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.

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.

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 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.

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.

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.

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.

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.

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.].

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.

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.

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.

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.

Raphe and ventrolateral medulla proteomics in epilepsy and sudden unexpected death in epilepsy.

Brainstem nuclei dysfunction is implicated in sudden unexpected death in epilepsy. In animal models, deficient serotonergic activity is associated with seizure-induced respiratory arrest. In humans, glia are decreased in the ventrolateral medullary pre-Botzinger complex that modulate respiratory rhythm, as well as in the medial medullary raphe that modulate respiration and arousal. Finally, sudden unexpected death in epilepsy cases have decreased midbrain volume. To understand the potential role of brainstem nuclei in sudden unexpected death in epilepsy, we evaluated molecular signalling pathways using localized proteomics in microdissected midbrain dorsal raphe and medial medullary raphe serotonergic nuclei, as well as the ventrolateral medulla in brain tissue from epilepsy patients who died of sudden unexpected death in epilepsy and other causes in diverse epilepsy syndromes and non-epilepsy control cases (n = 15-16 cases per group/region). Compared with the dorsal raphe of non-epilepsy controls, we identified 89 proteins in non-sudden unexpected death in epilepsy and 219 proteins in sudden unexpected death in epilepsy that were differentially expressed. These proteins were associated with inhibition of EIF2 signalling (P-value of overlap = 1.29 × 10-8, z = -2.00) in non-sudden unexpected death in epilepsy. In sudden unexpected death in epilepsy, there were 10 activated pathways (top pathway: gluconeogenesis I, P-value of overlap = 3.02 × 10-6, z = 2.24) and 1 inhibited pathway (fatty acid beta-oxidation, P-value of overlap = 2.69 × 10-4, z = -2.00). Comparing sudden unexpected death in epilepsy and non-sudden unexpected death in epilepsy, 10 proteins were differentially expressed, but there were no associated signalling pathways. In both medullary regions, few proteins showed significant differences in pairwise comparisons. We identified altered proteins in the raphe and ventrolateral medulla of epilepsy patients, including some differentially expressed in sudden unexpected death in epilepsy cases. Altered signalling pathways in the dorsal raphe of sudden unexpected death in epilepsy indicate a shift in cellular energy production and activation of G-protein signalling, inflammatory response, stress response and neuronal migration/outgrowth. Future studies should assess the brain proteome in relation to additional clinical variables (e.g. recent tonic-clonic seizures) and in more of the reciprocally connected cortical and subcortical regions to better understand the pathophysiology of epilepsy and sudden unexpected death in epilepsy.

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.

Flexural bending to approximate cortical forces exerted by electrocorticography (ECoG) arrays.

Objective.The force that an electrocorticography (ECoG) array exerts on the brain manifests when it bends to match the curvature of the skull and cerebral cortex. This force can negatively impact both short-term and long-term patient outcomes. Here we provide a mechanical characterization of a novel liquid crystal polymer (LCP) ECoG array prototype to demonstrate that its thinner geometry reduces the force potentially applied to the cortex of the brain.Approach.We built a low-force flexural testing machine to measure ECoG array bending forces, calculate their effective flexural moduli, and approximate the maximum force they could exerted on the human brain.Main results.The LCP ECoG prototype was found to have a maximal force less than 20% that of any commercially available ECoG arrays that were tested. However, as a material, LCP was measured to be as much as 24× more rigid than silicone, which is traditionally used in ECoG arrays. This suggests that the lower maximal force resulted from the prototype's thinner profile (2.9×-3.25×).Significance.While decreasing material stiffness can lower the force an ECoG array exhibits, our LCP ECoG array prototype demonstrated that flexible circuit manufacturing techniques can also lower these forces by decreasing ECoG array thickness. Flexural tests of ECoG arrays are necessary to accurately assess these forces, as material properties for polymers and laminates are often scale dependent. As the polymers used are anisotropic, elastic modulus cannot be used to predict ECoG flexural behavior. Accounting for these factors, we used our four-point flexure testing procedure to quantify the forces exerted on the brain by ECoG array bending. With this experimental method, ECoG arrays can be designed to minimize force exerted on the brain, potentially improving both acute and chronic clinical utility.

Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array.

One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30-70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100-1000 mm2 area to enable high-resolution mapping of neural activity. Here, we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings in nine patients with epilepsy. In addition, we recorded from four patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Recordings in epileptic patients had a significantly higher microseizure rate (2.01 events/min) than recordings in non-epileptic subjects (0.01 events/min; permutation test, P = 0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

Pilot study evaluating everolimus molecular mechanisms in tuberous sclerosis complex and focal cortical dysplasia.

BACKGROUND: Tuberous sclerosis complex (TSC) and some focal cortical dysplasias (FCDs) are associated with dysfunctional mTOR signaling, resulting in increased cell growth and ribosomal S6 protein phosphorylation (phospho-S6). mTOR inhibitors can reduce TSC tumor growth and seizure frequency, and preclinical FCD studies indicate seizure suppression. This pilot study evaluated safety of mTOR inhibitor everolimus in treatment resistant (failure of >2 anti-seizure medications) TSC and FCD patients undergoing surgical resection and to assess mTOR signaling and molecular pathways. METHODS AND FINDINGS: We evaluated everolimus in 14 treatment resistant epilepsy patients undergoing surgical resection (4.5 mg/m2 daily for 7 days; n = 4 Active, mean age 18.3 years, range 4-26; n = 10, Control, mean age 13.1, range 3-45). Everolimus was well tolerated. Mean plasma everolimus in Active participants were in target range (12.4 ng/ml). Brain phospho-S6 was similar in Active and Control participants with a lower trend in Active participants, with Ser235/236 1.19-fold (p = 0.67) and Ser240/244 1.15-fold lower (p = 0.66). Histologically, Ser235/236 was 1.56-fold (p = 0.37) and Ser240/244 was 5.55-fold lower (p = 0.22). Brain proteomics identified 11 proteins at <15% false discovery rate associated with coagulation system (p = 1.45x10-9) and acute phase response (p = 1.23x10-6) activation. A weighted gene correlation network analysis (WGCNA) of brain proteomics and phospho-S6 identified 5 significant modules. Higher phospho-S6 correlated negatively with cellular respiration and synaptic transmission and positively with organophosphate metabolic process, nuclear mRNA catabolic process, and neuron ensheathment. Brain metabolomics identified 14 increased features in Active participants, including N-acetylaspartylglutamic acid. Plasma proteomics and cytokine analyses revealed no differences. CONCLUSIONS: Short-term everolimus before epilepsy surgery in TSC and FCD resulted in no adverse events and trending lower mTOR signaling (phospho-S6). Future studies should evaluate implications of our findings, including coagulation system activation and everolimus efficacy in FCD, in larger studies with long-term treatment to better understand molecular and clinical effects. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov NCT02451696.

Shared computational principles for language processing in humans and deep language models.

Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.

Long-term priors influence visual perception through recruitment of long-range feedback.

Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors' influence on perception and provide constraints on theories about long-term priors' influence on perception.