Alcohol for seizure induction in the epilepsy monitoring unit.

RATIONALE: Seizure induction techniques are used in the epilepsy monitoring unit (EMU) to increase diagnostic yield and reduce length of stay. There are insufficient data on the efficacy of alcohol as an induction technique. METHODS: We performed a retrospective cohort study using six years of EMU data at our institution. We compared cases who received alcohol for seizure induction to matched controls who did not. The groups were matched on the following variables: age, reason for admission, length of stay, number of antiseizure medications (ASM) at admission, whether ASMs were tapered during admission, and presence of interictal epileptiform discharges. We used both propensity score and exact matching strategies. We compared the likelihood of epileptic seizures and nonepileptic events in cases versus controls using Kaplan-Meier time-to-event analysis, as well as odds ratios for these outcomes occurring at any time during the admission. RESULTS: We analyzed 256 cases who received alcohol (median dose 2.5 standard drinks) and 256 propensity score-matched controls. Cases who received alcohol were no more likely than controls to have an epileptic seizure (X2(1) = 0.01, p = 0.93) or nonepileptic event (X2(1) = 2.1, p = 0.14) in the first 48 h after alcohol administration. For the admission overall, cases were no more likely to have an epileptic seizure (OR 0.89, 95 % CI 0.61-1.28, p = 0.58), nonepileptic event (OR 0.97, CI 0.62-1.53, p = 1.00), nor require rescue benzodiazepine (OR 0.63, CI 0.35-1.12, p = 0.15). Stratified analyses revealed no increased risk of epileptic seizure in any subgroups. Sensitivity analysis using exact matching showed that results were robust to matching strategy. CONCLUSIONS: Alcohol was not an effective induction technique in the EMU. This finding has implications for counseling patients with epilepsy about the risks of drinking alcohol in moderation in their daily lives.

Clinical characteristics and surgical outcomes of epilepsy associated with temporal encephalocele: A systematic review.

Temporal encephaloceles (TE) are an under-identified, potentially intervenable cause of epilepsy. This systematic review consolidates the current data to identify the major clinical, neuroimaging, and EEG features and surgical outcomes of epilepsy associated with TE. Literature searches were carried out using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane Library databases from inception to December 7, 2023. Studies were included if they described clinical, neuroimaging, EEG, or surgical data in ≥5 patients with TE and epilepsy. Of 562 studies identified in the search, 24 met the eligibility criteria, reporting 423 unique patients with both epilepsy and TE. Compared to epilepsy patients without TE, those with TE had a higher mean age of seizure onset and were less likely to have a history of febrile seizures. Seizure semiologies were variable, but primarily mirrored temporal lobe onset patterns. Epilepsy patients with TE had a higher likelihood of having clinical or radiographic features of idiopathic intracranial hypertension (IIH) than those without. Brain MRI may show ipsilateral mesial temporal sclerosis (16 %). CT scans of the skull base usually revealed bony defects near the TE (90 %). Brain PET scans primarily showed ipsilateral temporal lobe hypometabolism (80 %), mostly in the anterior temporal lobe (67 %). Scalp EEG mostly lateralized ipsilateral to the implicated TE (92 % seizure onset) and localized to the temporal lobe (96 %). Intracranial EEG revealed seizure onset near the TE (11 of 12 cases including TE-adjacent electrodes) with variable timing of spread to the ipsilateral hippocampus. After surgical treatment of the TE, the rate of Engel I or ILAE 1 outcomes at one year was 75 % for lesionectomy, 85 % for anterior temporal lobectomy (ATL), and 80 % for ATL with amygdalohippocampectomy. Further studies are needed to better elucidate the relationship between IIH, TE, and epilepsy, improve the identification of TE, and optimize surgical interventions.

Spatiotemporal profile of atrophy in the first year following moderate-severe traumatic brain injury.

