Journal of Neurotrauma

Assessing quality of care is essential for improving the management of patients experiencing traumatic brain injury (TBI). This study aimed at devising a rigorous framework to evaluate the quality of TBI care provided by intensive care units (ICUs) and applying it to the Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe (CREACTIVE) consortium, which involved 83 ICUs from seven countries. The performance of the centers was assessed in terms of patients' outcomes, as measured by the 6-month Glasgow Outcome Scale-Extended (GOS-E). To account for the between-center differences in the characteristics of the admitted patients, we developed a multinomial logistic regression model estimating the probability of a four-level categorization of the GOS-E: good recovery (GR), moderate disability (MD), severe disability (SD), and death or vegetative state (D/VS). A total of 5928 patients admitted to the participating ICUs between March 2014 and March 2019 were analyzed. The model included 11 predictors and demonstrated good discrimination (area under the receiver operating characteristic [ROC] curve in the validation set for GR: 0.836, MD: 0.802, SD: 0.706, D/VS: 0.890) and calibration, both overall (Hosmer–Lemeshow test p value: 0.87) and in several subgroups, defined by prognostically relevant variables. The model was used as a benchmark for assessing quality of care by comparing the observed number of patients experiencing GR, MD, SD, and D/VS to the corresponding numbers expected in each category by the model, computing observed/expected (O/E) ratios. The four center-specific ratios were assembled with polar representations and used to provide a multidimensional assessment of the ICUs, overcoming the loss of information consequent to the traditional dichotomizations of the outcome in TBI research. The proposed framework can help in identifying strengths and weaknesses of current TBI care, triggering the changes that are necessary to improve patient outcomes.

After moderate to severe traumatic brain injury (TBI), sleep disturbance commonly emerges during the confused post-traumatic amnesia (PTA) recovery stage. However, the evaluation of early sleep disturbance during PTA, its recovery trajectory, and influencing factors is limited. This study aimed to evaluate sleep outcomes in patients experiencing PTA using ambulatory gold-standard polysomnography (PSG) overnight and salivary endogenous melatonin (a hormone that influences the sleep-wake cycle) assessment at two time-points. The relationships between PSG-derived sleep-wake parameters and PTA symptoms (i.e., agitation and cognitive disturbance) were also evaluated. In a patient subset, PSG was repeated after PTA had resolved to assess the trajectory of sleep disturbance.

Participants with PTA were recruited from Epworth HealthCare's inpatient TBI Rehabilitation Unit. Trained nurses administered overnight PSG at the patient bedside using the Compumedics Somté portable PSG device (Compumedics, Ltd., Australia). Two weeks after PTA had resolved, PSG was repeated. On a separate evening, two saliva specimens were collected (at 24:00 and 06:00) for melatonin testing. Results of routine daily hospital measures (i.e., Agitated Behavior Scale and Westmead PTA Scale) were also collected. Twenty-nine patients were monitored with PSG (mean: 41.6 days post-TBI; standard deviation [SD]: 28.3). Patients' mean sleep duration was reduced (5.6 h, SD: 1.2), and was fragmented with frequent awakenings (mean: 27.7, SD: 15.0). Deep, slow-wave restorative sleep was reduced, or completely absent (37.9% of patients). The use of PSG did not appear to exacerbate patient agitation or cognitive disturbance. Mean melatonin levels at both time-points were commonly outside of normal reference ranges. After PTA resolved, patients (n = 11) displayed significantly longer mean sleep time (5.3 h [PTA]; 6.5 h [out of PTA], difference between means: 1.2, p = 0.005). However, disturbances to other sleep-wake parameters (e.g., increased awakenings, wake time, and sleep latency) persisted after PTA resolved.

This is the first study to evaluate sleep disturbance in a cohort of patients as they progressed through the early TBI recovery phases. There is a clear need for tailored assessment of sleep disturbance during PTA, which currently does not form part of routine hospital assessment, to suggest new treatment paradigms, enhance patient recovery, and reduce its long-term impacts.

