A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury

Consensus paper on the assessment of adult patients with traumatic brain injury with Glasgow Coma Scale 13-15 at the emergency department: A multidisciplinary overview

Traumatic brain injury (TBI) is a common reason for presenting to emergency departments (EDs). The assessment of these patients is frequently hampered by various confounders, and diagnostics is still often based on nonspecific clinical signs. Throughout Europe, there is wide variation in clinical practices, including the follow-up of those discharged from the ED. The objective is to present a practical recommendation for the assessment of adult patients with an acute TBI, focusing on milder cases not requiring in-hospital care. The aim is to advise on and harmonize practices for European settings. A multiprofessional expert panel, giving consensus recommendations based on recent scientific literature and clinical practices, is employed. The focus is on patients with a preserved consciousness (Glasgow Coma Scale 13-15) not requiring in-hospital care after ED assessment. The main results of this paper contain practical, clinically usable recommendations for acute clinical assessment, decision-making on acute head computerized tomography (CT), use of biomarkers, discharge options, and needs for follow-up, as well as a discussion of the main features and risk factors for prolonged recovery. In conclusion, this consensus paper provides a practical stepwise approach for the clinical assessment of patients with an acute TBI at the ED. Recommendations are given for the performance of acute head CT, use of brain biomarkers and disposition after ED care including careful patient information and organization of follow-up for those discharged.

Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury

PURPOSE

Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.

METHODS

This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve.

RESULTS

According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 - 25.42, p = 0.002), 2.85 (95% CI: 1.11 - 7.31, p = 0.030), 2.62 (95% CI: 1.12 - 6.13, p = 0.027), 2.44 (95% CI: 1.25 - 4.76, p = 0.009), and 1.5 (95% CI: 1.10 - 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively.

CONCLUSION

Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

Identification and management of low-risk isolated traumatic brain injury patients initially treated at a rural level IV trauma center

STUDY OBJECTIVE

Our goal was to determine if low-risk, isolated mild traumatic brain injury (TBI) patients who were initially treated at a rural emergency department may have been safely managed without transfer to the tertiary referral trauma center.

METHODS

This was a retrospective observational analysis of isolated mild TBI patients who were transferred from a rural Level IV Trauma Center to a regional Level I Trauma Center between 2018 and 2022. Patients were risk-stratified according to the modified Brain Injury Guidelines (mBIG). Data abstracted from the electronic medical record included patient presentation, management, and outcomes.

RESULTS

250 patients with isolated mild TBI were transferred out to the Level I Trauma Center. Fall was the most common mechanism of injury (69.2%). 28 patients (11.2%) were categorized as low-risk (mBIG1). No mBIG1 patients suffered a progression of neurological injury, had worsening of intracranial hemorrhage on repeat head CT, or required neurosurgical intervention. 12/28 (42.9%) of mBIG1 patients had a hospital length of stay of 2 days or less, typically for observation. Those with longer lengths of stay were due to medical complications, such as sepsis, or difficulty in arranging disposition.

CONCLUSION

We propose that patients who meet mBIG1 criteria may be safely observed without transfer to a referral Level I Trauma Center. This would be of considerable benefit to patients, who would not need to leave their community, and would improve resource utilization in the region.

Fully automated sinogram-based deep learning model for detection and classification of intracranial hemorrhage

PURPOSE

To propose an automated approach for detecting and classifying Intracranial Hemorrhages (ICH) directly from sinograms using a deep learning framework. This method is proposed to overcome the limitations of the conventional diagnosis by eliminating the time-consuming reconstruction step and minimizing the potential noise and artifacts that can occur during the Computed Tomography (CT) reconstruction process.

METHODS

This study proposes a two-stage automated approach for detecting and classifying ICH from sinograms using a deep learning framework. The first stage of the framework is Intensity Transformed Sinogram Sythesizer, which synthesizes sinograms that are equivalent to the intensity-transformed CT images. The second stage comprises of a cascaded Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model that detects and classifies hemorrhages from the synthesized sinograms. The CNN module extracts high-level features from each input sinogram, while the RNN module provides spatial correlation of the neighborhood regions in the sinograms. The proposed method was evaluated on a publicly available RSNA dataset consisting of a large sample size of 8652 patients.

RESULTS

The results showed that the proposed method had a notable improvement as high as 27% in patient-wise accuracies when compared to state-of-the-art methods like ResNext-101, Inception-v3 and Vision Transformer. Furthermore, the sinogram-based approach was found to be more robust to noise and offset errors in comparison to CT image-based approaches. The proposed model was also subjected to a multi-label classification analysis to determine the hemorrhage type from a given sinogram. The learning patterns of the proposed model were also examined for explainability using the activation maps.

CONCLUSION

The proposed sinogram-based approach can provide an accurate and efficient diagnosis of ICH without the need for the time-consuming reconstruction step and can potentially overcome the limitations of CT image-based approaches. The results show promising outcomes for the use of sinogram-based approaches in detecting hemorrhages, and further research can explore the potential of this approach in clinical settings.

Preclinical characterization of macrophage-adhering gadolinium micropatches for MRI contrast after traumatic brain injury in pigs

The choroid plexus (ChP) of the brain plays a central role in orchestrating the recruitment of peripheral leukocytes into the central nervous system (CNS) through the blood-cerebrospinal fluid (BCSF) barrier in pathological conditions, thus offering a unique niche to diagnose CNS disorders. We explored whether magnetic resonance imaging of the ChP could be optimized for mild traumatic brain injury (mTBI). mTBI induces subtle, yet influential, changes in the brain and is currently severely underdiagnosed. We hypothesized that mTBI induces sufficient alterations in the ChP to cause infiltration of circulating leukocytes through the BCSF barrier and developed macrophage-adhering gadolinium [Gd(III)]-loaded anisotropic micropatches (GLAMs), specifically designed to image infiltrating immune cells. GLAMs are hydrogel-based discoidal microparticles that adhere to macrophages without phagocytosis. We present a fabrication process to prepare GLAMs at scale and demonstrate their loading with Gd(III) at high relaxivities, a key indicator of their effectiveness in enhancing image contrast and clarity in medical imaging. In vitro experiments with primary murine and porcine macrophages demonstrated that GLAMs adhere to macrophages also under shear stress and did not affect macrophage viability or functions. Studies in a porcine mTBI model confirmed that intravenously administered macrophage-adhering GLAMs provide a differential signal in the ChP and lateral ventricles at Gd(III) doses 500- to 1000-fold lower than those used in the current clinical standard Gadavist. Under the same mTBI conditions, Gadavist did not offer a differential signal at clinically used doses. Our results suggest that macrophage-adhering GLAMs could facilitate mTBI diagnosis.

