Cannabinoid treatments in epilepsy and seizure disorders.

Cannabis has been used to treat convulsions and other disorders since ancient times. In the last few decades, preclinical animal studies and clinical investigations have established the role of cannabidiol (CBD) in treating epilepsy and seizures and support potential therapeutic benefits for cannabinoids in other neurological and psychiatric disorders. Here, we comprehensively review the role of cannabinoids in epilepsy. We briefly review the diverse physiological processes mediating the central nervous system response to cannabinoids, including Δ9-tetrahydrocannabinol (Δ9-THC), cannabidiol, and terpenes. Next, we characterize the anti- and proconvulsive effects of cannabinoids from animal studies of acute seizures and chronic epileptogenesis. We then review the clinical literature on using cannabinoids to treat epilepsy, including anecdotal evidence and case studies as well as the more recent randomized controlled clinical trials that led to US Food and Drug Administration approval of CBD for some types of epilepsy. Overall, we seek to evaluate our current understanding of cannabinoids in epilepsy and focus future research on unanswered questions.

Temporal dynamics of short-term neural adaptation across human visual cortex.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses between lower and higher visual brain areas and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Temporal Dynamics of Neural Responses in Human Visual Cortex.

Neural responses to visual stimuli exhibit complex temporal dynamics, including subadditive temporal summation, response reduction with repeated or sustained stimuli (adaptation), and slower dynamics at low contrast. These phenomena are often studied independently. Here, we demonstrate these phenomena within the same experiment and model the underlying neural computations with a single computational model. We extracted time-varying responses from electrocorticographic recordings from patients presented with stimuli that varied in duration, interstimulus interval (ISI) and contrast. Aggregating data across patients from both sexes yielded 98 electrodes with robust visual responses, covering both earlier (V1-V3) and higher-order (V3a/b, LO, TO, IPS) retinotopic maps. In all regions, the temporal dynamics of neural responses exhibit several nonlinear features. Peak response amplitude saturates with high contrast and longer stimulus durations, the response to a second stimulus is suppressed for short ISIs and recovers for longer ISIs, and response latency decreases with increasing contrast. These features are accurately captured by a computational model composed of a small set of canonical neuronal operations, that is, linear filtering, rectification, exponentiation, and a delayed divisive normalization. We find that an increased normalization term captures both contrast- and adaptation-related response reductions, suggesting potentially shared underlying mechanisms. We additionally demonstrate both changes and invariance in temporal response dynamics between earlier and higher-order visual areas. Together, our results reveal the presence of a wide range of temporal and contrast-dependent neuronal dynamics in the human visual cortex and demonstrate that a simple model captures these dynamics at millisecond resolution.SIGNIFICANCE STATEMENT Sensory inputs and neural responses change continuously over time. It is especially challenging to understand a system that has both dynamic inputs and outputs. Here, we use a computational modeling approach that specifies computations to convert a time-varying input stimulus to a neural response time course, and we use this to predict neural activity measured in the human visual cortex. We show that this computational model predicts a wide variety of complex neural response shapes, which we induced experimentally by manipulating the duration, repetition, and contrast of visual stimuli. By comparing data and model predictions, we uncover systematic properties of temporal dynamics of neural signals, allowing us to better understand how the brain processes dynamic sensory information.

Brain molecular mechanisms in Rasmussen encephalitis.

OBJECTIVE: This study was undertaken to identify molecular mechanisms in brain tissue of Rasmussen encephalitis (RE) when compared to people with non-RE epilepsy (PWE) and control cases using whole exome sequencing (WES), RNAseq, and proteomics. METHODS: Frozen brain tissue (ages = 2-19 years) was obtained from control autopsy (n = 14), surgical PWE (n = 10), and surgical RE cases (n = 27). We evaluated WES variants in RE associated with epilepsy, seizures, RE, and human leukocyte antigens (HLAs). Differential expression was evaluated by RNAseq (adjusted p < .05) and label-free quantitative mass spectrometry (false discovery rate < 5%) in the three groups. RESULTS: WES revealed no common pathogenic variants in RE, but several rare and likely deleterious variants of unknown significance (VUS; ANGPTL7/MTOR, SCN1A, FCGR3B, MTOR) and more common HLA VUS in >25% of RE cases (HLA-DRB1, HLA-DQA2), all with allele frequency < 5% in the general population. RNAseq in RE versus PWE (1516 altered transcripts) revealed significant activation of crosstalk between dendritic and natural killer cells (p = 7.94 × 10-6 , z = 2.65), in RE versus control (7466 transcripts) neuroinflammation signaling activation (p = 6.31 × 10-13 , z = 5.07), and in PWE versus control (945 transcripts) phagosome formation activation (p = 2.00 × 10-13 , z = 5.61). Proteomics detected fewer altered targets. SIGNIFICANCE: In RE, we identified activated immune signaling pathways and immune cell type annotation enrichment that suggest roles of the innate and adaptive immune responses, as well as HLA variants that may increase vulnerability to RE. Follow-up studies could evaluate cell type density and subregional localization associated with top targets, clinical history (neuropathology, disease duration), and whether modulating crosstalk between dendritic and natural killer cells may limit disease progression.

