Modeling Heterogeneity in Preclinical Traumatic Brain Injury Studies

Michelle LaPlaca, Ph.D.


Coulter Department of Biomedical Engineering

Georgia Tech and Emory University

Seminar Information

Seminar Date
October 28, 2022 - 2:00 PM

The FUNG Auditorium - PFBH



Traumatic brain injury (TBI) is a leading cause of death and disability in the U.S. and in the world.  TBI is highly heterogeneous, rendering diagnosis and treatment extremely challenging across all injury severities, but in mild TBI (mTBI) the problem is magnified due to subjective symptoms and the absence of gross pathoanatomic features. Clinical population heterogeneity, however is not captured in most preclinical animal studies, which largely include only single strain, sex, and age. Furthermore, premorbid, psychosocial, and genetic factors that contribute to variability are infrequently captured in preclinical studies.  In this talk I will discuss two topics related to preclinical study of TBI and mTBI.  First, I will introduce a new interagency center that is developing preclinical data elements and data sharing platforms for TBI research.  These data tools are applicable to other fields and are timely with upcoming federal and journal data sharing mandates.  Common data elements are intended to increase rigor and reproducibility.  I will present initial capabilities in pooling several datasets to highlight missing values and mapping challenges.  In addition, one can systematically add heterogeneity to preclinical studies.  We have varied sex, strain, and the number of head impacts and found deficits in working memory acutely that resolved by three days post-injury.  We also have examined the effects of a high fat diet and chronic stress after injury on acute neurological recovery and anxiety.  Stressed animals with TBI took longer to recover and when high fat diet was added, recovery time was further extended.  However, evidence of an adaptation to stress may explain an acquired resilience against anxiety and depressive-like behavior.  It is clear that complex relationships exist among experimental factors that lend support for data science approaches.  Certainly, if parallels between preclinical and clinical TBI are to be made, we need to better understand heterogeneity at both levels. It is our goal to improve translation by adding variables to preclinical study designs that will better represent human variability, while recognizing significant challenges.

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.