Frank G. Hillary - US grants

DESCRIPTION (provided by applicant): Each year 230,000 people are hospitalized and survive moderate and severe traumatic brain injury (TBI). As a result, a large number of individuals with TBI endure life-long impairment and disability. Acute rating scales such as the Glasgow Coma Scale (GCS) have shown limited predictive validity regarding patient outcome and traditional neuroimaging techniques such as CT and MRI maintain limited correlations with brain injury severity and cognitive functioning. Continued advances in neuroimaging, however, have provided researchers with an important opportunity to study the pathophysiology of brain dysfunction following TBI. According to the NCMRR, "the neurobiology of TBI in humans should be studied with modern imaging techniques". The purpose of this study is to correlate proton magnetic resonance spectroscopy (MRS), an advanced neuroimaging technique, with behavioral measures of TBI severity and cognitive outcome. MRS measures the concentration of cerebral metabolites such as N-acetylaspartate (NAA), choline (Cho), and glutamate (Glu). While MRS has shown promise in predicting brain injury severity and patient outcome, the exact protocols for using MRS with TBI remain undetermined and the purpose of the proposed study is to examine three critical areas: (1) the post-injury time period when the MRS data should be acquired (e.g., within one week or within one month of injury); (2) how metabolites should be measured (i.e., absolute concentrations or changes in concentration over time); and (3) the brain locations best suited for MRS data acquisition (i.e., acquisition near lesion sites or acquisition at sites remote from probable brain lesion). The proposed study will make determinations in these three areas through the use of two acute MRS scans following TBI to measure concentrations of NAA, Cho and Glu and their correlation with injury severity and cognitive variables. In addition, correlation of acute MRS data with behavioral data (e.g., duration of loss of consciousness, duration of post-traumatic amnesia) will elucidate the relationship between changes in brain metabolism and changes in patient behavior during acute recovery from TBI. The present proposal will employ a promising, noninvasive neuroimaging technique, MRS, to determine the most appropriate protocols (i.e., timing, metabolic measurement, brain location for data acquisition) for application of MRS to acute TBI. With an established protocol for using MRS, this instrument should prove useful for determining the effectiveness of acute interventions (e.g. hypothermia, pharmacologic intervention) and for predicting the acute course of patient recovery.

Brain; CRISP; Cognitive; Cognitive Disturbance; Cognitive Impairment; Cognitive decline; Cognitive function abnormal; Computer Retrieval of Information on Scientific Projects Database; Disturbance in cognition; Encephalon; Encephalons; Evaluation; Funding; Grant; Impaired cognition; Institution; Intermediary Metabolism; Investigators; METBL; MR Imaging; MR Tomography; MRI; Magnetic Resonance Imaging; Magnetic Resonance Imaging Scan; Measures; Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance; Memory, Immediate; Memory, Short-Term; Memory, Shortterm; Metabolic; Metabolic Processes; Metabolism; NIH; NMR Imaging; NMR Tomography; National Institutes of Health; National Institutes of Health (U.S.); Nervous System, Brain; Nuclear Magnetic Resonance Imaging; Purpose; Research; Research Personnel; Research Resources; Researchers; Resources; Short-Term Memory; Source; Structure; Trauma, Brain; Traumatic Brain Injury; Traumatic encephalopathy; United States National Institutes of Health; Zeugmatography; cognitive dysfunction; cognitive loss; cognitively impaired; memory process; processing speed; traumatic brain damage; working memory

Project Summary/Abstract Traumatic brain injury (TBI) is a major public health issue globally, and while neuroimaging has been useful in understanding disruption in brain structure and function after injury, there are a number of factors that attenuate its prognostic ability. For example, there is tremendous heterogeneity in outcome after injury which is only partially explained by injury severity. Cost frequently limits sample size in neuroimaging studies, yet given the myriad factors that have been shown to influence patient outcome (age, injury severity, socioeconomic status), small samples and mass univariate testing often result in many studies being grossly under-powered. One solution is to combine data and create novel data sharing platforms, and the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium has supported this kind of collaboration for over a decade across a range of clinical disorders. The goal of this proposal is to develop tools and data processing procedures for use in the ENIGMA Brain Injury working group. In the R61 phase, we aim to develop and test a workflow for harmonized processing of behavioral data (Aim 1) as well as structural and functional (resting-state) MRI data (Aim 2). For Aim 1 of the R61, the goal is to offer a decision tree of procedures that is data-dependent, allowing investigators to establish common cognitive endpoints across cohorts that collect a range of neuropsychological and clinical measures. This proposal will create sharable procedures, flexible tools, and generalizable guidelines for best practices for extracting common cognitive endpoints from distinct behavioral test batteries (R61 Aim 1). In Aim 2 of the R61, we develop an image processing pipeline called Harmonization and Aggregation for Functional and structural imaging data PIPEline; HAF-PIPE) that allows for aggregation of non-equivalent imaging data. A primary goal is to decentralize ComBat, an open-source data harmonization tool, so that it can be used in a virtual sharing environment. Following satisfaction of the R61 Go/No-Go criteria, which is the curation of the dataset including 13 cohorts, extraction of common cognitive endpoints, and creation of HAF- PIPE, we will move to the R33 phase. In the R33 phase, we will leverage the large, harmonized dataset and apply a machine learning technique (CorEx - Correlation Explanation) to identify patient clusters within each patient population studied. HAF-PIPE and the procedures and guidelines from the R61 phase will then be extended to additional patient populations and made available to other ENIGMA working groups. The harmonized data, along with the tools and procedures for creating them, will be accessible to researchers following proposal submission and approval as a curated dataset. With success, this proposal holds the promise of significantly advancing data curation, harmonization, and sharing in the clinical neurosciences. We anticipate that our proposal will significantly advance our understanding of factors that impact outcome after injury and will yield a tool that will be useful across the neuroimaging community.