Biology subjects

Mehta, A. D.

Publications and source records attributed to Mehta, A. D..

14 recordsLinked to original sources

bioRxiv2026

Temporal Dynamics of BOLD fMRI Predict Intracranially-Confirmed Seizure Onset Zones in Drug-Resistant Epilepsy

ObjectiveIn individuals with drug-resistant epilepsy, accurately identifying the brain regions where seizures originate is a critical prerequisite to guide surgical treatment and achieve seizure freedom. To accomplish this, intracranial EEG is considered the gold standard, providing the spatiotemporal high-resolution data necessary to pinpoint epileptogenic activity. However, this precision is achieved through an invasive procedure with significant patient burden, which is fundamentally limited by the electrode placement and spatial coverage. MethodsIn this study, we investigated the potential utility of preoperative resting-state fMRI to non-invasively map alterations in brain dynamics at the whole brain level. Region-wise brain dynamics were quantified with complementary measures of local autocorrelation decay rates. We assessed the capacity of these derived features to effectively identify intracranial EEG confirmed seizure onset zones in 18 individuals with drug-resistant medial temporal lobe epilepsy. Overall, the study cohort contained 3867 implanted electrodes of which 159 classified as seizure onset zones by two independent board-certified epileptologists. ResultsOverall, our findings reveal more constrained temporal dynamics for brain regions associated with seizure onsets compared to non-seizure onset zones. Individual-level prediction showed a performance better than chance in 15 of the 18 patients. The overall predictive performance across all patients yielded a median AUC of 0.81, a median true positive rate of 0.75, and a median true negative rate of 0.83. Furthermore, in a subset of 13 patients, those with negative seizure outcomes showed higher probabilities of seizure onset zone predictions outside the resection area compared to those with good outcomes. SignificanceOverall, our findings suggest that altered temporal dynamics derived from preoperative resting-state fMRI represent a promising non-invasive approach for delineating epileptogenic tissue, potentially informing intervention strategies and guiding electrode placement.

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bioRxiv2026

Deciphering epileptogenic and activity-dependent gene programs in the human brain

Neuronal activity is fundamental to brain function, yet chronically elevated activity underlies neurological disorders such as drug-resistant epilepsy (DRE). In animal models, activity induces defined transcriptional programs within activated neurons; however, the nature, cellular specificity, and pathological relevance of such programs in the human brain remain poorly understood. Here, we apply single-nucleus and spatial transcriptomics to epileptogenic, non-epileptogenic, and intraoperatively stimulated non-epileptogenic cortical tissue obtained from individuals with DRE. Across 26 cell types profiled, glutamatergic neurons projecting from cortical layers 2/3, 5, and 6 to intratelencephalic targets exhibit pronounced sensitivity to the epileptogenic microenvironment, inducing shared immediate-early genes alongside cell-type-specific programs linked to synaptic remodeling and cellular stress. Approximately one-third of transcripts enriched in the epileptogenic microenvironment were also induced by acute stimulation, suggesting that a fraction of epilepsy-associated gene expression reflects conserved responses to heightened activity rather than disease-specific programs. While transcripts induced by both epileptogenic and acute activity converged upon immediate-early genes and heat-shock proteins, only acute stimulation triggered a rapid, multicellular induction of transcripts involved in mitochondrial ATP synthesis. This divergence suggests that neurons within epileptogenic cortex may be unable to mount appropriate metabolic adaptations to sustained energetic demands. In parallel, both microglia and circulating CD14+ monocytes exhibit signs of immune activation in epilepsy, suggesting myeloid-driven inflammatory rewiring that extends beyond the brain. Together, these findings illuminate human activity-dependent gene programs and reveal signatures of neuronal vulnerability and inflammation in DRE.