The potential influence of pituitary-related hormones (including both pituitary gland and target gland hormones) on functional recovery after traumatic brain injury has been observed. However, the relationship between these hormones and the recovery of consciousness in patients with disorders of consciousness (DOC) remains unclear. In this retrospective and observational study, 208 patients with DOC were recruited. According to the Glasgow Outcome Scale (GOS) scores after 6 months, patients with DOC were categorized into two subgroups: a favorable prognosis subgroup (n = 38) comprising those who regained consciousness (GOS score ≥3), and a poor prognosis subgroup (n = 156) comprising those who remained in DOC (GOS score <3). Comparative analyses of pituitary-related hormone levels between the two subgroups were conducted. Further, a binary logistic regression analysis was conducted to assess the predictive value of pituitary-related hormones for the patients' prognosis. The favorable prognosis subgroup showed a significant increase in adrenocorticotropic hormone (ACTH) levels (p = 0.036). Moreover, higher ACTH levels and shorter days since injury were significantly associated with a better prognosis, with odds ratios (ORs) of 0.928 (95% confidence interval [CI] = 0.873-0.985, p = 0.014) and 1.015 (95% CI = 1.005-1.026, p = 0.005), respectively. A subsequent receiver operating characteristic (ROC) analysis demonstrated the potential to predict patients' prognosis with an area under the curve value of 0.78, an overall accuracy of 75.5%, a sensitivity of 77.5%, and a specificity of 66.7%. Our findings indicate that ACTH levels could serve as a clinically valuable and convenient predictor for patients' prognosis.

Pre-injury migraines might be a risk factor for prolonged recovery after sport-related concussion (SRC). We sought to examine whether a pre-injury history of migraines is associated with worse recovery following SRC in collegiate athletes. Data were collected through a prospective concussion surveillance system in 11 National Collegiate Athletic Association (NCAA) Division III college athletic programs between September 2014 and March 2020. Our primary independent variable, pre-injury migraines, were self-reported by the athletes. Between those with and without migraines, the outcomes of days to return-to-learn (RTL) without academic accommodations and return-to-play (RTP) were compared using Mann–Whitney U tests. Each athlete's RTL and RTP status was dichotomized (i.e., returned vs. not returned) at various time points for RTL (i.e., 7/14/21/28 days) and RTP (i.e., 14/21/28/56 days). Chi-squared tests were performed to compare the proportions of RTL and RTP status between groups. Multivariable regressions analyzed potential predictors of RTL and RTP adjusting for age, gender, prior concussions, other health conditions, and symptom severity. Of 1409 athletes with an SRC, 111 (7.9%) had a pre-injury history of migraines. Compared with those without migraines, those with migraines had longer median (interquartile range [IQR]) days to RTL (migraines = 7.0 [3.0–12.3] vs. no migraines = 5.0 [2.0–10.0], U = 53,590.5, p = 0.022). No differences were found in RTP between the two groups (migraines = 16.0 [10.0–33.0] vs. nχo migraines 15.0 [11.0–23.0], U = 38,545.0, p = 0.408). Regarding RTL, significantly lower proportions of athletes in the migraine group had fully RTL, without accommodations, at ≤14 days (77.5% vs. 85.2%, χ² = 4.33, p = 0.037), ≤21 days (85.3% vs. 93.0%, χ² = 7.99, p = 0.005), and ≤28 days (88.2% vs. 95.6%, χ² = 10.60, p = 0.001). Regarding RTP, a significantly lower proportion of athletes in the migraine group RTP at ≤28 days (72.0% vs. 82.7%, χ² = 5.40, p = 0.020) and ≤56 days (84.0% vs. 93.0%, χ² = 8.19, p = 0.004). In a multivariable model predicting RTL that was adjusted for age, gender, acute concussion symptoms, and other health variables (e.g., attention-deficit/hyperactivity disorder [ADHD], history of mental health difficulties), pre-injury history of migraine was associated with longer RTL (β = 0.06, p = 0.030). In a multivariable model predicting RTP, pre-injury history of migraine was not associated with RTP (β = 0.04, p = 0.192). In collegiate athletes, pre-injury migraine history was independently associated with longer RTL but not RTP. When comparing the proportions of those with successful RTP by days, significantly lower proportions of those with migraines showed successful RTP at ≤28 days and ≤56 days. Futures studies should study the generalizability of our findings in other school levels.

