Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms.

Mild traumatic brain injury (mTBI) is a significant health burden among military service members. Although mTBI was once considered relatively benign compared to more severe TBIs, a growing body of evidence has demonstrated the devastating neurological consequences of mTBI, including chronic post-concussion symptoms and deficits in cognition, memory, sleep, vision, and hearing. The discovery of reliable biomarkers for mTBI has been challenging due to under-reporting and heterogeneity of military-related mTBI, unpredictability of pathological changes, and delay of post-injury clinical evaluations. Moreover, compared to more severe TBI, mTBI is especially difficult to diagnose due to the lack of overt clinical neuroimaging findings. Yet, advanced neuroimaging techniques using magnetic resonance imaging (MRI) hold promise in detecting microstructural aberrations following mTBI. Using different pulse sequences, MRI enables the evaluation of different tissue characteristics without risks associated with ionizing radiation inherent to other imaging modalities, such as X-ray-based studies or computerized tomography (CT). Accordingly, considering the high morbidity of mTBI in military populations, debilitating post-injury symptoms, and lack of robust neuroimaging biomarkers, this review (1) summarizes the nature and mechanisms of mTBI in military settings, (2) describes clinical characteristics of military-related mTBI and associated comorbidities, such as post-traumatic stress disorder (PTSD), (3) highlights advanced neuroimaging techniques used to study mTBI and the molecular mechanisms that can be inferred, and (4) discusses emerging frontiers in advanced neuroimaging for mTBI. We encourage multi-modal approaches combining neuropsychiatric, blood-based, and genetic data as well as the discovery and employment of new imaging techniques with big data analytics that enable accurate detection of post-injury pathologic aberrations related to tissue microstructure, glymphatic function, and neurodegeneration. Ultimately, this review provides a foundational overview of military-related mTBI and advanced neuroimaging techniques that merit further study for mTBI diagnosis, prognosis, and treatment monitoring.

An examination of volumetric and cortical thickness correlates of posttraumatic nightmares in male active duty service members.

Posttraumatic nightmares commonly occur after a traumatic experience. Despite significant deleterious effects on well-being and their role in posttraumatic stress disorder, posttraumatic nightmares remain understudied. The neuroanatomical structures of the amygdala, medial prefrontal cortex, hippocampus, and anterior cingulate cortex constitute the AMPHAC model (Levin and Nielsen, 2007), which is implicated in the neurophysiology of disturbing dreams of which posttraumatic nightmares is a part. However, this model has not been investigated using neuroimaging data. The present study sought to determine whether there are structural differences in the AMPHAC regions in relation to the occurrence of posttraumatic nightmares. Data were obtained from treatment-seeking male active duty service members (N = 351). Posttraumatic nightmares were not significantly related to gray matter volume, cortical surface area, or cortical thickness of any the AMPHAC regions when controlling for age and history of mild traumatic brain injury. Although the present analyses do not support an association between structural measures of AMPHAC regions and posttraumatic nightmares, we suggest that functional differences within and/or between these brain regions may be related to the occurrence of posttraumatic nightmares because functional and structural associations are distinct. Future research should examine whether functional differences may be associated with posttraumatic nightmares.

Acquired Sleep-Related Hypermotor Epilepsy with Disrupted White Matter Tracts Assessed by Multishell Diffusion Magnetic Resonance Imaging.

Sleep-related hypermotor epilepsy (SHE) (previously frontal lobe epilepsy) is a rare seizure disorder commonly misdiagnosed or unrecognized, causing negative patient sequelae. While usually reported in familial studies, it is more commonly acquired. Diagnosis is a challenge due to its low incidence in comparison with the more common sleep disorders or psychogenic etiologies in the differential diagnosis. Diagnosis is scaled on degree of certainty based on described or clinically documented semiology, with video EEG as a helpful, but not necessary, adjunct. Current treatment is similar to other focal epilepsies. We studied a 36-year-old active duty male soldier who presented with 2 years of predominantly sleep related, abrupt, short, and anamnestic hyperkinetic movements with unstructured vocalizations. Prior workup included non-contributory video electroencephalograph (EEG) and polysomnography as well as normal brain magnetic resonance imaging (MRI). Treatments for presumed psychiatric and parasomnia disturbances were not effective in establishing diagnosis or relief. Evaluation at our tertiary, multidisciplinary care institution recorded events consistent with the diagnosis of clinical SHE. He was enrolled in an advanced multishell diffusion-weighted imaging MRI research study to evaluate white matter tracts, given his history of mild, repetitive, non-penetrating traumatic brain injury, not otherwise requiring hospitalization. Multishell diffusion MRI tractography found changes not previously described in the right frontal lobe white matter tracts. These changes were consistent with neurological localization and serve as a potential nidus for this patient's seizure disorder. Misdiagnosis of SHE can result in detrimental biopsychosocial sequelae of untreated epilepsy, unnecessary or harmful intervention, or the stigmata of a behavioral disorder. Further investigation into diagnosis and etiology of acquired SHE is needed. Assessment for white matter abnormalities can potentially provide information into pathogenesis of epilepsy disorders.

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging.

The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.

Dynamic Causal Modeling applied to fMRI data shows high reliability.

Sensitivity, specificity, and reproducibility are vital to interpret neuroscientific results from functional magnetic resonance imaging (fMRI) experiments. Here we examine the scan-rescan reliability of the percent signal change (PSC) and parameters estimated using Dynamic Causal Modeling (DCM) in scans taken in the same scan session, less than 5 min apart. We find fair to good reliability of PSC in regions that are involved with the task, and fair to excellent reliability with DCM. Also, the DCM analysis uncovers group differences that were not present in the analysis of PSC, which implies that DCM may be more sensitive to the nuances of signal changes in fMRI data.

Automatic physiological waveform processing for FMRI noise correction and analysis.

Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.

Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses.

Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.

Persistence and brain circuitry.

The orbitofrontal and adjacent medial prefrontal cortex may play an important role in normal social functioning and affect modulation. Recent anatomical studies of this area of the prefrontal cortex have demonstrated a striking correspondence of fine-grained architectonic partitioning schemes in humans and nonhuman primates. This finding allows neurophysiological recording and anatomical connectivity data in animals to be considered together with functional imaging data and lesion studies in humans. In a functional MRI study, we show that individual differences in Persistence, a dimensional trait assessed with a seven-factor personality model, may be linked to specific areas in the lateral orbital and medial prefrontal cortex and the ventral striatum. These areas are part of an anatomical circuit that has been defined in nonhuman primates and has been implicated in functions related to behavioral persistence. These findings represent a fresh approach to linking normal individual differences in personality and behavior to specific neuronal structures and subsystems.

A parametric study of mental spatial transformations of bodies.

TWO CLASSES OF MENTAL SPATIAL TRANSFORMATION CAN BE DISTINGUISHED: Object-based spatial transformations are imagined movements of objects; and egocentric perspective transformations are imagined movements of one's point of view. The hypothesis that multiple neural systems contribute to these mental imagery operations was tested with functional MRI. Participants made spatial judgments about pictures of human bodies, and brain activity was analyzed as a function of the judgment required and the time taken to respond. Areas in right temporal, occipital and parietal cortex and the medial superior cerebellum appear to be differentially involved in object-based spatial transformations. Additionally, midline structures and lateral parietal cortex were found to decrease in activity during the spatial reasoning tasks, independently of the judgment required or of the latency of response. The results are discussed in terms of a model of spatial reasoning that postulates specialized subsystems for performing object-based and egocentric perspective image transformations.