Biology subjects

Flinker, A.

Publications and source records attributed to Flinker, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Normalization accounts for temporal dynamics in human somatosensory cortex

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statementHow the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

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bioRxiv2026

Prediction of Stimulation-Defined Eloquent Cortex Using Graph-Theoretical Connectivity from Electrocorticography During Presurgical Mapping

Background and ObjectivesElectrical stimulation mapping (ESM) is the clinical gold standard for identifying eloquent cortex during presurgical evaluation but is time-intensive, constrained by incomplete cortical sampling, and limited by patient tolerance. We investigated whether features derived from electrocorticography (ECoG), including functional connectivity measures, can provide complementary information for identifying functionally critical cortex and examined how predictive performance varies across functional domains, behavioral tasks, and data quantity. MethodsFourteen patients undergoing intracranial monitoring for epilepsy surgery performed speech production tasks while ECoG was recorded independently of stimulation mapping. Graph-theoretic functional connectivity features derived from high-gamma activity (70-150 Hz), combined with anatomical region encoding, were used to train machine learning classifiers, and predictive performance was evaluated using leave-one-subject-out validation with ESM-defined functional deficits used as ground-truth outcomes. ResultsFourteen patients were included. Performance differed across functional domains, with motor-critical electrodes identified with the highest accuracy (ROC-AUC 0.929 {+/-} 0.061; PR-AUC 0.755 {+/-} 0.191), followed by speech arrest (ROC-AUC 0.793 {+/-} 0.103; PR-AUC 0.550 {+/-} 0.196), whereas language-critical electrodes were more difficult to robustly predict (ROC-AUC 0.761 {+/-} 0.160; PR-AUC 0.385 {+/-} 0.167). Additional signal-derived features provided limited benefit beyond anatomical and connectivity features. Performance also varied across tasks and with the number of available trials, with motor prediction remaining stable across trial counts and speech arrest prediction improving with increasing trial counts up to approximately 10 trials before plateauing. In addition, evaluation at the level of stimulation pairs increased sensitivity, with only modest changes in overall performance. DiscussionGraph-theoretic connectivity analysis of ECoG provides complementary information for identifying stimulation-defined functional criticality and supports presurgical mapping. Differences in predictive performance across functional domains likely reflect underlying neuro-physiologic organization, with connectivity providing the clearest improvement for speech arrest prediction. Combined-label prediction strategies increased sensitivity at the expense of specificity, reflecting a clinically relevant tradeoff between broad detection of critical cortex and precise localization. These findings suggest that connectivity-informed approaches may help guide task selection and improve mapping efficiency while complementing electrical stimulation mapping.

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bioRxiv2026

Neural Dynamics of Automatic Speech Production

Speech is a defining human behavior, and this ability depends critically on speech motor cortex. While the ventral precentral and postcentral gyri are classically regarded as chiefly articulatory and somatosensory regions, a growing body of literature challenges this simplification. Most prior research, however, has examined cued or structured speech production tasks, neglecting the automatic, overlearned speech commonly utilized in clinical assessment. Consequently, the neural dynamics and precise timing of cortical recruitment during automatic speech remain poorly understood. Here, we present intracranial electrocorticography (ECoG) recordings from the left perisylvian cortex in participants performing automatic speech such as counting and recitation of overlearned sequences. We investigate neural dynamics using encoding (multivariate temporal response function) and decoding (deep neural network speech synthesis) models. We show that automatic speech engages a distributed network across superior temporal, precentral, and post-central cortices, characterized by attenuated pre-articulatory activity and weaker frontal encoding. Furthermore, two complementary decoding strategies reveal that speech motor cortex represents a mixture of feedforward and feedback signals, with a subset of sites exhibiting exclusively feed-forward dynamics. These results delineate the spatiotemporal cortical organization of automatic speech and establish that the speech motor cortex supports more complex dynamics than purely feedforward control.

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bioRxiv2025

Speaker Identity is Robustly Encoded in Spatial Patterns of Intracranial EEG for Attention Decoding

The human auditory cortex robustly tracks attended speech, yet it remains unclear if speaker identity is encoded in spatial patterns of neural activity independent of temporal dynamics. Here, we demonstrate that the identity of an attended speaker is reliably reflected in distinct, time-invariant spatial activation maps in human intracranial EEG (iEEG). Leveraging these "neural fingerprints", we developed a novel framework for Auditory Attention Decoding (AAD) that shifts from traditional temporal envelope tracking to spatial speaker identification. By decoupling the decoding of "who" is speaking from "when" they are speaking, our modular system achieves state-of-the-art speech extraction, particularly in short time windows (<2 seconds) where temporal models typically fail. Furthermore, we observed a reciprocal shift in neural activity during attentional switches, confirming that these spatial codes dynamically track listener intent. These findings establish that speaker identity is a robust, spatially distributed feature in the auditory cortex, offering a high-speed, complementary mechanism for neuro-steered hearing technologies.

