医学
神经认知
磁共振成像
灌注
灌注扫描
内科学
放射科
认知
精神科
作者
Sen Hee Tay,Mary Charlotte Stephenson,Nur Azizah Allameen,Raymond Yeow Seng Ngo,Nazir Ismail,V. Wang,John J. Totman,Dennis Lai-Hong Cheong,Sriram Narayanan,Bernett Teck Kwong Lee,Anselm Mak
出处
期刊:Rheumatology
[Oxford University Press]
日期:2023-05-15
卷期号:63 (2): 414-422
被引量:4
标识
DOI:10.1093/rheumatology/kead221
摘要
Abstract Objective To study whether multimodal brain MRI comprising permeability and perfusion measures coupled with machine learning can predict neurocognitive function in young patients with SLE without neuropsychiatric manifestations. Methods SLE patients and healthy controls (HCs) (≤40 years of age) underwent multimodal structural brain MRI that comprised voxel-based morphometry (VBM), magnetization transfer ratio (MTR) and dynamic contrast-enhanced (DCE) MRI in this cross-sectional study. Neurocognitive function assessed by Automated Neuropsychological Assessment Metrics was reported as the total throughput score (TTS). Olfactory function was assessed. A machine learning–based model (i.e. glmnet) was constructed to predict TTS. Results Thirty SLE patients and 10 HCs were studied. Both groups had comparable VBM, MTR, olfactory bulb volume (OBV), olfactory function and TTS. While after correction for multiple comparisons the uncorrected increase in the blood–brain barrier (BBB) permeability parameters compared with HCs did not remain evident in SLE patients, DCE-MRI perfusion parameters, notably an increase in right amygdala perfusion, was positively correlated with TTS in SLE patients (r = 0.636, false discovery rate P < 0.05). A machine learning–trained multimodal MRI model comprising alterations of VBM, MTR, OBV and DCE-MRI parameters mainly in the limbic system regions predicted TTS in SLE patients (r = 0.644, P < 0.0005). Conclusion Multimodal brain MRI demonstrated increased right amygdala perfusion that was associated with better neurocognitive performance in young SLE patients without statistically significant BBB leakage and microstructural abnormalities. A machine learning–constructed multimodal model comprising microstructural, perfusion and permeability parameters accurately predicted neurocognitive performance in SLE patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI