NeuroAI Laboratory
Projects
Neurobiological Heterogeneity Mechanisms of Schizophrenia
Goal
Neurobiological heterogeneity in schizophrenia is poorly understood and impedes precision diagnosis, prognosis and treatment. The goal of this project is to systematically investigate heterogeneity signatures using machine learning/AI methodologies, along with multimodal neuroimaging and non-imaging big datasets, in order to improve schizophrenia diagnosis and prognosis.
Characterizing Alzheimer’s Disease Imaging Biomarkers and Mapping with Cognition and Genetics Using Machine Learning
Goal
The project aims to optimally quantify neuroimaging biomarkers and build novel explainable AI predictive algorithms with the goals of better understanding Alzheimer’s disease neurobiological mechanisms using neuroimaging, cognition and genetics.
Neuropsychiatric Mechanisms of Alzheimer’s Disease Using Machine Learning
Goal
The project develops and applies novel machine learning/AI algorithms in a large-scale dataset with the goals of i) better understanding neuropsychiatric mechanisms of Alzheimer’s disease and ii) aiding toward precision medicine approaches for diagnosing and treating Alzheimer’s disease with neuropsychiatric behavior.
Our People
The lab, led by Ganesh Chand, PhD, leverages a multidisciplinary team approach with a research focus in developing novel computational methodologies, optimally quantifying multimodal neuroimaging and non-imaging big data, understanding neurobiological mechanisms in neuropsychiatric and neurological disorders and improving diagnostics, prognostics and personalized medicine efforts for these disorders.
