Faculty Bibliography

In the phase 3 randomized controlled trial (RCT; NCT03355209) of fenfluramine in Lennox-Gastaut syndrome (LGS), patients in fenfluramine treatment groups (0.2 mg/kg/day, 0.7 mg/kg/day) experienced greater reduction from baseline in frequency of seizures associated with a fall versus placebo, which was sustained in the open-label extension (OLE) study (NCT03355209). In this post hoc analysis, trajectories of fenfluramine effectiveness and safety, along with dose changes over time, are described for patients with LGS randomized to placebo in RCT who switched to fenfluramine in OLE (PBO-FFA) and those who received fenfluramine in both RCT/OLE (FFA-FFA). Among patients who completed 12 months in OLE (N = 151), numerical improvements in effectiveness outcomes were seen in the PBO-FFA group (n = 59) after initiating fenfluramine and were similar to those in the FFA-FFA group (n = 92). Regression to the mean was not observed in the PBO-FFA group, suggesting that changes were due to fenfluramine. Incidence of the most commonly reported treatment-emergent adverse events increased in the PBO-FFA group after fenfluramine initiation but decreased in the FFA-FFA group in OLE. These data demonstrate rapid improvement in seizure frequency and global functioning in both groups with continued clinical improvement as the mean fenfluramine dose was increased. These results confirm that sustained fenfluramine treatment is effective and tolerable. PLAIN LANGUAGE SUMMARY: This study assessed the change over time in the number of seizures, overall improvement, and side effects in patients with LGS receiving placebo (no active medicine) or fenfluramine in a 14-week study; all patients later received fenfluramine in the extension study. Overall, the number of seizures (associated with a fall) decreased once patients initially receiving placebo changed to fenfluramine (optimal effect around Month 4 while receiving a higher dose), but as expected, common side effects were reported more frequently once patients began fenfluramine treatment. Patients, parents, and doctors should be aware of this time course to allow fenfluramine enough time to work.

[This corrects the article DOI: 10.7759/cureus.113024.].

Humans are the only species with the ability to systematically combine words to convey an unbounded number of complex meanings. This process is guided by combinatorial processes whose underlying neural mechanisms remain obscured by inherent limitations of noninvasive brain measures and a near-total focus on comprehension paradigms. Here, we address these limitations with high-resolution neurosurgical recordings (electrocorticography) and a controlled sentence production experiment. We uncover distinct cortical networks encoding word-level and higher-order information. These networks exhibited a hybrid spatial organization: broadly distributed across traditional language areas but with focal concentrations of sensitivity to semantic and structural contrasts in canonical language regions. In contrast to previous comprehension-based findings, we find that these networks are largely nonoverlapping. Most notably, higher-order linguistic information showed an unexpected dissociation from local activity magnitude. This result establishes an operational dissociation between activity magnitude and information content, pointing toward a potentially distinct neural coding scheme for higher-order language, with important implications for the neurobiology of language.

Diagnostic MRI evaluation of temporal lobe epilepsy (TLE) depends on the subjective visual interpretation of MRI images. These interpretations could be enhanced by quantitative artificial intelligence (AI) support tools. Humans often make sequential and conditional decisions during their radiological interpretations, such as whether an abnormality is present and, if present, characterizing the abnormality. It is not known whether it is superior to train AI to treat every decision separately in a similar step-wise manner or to train a model holistically on all decisions simultaneously. Here, we analysed three large epilepsy MRI datasets [n = 3676, 2320 people with epilepsy and 1356 healthy controls (HC)] to perform two tasks: (i) establish the presence of a TLE pattern on MRI and (ii) determine TLE pattern lateralization. We compared Step-wise models that independently classify TLE versus HC and lateralize patients as left TLE (L-TLE) or right TLE (R-TLE), against a simultaneous model trained to distinguish all three classes in a single step. To do this, 3D volumetric T1-weighted images were input into an EfficientNetV2 model multiple times to ensure reproducibility of results. Class prediction, model classification confidence and saliency maps were output for interpretability. Step-wise models outperformed the Simultaneous model on both tasks (both Ps < 0.001), with an average ∼2.8% accuracy increase for discriminating HC from TLE and an average 12.7% accuracy increase for distinguishing L-TLE from R-TLE. For both the Step-wise and Simultaneous models, important features discriminating TLE from HC included the known TLE limbic pattern involving the hippocampus, parahippocampal cortical regions, cingulate cortex and lateral temporal regions. However, there was less concordance between the Step-wise and Simultaneous models for the L-TLE versus R-TLE task (all Fisher's Zs > 10.5, Ps < 0.001); the Step-wise model focused less on subcortical regions such as the thalamus and hippocampus and focused more on distributed cortical pathology. Across the two Step-wise models, 95.1% of TLE patients had accurate classifications in either HC versus TLE and/or L-TLE versus R-TLE tasks. These results included 69.6% of patients being both correctly labelled as TLE and lateralized, 13.9% being correctly labelled TLE but lateralized incorrectly and 11.6% being lateralized correctly but not detected as TLE. These findings provide evidence that diagnostic tasks with simpler, Step-wise AI models may enhance diagnostic performance and interpretability in clinical workflows. Future AI clinical support tools can leverage this step-wise approach in the early identification of TLE-related structural patterns, supporting timely diagnosis and treatment decisions.

