Machine‐learning derived MRI‐based atrophy biomarker predicts long‐term cognitive decline in stroke or transient ischemic attack

Abstract

AbstractBackgroundAlzheimer’s disease‐resemblance atrophy index (AD‐RAI) is a machine‐learning derived MRI‐based brain atrophy biomarker that is valid in predicting cognitive decline in subjects with AD. We investigated the performance of AD‐RAI in predicting long‐term cognitive decline in subjects with stroke or transient ischemic attack (TIA).MethodWe recruited consecutive dementia‐free stroke/TIA subjects who had brain MRI at baseline (i.e., within 3‐6 months after the index event) and cognitive data at both baseline and 3 years. We defined cognitive decline as an increase in clinical dementia rating scale from 0 to 0.5 or above or from 0.5 to 1 or above at 3 years when compared with baseline. We investigated the association between AD‐RAI, traditional brain atrophy biomarkers (hippocampus volume [HV], hippocampal fraction [HF], total brain volume [TBV], TBV/intracranial volume [ICV] ratio, ventricular‐brain‐ratio, presence of medial temporal lobe atrophy [MTLA]), and cerebral small vessel disease biomarkers (white matter hyperintensity [WMH]) volume, WMHV/ICV ratio presence of confluent WMH, presence of >/ = 3 lacunes) with cognitive decline.ResultOf 231 participants (mean age 66.0 ± 10.9, 124 [53.7] male), 55(23.8) had cognitive decline at 3 years. Among all the imaging biomarkers, AD‐RAI and HV were associated with cognitive decline in univariate regression. Such a relationship was still significant with AD‐RAI after adjusted for age, gender, and education (aOR [95%CI] 3.900 [1.221‐12.458]). Among all imaging biomarkers, only AD‐RAI was associated with slope of Montreal cognitive assessment (MoCA) after adjusted to age, gender, education (ß(SE) ‐0.742[0.242], p = 0.002).ConclusionAD‐RAI predicted long term cognitive decline in subjects with stroke/TIA.