Biology subjects

Devinsky, O.

Publications and source records attributed to Devinsky, O..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

ClearScope: a fully integrated light sheet theta microscope for sub-cellular resolution imaging without lateral size constraints

Three-dimensional (3D) ex vivo imaging of cleared intact brains of animal models and large human and non-human primate postmortem brain specimens is important for understanding the physiological neural network connectivity patterns and the pathological alterations underlying neuropsychiatric and neurological disorders. Light-sheet microscopy has emerged as a highly effective imaging modality for rapid high-resolution imaging of large cleared samples. However, the orthogonal arrangements of illumination and detection optics in light sheet microscopy limits the size of specimen that can be imaged. Recently developed light sheet theta microscopy (LSTM) technology addressed this by utilizing a unique arrangement of two illumination light paths oblique to the detection light path, while allowing perpendicular arrangement of the detection light path relative to the specimen surface. Here, we report development of a next-generation, fully integrated, and user-friendly LSTM system for rapid sub-cellular resolution imaging uniformly throughout a large specimen without constraining the lateral (XY) size. In addition, we provide a seamlessly integrated workflow for image acquisition, data storage, pre- and post-processing, enhancement, and quantitative analysis. We demonstrate the system performance by high-resolution 3D imaging of intact mouse brains and human brain samples, and complete data analysis including digital neuron tracing, vessel reconstruction and design-based stereological analysis in 3D. This technically enhanced and user-friendly LSTM implementation will enable rapid quantitative mapping of molecular and cellular features of interests in diverse types of very large samples.

neuroscience↗

bioRxiv2024

A low-activity cortical network selectively encodes syntax

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 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 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 non-overlapping. Most strikingly, our data re-veal an unexpected property of higher-order linguistic information: it is encoded independent of neural activity levels. These results show that activity magnitude and information content are dissociable, with important implications for studying the neurobiology of language. Teaser"Brain recordings during speech reveal complex linguistic information throughout cortex, independent of neural activity levels."

neuroscience↗

bioRxiv2024

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 havent 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 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.

neuroscience↗

bioRxiv2024

ALIGNING BRAINS INTO A SHARED SPACE IMPROVES THEIR ALIGNMENT TO LARGE LANGUAGE MODELS

AO_SCPLOWBSTRACTC_SCPLOWRecent studies have shown that large language models (LLMs) can accurately predict neural activity measured using electrocorticography (ECoG) during natural language processing. To predict word-by-word neural activity, most prior work has estimated and evaluated encoding models within each electrode and subject--without evaluating how these models generalize across individual brains. In this paper, we analyze neural responses in 8 subjects while they listened to the same 30-minute podcast episode. We use a shared response model (SRM) to estimate a shared information space across subjects. We show that SRM significantly improves LLM-based encoding model performance. We also show that we can use this shared space to denoise the individual brain responses by projecting back into the individualized electrode space, and this process achieves a mean 38% improvement in encoding performance. The strongest improvement was observed for brain areas specialized for language comprehension, specifically in the superior temporal gyrus (STG) and inferior frontal gyrus (IFG). Critically, estimating a shared space allows us to construct encoding models that better generalize across individuals.

neuroscience↗

bioRxiv2024

A left-lateralized dorsolateral prefrontal network for naming

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 process relevant to daily auditory discourse remain poorly understood. Here, we recorded neurosurgical electrocorticographic (ECoG) data from 48 patients and dissociated two key language networks that highly overlap in time and space critical for 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 invariant to input modalities. Functionally, we confirmed that the semantic processing network encodes word surprisal during sentence perception. These findings elucidate neurophysiological mechanisms underlying the processing of semantic auditory inputs ranging from passive language comprehension to conversational speech.

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bioRxiv2024

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals

ObjectiveThis study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. ApproachWe propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-patient models exploiting data from multiple participants. Main ResultsThe subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. SignificanceThe proposed SwinTW decoder enables future speech neuropros-theses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests that such a model can be applied to new patients that do not have paired acoustic and neural data, providing an advance in neuroprostheses for people with speech disability, where acoustic-neural training data is not feasible.

neuroscience↗

bioRxiv2024

Discovery of novel compounds and target mechanisms using a high throughput, multiparametric phenotypic screen in a human neuronal model of Tuberous Sclerosis

