Faculty Bibliography

Mobile applications are widely used in epilepsy, although their impact on clinical effectiveness (CE) and their feasibility, acceptability, and usability (FAU) remain unclear. We conducted a systematic review investigating CE and FAU of epilepsy mobile applications using MEDLINE and Embase from database inception to June 21, 2024. We followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting standards. The protocol was registered on PROSPERO (CRD42019134848). In duplicate, we determined study quality using the Newcastle-Ottawa Quality Assessment Scale (NOQAS) and the Joanna Briggs Critical Appraisal Checklist (to determine eligibility for inclusion), risk of bias using the Cochrane Risk of Bias tool, and usability study quality using the 15-point Silva scale. We identified 8953 studies, of which 20 were included. Twelve (60.0%) addressed CE, nine (45.0%) acceptability, five (25.0%) usability, and eight (40.0%) feasibility. Five (25.0%) evaluated CE and FAU. Studies comprised prospective cohort (n = 9, 45.0%), pilot (n = 3, 15.0%), randomized controlled trial (n = 7, 35.0%), and pre/post (n = 1, 5.0%) designs. Most apps were used for self-management or to enhance education or communication between patients and providers. Cohort studies demonstrated fair quality (median NOQAS score = 5, interquartile range [IQR] = 5.0-5.8), whereas of seven randomized controlled trials, four (57.1%) had some concern for bias. Usability studies demonstrated high quality (median Silva score = 10, IQR = 10-11). Apps were predominantly intended for patient use (n = 9, 75.0%). Symptom reporting and medication management were the most common app targets in both CE and FAU studies (n = 8, 66.7%; n = 9, 69.2%), although FAU studies more frequently used monitoring or tracking (n = 10, 76.9%) and reminder setting (n = 10, 76.9%) than CE apps (n = 7, 58.3%). Investigations of application use most commonly studied CE and patient-facing apps. Additional high-quality evidence is necessary to evaluate the CE and FAU of app use in epilepsy to work toward the standardization of FAU metrics and development of implementation guidelines.

Typical carotid webs are nonatherosclerotic shelf-like projections of fibromyxoid tissue extending from the posterior wall of the proximal internal carotid artery (ICA). Carotid webs may precipitate acute embolic stroke, especially in younger patients. We describe our experience with pathology-proven carotid webs of atypical appearance, or atypical carotid webs (ACWs), a subset of carotid webs exhibiting abnormal location, morphology, or association with atherosclerotic changes. Our electronic medical record database was queried for all imaging impressions containing "carotid web," "shelf," or "protrusion" from 2018-2024. Imaging was reviewed by an experienced neuroradiologist and neurosurgeon. Patients with typical carotid webs or those with different diagnoses (e.g. dissection/thrombus) were excluded. Twenty-seven patients were treated for typical carotid webs; 24 were treated with carotid endarterectomy (CEA) and had pathology-confirmed webs. Five patients (three male) were identified to have ACWs and included in this report. Mean age was 43.6 years. All ACWs were identified by computed tomography angiography (CTA). All patients presented with acute ischemic stroke or transient ischemic attack (TIA). One web was located on the anterior ICA wall, three were of abnormal morphology different from a "shelf-like" projection, and one was associated with atherosclerotic change. No patients experienced a further stroke or TIA following CEA. ACWs may precipitate ischemic stroke and can be treated and definitively diagnosed with CEA. Due to their unusual appearance, ACWs may evade radiographic identification or be misdiagnosed. As ACWs have not been previously reported in the literature, awareness of their existence must be raised to increase their detection and treatment.

