Faculty Bibliography

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants, all female) and intracranial electroencephalography (iEEG, 2 participants, both male) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time.Significance statement How the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30 min naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a logarithmic relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

OBJECTIVES/OBJECTIVE:Distinguishing Idiopathic Generalized Epilepsy (IGE) from focal epilepsy (FE) can be challenging when IGE cases present with asymmetric clinical or electroencephalographic (i.e., atypical) features. We aimed to identify factors that lead to discordance in IGE diagnoses among epileptologists. METHODS:41 patients with IGE and 6 patients with FE or mixed focal and generalized epilepsy followed at the NYU Comprehensive Epilepsy Center for ≥5 years were identified. IGE cases included typical (n = 18) and atypical (n = 23) presentations. Anonymized summaries of patients at initial presentation and 5-year follow-up were presented in random order to epileptologists who categorized them as generalized epilepsy (GE), FE, or other. Factors leading to discordance were identified. RESULTS:Inter-rater agreement was moderate for atypical IGE cases at initial presentation (AC1 = 0.54, 95% CI 0.31-0.77, p < 0.01) and 5-year follow-up (AC1 = 0.48, 95% CI 0.25-0.72, p < 0.01). For atypical cases, asymmetric epileptiform activity was most commonly associated with increased discordance. DISCUSSION/CONCLUSIONS:Epileptologists may underdiagnose IGE when patients have asymmetric EEG or clinical features. Review of one time point (i.e., vignettes) may mask the diversity of features that may support a localized epilepsy when serial reviews reveal a generalized epilepsy.

INTRODUCTION/BACKGROUND:Juvenile myoclonic epilepsy (JME) is a genetic generalized epilepsy syndrome with onset typically in adolescence and a chronic course requiring long-term antiseizure medications (ASMs). Valproate (VPA) is the most effective treatment for seizure control in JME but use is limited by metabolic, cognitive, and teratogenic adverse effects (AEs). Levetiracetam (LEV) is an alternative ASM when VPA is contraindicated or not tolerated. Comparisons of the efficacy and long-term tolerability of VPA and LEV remain limited. METHODS:statistic. RESULTS: = 21.2%). Sensitivity analyses confirmed the robustness of the pooled estimates. CONCLUSION/CONCLUSIONS:VPA was associated with higher seizure remission rates and lower treatment discontinuation compared with LEV; however, these findings must be interpreted with caution given the substantial heterogeneity, the predominance of observational studies, and the serious risk of bias identified in most included studies VPA also demonstrated lower rates of treatment discontinuation, despite a higher burden of cognitive impairment and weight gain. No relevant differences were observed regarding dizziness. Large-scale randomized trials with standardized outcome definitions and longer follow-up are needed to define the comparative risk-benefit profiles of LEV and VPA in JME.

OBJECTIVE:To assess the efficacy and safety of fenfluramine in patients with Dravet syndrome (DS) stratified by age, number of previously attempted antiseizure medications (ASMs), and SCN1A pathogenic variant status. METHODS:In this post hoc analysis, data from three randomized controlled trials (RCTs) in patients with DS (2-18 years) were pooled and stratified by age (<4; ≥4 years), number of previous ASMs (1-3; 4-6; ≥7), and SCN1A pathogenic variant status (SCN1A+; SCN1A-). Stratified groups were assessed and compared with the pooled placebo group (change in monthly convulsive seizure frequency [MCSF], longest convulsive seizure-free interval, and Clinical Global Impression-Improvement [CGI-I] scale scores rated by parents/caregivers and investigators), and safety (treatment-emergent adverse events [TEAEs]: frequency, days to onset, and proportion resolved). RESULTS:Among 348 patients included in the RCTs, 216 were randomized to fenfluramine (0.7 mg/kg/day, n = 88; 0.4 mg/kg/day [with stiripentol], n = 43; 0.2 mg/kg/day, n = 85) and 132 to placebo. Compared with placebo, fenfluramine treatment (all doses combined) resulted in greater MCSF reductions, greater increases in longest convulsive seizure-free intervals, and a higher proportion of parents/caregivers and investigators reporting clinically meaningful improvement ("Much Improved", "Very Much Improved") on CGI-I scores across all stratified groups. CGI-I scores were consistent across fenfluramine doses in most stratified groups, but patients with the fewest number of previous ASMs had the greatest frequency of clinically meaningful improvement on investigator-rated CGI-I scores. Safety outcomes were similar across all strata. Most TEAEs resolved by end-of-study. SIGNIFICANCE/CONCLUSIONS:Fenfluramine treatment was associated with improved seizure outcomes and global functioning compared with placebo regardless of age, number of previous ASMs, and SCN1A status in patients with DS. Fenfluramine was well-tolerated; no new safety signals were identified. Further studies with larger sample sizes (including adults) and a priori inferential analyses of stratified groups are warranted. PLAIN LANGUAGE SUMMARY/CONCLUSIONS:Patients with Dravet syndrome struggle with seizures and everyday life. In three studies, patients aged 2-18 years received fenfluramine or placebo (sugar pill). Fenfluramine lowered seizures without many side effects. Researchers combined results from these studies to see how fenfluramine worked in different patient groups based on age, number of previous medications, and a gene called SCN1A. They looked at seizure reduction and whether doctors felt patients had improved. In all groups, fenfluramine worked better than placebo, with similar side effects. Researchers believe fenfluramine helped these patients, but some groups were small, so these results need to be confirmed.

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.