Traumatic brain injury (TBI) triggers progressive neurodegeneration resulting in brain atrophy that continues months-to-years following injury. However, a comprehensive characterization of the spatial and temporal evolution of TBI-related brain atrophy remains incomplete. Utilizing a sensitive and unbiased morphometry analysis pipeline optimized for detecting longitudinal changes, we analyzed a sample consisting of 37 individuals with moderate-severe TBI who had primarily high-velocity and high-impact injury mechanisms. They were scanned up to three times during the first year after injury (3 months, 6 months, and 12 months post-injury) and compared with 33 demographically matched controls who were scanned once. Individuals with TBI already showed cortical thinning in frontal and temporal regions and reduced volume in the bilateral thalami at 3 months post-injury. Longitudinally, only a subset of cortical regions in the parietal and occipital lobes showed continued atrophy from 3 to 12 months post-injury. Additionally, cortical white matter volume and nearly all deep gray matter structures exhibited progressive atrophy over this period. Finally, we found that disproportionate atrophy of cortex along sulci relative to gyri, an emerging morphometric marker of chronic TBI, was present as early as 3 month post-injury. In parallel, neurocognitive functioning largely recovered during this period despite this pervasive atrophy. Our findings demonstrate msTBI results in characteristic progressive neurodegeneration patterns that are divergent across regions and scale with the severity of injury. Future clinical research using atrophy during the first year of TBI as a biomarker of neurodegeneration should consider the spatiotemporal profile of atrophy described in this study.

A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit.

OBJECTIVE: Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU. METHODS: We studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures. RESULTS: Seizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay. SIGNIFICANCE: A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy.

OBJECTIVE: Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS: We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS: We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE: The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

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.

Connectomic assessment of injury burden and longitudinal structural network alterations in moderate-to-severe traumatic brain injury.

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease.

Subarachnoid Hemorrhage in the Setting of Electroconvulsive Therapy in a Patient With an Unsecured Cerebral Aneurysm: A Case Report and Review of the Literature.

OBJECTIVES: The aim of the study was to report a case of a patient who experienced a fatal aneurysmal subarachnoid hemorrhage during the course of electroconvulsive therapy (ECT). METHODS: This is a case report and review of the literature. RESULTS: Electroconvulsive therapy is widely viewed as a safe and effective treatment for major depressive disorder; however, there remains some uncertainty regarding the safety of ECT in patients with cerebral aneurysms. Many cases exist documenting the safety of ECT in patients with both secured and unsecured cerebral aneurysms; however, these patients had aneurysms that were associated with a low (ie, ≤2.6%) 5-year cumulative rupture rate, whereas the patient in our case had an aneurysm associated with a 14.5% five-year cumulative rupture rate. CONCLUSIONS: We stress that ECT should not be viewed as a universally safe procedure in patients with cerebral aneurysms and the 5-year cumulative aneurysm rupture rate should be used in the discussion of the risk-benefit ratio of ECT in patients with unsecured cerebral aneurysms.

Does the Magnitude of the Electrocardiogram QT Interval Dispersion Predict Stroke Outcome?

BACKGROUND: QT dispersion, maximal interlead difference in QT interval on 12-lead electrocardiogram (ECG), measures cardiac repolarization abnormalities. Data are conflicting whether QT dispersion predicts adverse outcome in acute ischemic stroke (AIS) patients. Our objective is to determine if QT dispersion predicts: (1) short-term clinical outcome in AIS, and (2) stroke location (insular versus noninsular cortex). METHODS: Admission ECGs from 412 consecutive patients with acute stroke symptoms from 2 university-based stroke centers were reviewed. QT dispersion was measured. A neuroradiologist reviewed brain imaging for insular cortex involvement. Favorable clinical outcomes at discharge were modified Rankin Scale (mRS) score of 0-1, discharge National Institutes of Health Stroke Scale (NIHSS) score less than 2, and discharge to home. Multiple logistic regressions were performed for each outcome measure and to determine the association between insular infarct and QT dispersion. RESULTS: Of 145 subjects in the final analysis, median age was 65 years (interquartile range [IQR] 56-75), male patients were 38%, black patients were 68%, median QT dispersion was 78 milliseconds (IQR 59-98), and median admission NIHSS score was 4 (IQR 2-6). QT dispersion did not predict short-term clinical outcome for mRS score (odds ratio [OR] = 1.001, 95% confidence interval [CI] .99-1.01, P = .85), NIHSS at discharge (OR = .994, 95% CI .98-1.01, P = .30), or discharge disposition (OR = 1.001, 95% CI .99-1.01, P = .81). Insular cortex involvement did not correlate with QT dispersion magnitude (OR = 1.009, 95% CI .99-1.02, P = .45). CONCLUSIONS: We could not demonstrate that QT dispersion is useful in predicting short-term clinical outcome at discharge in AIS. Further, the magnitude of QT dispersion did not predict insular cortical stroke location.