Background A landmark study of 1,600 Italian-Americans in Roseto, PA, challenged the prevailing view that high saturated fat intake was a major cause of myocardial infarction (MI). Despite similar rates of cigarette smoking and obesity, and even higher levels of saturated fat consumption compared to neighboring towns, Rosetans experienced far lower MI death rates. More than 50 years later, it remains uncertain whether Roseto's residents had better heart health than the average American and, if so, what protective factors may have been responsible. Methodology We compared MI deaths in Roseto and neighboring towns to the contemporaneous Framingham Heart Study cohort matched for age and sex. Results We found no evidence that MI deaths were lower in Roseto, PA, than in Framingham, MA when controlling for age and sex. While the role of social support in health has been established in subsequent studies, methodological issues, confounding factors, and biases challenge the validity of the Roseto study. Conclusions The dramatically lower MI and MI mortality rates among males in Roseto reflect biases in sampling and comparison populations, which also impacted the contrasting Diet-Heart Hypothesis that saturated fats cause heart disease. Although social support enhances health outcomes, the Roseto study neither supported nor refuted this connection.

Obstructive sleep apnea (OSA) is a common but underdiagnosed and undertreated sleep disorder among people with epilepsy (PWE). In PWE, this sleep disorder is often managed as a comorbid condition rather than a contributor to epilepsy outcomes. For many years, OSA has been associated with higher seizure burden and interictal epileptiform discharges. Emerging evidence links OSA to late onset epilepsy (LOE) and increased risk markers for sudden unexpected death in epilepsy (SUDEP). This evidence also suggests that treating OSA with continuous positive airway pressure may improve seizure control. This critical review of the literature posits that OSA should be viewed as a modifiable risk factor for PWE. We apply the Bradford Hill criteria for causation as a framework to appraise the evidence connecting OSA with (1) seizure severity, (2) incident LOE, and (3) SUDEP risk. OSA supports eight of nine Bradford Hill criteria to varying degrees: strength, consistency, temporality, biological gradient, plausibility, coherence, analogy, and experiment, but not specificity. Key limitations include confounding, selection and adherence biases, and limited randomized evidence. However, the evidence supports integrating systematic OSA screening and evidence-based treatment into epilepsy care. Future research should prioritize randomized trials to assess the impact of OSA treatment on epilepsy incidence, severity, and SUDEP risk.

Prediction of future events is essential for guiding effective actions in dynamic environments. Studies on the neural mechanisms for time-forward predictions have typically used stimuli with relatively simple statistical regularities. Here, we investigated time-forward predictions using stimuli containing statistical regularities similar to those found in natural auditory stimuli. Using intracranial EEG recordings in neurosurgical patients, we found that prediction signals were primarily carried by low-frequency activity across widespread cortical regions, including sensory, parietal, and frontal areas. Prediction-error (PE) signals were found in both low- and high-frequency activity, with high-frequency components localized mainly to sensory areas. Contrary to previous hypotheses, prediction and PE signals did not show a clear spatial or spectral segregation. Directed connectivity between brain regions decreased over the course of the stimulus sequence as predictability increased, except for pathways originating from the parietal cortex. These results reveal integrative and distributed predictive processing and highlight the dorsal auditory pathway in time-forward prediction.