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bioRxiv2025

Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech

Large language models (LLMs) capture long-range contextual structure in natural language and have recently been shown to align with the human brains contextualized linguistic encoding. This makes them a promising computational probe for studying how context-dependent linguistic information is represented during natural speech perception. Speech perception often occurs in multi-talker environments, where attention must dynamically select among competing streams, yet how contextual information from attended and unattended speech is neurally encoded remains underexplored. Here, we investigate how auditory attention modulates neural tracking of context-dependent linguistic representations using electrocorticography (ECoG) and stereoelectroencephalography (sEEG) recordings from three epilepsy patients engaged in a two-conversation "cocktail party" paradigm. To model neural responses to attended and unattended speech streams, we used contextual word embeddings generated by large language models. We find that LLM-derived features reliably predict brain activity for the attended stream and that contextual information from the unattended stream also contributes to neural prediction. Importantly, these contributions extend beyond low-level acoustic features and shallow syntactic information, and depend on the surrounding linguistic context. Moreover, neural tracking of the unattended stream reflects shorter-range contextual integration than that of the attended stream. Together, these findings indicate that neural responses to speech reflect context-dependent linguistic representations from multiple concurrent speech streams, with attention modulating the depth and timescale of contextual integration. Our results highlight the utility of LLMs for probing higher-level linguistic representations in complex, naturalistic listening environments.

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bioRxiv2025

Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems

Transforming continuous acoustic speech signals into discrete linguistic meaning is a remarkable computational feat accomplished by both the human brain and modern artificial intelligence. A key scientific question is whether these biological and artificial systems, despite their different architectures, converge on similar strategies to solve this challenge. While ASR systems now achieve human-level performance, research on their parallels with the brain has been limited by biologically implausible, non-causal models and comparisons that stop at predicting brain activity without detailing the alignment of the underlying representations. Furthermore, studies using text-based models overlook the crucial acoustic stages of speech processing. Here, using high-resolution intracranial recordings and a causal, recurrent ASR model, we bridge these gaps by uncovering a striking correspondence between the brains processing hierarchy and the models internal representations. Specifically, we demonstrate a deep alignment in their algorithmic approach: neural activity in distinct cortical regions maps topographically to corresponding model layers, and critically, the representational content at each stage follows a parallel progression from acoustic to phonetic, lexical, and semantic information. This work thus moves beyond demonstrating simple model-brain alignment to specifying the shared underlying representations at each stage of processing, providing direct evidence that both systems converge on a similar computational strategy for transforming sound into meaning.

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bioRxiv2024

The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models

Music perception involves the intricate processing of individual notes and their contextual relationships within a piece. However, how the brain encodes and organizes these features, particularly in relation to musical expertise, remains unclear. Using noninvasive and invasive electrophysiological recordings alongside generative music models, we reveal that musicians exhibit neural encoding that is more attuned to the separation and integration of complex musical structures, with a pronounced left-hemispheric bias compared to non-musicians. Invasive recordings further highlight a hierarchical and spatially organized representation of musical features and context across brain regions. These findings advance our understanding of how the brain processes music and the role of musical training in shaping auditory cognition.

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bioRxiv2024

Motifs of human hippocampal and cortical high frequency oscillations structure processing and memory of naturalistic stimuli

The discrete events of our narrative experience are organized by the neural substrate that underlies episodic memory. This narrative process is segmented into discrete units by event boundaries. This permits a replay process that acts to consolidate each event into a narrative memory. High frequency oscillations (HFOs) are a potential mechanism for synchronizing neural activity during these processes. Here, we use intracranial recordings from participants viewing and freely recalling a naturalistic stimulus. We show that hippocampal HFOs increase following event boundaries and that coincident hippocampal-cortical HFOs (co-HFOs) occur in cortical regions previously shown to underlie event segmentation (inferior parietal, precuneus, lateral occipital, inferior frontal cortices). We also show that event-specific patterns of co-HFOs that occur during event viewing re-occur following the subsequent three event boundaries (in decaying fashion) and also during recall. This is consistent with models that support replay as a mechanism for memory consolidation. Hence, HFOs may coordinate activity across brain regions serving widespread event segmentation, encode naturalistic memory, and bind representations to assemble memory of a coherent, continuous experience.