The purpose of this study was to differentiate clinically meaningful improvement or deterioration from normal fluctuations in patients with disorders of consciousness (DoC) following severe brain injury. We computed indices of responsiveness for the Coma Recovery Scale-Revised (CRS-R) using data from a clinical trial of 180 participants with DoC. We used CRS-R scores from baseline (enrollment in a clinical trial) and a 4-week follow-up assessment period for these calculations. To improve precision, we transformed ordinal CRS-R total scores (0–23 points) to equal-interval measures on a 0–100 unit scale using Rasch Measurement theory. Using the 0–100 unit total Rasch measures, we calculated distribution-based 0.5 standard deviation (SD) minimal clinically important difference, minimal detectable change using 95% confidence intervals, and conditional minimal detectable change using 95% confidence intervals. The distribution-based minimal clinically important difference evaluates group-level changes, whereas the minimal detectable change values evaluate individual-level changes. The minimal clinically important difference and minimal detectable change are derived using the overall variability across total measures at baseline and 4 weeks. The conditional minimal detectable change is generated for each possible pair of CRS-R Rasch person measures and accounts for variation in standard error across the scale. We applied these indices to determine the proportions of participants who made a change beyond measurement error within each of the two subgroups, based on treatment arm (amantadine hydrochloride or placebo) or categorization of baseline Rasch person measure to states of consciousness (i.e., unresponsive wakefulness syndrome and minimally conscious state). We compared the proportion of participants in each treatment arm who made a change according to the minimal detectable change and determined whether they also changed to another state of consciousness. CRS-R indices of responsiveness (using the 0–100 transformed scale) were as follows: 0.5SD minimal clinically important difference = 9 units, minimal detectable change = 11 units, and the conditional minimal detectable change ranged from 11 to 42 units. For the amantadine and placebo groups, 70% and 58% of participants showed change beyond measurement error using the minimal detectable change, respectively. For the unresponsive wakefulness syndrome and minimally conscious state groups, 54% and 69% of participants changed beyond measurement error using the minimal detectable change, respectively. Among 115 participants (64% of the total sample) who made a change beyond measurement error, 29 participants (25%) did not change state of consciousness. CRS-R indices of responsiveness can support clinicians and researchers in discerning when behavioral changes in patients with DoC exceed measurement error. Notably, the minimal detectable change can support the detection of patients who make a “true” change within or across states of consciousness. Our findings highlight that the continued use of ordinal scores may result in incorrect inferences about the degree and relevance of a change score.

Traumatic brain injury (TBI) persists as a substantial clinical dilemma, largely because of the absence of effective treatments. This challenge is exacerbated by the hindered clearance of intracranial metabolic byproducts and the continual accrual of deleterious proteins. The glymphatic system (GS) and meningeal lymphatic vessels (MLVs), key elements of the intracranial lymphatic network, play critical roles in the clearance of harmful substances. Cannabidiol (CBD) has shown promise in reducing metabolite overload and bolstering cognitive performance in various neurodegenerative diseases. The precise mechanisms attributing to its beneficial effects in TBI scenarios, however, are yet to be distinctly understood. Utilizing a fluid percussion injury paradigm, our research adopted a multifaceted approach, encompassing behavioral testing, immunofluorescence and immunohistochemical analyses, laser speckle imaging, western blot techniques, and bilateral cervical efferent lymphatic ligation. This methodology aimed to discern the influence of CBD on both neurological outcomes and intracranial lymphatic clearance in a murine TBI model. We observed that CBD administration notably ameliorated motor, memory, and cognitive functions, concurrently with a significant reduction in the concentration of phosphorylated tau protein and amyloid-β. In addition, CBD expedited the turnover and elimination of intracranial tracers, increased cerebral blood flow, and enhanced the efficacy of fluorescent tracer migration from MLVs to deep cervical lymph nodes (dCLNs). Remarkably, CBD treatment also induced a reversion in aquaporin-4 (AQP-4) polarization and curtailed neuroinflammatory indices. A pivotal discovery was that the surgical interruption of efferent lymphatic conduits in the neck nullified CBD's positive contributions to intracranial waste disposal and cognitive improvement, yet the anti-neuroinflammatory actions remained unaffected. These insights suggest that CBD may enhance intracranial metabolite clearance, potentially via the regulation of the intracranial lymphatic system, thereby offering neurofunctional prognostic improvement in TBI models. Our findings underscore the potential therapeutic applicability of CBD in TBI interventions, necessitating further comprehensive investigations and clinical validations to substantiate these initial conclusions.