Relationship between QT interval dispersion in acute stroke and stroke prognosis: a systematic review.

BACKGROUND: QT dispersion (QTd) has been proposed as an indirect electrocardiography (ECG) measure of heterogeneity of ventricular repolarization. The predictive value of QTd in acute stroke remains controversial. We aimed to clarify the relationship between QTd and acute stroke and stroke prognosis. METHODS: A systematic review of the literature was performed using prespecified medical subjects heading terms, Boolean logic, and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Eligible studies included ischemic or hemorrhagic stroke and provided QTd measurements. RESULTS: Two independent reviewers identified 553 publications. Sixteen articles were included in the final analysis. There were a total of 888 stroke patients: 59% ischemic and 41% hemorrhagic. There was considerable heterogeneity in study design, stroke subtypes, ECG assessment time, control groups, and comparison groups. Nine studies reported a significant association between acute stroke and baseline QTd. Two studies reported that QTd increases are specifically related to hemorrhagic strokes, involvement of the insular cortex, right-side lesions, larger strokes, and increases in 3,4-dihydroxyphenylethylene glycol in hemorrhagic stroke. Three studies reported QTd to be an independent predictor of stroke mortality. One study each reported increases in QTd in stroke patients who developed ventricular arrhythmias and cardiorespiratory compromise. CONCLUSIONS: There are few well-designed studies and considerable variability in study design in addressing the significance of QTd in acute stroke. Available data suggest that stroke is likely to be associated with increased QTd. Although some evidence suggests a possible prognostic role of QTd in stroke, larger and well-designed studies need to confirm these findings.

Enhancing the Diagnostic Utility of ASL Imaging in Temporal Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework.

Background and Significance: Positron Emission Tomography (PET) using fluorodeoxyglucose (FDG-PET) is a standard imaging modality for detecting areas of hypometabolism associated with the seizure onset zone (SOZ) in temporal lobe epilepsy (TLE). However, FDG-PET is costly and involves the use of a radioactive tracer. Arterial Spin Labeling (ASL) offers an MRI-based quantification of cerebral blood flow (CBF) that could also help localize the SOZ, but its performance in doing so, relative to FDG-PET, is limited. In this study, we seek to improve ASL's diagnostic performance by developing a deep learning framework for synthesizing FDG-PET-like images from ASL and structural MRI inputs. Methods: We included 68 epilepsy patients, out of which 36 had well lateralized TLE. We compared the coupling between FDG-PET and ASL CBF values in different brain regions, as well as the asymmetry of these values across the brain. We additionally assessed each modality's ability to lateralize the SOZ across brain regions. Using our paired PET-ASL data, we developed FlowGAN, a generative adversarial neural network (GAN) that synthesizes PET-like images from ASL and T1-weighted MRI inputs. We tested our synthetic PET images against the actual PET images of subjects to assess their ability to reproduce clinically meaningful hypometabolism and asymmetries in TLE. Results: We found variable coupling between PET and ASL CBF values across brain regions. PET and ASL had high coupling in neocortical temporal and frontal brain regions (Spearman's r > 0.30, p < 0.05) but low coupling in mesial temporal structures (Spearman's r < 0.30, p > 0.05). Both whole brain PET and ASL CBF asymmetry values provided good separability between left and right TLE subjects, but PET (AUC = 0.96, 95% CI: [0.88, 1.00]) outperformed ASL (AUC = 0.81; 95% CI: [0.65, 0.96]). FlowGAN-generated images demonstrated high structural similarity to actual PET images (SSIM = 0.85). Globally, asymmetry values were better correlated between synthetic PET and original PET than between ASL CBF and original PET, with a mean correlation increase of 0.15 (95% CI: [0.07, 0.24], p<0.001, Cohen's d = 0.91). Furthermore, regions that had poor ASL-PET correlation (e.g. mesial temporal structures) showed the greatest improvement with synthetic PET images. Conclusions: FlowGAN improves ASL's diagnostic performance, generating synthetic PET images that closely mimic actual FDG-PET in depicting hypometabolism associated with TLE. This approach could improve non-invasive SOZ localization, offering a promising tool for epilepsy presurgical assessment. It potentially broadens the applicability of ASL in clinical practice and could reduce reliance on FDG-PET for epilepsy and other neurological disorders.