Metabolomic, proteomic, and transcriptomic changes in adults with epilepsy on modified Atkins diet.

OBJECTIVE: High-fat and low-carbohydrate diets can reduce seizure frequency in some treatment-resistant epilepsy patients, including the more flexible modified Atkins diet (MAD), which is more palatable, mimicking fasting and inducing high ketone body levels. Low-carbohydrate diets may shift brain energy production, particularly impacting neuron- and astrocyte-linked metabolism. METHODS: We evaluated the effect of short-term MAD on molecular mechanisms in adult epilepsy patients from surgical brain tissue and plasma compared to control participants consuming a nonmodified higher carbohydrate diet (n = 6 MAD, mean age = 43.7 years, range = 21-53, diet for average 10 days; n = 10 control, mean age = 41.9 years, range = 28-64). RESULTS: By metabolomics, there were 13 increased metabolites in plasma (n = 15 participants with available specimens), which included 4.10-fold increased ketone body 3-hydroxybutyric acid, decreased palmitic acid in cortex (n = 16), and 11 decreased metabolites in hippocampus (n = 6), which had top associations with mitochondrial functions. Cortex and plasma 3-hydroxybutyric acid levels had a positive correlation (p = .0088, R2  = .48). Brain proteomics and RNAseq identified few differences, including 2.75-fold increased hippocampal MT-ND3 and trends (p < .01, false discovery rate > 5%) in hippocampal nicotinamide adenine dinucleotide (NADH)-related signaling pathways (activated oxidative phosphorylation and inhibited sirtuin signaling). SIGNIFICANCE: Short-term MAD was associated with metabolic differences in plasma and resected epilepsy brain tissue when compared to control participants, in combination with trending expression changes observed in hippocampal NADH-related signaling pathways. Future studies should evaluate how brain molecular mechanisms are altered with long-term MAD in a larger cohort of epilepsy patients, with correlations to seizure frequency, epilepsy syndrome, and other clinical variables. [Clinicaltrials.gov NCT02565966.].

Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy.

OBJECTIVE: Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (µECoG) arrays, which provide high spatial resolution over large cortical surface areas. METHODS: We used novel liquid crystal polymer thin-film µECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. RESULTS: We found visually delineable subregions of elevated HFO activity within each µECoG recording. Forty-seven percent of HFOs occurred on single 200-µm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our µECoG recordings. SIGNIFICANCE: These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.

Serotonin receptor expression in hippocampus and temporal cortex of temporal lobe epilepsy patients by postictal generalized electroencephalographic suppression duration.