Moderate traumatic brain injury (mTBI) involves a series of complex pathophysiological processes in not only the area in direct contact with mechanical violence but also in other brain regions far from the injury site, which may be important factors influencing subsequent neurological dysfunction or death. The medulla oblongata (MO) is a key area for the maintenance of basic respiratory and circulatory functions, whereas the pathophysiological processes after mTBI have rarely drawn the attention of researchers. In this study, we established a closed-head cortical contusion injury model, identified 6 different time points that covered the acute, subacute, and chronic phases, and then used nontargeted metabolomics to identify and analyze the changes in differential metabolites (DMs) and metabolic pathways in the MO region. Our results showed that the metabolic profile of the MO region underwent specific changes over time: harmaline, riboflavin, and dephospho-coenzyme A were identified as the key DMs and play important roles in reducing inflammation, enhancing antioxidation, and maintaining homeostasis. Choline and glycerophospholipid metabolism was identified as the key pathway related to the changes in MO metabolism at different phases. In addition, we confirmed increases in the levels of inflammatory factors and the activation of astrocytes and microglia by Western blot and immunofluorescence staining, and these findings were consistent with the nontargeted metabolomic results. These findings suggest that neuroinflammation plays a central role in MO neuropathology after mTBI and provide new insights into the complex pathophysiologic mechanisms involved after mTBI.

In the past decade, signature clinical neuropathology of blast-induced traumatic brain injury has been under intense debate, but interface astroglial scarring (IAS) seems to be convincing. In this study, we examined whether IAS could be replicated in the rat brain exposed to a laser-induced shock wave(s) (LISW[s]), a tool that can produce a pure shock wave (primary mechanism) without dynamic pressure (tertiary mechanism). Under certain conditions, we observed astroglial scarring in the subpial glial plate (SGP), gray-white matter junctions (GM-WM), ventricular wall (VW), and regions surrounding cortical blood vessels, accurately reproducing clinical IAS. We also observed shock wave impulse-dependent meningeal damage (dural microhemorrhage) in vivo by transcranial near-infrared (NIR) reflectance imaging. Importantly, there were significant correlations between the degree of dural microhemorrhage and the extent of astroglial scarring more than 7 days post-exposure, suggesting an association of meningeal damage with astroglial scarring. The results demonstrated that the primary mechanism alone caused the IAS and meningeal damage, both of which are attributable to acoustic impedance mismatching at multi-layered tissue boundaries. The time course of glial fibrillary acidic protein (GFAP) immunoreactivity depended not only on the LISW conditions but also on the regions. In the SGP, significant increases in GFAP immunoreactivity were observed at 3 days post-exposure, whereas in the GM-WM and VW, GFAP immunoreactivity was not significantly increased before 28 days post-exposure, suggesting different pathological mechanisms. With the high-impulse single exposure or the multiple exposure (low impulse), fibrotic reaction or fibrotic scar formation was observed, in addition to astroglial scarring, in the cortical surface region. Although there are some limitations, this seems to be the first report on the shock-wave-induced IAS rodent model. The model may be useful to explore potential therapeutic approaches for IAS.

Sleep–wake disturbances (SWDs) are one of the most common complaints following traumatic brain injury (TBI). The high prevalence and socioeconomic burden of SWDs post-TBI have only been recognized in the past decade. Common SWDs induced by TBI include excessive daytime sleepiness (EDS), hypersomnia, insomnia, obstructive sleep apnea (OSA), and circadian rhythm sleep disorders. Sleep disturbances can significantly compromise quality of life, strain interpersonal relationships, diminish work productivity, exacerbate other clinical conditions, and impede the rehabilitation process of TBI patients. Consequently, the prompt regulation and enhancement of sleep homeostasis in TBI patients is of paramount importance. Although studies have shown that abnormal neural network function, neuroendocrine changes, disturbance of sleep–wake regulators, and immune inflammatory responses related to brain structural damage induced by TBI are involved in the development of SWDs, the exact neuropathological mechanisms are still poorly understood. Therefore, we systematically review the current clinical and experimental studies on the characteristics and possible neural mechanisms of post-TBI SWDs. Elucidating the neural underpinnings of post-TBI SWDs holds the potential to diversify and enhance therapeutic approaches for these conditions. Such advancements could hasten the recuperation of TBI patients and ameliorate their overall quality of life. It is our aspiration that departments specializing in neurosurgery, rehabilitation, and neuropsychiatry will be able to recognize and address these conditions promptly, thereby facilitating the healing journey of affected individuals.

