$3.2 Million Grant Supports Study of Neuropsychiatric Symptoms in Alzheimer’s Disease
Alzheimer’s disease (AD) is currently ranked as the seventh leading cause of death in the U.S. and is the most common cause of dementia among older adults. In addition to declining cognitive functioning, some people with the disease develop neuropsychiatric behaviors such as apathy, depression, aggression or anxiety, while others do not, and the underlying cause and mechanisms are unknown. This poses additional challenges for the clinical team’s treatment plan as well as for the patients and their caregivers’ daily lives.
Ganesh B. Chand, PhD, assistant professor of radiology at WashU Medicine Mallinckrodt Institute of Radiology (MIR), received a four-year, $3.2 million grant from the National Institutes of Health to study the neuropsychiatric mechanisms in AD. He and his team will develop new machine learning and artificial intelligence (AI) algorithms to validate and use on large-scale multimodal imaging, behavioral, clinical and genetic datasets. With a new emphasis on the neuropsychiatric aspects of AD, this project seeks to significantly advance the understanding of disease mechanisms, creating a path to improve diagnosis and treatment for patients.
Central to their study are biomarkers measured using amyloid PET, tau PET and MRI scans. These markers play a crucial role in understanding the multivariate relationships between neuropsychiatric symptoms and genotypes.
By leveraging extensive data from sources including the WashU Knight Alzheimer’s Disease Research Center, Chand’s study aims to be the first to systematically investigate the intricate relationships among these biomarkers, neuropsychiatric symptoms, vascular risks and genetic predispositions.
As the principal investigator of the NeuroAI Laboratory within MIR’s Precision Radiotheranostics Translation Center (PRTC), Chand and his team are focused on developing innovative computational methods to study brain health and neuropsychiatric and neurological disorders such as schizophrenia and Alzheimer’s disease. They use AI and multimodal neuroimaging — including positron emission tomography (PET), functional magnetic resonance imaging (fMRI)/MRI and EEG — as well as behavioral, clinical and genetic approaches to design predictive models with the ultimate aim of advancing precision medicine for these disorders.
