Cardiometabolic disease, cognitive decline, and brain structure in middle and older age

INTRODUCTION

The presence of multiple cardiometabolic diseases (CMDs) has been linked to increased dementia risk, but the combined influence of CMDs on cognition and brain structure across the life course is unclear.

METHODS

In the UK Biobank, 46,562 dementia-free participants completed a cognitive test battery at baseline and a follow-up visit 9 years later, at which point 39,306 also underwent brain magnetic resonance imaging. CMDs (diabetes, heart disease, and stroke) were ascertained from medical records. Data were analyzed using age-stratified (middle age [< 60] versus older [≥ 60]) mixed-effects models and linear regression.

RESULTS

A higher number of CMDs was associated with significantly steeper global cognitive decline in older (β = -0.008; 95% confidence interval: -0.012, -0.005) but not middle age. Additionally, the presence of multiple CMDs was related to smaller total brain volume, gray matter volume, white matter volume, and hippocampal volume and larger white matter hyperintensity volume, even in middle age.

DISCUSSION

CMDs are associated with cognitive decline in older age and poorer brain structural health beginning already in middle age.

Highlights

We explored the association of CMDs with cognitive decline and brain MRI measures.CMDs accelerated cognitive decline in older (≥60y) but not middle (<60) age.CMDs were associated with poorer brain MRI parameters in both middle and older age.Results highlight the connection between CMDs and cognitive/brain aging.

Sex differences in brain atrophy in dementia with Lewy bodies

INTRODUCTION

Sex influences neurodegeneration, but it has been poorly investigated in dementia with Lewy bodies (DLB). We investigated sex differences in brain atrophy in DLB using magnetic resonance imaging (MRI).

METHODS

We included 436 patients from the European-DLB consortium and the Mayo Clinic. Sex differences and sex-by-age interactions were assessed through visual atrophy rating scales (n = 327; 73 ± 8 years, 62% males) and automated estimations of regional gray matter volume and cortical thickness (n = 165; 69 ± 9 years, 72% males).

RESULTS

We found a higher likelihood of frontal atrophy and smaller volumes in six cortical regions in males and thinner olfactory cortices in females. There were significant sex-by-age interactions in volume (six regions) and cortical thickness (seven regions) across the entire cortex.

DISCUSSION

We demonstrate that males have more widespread cortical atrophy at younger ages, but differences tend to disappear with increasing age, with males and females converging around the age of 75.

HIGHLIGHTS

Male DLB patients had higher odds for frontal atrophy on radiological visual rating scales. Male DLB patients displayed a widespread pattern of cortical gray matter alterations on automated methods. Sex differences in gray matter measures in DLB tended to disappear with increasing age.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.