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep–wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep–wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period.

Impairment in visual function is common after traumatic brain injury (TBI) in the clinical setting, a phenomenon that translates to pre-clinical animal models as well. In Morris et al. (2021), we reported histological changes following weight-drop-induced TBI in a rodent model including retinal ganglion cell (RGC) loss, decreased electroretinogram (ERG) evoked potential, optic nerve diameter reduction, induced inflammation and gliosis, and loss of myelin accompanied by markedly impaired visual acuity. In this review, we will describe several pre-clinical TBI models that result in injuries to the visual system, indicating that visual function may be impaired following brain injury induced by a number of different injury modalities. This underscores the importance of understanding the role of the visual system and the potential detrimental sequelae to this sensory modality post-TBI. Given that most commonly employed behavioral tests such as the Elevated Plus Maze and Morris Water Maze rely on an intact visual system, interpretation of functional deficits in diffuse models may be confounded by off- target effects on the visual system.

Traumatic brain injuries (TBIs) are a large societal and individual burden. In the first year of life, the vast majority of these injuries are the result of inflicted abusive events by a trusted caregiver. Abusive head trauma (AHT) in infants, formerly known as shaken baby syndrome, is the leading cause of inflicted mortality and morbidity in this population. In this review we address clinical diagnosis, symptoms, prognosis, and neuropathology of AHT, emphasizing the burden of repetitive AHT. Next, we consider existing animal models of AHT, and we evaluate key features of an ideal model, highlighting important developmental milestones in children most vulnerable to AHT. We draw on insights from other injury models, such as repetitive, mild TBIs (RmTBIs), post-traumatic epilepsy (PTE), hypoxic-ischemic injuries, and maternal neglect, to speculate on key knowledge gaps and underline important new opportunities in pre-clinical AHT research. Finally, potential treatment options to facilitate healthy development in children following an AHT are considered. Together, this review aims to drive the field toward optimized, well-characterized animal models of AHT, which will allow for greater insight into the underlying neuropathological and neurobehavioral consequences of AHT.

In previous studies, the incidence of traumatic intracranial aneurysms (TICAs) after civilian gunshot wound to the head (cGSWH) was ∼3%. Given the use of delayed vessel imaging, we hypothesize that a significant fraction of TICAs is missed on initial non-contrasted scans. This study was designed to characterize acute TICAs using admission computed tomographic angiography (aCTA) in cGSWH. Over the period from 2017 to 2022, 341 patients were admitted to R. Adams Cowley Shock Trauma Center with cGSWH; 136 subjects had aCTA ∼3 (standard deviation [SD] 3.5) h post-injury. Demographics, clinical findings, imaging techniques, endovascular/surgical interventions, and outcomes were analyzed. Mean age was 34.7 (SD 13.1), male:female ratio was 120:16. Average admission Glasgow Coma Scale (GCS) score was 6 (SD 3.9). Entry site was frontal in 41, temporal in 55, parietal in 18, occipital in 6, suboccipital in 9, temporo-parietal in 1, and frontobasal-temporal in 6. Projectiles crossed multiple dural compartments in 76 (55%) patients. 35 TICAs were diagnosed in 28 subject: 24 were located along the middle cerebral artery (MCA), 6 in the anterior cerebral artery (ACA), 3 in the internal carotid artery (ICA), 1 in the posterior cerebral artery (PCA), and 1 in the middle meningeal artery (MMA). Eleven TICAs resolved spontaneously in nine patients. Eight aneurysms were treated by endovascular means, two via combined endovascular/open approaches. Forty-nine patients died, 10 of whom had 15 TICAs. Eighty patients developed intracerebral hematoma s (ICHs). Regression models showed that the presence of an ICH was the main predictor of TICA in cGSWH. Larger ICHs (average 22.3 cc vs. 9.4 cc in patients with and without aneurysms, respectively) in patients with cGSWH suggest hidden TICAs. Nearly 30% of patients had spontaneous resolution within 1 week. When CTA was performed acutely, TICAs were 10 times more frequent in cGSWH than in previous literature, and those patients were more likely to proceed to surgery. Almost one third of patients in this series died from the devastating effects of cGSWH.