OBJECTIVE: Prolonged postictal generalized electroencephalographic suppression (PGES) is a potential biomarker for sudden unexpected death in epilepsy (SUDEP), which may be associated with dysfunctional autonomic responses and serotonin signaling. To better understand molecular mechanisms, PGES duration was correlated to 5HT1A and 5HT2A receptor protein expression and RNAseq from resected hippocampus and temporal cortex of temporal lobe epilepsy patients with seizures recorded in preoperative evaluation. METHODS: Analyses included 36 cases (age = 14-64 years, age at epilepsy onset = 0-51 years, epilepsy duration = 2-53 years, PGES duration = 0-93 s), with 13 cases in all hippocampal analyses. 5HT1A and 5HT2A protein was evaluated by Western blot and histologically in hippocampus (n = 16) and temporal cortex (n = 9). We correlated PGES duration to our previous RNAseq dataset for serotonin receptor expression and signaling pathways, as well as weighted gene correlation network analysis (WGCNA) to identify correlated gene clusters. RESULTS: In hippocampus, 5HT2A protein by Western blot positively correlated with PGES duration (p = .0024, R2  = .52), but 5HT1A did not (p = .87, R2  = .0020). In temporal cortex, 5HT1A and 5HT2A had lower expression and did not correlate with PGES duration. Histologically, PGES duration did not correlate with 5HT1A or 5HT2A expression in hippocampal CA4, dentate gyrus, or temporal cortex. RNAseq identified two serotonin receptors with expression that correlated with PGES duration in an exploratory analysis: HTR3B negatively correlated (p = .043, R2  = .26) and HTR4 positively correlated (p = .049, R2  = .25). WGCNA identified four modules correlated with PGES duration, including positive correlation with synaptic transcripts (p = .040, Pearson correlation r = .52), particularly potassium channels (KCNA4, KCNC4, KCNH1, KCNIP4, KCNJ3, KCNJ6, KCNK1). No modules were associated with serotonin receptor signaling. SIGNIFICANCE: Higher hippocampal 5HT2A receptor protein and potassium channel transcripts may reflect underlying mechanisms contributing to or resulting from prolonged PGES. Future studies with larger cohorts should assess functional analyses and additional brain regions to elucidate mechanisms underlying PGES and SUDEP risk.

Coding and non-coding transcriptome of mesial temporal lobe epilepsy: Critical role of small non-coding RNAs.

Our understanding of mesial temporal lobe epilepsy (MTLE), one of the most common form of drug-resistant epilepsy in humans, is derived mainly from clinical, imaging, and physiological data from humans and animal models. High-throughput gene expression studies of human MTLE have the potential to uncover molecular changes underlying disease pathogenesis along with novel therapeutic targets. Using RNA- and small RNA-sequencing in parrallel, we explored differentially expressed genes in the hippocampus and cortex of MTLE patients who had undergone surgical resection and non-epileptic controls. We identified differentially expressed genes in the hippocampus of MTLE patients and differentially expressed small RNAs across both the cortex and hippocampus. We found significant enrichment for astrocytic and microglial genes among up-regulated genes, and down regulation of neuron specific genes in the hippocampus of MTLE patients. The transcriptome profile of the small RNAs reflected disease state more robustly than mRNAs, even across brain regions which show very little pathology. While mRNAs segregated predominately by brain region for MTLE and controls, small RNAs segregated by disease state. In particular, our data suggest that specific miRNAs (e.g., let-7b-3p and let-7c-3p) may be key regulators of multiple pathways related to MTLE pathology. Further, we report a strong association of other small RNA species with MTLE pathology. As such we have uncovered novel elements that may contribute to the establishment and progression of MTLE pathogenesis and that could be leveraged as therapeutic targets.

Basal forebrain dynamics during nonassociative and associative olfactory learning.

Cholinergic and GABAergic projections from the horizontal diagonal band (HDB) and medial preoptic area (MCPO) of the basal forebrain to the olfactory system are associated with odor discrimination and odor learning, as well as modulation of neural responses in olfactory structures. Whereas pharmacological and lesion studies give insights into the functional role of these modulatory inputs on a slow timescale, the response dynamics of neurons in the HDB/MCPO during olfactory behaviors have not been investigated. In this study we examined how these neurons respond during two olfactory behaviors: spontaneous investigation of odorants and odor-reward association learning. We observe rich heterogeneity in the response dynamics of individual HDB/MCPO neurons, with a substantial fraction of neurons exhibiting task-related modulation. HDB/MCPO neurons show both rapid and transient responses during bouts of odor investigation and slow, long-lasting modulation of overall response rate based on behavioral demands. Specifically, baseline rates were higher during the acquisition phase of an odor-reward association than during spontaneous investigation or the recall phase of an odor reward association. Our results suggest that modulatory projections from the HDB/MCPO are poised to influence olfactory processing on multiple timescales, from hundreds of milliseconds to minutes, and are therefore capable of rapidly setting olfactory network dynamics during odor processing and learning.

Distinct roles of bulbar muscarinic and nicotinic receptors in olfactory discrimination learning.

