Functional and anatomical connectivity predict brain stimulation's mnemonic effects.

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, stiff person syndrome (SPS) is an RND characterized by painful muscle spasms and rigidity. Leveraging underutilized electronic health records (EHR), this study showcased a machine-learning-based framework to identify clinical features that optimally characterize the diagnosis of SPS. METHODS: A machine-learning-based feature selection approach was employed on 319 items from the past medical histories of 48 individuals (23 with a diagnosis of SPS and 25 controls) with elevated serum autoantibodies against glutamic-acid-decarboxylase-65 (anti-GAD65) in Dartmouth Health's EHR to determine features with the highest discriminatory power. Each iteration of the algorithm implemented a Support Vector Machine (SVM) model, generating importance scores-SHapley Additive exPlanation (SHAP) values-for each feature and removing one with the least salient. Evaluation metrics were calculated through repeated stratified cross-validation. RESULTS: Depression, hypothyroidism, GERD, and joint pain were the most characteristic features of SPS. Utilizing these features, the SVM model attained precision of 0.817 (95% CI 0.795-0.840), sensitivity of 0.766 (95% CI 0.743-0.790), F-score of 0.761 (95% CI 0.744-0.778), AUC of 0.808 (95% CI 0.791-0.825), and accuracy of 0.775 (95% CI 0.759-0.790). CONCLUSIONS: This framework discerned features that, with further research, may help fully characterize the pathologic mechanism of SPS: depression, hypothyroidism, and GERD may respectively represent comorbidities through common inflammatory, genetic, and dysautonomic links. This methodology could address diagnostic challenges in neurology by uncovering latent associations and generating hypotheses for RNDs.

Accelerated long-term forgetting of recall and recognition memory in people with epilepsy.

PURPOSE: Persons with epilepsy (PWE) report memory deficits as one of the most distressing aspects of their disorder. Recently, a long-term memory deficit known as Accelerated Long-Term Forgetting (ALF) has been described in PWE. ALF is characterized by the initial retention of learned information, followed by an accelerated rate of memory decay. However, the rate of ALF varies widely across literature and it is unclear how it impacts different memory retrieval types. The current study aimed to capture the time course of ALF on both free recall and recognition memory using a movie-based task in PWE. METHODS: A sample of 30 PWE and 30 healthy comparison (HC) subjects watched a nature documentary and were tested on their recall and recognition of the film's content immediately after viewing and at delays of 24 hours, 48 hours, and 72 hours. Participants also rated the confidence they had in their recognition memory trial responses. RESULTS: For recall, PWE exhibit ALF at 72 hours (ß = -19.840, SE = 3.743, z(226) = -5.301, p < 0.001). For recognition, PWE had decreased performance compared to controls at the 24-hour (ß = -10.165, SE = 4.174, z(224) = -3.166, p = 0.004), 48-hour (ß = -8.113, SE = 3.701, z(224) = -2.195, p = 0.044), and 72-hour (ß = -10.794, SE = 3.017, z(224) = -3.295, p = 0.003) delays. The PWE group showed positive correlations (tau = 0.165, p < 0.001) between confidence ratings and accuracy, with higher confidence reflecting successful recognition. PWE were 49% less likely to answer either retrieval type correctly at 72 hours (OR 0.51, 95% CI [0.35, 0.74], p < 0.001). Left hemispheric seizure onset decreased the odds of successful retrieval by 88% (OR 0.12, 95% CI [0.01, 0.42], p = 0.019). CONCLUSIONS: These findings provide evidence of ALF in PWE, with a differential impact on recall and recognition memory. This further supports the call to include ALF assessments in standard memory evaluations in PWE. Additionally, identifying the neural correlates of ALF in the future will be important in developing targeted therapies to alleviate the burden of memory impairment for PWE.

MEW network self-management program characteristics and lessons learned through the RE-AIM framework.