Tuberous sclerosis complex (TSC) is a rare genetic disorder caused by mutations in the mTOR pathway genes TSC1 or TSC2. TSC can affect multiple organs including the brain, and most patients (75-90%) present with seizures during early childhood and intractable epilepsy throughout life. mTOR inhibitors, part of the current standard of care, lack the optimal characteristics to fully address patient phenotypes. Here, we report on the application of our all-optical electrophysiology platform for phenotypic screening in a human neuronal model of TSC. We used CRISPR/Cas9-isogenic TSC2-/- iPS cell lines to identify disease-associated changes to neuronal morphology, transcript expression and neuronal excitability. We established a robust multiparametric electrophysiological phenotype which we then validated in TSC patient-derived neurons. We used this phenotype to conduct a screen of [~]30,000 small molecule compounds in human iPS cell-derived neurons and identified chemical scaffolds that rescued the functional TSC disease parameters. Confirmed hits may act via different mechanisms than direct mTOR pathway inhibition. This strategy provides molecular starting points for therapeutic development in TSC and a framework for phenotype discovery and drug screening in other neurological disorders.

neuroscience↗

bioRxiv2024

Closed-loop electrical stimulation to prevent focal epilepsy progression and long-term memory impairment

Interictal epileptiform discharges (IEDs) are ubiquitously expressed in epileptic networks and disrupt cognitive functions. It is unclear whether addressing IED-induced dysfunction could improve epilepsy outcomes as most therapeutics target seizures. We show in a model of progressive hippocampal epilepsy that IEDs produce pathological oscillatory coupling which is associated with prolonged, hypersynchronous neural spiking in synaptically connected cortex and expands the brain territory capable of generating IEDs. A similar relationship between IED-mediated oscillatory coupling and temporal organization of IEDs across brain regions was identified in human subjects with refractory focal epilepsy. Spatiotemporally targeted closed-loop electrical stimulation triggered on hippocampal IED occurrence eliminated the abnormal cortical activity patterns, preventing spread of the epileptic network and ameliorating long-term spatial memory deficits in rodents. These findings suggest that stimulation-based network interventions that normalize interictal dynamics may be an effective treatment of epilepsy and its comorbidities, with a low barrier to clinical translation. One-Sentence SummaryTargeted closed-loop electrical stimulation prevents spread of the epileptic network and ameliorates long-term spatial memory deficits.

neuroscience↗

bioRxiv2024

Wavelet Phase Coherence of Ictal Scalp EEG-Extracted Muscle Activity (SMA) as a Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP)

ObjectiveApproximately 50 million people worldwide have epilepsy and 8-17% of the deaths in patients with epilepsy are attributed to sudden unexpected death in epilepsy (SUDEP). The goal of the present work was to establish a biomarker for SUDEP so that preventive treatment can be instituted. ApproachSeizure activity in patients with SUDEP and non-SUDEP was analyzed, specifically, the scalp EEG extracted muscle activity (SMA) and the average wavelet phase coherence (WPC) during seizures was computed for two frequency ranges (1-12 Hz, 13-30 Hz) to identify differences between the two groups. Main resultsIctal SMA in SUDEP patients showed a statistically higher average WPC value when compared to non-SUDEP patients for both frequency ranges. Area under curve for a cross-validated logistic classifier was 81%. SignificanceAverage WPC of ictal SMA is a candidate biomarker for early detection of SUDEP.

neuroscience↗

bioRxiv2024

Modeling and Correction of Protein Conformational Disease in iPSC-derived Neurons through Personalized Base Editing

AbstractAltered protein conformation can cause incurable neurodegenerative disorders. Mutations in SERPINI1, the gene encoding neuroserpin, can alter protein conformation resulting in cytotoxic aggregation leading to neuronal death. Familial encephalopathy with neuroserpin inclusion bodies (FENIB) is a rare autosomal dominant progressive myoclonic epilepsy that progresses to dementia and premature death. We developed HEK293T and induced pluripotent stem cell (iPSC) models of FENIB, harboring a patient-specific pathogenic SERPINI1 variant or stably overexpressing mutant neuroserpin fused to GFP (MUT NS-GFP). Here, we utilized a personalized adenine base editor (ABE)-mediated approach to correct the pathogenic variant efficiently and precisely to restore neuronal dendritic morphology. ABE-treated MUT NS-GFP cells demonstrated reduced inclusion size and number. Using an inducible MUT NS-GFP neuron system, we identified early prevention of toxic protein expression allowed aggregate clearance, while late prevention halted further aggregation. To address several challenges for clinical applications of gene correction, we developed a neuron-specific engineered virus-like particle to optimize neuronal ABE delivery, resulting in higher correction efficiency. Our findings provide a targeted strategy which may treat FENIB and potentially other neurodegenerative diseases due to altered protein conformation such as Alzheimers and Huntingtons diseases.

