Several recent meta-analyses suggest that the semi-acute stage of mild traumatic brain injury (mTBI) is associated with mild cognitive deficits in attention, memory and executive functioning. However, routine clinical imaging (MRI and CT) scans are usually insensitive to both the neuronal pathology underlying these acute cognitive deficits as well as to the subsequent recovery process that occurs in the majority (80-90%) of patients. Our currently funded NIH work investigates attentional deficits that are commonly associated with mTBI both semi-acutely (within 3 weeks of injury) and in the more chronic injury phase (3-5 months) using neuropsychological testing coupled with an extensive magnetic resonance imaging (MRI) battery. Specifically, we are using functional MRI (FMRI), diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (1H-MRS) to quantify the cognitive and neurophysiological changes that occur following mTBI. Our preliminary data (16 mTBI patients and 16 matched controls) suggest subtle cognitive deficits, metabolic abnormalities and hemodynamic disturbances during the semi-acute stages of mTBI. However, our current multimodal imaging protocol lacks a direct measure of neuronal functioning, which would provide crucial information regarding potential mechanisms underlying these metabolic and vascular abnormalities. Therefore, this competitive revision (Notice Number NOT-OD-09-058;NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications) seeks to add magnetoencephalography (MEG) as a direct measure of the synchronous firing of neuronal ensembles (i.e., neuron electrophysiology) to complement our ongoing NIH funded work. Twenty-seven well- characterized mild TBI patients and 15 non-cranial trauma controls will undergo neuropsychological testing and an extensive imaging battery 3 weeks and 3-5 months post injury. Participants will be asked to perform both a spatial orienting and a resting-state task during the collection of FMRI (currently funded work) and MEG (competitive revision) data. Amongst all of the imaging modalities, the combination of information from MEG and FMRI is likely to be the most synergistic due to the increased spatial (FMRI) and temporal (MEG) resolution that each modality provides. This hypothesis will be directly tested by applying novel multivariate statistical techniques (joint independent component analyses;J-ICA) to the acquired data. The utility of J-ICA to capitalize on the unique information present in each individual imaging modality has not been studied, and, more importantly, neither has its ability to tell both when (MEG) and where (FMRI) pathological responses are occurring within the brain following mTBI. The impact and innovation of the current proposal lies on several levels. Foremost, it addresses an important gap in our knowledge regarding the development of standardized protocols that are capable of capturing the dynamic neurological changes that occur after mTBI. While it is unlikely that neuroimaging techniques alone will ever be able to provide an independent objective diagnosis, it is likely that they will provide incremental information important for both differential diagnosis and predictions about future outcome. Second, the addition of MEG ensures we are directly measuring neuronal as well as metabolic (1H-MRS) and vascular (FMRI) responses. This is critical given that frank neuronal dysfunction could potentially drive both the metabolic and vascular abnormalities that we are currently observing in our semi-acutely injured mTBI patients. MEG provides exquisite temporal resolution that will permit an evaluation of when (on the millisecond level) the pathological neuronal response is occurring (M50 versus M100 versus M300). To date, there have only been a few studies that have utilized MEG to study mTBI and none have been conducted during the semi-acute stage of injury in an unselected population. Finally, a longitudinal study of mild TBI during both the semi-acute and chronic phase that combines these neuroimaging modalities will provide the foundation for a human recovery model in TBI. Although current animal models exist, these are a poor substitute for the complexities inherent in human cognition.

Public Health Relevance

In the United States alone, there are approximately 1.2 million mild traumatic brain injury (mTBI) cases per year that result in an estimated cost of $56 billion dollars. The symptoms of mTBI can range from severe physical and mental disability to subtle problems with attention, concentration, or emotional control. Cognitive difficulties are often present in the first few weeks of injury, but typically remit 3-5 months post injury in the majority (approximately 80-90%) of patients. The first step for understanding these cognitive difficulties is to develop biomarkers that are sensitive to neuronal injury and the subsequent recovery process, which will be critical not only for mTBI, but also for more severe forms of TBI. However, the neuropathology underlying cognitive deficits in the acute or chronic phases of mild TBI is often subtle and difficult to detect with conventional imaging techniques. Our currently funded NIH work with magnetic resonance imaging (MRI) techniques provide preliminary evidence of abnormal metabolic and vascular responses in the semi-acute phase of mTBI, which we have also shown to correlate with cognitive dysfunction. This competitive revision will add a direct measurement of synchronous neuronal firing (magnetoencephalography;MEG) to our currently funded protocol. The fusion of high-resolution spatial (FMRI) and temporal (MEG) information, coupled with behavioral and neuropsychological measures over time, will provide unprecedented insight into the foundation of a mTBI recovery model in humans.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Grants (R21)
Project #
3R21NS064464-01A1S1
Application #
7836382
Study Section
Special Emphasis Panel (ZRG1-BDCN-Y (95))
Program Officer
Hicks, Ramona R
Project Start
2009-03-01
Project End
2012-02-28
Budget Start
2009-09-30
Budget End
2012-02-28
Support Year
1
Fiscal Year
2009
Total Cost
$507,037
Indirect Cost
Name
The Mind Research Network
Department
Type
DUNS #
098640696
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
Vergara, Victor M; Mayer, Andrew R; Damaraju, Eswar et al. (2017) The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury. Brain Behav 7:e00809
Vergara, Victor M; Mayer, Andrew R; Damaraju, Eswar et al. (2017) Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy. J Neurotrauma 34:1045-1053
Vergara, Victor M; Mayer, Andrew R; Damaraju, Eswar et al. (2017) The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA. Neuroimage 145:365-376
Cheng, Yilong; Wei, Hua; Tan, James-Kevin Y et al. (2016) Nano-Sized Sunflower Polycations As Effective Gene Transfer Vehicles. Small 12:2750-8
Cheng, Yilong; Yumul, Roma C; Pun, Suzie H (2016) Virus-Inspired Polymer for Efficient In?Vitro and In?Vivo Gene Delivery. Angew Chem Int Ed Engl 55:12013-7
Mayer, Andrew R; Ling, Josef M; Allen, Elena A et al. (2015) Static and Dynamic Intrinsic Connectivity following Mild Traumatic Brain Injury. J Neurotrauma 32:1046-55
Mayer, Andrew R; Hanlon, Faith M; Dodd, Andrew B et al. (2015) A functional magnetic resonance imaging study of cognitive control and neurosensory deficits in mild traumatic brain injury. Hum Brain Mapp 36:4394-406
Vergara, Victor M; Damaraju, Eswar; Mayer, Andrew B et al. (2015) The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging. Conf Proc IEEE Eng Med Biol Soc 2015:5432-5
Zuo, Xi-Nian; Anderson, Jeffrey S; Bellec, Pierre et al. (2014) An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 1:140049
Franco, Alexandre R; Mannell, Maggie V; Calhoun, Vince D et al. (2013) Impact of analysis methods on the reproducibility and reliability of resting-state networks. Brain Connect 3:363-74

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