Michael Tobia

Project Title: Olfactory function and brain connectivity as biomarkers for senescent racial/ethnic health disparities

The prevalence of Alzheimer’s disease (AD) and related dementias among racial/ethnic minority groups represents a national health disparity, which is particularly relevant to the population of South Florida. Olfactory loss, defined as reduced sensory acuity and cognitive functioning involving the sense of smell, is a well-established prodromal biomarker of cognitive impairment associated with AD and other dementias. Research has shown there is a significant racial/ethnic disparity in olfactory ability such that the age of onset and rate of decline among Hispanics and African Americans parallels the disparity with respect to AD prevalence. A brain-based assessment that probes the integrity of olfactory-related brain regions and brain networks may provide prognostic insight into olfactory dysfunction or disease trajectory which pre-dates the onset of overt sensory and/or neurocognitive impairments, and would allow for early/preventive health programs to intervene more successfully. This pilot project represents the first step in a research program aiming to discover the impact of health disparity on brain function, with the ultimate goal of identifying prognostic brain-based biomarkers for AD and dementia among the racially and ethnically diverse aging population residing in the South Florida region.

Research Interests

Brain imaging & MRI methods, signal processing, brain connectivity & mental health, data fusion, impact of health disparity on brain systems

Senior Research Scientist
Physics

Michael Tobia is a Senior Research Scientist at FIU’s Neuroinformatics and Brain Connectivity (NBC) Lab (PIs: Laird, Sutherland) and is affiliated with FIU’s Center for Imaging Science (CIS). Tobia has a multidisciplinary background spanning the domains of cognitive and brain sciences, as well as non-invasive magnetic resonance (MR) imaging and signal processing techniques. His research utilizes computational modeling, univariate and multivariate analyses of brain imaging data, including fMRI (both task-based and resting-state) and diffusion-weighted MRI, as well as behavioral data to interrogate brain structure and function. His research focuses on functional connectivity of large-scale brain networks, the relationships among time-varying characteristics of brain signals and cognition in mental health, and multivariate methods for data fusion.