Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease
Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, ca...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
|
| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158223001845 |
| _version_ | 1797743876485677056 |
|---|---|
| author | Beini Hu Laurent Younes Xuan Bu Chin-Fu Liu J. Tilak Ratnanather Jane Paulsen Nellie Georgiou-Karistianis Michael I. Miller Christopher Ross Andreia V. Faria |
| author_facet | Beini Hu Laurent Younes Xuan Bu Chin-Fu Liu J. Tilak Ratnanather Jane Paulsen Nellie Georgiou-Karistianis Michael I. Miller Christopher Ross Andreia V. Faria |
| author_sort | Beini Hu |
| collection | DOAJ |
| description | Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics. |
| first_indexed | 2024-03-12T15:01:48Z |
| format | Article |
| id | doaj.art-b545347e1d73406e9745918844e75b31 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-03-12T15:01:48Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-b545347e1d73406e9745918844e75b312023-08-14T04:07:39ZengElsevierNeuroImage: Clinical2213-15822023-01-0139103493Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s diseaseBeini Hu0Laurent Younes1Xuan Bu2Chin-Fu Liu3J. Tilak Ratnanather4Jane Paulsen5Nellie Georgiou-Karistianis6Michael I. Miller7Christopher Ross8Andreia V. Faria9Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USADepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USADepartment of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USADepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USADepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USADepartment of Psychiatry, Neurology, Psychological Brain Sciences, University of Iowa, USA; Department Neurology, University of Wisconsin-Madison, USASchool of Psychological Sciences and Turner Institute of Brain and Mental Health, Monash University, AustraliaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USADepartment of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USADepartment of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Corresponding author.Changes in the brain of patients with Huntington's disease (HD) begin years before clinical onset, so it remains critical to identify biomarkers to track these early changes. Metrics derived from tensor modeling of diffusion-weighted MRIs (DTI), that indicate the microscopic brain structure, can add important information to regional volumetric measurements. This study uses two large-scale longitudinal, multicenter datasets, PREDICT-HD and IMAGE-HD, to trace changes in DTI of HD participants with a broad range of CAP scores (a product of CAG repeat expansion and age), including those with pre-manifest disease (i.e., prior to clinical onset). Utilizing a fully automated data-driven approach to study the whole brain divided in regions of interest, we traced changes in DTI metrics (diffusivity and fractional anisotropy) versus CAP scores, using sigmoidal and linear regression models. We identified points of inflection in the sigmoidal regression using change-point analysis. The deep gray matter showed more evident and earlier changes in DTI metrics over CAP scores, compared to the deep white matter. In the deep white matter, these changes were more evident and occurred earlier in superior and posterior areas, compared to anterior and inferior areas. The curves of mean diffusivity vs. age of HD participants within a fixed CAP score were different from those of controls, indicating that the disease has an additional effect to age on the microscopic brain structure. These results show the regional and temporal vulnerability of the white matter and deep gray matter in HD, with potential implications for experimental therapeutics.http://www.sciencedirect.com/science/article/pii/S2213158223001845DTIMRIHuntington's diseaseLongitudinalCross-sectional |
| spellingShingle | Beini Hu Laurent Younes Xuan Bu Chin-Fu Liu J. Tilak Ratnanather Jane Paulsen Nellie Georgiou-Karistianis Michael I. Miller Christopher Ross Andreia V. Faria Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease NeuroImage: Clinical DTI MRI Huntington's disease Longitudinal Cross-sectional |
| title | Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease |
| title_full | Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease |
| title_fullStr | Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease |
| title_full_unstemmed | Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease |
| title_short | Mixed longitudinal and cross-sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest Huntington’s disease |
| title_sort | mixed longitudinal and cross sectional analyses of deep gray matter and white matter using diffusion weighted images in premanifest and manifest huntington s disease |
| topic | DTI MRI Huntington's disease Longitudinal Cross-sectional |
| url | http://www.sciencedirect.com/science/article/pii/S2213158223001845 |
| work_keys_str_mv | AT beinihu mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT laurentyounes mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT xuanbu mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT chinfuliu mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT jtilakratnanather mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT janepaulsen mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT nelliegeorgioukaristianis mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT michaelimiller mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT christopherross mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease AT andreiavfaria mixedlongitudinalandcrosssectionalanalysesofdeepgraymatterandwhitematterusingdiffusionweightedimagesinpremanifestandmanifesthuntingtonsdisease |