Prediction of amyloid pathology in cognitively unimpaired individuals using voxel-wise analysis of longitudinal structural brain MRI

Abstract

Abstract Background Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer’s disease (AD) pathophysiologic continuum constituting what has been established as “AD signature”. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration. Method Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), and 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cutoffs ( 2.5 years, and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC = 0.87 (95%CI 0.72–0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior, and lateral temporal lobes; precuneus; caudate heads; basal forebrain; and lateral ventricles. Conclusions Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, the presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid-positive individuals, this longitudinal voxel-wise classifier is expected to avoid 55% of unnecessary CSF and/or PET scans and reduce economic cost by 40%.

Keywords

• Preclinical AD signature
• Jacobian determinant
• MRI
• Machine learning
• Longitudinal voxel-wise analysis