Patient Characterization Protocols for Psychophysiological Studies of Traumatic Brain Injury and Post-TBI Psychiatric Disorders

Prediction analysis of TBI 24-h survival outcome based on machine learning

Background

Traumatic brain injury (TBI) is the major reason for the death of young people and is well known for its high mortality and morbidity. This paper aim to predict the 24h survival of patients with TBI.

Methods

A total of 1224 samples were involved in this analysis, and the clinical indicators involved included age, gender, blood pressure, MGAP and other fields, among which the target variable was "outcome", which was a binary variable. The methods mainly involved in this paper include data visualization analysis, single factor analysis, feature engineering analysis, random forest model (RF), K-Nearst Neighbors (KNN) model, and so on. Logistic regression model (LR) and deep neural network model (DNN). We will oversample the training set using the SMOTE method because of the very unbalanced labeling of the sample itself.

Results

Although the accuracy of all models is very high, the recall rate is relatively low. The DNN model with the best performance only reaches 0.17, and the corresponding AUC is 0.80. After resampling, we find that the recall rate of positive samples of all models has increased a lot, but the AUC of some models has decreased. Finally, the optimal model is LR, whose positive sample recall rate is 0.67 and AUC is 0.82.

Conclusion

Through resampling, we obtained that the best model is the RF model, whose recall rate and AUC are the best, and the AUC level is about 0.87, indicating that the accuracy performance of the model is still good.

The impact of childhood acquired brain injury on siblings: a scoping review

OBJECTIVE

(a) To analyze the extent and nature of research on the impact of childhood acquired brain injury (ABI) on siblings, (b) to synthetize in a descriptive way the results of these studies and propose perspectives of care/support.

METHOD

A literature search of 3 databases was performed up to August 2022. Studies addressing issues around siblings of children with ABI were included in the scoping review.

RESULTS

25 articles were identified and analyzed. Results indicate that there is a paucity of research on this issue. However, interest in the subject has increased over past decades. Despite variable results, the current literature highlights the negative impact of ABI on family functioning and relationships. The trajectory and quality of life of siblings of children with ABI are modified. ABI causes intense and mixed emotions, psychological distress, behavioral difficulties and social stigma. Siblings have varied ways of coping with ABI and express particular needs that must be addressed.

CONCLUSION

There is a significant impact of childhood ABI on siblings' subsequent life. Existing studies on this subject are few, heterogeneous, and sometimes contradictory. Further studies on this theme therefore appear necessary in order to propose appropriate support for patients' siblings according to their age and situation.

Utilizing the BAT-LQ to assess TBI incidence in a college student population

OBJECTIVE

Although the annual number of traumatic brain injuries (TBIs) reported in the US exceeds two million, data suggests that this is an underestimate. The goal of this study was to understand lifetime TBI incidence among a sample of college students. Additionally, this study examined whether a single yes/no question regarding TBI history was sufficient to gather accurate information about TBI incidence in college students.

DESIGN

Participants were asked a single TBI question and administered the BAT-LQ.

MAIN MEASURES

The BAT-LQ is a screening tool designed to assess for probable lifetime TBIs.

RESULTS

Data from 121 participants were analyzed for this study. On the single-question, 24.8% of participants reported experiencing a TBI. However, upon further prompting, 76.8% of all participants reported experiencing a blow to the head accompanied by at least one diagnostic symptom of a TBI, suggesting a probable TBI based on best-practice diagnosis guidelines.

CONCLUSION

The results of this study suggest that increased education about TBI is warranted to ensure that individuals receive care for probable TBIs, as many individuals likely lack knowledge about what constitutes a TBI diagnosis. Additionally, the results suggest that a single question may not be sufficient to capture true lifetime TBI incidence.

Afferent Visual Manifestations of Traumatic Brain Injury

Traumatic brain injury (TBI) causes structural and functional damage to the central nervous system including the visual pathway. Defects in the afferent visual pathways affect visual function and in severe cases cause complete visual loss. Visual dysfunction is detectable by structural and functional ophthalmic examinations that are routine in the eye clinic, including examination of the pupillary light reflex and optical coherence tomography (OCT). Assessment of pupillary light reflex is a non-invasive assessment combining afferent and efferent visual function. While a assessment using a flashlight is relatively insensitive, automated pupillometry has 95% specificity and 78.1% sensitivity in detecting TBI-related visual and cerebral dysfunction with an area under the curve of 0.69-0.78. OCT may also serve as a noninvasive biomarker of TBI severity, demonstrating changes in the retinal ganglion cell layer and nerve fiber layer throughout the range of TBI severity even in the absence of visual symptoms. This review discusses the impact of TBI on visual structure and function.