Lifetime Blast Exposure Is Not Related to White Matter Integrity in Service Members and Veterans With and Without Uncomplicated Mild Traumatic Brain Injury

This study examines the impact of lifetime blast exposure on white matter integrity in service members and veterans (SMVs). Participants were 227 SMVs, including those with a history of mild traumatic brain injury (mTBI; n = 124), orthopedic injury controls (n = 58), and non-injured controls (n = 45), prospectively enrolled in a Defense and Veterans Brain Injury Center (DVBIC)/Traumatic Brain Injury Center of Excellence (TBICoE) study. Participants were divided into three groups based on number of self-reported lifetime blast exposures: none (n = 53); low (i.e., 1-9 blasts; n = 81); and high (i.e., ≥10 blasts; n = 93). All participants underwent diffusion tensor imaging (DTI) at least 11 months post-injury. Tract-of-interest (TOI) analysis was applied to investigate fractional anisotropy and mean, radial, and axial diffusivity (AD) in left and right total cerebral white matter as well as 24 tracts. Benjamini-Hochberg false discovery rate (FDR) correction was used. Regressions investigating blast exposure and mTBI on white matter integrity, controlling for age, revealed that the presence of mTBI history was associated with lower AD in the bilateral superior longitudinal fasciculus and arcuate fasciculus and left cingulum (βs = -0.255 to -0.174; ps < 0.01); however, when non-injured controls were removed from the sample (but orthopedic injury controls remained), these relationships were attenuated and did not survive FDR correction. Regression models were rerun with modified post-traumatic stress disorder (PTSD) diagnosis added as a predictor. After FDR correction, PTSD was not significantly associated with white matter integrity in any of the models. Overall, there was no relationship between white matter integrity and self-reported lifetime blast exposure or PTSD.

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Non-Invasive Continuous Optical Monitoring of Cerebral Blood Flow after Traumatic Brain Injury in Mice Using Fiber Camera-Based Speckle Contrast Optical Spectroscopy

Neurocritical care focuses on monitoring cerebral blood flow (CBF) to prevent secondary brain injuries before damage becomes irreversible. Thus, there is a critical unmet need for continuous neuromonitoring methods to quantify CBF within the vulnerable cortex continuously and non-invasively. Animal models and imaging biomarkers can provide valuable insights into the mechanisms and kinetics of head injury, as well as insights for potential treatment strategies. For this purpose, we implemented an optical technique for continuous monitoring of blood flow changes after a closed head injury in a mouse model, which is based on laser speckle contrast imaging and a fiber camera-based approach. Our results indicate a significant decrease (~10%, p-value < 0.05) in blood flow within 30 min of a closed head injury. Furthermore, the low-frequency oscillation analysis also indicated much lower power in the trauma group compared to the control group. Overall, blood flow has the potential to be a biomarker for head injuries in the early phase of a trauma, and the system is useful for continuous monitoring with the potential for clinical translation.

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

Optimization of Nutrition after Brain Injury: Mechanistic and Therapeutic Considerations

Emerging science continues to establish the detrimental effects of malnutrition in acute neurological diseases such as traumatic brain injury, stroke, status epilepticus and anoxic brain injury. The primary pathological pathways responsible for secondary brain injury include neuroinflammation, catabolism, immune suppression and metabolic failure, and these are exacerbated by malnutrition. Given this, there is growing interest in novel nutritional interventions to promote neurological recovery after acute brain injury. In this review, we will describe how malnutrition impacts the biomolecular mechanisms of secondary brain injury in acute neurological disorders, and how nutritional status can be optimized in both pediatric and adult populations. We will further highlight emerging therapeutic approaches, including specialized diets that aim to resolve neuroinflammation, immunodeficiency and metabolic crisis, by providing pre-clinical and clinical evidence that their use promotes neurologic recovery. Using nutrition as a targeted treatment is appealing for several reasons that will be discussed. Given the high mortality and both short- and long-term morbidity associated with acute brain injuries, novel translational and clinical approaches are needed.

Plasma phosphorylated tau181 as a biomarker of mild traumatic brain injury: findings from THINC and NCAA-DoD CARE Consortium prospective cohorts

Objective

The aim of this study was to investigate phosphorylated tau (p-tau181) protein in plasma in a cohort of mild traumatic brain injury (mTBI) patients and a cohort of concussed athletes.

Methods

This pilot study comprised two independent cohorts. The first cohort-part of a Traumatic Head Injury Neuroimaging Classification (THINC) study-with a mean age of 46 years was composed of uninjured controls (UIC, n = 30) and mTBI patients (n = 288) recruited from the emergency department with clinical computed tomography (CT) and research magnetic resonance imaging (MRI) findings. The second cohort-with a mean age of 19 years-comprised 133 collegiate athletes with (n = 112) and without (n = 21) concussions. The participants enrolled in the second cohort were a part of a multicenter, prospective, case-control study conducted by the NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium at six CARE Advanced Research Core (ARC) sites between 2015 and 2019. Blood was collected within 48 h of injury for both cohorts. Plasma concentration (pg/ml) of p-tau181 was measured using the Single Molecule Array ultrasensitive assay.

Results

Concentrations of plasma p-tau181 in both cohorts were significantly elevated compared to controls within 48 h of injury, with the highest concentrations of p-tau181 within 18 h of injury, with an area under the curve (AUC) of 0.690-0.748, respectively, in distinguishing mTBI patients and concussed athletes from controls. Among the mTBI patients, the levels of plasma p-tau181 were significantly higher in patients with positive neuroimaging (either CT+/MRI+, n = 74 or CT-/MRI+, n = 89) compared to mTBI patients with negative neuroimaging (CT-/MRI-, n = 111) findings and UIC (P-values < 0.05).

Conclusion

These findings indicate that plasma p-tau181 concentrations likely relate to brain injury, with the highest levels in patients with neuroimaging evidence of injury. Future research is needed to replicate and validate this protein assay's performance as a possible early diagnostic biomarker for mTBI/concussions.

Plasma neurofilament light admission levels and development of axonal pathology in mild traumatic brain injury

BACKGROUND

It is known that blood levels of neurofilament light (NF-L) and diffusion-weighted magnetic resonance imaging (DW-MRI) are both associated with outcome of patients with mild traumatic brain injury (mTBI). Here, we sought to examine the association between admission levels of plasma NF-L and white matter (WM) integrity in post-acute stage DW-MRI in patients with mTBI.

METHODS

Ninety-three patients with mTBI (GCS ≥ 13), blood sample for NF-L within 24 h of admission, and DW-MRI ≥ 90 days post-injury (median = 229) were included. Mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated from the skeletonized WM tracts of the whole brain. Outcome was assessed using the Extended Glasgow Outcome Scale (GOSE) at the time of imaging. Patients were divided into CT-positive and -negative, and complete (GOSE = 8) and incomplete recovery (GOSE < 8) groups.

RESULTS

The levels of NF-L and FA correlated negatively in the whole cohort (p = 0.002), in CT-positive patients (p = 0.016), and in those with incomplete recovery (p = 0.005). The same groups showed a positive correlation with mean MD, AD, and RD (p < 0.001-p = 0.011). In CT-negative patients or in patients with full recovery, significant correlations were not found.

CONCLUSION

In patients with mTBI, the significant correlation between NF-L levels at admission and diffusion tensor imaging (DTI) measurements of diffuse axonal injury (DAI) over more than 3 months suggests that the early levels of plasma NF-L may associate with the presence of DAI at a later phase of TBI.

Anti-inflammatory interleukin 1 receptor antagonist concentration in plasma correlates with blood-brain barrier integrity in the primary lesion area in traumatic brain injury patients

Purpose

Blood-brain barrier (BBB) dysregulation and pro-inflammatory signalling molecules are secondary factors that have been associated with injury severity and long-term clinical outcome following traumatic brain injury (TBI). However, the association between BBB permeability and inflammation is unknown in human TBI patients. In this study, we investigated whether BBI integrity as measured by Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Imaging (MRI) correlates with plasma levels of immunological markers following TBI.