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bioRxiv2025

Mithra: An Open-Source and Cross-Platform Visualization Toolbox for Human Intracranial Recordings

Intracranial electrophysiological recordings, including electrocorticography (ECoG) and stereo-EEG (sEEG), are increasingly used across research programs to study human brain function due to their high spatiotemporal resolution. Numerous tools exist for electrode localization and visualization; however, most focus on subject-level visualization and provide only partial solutions for systematic within- and across-subject analyses. It is especially important to preserve subject-specific anatomical locations while mapping electrodes to standardized spaces for analysis across multiple subjects. To address these needs, we present Mithra1: a toolkit for visualizing intracranial recordings, implemented in both Python and MATLAB. The toolbox enables visualization of electrodes alongside the brains pial surface and anatomical annotations, supports localization in common average spaces such as the MNI and FreeSurfer average brains, and maintains alignment with individual anatomy. It further allows electrode projection onto the cortical surface to generate Gaussian heatmaps representing the spatial distribution of neural activity. By integrating these capabilities into a unified framework, our toolbox provides a flexible, cross-platform solution for systematic within- and across-subject electrode visualization and analysis.

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bioRxiv2025

The Road Not Taken: Unsaid Word Alternatives are Represented in the Brain

Human language allows multiple ways to express the same thought, implying that several lexical alternatives may exist in parallel before a single word is spoken or heard. We test the multiple alternatives-co-activation hypothesis by combining high-density ECoG during spontaneous dialogue with behavioral paradigms and ranked next-word predictions from large language models (LLMs). Behaviorally, words that LLMs rank as more likely continuations are recognized faster in a preregistered lexical decision task and produced with shorter inter-word intervals in free speech, indicating graded anticipatory activation of alternatives. Neurally, encoding models reveal that activity in classical language regions (IFG, STG) prior to word onset is predicted by embeddings of multiple top-ranked alternatives, not only by the word ultimately used; critically, mean embeddings that pool the top candidates outperform single-candidate embeddings, and the effect persists with arbitrary (non-semantic) embeddings that control for distributional similarity. Extending beyond a handful of options, encoding strength increases as embeddings are averaged across larger top-k sets, implying that a broad cohort of lexical candidates is simultaneously represented. Finally, models trained in comprehension generalize to production (and vice versa), preserving rank order and suggesting a shared neural code for candidate sets across modalities. Together, these findings provide direct evidence that the brain co-activates unsaid alternatives during natural language use and identify parallel candidate activation as a computational principle common to human comprehension, human production, and artificial language modeling.

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bioRxiv2025

Decoding state specific connectivity during speech production and perception

Understanding how dynamic brain networks support language perception and production is central to cognitive neuroscience. A vast network based literature has employed functional connectivity (FC), primarily using resting-state and task-based fMRI. However, methodological limitations have hindered this approach in language processing, particularly during speech production. Here, we address this gap by employing a large cohort of electrocorticographic (ECoG) patients (N=42) to investigate the networks driving speech perception and production. We acquired data while patients were engaged in a controlled battery of speech production tasks focusing on five cognitive states (auditory perception, picture perception, reading perception, speech production, and baseline). Using linear classifiers we were able to robustly decode cognitive states from single-trial FC (i.e. Pearson correlations) of the neural activity patterns, achieving a mean accuracy of 64.4%. These classifiers revealed distinct network signatures underlying auditory and visual perception as well as speech production via stable network connectivity. Importantly, the network signatures included both regions with robust local neural activity and those with minimal or no detectable activation. Such signatures indicate that even low-activity regions contribute critically to differentiating cognitive states. Our findings underscore the significance of functional connectivity analysis as a complementary dimension to investigating local neural activity, and suggest that the functional networks supporting speech extend beyond the most metabolically active regions.

