Faculty Bibliography

The ability to connect the form and meaning of a concept, known as word retrieval, is fundamental to human communication. While various input modalities could lead to identical word retrieval, the exact neural dynamics supporting this convergence relevant to daily auditory discourse remain poorly understood. Here, we leveraged neurosurgical electrocorticographic (ECoG) recordings from 48 patients and dissociated two key language networks that highly overlap in time and space integral to word retrieval. Using unsupervised temporal clustering techniques, we found a semantic processing network located in the middle and inferior frontal gyri. This network was distinct from an articulatory planning network in the inferior frontal and precentral gyri, which was agnostic to input modalities. Functionally, we confirmed that the semantic processing network encodes word surprisal during sentence perception. Our findings characterize how humans integrate ongoing auditory semantic information over time, a critical linguistic function from passive comprehension to daily discourse.

OBJECTIVE:This study aimed to externally validate the Memory Assessment Clinics Scale for Epilepsy (MAC-E), a brief self-report measure of subjective memory complaints in adults with epilepsy. METHODS:A cross-sectional study was conducted including adults with focal pharmacoresistant epilepsy from three Level 4 epilepsy centers in the U.S., who completed the MAC-E as part of a clinical neuropsychological evaluation. Confirmatory factor analysis was conducted, and goodness-of-fit criteria were calculated to assess model fit: comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR). Item response theory models were constructed, and Mokken analysis was used to assess discrimination and unidimensionality. Internal consistency was evaluated with McDonald's Omega. RESULTS:values for each of the 5 factors (0.58-0.91 and 0.34-0.82, respectively). MAC-E items demonstrated high levels of discrimination as well as the ability to evaluate across the entirety of each latent trait. Score responses were uniformly distributed across latent traits, and unidimensionality was established by factor (all H coefficients > 0.4). Internal consistency was high across factors (omega range: 0.77-0.88). CONCLUSIONS:Results of this study demonstrate good external validation of the MAC-E in an independent, multicenter cohort of adults with epilepsy. These findings provide further support that the MAC-E is a psychometrically valid, self-report instrument to assess every-day memory abilities in adults with epilepsy in both clinical and research settings.

Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. It remains an untested assumption that insights from this literature generalize to more naturalistic utterances like sentences. Here, we investigate this using high-resolution neurosurgical recordings (ECoG) and an overt production experiment where patients produce six words in isolation (picture naming) and in sentences (scene description). We trained machine learning models to identify the unique brain activity pattern for each word during picture naming, and used these patterns to decode which words patients were processing while they produced sentences. Our findings reveal that words share cortical representations across tasks. In sensorimotor cortex, words were consistently activated in the order in which they were said in the sentence. However, in inferior and middle frontal gyri (IFG and MFG), the order in which words were processed depended on the syntactic structure of the sentence. This dynamic interplay between sentence structure and word processing reveals that sentence production is not simply a sequence of single word production tasks, and highlights a regional division of labor within the language network. Finally, we argue that the dynamics of word processing in prefrontal cortex may impose a subtle pressure on language evolution, explaining why nearly all the world's languages position subjects before objects.

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.

OBJECTIVES/OBJECTIVE:Late-onset epilepsy has the highest incidence among all age groups affected by epilepsy and often occurs in the absence of known clinical risk factors such as stroke and dementia. There is increasing evidence that brain changes contributing to epileptogenesis likely start years before disease onset, and we aim to relate cognitive and imaging correlates of subclinical brain injury to incident late-onset epilepsy in a large, community-based cohort. METHODS:We studied Offspring Cohort of the Framingham Heart Study participants 45 years or older, who were free of prevalent stroke, dementia, or epilepsy, and had neuropsychological (NP) evaluation and brain magnetic resonance imaging (MRI). Cognitive measures included Visual Reproduction Delayed Recall, Logical Memory Delayed Recall, Similarities, Trail Making Test B minus A (TrTB-TrTA; attention and executive function), and a global measure of cognition derived from principal component analysis. MRI measures included total cerebral brain volume, cortical gray matter volume (CGMV), white matter hyperintensity volume (WMHV), and hippocampal volume. Incident epilepsy was identified through a review of administrative data and medical records. Cox proportional hazards regression models were used for the analyses. All analyses were adjusted for age, sex, and educational level (cognition only). RESULTS:Among participants who underwent NP testing (n = 2349, 45.81% male), 31 incident epilepsy cases were identified during follow-up. Better performance on the TrTB-TrTA was associated with a lower risk of developing epilepsy (hazard ratio [HR] .25, 95% confidence interval [CI] .08-.73; p = .011). In the subgroup of participants with MRI (n = 2056, 46.01% male), 27 developed epilepsy. Higher WMHV was associated with higher epilepsy risk (HR 1.5, 95%CI 1.01-2.20; p = .042), but higher CGMV (HR .73, 95% CI .57-.93; p = .001) was associated with lower incidence of epilepsy. SIGNIFICANCE/CONCLUSIONS:Better performance on the (TrTB-TrTA), a measure of executive function and attention, and higher cortical volumes are associated with lower risk of developing epilepsy. Conversely, higher WMHV, a measure of occult vascular injury, increases the risk. Our study shows that non-invasive tests performed in mid-life may help identify people at risk for developing epilepsy later in life.

When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals; however, in humans, its exact origin and temporal dynamics remain unknown. We report electrocorticographic recordings in neurosurgical patients and a connectivity analysis framework based on Granger causality that reveals major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to the ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis.

Epilepsy affects approximately 1% of the world population. Patients have recurrent seizures, increased physical and psychiatric comorbidities, and higher mortality rate than the general population. Over the last 40 years, research has resulted in 20 new antiseizure medications (ASMs) approved between 1990 and 2018. In spite of this, up to one-third of patients (~ 1 million patients in the USA) have drug-resistant epilepsy (DRE), with little change between 1982 and 2018, a period of intense new ASM development. A minority of patients with DRE may benefit from surgical treatment, but this specialized care remains challenging to scale. Therefore, the greatest hope for breakthroughs for patients with DRE is in pharmacologic therapies. Recently, several advances promise to change the outcomes for patients with DRE. Cenobamate, a drug with dual mechanisms of modulating sodium channel currents and GABA-A receptors, achieves 90-100% seizure reduction in 25-33% of patients with focal DRE, a response not observed with other ASMs. Fenfluramine, a serotonin-acting drug, dramatically reduces the frequency of convulsive seizures in Dravet syndrome, a devastating developmental epileptic encephalopathy with severe DRE. Both drugs reduce mortality. In addition, the possibility of DRE prevention was recently raised in patients with tuberous sclerosis complex, a relatively common genetic form of epilepsy. A paradigm shift is emerging in the treatment of epilepsy. Seizure freedom has become attainable in a significant proportion of patients with focal DRE, and dramatic seizure reduction has been achieved in a developmental encephalopathy. Coupled with a rich pipeline of new compounds under clinical development, the long sought-after breakthrough in the treatment of epilepsy may finally be in sight.

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. However, neuroscience researchers haven't kept up with the quick progress in LLM development. 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-minute 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 log-linear 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.