The olfactory bulb (OB) and piriform cortex receive dense cholinergic projections from the basal forebrain. Cholinergic modulation within the piriform cortex has long been proposed to serve important functions in olfactory learning and memory. We here investigate how olfactory discrimination learning is regulated by cholinergic modulation of the OB inputs to the piriform cortex. We examined rats' performance on a two-alternative choice odor discrimination task following local, bilateral blockade of cholinergic nicotinic and/or muscarinic receptors in the OB. Results demonstrate that acquisition, but not recall, of novel discrimination problems is impaired following blockade of OB cholinergic receptors, although the relative contribution of muscarinic and nicotinic receptors depends on task difficulty. Blocking muscarinic receptors impairs learning for nearly all odor sets, whereas blocking nicotinic receptors only affects performance for perceptually similar odors. This pattern of behavioral effects is consistent with predictions from a model of cholinergic modulation in the OB and piriform cortex (de Almeida et al., 2013). Model simulations suggest that muscarinic and nicotinic receptors may serve complementary roles in regulating coherence and sparseness of the OB network output, which in turn differentially regulate the strength and overlap in cortical odor representations. Overall, our results suggest that muscarinic receptor blockade results in a bona fide learning impairment that may arise because cortical neurons are activated less often. Behavioral impairment following nicotinic receptor blockade may not be due to the inability of the cortex to learn, but rather arises because the cortex is unable to resolve highly overlapping input patterns.

Dual sensitivity of inferior colliculus neurons to ITD in the envelopes of high-frequency sounds: experimental and modeling study.

Human listeners are sensitive to interaural time differences (ITDs) in the envelopes of sounds, which can serve as a cue for sound localization. Many high-frequency neurons in the mammalian inferior colliculus (IC) are sensitive to envelope-ITDs of sinusoidally amplitude-modulated (SAM) sounds. Typically, envelope-ITD-sensitive IC neurons exhibit either peak-type sensitivity, discharging maximally at the same delay across frequencies, or trough-type sensitivity, discharging minimally at the same delay across frequencies, consistent with responses observed at the primary site of binaural interaction in the medial and lateral superior olives (MSO and LSO), respectively. However, some high-frequency IC neurons exhibit dual types of envelope-ITD sensitivity in their responses to SAM tones, that is, they exhibit peak-type sensitivity at some modulation frequencies and trough-type sensitivity at other frequencies. Here we show that high-frequency IC neurons in the unanesthetized rabbit can also exhibit dual types of envelope-ITD sensitivity in their responses to SAM noise. Such complex responses to SAM stimuli could be achieved by convergent inputs from MSO and LSO onto single IC neurons. We test this hypothesis by implementing a physiologically explicit, computational model of the binaural pathway. Specifically, we examined envelope-ITD sensitivity of a simple model IC neuron that receives convergent inputs from MSO and LSO model neurons. We show that dual envelope-ITD sensitivity emerges in the IC when convergent MSO and LSO inputs are differentially tuned for modulation frequency.

Temporal dynamics of short-term neural adaptation across human visual cortex.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language.

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.

A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by word, from one brain to another in natural conversations. Linguistic content emerges in the speaker's brain before word articulation and rapidly re-emerges in the listener's brain after word articulation. The contextual embeddings better capture word-by-word neural alignment between speaker and listener than syntactic and articulatory models. Our findings indicate that the contextual embeddings learned by LLMs can serve as an explicit numerical model of the shared, context-rich meaning space humans use to communicate their thoughts to one another.

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns.

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language. To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation for each word (i.e., a brain embedding) in each patient. Using stringent zero-shot mapping we demonstrate that brain embeddings in the IFG and the DLM contextual embedding space have common geometric patterns. The common geometric patterns allow us to predict the brain embedding in IFG of a given left-out word based solely on its geometrical relationship to other non-overlapping words in the podcast. Furthermore, we show that contextual embeddings capture the geometry of IFG embeddings better than static word embeddings. The continuous brain embedding space exposes a vector-based neural code for natural language processing in the human brain.

Localized proteomic differences in the choroid plexus of Alzheimer's disease and epilepsy patients.

