Blast-related injury and loss of consciousness is common in military TBI. Structural MR imaging demonstrates a high incidence of white matter T2-weighted hyperintense areas and pituitary abnormalities, with a low incidence of microhemorrhage in the chronic phase.

A definitive diagnosis of mild traumatic brain injury (mTBI) is difficult due to the absence of biomarkers in standard clinical imaging. The brain is a complex network of interconnected neurons and subtle changes can modulate key networks of cognitive function. The resting state default mode network (DMN) has been shown to be sensitive to changes induced by pathology. This study seeks to determine whether quantitative measures of the DMN are sensitive in distinguishing mTBI subjects. Resting state functional magnetic resonance imaging data were obtained for healthy (n=12) and mTBI subjects (n=15). DMN maps were computed using dual-regression Independent Component Analysis (ICA). A goodness-of-fit (GOF) index was calculated to assess the degree of spatial specificity and sensitivity between healthy controls and mTBI subjects. DMN regions and neuropsychological assessments were examined to identify potential relationships. The resting state DMN maps indicate an increase in spatial coactivity in mTBI subjects within key regions of the DMN. Significant coactivity within the cerebellum and supplementary motor areas of mTBI subjects were also observed. This has not been previously reported in seed-based resting state network analysis. The GOF suggested the presence of high variability within the mTBI subject group, with poor sensitivity and specificity. The neuropsychological data showed correlations between areas of coactivity within the resting state network in the brain with a number of measures of emotion and cognitive functioning. The poor performance of the GOF highlights the key challenge associated with mTBI injury: the high variability in injury mechanisms and subsequent recovery. However, the quantification of the DMN using dual-regression ICA has potential to distinguish mTBI from healthy subjects, and provide information on the relationship of aspects of cognitive and emotional functioning with their potential neural correlates.

Perfusion deficits in patients with mild traumatic brain injury (TBI) from a military population were characterized by dynamic susceptibility contrast perfusion imaging. Relative cerebral blood flow (rCBF) was calculated by a model-independent deconvolution approach from the tracer concentration curves following a bolus injection of gadolinium diethylenetriaminepentaacetate (Gd-DTPA) using both manually and automatically selected arterial input functions (AIFs). Linear regression analysis of the mean values of rCBF from selected regions of interest showed a very good agreement between the two approaches, with a regression coefficient of R = 0.88 and a slope of 0.88. The Bland-Altman plot also illustrated the good agreement between the two approaches, with a mean difference of 0.6 ± 12.4 mL/100 g/min. Voxelwise analysis of rCBF maps from both approaches demonstrated multiple clusters of decreased perfusion (p < 0.01) in the cerebellum, cuneus, cingulate and temporal gyrus in the group with mild TBI relative to the controls. MRI perfusion deficits in the cerebellum and anterior cingulate also correlated (p < 0.01) with neurocognitive results, including the mean reaction time in the Automated Neuropsychological Assessment Metrics and commission error and detection T-scores in the Continuous Performance Test, as well as neurobehavioral scores in the Post-traumatic Stress Disorder Checklist-Civilian Version. In conclusion, rCBF calculated using AIFs selected from an automated approach demonstrated a good agreement with the corresponding results using manually selected AIFs. Group analysis of patients with mild TBI from a military population demonstrated scattered perfusion deficits, which showed significant correlations with measures of verbal memory, speed of reaction time and self-report of stress symptoms.

