Modeling Heterogeneity in Preclinical Traumatic Brain Injury Studies

Michelle LaPlaca, Ph.D.

Professor

Coulter Department of Biomedical Engineering

Georgia Tech and Emory University


Seminar Information

Seminar Date
October 28, 2022 - 2:00 PM

Location
The FUNG Auditorium - PFBH

LaPlaca

Speaker Bio

Michelle C. LaPlaca, Ph.D. is a Professor in the Coulter Department of Biomedical Engineering at Georgia Tech and Emory University in Atlanta, GA, and a Research Biomedical Engineer at the Atlanta VA Medical Center.  She earned a Ph.D. in Bioengineering in injury biomechanics and completed postdoctoral training in Neurosurgery, both at the University of Pennsylvania in Philadelphia, PA.  She joined the Coulter Department of Biomedical Engineering in 1998 and is a member of the Institute for Bioengineering and Bioscience, as well as a program faculty member in the Bioengineering graduate program at Georgia Tech and the Neuroscience graduate program at Emory. She has testified before the US Senate on traumatic brain injury (TBI), and served as President and Program Chair of the National Neurotrauma Society.  Dr. LaPlaca is an elected fellow in the American Institute for Medical and Biological Engineering (AIMBE), is Associate Editor at the Journal of Neurotrauma, is on the editorial board of Experimental Neurology, and is on the Board of Directors of the Brain Injury Association of Georgia (BIAG).  Dr. LaPlaca’s research interests are to better understand TBI heterogeneity, as it pertains to stress, identify novel TBI biomarkers and the role of glymphatic system in biomarker efflux, and develop multimodal concussion assessment tools.  She participated in the development of preclinical common data elements (CDEs) within the FITBIR framework and currently is part of the VA interagency center PRECISE-TBI (PRE Clinical Interagency reSearch resourcE-TBI) to expand CDEs and provide end-to-end informatic tools to improve rigor, reproducibility, and transparency in research.  By working at both pre-clinical and clinical levels and using data science tools to study the complexity of TBI, this approach promotes successful clinical translation.