RATIONALE: The promotion of evidence-based self-management support for people living with chronic conditions such as epilepsy is a public health priority. Epilepsy self-management encompasses three general areas: (1) treatment management, (2) seizure management, and (3) lifestyle management. Interventions focusing on self-management have increased quality of life and adherence to treatment. This study assesses and synthesizes the Managing Epilepsy Well Network (MEWN) program implementation experiences using the RE-AIM framework. This research informs the quality and rigor of MEWN program dissemination and implementation efforts to assess whether these programs are being implemented and their scalability. METHODS: The study data were derived from a MEWN Self-management Program Survey conducted with currently active MEWN researchers through an online survey and review of program publications and archival documents. Survey data were obtained from either the principal investigator or study team for the UPLIFT, HOBSCOTCH, SMART, MINDSET, TIME, and PACES programs. The survey questionnaire included 6 sections consisting of 68 questions and focused on the RE-AIM dimensions and respondent characteristics. The RE-AIM dimensions included: (1) Reach, (2) Effectiveness, (3) Adoption (number of and type of adopting sites), (4) Implementation (retention rate, barriers to implementation), and (5) Maintenance. RESULTS: Across the MEWN programs, participation (44-120 individuals) and delivery methods (community, clinic, or asynchronous; group or individual) ranged with most programs predominantly reaching White or African American participants. Common program outcome measures included clinical outcomes (e.g., depression, quality of life, seizure frequency) and indicators of self-management behaviors (e.g., problem-solving; self-efficacy). Initial efficacy trials suggested programs were effective in changing some of their targeted outcomes (effectiveness). Most programs were implemented in clinical settings and several programs are being replicated or adapted to different geographical (e.g., urban, rural, suburban) or demographic (e.g., race, age) settings (adoption). Program delivery methods involved a mixture of program staff, peer educators, and researchers. Implementation enabling factors included partnerships with local epilepsy organizations and the inclusion of peer educators. Retention rates for all programs averaged 83.6%. Internal barriers included recruitment and lack of sufficient resources for participants. External barriers included clinical staff buy-in, staffing, and insufficient funding for support staff. Despite uncertain funding, all programs offered next steps to sustain their initiatives such as packaging their programs, initiating adoption with regional organizations, and supporting organizational readiness (maintenance). Dissemination efforts included partnering with other organizations, provision of training and technical assistance, and partnering with national organizations on grant opportunities to scale up existing programs. CONCLUSION: These data showcase the impact of the MEWN self-management interventions on health and quality of life. These programs are employing training, readiness assessment, technical assistance, and development of partnerships to increase program scalability. Finally, program adaptations are being conducted to expand the interventions to other populations to address health inequalities. The lessons learned are critical for other interventions attempting to increase the translation of their programs to other settings.

Practical considerations in epilepsy neurostimulation.

Neuromodulation is a key therapeutic tool for clinicians managing patients with drug-resistant epilepsy. Multiple devices are available with long-term follow-up and real-world experience. The aim of this review is to give a practical summary of available neuromodulation techniques to guide the selection of modalities, focusing on patient selection for devices, common approaches and techniques for initiation of programming, and outpatient management issues. Vagus nerve stimulation (VNS), deep brain stimulation of the anterior nucleus of the thalamus (DBS-ANT), and responsive neurostimulation (RNS) are all supported by randomized controlled trials that show safety and a significant impact on seizure reduction, as well as a suggestion of reduction in the risk of sudden unexplained death in epilepsy (SUDEP). Significant seizure reductions are observed after 3 months for DBS, RNS, and VNS in randomized controlled trials, and efficacy appears to improve with time out to 7 to 10 years of follow-up for all modalities, albeit in uncontrolled follow-up or retrospective studies. A significant number of patients experience seizure-free intervals of 6 months or more with all three modalities. Number and location of epileptogenic foci are important factors affecting efficacy, and together with comorbidities such as severe mood or sleep disorders, may influence the choice of modality. Programming has evolved-DBS is typically initiated at lower current/voltage than used in the pivotal trial, whereas target charge density is lower with RNS, however generalizable optimal parameters are yet to be defined. Noninvasive brain stimulation is an emerging stimulation modality, although it is currently not used widely. In summary, clinical practice has evolved from those established in pivotal trials. Guidance is now available for clinicians who wish to expand their approach, and choice of neuromodulation technique may be tailored to individual patients based on their epilepsy characteristics, risk tolerance, and preferences.

Temporal lobe interictal spikes disrupt encoding and retrieval of verbal memory: A subregion analysis.

OBJECTIVE: The medial temporal lobe (MTL) encodes and recalls memories and can be a predominant site for interictal spikes (IS) in patients with focal epilepsy. It is unclear whether memory deficits are due to IS in the MTL producing a transient decline. Here, we investigated whether IS in the MTL subregions and lateral temporal cortex impact episodic memory encoding and recall. METHODS: Seventy-eight participants undergoing presurgical evaluation for medically refractory focal epilepsy with depth electrodes placed in the temporal lobe participated in a verbal free recall task. IS were manually annotated during the pre-encoding, encoding, and recall epochs. We examined the effect of IS on word recall using mixed-effects logistic regression. RESULTS: IS in the left hippocampus (odds ratio [OR] = .73, 95% confidence interval [CI] = .63-.84, p < .001) and left middle temporal gyrus (OR = .46, 95% CI = .27-.78, p < .05) during word encoding decreased subsequent recall performance. Within the left hippocampus, this effect was specific for area CA1 (OR = .76, 95% CI = .66-.88, p < .01) and dentate gyrus (OR = .74, 95% CI = .62-.89, p < .05). IS in other MTL subregions or inferior and superior temporal gyrus and IS occurring during the prestimulus window did not affect word encoding (p > .05). IS during retrieval in right hippocampal (OR = .22, 95% CI = .08-.63, p = .01) and parahippocampal regions (OR = .24, 95% CI = .07-.8, p < .05) reduced the probability of recalling a word. SIGNIFICANCE: IS in medial and lateral temporal cortex contribute to transient memory decline during verbal episodic memory.