OBJECTIVE:To evaluate cannabidiol (CBD) in pediatric patients with autism spectrum disorder (ASD), fluent verbal language and an estimated full-scale IQ of 80 or above. BACKGROUND:Preliminary evidence suggests CBD may ameliorate challenges associated with ASD. Whether CBD benefits pediatric ASD without accompanying intellectual or language impairment remains unknown. METHODS:, 100 mg/mL) at 3, 6, or 9 mg/kg/day using a Bayesian optimal interval dosing design. The primary endpoint was the CBD dose associated with the highest response rate (i.e., Clinical Global Impression Scale-Improvement [CGI-I] score = 1 or 2) on a target symptom domain designated individually based on informant report, standardized scales, and clinical observation. Secondary endpoints were effect sizes of changes from baseline in measures assessing ASD core and associated symptoms, and global functioning. Adverse events (AEs) were assessed weekly. Plasma CBD levels and clinical labs were obtained at the final visit. RESULTS:= 1.36, 95% CI [0.78-1.93]).Of 222 reported AEs, 27 unique AEs were considered treatment-related. Most AEs (93%) were mild and expected (82%); none was severe. The most frequent related AEs were increased salivation (30%), increased sleep duration (39%), sleepiness/sedation (26%), increased dream activity (35%), and polyuria (22%). Vital signs, physical exams, weight, liver function tests, and complete blood counts were unaffected. CBD plasma levels did not correlate with response. CONCLUSIONS:In this preliminary study, CBD was well tolerated; AEs were mild-moderate. Mean SRS2-T and subscores decreased significantly with large effect sizes, shifting from the severe to the moderate range. CLINICAL TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier NCT03900923.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Direct electrocortical stimulation (DES) is the gold standard for mapping eloquent cortex, yet existing functional atlases are limited by sampling biases and density-based methods that obscure a region's true functional probability. Consequently, interpatient variability and the functional contributions of nontraditional language areas, such as the middle frontal gyrus, remain poorly characterized, particularly in epilepsy populations where functional reorganization is common. We therefore developed a probabilistic functional atlas of extraoperative DES and applied data-driven methods to characterize the functional organization of language, motor, and sensory cortex. METHODS:This was a retrospective observational study of patients undergoing intracranial monitoring for drug-resistant epilepsy (2008-2023). Electrical stimulation was delivered to intracranial electrodes during language tasks, and language, motor, and sensory findings were recorded. Positive and negative stimulation sites were analyzed in standard patient space and using the Human Connectome Project parcellation atlas. A multilevel statistical framework, including probability mapping, bootstrapped region-of-interest analyses, and kernel density estimation, defined structure-function relationships. Generalized linear mixed-effects models assessed the influence of clinical variables on language disruption. RESULTS:= 0.003) independently predicted a lower probability of language disruption in the temporal lobe. DISCUSSION/CONCLUSIONS:This extraoperative DES atlas provides a comprehensive benchmark for understanding functional cortical organization in epilepsy. We add evidence to the growing literature that language and motor systems are more distributed and variable than classically described. Substantial interpatient variability underscores the necessity of individualized mapping to guide safe neurosurgical planning. Limitations include the retrospective design and sampling bias inherent to electrode placement.

OBJECTIVE:Diagnostic and treatment delays in infantile epileptic spasms syndrome (IESS) increase the risk of poor neurodevelopmental outcomes. Early clinical recognition of IESS is essential, especially in regions lacking expedited access to electroencephalograms (EEG). This study aimed to determine clinicians' accuracy at recognizing infantile epileptic spasms (ES) based on smartphone videos, and the impact of brief IESS education on accuracy, diagnostic confidence, and willingness to treat without EEG. METHODS:This multicenter prospective cohort study took place over seven sessions globally from 2022 to 2023. Smartphone videos of children from the US and South Africa with EEG-confirmed diagnoses of IESS (6 videos) and non-epileptic ES-mimickers (3 videos) were obtained. Staff physicians and trainee participants from multiple subspecialties worldwide viewed videos three times: (1) baseline viewing, (2) after brief IESS training, and (3) with clinical history. Surveys on diagnosis and management were completed after each viewing. RESULTS:Of 187 participants who attended a session and initiated a survey, 180 (80 trainees [44%]) met the inclusion criteria. Initial diagnostic accuracy averaged 64% (95% confidence interval [CI]: 62-66%) and improved to 72% (69-74%) after IESS training and clinical history (V + T + CHx). Area under the curve for diagnostic performance of smartphone videos was 0.80 (0.78-0.82), and sensitivity was 0.85 (0.83-0.88) after V + T + CHx. The odds of making a correct diagnosis increased by 86% (OR 1.86, CI 1.59-2.18, p < 0.001) after V + T + CHx. Diagnostic confidence and clinician comfort level treating ES without EEG also improved significantly after V + T + CHx (by 0.36 points and 0.45 points, respectively, on 5-point Likert scales, p < 0.001). Diagnostic accuracy correlated strongly with increased diagnostic confidence and increased clinician comfort level managing patients without an EEG (p < 0.001). Staff physicians had a 24% higher likelihood of making a correct diagnosis than trainees. SIGNIFICANCE/CONCLUSIONS:Smartphone videos, especially when enhanced by brief IESS training, can facilitate triage and early identification of infantile ES, reducing diagnostic delays in this time-sensitive condition. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:Infantile epileptic spasms syndrome is associated with severe developmental impacts, which can be worsened by delayed treatment. Rapid diagnosis is critical, especially in resource-limited settings lacking specialists and timely access to diagnostic tests. Our study found that clinician participants identified epileptic spasms, the hallmark seizure type of this condition, based on video alone with moderately high accuracy, and accuracy improved after education and clinical information. Thus, smartphone videos, particularly when enhanced by brief training, may be an effective tool to triage movements concerning for epileptic spasms, potentially improving resource allocation and reducing diagnostic delays in this urgent childhood epilepsy condition.