Biology subjects

Tostaeva, G.

Publications and source records attributed to Tostaeva, G..

6 recordsLinked to original sources

bioRxiv2026

Toward A Whole-Brain Interface Enabled by High-Resolution Surface Electrophysiology

The goal of building a whole-brain interface - capable of generating a real-time digital representation of the state of the entire central nervous system - has long been framed as a hardware challenge of recording simultaneously from ever-larger numbers of microelectrodes. Advances in high-resolution neural interfaces now enable electrophysiologic interaction with the human nervous system at spatial and temporal resolutions previously unattainable without disrupting tissue architecture or physiology. These capabilities create new opportunities for mechanistic neuroscience, neuroprosthetics, and precision-guided neurosurgery. With the advent of modern microelectrode array technology, however, the whole-brain interface has also become a data challenge. Rather than pursuing large-scale human brain recording purely through a single device placed in a single patient, we demonstrate here that systematic multi-subject aggregation, across brain regions and behavioral paradigms, in a common anatomical framework, can achieve something functionally similar to a whole-brain interface. Here we present an approach to a functional atlas of the human central nervous system derived from high-resolution surface electrophysiology recordings obtained in the first 70 patients implanted with an anatomically conformal, thin-film neural interface positioned in more than 100 locations. The atlas comprises >100,000 electrode deployments and catalogs placement across a broad range of neuroanatomic targets spanning the motor, sensory, language, auditory, and associative domains, as well as brainstem and spinal cord structures that have historically been difficult to access at high electrophysiologic resolution. Across participants, aggregated recordings reveal somatotopic organization, including digit representations that follow a lateral-to-medial ordering consistent with classical homuncular maps, with transitions between adjacent representations occurring over spatial scales of approximately 0.8 - 2.0 mm; canonical frequency-band signatures, such as movement-related beta-band suppression and focal high-gamma (70 - 200 Hz) increases during motor and speech behaviors; and task-modulated population dynamics in both evoked and spontaneous activity across cortical, brainstem, and spinal cord regions. These findings yield insight into patterns of mesoscale electrophysiological dynamics conserved across the entire human central nervous system, suggesting that fine-scale functional organization is preserved across individuals when aligned to common anatomical landmarks. We further demonstrate several applications of these high-resolution recordings, including real-time neural decoding from upper extremity motor cortex to enable real-time neuroprosthetic control, and from ventrolateral cortex to decode cortical correlates of spoken language, facial movements, and complex facial expressions of emotion, supporting initial feasibility of combined verbal and nonverbal communication interfaces. Together, these results establish a paradigm for approaching large-scale interfaces with the human brain, as well as a starting point for a scalable, extensible atlas of human electrophysiologic function designed to accelerate discovery across basic neuroscience, translational research, and clinical neurotechnology.

neuroscience↗

bioRxiv2026

Classification of image category based on spatially distributed, transient high-frequency events

Transient high-frequency activity (HFA) in local field potentials exhibits substantial variability across trials and behavioral states, yet its functional role in sensory representation remains poorly understood. Here, we tested whether transient HFA events carry stimulus-related information through their temporal, spectral, and phase-dependent properties. Using intracranial recordings from 21 human participants performing a visual localizer task, we extracted transient HFA events and quantified their features using information-theoretic and decoding analyses. Individual event features carried only modest information about stimulus identity, and response magnitude and morphology-related properties contributed minimally to decoding performance. Instead, decoding was dominated by temporal alignment and low-frequency phase. Critically, representing transient events as distributed low-frequency phase configurations across electrodes substantially improved cross-trial decoding performance, whereas disrupting distributed phase structure eliminated this decoding advantage. These findings indicate that stimulus-related structure in transient HFA does not primarily arise from isolated local event properties, but instead emerges through distributed, phase-dependent network dynamics. More broadly, the results provide a framework for understanding how transient high-frequency neural activity contributes to sensory representations across distributed cortical networks.

neuroscience↗

bioRxiv2026

Coordinated human prefrontal dynamics sustain task-state representations during learning

Making decisions in complex, real-world environments is challenging. Biologically plausible strategies like reinforcement learning (RL) require attention toward reward-predictive stimuli to define task states, yet how attention and decision processes coordinate in the human brain remains unclear. We hypothesized this arises through interactions between orbitofrontal (OFC) value-based mechanisms and lateral prefrontal (LPFC) attention filtering. To test this, we combined behavioral modeling with local field potential (LFP) and single-unit recordings in 22 subjects performing a multidimensional RL task. Reward expectations were encoded in OFC and LPFC, as reflected in high-frequency LFP and OFC single-unit spiking, but modulated by attention only in LPFC. Theta LFPs encoded reward expectations and indexed attention-dependent LPFC-OFC coordination, with value-related coupling emerging pre-choice in high-attention subjects and post-choice in low-attention subjects. These findings show that prefrontal circuits dynamically coordinate to encode attention-weighted value signals, shaping state representations and providing a tractable solution to learning in complex environments.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Hippocampal sharp wave ripples and coincident cortical ripples orchestrate human semantic networks

Episodic memory function is predicated upon the precise coordination between the hippocampus and widespread cortical regions. However, our understanding of the neural mechanisms involved in this process is incomplete. In this study, human subjects undergoing intracranial electroencephalography (iEEG) monitoring performed a list learning task. We show sharp-wave ripple (SWR)-locked reactivation of specific semantic processing regions during free recall. This cortical activation consists of both broadband high frequency (non-oscillatory) and cortical ripple (oscillatory) activity. SWRs and cortical ripples in the anterior temporal lobe, a major semantic hub, co-occur and increase in rate prior to recall. Coincident hippocampal-ATL ripples are associated with a greater increase in cortical reactivation, show specificity in location based on recall content, and are preceded by cortical theta oscillations. These findings may represent a reactivation of hippocampus and cortical semantic regions orchestrated by an interplay between hippocampal SWRs, cortical ripples, and theta oscillations.

neuroscience↗

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.

neuroscience↗