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral (“what”) visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

Traumatic axonal injury (TAI) is a common finding on magnetic resonance imaging (MRI) in patients with moderate–severe traumatic brain injury (TBI), and the burden of TAI is associated with outcome in this patient group. Lesion mapping offers a way to combine imaging findings from numerous individual patients into common lesion maps where the findings from a whole patient cohort can be assessed. The aim of this study was to evaluate the spatial distribution of TAI lesions on different MRI sequences and its associations to outcome with use of lesion mapping. Included prospectively were 269 patients (8–70 years) with moderate or severe TBI and MRI within six weeks after injury. The TAI lesions were evaluated and manually segmented on fluid-attenuated inversed recovery (FLAIR), diffusion weighted imaging (DWI), and either T2* gradient echo (T2*GRE) or susceptibility weighted imaging (SWI). The segmentations were registered to the Montreal Neurological Institute space and combined to lesion frequency distribution maps. Outcome was assessed with Glasgow Outcome Scale Extended (GOSE) score at 12 months. The frequency and distribution of TAI was assessed qualitatively by visual reading. Univariable associations to outcome were assessed qualitatively by visual reading and also quantitatively with use of voxel-based lesion-symptom mapping (VLSM). The highest frequency of TAI was found in the posterior half of corpus callosum. The frequency of TAI was higher in the frontal and temporal lobes than in the parietal and occipital lobes, and in the upper parts of the brainstem than in the lower. At the group level, all voxels in mesencephalon had TAI on FLAIR. The patients with poorest outcome (GOSE scores ≤4) had higher frequencies of TAI. On VLSM, poor outcome was associated with TAI lesions bilaterally in the splenium, the right side of tectum, tegmental mesencephalon, and pons. In conclusion, we found higher frequency of TAI in posterior corpus callosum, and TAI in splenium, mesencephalon, and pons were associated with poor outcome. If lesion frequency distribution maps containing outcome information based on imaging findings from numerous patients in the future can be compared with the imaging findings from individual patients, it would offer a new tool in the clinical workup and outcome prediction of the patient with TBI.

Traumatic brain injury (TBI) causes significant neurophysiological deficits and is typically associated with rapid head accelerations common in sports-related incidents and automobile accidents. There are over 1.5 million TBIs in the United States each year, with children aged 0–4 being particularly vulnerable. TBI diagnosis is currently achieved through interpretation of clinical signs and symptoms and neuroimaging; however, there is increasing interest in minimally invasive fluid biomarkers to detect TBI objectively across all ages. Pre-clinical porcine models offer controlled conditions to evaluate TBI with known biomechanical conditions and without comorbidities. The objective of the current study was to establish pediatric porcine healthy reference ranges (RRs) of common human serum TBI biomarkers and to report their acute time-course after nonimpact rotational head injury. A retrospective analysis was completed to quantify biomarker concentrations in porcine serum samples collected from 4-week-old female (n = 215) and uncastrated male (n = 6) Yorkshire piglets. Subjects were assigned to one of three experimental groups (sham, sagittal-single, sagittal-multiple) or to a baseline only group. A rapid nonimpact rotational head injury model was used to produce mild-to-moderate TBI in piglets following a single rotation and moderate-to-severe TBI following multiple rotations. The Quanterix Simoa Human Neurology 4-Plex A assay was used to quantify glial fibrillary acidic protein (GFAP), neurofilament light (Nf-L), tau, and ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1). The 95% healthy RRs for females were calculated and validated for GFAP (6.3–69.4 pg/mL), Nf-L (9.5–67.2 pg/mL), and UCH-L1 (3.8–533.7 pg/mL). Rising early, GFAP increased significantly above the healthy RRs for sagittal-single (to 164 and 243 pg/mL) and increased significantly higher in sagittal-multiple (to 494 and 413 pg/mL) groups at 30 min and 1 h postinjury, respectively, returning to healthy RRs by 1-week postinjury. Rising later, Nf-L increased significantly above the healthy RRs by 1 day in sagittal-single (to 69 pg/mL) and sagittal-multiple groups (to 140 pg/mL) and rising further at 1 week (single = 231 pg/mL, multiple = 481 pg/mL). Sagittal-single and sagittal-multiple UCH-L1 serum samples did not differ from shams or the healthy RRs. Sex differences were observed but inconsistent. Serum GFAP and Nf-L levels had distinct time-courses following head rotations in piglets, and both corresponded to load exposure. We conclude that serum GFAP and Nf-L offer promise for early TBI diagnosis and intervention decisions for TBI and other neurological trauma.