Posttraumatic Epilepsy and Dementia Risk.

Importance: Although both head injury and epilepsy are associated with long-term dementia risk, posttraumatic epilepsy (PTE) has only been evaluated in association with short-term cognitive outcomes. Objective: To investigate associations of PTE with dementia risk. Design, Setting, and Participants: The Atherosclerosis Risk in Communities (ARIC) study initially enrolled participants from 1987 to 1989 and this prospective cohort study uses data through December 31, 2019, with a median follow-up of 25 years. Data were analyzed between March 14, 2023, and January 2, 2024. The study took place in 4 US communities in Minnesota, Maryland, North Carolina, and Mississippi. Of 15 792 ARIC study participants initially enrolled, 2061 were ineligible and 1173 were excluded for missing data, resulting in 12 558 included participants. Exposures: Head injury was defined by self-report and International Classification of Diseases (ICD) diagnostic codes. Seizure/epilepsy was defined using ICD codes. PTE was defined as a diagnosis of seizure/epilepsy occurring more than 7 days after head injury. Head injury, seizure/epilepsy, and PTE were analyzed as time-varying exposures. Main Outcomes and Measures: Dementia was defined using cognitive assessments, informant interviews, and ICD and death certificate codes. Adjusted Cox and Fine and Gray proportional hazards models were used to estimate dementia risk. Results: Participants had a mean (SD) age of 54.3 (5.8) years at baseline, 57.7% were female, 28.2% were of self-reported Black race, 14.4% were ultimately categorized as having head injury, 5.1% as having seizure/epilepsy, and 1.2% as having PTE. Over a median follow-up of 25 (25th to 75th percentile, 17-30) years, 19.9% developed dementia. In fully adjusted models, compared with no head injury and no seizure/epilepsy, PTE was associated with 4.56 (95% CI, 4.49-5.95) times the risk of dementia, while seizure/epilepsy was associated with 2.61 (95% CI, 2.21-3.07) times the risk and head injury with 1.63 (95% CI, 1.47-1.80) times the risk. The risk of dementia associated with PTE was significantly higher than the risk associated with head injury alone and with nontraumatic seizure/epilepsy alone. Results were slightly attenuated in models accounting for the competing risks of mortality and stroke, but patterns of association remained similar. In secondary analyses, the increased dementia risk associated with PTE occurring after first vs second head injury and after mild vs moderate/severe injury was similar. Conclusions and Relevance: In this community-based cohort, there was an increased risk of dementia associated with PTE that was significantly higher than the risk associated with head injury or seizure/epilepsy alone. These findings provide evidence that PTE is associated with long-term outcomes and supports both the prevention of head injuries via public health measures and further research into the underlying mechanisms and the risk factors for the development of PTE, so that efforts can also be focused on the prevention of PTE after a head injury.

Association between structural brain MRI abnormalities and epilepsy in older adults.

OBJECTIVE: To determine the association between brain MRI abnormalities and incident epilepsy in older adults. METHODS: Men and women (ages 45-64 years) from the Atherosclerosis Risk in Communities study were followed up from 1987 to 2018 with brain MRI performed between 2011 and 2013. We identified cases of incident late-onset epilepsy (LOE) with onset of seizures occurring after the acquisition of brain MRI. We evaluated the relative pattern of cortical thickness, subcortical volume, and white matter integrity among participants with incident LOE after MRI in comparison with participants without seizures. We examined the association between MRI abnormalities and incident LOE using Cox proportional hazards regression. Models were adjusted for demographics, hypertension, diabetes, smoking, stroke, and dementia status. RESULTS: Among 1251 participants with brain MRI data, 27 (2.2%) developed LOE after MRI over a median of 6.4 years (25-75 percentile 5.8-6.9) of follow-up. Participants with incident LOE after MRI had higher levels of cortical thinning and white matter microstructural abnormalities before seizure onset compared to those without seizures. In longitudinal analyses, greater number of abnormalities was associated with incident LOE after controlling for demographic factors, risk factors for cardiovascular disease, stroke, and dementia (gray matter: hazard ratio [HR]: 2.3, 95% confidence interval [CI]: 1.0-4.9; white matter diffusivity: HR: 3.0, 95% CI: 1.2-7.3). INTERPRETATION: This study demonstrates considerable gray and white matter pathology among individuals with LOE, which is present prior to the onset of seizures and provides important insights into the role of neurodegeneration, both of gray and white matter, and the risk of LOE.

