Current Research
Research Goals
The focus of the research in our laboratory is to understand recovery of function after neurological disruption, and we focus this work primarily on understanding recovery after severe traumatic brain injury, where individuals experience coma from days to weeks after injury. To achieve this broad goal, we take both a cognitive neuroscience and clinical neuropsychological perspective to understanding recovery after catastrophic injury (see below). The research methods employed in our lab range from statistical modeling of brain networks using brain imaging methods to documenting cognitive and emotional functioning via interviews, surveys, and cognitive testing. Research is conducted within the Department of Psychology at University Park (UP) but we also we have close collaborative relationships with investigators at the Kessler Foundation in New Jersey (Drs. John DeLuca, Katya Dobryakova, Helen Genova), The Mind Research Network in Albuquerque New Mexico (Dr. Vincent Calhoun), the University of Utah (Dr. Elisabeth Wilde), MossRehab in Philadelphia (Dr. Amanda Rabinowitz) and the University of Michigan (Dr. Benjamin Hampstead). International collaborative efforts through ENIGMA involve investigators in 5 different countries (see http://enigma.ini.usc.edu/ongoing/enigma-tbi/enigma-adult-mstbi/).
Active Projects
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease (AD). ADNI researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. We are working on a few studies examining hyperconnectivity in these data. To learn more about ADNI, click here.
Enhancing Neuroimaging Genetics by Meta-Analysis (ENIGMA)
Our lab is a member of the Moderate-Severe TBI (msTBI) Working Group for the ENIGMA Consortium, where our lab head Dr. Hillary serves as a co-principal investigator. This collaboration focuses on msTBI across the lifespan in adults, investigating both acute and chronic TBI, the long-term effects of TBI on aging, and the impact of interventions on these long-term effects. We accomplish this by analyzing datasets across our participating sites, to examine structural and diffusion magnetic resonance imaging (MRI), functional brain imaging: fMRI, positron emission tomography (PET), and arterial spin labeling (ASL), MR spectroscopy, as well as clinical, genetic, and behavioral data (both performance-based and survey collection). To learn more, click here.
Examining Elderly Traumatic Brain Injury and Risk for Neurodegeneration
Data collection is ongoing through a grant which seeks to understand the risk factors for Alzheimer’s disease after TBI, including time since diagnosis, ethnicity, and genetic predictors. In Aim 1 the goal is to collect data in a large group of individuals with TBI to understand these interacting factors in predicting cognitive decline. Then in Aim 2, in a sub-group of individuals we use brain imaging methods in order to determine the network response associated with neurodegeneration decades post TBI. Ultimately, the ability to monitor the neural network response to injury-specific factors in combination with risk/resiliency factors (e.g., genetic, health) may bring greater precision to rehabilitation in TBI and aid in identifying patients at risk for neurodegeneration years prior to onset.
Head Trauma in Women
Over the past few decades, significant advancements have been made in understanding the effects of head injury on the brain. In most observational and clinical TBI studies, however, women only make up about 30% of the total participants. Even in pre-clinical models, there is bias against the inclusion of female animal subjects, as there is an assumption that female behavior is more variable due to cyclic changes in sex hormone levels. A recent meta-analysis provides evidence against this theory; regardless, there remains a dearth of literature on the prevalence and sequelae of TBI in women. There is now preliminary evidence of sex differences in male and female TBI survivors (e.g. rates of reporting, risk across the lifespan, recovery outcomes), but more extensive work is needed to improve treatment and prevention. We therefore seek to examine the effects of sub-acute and chronic TBI as well as hypoxic brain injury (HBI) on neuronal, cognitive, and psychological outcomes in women who have experienced TBI. Specific aims include assessment of neural network response and cognitive functioning in the subacute and chronic stages of TBI and HBI in women, and assessment of risk factors for poor outcome from TBI and HBI in women.
Archival Data (Inactive Data Collection)
Pennsylvania Trauma Outcome Study (PTOS)
PTOS is a trauma registry collecting de-identified data from its participating hospitals state- wide within Pennsylvania. Our current data drawn from this registry spans 1986 until mid- 2019, and contains over 500,000 cases to date encompassing both TBI and non-TBI injury patients with over 1,400 variables collected both at the scene of injury and during a patient’s hospital stay.