OBJECTIVE:Seizures can impact cognition both acutely and chronically. However, among those without significant comorbidities and broadly average cognition at epilepsy onset, the relationship between cognitive function at the time of diagnosis and long-term seizure control has been relatively unexplored. This analysis investigated associations between participant characteristics including specific aspects of cognitive performance at the time of focal epilepsy diagnosis and antiseizure medication (ASM) treatment resistance. METHODS:This was a secondary analysis of Human Epilepsy Project (HEP) data, which enrolled people with newly diagnosed focal epilepsy and broadly average cognition (estimated IQ ≥ 70) from June 29, 2012, to September 1, 2019. Participants analyzed in this study were between 18 and 60 years old, and scored within an acceptable range (i.e., Standard Score of ≥80) on measures estimating premorbid cognitive ability were offered the Cogstate Brief Battery (CBB). Participant characteristics were analyzed, including the presence of any anxiety disorders or depression, and summary CBB scores. HEP participants who were classified by the study as treatment resistant if they had experienced failure to achieve seizure freedom after two adequate trials of ASMs. Treatment resistance was modeled using multiple logistic regression to assess for independent associations between attention and working memory after correcting for the presence of the other potentially explanatory variables. RESULTS:200 HEP participants had comprehensive enrollment records including CBB results and complete seizure outcome data for analysis in this study. After correcting for potentially confounding variables, there were no independent associations between cognitive measures on the CBB at the time of enrollment and subsequent development of ASM treatment resistance. Specifically, z-scores for reaction time on the CBB (an average of the CBB Identification and Detection tests) were not associated with treatment resistance (p = 0.51) and z-scores for memory performance (an average of the CBB One Card Learning test and One Back tests) were not associated with treatment resistance (p = 0.24). There were no significant independent associations between age or the presence of depression or anxiety disorders at the time of CBB testing and treatment resistance. However, there was an independent association between employment status and treatment resistance, with those who were employed or students (>18 years old) at the time of enrollment and CBB testing having 0.35 times lower odds of treatment resistance (95 %CI 0.15-0.81, p = 0.01). SIGNIFICANCE/CONCLUSIONS:The findings from this study suggest that in otherwise healthy people with new onset focal epilepsy who have broadly average intelligence, attention and working memory as measured by the CBB at the time of diagnosis is not associated with treatment resistance. Although performance on cognitive testing at epilepsy onset may not be predictive of risk of treatment resistance in this population, other individual characteristics such as employment status at the time of diagnosis may be indirect markers of long-term seizure outcomes and require further investigation.

Fatigue is a common and often debilitating feature of multiple sclerosis (MS) that lacks reliably effective treatment options for most patients. Transcranial direct current stimulation (tDCS), a safe and well-tolerated type of noninvasive brain stimulation, is a low-cost and home-based approach with the potential to reduce fatigue in MS. We conducted a double-blind, sham-controlled, randomized clinical trial to compare active vs. low-dose (sham) tDCS paired with computer-based cognitive training, delivered as a home-based intervention, to reduce MS-related fatigue. Participants with MS-related fatigue, but without depression, were stratified by neurologic disability using the Extended Disability Status Scale (EDSS) and randomized to complete 30 daily sessions over six weeks of either active or sham tDCS paired with online cognitive training (BrainHQ). The primary outcome was the change in PROMIS Fatigue score from baseline to the end of the intervention. A total of 117 participants were randomized, with 92% completing all treatment sessions. Both groups showed significant reductions in fatigue, with no significant difference between them. This suggests that tDCS does not provide any additional benefit over cognitive training alone in reducing fatigue, but confirms the feasibility and tolerance of this home-based intervention.

BACKGROUND:Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT). METHODS:We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication. RESULTS:The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75). CONCLUSIONS:We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

BACKGROUND:Brain arteriovenous malformations (AVMs) are abnormal connections between feeding arteries and draining veins, associated with significant risks of haemorrhage, seizures and other neurological deficits. Preoperative embolization is commonly used as an adjunct to microsurgical resection, with the aim of reducing intraoperative complications and improving outcomes. However, the efficacy and safety of this approach remain controversial. METHODS:This study is a subanalysis of the Multicenter International Study for Treatment of Brain AVMs consortium. We retrospectively analysed 486 patients with brain AVMs treated with microsurgical resection between January 2010 and December 2023. Patients were divided into two groups: those who underwent microsurgery alone (n=245) and those who received preoperative embolization, followed by microsurgery (n=241). Propensity score matching was employed, resulting in 288 matched patients (144 in each group). The primary outcomes were rates of complete AVM obliteration and functional outcomes (measured by the modified Rankin Scale (mRS)). Secondary outcomes included complication rates, mortality, hospital length of stay and postsurgical rupture. RESULTS:After matching, the complete obliteration rate was 97% with no significant difference between the microsurgery-only group and the preoperative embolization group (p=0.12). The proportion of patients with an mRS score of 0-2 at the last follow-up was similar in both groups (83% vs 84%; p=0.67). The median hospital stay was significantly longer for the embolisation group (9 days vs 7 days; p=0.017). Complication rates (24% vs 22%; p=0.57) and mortality rates (4.9% vs 2.1%; p=0.20) were comparable between the two groups. No significant differences were observed in postsurgical rupture, recurrence or retreatment rates. CONCLUSIONS:In this large multicentre study, preoperative embolization did not significantly improve AVM obliteration rates, functional outcomes or reduce complications compared with microsurgery alone.

Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.