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bioRxiv2024

Brain-controlled augmented hearing for spatially moving conversations in multi-talker environments

Focusing on a specific conversation amidst multiple interfering talkers presents a significant challenge, especially for the hearing-impaired. Brain-controlled assistive hearing devices aim to alleviate this problem by separating complex auditory scenes into distinct speech streams and enhancing the attended speech based on the listeners neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, we present a realistic AAD task that mirrors the dynamic nature of acoustic settings. This task involves focusing on one of two concurrent conversations, with multiple talkers taking turns and moving continuously in space with background noise. Invasive electroencephalography (iEEG) data were collected from three neurosurgical patients as they focused on one of the two moving conversations. We propose an enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve auditory attention decoding accuracy. Our subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of our approach in real-world scenarios and marks a significant step towards developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences. TAKEAWAYS- Brain-controlled hearing device for scenarios with moving conversations in multi-talker settings, closely mimicking real-world listening environments - Developed a binaural speech separation model that separates speech of moving talkers while retaining their spatial locations, enhancing auditory perception and auditory attention decoding - Proposed system enhances speech intelligibility and reduces listening effort in realistic acoustic scenes

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bioRxiv2023

Intracranial electroencephalography reveals effector-independent evidence accumulation dynamics in multiple human brain regions

Neural representations of perceptual decision formation that are abstracted from specific motor requirements have previously been identified in humans using non-invasive electrophysiology, however, it is currently unclear where these originate in the brain. Here, we capitalized on the high spatiotemporal precision of intracranial EEG to localize such abstract decision signals. Presurgical epilepsy patients judged the direction of random-dot stimuli and responded either with a speeded button press (N=23), or vocally, after a randomized delay (N=11). We found a widely distributed motor-independent network of regions where high-frequency activity exhibited key characteristics consistent with evidence accumulation, including a gradual build-up that was modulated by the strength of the sensory evidence, and an amplitude that predicted subjects choice accuracy and response time. Our findings offer a new view on the brain networks governing human decision making.

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bioRxiv2021

Enhancing neuronal plasticity through intracranial theta burst stimulation in the human sensorimotor cortex.

The progress of therapeutic neuromodulation greatly depends on improving stimulation parameters to most efficiently induce neuroplasticity effects. Intermittent Theta Burst stimulation (iTBS), a form of electrical stimulation that mimics the natural brain activity patterns, has shown efficacy in inducing such effects in animal studies and rhythmic Transcranial Magnetic Stimulation (rTMS) studies in humans. However, little is known about the potential neuroplasticity effects of iTBS applied through intracranial electrodes in humans which could have implications for deep brain stimulation therapies. This study characterizes the physiological effects of cortical iTBS in the human cortex and compare them with single pulse alpha stimulation, another frequently used paradigm in rTMS research. We applied these stimulation paradigms to well-defined regions in the sensorimotor cortex which elicited contralateral hand or arm muscle contractions during electrical stimulation mapping in epilepsy patients implanted with intracranial electrodes. Treatment effects were evaluated using effective connectivity and beta oscillations coherence measures in areas connected to the treatment site as defined with cortico-cortical evoked potentials. Our results show that iTBS increases beta band synchronization within the sensorimotor network indicating a potential neuroplasticity effect. The effect is specific to the sensorimotor system, the beta frequency band and the stimulation pattern (no effect was found with single-pulse alpha stimulation). The effects outlasted the stimulation by three minutes. By characterizing the neurophysiological effects of iTBS within well-defined cortical networks, we hope to provide an electrophysiological framework that allows clinicians and researchers to optimize brain stimulation protocols which may have translational value.

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bioRxiv2019

Intracortical dynamics underlying repetitive stimulation predicts changes in network connectivity

Targeted stimulation can be used to modulate the activity of brain networks. Previously we demonstrated that direct electrical stimulation produces predictable post-stimulation changes in brain excitability. However, understanding the neural dynamics during stimulation and its relationship to post-stimulation effects is limited but critical for treatment optimization. Here, we applied 10Hz direct electrical stimulation across several cortical regions in 14 patients implanted with intracranial electrodes for seizure monitoring. The stimulation train was characterized by a consistent increase in high gamma (70-170Hz) power. Immediately post-train, low-frequency (1-8Hz) power increased, resulting in an evoked response that was highly correlated with the neural response during stimulation. Using two measures of network connectivity, cortico-cortical evoked potentials (indexing effective connectivity) and theta coherence (indexing functional connectivity), we found a stronger response to stimulation in regions that were highly connected to the stimulation site. In these regions, repeated cycles of stimulation trains and rest progressively altered the stimulation response. Finally, after just 2 minutes (10%) of repetitive stimulation, we were able to predict post-stimulation connectivity changes with high discriminability. Taken together, this work reveals a relationship between stimulation dynamics and post-stimulation connectivity changes in humans. Thus, measuring neural activity during stimulation can inform future plasticity-inducing protocols.