Methods

Thirty-two TBI patients recruited from a neurosurgical unit were included in the study. Structural three-dimensional T1-weighted and DCE-MRI images were acquired on a 3T MRI at the earliest opportunity once the participant was sufficiently stable after patient admission to hospital. Blood sampling was performed on the same day as the MRI. The location and extents of the haemorrhagic and contusional lesions were identified. Immunological biomarkers were quantified from the participants' plasma using a multiplex immunoassay. Demographic and clinical information, including age and Glasgow Coma Scale (GCS) were also collected and the immunological biomarker profiles were compared across controls and the TBI severity sub-groups. Contrast agent leakiness through blood-brain barriers (BBB) in the contusional lesions were assessed by fitting DCE-MRI using Patlak model and BBB leakiness characteristics of the participants were correlated with the immunological biomarker profiles.

Results

TBI patients showed reduced plasma levels of interleukin (IL)-1β, IFN-γ, IL-13, and chemokine (C-C motif) ligands (CCL)2 compared to controls and significantly higher levels of platelet-derived growth factor (PDGF-BB), IL-6, and IL-8. BBB leakiness of the contusional lesions did not significantly differ across different TBI severity sub-groups. IL-1ra levels significantly and positively correlated with the contusional lesion's BBB integrity as measured with DCE-MRI via an exponential curve relationship.

Discussion

This is the first study to combine DCE-MRI with plasma markers of inflammation in acute TBI patients. Our finding that plasma levels of the anti-inflammatory cytokine IL-1ra correlated negatively with increased leakiness of the BBB.

Neurophysiological Markers to Guide Return to Sport After Sport-Related Concussion

SUMMARY

Sport-related concussion (SRC) has been defined as a subset of mild traumatic brain injury (mTBI), without structural abnormalities, reflecting a functional disturbance. Over the past decade, SRC has gained increasing awareness and attention, which coincides with an increase in incidence rates. Because this injury has been considered one of the most challenging encounters for clinicians, there is a need for objective biomarkers to aid in diagnosis (i.e., presence/severity) and management (i.e., return to sport) of SRC/mTBI.The primary aim of this article was to present state-of-the-art neurophysiologic methods (e.g., electroencephalography, magnetoencephalography, transcranial magnetic stimulation, and autonomic nervous system) that are appropriate to investigate the complex pathophysiological process of a concussion. A secondary aim was to explore the potential for evidence-based markers to be used in clinical practice for SRC management. The article concludes with a discussion of future directions for SRC research with specific focus on clinical neurophysiology.

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Objective

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.

Method

Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Result

The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.

Conclusion

The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Conventional MR Imaging in Trauma Management in Adults

MR imaging has been shown to have higher sensitivity than computed tomography (CT) for traumatic intracranial soft tissue injuries as well as most cases of intracranial hemorrhage, thus making it a significant adjunct to CT in the management of traumatic brain injury, mostly in the subacute to chronic phase, but may also be of use in the acute phase, when there are persistent neurologic symptoms unexplained by prior imaging.

White Matter Integrity Relates to Cognition in Service Members and Veterans after Complicated Mild, Moderate, and Severe Traumatic Brain Injury, But Not Uncomplicated Mild Traumatic Brain Injury

The extant literature investigating the relationship between diffusion tensor imaging (DTI) and cognition following traumatic brain injury (TBI) is limited by small sample sizes and inappropriate control groups. The present study examined DTI metric differences between service members and veterans (SMVs) with bodily injury (Trauma Control; TC), uncomplicated mild TBI (mTBI), complicated mild TBI (compTBI), and severe-moderate TBI combined (smTBI), and how DTI metrics related to cognition within each group. Participants were 226 SMVs (56 TC, 112 mTBI, 29 compTBI, 29 smTBI) with valid neuropsychological testing and DTI at least 11 months post-injury. The smTBI group demonstrated decreased fractional anisotropy (FA) and increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of the cerebral white matter (CWM) and several individual white matter tracts compared with the TC, mTBI, and compTBI groups (all ps < 0.05; rs = 0.17 to 0.49). The TC, mTBI, and compTBI groups did not differ in terms of any DTI metrics. Within the smTBI group, FA, AD, MD, and RD of the total CWM and several white matter tracts were related to Processing Speed (|rs|: 0.43 to 0.66; ps < 0.05), and/or Delayed Memory (|rs|: 0.41 to 0.67; ps < 0.05). In the compTBI group, Processing Speed was related to left arcuate fasciculus and superior longitudinal fasciculus (SLF) FA, MD, and RD, as well as left uncinate fasciculus MD and RD. In contrast, there were no significant relationships between DTI metrics and cognition/emotional functioning within the mTBI or TC groups. Overall, findings suggest a dose-response relationship between TBI severity and the strength of the relationship between white matter integrity and cognitive performance, with essentially no relationship in mTBI, some findings in compTBI, and several strongly significant relationships in smTBI. In contrast to previously reported findings, there were no differences in DTI metrics between controls, mTBI, and compTBI, and DTI metrics were unrelated to cognition in our relatively large mTBI group.

Near-infrared-IIb emitting single-atom catalyst for imaging-guided therapy of blood-brain barrier breakdown after traumatic brain injury

The blood-brain barrier breakdown, as a prominent feature after traumatic brain injury, always triggers a cascade of biochemical events like inflammatory response and free radical-mediated oxidative damage, leading to neurological dysfunction. The dynamic monitoring the status of blood-brain barrier will provide potent guidance for adopting appropriate clinical intervention. Here, we engineer a near-infrared-IIb Ag₂Te quantum dot-based Mn single-atom catalyst for imaging-guided therapy of blood-brain barrier breakdown of mice after traumatic brain injury. The dynamic change of blood-brain barrier, including the transient cerebral hypoperfusion and cerebrovascular damage, could be resolved with high spatiotemporal resolution (150 ms and ~ 9.6 µm). Notably, the isolated single Mn atoms on the surface of Ag₂Te exhibited excellent catalytic activity for scavenging reactive oxygen species to alleviate neuroinflammation in brains. The timely injection of Mn single-atom catalyst guided by imaging significantly promoted the reconstruction of blood-brain barrier and recovery of neurological function after traumatic brain injury.

Neurovascular hypoxia after mild traumatic brain injury in juvenile mice correlates with heart-brain dysfunctions in adulthood

AIM

Retrospective studies suggest that mild traumatic brain injury (mTBI) in pediatric patients may lead to an increased risk of cardiac events. However, the exact functional and temporal dynamics and the associations between heart and brain pathophysiological trajectories are not understood.

METHODS

A single impact to the left somatosensory cortical area of the intact skull was performed on juvenile mice (17 days postnatal). Cerebral 3D photoacoustic imaging was used to measure the oxygen saturation (sO₂ ) in the impacted area 4 h after mTBI followed by 2D and 4D echocardiography at days 7, 30, 90, and 190 post-impact. At 8 months, we performed a dobutamine stress test to evaluate cardiac function. Lastly, behavioral analyses were conducted 1 year after initial injury.

RESULTS

We report a rapid and transient decrease in cerebrovascular sO₂ and increased hemoglobin in the impacted left brain cortex. Cardiac analyses showed long-term diastolic dysfunction and a diminished systolic strain response under stress in the mTBI group. At the molecular level, cardiac T-p38MAPK and troponin I expression was pathologic modified post-mTBI. We found linear correlations between brain sO₂ measured immediately post-mTBI and long-term cardiac strain after 8 months. We report that initial cerebrovascular hypoxia and chronic cardiac dysfunction correlated with long-term behavioral changes hinting at anxiety-like and memory maladaptation.