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bioRxiv2025

A probabilistic functional atlas based on extraoperative electrocortical stimulation mapping

BackgroundDirect electrocortical stimulation (DES) is the clinical gold standard for identifying eloquent cortex and guiding neurosurgical intervention, yet prior intraoperative DES during awake craniotomies have been limited by intraoperative sampling constraints and density-based rather than probabilistic analyses. The probability of typical and atypical cortical organization has not been fully explored, especially in epilepsy populations. We sought to generate a probabilistic atlas of motor, sensory, and language functions using extraoperative DES in a large cohort of patients with epilepsy. MethodsWe retrospectively analyzed 2,124 extraoperative DES trials from 125 patients undergoing intracranial monitoring (2008-2023). Positive and negative trials were mapped to Montreal Neurological Institute space, parcellated with the Human Connectome Project atlas, and analyzed using probability mapping, bootstrapped region-of-interest hit probabilities, hierarchical clustering, and kernel density estimation. Mixed-effects models assessed clinical predictors of language disruption. ResultsProbabilistic maps revealed regions of increased likelihood for eliciting functional responses in expected sensorimotor and language territories, but also demonstrated marked variability and deviations from expected cortical locations. Language disruption occurred in 338 trials, motor in 520, and sensory in 370. Instead of observing high probabilities and low inter-patient variability isolated to classic perisylvian locations (e.g., Brocas and Wernickes areas), the likelihood of language disruption followed graded probabilistic gradients with high inter-patient variability. The middle frontal gyrus emerged as a consistent locus of naming and speech arrest. Motor phenomena extended into parietal association cortex. Higher-order experiences, including forced thoughts and feelings of presence, were reproducibly evoked from frontal and temporoparietal sites. Early seizure onset and temporal lobe lesions predicted lower naming disruption probabilities. ConclusionsThis extraoperative DES atlas, the largest to date, demonstrates that eloquent cortical functions are organized along probabilistic continua rather than fixed regions. Findings highlight the middle frontal gyrus as a critical language node, extend motor mapping into parietal cortex, and delineate reproducible experiential phenomena. Substantial inter-patient variability underscores the necessity of individualized mapping in surgical planning.

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bioRxiv2025

Frontal cortex organization supporting audiovisual processing during naturalistic viewing

AbstractOur brains dynamically adapt to a multisensory world by orchestrating diverse inputs across sensory streams. This process engages multiple brain regions, but it remains unclear how audiovisual stimuli are represented and evolve over time, especially in naturalistic scenarios. Here, we employed a movie-viewing paradigm to explore this question. We recorded intracranial electrocorticography (iEEG) to measure brain activity in 19 participants watching a short multilingual movie. Using unsupervised clustering and supervised encoding models, we identified a robust modality-specific gradient in the frontal cortex, wherein the ventral division primarily processes auditory information and the dorsal division processes visual inputs. Further, we found that this cortical organization dynamically changed, adapting to different movie contexts. This result potentially reflects flexible audiovisual-resource assignment to construct a coherent percept of the movie. Leveraging behavioral ratings, we found that the frontal cortex is the primary site in this modality assignment process. Together, our findings shed new light on the functional architecture of the frontal cortex underlying flexible multisensory representation and integration in natural contexts.

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bioRxiv2025

The neurocomputational mechanisms of hierarchical linguistic predictions during narrative comprehension

Language comprehension requires a listener to predict the upcoming inputs of linguistic units with multiple timescales based on previous contexts, but how the prediction process is hierarchically represented and implemented in the human brain remains unclear. Combining the natural language processing (NLP) approach and functional Magnetic Resonance Imaging (fMRI) in a narrative comprehension task, we first applied the group-based general linear model (gGLM) to identify the neural underpinnings associated with the language prediction on word and sentence. Our results revealed a cortical architecture supporting the prediction, extending from the superior temporal cortices to the regions in the default mode network. Then, we investigated how these adjacent levels interact with each other by testing two rival hypotheses: the continuous updating hypothesis posits that the higher level of the representational hierarchy is continuously updated as inputs unfold over time, while the sparse updating hypothesis states that the higher level is only updated at the end of their preferred timescales of linguistic units. By conducting computational modeling and autocorrelation analysis, we found the sparse model outperformed the continuous model and the updating might occur at the sentence boundaries. Together, our results extend the linguistic prediction from the small timescales such as words to large timescales such as sentences, providing novel insights into the neurocomputational mechanisms of information updating within the linguistic prediction hierarchy.

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bioRxiv2025

The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brains linguistic capabilities. ECoG offers a high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research endeavors, from auditory perception to semantic integration. In addition to the neural data, we extract linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.

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bioRxiv2025

Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search

Prior behavioral work showed that event structure supports our ability to search through continuous naturalistic memories. We hypothesized that, neurally, this memory search process involves a division of labor between slowly unfurling neocortical states representing event knowledge and fast hippocampal-neocortical communication supporting retrieval of new information at transitions between events. In a sample of ten intracranial EEG patients we tracked slow neural state-patterns as patients viewed a movie and then searched their memories in a structured naturalistic interview. As patients answered questions ("after A, when does B happen next?"), state-patterns from movie-viewing were reinstated in neocortex. During the critical memory-search period, states unfurled in a forward direction, realizing a trajectory from A to B in memory. Punctate moments of state-transition during memory-search were marked by low-frequency power decreases in cortex and preceded by power decreases in hippocampus that correlated with reinstatement. Connectivity-analysis revealed information-flow from hippocampus to cortex underpinning state-transitions.