(See our Select Publications for the projects below that have been successfully published in peer-reviewed research journals)
Studies utilizing data from PTOS have examined:
Descriptive trends for patients discharged to homeless. Despite carrying an elevated risk for TBI, individuals facing housing insecurity face specific challenges to be identified and included consistently in TBI research. Furthermore, existing literature has described that many homeless patients experience their first TBI prior to their first episode of homelessness, raising concerns for the impacts of repetitive neurotrauma. Given the data we do have, do these patients discharged to homeless carry a specific pattern of pre-existing conditions (PECs) that may lead to, result from, or exacerbate housing insecurity in comparison to a similar cohort discharged to home? Controlling for injury severity, do these homeless patients suffer from worse outcomes in comparison to those not battling homelessness?
Impact of PECs and their role in early recovery. More specifically, are there patterns to accrual of specific PECs that can be examined using analytic methods specifically designed for Big Data? Do these patterns confer differential risk for pathological outcome following moderate-severe TBI? Findings indicate (consistent with other literature) that PECs vary by age, with PECs at younger age potentially more modifiable with early intervention, and that consideration of the disease burden for these TBI patients (regardless of age) may be underserved by relying on a single condition, rather than the multiple morbidity demonstrated by our results.
Discrepancies in incident rates of TBIs in racial and ethnic minorities in the population compared to their inclusion in the literature, as well as describing what these findings signify about the incident rate, reporting, and anticipated outcomes for TBIs in these populations. Results indicated that African American patients sustain more TBIs than would be expected within the study sample, White patients are more frequently discharged to further care than patients of color, and that 78% of the reviewed literature did not report racial/ethnic demographic information.
Mixed literature surrounding alcohol use at the time of injury and implications for outcome following moderate-severe TBI. Results indicated that while rates for alcohol positive TBI remained stable over the 18-year period examined, alcohol-positive TBI interacts with both age of the TBI patient and their respective mechanisms of injury (i.e. motor vehicle accidents for younger patients and falls for elderly patients).
Describing injury-related variables and trends for older adults (65 years and up) suffering a moderate-severe TBI: mechanism of injury, injury severity, hospital length of stay, and functional status at discharge. Findings indicated that the incidence of elderly TBI approximately doubled between 1992 and 2009, and that the increase in elderly TBI is greatest for individuals between the ages of 83 and 90. Furthermore, this age group had the poorest outcomes following TBI. Prevention and awareness of TBI in the elderly is imperative in reducing the likelihood of injury and disability
Longitudinal Examination of Recovery from Moderate to Severe TBI
While the research in our lab examines a number of TBI-related factors including cognition and outcome, over the past 10 years one important emphasis of the work in our lab has been to understand systems-level plasticity after TBI. In this work, we use both behavioral and functional brain imaging methods in humans and detail cross-sectional approaches with longitudinal designs to understand patient outcome during critical recovery windows.
New Learning and Neural Network Change After TBI
The purpose of this study is to examine the allocation of neural resources in cases of neurologic compromise due to TBI using BOLD fMRI. We examine the role of increased neural activity in working memory dysfunction following TBI and test competing hypotheses regarding the nature of neural recruitment. These hypotheses include 1) brain reorganization, which supposes that additional prefrontal cortex recruitment reflects underlying changes in the neural substrate and/or changes in the functional network associated with working memory tasks; 2) neural compensation, which is similar to brain reorganization but it makes no inferences about alterations in the underlying neural substrate; and 3) a natural support mechanism that is neither permanent, nor operating to bolster cognitive functioning, and implies a negative performance/activation relationship. The overarching goal of this investigation is to test the viability of these explanations through several methodological manipulations. First, we emphasize the basic relationship between brain involvement and task performance, which helps to dissociate the third from the first two explanations presented above. Second, through the use of task repetition and practice, we can measure how neural networks are altered as task performance improves.