CONCLUSION

Experimental juvenile mTBI induces time-dependent cardiac dysfunction that corresponds to the initial neurovascular sO₂ dip and is associated with long-term behavioral modifications. These imaging biomarkers of the heart-brain axis could be applied to improve clinical pediatric mTBI management.

Automatically Diagnosing Skull Fractures Using an Object Detection Method and Deep Learning Algorithm in Plain Radiography Images

OBJECTIVE

Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability.

METHODS

A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance.

RESULTS

In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anteriorposterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor.

CONCLUSION

The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.

Temporary or Permanent? A Clinical Challenge in the Evaluation of Traumatic Brain Injury Patients with Unconsciousness and Normal Initial Head CT

BACKGROUND

Traumatic brain injury (TBI) patients with unconsciousness and normal initial head computed tomography (CT) present a clinical dilemma for physicians and neurosurgeons in the emergency department (ED). We recorded how long it took for patients to regain consciousness and evaluated the patients' characteristics.

METHODS

From 2018 to 2020, TBI patients with unconsciousness and normal initial head CT [Glasgow coma scale (GCS) score < 13, negative CT scan and normal laboratory test results] were evaluated. Patients who regained consciousness were analyzed. Multivariate logistic regression (MLR) analyses were used to evaluate independent factors for regaining consciousness.

RESULTS

A total of 77 patients were included in this study. Fifty-eight (75.3%) patients regained consciousness, most within one day (43.1%). Nineteen (24.7%) patients never regained consciousness. MLR analysis showed that initial GCS score (odds 1.85, p = 0.017), early airway protection in ED (odds 25.02, p = 0.018) and 72-h GCS score improvement by two points (odds 0.02, p = 0.001) were independent factors for regaining consciousness. Overall, 94.1% of patients who received early airway protection and improved 2 points in 72-h GCS score regained consciousness. The association between days to M5 status and days to M6 status (consciousness) was highly significant. Fewer days to M5 status were highly associated with needing fewer days to regain consciousness.

CONCLUSIONS

For TBI patients with unconsciousness and normal initial head CT, a higher probability of regaining consciousness was observed in those who underwent early airway protection and who improved 2 points in 72-h GCS score. Regaining consciousness within a short period could be expected in patients with M5 status.

Traumatic Brain Injury and Secondary Neurodegenerative Disease

Traumatic brain injury (TBI) is a devastating event with severe long-term complications. TBI and its sequelae are one of the leading causes of death and disability in those under 50 years old. The full extent of secondary brain injury is still being intensely investigated; however, it is now clear that neurotrauma can incite chronic neurodegenerative processes. Chronic traumatic encephalopathy, Parkinson's disease, and many other neurodegenerative syndromes have all been associated with a history of traumatic brain injury. The complex nature of these pathologies can make clinical assessment, diagnosis, and treatment challenging. The goal of this review is to provide a concise appraisal of the literature with focus on emerging strategies to improve clinical outcomes. First, we review the pathways involved in the pathogenesis of neurotrauma-related neurodegeneration and discuss the clinical implications of this rapidly evolving field. Next, because clinical evaluation and neuroimaging are essential to the diagnosis and management of neurodegenerative diseases, we analyze the clinical investigations that are transforming these areas of research. Finally, we briefly review some of the preclinical therapies that have shown the most promise in improving outcomes after neurotrauma.

Impact of acute stress on neural indices of positive and negative reinforcement processing in cannabis users

OBJECTIVE

Chronic cannabis use is maintained in part through dysregulated stress and reward response systems. Specifically, stress-related negative affect is thought to act as a salient motivator for chronic substance use. Models of addiction posit that the transition from positive to negative reinforcement motives for substance use is a key mechanism of disordered use. However, research in substance-using samples has not assessed stress-related neural processing of both positive and negative reinforcement.

METHOD

Therefore, the present study utilized laboratory stress induction to examine how stress affects the reward positivity, an event-related potential sensitive to both positive (RewP) and negative (relief-RewP) reinforcement, in 87 cannabis users (58.10% female, Mage = 19.40) varying in cannabis use disorder (CUD) severity and, as part of larger study aims, history of traumatic brain injury (TBI). We predicted greater CUD severity would be associated with a blunted RewP and enhanced relief-RewP, particularly after stress induction, independent of TBI status.

RESULTS

Findings indicated that CUD severity was not associated with RewP/relief-RewP amplitude regardless of acute stress. Exploratory analyses revealed, however, that among those with history of TBI +, CUD severity was associated with greater stress-elicited blunting of the RewP and enhancement of the relief-RewP.

CONCLUSION

Although initial findings contradict current allostatic models of addiction, exploratory findings suggest that history of TBI, and potentially other confounding variables related to increased risk of TBI experience, may influence the extent to which stressful experiences modulate the neurophysiology of both positive and negative reinforcement reward processing in CUD. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Scoping Review of Magnetic Resonance Modalities Used in Detection of Persistent Postconcussion Symptoms in Pediatric Populations

Up to 30% of youth with concussion experience PPCSs (PPCS) lasting 4 weeks or longer, and can significantly impact quality of life. Magnetic resonance imaging (MRI) has the potential to increase understanding of causal mechanisms underlying PPCS. However, there are no clear modalities to assist in detecting PPCS. This scoping review aims to synthesize findings on utilization of MRI among children and youth with PPCS, and summarize progress and limitations. Thirty-six studies were included from 4907 identified papers. Many studies used multiple modalities, including (1) structural (n = 27) such as T1-weighted imaging, diffusion weighted imaging, and susceptibility weighted imaging; and (2) functional (n = 23) such as functional MRI and perfusion-weighted imaging. Findings were heterogeneous among modalities and regions of interest, which warrants future reviews that report on the patterns and potential advancements in the field. Consideration of modalities that target PPCS prediction and sensitive modalities that can supplement a biopsychosocial approach to PPCS would benefit future research.

Traumatic brain injury: progress and challenges in prevention, clinical care, and research

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents.

Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery.

The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition.

Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers.

In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI.

Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation.

Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority.

This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1 ); system of care (section 2 ); clinical management (section 3 ); characterisation of TBI (section 4 ); outcome assessment (section 5 ); prognosis (Section 6 ); and new directions for acquiring and implementing evidence (section 7 ). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients' brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by 7.5% in dice score, 23.91% insensitivity, and 7.09% in specificity. Depth-wise separable MI-Unet showed a performance increase by 10.83% in dice score, 2.97% in sensitivity, and 12.72% in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of 9.71% in dice score, 3.56% in sensitivity, and 12.6% in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by 15.45% in dice score, 20.56% in sensitivity, and 12.22% in specificity.

Disruption of white matter integrity and its relationship with cognitive function in non-severe traumatic brain injury

Background

Impairment in cognitive function is a recognized outcome of traumatic brain injury (TBI). However, the degree of impairment has variable relationship with TBI severity and time post injury. The underlying pathology is often due to diffuse axonal injury that has been found even in mild TBI. In this study, we examine the state of white matter putative connectivity in patients with non-severe TBI in the subacute phase, i.e., within 10 weeks of injury and determine its relationship with neuropsychological scores.

Methods

We conducted a case-control prospective study involving 11 male adult patients with non-severe TBI and an age-matched control group of 11 adult male volunteers. Diffusion MRI scanning and neuropsychological tests were administered within 10 weeks post injury. The difference in fractional anisotropy (FA) values between the patient and control groups was examined using tract-based spatial statistics. The FA values that were significantly different between patients and controls were then correlated with neuropsychological tests in the patient group.

