Sheelakumari Raghavan

Precise disease heterogeneity and progression quantification in MSA and Parkinson's disease using machine learning featured image

Precise disease heterogeneity and progression quantification in MSA and Parkinson's disease using machine learning

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.

robel-k.-gebre

Chapter 8 - Artificial intelligence: Relevance and applications in dementia

A book chapter reviewing AI applications in dementia research including imaging biomarkers, machine learning classification, and disease progression modeling.

sheelakumari-raghavan

Diffusion Imaging Measures to Disentangle SVD-related Hypertensive Arteriopathy versus Cerebral Amyloid Angiopathy

We used diffusion imaging measures to distinguish between hypertensive arteriopathy and cerebral amyloid angiopathy contributions to small vessel disease.

sheelakumari-raghavan

Vascular risk, gait, behavioral, and plasma indicators of VCID

We examined vascular risk factors, gait measures, behavioral symptoms, and plasma biomarkers as indicators of vascular contributions to cognitive impairment and dementia.

sheelakumari-raghavan

Non-imaging measures to predict variability in white matter changes relevant to VCID

We evaluated non-imaging measures for predicting variability in white matter changes relevant to vascular contributions to cognitive impairment and dementia.

sheelakumari-raghavan

White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease

We characterized white matter degeneration pathways associated with regional tau deposition in Alzheimer's disease.

jianqiao-tian