We developed a machine learning framework to quantify disease heterogeneity and progression in multiple system atrophy and Parkinson's disease using structural and diffusion MRI.
A book chapter reviewing AI applications in dementia research including imaging biomarkers, machine learning classification, and disease progression modeling.
We used diffusion imaging measures to distinguish between hypertensive arteriopathy and cerebral amyloid angiopathy contributions to small vessel disease.
We tested whether combining plasma biomarkers, genetics, cardiovascular risk, and lifestyle measures with MRI improves prediction of cognitive decline beyond any single data …
We introduce THETA (Tau Heterogeneity Evaluation in Alzheimer's Disease), a novel tau PET summary metric that captures the full spatial heterogeneity of tau deposition across the …
We applied AI methods to neuroimaging data to detect hypersomnia and characterize its neurobiological correlates.
We developed machine learning models integrating polygenic risk scores for enhanced prediction of Alzheimer's disease imaging and fluid biomarker endophenotypes.
We examined vascular risk factors, gait measures, behavioral symptoms, and plasma biomarkers as indicators of vascular contributions to cognitive impairment and dementia.
We demonstrated that genetic risk scores enhance the diagnostic accuracy of plasma biomarkers for detecting brain amyloidosis.
We characterized white matter degeneration pathways associated with regional tau deposition in Alzheimer's disease.
We conducted a systematic comparison of cross-scanner harmonization methods for structural MRI and demonstrated that widely adopted techniques may not perform as reliably as the …
We developed the first deep learning application for detecting hip osteoarthritis from 2-D summation images derived from routine clinical CT scans.
We developed a filter using alkali-activated blast furnace slag for dye removal from wastewater.
We identified CT-based bone structural characteristics that discriminate patients with low-energy acetabular fractures from matched controls.
We identified structural risk factors for low-energy acetabular fractures in the elderly using computed tomography.