Biology subjects

Pesaran, B.

Publications and source records attributed to Pesaran, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Scalable hardware and software for high-density, multi-regional electrophysiology in non-human primates

Understanding how distributed neural circuits generate complex behaviors requires long-duration recordings from populations of individual neurons across multiple brain regions. High-density Neuropixels probes have revolutionized rodent systems neuroscience. Extending these capabilities to non-human primates (NHPs) for chronic, multi-regional recordings requires compact, implantable microdrive hardware and reliable automated spike-sorting software. NHPs provide access to complex and flexible behaviors that are difficult to study in other model systems. Extending the Neuropixels revolution to NHPs therefore depends on integrating hardware and software for long-duration, multi-regional recordings. Here, we present a scalable hardware-software platform co-designed for high-density neural data acquisition and reliable single-unit identification in NHPs. The Multi-regional Implantable NeuroDrive (MIND) is a lightweight, implantable microdrive that provides stable recordings from multiple independently positioned Neuropixels probes and is designed to support chronic, ambulatory, head-free recordings. We paired MIND with UnitAssay, an automated post-sorting curation framework that identifies and curates characteristic spike-sorting errors with minimal computational cost. Together, MIND and UnitAssay provide an integrated platform for implanted, multi-regional recordings in NHPs performing complex and naturalistic behaviors. We tested MIND and UnitAssay across parietal, prefrontal, and motor cortices in rhesus macaques performing reaching and eye-movement tasks. MIND enabled stable multi-probe recordings across cortical regions and durations. UnitAssay enabled automated curation of large-scale spike-sorting output and improved reliable single-unit identification relative to Kilosort 4 labels and standard SpikeInterface quality control. Together, MIND and UnitAssay provide a scalable solution for high-density, multi-regional, long-duration electrophysiology in NHPs.

neuroscience↗

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↗

bioRxiv2025

Functional populations in prefrontal cortex related to working memory encoding and maintenance

Nonlinear mixed selectivity, with neurons responding to diverse combinations of task-relevant variables, has been proposed as a key mechanism to enable flexible behavior and cognition. However, it is debated whether the structure of neural population responses in fronto-parietal cortices is better described as random mixed-selective or as non-random, that is, in terms of multiple subpopulations with stereotypical response profiles. Here, we show that neural activity in the macaque prefrontal cortex during a working memory and a visual response task is organized into subpopulations that provide a comprehensive description of the low-dimensional population dynamics. First, analysis of the demixed Principal Components shows that the neural code faithfully represents stimulus identity, task condition, and elapsed time during the trial. Second, a model-free analysis of the population structure reveals a significant degree of clustering, implying a non-random distribution of feature selectivity that is incompatible with random mixed selectivity. Closer inspection shows that stimulus-selective neurons also tend to be task-selective. Third, examining the contribution of stimulus-selective neurons to task-condition-related variance reveals two contrasting activity profiles that correspond to functionally different populations. One population responds during visual stimulation while the other activates during memory maintenance. Finally, the observed neural geometry explains how stable task and stimulus information can be read out from the population response using a linear decoder. Our results highlight that despite the heterogeneity of prefrontal responses during working memory, neurons do not represent random mixtures of task features but are structured according to neural subpopulations.

neuroscience↗

bioRxiv2024

Personalized cue-reactive delta-theta oscillations guide deep brain stimulation for opioid use disorder

Abstract / SummarySubstance use disorder (SUD) is a significant public health concern, with over 30% of the affected population not responding to available treatments. Severe SUD is characterized by drug-cue reactivity that has been reported to predict treatment-failure. We leveraged this pathophysiological feature to optimize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in an adult with SUD. A personalized drug cue-reactivity task was administered while recording NAc region electrophysiology from a lead externalized for clinical purposes. We identified a drug cue-evoked signal in the ventral NAc associated with intensification of opioid-related cravings, which attenuated subsequent to stimulation delivered to the same area. DBS was then programmed to engage this focal region, which resulted in sustained suppression of drug-related cravings. This finding heralds the potential for personalized strategies to optimize DBS for SUD.

neuroscience↗

bioRxiv2024

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

neuroscience↗

bioRxiv2024

Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device

Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-m-thick, mechanically flexible micro-electrocorticography (ECoG) BCI, integrating a 256x256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

neuroscience↗

bioRxiv2023

Event Detection and Classification from Multimodal Time Series with Application to Neural Data

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.

neuroscience↗

bioRxiv2023

Causal evidence of coherent theta rhythms in the modulation of multiregional communication

