Award or grant: National Institutes of Health (NIH)/National Institute of Neurological Disorders and Stroke (NINDS), 1R01NS102646-01A1
Traumatic Brain Injury (TBI) is a leading cause of death and long-term disability, and there are more than 5.3 million persons in the U.S. alone with chronic executive attention and cognitive dysfunction. There is a fundamental gap in knowledge of the functional and structural mechanisms underlying executive attention impairments after TBI. Without this knowledge, it will not be possible to establish reliable ways to predict potential for recovery or, ultimately, create individualized therapies. The long-term goal of this integrated research effort (conducted with the team of Kristen Dams-O’Connor, M.D.) is to identify mechanism(s) underlying cognitive deficits in TBI patients, as this will enable accurate classification of their impairments, more accurate prognoses, and precise evaluation of intervention effectiveness.
The overall objective of this proposal is to relate clinically applicable electroencephalogram (EEG) metrics of executive attention to quantitative metrics of structural connectivity alterations within the anterior forebrain mesocircuit (medial frontal cortex, striatum and central thalamus), and to evaluate their role in predicting cognitive outcomes after TBI. The central hypothesis is that individually measured electrophysiologic responses and anatomical injuries within the anterior forebrain mesocircuit of TBI subjects will correlate with executive attention deficits, as measured by the attention network test (ANT), and accurately predict broad cognitive outcomes. This hypothesis is based on preliminary work from two studies of EEG and diffusion magnetic resonance imaging (MRI) in TBI patients, as well as related published research supporting the underlying model in more severely brain-injured subjects. The rationale underlying the proposed research is that characterizing the relationship between the anterior forebrain mesocircuit and executive attention deficits at an individual level, using both physiological and anatomical measurements, will allow insight into the biological underpinnings of the deficits and help frame mechanistic approaches to future diagnosis and therapy. Guided by strong preliminary data, this hypothesis will be tested with two Specific Aims.
The first Aim is to determine the extent to which executive attentional impairment, measured with the ANT, relates to injury-related changes in the anterior forebrain mesocircuit (a) physiology (EEG) and (b) white matter connectivity (diffusion MRI). Part (c) of Aim 1 will integrate the two modalities and relate them back to clinically applicable EEG. Aim 2 is to (a) cross-sectionally relate and (b) longitudinally predict cognitive outcomes via cutting-edge machine-learning techniques applied to imaging metrics collected in Aim 1. The approach is innovative, in our opinion, because they propose to link attentional impairments, as measured by the ANT, to measures of physiology and connectivity on an individual basis and predict cognitive outcomes using machine learning. The proposed research is significant because knowledge of the biology underlying attention impairment will allow for its evaluation as a prognostic measure and provide targets for effective individualized interventions. Ultimately, such knowledge has the potential to enable development of therapies that can dramatically improve the quality of life for millions who remain unable to return to prior levels of functioning within their communities after TBI.