Faculty Bibliography

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.

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:This study was undertaken to evaluate the safety and effectiveness of responsive thalamic stimulation as adjunctive therapy for drug-resistant idiopathic generalized epilepsy (IGE) with generalized tonic-clonic seizures (GTCSs). METHODS:NAUTILUS is a prospective, multicenter, single-blind, randomized sham-controlled pivotal trial. Patients were ≥12 years of age with drug-resistant IGE and ≥2 GTCSs over a 3-month baseline. Bilateral depth leads were targeted to the centromedian thalamus. One month later, patients were randomized to Active (responsive stimulation, n = 44) or Sham (no stimulation, n = 43). The effectiveness evaluation period (EEP) began 3 months postimplant through 1 year. After a second GTCS in the EEP, patients transitioned to open-label active stimulation. The primary safety endpoint was the serious adverse device-related event (SADE) rate at 84 days postimplant. The primary effectiveness endpoint was time-to-second-GTCS during the EEP. Additional endpoints included median percent change in days with any generalized seizure, GTCS frequency, and responder rate (RR). RESULTS:Eighty-seven patients were implanted across 23 US centers. The SADE rate was significantly below the performance goal (6.9%, p < .0001), with no adverse effects on cognition, mood, or sleep. The prespecified primary effectiveness endpoint was not significant. However, a post hoc mixed-effects model considering all EEP days demonstrated greater GTCS reduction in the originally randomized Active group (61%) compared to patients originally randomized to Sham (49%, p = .030). Eighteen-month outcomes included 76.8% median GTCS reduction, 62.5% RR, 40% GTCS-free at that timepoint, and 77.8% median reduction in days with any generalized seizure. More than 90% of patients and 86% of physicians reported improvement on Global Impression of Change scales. SIGNIFICANCE/CONCLUSIONS:NAUTILUS is the first randomized controlled neuromodulation trial in IGE. Responsive thalamic stimulation provided a clinically meaningful and durable reduction in seizures with an acceptable safety profile, offering a much-needed option for drug-resistant IGE.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Neuromodulation therapies are approved for the treatment of focal epilepsy based on data from randomized controlled trials (RCTs). After approval of a responsive direct brain stimulation device (The RNS System for focal epilepsy), the Food and Drug Administration required a prospective study to evaluate whether real-world safety and effectiveness differed from outcomes in the RCT. METHODS:This open-labeled study enrolled adult participants who met the RNS System-approved indication for use. The primary effectiveness end point was median percent change in seizure frequency at 3 years of treatment. Interim safety is presented; the primary safety endpoint analysis will be conducted at 5 years. RESULTS:). DISCUSSION/CONCLUSIONS:This prospective real-world study contributes to the body of evidence that adjunctive direct brain-responsive neurostimulation provides significant and sustained reductions in the frequency of focal seizures. Seizure reductions were greater and were achieved faster than in the RCT and long-term treatment trials but were similar to a more recent retrospective multicenter real-world study. As in the preapproval studies, treatment was well-tolerated and safe, and the SUDEP rate was low. The RNS System showed similar safety and improved seizure outcomes in real-world use compared with the RCT. Improvements in efficacy may reflect changes in programming practices. Future research efforts will focus on using the brain data obtained by the device to optimize detection and stimulation paradigms for each patient. TRIAL REGISTRATION INFORMATION/UNASSIGNED:ClinicalTrials.gov, NCT02403843, submitted March 26, 2015. CLASSIFICATION OF EVIDENCE/METHODS:This study provides Class IV evidence that in adults with refractory focal-onset seizures, direct brain-responsive neurostimulation reduces seizure frequency without serious adverse events up to 3 years.

The ability to quickly learn and generalize is one of the brain's most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7 T fMRI, and intracranial recordings, we identify the high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

Recent research demonstrates that large language models can predict neural activity recorded via electrocorticography during natural language processing. To predict word-by-word neural activity, most prior work evaluates encoding models within individual electrodes and participants, limiting generalizability. Here we analyze electrocorticography data from eight participants listening to the same 30-min podcast. Using a shared response model, we estimate a common information space across participants. This shared space substantially enhances large language model-based encoding performance and enables denoising of individual brain responses by projecting back into participant-specific electrode spaces-yielding a 37% average improvement in encoding accuracy (from r = 0.188 to r = 0.257). The greatest gains occur in brain areas specialized for language comprehension, particularly the superior temporal gyrus and inferior frontal gyrus. Our findings highlight that estimating a shared space allows us to construct encoding models that better generalize across individuals.

Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy aligns with the temporal dynamics of language comprehension in the brain. Using electrocorticography (ECoG) data from participants listening to a 30-minute narrative, we show that deeper LLM layers correspond to later brain activity, particularly in Broca's area and other language-related regions. We extract contextual embeddings from GPT-2 XL and Llama-2 and use linear models to predict neural responses across time. Our results reveal a strong correlation between model depth and the brain's temporal receptive window during comprehension. We also compare LLM-based predictions with symbolic approaches, highlighting the advantages of deep learning models in capturing brain dynamics. We release our aligned neural and linguistic dataset as a public benchmark to test competing theories of language processing.

Syntax, the abstract structure of language, is a hallmark of human cognition. Despite its importance, its neural underpinnings remain obscured by inherent limitations of non-invasive 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 three syntactic networks that are broadly distributed across traditional language regions, but with focal concentrations in middle and inferior frontal gyri. In contrast to previous findings from comprehension studies, these networks process syntax mostly to the exclusion of words and meaning, supporting a cognitive architecture with a distinct syntactic system. Most strikingly, our data reveal an unexpected property of syntax: it is encoded independent of neural activity levels. We propose that this "low-activity coding" scheme represents a novel mechanism for encoding information, reserved for higher-order cognition more broadly.

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain's linguistic capabilities. ECoG offers high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research questions, from auditory perception to narrative integration. In addition to the neural data, we extracted linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.