MRI assessment of whole-brain structural changes in aging
Received 12 April 2017
Accepted for publication 27 June 2017
Published 9 August 2017 Volume 2017:12 Pages 1251—1270
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Richard Walker
Hui Guo,1,2 William Siu,1,3 Ryan CN D’Arcy,1,4 Sandra E Black,5,6 Lukas A Grajauskas,1,4 Sonia Singh,7,8 Yunting Zhang,2 Kenneth Rockwood,9,10 Xiaowei Song1,4,9
On behalf of The Alzheimer’s Disease Neuroimaging Initiative and the National Alzheimer’s Coordinating Center
1Health Sciences and Innovation, ImageTech Laboratory, Fraser Health Authority, Surrey, BC, Canada; 2Department of Diagnostic Imaging, Tianjin Medical University General Hospital, Tianjin, China; 3Department of Medical Imaging, Fraser Health Authority, Surrey, 4Departments of Applied Sciences and Computing Science, Simon Fraser University, Burnaby, BC, 5Department of Medicine (Neurology), 6LC Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre & University of Toronto, Toronto, ON, 7Department of Family Medicine, University of British Columbia, Vancouver, BC, 8Department of Research and Evaluation, Fraser Health, Surrey, BC, 9Department of Medicine (Geriatric Medicine), Dalhousie University, 10Centre for Healthcare of the Elderly, Nova Scotia Health Authority, Halifax, NS, Canada
Purpose: One of the central features of brain aging is the accumulation of multiple age-related structural changes, which occur heterogeneously in individuals and can have immediate or potential clinical consequences. Each of these deficits can coexist and interact, producing both independent and additive impacts on brain health. Many of the changes can be visualized using MRI. To collectively assess whole-brain structural changes, the MRI-based Brain Atrophy and Lesion Index (BALI) has been developed. In this study, we validate this whole-brain health assessment approach using several clinical MRI examinations.
Materials and methods: Data came from three independent studies: the Alzheimer’s Disease Neuroimaging Initiative Phase II (n=950; women =47.9%; age =72.7±7.4 years); the National Alzheimer’s Coordinating Center (n=722; women =55.1%; age =72.7±9.9 years); and the Tianjin Medical University General Hospital Research database on older adults (n=170; women =60.0%; age =62.9±9.3 years). The 3.0-Tesla MRI scans were evaluated using the BALI rating scheme on the basis of T1-weighted (T1WI), T2-weighted (T2WI), T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), and T2*-weighted gradient-recalled echo (T2*GRE) images.
Results: Atrophy and lesion changes were commonly seen in each MRI test. The BALI scores based on different sequences were highly correlated (Spearman r2>0.69; P<0.00001). They were associated with age (r2>0.29; P<0.00001) and differed by cognitive status (χ2>26.48, P<0.00001). T2-FLAIR revealed a greater level of periventricular (χ2=29.09) and deep white matter (χ2=26.65, P<0.001) lesions than others, but missed revealing certain dilated perivascular spaces that were seen in T2WI (P<0.001). Microhemorrhages occurred in 15.3% of the sample examined and were detected using only T2*GRE.
Conclusion: The T1WI- and T2WI-based BALI evaluations consistently identified the burden of aging and dementia-related decline of structural brain health. Inclusion of additional MRI tests increased lesion differentiation. Further research is to integrate MRI tests for a clinical tool to aid the diagnosis and intervention of brain aging.
Keywords: aging, brain atrophy and lesion index (BALI), brain health, MRI pulse sequences, structural brain changes
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