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.

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.

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.

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: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.