Subcortical Functional Connectivity Gradients in Temporal Lobe Epilepsy.

Background and Motivation: Functional gradients have been used to study differences in connectivity between healthy and diseased brain states, however this work has largely focused on the cortex. Because the subcortex plays a key role in seizure initiation in temporal lobe epilepsy (TLE), subcortical functional-connectivity gradients may help further elucidate differences between healthy brains and TLE, as well as differences between left (L)-TLE and right (R)-TLE. Methods: In this work, we calculated subcortical functional-connectivity gradients (SFGs) from resting-state functional MRI (rs-fMRI) by measuring the similarity in connectivity profiles of subcortical voxels to cortical gray matter voxels. We performed this analysis in 23 R-TLE patients and 32 L-TLE patients (who were otherwise matched for age, gender, disease specific characteristics, and other clinical variables), and 16 controls. To measure differences in SFGs between L-TLE and R-TLE, we quantified deviations in the average functional gradient distributions, as well as their variance, across subcortical structures. Results: We found an expansion, measured by increased variance, in the principal SFG of TLE relative to controls. When comparing the gradient across subcortical structures between L-TLE and R-TLE, we found that abnormalities in the ipsilateral hippocampal gradient distributions were significantly different between L-TLE and R-TLE. Conclusion: Our results suggest that expansion of the SFG is characteristic of TLE. Subcortical functional gradient differences exist between left and right TLE and are driven by connectivity changes in the hippocampus ipsilateral to the seizure onset zone.

Subcortical functional connectivity gradients in temporal lobe epilepsy.

BACKGROUND AND MOTIVATION: Functional gradients have been used to study differences in connectivity between healthy and diseased brain states, however this work has largely focused on the cortex. Because the subcortex plays a key role in seizure initiation in temporal lobe epilepsy (TLE), subcortical functional-connectivity gradients may help further elucidate differences between healthy brains and TLE, as well as differences between left (L)-TLE and right (R)-TLE. METHODS: In this work, we calculated subcortical functional-connectivity gradients (SFGs) from resting-state functional MRI (rs-fMRI) by measuring the similarity in connectivity profiles of subcortical voxels to cortical gray matter voxels. We performed this analysis in 24 R-TLE patients and 31 L-TLE patients (who were otherwise matched for age, gender, disease specific characteristics, and other clinical variables), and 16 controls. To measure differences in SFGs between L-TLE and R-TLE, we quantified deviations in the average functional gradient distributions, as well as their variance, across subcortical structures. RESULTS: We found an expansion, measured by increased variance, in the principal SFG of TLE relative to controls. When comparing the gradient across subcortical structures between L-TLE and R-TLE, we found that abnormalities in the ipsilateral hippocampal gradient distributions were significantly different between L-TLE and R-TLE. CONCLUSION: Our results suggest that expansion of the SFG is characteristic of TLE. Subcortical functional gradient differences exist between left and right TLE and are driven by connectivity changes in the hippocampus ipsilateral to the seizure onset zone.

Structural brain network deviations predict recovery after traumatic brain injury.

OBJECTIVE: Traumatic brain injury results in diffuse axonal injury and the ensuing maladaptive alterations in network function are associated with incomplete recovery and persistent disability. Despite the importance of axonal injury as an endophenotype in TBI, there is no biomarker that can measure the aggregate and region-specific burden of axonal injury. Normative modeling is an emerging quantitative case-control technique that can capture region-specific and aggregate deviations in brain networks at the individual patient level. Our objective was to apply normative modeling in TBI to study deviations in brain networks after primarily complicated mild TBI and study its relationship with other validated measures of injury severity, burden of post-TBI symptoms, and functional impairment. METHOD: We analyzed 70 T1-weighted and diffusion-weighted MRIs longitudinally collected from 35 individuals with primarily complicated mild TBI during the subacute and chronic post-injury periods. Each individual underwent longitudinal blood sampling to characterize blood protein biomarkers of axonal and glial injury and assessment of post-injury recovery in the subacute and chronic periods. By comparing the MRI data of individual TBI participants with 35 uninjured controls, we estimated the longitudinal change in structural brain network deviations. We compared network deviation with independent measures of acute intracranial injury estimated from head CT and blood protein biomarkers. Using elastic net regression models, we identified brain regions in which deviations present in the subacute period predict chronic post-TBI symptoms and functional status. RESULTS: Post-injury structural network deviation was significantly higher than controls in both subacute and chronic periods, associated with an acute CT lesion and subacute blood levels of glial fibrillary acid protein (r = 0.5, p = 0.008) and neurofilament light (r = 0.41, p = 0.02). Longitudinal change in network deviation associated with change in functional outcome status (r = -0.51, p = 0.003) and post-concussive symptoms (BSI: r = 0.46, p = 0.03; RPQ: r = 0.46, p = 0.02). The brain regions where the node deviation index measured in the subacute period predicted chronic TBI symptoms and functional status corresponded to areas known to be susceptible to neurotrauma. CONCLUSION: Normative modeling can capture structural network deviations, which may be useful in estimating the aggregate and region-specific burden of network changes induced by TAI. If validated in larger studies, structural network deviation scores could be useful for enrichment of clinical trials of targeted TAI-directed therapies.