Results

Several clusters with peak voxels of significant FA reductions (p < 0.05) in the white matter skeleton were seen in patients compared to the control group. These clusters were located in the superior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus, and cingulum, as well as white matter fibers in the area of genu of corpus callosum, anterior corona radiata, superior corona radiata, anterior thalamic radiation and part of inferior frontal gyrus. Mean global FA magnitude correlated significantly with MAVLT immediate recall scores while matrix reasoning scores correlated positively with FA values in the area of right superior fronto-occipital fasciculus and left anterior corona radiata.

Conclusion

The non-severe TBI patients had abnormally reduced FA values in multiple regions compared to controls that correlated with several measures of executive function during the sub-acute phase of TBI.

Molecular diagnostics in neurotrauma: Are there reliable biomarkers and effective methods for their detection?

To date, a large number of studies are being carried out in the field of neurotrauma, researchers not only establish the molecular mechanisms of the course of the disorders, but are also involved in the search for effective biomarkers for early prediction of the outcome and therapeutic intervention. Particular attention is paid to traumatic brain injury and spinal cord injury, due to the complex cascade of reactions in primary and secondary injury that affect pathophysiological processes and regenerative potential of the central nervous system. Despite a wide range of methods available methods to study biomarkers that correlate with the severity and degree of recovery in traumatic brain injury and spinal cord injury, development of reliable test systems for clinical use continues. In this review, we evaluate the results of recent studies looking for various molecules acting as biomarkers in the abovementioned neurotrauma. We also summarize the current knowledge of new methods for studying biological molecules, analyzing their sensitivity and limitations, as well as reproducibility of results. In this review, we also highlight the importance of developing reliable and reproducible protocols to identify diagnostic and prognostic biomolecules.

Amide Proton Transfer-Weighted Magnetic Resonance Imaging for Detecting Severity and Predicting Outcome after Traumatic Brain Injury in Rats

After traumatic brain injury (TBI), early assessment of secondary injury severity is critically important for estimating prognosis and treatment stratification. Currently, secondary injury severity is difficult to estimate. The objective of this study was to investigate the capacity of non-invasive amide proton transfer-weighted (APTw) magnetic resonance imaging (MRI) techniques to assess TBI injury in different brain regions and predict long-term neurobehavior outcomes. Fifty-five male and female rats were subjected to a controlled cortical impact with one of three different impactor depths to produce different degrees of TBI. Multi-parameter MRI data were acquired on a 4.7-Tesla scanner at 1 h, 1 day, and 3 days. Immunofluorescence staining was used to detect activated microglia at 3 days, and neurobehavioral tests were performed to assess long-term outcomes after 28 days. The APTw signal in the injury core at 1 day correlated with deficits in sensorimotor function, the sucrose preference test (a test for anhedonia), and spatial memory function on the Barnes maze. The APTw signal in the perilesion ipsilateral cortex gradually increased after TBI, and the value at 3 days correlated with microglia density at 3 days and with spatial memory decline and anhedonia at 28 days. The correlation between APTw and activated microglia was also observed in the ipsilateral thalamus, and its correlation to memory deficit and depression was evident in other ipsilateral sites. These results suggest that APTw imaging can be used for detecting secondary injury and as a potential predictor of long-term outcomes from TBI.

Pediatric Sports-Related Concussion: An Approach to Care

Sports-related concussion (SRC) is a common sports injury in children and adolescents. With the vast amount of youth sports participation, an increase in awareness of concussion and evidence that the injury can lead to consequences for school, sports and overall quality of life, it has become increasingly important to properly diagnose and manage concussion. SRC in the student athlete is a unique and complex injury, and it is important to highlight the differences in the management of child and adolescent concussion compared with adults. This review focuses on the importance of developing a multimodal systematic approach to diagnosing and managing pediatric sports-related concussion, from the sidelines through recovery.

Combining MRI and cognitive evaluation to classify concussion in university athletes

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R² = 0.32, p < .001), and performed worse on tests of concentration (∆R² = 0.07, p < .05) and delayed memory (∆R² = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

The Legacy of the TTASAAN Report-Premature Conclusions and Forgotten Promises: A Review of Policy and Practice Part I

Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease-comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.

Neuroimaging Biomarkers of New-Onset Psychiatric Disorders Following Traumatic Brain Injury

Traumatic brain injury (TBI) has traditionally been associated with cognitive and behavioral changes during both the acute and chronic phases of injury. Because of its noninvasive nature, neuroimaging has the potential to provide unique information on underlying macroscopic and microscopic biological mechanisms that may serve as causative agents for these neuropsychiatric sequelae. This broad scoping review identifies at least 4 common macroscopic pathways that exist between TBI and new-onset psychiatric disorders, as well as several examples of how neuroimaging is currently being utilized in clinical research. The review then critically examines the strengths and limitations of neuroimaging for elucidating TBI-related microscopic pathology, such as microstructural changes, neuroinflammation, proteinopathies, blood-brain barrier damage, and disruptions in cellular signaling. A summary is then provided for how neuroimaging is currently being used to investigate TBI-related pathology in new-onset neurocognitive disorders, depression, and posttraumatic stress disorder. Identified gaps in the literature include a lack of prospective studies to definitively associate imaging findings with the development of new-onset psychiatric disorders, as well as antemortem imaging studies subsequently confirmed with postmortem correlates in the same study cohort. Although the spatial resolution and specificity of imaging biomarkers has greatly improved over the last 2 decades, we conclude that neuroimaging biomarkers do not yet exist for the definitive in vivo diagnosis of cellular pathology. This represents a necessary next step for further elucidating causal relationships between TBI and new-onset psychiatric disorders.

First-in-clinical application of a time-gated diffuse correlation spectroscopy system at 1064 nm using superconducting nanowire single photon detectors in a neuro intensive care unit

Recently proposed time-gated diffuse correlation spectroscopy (TG-DCS) has significant advantages compared to conventional continuous wave (CW)-DCS, but it is still in an early stage and clinical capability has yet to be established. The main challenge for TG-DCS is the lower signal-to-noise ratio (SNR) when gating for the deeper traveling late photons. Longer wavelengths, such as 1064 nm have a smaller effective attenuation coefficient and a higher power threshold in humans, which significantly increases the SNR. Here, we demonstrate the clinical utility of TG-DCS at 1064 nm in a case study on a patient with severe traumatic brain injury admitted to the neuro-intensive care unit (neuroICU). We showed a significant correlation between TG-DCS early (ρ = 0.67) and late (ρ = 0.76) gated against invasive thermal diffusion flowmetry. We also analyzed TG-DCS at high temporal resolution (50 Hz) to elucidate pulsatile flow data. Overall, this study demonstrates the first clinical translation capability of the TG-DCS system at 1064 nm using a superconducting nanowire single-photon detector.