Stress- and trauma-related blockade of episodic-autobiographical memory processing

Memory disorders without a direct neural substrate still belong to the riddles in neuroscience. Although they were for a while dissociated from research and clinical arenas, risking becoming forgotten diseases, they sparked novel interests, paralleling the refinements in functional neuroimaging and neuropsychology. Although Endel Tulving has not fully embarked himself on exploring this field, he had published at least one article on functional amnesia (Schacter et al., 1982) and ignited a seminal article on amnesia with mixed etiology (Craver et al., 2014). Most importantly, the research of Endel Tulving has provided the researchers and clinicians in the field of dissociative or functional amnesia with the best framework for superiorly understanding these disorders through the lens of his evolving concept of episodic memory and five long term memory systems classification, which he developed and advanced. Herein we use the classification of long-term memory systems of Endel Tulving as well as his concepts and views on autonoetic consciousness, relationships between memory systems and relationship between episodic memory and emotion to describe six cases of dissociative amnesia that put a challenge for researchers and clinicians due to their atypicality. We then discuss their possible triggering and maintaining mechanisms, pointing to their clinical heterogeneity and multifaceted causally explanatory frameworks.

Increased short- and long-term risk of sleep disorders in people with traumatic brain injury

This study aims to evaluate the relationship between traumatic brain injury (TBI) and sleep disorders (SDs). We first initiated a questionnaire-based clinical survey to assess sleep problems in the early stage after a TBI, followed by a population-based cohort study to evaluate the long-term risk of SDs in TBI patients. For short-term clinical survey, mild (m)TBI patients and healthy controls were recruited to evaluate the sleep quality and daytime sleepiness using the Pittsburg Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) within two weeks after a TBI. For long-term observation, a 5-year nationwide population-based cohort study that utilized a large administrative database was conducted. In the short-term survey, 236 mTBI patients and 223 controls were analyzed. Total scores of the PSQI and ESS were significantly higher in mTBI patients than in the controls. In the long-term cohort study, 6932 TBI cases and 34,660 matched controls were included. TBI cases had a 1.36-fold greater risk of SDs compared to the non-TBI controls during the 5-year follow-up period. Results showed that patients with TBI had a significantly higher risk of SDs than did controls both in the early stage and during a 5-year follow-up period.

Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information

Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain. This study proposed a new method named multivariate permutation conditional mutual information (MPCMI) to quantitatively estimate the coupling strength of multivariate neural signals (MNS). The performance of the MPCMI method was validated on the simulated MNS generated by multi-channel neural mass model (MNMM). The coupling strength feature of simulated MNS extracted by MPCMI showed better performance compared with standard methods, such as permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied to estimate the coupling strengths of two-channel resting-state electroencephalographic (rsEEG) signals from different brain regions of 19 patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 frequency bands. Empirical results showed that the MPCMI could effectively extract the coupling strength features that were significantly different between the aMCI and the NC. Hence, the proposed MPCMI method could be an effective estimate of coupling strengths of MNS, and might be a viable biomarker for clinical applications.

Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping

BACKGROUND AND PURPOSE

Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology.

MATERIALS AND METHODS

Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident.

RESULTS

Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping; they were worse on preconditioned quantitative susceptibility mapping.

CONCLUSIONS

Preconditioned quantitative susceptibility mapping MR imaging can bring the benefits of quantitative susceptibility mapping imaging to clinical practice without the limitations of mask-based quantitative susceptibility mapping, especially for evaluating cerebral microhemorrhage-associated pathologies, such as traumatic brain injury.

Procedures for the Comparative Testing of Noninvasive Neuroassessment Devices

A sequential process for comparison testing of noninvasive neuroassessment devices is presented. Comparison testing of devices in a clinical population should be preceded by computational research and reliability testing with healthy populations, as opposed to proceeding immediately to testing with clinical participants. A five-step process is outlined as follows: 1. Complete a preliminary literature review identifying candidate measures. 2. Conduct systematic simulation studies to determine the computational properties and data requirements of candidate measures. 3. Establish the test-retest reliability of each measure in a healthy comparison population and the clinical population of interest. 4. Investigate the clinical validity of reliable measures in appropriately defined clinical populations. 5. Complete device usability assessment (weight, simplicity of use, cost effectiveness, ruggedness) only for devices and measures that are promising after steps 1 through 4 are completed. Usability may be considered throughout the device evaluation process but such considerations are subordinate to the higher priorities addressed in steps 1 through 4.

