Randy L Buckner; Abraham Z Snyder; Douglas N Greve; Rahul S R Desikan; Evelina Busa; John C Morris; Anders M Dale; Bruce Fischl Journal: Cereb Cortex Date: 2004-03-28 Impact factor: 5.357
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