Neuroimaging correlates of depression after traumatic brain injury: A systematic review

Depression is the most frequent neuropsychiatric complication after traumatic brain injury (TBI) and is associated with poorer outcomes. Neuroimaging has the potential to improve our understanding of the neural correlates of depression after TBI and may improve our capacity to accurately predict and effectively treat this condition. We conducted a systematic review of structural and functional neuroimaging studies that examined the association between depression after TBI, and neuroimaging measures. Electronic searches were conducted in four databases and were complemented by manual searches. In total, 2,035 citations were identified and, ultimately, 38 articles were included totaling 1,793 individuals (median [25%-75%] sample size of 38.5 (21.8-54.3) individuals). The most frequently used modality was structural magnetic resonance imaging (MRI) (n=17, 45%), followed by diffusion tensor imaging (n=11, 29%), resting-state functional MRI (n=10, 26%), task-based functional MRI (n=4, 8%), and positron emission tomography (n=2, 4%). Most studies (n=27, 71%) were cross-sectional. Overall, depression after TBI was associated with lower grey matter measures (volume, thickness, and/or density) and greater white matter damage. However, identification of specific brain areas was somewhat inconsistent. Findings that were replicated in more than one study included reduced grey matter in the rostral anterior cingulate cortex, prefrontal cortex and hippocampus, and damage in five white matter tracts (cingulum, internal capsule, superior longitudinal fasciculi, anterior, and posterior corona radiata). This systematic review found that the available data did not converge on a clear neuroimaging biomarker for depression after TBI. However, there are promising targets that warrant further study.

Lipidome Alterations following Mild Traumatic Brain Injury in the Rat

Traumatic brain injury (TBI) poses a major health challenge, with tens of millions of new cases reported globally every year. Brain damage resulting from TBI can vary significantly due to factors including injury severity, injury mechanism and exposure to repeated injury events. Therefore, there is need for robust blood biomarkers. Serum from Sprague Dawley rats was collected at several timepoints within 24 h of mild single or repeat closed head impacts. Serum samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) in positive and negative ion modes. Known lipid species were identified through matching to in-house tandem MS databases. Lipid biomarkers have a unique potential to serve as objective molecular measures of injury response as they may be liberated to circulation more readily than larger protein markers. Machine learning and feature selection approaches were used to construct lipid panels capable of distinguishing serum from injured and uninjured rats. The best multivariate lipid panels had over 90% cross-validated sensitivity, selectivity, and accuracy. These mapped onto sphingolipid signaling, autophagy, necroptosis and glycerophospholipid metabolism pathways, with Benjamini adjusted p-values less than 0.05. The novel lipid biomarker candidates identified provide insight into the metabolic pathways altered within 24 h of mild TBI.

Regional brain volume prior to treatment is linked to outcome after cognitive rehabilitation in traumatic brain injury

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Advanced neuroimaging has potential to inform new practices in cognitive rehabilitation.

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Regional brain volume was linked to effect of cognitive rehabilitation in traumatic brain injury.

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The most robust effects were observed in midline fronto-parietal brain regions.

Cognitive rehabilitation is useful for many after traumatic brain injury (TBI), but we lack critical knowledge about which patients benefit the most from different approaches. Advanced neuroimaging techniques have provided important insight into brain pathology and systems plasticity after TBI, and have potential to inform new practices in cognitive rehabilitation. In this study, we aimed to identify candidate structural brain measures with relevance for rehabilitation of cognitive control (executive) function after TBI. Twenty-eight patients (9 female, mean age 40.5 (SD = 13.04) years) with TBI (>21 months since injury) that participated in a randomized controlled cognitive rehabilitation trial (NCT02692352) were included in the analyses. Regional brain volume was extracted from T1-weighted MRI scans before treatment using tensor-based morphometry. Both positive and negative associations between treatment outcome (everyday cognitive control function) and regional brain volume were observed. The most robust associations between regional brain volume and improvement in function were observed in midline fronto-parietal regions, including the anterior and posterior cingulate cortices. The study provides proof of concept and valuable insight for planning future studies focusing on neuroimaging in cognitive rehabilitation after TBI.

Cerebral Microbleeds May Be Less Detectable by Susceptibility Weighted Imaging MRI From 24 to 72 Hours After Traumatic Brain Injury

Purpose: A former rodent study showed that cerebral traumatic microbleeds (TMBs) may temporarily become invisible shortly after injury when detected by susceptibility weighted imaging (SWI). The present study aims to validate this phenomenon in human SWI.

Methods: In this retrospective study, 46 traumatic brain injury (TBI) patients in various forms of severity were included and willingly complied with our strict selection criteria. Clinical parameters potentially affecting TMB count, Rotterdam and Marshall CT score, Mayo Clinic Classification, contusion number, and total volume were registered. The precise time between trauma and MRI [5 h 19 min to 141 h 54 min, including SWI and fluid-attenuated inversion recovery (FLAIR)] was individually recorded; TMB and FLAIR lesion counts were assessed. Four groups were created based on elapsed time between the trauma and MRI: 0–24, 24–48, 48–72, and >72 h. Kruskal–Wallis, ANOVA, Chi-square, and Fisher’s exact tests were used to reveal differences among the groups within clinical and imaging parameters; statistical power was calculated retrospectively for each comparison.

Results: The Kruskal–Wallis ANOVA with Conover post hoc analysis showed significant (p = 0.01; 1−β > 0.9) median TMB number differences in the subacute period: 0–24 h = 4.00 (n = 11); 24–48 h = 1 (n = 14); 48–72 h = 1 (n = 11); and 72 h ≤ 7.5 (n = 10). Neither clinical parameters nor FLAIR lesions depicted significant differences among the groups.

Conclusion: Our results demonstrate that TMBs on SWI MRI may temporarily become less detectable at 24–72 h following TBI.

Free Water Volume Fraction: An Imaging Biomarker to Characterize Moderate-to-Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a major clinical and public health problem with few therapeutic interventions successfully translated to the clinic. Identifying imaging-based biomarkers characterizing injury severity and predicting long-term functional and cognitive outcomes in TBI patients is crucial for treatment. TBI results in white matter (WM) injuries, which can be detected using diffusion tensor imaging (DTI). Trauma-induced pathologies lead to accumulation of free water (FW) in brain tissue, and standard DTI is susceptible to the confounding effects of FW. In this study, we applied FW DTI to estimate free water volume fraction (FW-VF) in patients with moderate-to-severe TBI and demonstrated its association with injury severity and long-term outcomes. DTI scans and neuropsychological assessments were obtained longitudinally at 3, 6, and 12 months post-injury for 34 patients and once in 35 matched healthy controls. We observed significantly elevated FW-VF in 85 of 90 WM regions in patients compared to healthy controls (p < 0.05). We then presented a patient-specific summary score of WM regions derived using Mahalanobis distance. We observed that MVF at 3 months significantly predicted functional outcome (p = 0.008), executive function (p = 0.005), and processing speed (p = 0.01) measured at 12 months and was significantly correlated with injury severity (p < 0.001). Our findings are an important step toward implementing MVF as a biomarker for personalized therapy and rehabilitation planning for TBI patients.

Biofluid Biomarkers in Traumatic Brain Injury: A Systematic Scoping Review

Emerging evidence suggests that biofluid-based biomarkers have diagnostic and prognostic potential in traumatic brain injuries (TBI). However, owing to the lack of a conceptual framework or comprehensive review, it is difficult to visualize the breadth of materials that might be available. We conducted a systematic scoping review to map and categorize the evidence regarding biofluid-based biochemical markers of TBI. A comprehensive search was undertaken in January 2019. Of 25,354 records identified through the literature search, 1036 original human studies were included. Five hundred forty biofluid biomarkers were extracted from included studies and classified into 19 distinct categories. Three categories of biomarkers including cytokines, coagulation tests, and nerve tissue proteins were investigated more than others and assessed in almost half of the studies (560, 515, and 502 from 1036 studies, respectively). S100 beta as the most common biomarker for TBI was tested in 21.2% of studies (220 articles). Cortisol was the only biomarker measured in blood, cerebrospinal fluid, urine, and saliva. The most common sampling time was at admission and within 24 h of injury. The included studies focused mainly on biomarkers from blood and central nervous system sources, the adult population, and severe and blunt injuries. The most common outcome measures used in studies were changes in biomarker concentration level, Glasgow coma scale, Glasgow outcome scale, brain computed tomography scan, and mortality rate. Biofluid biomarkers could be clinically helpful in the diagnosis and prognosis of TBI. However, there was no single definitive biomarker with accurate characteristics. The present categorization would be a road map to investigate the biomarkers of the brain injury cascade separately and detect the most representative biomarker of each category. Also, this comprehensive categorization could provide a guiding framework to design combined panels of multiple biomarkers.

