Biology subjects

Gifford, K. A.

Publications and source records attributed to Gifford, K. A..

4 recordsLinked to original sources

bioRxiv2025

Sex-specific Associations of Gene Expression with Alzheimer's Disease Neuropathology and Ante-mortem Cognitive Performance

The biological mechanisms underlying womens increased Alzheimers disease (AD) prevalence remain undefined. Previous case/control studies have identified sex-biased molecular pathways, but sex-specific relationships between gene expression and AD endophenotypes, particularly sex chromosomes, are underexplored. With bulk transcriptomic data across 3 brain regions from 767 decedents, we investigated sex-specific associations between gene expression and post-mortem {beta}-amyloid and tau as well as antemortem longitudinal cognition. Of 23,118 significant gene associations, 10% were significant in one sex and not the other (sex-specific). Most sex-specific gene associations were identified in females (73%) and associated with tau tangles and longitudinal cognition (90%). Four X-linked genes, MCF2, HDAC8, FTX, and SLC10A3, demonstrated significant sex differences in their associations with AD endophenotypes (i.e., significant sex x gene interaction). Our results also uncovered sex-specific biological pathways, including a female-specific role of neuroinflammation and neuronal development, reinforcing the potential for sex-aware analyses to enhance precision medicine approaches in AD.

neuroscience↗

bioRxiv2023

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

The greatest known risk factor for Alzheimers 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. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: {beta}=-1.094, p=6.32x10-7; T1: {beta}=-1.331, p=6.52x10-7; FW+T1: {beta}=-1.476, p=2.53x10-10; executive function, FW: {beta}=-1.276, p=1.46x10-9; T1: {beta}=-1.337, p=2.52x10-7; FW+T1: {beta}=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: {beta}=-0.091, p=4.62x10-11; T1: {beta}=-0.097, p=1.40x10-8; FW+T1: {beta}=-0.101, p=1.35x10-11; executive function, FW: {beta}=-0.125, p=1.20x10-10; T1: {beta}=-0.163, p=4.25x10-12; FW+T1: {beta}=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

neuroscience↗

bioRxiv2022

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1,184 participants

Quantifying the microstructural and macrostructural geometrical features of the human brains connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal datasets totaling in 1184 subjects and 2236 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging dataset, Cambridge Centre for Ageing Neuroscience dataset, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could have widespread implications for a variety of neurodegenerative disorders.

neuroscience↗

bioRxiv2020

Genetic Variants and Functional Pathways Associated with Resilience to Alzheimer's Disease

Approximately 30% of older adults exhibit the neuropathologic features of Alzheimers disease (AD) without signs of cognitive impairment. Yet, little is known about the genetic factors that allow these potentially resilient individuals to remain cognitively normal in the face of substantial neuropathology. We performed a large, genome-wide association study (GWAS) of two previously validated metrics of cognitive resilience quantified using a latent variable modeling approach and representing better-than-predicted cognitive performance for a given level of neuropathology. Data were harmonized across 5,108 participants from a clinical trial of AD and three longitudinal cohort studies of cognitive aging. All analyses were run across all participants and repeated restricting the sample to individuals with normal cognition to identify variants at the earliest stages of disease. As expected, all resilience metrics were genetically correlated with cognitive performance and education attainment traits (p-values<2.5x10-20), and we observed novel correlations with neuropsychiatric conditions (p-values<7.9x10-4). Notably, neither resilience metric was genetically correlated with clinical AD (p-values>0.42) nor associated with APOE (p-values>0.13). In single variant analyses, we observed a genome-wide significant locus among participants with normal cognition on chromosome 18 upstream of ATP8B1 (index SNP rs2571244, MAF=0.08, p=2.3x10-8). The top variant at this locus (rs2571244) was significantly associated with methylation in prefrontal cortex tissue at multiple CpG sites, including one just upstream of ATPB81 (cg19596477; p=2x10-13). Overall, this comprehensive genetic analysis of resilience implicates a putative role of vascular risk, metabolism, and mental health in protection from the cognitive consequences of neuropathology, while also providing evidence for a novel resilience gene along the bile acid metabolism pathway. Furthermore, the genetic architecture of resilience appears to be distinct from that of clinical AD, suggesting that a shift in focus to molecular contributors to resilience may identify novel pathways for therapeutic targets.

genetics↗