摘要
Neuro myelitis optica spectrum disorder(NMOSD) is an inflammatory demyelinating disease of the central nervous system.However,whether and how cortical changes occur in NMOSD with normal-appearing brain tissue,or whether any cortical changes correlate with clinical chara cteristics,is not completely clear.The current study recruited 43 patients with NMOSD who had normal-appearing brain tissue and 45 healthy controls matched for age,sex,and educational background from December 2020 to February 2022.A surface-based morphological analysis of high-resolution T1-weighted structural magnetic resonance images was used to calculate the cortical thickness,sulcal depth,and gyrification index.Analysis showed that cortical thickness in the bilate ral rostral middle frontal gyrus and left superior frontal gyrus was lower in the patients with NMOSD than in the control participants.Subgroup analysis of the patients with NMOSD indicated that compared with those who did not have any optic neuritis episodes,those who did have such episodes exhibited noticeably thinner cortex in the bilateral cuneus,superior parietal co rtex,and pericalcarine co rtex.Correlation analysis indicated that co rtical thickness in the bilateral rostral middle frontal gyrus was positively correlated with scores on the Digit Symbol Substitution Test and negatively correlated with scores on the Trail Making Test and the Expanded Disability Status Scale.These results are evidence that cortical thinning of the bilateral regional frontal cortex occurs in patients with NMOSD who have normal-appearing brain tissue,and that the degree of thinning is correlated with clinical disability and cognitive function.These findings will help im prove our understanding of the imaging chara cteristics in NMOSD and their potential clinical significance.
基金
Clinical Research Center for Medical Imaging in Hunan Province,No.2020SK4001
Science and Technology Innovation Program of Hunan Province,No.2021RC4016
Accurate Localization Study of Mild Traumatic Brain Injury Based on Deep Learning Through Multimodal Image and Neural Network,No.2021gfcx05 (all to JL)。