molecular biology↗

bioRxiv2023

Speech-induced suppression and vocal feedback sensitivity in human cortex

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.

neuroscience↗

bioRxiv2023

A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis

Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring speech function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity, and high dimensionality, and the limited publicly available source code. Here, we present a novel deep learning-based neural speech decoding framework that includes an ECoG Decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable Speech Synthesizer that maps speech parameters to spectrograms. We develop a companion audio-to-audio auto-encoder consisting of a Speech Encoder and the same Speech Synthesizer to generate reference speech parameters to facilitate the ECoG Decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Among three neural network architectures for the ECoG Decoder, the 3D ResNet model has the best decoding performance (PCC=0.804) in predicting the original speech spectrogram, closely followed by the SWIN model (PCC=0.796). Our experimental results show that our models can decode speech with high correlation even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. We successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with speech deficits resulting from left hemisphere damage. Further, we use an occlusion analysis to identify cortical regions contributing to speech decoding across our models. Finally, we provide open-source code for our two-stage training pipeline along with associated preprocessing and visualization tools to enable reproducible research and drive research across the speech science and prostheses communities.

neuroscience↗

bioRxiv2023

Timing and location of speech errors induced by direct cortical stimulation

Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g., planning versus motor execution). In this retrospective observational study, we analyzed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 seconds. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 seconds), superior temporal gyrus (0.51 seconds), and middle temporal gyrus (0.54 seconds), followed by relatively long latencies in sensorimotor cortex (0.72 seconds) and especially long latencies in inferior frontal gyrus (0.95 seconds). Nonparametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.

neuroscience↗

bioRxiv2023

Temporal dynamics of 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. Ventraland 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. Author summaryNeural responses in visual cortex adapt over time, with reduced responses to prolonged and repeated stimuli. Here, we examine how adaptation patterns differ across the visual hierarchy in neural responses recorded from human visual cortex with high temporal and spatial precision. To identify possible neural computations underlying short-term adaptation, we fit the response time courses using a temporal divisive normalization model. The model accurately predicts prolonged and repeated responses in lower and higher visual areas, and reveals differences in temporal adaptation across the visual hierarchy and stimulus categories. Our model suggests that differences in adaptation patterns result from differences in divisive normalization dynamics. Our findings shed light on how information is integrated in the brain on a millisecond-time scale and offer an intuitive framework to study the emergence of neural dynamics across brain areas and stimuli.

neuroscience↗

bioRxiv2023

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 speakers brain before word articulation, and the same linguistic content rapidly reemerged in the listeners 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.

neuroscience↗

bioRxiv2023

Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations

Humans effortlessly use the continuous acoustics of speech to communicate rich linguistic meaning during everyday conversations. In this study, we leverage 100 hours (half a million words) of spontaneous open-ended conversations and concurrent high-quality neural activity recorded using electrocorticography (ECoG) to decipher the neural basis of real-world speech production and comprehension. Employing a deep multimodal speech-to-text model named Whisper, we develop encoding models capable of accurately predicting neural responses to both acoustic and semantic aspects of speech. Our encoding models achieved high accuracy in predicting neural responses in hundreds of thousands of words across many hours of left-out recordings. We uncover a distributed cortical hierarchy for speech and language processing, with sensory and motor regions encoding acoustic features of speech and higher-level language areas encoding syntactic and semantic information. Many electrodes--including those in both perceptual and motor areas--display mixed selectivity for both speech and linguistic features. Notably, our encoding model reveals a temporal progression from language-to-speech encoding before word onset during speech production and from speech-to-language encoding following word articulation during speech comprehension. This study offers a comprehensive account of the unfolding neural responses during fully natural, unbounded daily conversations. By leveraging a multimodal deep speech recognition model, we highlight the power of deep learning for unraveling the neural mechanisms of language processing in real-world contexts.

neuroscience↗

bioRxiv2023

Do cortical co-ripples bind lines, letters, words, meanings, strategy and action in reading?

Whether high-frequency phase-locked oscillations facilitate integration ( binding) of information across widespread cortical areas is controversial. Here we show with intracranial EEG that cortico-cortical co-ripples ([~]100ms long [~]90Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200-400ms post-word-onset. Semantically-specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400-800ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero-lag over long distances (>12cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition. One Sentence SummaryWidespread visual, wordform, semantic, executive and response areas phase-lock at 90Hz during integrative semantic processing.

neuroscience↗

bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal 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 StatementThe 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. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

neuroscience↗