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bioRxiv2024

From single words to sentence production: Shared cortical representations but distinct temporal dynamics

Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. However, it remains a largely untested assumption that the principles of word production generalize to more naturalistic utterances like sentences. Here, we investigate this using high-resolution neurosurgical recordings (ECoG) and an overt production experiment where patients produced six words in isolation (picture naming) and in sentences (scene description). We trained machine learning classifiers to identify the unique brain activity patterns for each word during picture naming, and used these patterns to decode which words patients were processing while they produced sentences. Our findings confirm that words share cortical representations across tasks, but reveal a division of labor within the language network. In sensorimotor cortex, words were consistently activated in the order in which they were said in the sentence. However, in inferior and middle frontal gyri (IFG and MFG), the order in which words were processed depended on the syntactic structure of the sentence. Deeper analysis of this pattern revealed a spatial code for representing a words position in the sentence, with subjects selectively encoded in IFG and objects in MFG. Finally, we argue that the processes we observe in prefrontal cortex may impose a subtle pressure on language evolution, explaining why nearly all the worlds languages position subjects before objects.

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bioRxiv2024

Enhanced Structural Brain Connectivity Analyses Using High Diffusion-weighting Strengths

Tractography is a unique modality for the in vivo measurement of structural connectivity, crucial for understanding brain networks and neurological conditions. With increasing b-value, the diffusion-weighting signal becomes primarily sensitive to the intra-axonal signal. However, it remains unclear how tractography is affected by this observation. Here, using open-source datasets, we showed that at high b-values, DWI reduces the uncertainty in estimating fiber orientations. Specifically, we found the ratio of biologically-meaningful longer-range connections increases, accompanied with downstream impact of redistribution of connectome and network metrics. However, when going beyond b=6000 s/mm2, the loss of SNR imposed a penalty. Lastly, we showed that the data reaches satisfactory reproducibility with b-value above 1200 s/mm2. Overall, the results suggest that using b-values above 2500 s/mm2 is essential for more accurate connectome reconstruction by reducing uncertainty in fiber orientation estimation, supporting the use of higher b-value protocols in standard diffusion MRI scans and pipelines.

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bioRxiv2024

Temporal integration in human auditory cortex is predominantly yoked to absolute time, not structure duration

Sound structures such as phonemes and words have highly variable durations. Thus, there is a fundamental difference between integrating across absolute time (e.g., 100 ms) vs. sound structure (e.g., phonemes). Auditory and cognitive models have traditionally cast neural integration in terms of time and structure, respectively, but the extent to which cortical computations reflect time or structure remains unknown. To answer this question, we rescaled the duration of all speech structures using time stretching/compression and measured integration windows in the human auditory cortex using a new experimental/computational method applied to spatiotemporally precise intracranial recordings. We observed significantly longer integration windows for stretched speech, but this lengthening was very small ([~]5%) relative to the change in structure durations, even in non-primary regions strongly implicated in speech-specific processing. These findings demonstrate that time-yoked computations dominate throughout the human auditory cortex, placing important constraints on neurocomputational models of structure processing.

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bioRxiv2024

Information-making processes in the speaker's brain drive human conversations forward

Natural human conversation is driven by the exchange of information-rich messages that surprise the listener and deviate from predictable context. While extensive research has characterized how the brain processes unexpected linguistic input during comprehension, the neural mechanisms underlying the generation of such information by the speaker remain poorly understood. In this study, we hypothesize that the speakers brain actively creates information through extra neural computation, transforming internal thoughts into novel, meaningful linguistic outputs. Utilizing a unique 24/7 dataset of continuous electrocorticography (ECoG) comprising approximately 100 hours of spontaneous natural conversations, we contrast the neural basis of speech production and comprehension within the same participants. Behaviorally, we find that speakers take longer pausing for an additional 100-150 milliseconds before producing information-rich, improbable words, even when controlling for word identity and frequency. Neurally, we provide converging evidence for a previously unreported process: generating information-rich words elicits significantly stronger neural activity (ERP) and enhanced neural encoding and decoding in language-related areas starting 100-500 ms before articulation. This pattern contrasts sharply with speech comprehension, where enhanced neural activity for predictable words occurs before onset, while responses to improbable words emerge only after onset as prediction errors. Furthermore, we demonstrate that large language models (LLMs) mirror this biological process, requiring deeper internal computation across layers to generate improbable versus probable words. Together, these results reveal that the speakers brain is not merely a transmission channel but an active generator of information, recruiting extra neural resources to create novel content that diverges from listener expectations.

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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."

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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.

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