Persistent symptoms are common after a mild traumatic brain injury (mTBI). The Post-Concussion Symptoms (PoCS) Rule is a newly developed clinical decision rule for the prediction of persistent post-concussion symptoms (PPCS) 3 months after an mTBI. The PoCS Rule includes assessment of demographic and clinical characteristics and headache presence in the emergency department (ED), and follow-up assessment of symptoms at 7 days post-injury using two thresholds (lower/higher) for symptom scoring. We examined the PoCS Rule in an independent sample. We analyzed a clinical trial that recruited participants with mTBI from EDs in Greater Vancouver, Canada. The primary analysis used data from 236 participants, who were randomized to a usual care control group, and completed the Rivermead Postconcussion Symptoms Questionnaire at 3 months. The primary outcome was PPCS, as defined by the PoCS authors. We assessed the overall performance of the PoCS rule (area under the receiver operating characteristic curve [AUC]), sensitivity, and specificity. More than 40% of participants (median age 38 years, 59% female) reported PPCS at 3 months. Most participants (88%) were categorized as being at medium risk based on the ED assessment, and a majority were considered as being at high risk according to the final PoCS Rule (81% using a lower threshold and 72% using a higher threshold). The PoCS Rule showed a sensitivity of 93% (95% confidence interval [CI], 88–98; lower threshold) and 85% (95% CI, 78–92; higher threshold), and a specificity of 28% (95% CI, 21–36) and 37% (95% CI, 29–46), respectively. The overall performance was modest (AUC 0.61, 95% CI 0.59, 0.65). In conclusion, the PoCS Rule was sensitive for PPCS, but had a low specificity in our sample. Follow-up assessment of symptoms can improve risk stratification after mTBI.

Traumatic brain injuries (TBIs) can lead to long-lasting cognitive impairments, and some survivors experience cognitive decline post-recovery. Early detection of decline is important for care planning, and understanding risk factors for decline can elucidate targets for prevention. While neuropsychological testing is the gold standard approach to characterizing cognitive function, there is a need for brief, scalable tools that are capable of detecting clinically significant changes in post-TBI cognition. This study examines whether a clinically significant change can be detected using the Brief Test of Adult Cognition by Telephone (BTACT) in a sample of individuals with chronic TBI and investigates whether potentially modifiable factors are associated with cognitive decline. Ninety participants aged 40 or older with complicated mild-to-severe TBI participated in two telephone-based study visits ∼1 year apart. Demographic, head trauma exposure, comorbid medical conditions, physical, and psychosocial functioning data were collected via self-report. The BTACT, a brief measure of global cognitive function, was used to assess cognitive performance across six domains. A reliable change index for quantifying clinically significant changes in BTACT performance was calculated. Results revealed cognitive decline in 10–27% of participants across various cognitive domains. More specifically, only depressive symptoms, including depressed affect and anhedonia, were significantly associated with cognitive decline after correcting for multiple comparisons using false discovery rate (FDR). Other factors such as the number of blows to the head, male gender, dyspnea, increased anxiety symptoms, seizures, illicit drug use, and fewer cardiovascular comorbidities should be considered hypothesis generating. Importantly, age was not a significant predictor of cognitive decline, which challenges the assumption that cognitive decline is solely related to the natural aging process. It suggests that there are unique factors associated with TBI that impact cognitive function, and these factors can affect individuals across the lifespan. The BTACT is a brief and sensitive tool for identifying clinically meaningful changes in cognitive function over a relatively brief period (i.e., 1 year) in a sample of individuals in the chronic stages of TBI (i.e., x̄ = 6.7 years post-TBI). Thus, the BTACT may be useful in surveillance efforts aimed at understanding and detecting decline, particularly in situations where in-person cognitive screening is impractical or unfeasible. We also identified potentially modifiable targets for the prevention of post-TBI cognitive decline. These findings can offer insights into treatment goals and preventive strategies for individuals at risk for cognitive decline, as well as help to facilitate early identification efforts.