A5091370913- Functional Brain Connectivity Studies
- Advanced Neuroimaging Techniques and Applications
- Alzheimer's disease research and treatments
- Dementia and Cognitive Impairment Research
- Health, Environment, Cognitive Aging
- Machine Learning in Bioinformatics
- Statistical Methods and Inference
- Brain Tumor Detection and Classification
- Adipose Tissue and Metabolism
- Sparse and Compressive Sensing Techniques
- Neuroinflammation and Neurodegeneration Mechanisms
- Acute Ischemic Stroke Management
- Bioinformatics and Genomic Networks
- Tryptophan and brain disorders
- Cerebrovascular and Carotid Artery Diseases
- Mitochondrial Function and Pathology
- Radiation Dose and Imaging
- Genomics and Rare Diseases
- Advanced MRI Techniques and Applications
- Retinal Imaging and Analysis
- Neurogenesis and neuroplasticity mechanisms
- Phosphodiesterase function and regulation
- Probabilistic and Robust Engineering Design
- Stroke Rehabilitation and Recovery
- Medical Imaging Techniques and Applications
Stanford University
2025
Duke University
2022-2025
Duke Medical Center
2023-2024
Duke University Hospital
2024
Duke University Health System
2024
University of North Carolina at Charlotte
2021-2023
Allameh Tabataba'i University
2015
Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with 20 controls) reveal location, extent, dynamics of AMD-related changes. Voxel-based analyses at first visit identified reduced volume in cuneate gyrus, associated vision, temporal bilateral cingulate higher cognition memory. The second occurred 2 years after...
Abstract Alzheimer’s disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Early detection understanding of risk factors their downstream effects are therefore crucial. Animal models provide valuable tools to study these prodromal stages. We investigated various levels genetic for AD using mice expressing the three major human APOE alleles in place mouse APOE. leverage utilizing high-resolution...
Abstract The selective vulnerability of brain networks in individuals at risk for Alzheimer’s disease (AD) may help differentiate pathological from normal aging asymptomatic stages, allowing the implementation more effective interventions. We used a sample 72 people across age span, enriched APOE4 genotype to reveal vulnerable associated with composite AD factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed high weight subgraphs involving cuneus,...
Abstract Alzheimer’s disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, critical risk factor for AD, is typically assessed by comparing physiological estimated brain ages. This study utilizes mouse models expressing human alleles of APOE nitric oxide synthase 2 (hNOS2), replicating genetic risks AD alongside human-like immune response. We developed multivariate model that incorporates...
Background Cardiovascular disease (CVD) is associated with the apolipoprotein E (APOE) gene and lipid metabolism. This study aimed to develop an imaging-based pipeline comprehensively assess cardiac structure function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT). Methods 123 mice grouped based on genotype (APOE2, APOE3, APOE4, knockout (KO)), gender, human NOS2 factor, diet (control or high fat) were used this study. The included PCCT...
Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed modifiable risk factors influence susceptibility AD are under intense investigation, yet the impact of unique on brain networks is difficult disentangle, their interactions remain unclear. To model multiple including APOE genotype, age, sex, diet, immunity we leveraged mice expressing human NOS2 genes, conferring a...
In this paper, we propose methods for functional predictor selection and the estimation of smooth coefficients simultaneously in a scalar-on-function regression problem under high-dimensional multivariate data setting. particular, develop two group-sparse generic Hilbert space infinite dimension. We show convergence algorithms consistency (oracle property) infinite-dimensional spaces. Simulation studies effectiveness both coefficients. The applications to magnetic resonance imaging (fMRI)...
<title>Abstract</title> Alzheimer’s disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects the impact of risk factors their long-range brain connections. To detect vulnerable connections, devised a multiscale, hierarchical method spatial clustering whole tractogram examined age APOE allelic variation cognitive abilities bundle properties including texture e.g., mean fractional anisotropy,...
We compared the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions predict patient outcome using single-contrast (DWI) dual-contrast images (T2w FLAIR DWI). The predicted lesion segmentation metrics location relative corticospinal tract correlated with post-stroke measured by National Institutes Health Stroke Scale (NIHSS). multi-modal CNN achieved best results mean Dice 0.74. highest correlation was for weighted-lesion load both...
The brain connectome helds promise to detect subtle changes in individuals at risk for Alzheimer's disease. We imaged using high resolution diffusion imaging 72 subjects enriched the APOE4 genotype reveal vulnerable networks associated with a composite AD factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed weight subgraphs involving cuneus, temporal, cingulate cortex, cerebellum. Our results have identified structural weights several factors...
Abstract Background Alzheimer's disease (AD) is multifactorial, thus multivariate analyses help untangle its effects. We employed multiple contrast MRI to reveal age‐related brain changes in populations at risk for AD, due APOE4 carriage. assessed volume and microstructure using diffusion weighted imaging, quantitative magnetic susceptibility maps (QSM) reflective primarily of cerebral iron metabolism. Method Our study included 48 non carriers, 42 with age ranging from 20.2 83; males...
Abstract Background Alzheimer's disease (AD) causes a steady degradation of connections inside the brain. The apolipoprotein E is protein where one its subtypes, APOE4, major genetic risk factor for developing late onset AD. Using combination tensor network PCA (TN‐PCA) and bundle analysis, we sought to determine which specific differentiate APOE4 individuals relative non‐APOE4 carriers, whether these changes increase with age. Method Our study included 77 from 20 83 years old, 37 male 40...
Abstract Background While we do not yet have the means to detect early Alzheimer’s disease (AD), studying subjects at risk conferred by presence of APOE4 allele, can provide useful information before clinical onset. We show that using symmetric bilinear regression with L1 penalty (SBL) individual (DTI, fMRI) and fused connectomes, identify vulnerable regions changing in association hallmark AD biomarkers measured cerebrospinal fluid: amyloid beta Aβ42/40, phosphorylated tau (PTAU),...
Alzheimer's disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects the impact of risk factors their long-range brain connections. To detect vulnerable connections, devised a multiscale, hierarchical method spatial clustering whole tractogram examined age APOE allelic variation cognitive abilities bundle properties including texture e.g., mean fractional anisotropy, variability, geometric...
Motivation: Understanding Alzheimer&rsquo;s disease (AD) requires decoding the complex interplay of risk factors, particularly how age-related structural connectivity changes affect AD onset and progression. Goal(s): Using a state-of-the-art deep learning method, we aim to identify key brain connections, predict age assess factors with connectomes behavioral data from mouse models humanized APOE genotypes. Approach: Our Feature Attention Graph Neural Network (FAGNN) integrates...
ABSTRACT Alzheimer’s disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial factor for AD, which can be estimated by disparity between physiological age brain age. To model AD more effectively, integrating biological, genetic, cognitive markers essential. Here, we utilized mouse models expressing major APOE human alleles nitric oxide synthase 2 replicate genetic humanized innate...
Abstract Brain connectomes provide untapped potential for identifying individuals at risk Alzheimer’s disease (AD), and can help novel targets based on selective circuit vulnerability. Age, APOE4 genotype, female sex are thought to contribute the vulnerability of brain networks in disease, a manner that differentiates pathological versus normal aging. These may predict pathology otherwise hard detect, decades before overt manifestation cognitive decline. Uncovering network biomarkers...