Biology subjects

Cordes, D.

Publications and source records attributed to Cordes, D..

4 recordsLinked to original sources

bioRxiv2026

Association of object mnemonic discrimination deficit with age and AD biomarkers in older adults

Background Blood-based Alzheimers disease (AD) biomarkers provide scalable detection of AD pathology, yet their relationships with hippocampal-dependent mnemonic discrimination remain unclear. This study examined how object mnemonic discrimination relates to age, hippocampal structure, and plasma AD biomarkers in cognitively normal (CN) older adults and individuals with amnestic mild cognitive impairment (aMCI). Methods Forty-six CN and forty-one aMCI participants completed an object mnemonic similarity task (MST). Mnemonic discrimination was quantified using the overall lure discrimination index (LDI) and similarity-specific LDIs. Plasma pTau181, pTau217, pTau231, and GFAP were assayed, and hippocampal volumes were derived from 7T MR scanner. Multiple linear regression models assessed associations between LDI and biomarkers, controlling for age, sex, education, APOE4 status, and diagnosis. Repeated-measures ANCOVA and linear mixed-effects models evaluated similarity-dependent age effects. ROC analyses and stepwise logistic regression quantified diagnostic classification performance. Results LDI was significantly reduced in aMCI compared to CN. Total hippocampal volume and plasma pTau217 showed the strongest associations with LDI, exceeding those of pTau181, pTau231, and GFAP. LDI discriminated aMCI from CN with high accuracy (AUC = 0.836), comparable to hippocampal volume and plasma biomarkers. A combined model including LDI, pTau181, pTau217, and hippocampal volume achieved the highest diagnostic accuracy (AUC = 0.931). Age effects were similarity-dependent: discrimination declined with age only for low-similarity lures, independent of diagnosis. Sex differences emerged within aMCI, with females showing lower LDI than males. Conclusions Object mnemonic discrimination provides a sensitive behavioral marker linked to hippocampal volume and plasma pTau217, showing discriminative power comparable to established AD biomarkers in separating aMCI from CN participants. Similarity-dependent age effects highlight age-related deficits are not universal for lures with varying similarity levels.

neuroscience↗

bioRxiv2023

Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal GWAS model

BackgroundThe development and progression of Alzheimers disease (AD) is a complex process that can change over time, during which genetic influences on phenotypes may also fluctuate. Incorporating longitudinal phenotypes in genome wide association studies (GWAS) could help unmask genetic loci with time-varying effects. In this study, we incorporated a varying coefficient test in a longitudinal GWAS model to identify single nucleotide polymorphisms (SNPs) that may have time- or age-dependent effects in AD. MethodsGenotype data from 1,877 participants in the Alzheimers Neuroimaging Data Initiative (ADNI) were imputed using the Haplotype Reference Consortium (HRC) panel, resulting in 9,573,130 SNPs. Subjects longitudinal impairment status at each visit was considered as a binary and clinical phenotype. Participants composite standardized uptake value ratio (SUVR) derived from each longitudinal amyloid PET scan was considered as a continuous and biological phenotype. The retrospective varying coefficient mixed model association test (RVMMAT) was used in longitudinal GWAS to detect time-varying genetic effects on the impairment status and SUVR measures. Post-hoc analyses were performed on genome-wide significant SNPs, including 1) pathway analyses; 2) age-stratified genotypic comparisons and regression analyses; and 3) replication analyses using data from the National Alzheimers Coordinating Center (NACC). ResultsOur model identified 244 genome-wide significant SNPs that revealed time-varying genetic effects on the clinical impairment status in AD; among which, 12 SNPs on chromosome 19 were successfully replicated using data from NACC. Post-hoc age-stratified analyses indicated that for most of these 244 SNPs, the maximum genotypic effect on impairment status occurred between 70 to 80 years old, and then declined with age. Our model further identified 73 genome-wide significant SNPs associated with the temporal variation of amyloid accumulation. For these SNPs, an increasing genotypic effect on PET-SUVR was observed as participants age increased. Functional pathway analyses on significant SNPs for both phenotypes highlighted the involvement and disruption of immune responses- and neuroinflammation-related pathways in AD. ConclusionWe demonstrate that longitudinal GWAS models with time-varying coefficients can boost the statistical power in AD-GWAS. In addition, our analyses uncovered potential time-varying genetic variants on repeated measurements of clinical and biological phenotypes in AD.

neuroscience↗

bioRxiv2021

Towards Understanding Comprehensive Morphometric Changes and Its Correlation with Cognition and Exposure to Fighting in Active Professional Boxers

Professional athletes exposed to repetitive head impacts are at increased risk for developing a progressive neurological syndrome known as traumatic encephalopathy syndrome and neuropathology seen on autopsy called chronic traumatic encephalopathy (CTE). The early identification of individuals at increased risk for CTE is important and the search for biomarkers is underway. In this study, we utilized data from a large cohort study to compare differences in regional brain volumes, cortical thickness, voxel-based morphometric (VBM)-derived measures, and graph-theoretical measures derived from large-scale topographical maps in active professional boxers. We compared the above morphometric measures between active professional boxers with low cognitive scores (impaired boxers) and active professional boxers with intact cognitive scores (nonimpaired boxers). The cognitive scores were evaluated through neuropsychological evaluation. As an exploratory analysis, we also examined the power of various machine-learning algorithms to identify impaired and nonimpaired boxers using both group-level regression-driven analysis and previously identified hypothesis-driven cortical thickness and volumetric measures. We found significant group-level differences between impaired and nonimpaired boxers in cortical thickness in a single brain region (right precuneus), differences in VBM-derived gray matter density encompassing the caudate, putamen, and thalamus; and white matter density encompassing the right paracentral lobule, but no differences in any graph-theoretical network properties. Additionally, we found that a priori hypothesis-driven T1-derived cortical thickness and volumetric analysis performed better than traditional regression-based analysis. Overall, this study suggests that neuroanatomical differences exist between impaired and nonimpaired active professional boxers, and that hypothesis-driven techniques are likely necessary to become reliable biomarkers.

neuroscience↗

bioRxiv2019

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.

neuroscience↗