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bioRxiv2018

Face-selective units in human ventral temporal cortex reactivate during free recall

Properties of face-responsive individual neurons in the human ventral temporal cortex (VTC) have yet to be studied, and their role in conscious perception remains unknown. To explore this, we implanted microelectrodes into the VTCs of eight human subjects undergoing invasive epilepsy monitoring. Most (26 of 33) category-selective units showed specificity for face stimuli, with a range of response profiles. Different face exemplars evoked consistent and discriminable responses in the population of units sampled. During a free recall task, face-selective units selectively reactivated in the absence of visual stimulation during the 2-second window prior to face recall events. Furthermore, the identity of the recalled face could be predicted by comparing activity preceding recall events to activity evoked by visual stimulation.

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bioRxiv2018

Deep Convolutional modeling of human face selective columns reveals their role in pictorial face representation

Despite the massive accumulation of systems neuroscience findings, their functional meaning remains tentative, largely due to the absence of realistically performing models. The discovery that deep convolutional networks achieve human performance in realistic tasks offers fresh opportunities for such modeling. Here we show that the face-space topography of face-selective columns recorded intra-cranially in 32 patients significantly matches that of a DCNN having human-level face recognition capabilities. Three modeling aspects converge in pointing to a role of human face areas in pictorial rather than person identification: First, the match was confined to intermediate layers of the DCNN. Second, identity preserving image manipulations abolished the brain to DCNN correlation. Third, DCNN neurons matching face-column tuning displayed view-point selective receptive fields. Our results point to a \"convergent evolution\" of pattern similarities in biological and artificial face perception. They demonstrate DCNNs as a powerful modeling approach for deciphering the function of human cortical networks.

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bioRxiv2018

Phase resetting in human auditory cortex to visual speech

Natural conversation is multisensory: when we can see the speakers face, visual speech cues influence our perception of what is being said. The neuronal basis of this phenomenon remains unclear, though there is indication that phase modulation of neuronal oscillations--ongoing excitability fluctuations of neuronal populations in the brain--provides a mechanistic contribution. Investigating this question using naturalistic audiovisual speech with intracranial recordings in humans, we show that neuronal populations in auditory cortex track the temporal dynamics of unisensory visual speech using the phase of their slow oscillations and phase-related modulations in high-frequency activity. Auditory cortex thus builds a representation of the speech streams envelope based on visual speech alone, at least in part by resetting the phase of its ongoing oscillations. Phase reset could amplify the representation of the speech stream and organize the information contained in neuronal activity patterns. SIGNIFICANCE STATEMENTWatching the speaker can facilitate our understanding of what is being said. The mechanisms responsible for this influence of visual cues on the processing of speech remain incompletely understood. We studied those mechanisms by recording the human brains electrical activity through electrodes implanted surgically inside the skull. We found that some regions of cerebral cortex that process auditory speech also respond to visual speech even when it is shown as a silent movie without a soundtrack. This response can occur through a reset of the phase of ongoing oscillations, which helps augment the response of auditory cortex to audiovisual speech. Our results contribute to discover the mechanisms by which the brain merges auditory and visual speech into a unitary perception.

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bioRxiv2017

Increasing Suppression Of Saccade-Related Transients Along The Human Visual Hierarchy

A key hallmark of visual perceptual awareness is robustness to instabilities arising from unnoticeable eye and eyelid movements. In previous human intracranial (iEEG) work (Golan et al., 2016) we found that excitatory broadband high-frequency activity transients, driven by eye blinks, are suppressed in higher-level but not early visual cortex. Here, we utilized the broad anatomical coverage of iEEG recordings in 12 eye-tracked neurosurgical patients to test whether a similar stabilizing mechanism operates following small saccades. We compared saccades (1.3{degrees}-3.7{degrees}) initiated during inspection of large individual visual objects with similarly-sized external stimulus displacements. Early visual cortex sites responded with positive transients to both conditions. In contrast, in both dorsal and ventral higher-level sites the response to saccades (but not to external displacements) was suppressed. These findings indicate that early visual cortex is highly unstable compared to higher-level visual regions which apparently constitute the main target of stabilizing extra-retinal oculomotor influences.

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