Static and Dynamic Factors Promoting Resilience following Traumatic Brain Injury: A Brief Review

Traumatic brain injury (TBI) is the greatest contributing cause of death and disability among children and young adults in the United States. The current paper briefly summarizes contemporary literature on factors that can improve outcomes (i.e., promote resilience) for children and adults following TBI. For the purpose of this paper, the authors divided these factors into static or unmodifiable factors (i.e., age, sex, intellectual abilities/education, and preinjury psychiatric history) and dynamic or modifiable factors (i.e., socioeconomic status, family functioning/social support, nutrition, and exercise). Drawing on human and animal studies, the research reviewed indicated that these various factors can improve outcomes in multiple domains of functioning (e.g., cognition, emotion regulation, health and wellness, behavior, etc.) following a TBI. However, many of these factors have not been studied across populations, have been limited to preclinical investigations, have been limited in their scope or follow-up, or have not involved a thorough evaluation of outcomes. Thus, although promising, continued research is vital in the area of factors promoting resilience following TBI in children and adults.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

A global coupling index of multivariate neural series with application to the evaluation of mild cognitive impairment

Recently, the synchronization between neural signals has been widely used as a key indicator of brain function. To understand comprehensively the effect of synchronization on the brain function, accurate computation of the synchronization strength among multivariate neural series from the whole brain is necessary. In this study, we proposed a method named global coupling index (GCI) to estimate the synchronization strength of multiple neural signals. First of all, performance of the GCI method was evaluated by analyzing simulated EEG signals from a multi-channel neural mass model, including the effects of the frequency band, the coupling coefficient, and the signal noise ratio. Then, the GCI method was applied to analyze the EEG signals from 12 mild cognitive impairment (MCI) subjects and 12 normal controls (NC). The results showed that GCI method had two major advantages over the global synchronization index (GSI) or S-estimator. Firstly, simulation data showed that the GCI method provided both a more robust result on the frequency band and a better performance on the coupling coefficients. Secondly, the actual EEG data demonstrated that GCI method was more sensitive in differentiating the MCI from control subjects, in terms of the global synchronization strength of neural series of specific alpha, beta1 and beta2 frequency bands. Hence, it is suggested that GCI is a better method over GSI and S-estimator to estimate the synchronization strength of multivariate neural series for predicting the MCI from the whole brain EEG recordings.

Statistical Issues in TBI Clinical Studies

The identification and longitudinal assessment of traumatic brain injury presents several challenges. Because these injuries can have subtle effects, efforts to find quantitative physiological measures that can be used to characterize traumatic brain injury are receiving increased attention. The results of this research must be considered with care. Six reasons for cautious assessment are outlined in this paper. None of the issues raised here are new. They are standard elements in the technical literature that describes the mathematical analysis of clinical data. The purpose of this paper is to draw attention to these issues because they need to be considered when clinicians evaluate the usefulness of this research. In some instances these points are demonstrated by simulation studies of diagnostic processes. We take as an additional objective the explicit presentation of the mathematical methods used to reach these conclusions. This material is in the appendices. The following points are made: (1) A statistically significant separation of a clinical population from a control population does not ensure a successful diagnostic procedure. (2) Adding more variables to a diagnostic discrimination can, in some instances, actually reduce classification accuracy. (3) A high sensitivity and specificity in a TBI versus control population classification does not ensure diagnostic successes when the method is applied in a more general neuropsychiatric population. (4) Evaluation of treatment effectiveness must recognize that high variability is a pronounced characteristic of an injured central nervous system and that results can be confounded by either disease progression or spontaneous recovery. A large pre-treatment versus post-treatment effect size does not, of itself, establish a successful treatment. (5) A procedure for discriminating between treatment responders and non-responders requires, minimally, a two phase investigation. This procedure must include a mechanism to discriminate between treatment responders, placebo responders, and spontaneous recovery. (6) A search for prodromes of neuropsychiatric disorders following traumatic brain injury can be implemented with these procedures.