Seizure- or Epilepsy-Related Emergency Department Visits Before and During the COVID-19 Pandemic - United States, 2019-2021.

Seizures, transient signs or symptoms caused by abnormal surges of electrical activity in the brain, can result from epilepsy, a neurologic disorder characterized by abnormal electrical brain activity causing recurrent, unprovoked seizures, or from other inciting causes, such as high fever or substance abuse (1). Seizures generally account for approximately 1% of all emergency department (ED) visits (2,3). Persons of any age can experience seizures, and outcomes might range from no complications for those with a single seizure to increased risk for injury, comorbidity, impaired quality of life, and early mortality for those with epilepsy (4). To examine trends in weekly seizure- or epilepsy-related (seizure-related) ED visits† in the United States before and during the COVID-19 pandemic, CDC analyzed data from the National Syndromic Surveillance Program (NSSP).§ Seizure-related ED visits decreased abruptly during the early pandemic period. By the end of 2020, seizure-related ED visits returned almost to prepandemic levels for persons of all ages, except children aged 0-9 years. By mid-2021, however, this age group gradually returned to baseline as well. Reasons for the decrease in seizure-related ED visits in 2020 among all age groups and the slow return to baseline among children aged 0-9 years compared with other age groups are unclear. The decrease might have been associated with fear of exposure to COVID-19 infection in EDs deterring parents or guardians of children from seeking care, adherence to mitigation measures including avoiding public settings such as EDs, or increased access to telehealth services decreasing the need for ED visits (5). These findings reinforce the importance of understanding factors associated with ED avoidance among persons with epilepsy or seizure, the importance that all eligible persons be up to date¶ with COVID-19 vaccination, and the need to encourage persons to seek appropriate care for seizure-related emergencies** to prevent adverse outcomes.

A pilot study assessing the feasibility and acceptability of Project UPLIFT adapted for patients with psychogenic nonepileptic seizures.

BACKGROUND: People with psychogenic nonepileptic seizures (PNES) are at elevated risk of multiple psychiatric comorbidities. Current treatment gaps highlight the need for time-limited, distance-delivered interventions that can be cost-effectively delivered to patients with PNES. Project UPLIFT is a self-management program addressing mood problems in epilepsy that has not previously been adapted for PNES. The purpose of this study was to assess the feasibility and acceptability of UPLIFT for patients with PNES. METHODS: Project UPLIFT was minimally adapted by a multidisciplinary research group at Dartmouth-Hitchcock Medical Center. Participants were recruited into a nonrandomized pilot study and were assessed at baseline, after completing the intervention, and at 1-month follow-up. RESULTS: The intervention was adapted and delivered without additional modification. A total of eight participants enrolled in the trial, and six participated in the first session. All six participants who started UPLIFT completed the intervention, with a 98% attendance record across the eight sessions. At follow-up, 100% reported that UPLIFT provided useful tools for daily life. All participants continued using UPLIFT after completing the program, and 83% felt it helped with seizure management. Preliminary results provide proof-of-concept for future efficacy trials. CONCLUSION: This study provides initial support for the feasibility and acceptability of Project UPLIFT, minimally adapted for patients with PNES. As a time-limited program that can be distance-delivered to groups of patients, UPLIFT may be well-suited for the healthcare environment brought on by the COVID-19 pandemic.

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention.

OBJECTIVE: To develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention. METHODS: Ninety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model's generalizability and performance with external data. RESULTS: All six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887). SIGNIFICANCE: We trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools.

Markers of Quality Care for Newly Diagnosed People With Epilepsy on Medicaid.

BACKGROUND: For newly diagnosed people with epilepsy (PWE), proper treatment is important to improve outcomes, yet limited data exist on markers of quality care. OBJECTIVE: Examine markers of quality care for newly diagnosed PWE. METHODS: Using Medicaid claims data (2010-2014) for 15 states we identified adults 18-64 years of age diagnosed with incident epilepsy in 2012 or 2013. We built 5 sequential logistic regression models to evaluate: (1) seeing a neurologist; (2) diagnostic evaluation; (3) antiepileptic medication adherence; (4) serum drug levels checked; and (5) being in the top quartile of number of negative health events (NHEs). We adjusted for demographics, comorbidities, county-level factors, and the outcomes from all prior models. RESULTS: Of 25,663 PWE, 37.3% saw a neurologist, with decreased odds for those of older age, those residing in counties with low-density of neurologists, and certain race/ethnicities; about 57% of PWE received at least 1 diagnostic test; and nearly 62% of PWE were adherent to their medication. The most common comorbidities were hypertension (37.1%) and psychoses (26.9%). PWE with comorbidities had higher odds of seeing a neurologist and to have NHEs. Substance use disorders were negatively associated with medication adherence and positively associated with high NHEs. CONCLUSIONS: There are notable differences in demographics among people with incident epilepsy who do or do not see a neurologist. Differences in NHEs persist, even after controlling for neurologist care and diagnostic evaluation. Continued attention to these disparities and comorbidities is needed in the evaluation of newly diagnosed PWE.