Search

The identification of healthy individuals harboring amyloid pathology represents one important challenge for secondary prevention clinical trials in Alzheimer's disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% of unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment. This recruitment strategy capitalizes on available MR scans to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for preclinical AD screening. This protocol could foster the development of secondary prevention strategies for AD. [ABSTRACT FROM AUTHOR]

Background: Gray matter changes associated with the progression of Alzheimer's disease (AD) have been thoroughly studied. However, alterations in white matter tracts have received less attention, particularly during early or preclinical stages of the disease.Objective: To identify the structural connectivity changes across the AD continuum.Methods: We performed probabilistic tractography in a total of 183 subjects on two independent samples that include control (n = 68) and preclinical AD individuals (n = 28), patients diagnosed with mild cognitive impairment (MCI) due to AD (n = 44), and AD patients (n = 43). We compared the connectivity between groups, and with CSF Aβ42 and tau biomarkers.Results: We observed disconnections in preclinical individuals, mainly located in the temporal lobe. This pattern of disconnection spread to the parietal and frontal lobes at the MCI stage and involved almost all the brain in AD. These findings were not driven by gray matter atrophy.Discussion: Using tractography, we were able to identify white matter changes between subsequent disease stages and, notably, also in preclinical AD. Therefore, this method may be useful for detecting early and specific brain structural changes during preclinical AD stage. [ABSTRACT FROM AUTHOR]