Neuromodulatory interventions seek to treat neuropsychiatric disorders by manipulating multiregional communication across the mesolimbic mood network. Modulations of multiregional communication are rarely measured directly and are often inferred from correlated neural activity such as neural coherence. Whether and how neural coherence reflects dynamic multiregional communication remains unclear. To address this limitation, we performed a causal-correlation analysis of theta-frequency (4-10 Hz) rhythms and mesolimbic multiregional communication. Selectively stimulating sender sites while recording from receiver sites revealed a mechanism of dynamic multiregional communication involving theta-coherent neural dynamics across a network of sender-receiver-modulator sites. Modulator site activity was highly theta-coherent with the receiver site activity, less theta-coherent with sender site activity, site specific and not shared by neighboring sites in the same region. These results reveal fundamental mechanisms of dynamic multiregional communication and support the use of theta-coherence as a target for neuromodulatory interventions in the mesolimbic mood network.

neuroscience↗

bioRxiv2023

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.

neuroscience↗

bioRxiv2023

Dynamical flexible inference of nonlinear latent structures in neural population activity

Inferring complex spatiotemporal dynamics in neural population activity is critical for investigating neural mechanisms and developing neurotechnology. These activity patterns are noisy observations of lower-dimensional latent factors and their nonlinear dynamical structure. A major unaddressed challenge is to model this nonlinear structure, but in a manner that allows for flexible inference, whether causally, non-causally, or in the presence of missing neural observations. We address this challenge by developing DFINE, a new neural network that separates the model into dynamic and manifold latent factors, such that the dynamics can be modeled in tractable form. We show that DFINE achieves flexible nonlinear inference across diverse behaviors and brain regions. Further, despite enabling flexible inference unlike prior neural network models of population activity, DFINE also better predicts the behavior and neural activity, and better captures the latent neural manifold structure. DFINE can both enhance future neurotechnology and facilitate investigations across diverse domains of neuroscience.

neuroscience↗

bioRxiv2023

Subdural CMOS optical probe (SCOPe) for bidirectional neural interfacing

Optical neurotechnologies use light to interface with neurons and can monitor and manipulate neural activity with high spatial-temporal precision over large cortical extents. While there has been significant progress in miniaturizing microscope for head-mounted configurations, these existing devices are still very bulky and could never be fully implanted. Any viable translation of these technologies to human use will require a much more noninvasive, fully implantable form factor. Here, we leverage advances in microelectronics and heterogeneous optoelectronic packaging to develop a transformative, ultrathin, miniaturized device for bidirectional optical stimulation and recording: the subdural CMOS Optical Probe (SCOPe). By being thin enough to lie entirely within the subdural space of the primate brain, SCOPe defines a path for the eventual human translation of a new generation of brain-machine interfaces based on light.

neuroscience↗

bioRxiv2023

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. During conflict i.e, when salience and goal each favor the selection of different targets, endogenous selection of the task-relevant target relies on top-down control. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, using a two-target free-choice luminance-reward selection task, we demonstrate that visual-movement neurons and not visual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent-beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

neuroscience↗

bioRxiv2022

Multi-block RNN Autoencoders Enable Broadband ECoG Signal Reconstruction

ObjectiveNeural dynamical models reconstruct neural data using dynamical systems. These models enable direct reconstruction and estimation of neural time-series data as well as estimation of neural latent states. Nonlinear neural dynamical models using recurrent neural networks in an encoder-decoder architecture have recently enabled accurate single-trial reconstructions of neural activity for neuronal spiking data. While these models have been applied to neural field potential data, they have only so far been applied to signal feature reconstruction (e.g. frequency band power), and have not yet produced direct reconstructions of broadband time-series data preserving signal phase and temporal resolution. ApproachHere we present two encoder-decoder model architectures - the RNN autoencoder (RAE) and multi-block RAE (MRAE) for direct time-series reconstruction of broadband neural data. We trained and tested models on multi-channel micro-Electricorticography (ECoG) recordings from non-human primate motor corticies during unconstrained behavior. Main ResultsWe show that RAE reconstructs micro-electrocorticography recordings, but has reconstruction accuracy that is band-limited to model scale. The MRAE architecture overcomes these time-bandwidth restrictions, yielding broadband (0-100 Hz), accurate reconstructions of ECoG data. SignificanceRAE and MRAE reconstruct broadband ECoG data through multiblock dynamical modeling. The MRAE overcomes time-bandwitdh restrictions to provide improved accuracy for long time duration signals. The reconstruction capabilities provided by these models for broadband neural signals like ECoG may enable the development of improved tools and analysis for basic scientific research and applications like brain-computer interfaces.