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Hand and arm impairment is common after stroke. Robotic stroke therapy will be more effective if hand and upper-arm training is integrated to help users practice reaching and grasping tasks. This article presents the design, development, and validation of a low-cost, functional electrical stimulation grasp-assistive glove for use with task-oriented robotic stroke therapy. Our glove measures grasp aperture while a user completes simple-to-complex real-life activities, and when combined with an integrated functional electrical stimulator, it assists in hand opening and closing. A key function is a new grasp-aperture prediction model, which uses the position of the end-effectors of two planar robots to define the distance between the thumb and index finger. We validated the accuracy and repeatability of the glove and its capability to assist in grasping. Results from five nondisabled subjects indicated that the glove is accurate and repeatable for both static hand-open and -closed tasks when compared with goniometric measures and for dynamic reach-to-grasp tasks when compared with motion analysis measures. Results from five subjects with stroke showed that with the glove, they could open their hands but without it could not. We present a glove that is a low-cost solution for in vivo grasp measurement and assistance.Abbreviations: 3-D = three-dimensional, ADL = activity of daily living, ADLER = Activities of Daily Living Exercise Robot, ANOVA = analysis of variance, DOF = degree of freedom, FES = functional electrical stimulation, GUI = graphical user interface, MCP = metacarpophalangeal, MMT = manual muscle test, PIP = proximal interphalangeal, SD = standard deviation, UL Model = Bilateral Upper-Limb Kinematic Model.

In the military, explosive blasts are a significant cause of mild traumatic brain injuries (mTBIs). The symptoms associated with blast mTBIs causes significant economic burdens and a diminished quality of life for many service members. At present, the distinction of the injury mechanism (blast versus non-blast) may not influence TBI diagnosis. However, using noninvasive imaging, this study reveals significant distinctions between the blast and non-blast TBI mechanisms. A cortical whole-brain thickness analysis was performed using structural high-resolution T1-weighted MRI to identify the effects of blasts in persistent mTBI (pmTBI) subjects. A total of 41 blast pmTBI subjects were individually age- and gender-matched to 41 non-blast pmTBI subjects. Using FreeSurfer, cortical thickness was quantified for the blast group, relative to the non-blast group. Cortical thinning was identified within the blast mTBI group, in two clusters bilaterally. In the left hemisphere, the cluster overlapped with the lateral orbitofrontal, rostral middle frontal, medial orbitofrontal, superior frontal, rostral anterior cingulate and frontal pole cortices ( p < 0.02, two-tailed, size = 1680 mm 2 ). In the right hemisphere, the cluster overlapped with the lateral orbitofrontal, rostral middle frontal, medial orbitofrontal, pars orbitalis, pars triangularis and insula cortices ( p < 0.002, two-tailed, cluster size = 2453 mm 2 ). Self-report assessments suggest significant differences in the Post-Traumatic Stress Disorder Checklist-Civilian Version ( p < 0.05, Bonferroni-corrected) and the Neurobehavioral Symptom Inventory ( p < 0.01, uncorrected) between the blast and non-blast mTBI groups. These results suggest that blast may cause a unique injury pattern related to a reduction in cortical thickness within specific brain regions which could affect symptoms. No other study has found cortical thickness difference between blast and non-blast mTBI groups and further replication is needed to confirm these initial observations.

Traumatic brain injury, depression and posttraumatic stress disorder (PTSD) are neurocognitive syndromes often associated with impairment of physical and mental health, as well as functional status. These syndromes are also frequent in military service members (SMs) after combat, although their presentation is often delayed until months after their return. The objective of this prospective cohort study was the identification of independent predictors of neurocognitive syndromes upon return from deployment could facilitate early intervention to prevent disability. We completed a comprehensive baseline assessment, followed by serial evaluations at three, six, and 12 months, to assess for new-onset PTSD, depression, or postconcussive syndrome (PCS) in order to identify baseline factors most strongly associated with subsequent neurocognitive syndromes. On serial follow-up, seven participants developed at least one neurocognitive syndrome: five with PTSD, one with depression and PTSD, and one with PCS. On univariate analysis, 60 items were associated with syndrome development at p < 0.15. Decision trees and ensemble tree multivariate models yielded four common independent predictors of PTSD: right superior longitudinal fasciculus tract volume on MRI; resting state connectivity between the right amygdala and left superior temporal gyrus (BA41/42) on functional MRI; and single nucleotide polymorphisms in the genes coding for myelin basic protein as well as brain-derived neurotrophic factor. Our findings require follow-up studies with greater sample size and suggest that neuroimaging and molecular biomarkers may help distinguish those at high risk for post-deployment neurocognitive syndromes.