Longitudinal Abnormalities in White Matter Extracellular Free Water Volume Fraction and Neuropsychological Functioning in Patients with Traumatic Brain Injury.

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes potentially associated with increases in brain extracellular water content, but the nature of extracellular free water abnormalities after neurotrauma and its relationship to measures typically thought to reflect traumatic axonal injury are not well characterized. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of a cohort with primarily complicated mild traumatic brain injury. We analyzed a cohort of 59 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 36 healthy controls. Patients underwent brain magnetic resonance imaging (MRI) at 2 weeks (n = 59) and 6 months (n = 29) post-injury, and controls underwent a single MRI. Of the participants with TBI, 50 underwent clinical neuropsychological assessment at 2 weeks and 28 at 6 months. For each subject, we derived a summary score representing deviations in whole brain white matter extracellular free water volume fraction (VF) and free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients at 2 weeks and 6 months post-injury relative to controls. SAS for VF exhibited moderate correlation with neuropsychological functioning, particularly on measures of executive function. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Association of Head Injury With Late-Onset Epilepsy: Results From the Atherosclerosis Risk in Communities Cohort.

BACKGROUND AND OBJECTIVES: Late-onset epilepsy (LOE; i.e., epilepsy starting in later adulthood) affects a significant number of individuals. Head injury is also a risk factor for acquired epilepsy, but the degree to which prior head injury may contribute to LOE is less well understood. Our objective was to determine the association between head injury and subsequent development of LOE. METHODS: Included were 8,872 participants enrolled in the Atherosclerosis Risk in Communities (ARIC) study with continuous Centers for Medicare Services fee-for-service (FFS) coverage (55.1% women, 21.6% Black). We identified head injuries through 2018 from linked Medicare fee for service claims for inpatient/emergency department care, active surveillance of hospitalizations, and participant self-report. LOE cases through 2018 were identified from linked Medicare FFS claims. We used Cox proportional hazards models to evaluate associations of head injury with LOE, adjusting for demographic, cardiovascular, and lifestyle factors. RESULTS: The adjusted hazard ratio (HR) for developing LOE after a history of head injury was 1.88 (95% confidence interval [CI] 1.44-2.43). There was evidence for dose-response associations with greater risk for LOE with increasing number of prior head injuries (HR 1.37, 95% CI 1.01-1.88 for 1 prior head injury and HR 3.55, 95% CI 2.51-5.02 for 2+ prior head injuries, compared to no head injuries) and with more severe head injury (HR 2.53, 95% CI 1.83-3.49 for mild injury and HR 4.90, 95% CI 3.15-7.64 for moderate/severe injury, compared to no head injuries). Associations with LOE were significant for head injuries sustained at older age (age ≥67 years: HR 4.01, 95% CI 2.91-5.54), but not for head injuries sustained at younger age (age < 67 years: HR 0.98, 95% CI 0.68-1.41). DISCUSSION: Head injury was associated with increased risk of developing LOE, particularly when head injuries were sustained at an older age, and there was evidence for higher risk for LOE after a greater number of prior head injuries and after more severe head injuries. CLASSIFICATION OF EVIDENCE: This study provides Class I evidence that an increased risk of late-onset epilepsy is associated with head injury and that this risk increases further with multiple and more severe head injuries.

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.