BACKGROUND:Hemorrhagic stroke survivors may have cognitive impairment. We sought to identify preadmission and admission factors associated with cognitive impairment after hemorrhagic stroke. DESIGN/METHODS:Patients with nontraumatic intracerebral or subarachnoid hemorrhage (ICH or SAH) were assessed 3-months post-bleed using the Quality of Life in Neurological Disorders (Neuro-QoL) Cognitive Function short form. Univariate and multivariate analysis were used to evaluate the relationship between poor cognition (Neuro-QoL t-score ≤50) and preadmission and admission factors. RESULTS:Of 101 patients (62 ICH and 39 SAH), 51 (50 %) had poor cognition 3-months post-bleed. On univariate analysis, poor cognition was associated with (p < 0.05): age [66.0 years (52.0-77.0) vs. 54.5 years (40.8-66.3)]; private insurance (37.3 % vs. 74.0 %); BMI > 30 (13.7 % vs. 34.0 %); and admission mRS score > 0 (41.2 % vs. 14.0 %), NIHSS score [8.0 (2.0-17.0) vs. 0.5 (0.0-4.0)], and APACHE II score [16.0 (11.0-19.0) vs. 9.0 (6.0-14.3)]. On multivariate analysis, poor cognition was associated with mRS score > 0 [OR 4.97 (1.30-19.0), p = 0.019], NIHSS score [OR 1.14 (1.02-1.28), p = 0.026], private insurance [OR 0.21 (0.06-0.76), p = 0.017] and BMI > 30 [OR 0.13 (0.03-0.56), p = 0.006]. CONCLUSIONS:Cognitive impairment after hemorrhagic stroke is less common in patients with BMI > 30 and private insurance. Heightened surveillance for non-obese patients without private insurance is suggested. Additional investigation into the relationship between cognition and both BMI and insurance type is needed.

IMPORTANCE/UNASSIGNED:The profile of gastrointestinal (GI) tract outcomes associated with the postacute and chronic phases of COVID-19 in children and adolescents remains unclear. OBJECTIVE/UNASSIGNED:To investigate the risks of GI tract symptoms and disorders during the postacute (28-179 days after documented SARS-CoV-2 infection) and the chronic (180-729 days after documented SARS-CoV-2 infection) phases of COVID-19 in the pediatric population. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This retrospective cohort study was performed from March 1, 2020, to September 1, 2023, at 29 US health care institutions. Participants included pediatric patients 18 years or younger with at least 6 months of follow-up. Data analysis was conducted from November 1, 2023, to February 29, 2024. EXPOSURES/UNASSIGNED:Presence or absence of documented SARS-CoV-2 infection. Documented SARS-CoV-2 infection included positive results of polymerase chain reaction analysis, serological tests, or antigen tests for SARS-CoV-2 or diagnosis codes for COVID-19 and postacute sequelae of SARS-CoV-2. MAIN OUTCOMES AND MEASURES/UNASSIGNED:GI tract symptoms and disorders were identified by diagnostic codes in the postacute and chronic phases following documented SARS-CoV-2 infection. The adjusted risk ratios (ARRs) and 95% CI were determined using a stratified Poisson regression model, with strata computed based on the propensity score. RESULTS/UNASSIGNED:The cohort consisted of 1 576 933 pediatric patients (mean [SD] age, 7.3 [5.7] years; 820 315 [52.0%] male). Of these, 413 455 patients had documented SARS-CoV-2 infection and 1 163 478 did not; 157 800 (13.6%) of those without documented SARS-CoV-2 infection had a complex chronic condition per the Pediatric Medical Complexity Algorithm. Patients with a documented SARS-CoV-2 infection had an increased risk of developing at least 1 GI tract symptom or disorder in both the postacute (8.64% vs 6.85%; ARR, 1.25; 95% CI, 1.24-1.27) and chronic (12.60% vs 9.47%; ARR, 1.28; 95% CI, 1.26-1.30) phases compared with patients without a documented infection. Specifically, the risk of abdominal pain was higher in COVID-19-positive patients during the postacute (2.54% vs 2.06%; ARR, 1.14; 95% CI, 1.11-1.17) and chronic (4.57% vs 3.40%; ARR, 1.24; 95% CI, 1.22-1.27) phases. CONCLUSIONS AND RELEVANCE/UNASSIGNED:In this cohort study, the increased risk of GI tract symptoms and disorders was associated with the documented SARS-CoV-2 infection in children or adolescents during the postacute or chronic phase. Clinicians should note that lingering GI tract symptoms may be more common in children after documented SARS-CoV-2 infection than in those without documented infection.