Ambica Mehndiratta1, Constantina A Treaba2, Valeria Barletta2, Elena Herranz2, Russell Ouellette1, Jacob A Sloane3, Eric C Klawiter4, Revere P Kinkel5, Caterina Mainero2. 1. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA. 2. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 3. Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA. 4. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 5. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA.
Abstract
BACKGROUND: Thalamic pathology is a marker for neurodegeneration and multiple sclerosis (MS) disease progression. OBJECTIVE: To characterize (1) the morphology of thalamic lesions, (2) their relation to cortical and white matter (WM) lesions, and (3) clinical measures, and to assess (4) the imaging correlates of thalamic atrophy. METHODS: A total of 90 MS patients and 44 healthy controls underwent acquisition of 7 Tesla images for lesion segmentation and 3 Tesla scans for atrophy evaluation. Thalamic lesions were classified according to the shape and the presence of a central venule. Regression analysis identified the predictors of (1) thalamic atrophy, (2) neurological disability, and (3) information processing speed. RESULTS: Thalamic lesions were mostly ovoid than periventricular, and for the great majority (78%) displayed a central venule. Lesion volume in the thalamus, cortex, and WM did not correlate with each other. Thalamic atrophy was only associated with WM lesion volume (p = 0.002); subpial and WM lesion volumes were associated with neurological disability (p = 0.016; p < 0.001); and WM and thalamic lesion volumes were related with cognitive impairment (p < 0.001; p = 0.03). CONCLUSION: Thalamic lesions are unrelated to those in the cortex and WM, suggesting that they may not share common pathogenic mechanisms and do not contribute to thalamic atrophy. Combined WM, subpial, and thalamic lesion volumes at 7 Tesla contribute to the disease severity.
BACKGROUND: Thalamic pathology is a marker for neurodegeneration and multiple sclerosis (MS) disease progression. OBJECTIVE: To characterize (1) the morphology of thalamic lesions, (2) their relation to cortical and white matter (WM) lesions, and (3) clinical measures, and to assess (4) the imaging correlates of thalamic atrophy. METHODS: A total of 90 MS patients and 44 healthy controls underwent acquisition of 7 Tesla images for lesion segmentation and 3 Tesla scans for atrophy evaluation. Thalamic lesions were classified according to the shape and the presence of a central venule. Regression analysis identified the predictors of (1) thalamic atrophy, (2) neurological disability, and (3) information processing speed. RESULTS:Thalamic lesions were mostly ovoid than periventricular, and for the great majority (78%) displayed a central venule. Lesion volume in the thalamus, cortex, and WM did not correlate with each other. Thalamic atrophy was only associated with WM lesion volume (p = 0.002); subpial and WM lesion volumes were associated with neurological disability (p = 0.016; p < 0.001); and WM and thalamic lesion volumes were related with cognitive impairment (p < 0.001; p = 0.03). CONCLUSION:Thalamic lesions are unrelated to those in the cortex and WM, suggesting that they may not share common pathogenic mechanisms and do not contribute to thalamic atrophy. Combined WM, subpial, and thalamic lesion volumes at 7 Tesla contribute to the disease severity.
Entities:
Keywords:
Multiple sclerosis; atrophy; central venule; ovoid; periventricular; thalamus
Authors: Caterina Mainero; T Benner; A Radding; A van der Kouwe; R Jensen; B R Rosen; R P Kinkel Journal: Neurology Date: 2009-07-29 Impact factor: 9.910
Authors: Christina J Azevedo; Steven Y Cen; Sankalpa Khadka; Shuang Liu; John Kornak; Yonggang Shi; Ling Zheng; Stephen L Hauser; Daniel Pelletier Journal: Ann Neurol Date: 2018-02-09 Impact factor: 10.422
Authors: M K Houtchens; R H B Benedict; R Killiany; J Sharma; Z Jaisani; B Singh; B Weinstock-Guttman; C R G Guttmann; R Bakshi Journal: Neurology Date: 2007-09-18 Impact factor: 9.910
Authors: Leonora K Fisniku; Declan T Chard; Jonathan S Jackson; Valerie M Anderson; Daniel R Altmann; Katherine A Miszkiel; Alan J Thompson; David H Miller Journal: Ann Neurol Date: 2008-09 Impact factor: 10.422