Noninvasive Optical Monitoring of Cerebral Blood Flow and EEG Spectral Responses after Severe Traumatic Brain Injury: A Case Report

Survivors of severe brain injury may require care in a neurointensive care unit (neuro-ICU), where the brain is vulnerable to secondary brain injury. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches in the neuro-ICU. Our goal is to address this need through combined measurements of EEG and functional optical spectroscopy (EEG-Optical) instrumentation and analysis to provide a complementary fusion of data about brain activity and function. We utilized the diffuse correlation spectroscopy method for assessing cerebral blood flow at the neuro-ICU in a patient with traumatic brain injury. The present case demonstrates the feasibility of continuous recording of noninvasive cerebral blood flow transients that correlated well with the gold-standard invasive measurements and with the frequency content changes in the EEG data.

A bioimpedance-based monitor for real-time detection and identification of secondary brain injury

Secondary brain injury impacts patient prognosis and can lead to long-term morbidity and mortality in cases of trauma. Continuous monitoring of secondary injury in acute clinical settings is primarily limited to intracranial pressure (ICP); however, ICP is unable to identify essential underlying etiologies of injury needed to guide treatment (e.g. immediate surgical intervention vs medical management). Here we show that a novel intracranial bioimpedance monitor (BIM) can detect onset of secondary injury, differentiate focal (e.g. hemorrhage) from global (e.g. edema) events, identify underlying etiology and provide localization of an intracranial mass effect. We found in an in vivo porcine model that the BIM detected changes in intracranial volume down to 0.38 mL, differentiated high impedance (e.g. ischemic) from low impedance (e.g. hemorrhagic) injuries (p < 0.001), separated focal from global events (p < 0.001) and provided coarse 'imaging' through localization of the mass effect. This work presents for the first time the full design, development, characterization and successful implementation of an intracranial bioimpedance monitor. This BIM technology could be further translated to clinical pathologies including but not limited to traumatic brain injury, intracerebral hemorrhage, stroke, hydrocephalus and post-surgical monitoring.

A Methodology to Compare Biomechanical Simulations With Clinical Brain Imaging Analysis Utilizing Two Blunt Impact Cases

According to the US Defense and Veterans Brain Injury Center (DVBIC) and Centers for Disease Control and Prevention (CDC), mild traumatic brain injury (mTBI) is a common form of head injury. Medical imaging data provides clinical insight into tissue damage/injury and injury severity, and helps medical diagnosis. Computational modeling and simulation can predict the biomechanical characteristics of such injury, and are useful for development of protective equipment. Integration of techniques from computational biomechanics with medical data assessment modalities (e.g., magnetic resonance imaging or MRI) has not yet been used to predict injury, support early medical diagnosis, or assess effectiveness of personal protective equipment. This paper presents a methodology to map computational simulations with clinical data for interpreting blunt impact TBI utilizing two clinically different head injury case studies. MRI modalities, such as T1, T2, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), were used for simulation comparisons. The two clinical cases have been reconstructed using finite element analysis to predict head biomechanics based on medical reports documented by a clinician. The findings are mapped to simulation results using image-based clinical analyses of head impact injuries, and modalities that could capture simulation results have been identified. In case 1, the MRI results showed lesions in the brain with skull indentation, while case 2 had lesions in both coup and contrecoup sides with no skull deformation. Simulation data analyses show that different biomechanical measures and thresholds are needed to explain different blunt impact injury modalities; specifically, strain rate threshold corresponds well with brain injury with skull indentation, while minimum pressure threshold corresponds well with coup–contrecoup injury; and DWI has been found to be the most appropriate modality for MRI data interpretation. As the findings from these two cases are substantiated with additional clinical studies, this methodology can be broadly applied as a tool to support injury assessment in head trauma events and to improve countermeasures (e.g., diagnostics and protective equipment design) to mitigate these injuries.

Traumatic brain injury biomarkers in pediatric patients: a systematic review

Traumatic brain injury (TBI) is the main cause of pediatric trauma death and disability worldwide. Recent studies have sought to identify biomarkers of TBI for the purpose of assessing functional outcomes. The aim of this systematic review was to evaluate the utility of TBI biomarkers in the pediatric population by summarizing recent findings in the medical literature. A total of 303 articles were retrieved from our search. An initial screening to remove duplicate studies yielded 162 articles. After excluding all articles that did not meet the inclusion criteria, 56 studies were gathered. Among the 56 studies, 36 analyzed serum biomarkers; 11, neuroimaging biomarkers; and 9, cerebrospinal fluid (CSF) biomarkers. Most studies assessed biomarkers in the serum, reflecting the feasibility of obtaining blood samples compared to obtaining CSF or performing neuroimaging. S100B was the most studied serum biomarker in TBI, followed by SNE and UCH-L1, whereas in CSF analysis, there was no unanimity. Among the different neuroimaging techniques employed, diffusion tensor imaging (DTI) was the most common, seemingly holding diagnostic power in the pediatric TBI clinical setting. The number of cross-sectional studies was similar to the number of longitudinal studies. Our data suggest that S100B measurement has high sensitivity and great promise in diagnosing pediatric TBI, ideally when associated with head CT examination and clinical decision protocols. Further large-scale longitudinal studies addressing TBI biomarkers in children are required to establish more accurate diagnostic protocols and prognostic tools.

The utilization of advance telemetry to investigate critical physiological parameters including electroencephalography in cynomolgus macaques following aerosol challenge with eastern equine encephalitis virus

Most alphaviruses are mosquito-borne and can cause severe disease in humans and domesticated animals. In North America, eastern equine encephalitis virus (EEEV) is an important human pathogen with case fatality rates of 30–90%. Currently, there are no therapeutics or vaccines to treat and/or prevent human infection. One critical impediment in countermeasure development is the lack of insight into clinically relevant parameters in a susceptible animal model. This study examined the disease course of EEEV in a cynomolgus macaque model utilizing advanced telemetry technology to continuously and simultaneously measure temperature, respiration, activity, heart rate, blood pressure, electrocardiogram (ECG), and electroencephalography (EEG) following an aerosol challenge at 7.0 log₁₀ PFU. Following challenge, all parameters were rapidly and substantially altered with peak alterations from baseline ranged as follows: temperature (+3.0–4.2°C), respiration rate (+56–128%), activity (-15-76% daytime and +5–22% nighttime), heart rate (+67–190%), systolic (+44–67%) and diastolic blood pressure (+45–80%). Cardiac abnormalities comprised of alterations in QRS and PR duration, QTc Bazett, T wave morphology, amplitude of the QRS complex, and sinoatrial arrest. An unexpected finding of the study was the first documented evidence of a critical cardiac event as an immediate cause of euthanasia in one NHP. All brain waves were rapidly (~12–24 hpi) and profoundly altered with increases of up to 6,800% and severe diffuse slowing of all waves with decreases of ~99%. Lastly, all NHPs exhibited disruption of the circadian rhythm, sleep, and food/fluid intake. Accordingly, all NHPs met the euthanasia criteria by ~106–140 hpi. This is the first of its kind study utilizing state of the art telemetry to investigate multiple clinical parameters relevant to human EEEV infection in a susceptible cynomolgus macaque model. The study provides critical insights into EEEV pathogenesis and the parameters identified will improve animal model development to facilitate rapid evaluation of vaccines and therapeutics.