neuroscience↗

bioRxiv2022

Modeling and Inference Methods for Switching Regime-Dependent Dynamical Systems with Multiscale Neural Observations

Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases. Here, we address both challenges by developing a Switching Multiscale Dynamical System model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity. We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.

neuroscience↗

bioRxiv2021

Where is all the nonlinearity: flexible nonlinear modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires modeling this transformation while both dissecting its potential nonlinearities and dissociating and preserving its nonlinear behaviorally relevant neural dynamics, which remain unaddressed. We present RNN PSID, a nonlinear dynamic modeling method that enables flexible dissection of nonlinearities, dissociation and preferential learning of neural dynamics relevant to specific behaviors, and causal decoding. We first validate RNN PSID in simulations and then use it to investigate nonlinearities in monkey spiking and LFP activity across four tasks and different brain regions. Nonlinear RNN PSID successfully dissociated and preserved nonlinear behaviorally relevant dynamics, thus outperforming linear and non-preferential nonlinear learning methods in behavior decoding while reaching similar neural prediction. Strikingly, dissecting the nonlinearities with RNN PSID revealed that consistently across all tasks, summarizing the nonlinearity only in the mapping from the latent dynamics to behavior was largely sufficient for predicting behavior and neural activity. RNN PSID provides a novel tool to reveal new characteristics of nonlinear neural dynamics underlying behavior.

neuroscience↗

bioRxiv2020

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

Neural-Matrix style, high-density electrode arrays for brain-machine interfaces (BMIs) and neuroscientific research require the use of multiplexing: Each recording channel can be routed to one of several electrode sites on the array. This capability allows the user to flexibly distribute recording channels to the locations where the most desirable neural signals can be resolved. For example, in the Neuropixel probe, 960 electrodes can be addressed by 384 recording channels. However, currently no adaptive methods exist to use recorded neural data to optimize/customize the electrode selections per recording context. Here, we present an algorithm called classification-based selection (CBS) that optimizes the joint electrode selections for all recording channels so as to maximize isolation quality of detected neurons. We show, in experiments using Neuropixels in non-human primates, that this algorithm yields a similar number of isolated neurons as would be obtained if all electrodes were recorded simultaneously. Neuron counts were 41-85% improved over previously published electrode selection strategies. The neurons isolated from electrodes selected by CBS were a 73% match, by spike timing, to the complete set of recordable neurons around the probe. The electrodes selected by CBS exhibited higher average per-recording-channel signal-to-noise ratio. CBS, and selection optimization in general, could play an important role in development of neurotechnologies for BMI, as signal bandwidth becomes an increasingly limiting factor. Code and experimental data have been made available1.

neuroscience↗

bioRxiv2019

Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification (PSID)

Neural activity exhibits dynamics that in addition to a behavior of interest also relate to other brain functions or internal states. Understanding how neural dynamics explain behavior requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop a novel preferential subspace identification (PSID) algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Applying PSID to large-scale neural activity in two monkeys performing naturalistic 3D reach-and-grasps uncovered new features for neural dynamics. First, PSID revealed the behaviorally relevant dynamics to be markedly lower-dimensional than otherwise implied. Second, PSID discovered distinct rotational dynamics that were more predictive of behavior. Finally, PSID more accurately learned the behaviorally relevant dynamics for each joint and recording channel. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed.

neuroscience↗

bioRxiv2019

A Causal Network Analysis of Neuromodulation in the Cortico-Subcortical Limbic Network

Neural decoding and neuromodulation technologies hold great promise for treating mood and other brain disorders in next-generation therapies that manipulate functional brain networks. Here, we perform a novel causal network analysis to decode multiregional communication in the primate mood processing network and determine how neuromodulation, short-burst tetanic microstimulation (SB-TetMS), alters multiregional network communication. The causal network analysis revealed a mechanism of network excitability that regulates when a sender stimulation site communicates with receiver sites. Decoding network excitability from neural activity at modulator sites predicted sender-receiver communication while SB-TetMS neuromodulation specifically disrupted sender-receiver communication. These results reveal specific network mechanisms of multiregional communication and suggest a new generation of brain therapies that combine neural decoding to predict multiregional communication with neuromodulation to disrupt multiregional communication.\n\nOne Sentence SummaryDecoding and modulating multiregional network communication.

neuroscience↗