| Literature DB >> 26945922 |
Veronica Popescu1, Roel Klaver2, Adriaan Versteeg1, Pieter Voorn2, Jos W R Twisk3, Frederik Barkhof1, Jeroen J G Geurts2, Hugo Vrenken1,4.
Abstract
Grey matter (GM) atrophy is a prominent aspect of multiple sclerosis pathology and an important outcome in studies. GM atrophy measurement requires accurate GM segmentation. Several methods are used in vivo for measuring GM volumes in MS, but assessing their validity in vivo remains challenging. In this postmortem study, we evaluated the correlation between postmortem MRI cortical volume or thickness and the cortical thickness measured on histological sections. Sixteen MS brains were scanned in situ using 3DT1-weighted MRI and these images were used to measure regional cortical volume using FSL-SIENAX, FreeSurfer, and SPM, and regional cortical thickness using FreeSurfer. Subsequently, cortical thickness was measured histologically in 5 systematically sampled cortical areas. Linear regression analyses were used to evaluate the relation between MRI regional cortical volume or thickness and histological cortical thickness to determine which postprocessing technique was most valid. After correction for multiple comparisons, we observed a significant correlation with the histological cortical thickness for FSL-SIENAX cortical volume with manual editing (std. β = 0.345, adjusted R(2) = 0.105, P = 0.005), and FreeSurfer cortical volume with manual editing (std. β = 0.379, adjusted R(2) = 0.129, P = 0.003). In addition, there was a significant correlation between FreeSurfer cortical thickness with manual editing and histological cortical thickness (std. β = 0.381, adjusted R(2) = 0.130, P = 0.003). The results support the use of FSL-SIENAX and FreeSurfer in cases of severe MS pathology. Interestingly none of the methods were significant in automated mode, which supports the use of manual editing to improve the automated segmentation. Hum Brain Mapp 37:2223-2233, 2016.Entities:
Keywords: FSL-SIENAX; FreeSurfer; SIENAX; SPM; brain atrophy; grey matter; multiple sclerosis
Mesh:
Year: 2016 PMID: 26945922 PMCID: PMC6867379 DOI: 10.1002/hbm.23168
Source DB: PubMed Journal: Hum Brain Mapp ISSN: 1065-9471 Impact factor: 5.038