In North America, EEEV causes the most severe mosquito-borne disease in humans highlighted by fatal encephalitis and permeant debilitating neurological sequelae in survivors. The first confirmed human cases were reported more than 80 years ago and since then multiple sporadic outbreaks have occurred including one of the largest in 2019. Unfortunately, most human infections are diagnosed at the on-set of severe neurological symptoms and consequently a detailed disease course in humans is lacking. This gap in knowledge is a significant obstacle in the development of appropriate animal models to evaluate countermeasures. Here, we performed a cutting-edge study by utilizing a new telemetry technology to understand the course of EEEV infection in a susceptible macaque model by measuring multiple physiological parameters relevant to human disease. Our study demonstrates that the infection rapidly produces considerable alterations in many critical parameters including the electrical activity of the heart and the brain leading to severe disease. The study also highlights the extraordinary potential of new telemetry technology to develop the next generation of animal models to comprehensively investigate pathogenesis as well as evaluate countermeasures to treat and/or prevent EEEV disease.

Acute traumatic brain injury and the use of head computed tomography scans in the emergency department

Introduction

The use of CT head scanning for traumatic brain injury (TBI) is a vital diagnostic tool, guided by risk stratification tools. This study aims to review the use of CT head scans and adherence to guidelines for TBI in two New Zealand emergency departments (EDs).

Methods

Retrospective observational study of patients referred for head CT from EDs to exclude a significant intracranial injury between 1st September 2018 and 31st August 2019. Clinical data were collected regarding presenting patterns, identification of injuries on CT scan and adherence to National Institute of Clinical Excellence (NICE) CT head guidelines.

Results

Out of 425 included cases, 41 (10%) patients had an intracranial injury seen on their CT head scan. Patients who reported loss (32% vs 20%, p < 0.05) or possible loss of consciousness (34% vs 22%, p < 0.05) and had a Glasgow Coma Score (GCS) <13 (17% vs 8%, p < 0.05) or focal neurology (10% vs 3%, p < 0.05) were more likely to have an intracranial injury on CT. Interestingly, 17 (41%) patients with CT diagnosed injuries had a GCS 15 and no focal neurology. NICE guidelines were adhered to in 364 (86%) of CT requests. In the 14% of cases that did not meet guideline criteria, all CT head scans were negative.

Conclusion

CT head scans are a valuable tool in TBI, and guidelines successfully identify those with significant intracranial injuries. However, the rate of significant injury for the total population requiring head CT remains low, with over 90% of head CTs in the population normal, despite high guideline compliance, perhaps identifying a role for novel objective tests in ED guidelines internationally.

Unravelling neuroinflammation in abusive head trauma with radiotracer imaging

Abusive head trauma (AHT) is a leading cause of mortality and morbidity in child abuse, with a mortality rate of approximately 25%. In survivors, the prognosis remains dismal, with high prevalence of cerebral palsy, epilepsy and neuropsychiatric disorders. Early and accurate diagnosis of AHT is challenging, both clinically and radiologically, with up to one-third of cases missed on initial examination. Moreover, most of the management in AHT is supportive, reflective of the lack of clear understanding of specific pathogenic mechanisms underlying secondary insult, with approaches targeted toward decreasing intracranial hypertension and reducing cerebral metabolism, cell death and excitotoxicity. Multiple studies have elucidated the role of pro- and anti-inflammatory cytokines and chemokines with upregulation/recruitment of microglia/macrophages, oligodendrocytes and astrocytes in severe traumatic brain injury (TBI). In addition, recent studies in animal models of AHT have demonstrated significant upregulation of microglia, with a potential role of inflammatory cascade contributing to secondary insult. Despite the histological and biochemical evidence, there is a significant dearth of specific imaging approaches to identify this neuroinflammation in AHT. The primary motivation for development of such imaging approaches stems from the need to therapeutically target neuroinflammation and establish its utility in monitoring and prognostication. In the present paper, we discuss the available data suggesting the potential role of neuroinflammation in AHT and role of radiotracer imaging in aiding diagnosis and patient management.

ACR Appropriateness Criteria® Head Trauma: 2021 Update

Head trauma (ie, head injury) is a significant public health concern and is a leading cause of morbidity and mortality in children and young adults. Neuroimaging plays an important role in the management of head and brain injury, which can be separated into acute (0-7 days), subacute (<3 months), then chronic (>3 months) phases. Over 75% of acute head trauma is classified as mild, of which over 75% have a normal Glasgow Coma Scale score of 15, therefore clinical practice guidelines universally recommend selective CT scanning in this patient population, which is often based on clinical decision rules. While CT is considered the first-line imaging modality for suspected intracranial injury, MRI is useful when there are persistent neurologic deficits that remain unexplained after CT, especially in the subacute or chronic phase. Regardless of time frame, head trauma with suspected vascular injury or suspected cerebrospinal fluid leak should also be evaluated with CT angiography or thin-section CT imaging of the skull base, respectively. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Traumatic Brain Injury: Ultrastructural Features in Neuronal Ferroptosis, Glial Cell Activation and Polarization, and Blood-Brain Barrier Breakdown

The secondary injury process after traumatic brain injury (TBI) results in motor dysfunction, cognitive and emotional impairment, and poor outcomes. These injury cascades include excitotoxic injury, mitochondrial dysfunction, oxidative stress, ion imbalance, inflammation, and increased vascular permeability. Electron microscopy is an irreplaceable tool to understand the complex pathogenesis of TBI as the secondary injury is usually accompanied by a series of pathologic changes at the ultra-micro level of the brain cells. These changes include the ultrastructural changes in different parts of the neurons (cell body, axon, and synapses), glial cells, and blood–brain barrier, etc. In view of the current difficulties in the treatment of TBI, identifying the changes in subcellular structures can help us better understand the complex pathologic cascade reactions after TBI and improve clinical diagnosis and treatment. The purpose of this review is to summarize and discuss the ultrastructural changes related to neurons (e.g., condensed mitochondrial membrane in ferroptosis), glial cells, and blood–brain barrier in the existing reports of TBI, to deepen the in-depth study of TBI pathomechanism, hoping to provide a future research direction of pathogenesis and treatment, with the ultimate aim of improving the prognosis of patients with TBI.

Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity

Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N^' nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR) graphs with N nodes where N<N^'. First, we formalize our GSR problem as a node feature embedding learning task. Once the HR nodes' embeddings are learned, the pairwise connectivity strength between brain ROIs can be derived through an aggregation rule based on a novel Graph U-Net architecture. While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology. Second, inspired by graph spectral theory, we break the symmetry of the U-Net architecture by super-resolving the low-resolution brain graph structure and node content with a GSR layer and two graph convolutional network layers to further learn the node embeddings in the HR graph. Third, to handle the domain shift between the ground-truth and the predicted HR brain graphs, we incorporate adversarial regularization to align their respective distributions. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub at https://github.com/basiralab/AGSR-Net.