Introduction: Alzheimer's disease (AD) and epilepsy are reciprocally related. Among sporadic AD patients, clinical seizures occur in 10-22% and subclinical epileptiform abnormalities occur in 22-54%. Cognitive deficits, especially short-term memory impairments, occur in most epilepsy patients. Common neurophysiological and molecular mechanisms occur in AD and epilepsy. The choroid plexus undergoes pathological changes in aging, AD, and epilepsy, including decreased CSF turnover, amyloid beta (Aß), and tau accumulation due to impaired clearance and disrupted CSF amino acid homeostasis. This pathology may contribute to synaptic dysfunction in AD and epilepsy. Methods: We evaluated control (n = 8), severe AD (n = 8; A3, B3, C3 neuropathology), and epilepsy autopsy cases (n = 12) using laser capture microdissection (LCM) followed by label-free quantitative mass spectrometry on the choroid plexus adjacent to the hippocampus at the lateral geniculate nucleus level. Results: Proteomics identified 2,459 proteins in the choroid plexus. At a 5% false discovery rate (FDR), 616 proteins were differentially expressed in AD vs. control, 1 protein in epilepsy vs. control, and 438 proteins in AD vs. epilepsy. There was more variability in the epilepsy group across syndromes. The top 20 signaling pathways associated with differentially expressed proteins in AD vs. control included cell metabolism pathways; activated fatty acid beta-oxidation (p = 2.00 x 10-7, z = 3.00), and inhibited glycolysis (p = 1.00 x 10-12, z = -3.46). For AD vs. epilepsy, the altered pathways included cell metabolism pathways, activated complement system (p = 5.62 x 10-5, z = 2.00), and pathogen-induced cytokine storm (p = 2.19 x 10-2, z = 3.61). Of the 617 altered proteins in AD and epilepsy vs. controls, 497 (81%) were positively correlated (p < 0.0001, R2 = 0.27). Discussion: We found altered signaling pathways in the choroid plexus of severe AD cases and many correlated changes in the protein expression of cell metabolism pathways in AD and epilepsy cases. The shared molecular mechanisms should be investigated further to distinguish primary pathogenic changes from the secondary ones. These mechanisms could inform novel therapeutic strategies to prevent disease progression or restore normal function. A focus on dual-diagnosed AD/epilepsy cases, specific epilepsy syndromes, such as temporal lobe epilepsy, and changes across different severity levels in AD and epilepsy would add to our understanding.

Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex.

Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz), but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance Statement: The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.

A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations.

Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to communicate their thoughts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We demonstrate that the linguistic embedding space can capture the linguistic content of word-by-word neural alignment between speaker and listener. Linguistic content emerged in the speaker's brain before word articulation, and the same linguistic content rapidly reemerged in the listener's brain after word articulation. These findings establish a computational framework to study how human brains transmit their thoughts to one another in real-world contexts.

An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory.

The relationship between conscious experience and brain activity has intrigued scientists and philosophers for centuries. In the last decades, several theories have suggested different accounts for these relationships. These theories have developed in parallel, with little to no cross-talk among them. To advance research on consciousness, we established an adversarial collaboration between proponents of two of the major theories in the field, Global Neuronal Workspace and Integrated Information Theory. Together, we devised and preregistered two experiments that test contrasting predictions of these theories concerning the location and timing of correlates of visual consciousness, which have been endorsed by the theories' proponents. Predicted outcomes should either support, refute, or challenge these theories. Six theory-impartial laboratories will follow the study protocol specified here, using three complementary methods: Functional Magnetic Resonance Imaging (fMRI), Magneto-Electroencephalography (M-EEG), and intracranial electroencephalography (iEEG). The study protocol will include built-in replications, both between labs and within datasets. Through this ambitious undertaking, we hope to provide decisive evidence in favor or against the two theories and clarify the footprints of conscious visual perception in the human brain, while also providing an innovative model of large-scale, collaborative, and open science practice.

N-Tools-Browser: Web-Based Visualization of Electrocorticography Data for Epilepsy Surgery.

Epilepsy affects more than three million people in the United States. In approximately one-third of this population, anti-seizure medications do not control seizures. Many patients pursue surgical treatment that can include a procedure involving the implantation of electrodes for intracranial monitoring of seizure activity. For these cases, accurate mapping of the implanted electrodes on a patient's brain is crucial in planning the ultimate surgical treatment. Traditionally, electrode mapping results are presented in static figures that do not allow for dynamic interactions and visualizations. In collaboration with a clinical research team at a Level 4 Epilepsy Center, we developed N-Tools-Browser, a web-based software using WebGL and the X-Toolkit (XTK), to help clinicians interactively visualize the location and functional properties of implanted intracranial electrodes in 3D. Our software allows the user to visualize the seizure focus location accurately and simultaneously display functional characteristics (e.g., results from electrical stimulation mapping). Different visualization modes enable the analysis of multiple electrode groups or individual anatomical locations. We deployed a prototype of N-Tools-Browser for our collaborators at the New York University Grossman School of Medicine Comprehensive Epilepsy Center. Then, we evaluated its usefulness with domain experts on clinical cases.