Biology subjects

Khalilian-Gourtani, A.

Publications and source records attributed to Khalilian-Gourtani, A..

10 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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↗

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↗

bioRxiv2022

A Corollary Discharge Circuit in Human Speech

When we vocalize, our brain distinguishes self-generated sounds from external ones. A corollary discharge signal supports this function in animals, however, in humans its exact origin and temporal dynamics remain unknown. We report Electrocorticographic (ECoG) recordings in neurosurgical patients and a novel connectivity approach based on Granger-causality that reveals major neural communications. We find a reproducible source for corollary discharge across multiple speech production paradigms localized to ventral speech motor cortex before speech articulation. The uncovered discharge predicts the degree of auditory cortex suppression during speech, its well-documented consequence. These results reveal the human corollary discharge source and timing with far-reaching implication for speech motor-control as well as auditory hallucinations in human psychosis. Significance statementHow do organisms dissociate self-generated sounds from external ones? A fundamental brain circuit across animals addresses this question by transmitting a blueprint of the motor signal to sensory cortices, referred to as a corollary discharge. However, in humans and non-human primates auditory system, the evidence supporting this circuit has been limited to its direct consequence, auditory suppression. Furthermore, an impaired corollary discharge circuit in humans can lead to auditory hallucinations. While hypothesized to originate in the frontal cortex, direct evidence localizing the source and timing of an auditory corollary discharge is lacking in humans. Leveraging rare human neurosurgical recordings combined with connectivity techniques, we elucidate the exact source and dynamics of the corollary discharge signal in human speech. One-sentence summaryWe reveal the source and timing of a corollary discharge from speech motor cortex onto auditory cortex in human speech.

neuroscience↗

bioRxiv2021

Distributed Feedforward and Feedback Processing across Perisylvian Cortex Supports Human Speech

Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and posterior cortical networks, but the degree and timing of their recruitment and dynamics remain unknown. We present a novel deep learning architecture that translates neural signals recorded directly from cortex to an interpretable representational space that can reconstruct speech. We leverage state-of-the-art learnt decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.

neuroscience↗