Autism Centers of Excellence (ACE)
In collaboration with University of Utah and Wisconsin-Madison, UF is part of an NIH funded longitudinal study following a cohort of autistic adults as they age to better understand the cognitive changes that occur during aging. A broad range of measures will be taken with the goal of identifying approaches to best support and promote health, well-being, and resilience in autistic adults and provide critical information that can be used to plan appropriate services and resources for these adults. Read more here.
ASD Motor Function Study
In this study we aim characterize parkinson-like symptoms in autism. Recent advances in neuroimaging and statistical modeling have enabled new approaches to investigating brain network architecture. We will apply structural covariance MRI (scMRI), an emerging technique utilizing standard anatomic MRI scans, as well as functional connectivity MRI (fcMRI), to derive network-level structure-function architecture in the human brain as it relates to neuromotor performance. Correlated regional variance in gray matter density (scMRI) and fcMRI signal will be analyzed in young adults to identify large scale, function critical network architectures that distinguish parkinsonian ASD subphenotypes.
Autism Phenome Project (APP)
The goal of APP is to describe different types of autism and enable more effective targeted treatments for defined subgroups of individuals with autism. Our lab is working with researchers at UC Davis to better understand brain development and mental health, and understand what factors at a young age can predict future health outcomes.
Other Studies
The DNNL is actively engaged in diverse projects, including collaborating with the University of Utah on a pediatric traumatic brain injury (pTBI) study and the International Pediatric Stroke Study (IPSS) with Toronto. Beyond these collaborations, our lab is dedicated to extensive data analysis from current and past initiatives. We utilize AI techniques and sophisticated scMRI analysis to investigate different aspects of neural network structure and development, among other areas.