Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease.

Introduction: The effects of sex and apolipoprotein E (APOE)-Alzheimer's disease (AD) risk factors-on white matter microstructure are not well characterized.

Methods: Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)-corrected and harmonized. This dataset included 4741 participants (age = 73.

Free-water: A promising structural biomarker for cognitive decline in aging and mild cognitive impairment.

Diffusion MRI derived free-water (FW) metrics show promise in predicting cognitive impairment and decline in aging and Alzheimer's disease (AD). FW is sensitive to subtle changes in brain microstructure, so it is possible these measures may be more sensitive than traditional structural neuroimaging biomarkers. In this study, we examined the associations among FW metrics (measured in the hippocampus and two AD signature meta-ROIs) with cognitive performance, and compared FW findings to those from more traditional neuroimaging biomarkers of AD.

Sex, racial, and -ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease.

Introduction: The effects of sex, race, and Apolipoprotein E () - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized.

Methods: Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.

Purpose: Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline.

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD.