Veteran perspectives of epilepsy care: Impact of Veteran satisfaction, knowledge, and proactivity.

OBJECTIVE: Veterans are at elevated risk of epilepsy due to higher rates of traumatic brain injury (TBI). However, little work has examined the extent to which quality of care is associated with key outcomes for Veterans with epilepsy (VWE). This study aimed to examine the impact of quality of care on three outcomes: patients' knowledge of epilepsy self-care, proactive epilepsy self-management, and satisfaction with care. METHOD: We conducted a cross-sectional study of Post-9/11 Veterans with validated active epilepsy who received VA care (n = 441). Veterans were surveyed on care processes using American Academy of Neurology epilepsy quality measures, and a patient-generated measure related to the use of emergency care. Outcome measures included epilepsy self-care knowledge, proactive epilepsy self-management, and satisfaction with epilepsy care. Covariates included sociodemographic and health status variables and a measure of patient-provider communication. An ordinary least-squares (OLS) regression model was used to determine if the quality of care was associated with the outcomes adjusting for multiple comparisons. RESULTS: Self-reported measures of quality of care were broadly associated with satisfaction with care and epilepsy knowledge. OLS modeling indicated that healthcare provider guidance on when to seek emergency care was significantly associated with higher Veteran satisfaction with care (p < 0.01). Veterans who were asked about seizure frequency at every visit by their provider also reported higher satisfaction with care (p < 0.01) and increased epilepsy knowledge (p < 0.01). Veteran-provider communication was positively associated with epilepsy knowledge and proactive epilepsy self-management. Veterans with epilepsy with drug resistance epilepsy were significantly less satisfied with their care and reported lower proactivity compared to epilepsy controlled with medications. Further analysis indicated Black VWEs reported lower scores on epilepsy self-care knowledge compared to Whites (p < 0.001). CONCLUSIONS: This study found that quality measures were associated with satisfaction and epilepsy knowledge but not associated with proactive self-management in multivariable models. The finding that better communication between providers and Veterans suggests that in addition to technical quality, interpersonal quality is important for patient outcomes. The secondary analysis identified racial disparities in epilepsy knowledge. This work offers opportunities to improve the quality of epilepsy care through the practice of patient-centered care models that reflect Veteran priorities and perceptions.

Traumatic Brain Injury and Early Onset Dementia in Post 9-11 Veterans.

OBJECTIVES: To assess traumatic brain injury (TBI)-related risks factors for early-onset dementia (EOD). BACKGROUND: Younger Post-9/11 Veterans may be at elevated risk for EOD due to high rates of TBI in early/mid adulthood. Few studies have explored the longitudinal relationship between traumatic brain injury (TBI) and the emergence of EOD subtypes. METHODS: This matched case-control study used data from the Veterans Health Administration (VHA) to identify Veterans with EOD. To address the low positive predictive value (PPV = 0.27) of dementia algorithms in VHA records, primary outcomes were Alzheimer's disease (AD) and frontotemporal dementia (FTD). Logistic regression identified conditions associated with dementia subtypes. RESULTS: The EOD cohort included Veterans with AD (n = 689) and FTD (n = 284). There were no significant demographic differences between the EOD cohort and their matched controls. After adjustment, EOD was significantly associated with history of TBI (OR: 3.05, 2.42-3.83), epilepsy (OR: 4.8, 3.3-6.97), other neurological conditions (OR: 2.0, 1.35-2.97), depression (OR: 1.35, 1.12-1.63) and cardiac disease (OR: 1.36, 1.1-1.67). CONCLUSION: Post-9/11 Veterans have higher odds of EOD following TBI. A sensitivity analysis across TBI severity confirmed this trend, indicating that the odds for both AD and FTD increased after more severe TBIs.

Criminal legal involvement among recently separated veterans: Findings from the LIMBIC study.

OBJECTIVE: This study investigated individual-level and neighborhood-level predictors of criminal legal involvement of veterans during the critical transition period from military to civilian life. HYPOTHESES: We hypothesized that substance use, mental health, and personality disorders will increase the incidence of criminal legal involvement, which will be highest among veterans living in socioeconomically disadvantaged neighborhoods after military discharge. METHOD: We analyzed data from a longitudinal cohort study of 418,624 veterans who entered Department of Veterans Affairs (VA) health care after leaving the military. Department of Defense (DoD) data on clinical diagnoses, demographics, and military history were linked to VA data on neighborhood of residence and criminal legal involvement. RESULTS: Criminal legal involvement in the 2 years following military discharge was most strongly predicted by younger age, substance use disorder, and being male. Other predictors included the military branch in which veterans served, deployment history, traumatic brain injury, serious mental illness, personality disorder, having fewer physical health conditions, and living in socioeconomically disadvantaged neighborhoods. These factors combined in multivariable analysis yielded a very large effect size for predicting criminal legal involvement after military separation (area under the curve = .82). The incidence of criminal legal involvement was 10 times higher among veterans with co-occurring substance use disorder, serious mental illness, and personality disorder than among veterans with none of these diagnoses, and these rates were highest among veterans residing in more socioeconomically disadvantaged neighborhoods. CONCLUSIONS: To our knowledge, this is the largest longitudinal study of risk factors for criminal legal involvement in veterans following military discharge. The findings supported the hypothesis that veterans with co-occurring mental disorders living in socioeconomically disadvantaged neighborhoods were at higher risk of criminal legal involvement, underscoring the complex interplay of individual-level and neighborhood-level risk factors for criminal legal involvement after veterans leave the military. These results can inform policy and programs, such as the DoD Transition Assistance Program (TAP) and the VA Military to Civilian Readiness Pathway program (M2C Ready), to enhance community reintegration and prevent criminal legal involvement among veterans transitioning from military to civilian life. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Method for Dynamically Detecting Secretions from Single Cells Using a Nanopore.

Secreted proteins mediate cell-to-cell communications. Thus, eavesdropping on the secretome could reveal the cellular phenotype, but it is challenging to detect the proteins because they are secreted only in minute amounts and then diluted in blood plasma or contaminated by cell culture medium or the lysate. In this pilot study, it is demonstrated that secretions from single cancer cells can be detected and dynamically analyzed through measurements of blockades in the electrolytic current due to single molecules translocating through a nanopore in a thin inorganic membrane. It is established that the distribution of blockades can be used to differentiate three different cancer cell lines (U937, MDA-MB-231, and MCF-7) in real time and quickly (<20 s). Importantly, the distinctive blockades associated with the chemokine CCL5, a prognostic factor for disease progression in breast cancer, along with other low-mass biomarkers of breast cancer (PI3, TIMP1, and MMP1) were identified in the context of the secretome of these three cell types, tracked with time, and used to provide information on the cellular phenotype.

Single-molecule protein identification by sub-nanopore sensors.

Recent advances in top-down mass spectrometry enabled identification of intact proteins, but this technology still faces challenges. For example, top-down mass spectrometry suffers from a lack of sensitivity since the ion counts for a single fragmentation event are often low. In contrast, nanopore technology is exquisitely sensitive to single intact molecules, but it has only been successfully applied to DNA sequencing, so far. Here, we explore the potential of sub-nanopores for single-molecule protein identification (SMPI) and describe an algorithm for identification of the electrical current blockade signal (nanospectrum) resulting from the translocation of a denaturated, linearly charged protein through a sub-nanopore. The analysis of identification p-values suggests that the current technology is already sufficient for matching nanospectra against small protein databases, e.g., protein identification in bacterial proteomes.

Micro- or nanorod and nanosphere structures derived from a series of phenyl-porphyrins.

We examine here a series of meso-phenyl porphyrin micro- and nanostructures. Optical absorption and emission spectroscopy imaging and atomic force microscopy are used to investigate the effect of peripheral groups in nano- and microstructures of 5,10,15,20-tetraphenylporphyrin (H2TPP) compared to three other phenylporphyrins, i.e. 5,10,15-triphenylporphyrin (H2-Tri-PP), 5,10-diphenylporphyrin (H25,10-BPP) and 5,15-diphenylporphyrin (H25,15-BPP) molecules. We show that nanospheres and nanorods are formed, the occurrence and properties of which are influenced by the number and position of the phenyl substituents.

The Impact of Non-Pain Factors on Pain Interference Among U.S. Service Members and Veterans with Symptoms of Mild Traumatic Brain Injury.

U.S. Service members and Veterans (SM/V) experience elevated rates of traumatic brain injury (TBI), chronic pain, and other non-pain symptoms. However, the role of non-pain factors on pain interference levels remains unclear among SM/Vs, particularly those with a history of TBI. The primary objective of this study was to identify factors that differentiate high/low pain interference, given equivalent pain intensity among U.S. SM/V participating in the ongoing Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) national multi-center prospective longitudinal observational study. An explainable machine learning was used to identify key predictors of pain interference conditioned on equivalent pain intensity. The final sample consisted of n = 1,577 SM/Vs who were predominantly male (87%), and 83.6% had a history of mild TBI(s) (mTBI), while 16.4% were TBI negative controls. The sample was categorized according to pain interference level (Low: 19.9%, Moderate: 52.5%, and High: 27.6%). Both pain intensity scores and pain interference scores increased with the number of mTBIs (p < 0.001), and there was evidence of a dose response between the number of injuries and pain scores. Machine learning models identified fatigue and anxiety as the most important predictors of pain interference, whereas emotional control was protective. Partial dependence plots identified that marginal effects of fatigue and anxiety were associated with pain interference (p < 0.001), but the marginal effect of mTBI was not significant in models considering all variables (p > 0.05). Non-pain factors are associated with functional limitations and disability experience among SM/V with an mTBI history. The functional effects of pain may be mediated through multiple other factors. Pain is a multi-dimensional experience that may benefit most from holistic treatment approaches that target comorbidities and build supports that promote recovery.

Cannabis use disorder contributes to cognitive dysfunction in Veterans with traumatic brain injury.

Background: While emerging evidence supports a link between traumatic brain injury (TBI) and progressive cognitive dysfunction in Veterans, there is insufficient information on the impact of cannabis use disorder (CUD) on long-term cognitive disorders. This study aimed to examine the incidences of cognitive disorders in Veterans with TBI and CUD and to evaluate their relationship. Methods: This retrospective cohort study used the US Department of Veterans Affairs and Department of Defense administrative data from the Long-term Impact of Military-Relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium Phenotype study. Diagnoses suggesting cognitive disorders after a TBI index date were identified using inpatient and outpatient data from 2003 to 2022. We compared the differential cognitive disorders incidence in Veterans who had the following: (1) no CUD or TBI (control group), (2) CUD only, (3) TBI only, and (4) comorbid CUD+TBI. Kaplan-Meier analyses were used to estimate the overall cognitive disorders incidence in the above study groups. The crude and adjusted Cox proportional hazards models were used to estimate crude and adjusted hazard ratios (HRs) for cognitive disorders. Results: A total of 1,560,556 Veterans [82.32% male, median (IQR) age at the time of TBI, 34.51 (11.29) years, and 61.35% white] were evaluated. The cognitive disorder incidence rates were estimated as 0.68 (95% CI, 0.62, 0.75) for CUD only and 1.03 (95% CI, 1.00, 1.06) for TBI only per 10,000 person-months of observations, with the highest estimated cognitive disorder incidence observed in participants with both TBI and CUD [1.83 (95% CI, 1.72, 1.95)]. Relative to the control group, the highest hazard of cognitive disorders was observed in Veterans with CUD+TBI [hazard ratio (HR), 3.26; 95% CI, 2.91, 3.65], followed by those with TBI only (2.32; 95 CI%, 2.13, 2.53) and with CUD (1.79; 95 CI%, 1.60, 2.00). Of note, in the CUD only subgroup, we also observed the highest risk of an early onset cognitive disorder other than Alzheimer's disease and Frontotemporal dementia. Discussion: The results of this analysis suggest that individuals with comorbid TBI and CUD may be at increased risk for early onset cognitive disorders, including dementia.

Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing.

Introduction: Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care. Methods: A medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes. Results: Veterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual. Discussion: This study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.

Microstructural Organization of Distributed White Matter Associated With Fine Motor Control in US Service Members With Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is the most common form of brain injury. While most individuals recover from mTBI, roughly 20% experience persistent symptoms, potentially including reduced fine motor control. We investigate relationships between regional white matter organization and subcortical volumes associated with performance on the Grooved Pegboard (GPB) test in a large cohort of military Service Members and Veterans (SM&Vs) with and without a history of mTBI(s). Participants were enrolled in the Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. SM&Vs with a history of mTBI(s) (n = 847) and without mTBI (n = 190) underwent magnetic resonance imaging and the GPB test. We first examined between-group differences in GPB completion time. We then investigated associations between GPB performance and regional structural imaging measures (tractwise diffusivity, subcortical volumes, and cortical thickness) in SM&Vs with a history of mTBI(s). Lastly, we explored whether mTBI history moderated associations between imaging measures and GPB performance. SM&Vs with mTBI(s) performed worse than those without mTBI(s) on the non-dominant hand GPB test at a trend level (p < 0.1). Higher fractional anisotropy (FA) of tracts including the posterior corona radiata, superior longitudinal fasciculus, and uncinate fasciculus were associated with better GPB performance in the dominant hand in SM&Vs with mTBI(s). These findings support that the organization of several white matter bundles are associated with fine motor performance in SM&Vs. We did not observe that mTBI history moderated associations between regional FA and GPB test completion time, suggesting that chronic mTBI may not significantly influence fine motor control.

Neuropsychological Profiles of Deployment-Related Mild Traumatic Brain Injury: A LIMBIC-CENC Study.

BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is a concern for US service members and veterans (SMV), leading to heterogeneous psychological and cognitive outcomes. We sought to identify neuropsychological profiles of mild TBI (mTBI) and posttraumatic stress disorder (PTSD) among the largest SMV sample to date. METHODS: We analyzed cross-sectional baseline data from SMV with prior combat deployments enrolled in the ongoing Long-term Impact of Military-relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium prospective longitudinal study. Latent profile analysis identified symptom profiles using 35 indicators, including physical symptoms, depression, quality of life, sleep quality, postconcussive symptoms, and cognitive performance. It is important to note that the profiles were determined independently of mTBI and probable PTSD status. After profile identification, we examined associations between demographic variables, mTBI characteristics, and PTSD symptoms with symptom profile membership. RESULTS: The analytic sample included 1,659 SMV (mean age 41.1 ± 10.0 years; 87% male); among them 29% (n = 480) had a history of non-deployment-related mTBI only, 14% (n = 239) had deployment-related mTBI only, 36% (n = 602) had both non-deployment and deployment-related mTBI, and 30% (n = 497) met criteria for probable PTSD. A 6-profile model had the best fit, with separation on all indicators (p < 0.001). The model revealed distinct neuropsychological profiles, representing a combination of 3 self-reported functioning patterns: high (HS), moderate (MS), and low (LS), and 2 cognitive performance patterns: high (HC) and low (LC). The profiles were (1) HS/HC: n=301, 18.1%; (2) HS/LC: n=294, 17.7%; (3) MS/HC: n=359, 21.6%; (4) MS/LC: n=316, 19.0%; (5) LS/HC: n=228, 13.7%; and (6) LS/LC: n=161, 9.7%. SMV with deployment-related mTBI tended to be grouped into lower functioning profiles and were more likely to meet criteria for probable PTSD. Conversely, SMV with no mTBI exposure or non-deployment-related mTBI were clustered in higher functioning profiles and had a lower likelihood of meeting criteria for probable PTSD. DISCUSSION: Findings suggest varied symptom and functional profiles in SMV, influenced by injury context and probable PTSD comorbidity. Despite diagnostic challenges, comprehensive assessment of functioning and cognition can detect subtle differences related to mTBI and PTSD, revealing distinct neuropsychological profiles. Prioritizing early treatment based on these profiles may improve prognostication and support efficient recovery.

Persistent MRI Findings Unique to Blast and Repetitive Mild TBI: Analysis of the CENC/LIMBIC Cohort Injury Characteristics.

INTRODUCTION: MRI represents one of the clinical tools at the forefront of research efforts aimed at identifying diagnostic and prognostic biomarkers following traumatic brain injury (TBI). Both volumetric and diffusion MRI findings in mild TBI (mTBI) are mixed, making the findings difficult to interpret. As such, additional research is needed to continue to elucidate the relationship between the clinical features of mTBI and quantitative MRI measurements. MATERIAL AND METHODS: Volumetric and diffusion imaging data in a sample of 976 veterans and service members from the Chronic Effects of Neurotrauma Consortium and now the Long-Term Impact of Military-Relevant Brain Injury Consortium observational study of the late effects of mTBI in combat with and without a history of mTBI were examined. A series of regression models with link functions appropriate for the model outcome were used to evaluate the relationships among imaging measures and clinical features of mTBI. Each model included acquisition site, participant sex, and age as covariates. Separate regression models were fit for each region of interest where said region was a predictor. RESULTS: After controlling for multiple comparisons, no significant main effect was noted for comparisons between veterans and service members with and without a history of mTBI. However, blast-related mTBI were associated with volumetric reductions of several subregions of the corpus callosum compared to non-blast-related mTBI. Several volumetric (i.e., hippocampal subfields, etc.) and diffusion (i.e., corona radiata, superior longitudinal fasciculus, etc.) MRI findings were noted to be associated with an increased number of repetitive mTBIs versus. CONCLUSIONS: In deployment-related mTBI, significant findings in this cohort were only observed when considering mTBI sub-groups (blast mechanism and total number/dose). Simply comparing healthy controls and those with a positive mTBI history is likely an oversimplification that may lead to non-significant findings, even in consortium analyses.

Phenotyping Depression After Mild Traumatic Brain Injury: Evaluating the Impact of Multiple Injury, Gender, and Injury Context.

The chronic mental health consequences of mild traumatic brain injury (TBI) are a leading cause of disability. This is surprising given the expectation of significant recovery after mild TBI, which suggests that other injury-related factors may contribute to long-term adverse outcomes. The objective of this study was to determine how number of prior injuries, gender, and environment/context of injury may contribute to depressive symptoms after mild TBI among deployed United States service members and veterans (SMVs). Data from the Long-term Impact of Military-Relevant Brain Injury Consortium Prospective Longitudinal Study was used to assess TBI injury characteristics and depression scores previously measured on the Patient Health Questionnaire-9 (PHQ-9) among a sample of 1456 deployed SMVs. Clinical diagnosis of mild TBI was defined via a multi-step process centered on a structured face-to-face interview. Logistical and linear regressions stratified by gender and environment of injury were used to model depressive symptoms controlling for sociodemographic and combat deployment covariates. Relative to controls with no history of mild TBI (n = 280), the odds ratios (OR) for moderate/severe depression (PHQ-9 ≥ 10) were higher for SMVs with one mild TBI (n = 358) OR: 1.62 (95% confidence interval [CI] 1.09-2.40, p = 0.016) and two or more mild TBIs (n = 818) OR: 1.84 (95% CI 1.31-2.59, p < 0.001). Risk differences across groups were assessed in stratified linear models, which found that depression symptoms were elevated in those with a history of multiple mild TBIs compared with those who had a single mild TBI (p < 0.001). Combat deployment-related injuries were also associated with higher depression scores than injuries occurring in non-combat or civilian settings (p < 0.001). Increased rates of depression after mild TBI persisted in the absence of post-traumatic stress disorder. Both men and women SMVs separately exhibited significantly increased depressive symptom scores if they had had combat-related mild TBI. These results suggest that contextual information, gender, and prior injury history may influence long-term mental health outcomes among SMVs with mild TBI exposure.

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Comorbidity and polypharmacy impact neurobehavioral symptoms and symptom validity failure among post-9/11 veterans with mild traumatic brain injury.

Objective: The study aimed to examine the association between post-concussive comorbidity burdens [post-traumatic stress disorder (PTSD), depression, and/or headache] and central nervous system (CNS) polypharmacy (five or more concurrent medications) with reported neurobehavioral symptoms and symptom validity screening among post-9/11 veterans with a history of mild traumatic brain injury (mTBI). Setting: Administrative medical record data from the Department of Veterans Affairs (VA) were used in the study. Participants: Post-9/11 veterans with mTBI and at least 2 years of VA care between 2001 and 2019 who had completed the comprehensive traumatic brain injury evaluation (CTBIE) were included in the study. Design: Retrospective cross-sectional design was used in the study. Main measures: Neurobehavioral Symptom Inventory (NSI), International Classification of Diseases, Ninth Revision, and Clinical Modification diagnosis codes were included in the study. Results: Of the 92,495 veterans with a history of TBI, 90% had diagnoses of at least one identified comorbidity (PTSD, depression, and/or headache) and 28% had evidence of CNS polypharmacy. Neurobehavioral symptom reporting and symptom validity failure was associated with comorbidity burden and polypharmacy after adjusting for sociodemographic characteristics. Veterans with concurrent diagnoses of PTSD, depression, and headache were more than six times more likely [Adjusted odds ratio = 6.55 (99% CI: 5.41, 7.92)]. to fail the embedded symptom validity measure (Validity-10) in the NSI. Conclusion: TBI-related multimorbidity and CNS polypharmacy had the strongest association with neurobehavioral symptom distress, even after accounting for injury and sociodemographic characteristics. Given the regular use of the NSI in clinical and research settings, these findings emphasize the need for comprehensive neuropsychological evaluation for individuals who screen positively for potential symptom overreporting, the importance of multidisciplinary rehabilitation to restore functioning following mTBI, and the conscientious utilization of symptom validity measures in research efforts.

Role of Deployment History on the Association Between Epilepsy and Traumatic Brain Injury in Post-9/11 Era US Veterans.

BACKGROUND AND OBJECTIVES: Traumatic brain injury (TBI) is a well-established epilepsy risk factor and is common among service members. Deployment-related TBI, where combat/blast may be more common, may have different outcomes than nondeployment-related TBI. This work examined associations of all TBI exposures (not just combat), and epilepsy, while adjusting for comorbidities associated with epilepsy, among veterans by deployment status. METHODS: The cohort included post-9/11 veterans with ≥2 years of care in both Veterans Health Administration and Defense Health Agency systems. We identified epilepsy using ICD-9/10-CM codes, antiseizure medication, and service-connected disability for epilepsy. We conducted a logistic regression model with interaction terms for conditions by deployment history that adjusted for demographics and military characteristics. RESULTS: The cohort (n = 938,890) included post-9/11 veterans of whom 27,436 (2.92%) had epilepsy. Most veterans had a history of deployment (70.64%), referred to as "deployed." Epilepsy was more common among veterans who were never deployed ("nondeployed") (3.85% vs 2.54%). Deployed veterans were more likely to have had TBI, compared with the nondeployed veterans (33.94% vs 14.24%), but nondeployed veterans with moderate/severe TBI had higher odds of epilepsy compared with deployed veterans (adjusted odds ratio [aOR] 2.92, 95% CI 2.68-3.17 vs aOR 2.01, 95% CI 1.91-2.11). Penetrating TBI had higher odds of epilepsy among the deployed veterans (aOR 5.33, 95% CI 4.89-5.81), whereas the odds of epilepsy for mild TBI did not significantly differ by deployment status. Although most neurologic conditions were more prevalent among the nondeployed veterans, they were often associated with higher odds of epilepsy in the deployed veterans. DISCUSSION: Deployment history had a significant differential impact on epilepsy predictors. As expected, penetrating TBI had a greater epilepsy impact among deployed veterans perhaps due to combat/blast. Some epilepsy predictors (moderate/severe TBI, multiple sclerosis, and Parkinson disease) had a stronger association in the nondeployed veterans suggesting a potential healthy warrior effect in which such conditions preclude deployment. Other neurologic conditions (e.g., brain tumor, Alzheimer disease/frontotemporal dementia) had a greater epilepsy impact in the deployed veterans. This may be attributable to deployment-related exposures (combat injury, occupational exposures). A better understanding of deployment effects is critical to provide targeted epilepsy prevention in veterans and military service members.

Harmonizing PTSD severity scales across instruments and sites.

OBJECTIVE: The variety of instruments used to assess posttraumatic stress disorder (PTSD) allows for flexibility, but also creates challenges for data synthesis. The objective of this work was to use a multisite mega analysis to derive quantitative recommendations for equating scores across measures of PTSD severity. METHOD: Empirical Bayes harmonization and linear models were used to describe and mitigate site and covariate effects. Quadratic models for converting scores across PTSD assessments were constructed using bootstrapping and tested on hold out data. RESULTS: We aggregated 17 data sources and compiled an n = 5,634 sample of individuals who were assessed for PTSD symptoms. We confirmed our hypothesis that harmonization and covariate adjustments would significantly improve inference of scores across instruments. Harmonization significantly reduced cross-dataset variance (28%, p < .001), and models for converting scores across instruments were well fit (median R² = 0.985) with an average root mean squared error of 1.46 on sum scores. CONCLUSIONS: These methods allow PTSD symptom severity to be placed on multiple scales and offers interesting empirical perspectives on the role of harmonization in the behavioral sciences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium.

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual's latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.

Multimodal Quality of Life Assessment in Post-9/11 Veterans With Epilepsy: Impact of Drug Resistance, Traumatic Brain Injury, and Comorbidity.

BACKGROUND AND OBJECTIVES: Epilepsy is defined by the occurrence of multiple unprovoked seizures, but quality of life (QOL) in people with epilepsy is determined by multiple factors, in which psychiatric comorbid conditions play a pivotal role. Therefore, understanding the interplay between comorbid conditions and QOL across epilepsy phenotypes is an important step toward improved outcomes. Here, we report the impact of QOL across distinct epilepsy phenotypes in a cohort of post-9/11 veterans with high rates of traumatic brain injury (TBI). METHODS: This observational cohort study from the Veterans Health Administration included post-9/11 veterans with epilepsy. A process integrating an epilepsy identification algorithm, chart abstraction, and self-reported measures was used to classify patients into 1 of 4 groups: (1) epilepsy controlled with medications, (2) drug-resistant epilepsy (DRE), (3) posttraumatic epilepsy (PTE), or (4) drug-resistant PTE (PT-DRE). Summary scores for 6 QOL measures were compared across the groups after adjustment for age, sex, and number of comorbid conditions. RESULTS: A total of 529 survey respondents with epilepsy were included in the analysis: 249 controls (i.e., epilepsy without DRE or PTE), 124 with DRE, 86 with PTE, and 70 with PT-DRE. DRE was more common in those with PTE compared with those with nontraumatic epilepsy (45% vs 33%, odds ratio 1.6 [95% CI 1.1-2.4], p = 0.01). Patients with PTE and PT-DRE had significantly more comorbid conditions in health records than those with nontraumatic epilepsy. Those with both PTE and DRE reported the lowest QOL across all 6 measures, and this persisted after adjustment for comorbid conditions and in further linear analyses. DISCUSSION: Among those with PTE, DRE prevalence was significantly higher than prevalence of nontraumatic epilepsies. PTE was also associated with higher burden of comorbidity and worse overall QOL compared to nontraumatic epilepsies. People with PTE are distinctly vulnerable to the comorbid conditions associated with TBI and epilepsy. This at-risk group should be the focus of future studies aimed at elucidating the factors associated with adverse health outcomes and developing antiepileptogenic therapies.

Association Between Traumatic Brain Injury and Subsequent Cardiovascular Disease Among Post-9/11-Era Veterans.

Importance: Traumatic brain injury (TBI) was common among US service members deployed to Iraq and Afghanistan. Although there is some evidence to suggest that TBI increases the risk of cardiovascular disease (CVD), prior reports were predominantly limited to cerebrovascular outcomes. The potential association of TBI with CVD has not been comprehensively examined in post-9/11-era veterans. Objective: To determine the association between TBI and subsequent CVD in post-9/11-era veterans. Design, Setting, and Participants: This was a retrospective cohort study conducted from October 1, 1999, to September 30, 2016. Participants were followed up until December 31, 2018. Included in the study were administrative data from the US Department of Veterans Affairs and the Department of Defense from the Long-term Impact of Military-Relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium. Participants were excluded if dates did not overlap with the study period. Data analysis was conducted between November 22, 2021, and June 28, 2022. Exposures: History of TBI as measured by diagnosis in health care records. Main Outcomes and Measures: Composite end point of CVD: coronary artery disease, stroke, peripheral artery disease, and cardiovascular death. Results: Of the 2 530 875 veterans from the consortium, after exclusions, a total of 1 559 928 veterans were included in the analysis. A total of 301 169 veterans (19.3%; median [IQR] age, 27 [23-34] years; 265 217 male participants [88.1]) with a TBI history and 1 258 759 veterans (80.7%; median [IQR] age, 29 [24-39] years; 1 012 159 male participants [80.4%]) without a TBI history were included for analysis. Participants were predominately young (1 058 054 [67.8%] <35 years at index date) and male (1 277 376 [81.9%]). Compared with participants without a history of TBI, diagnoses of mild TBI (hazard ratio [HR], 1.62; 95% CI, 1.58-1.66; P < .001), moderate to severe TBI (HR, 2.63; 95% CI, 2.51-2.76; P < .001), and penetrating TBI (HR, 4.60; 95% CI, 4.26-4.96; P < .001) were associated with CVD in adjusted models. In analyses of secondary outcomes, all severities of TBI were associated with the individual components of the composite outcome except penetrating TBI and CVD death. Conclusions and Relevance: Results of this cohort study suggest that US veterans with a TBI history were more likely to develop CVD compared with veterans without a TBI history. Given the relatively young age of the cohort, these results suggest that there may be an increased burden of CVD as these veterans age and develop other CVD risk factors. Future studies are needed to determine if the increased risk associated with TBI is modifiable.