Wilson Truccolo

· Pablo J. Salame Goldman Sachs Associate Professor of Computational NeuroscienceVerified

Brown University · Microbiology and Immunology

Active 2000–2025

h-index63

Citations14.7k

Papers23733 last 5y

Funding$4.2M

Faculty page Lab page Website

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About

The Truccolo Lab for Computational Neuroscience at Brown University studies how the collective dynamics (coordinated activity) in neuronal ensembles contribute to normal brain function, and how neurological disorders (e.g. epileptic seizures) result when these dynamics become pathological. We study collective neural dynamics at multiple scales, from the level of single-neuron activity, to population activity in neuronal ensembles, to larger-scale brain networks.

Research topics

Psychology
Medicine
Neuroscience

Selected publications

Downmodulation of Potassium Conductances Induces Epileptic Seizures in Cortical Network Models Via Multiple Synergistic Factors
Journal of Neuroscience · 2025-01-29 · 2 citations
articleOpen accessSenior author
Voltage-gated potassium conductances g K play a critical role not only in normal neural function, but also in many neurological disorders and related therapeutic interventions. In particular, in an important animal model of epileptic seizures, 4-aminopyridine (4-AP) administration is thought to induce seizures by reducing g K in cortex and other brain areas. Interestingly, 4-AP has also been useful in the treatment of neurological disorders such as multiple sclerosis and spinal cord injury, where it is thought to improve action potential propagation in axonal fibers. Here, we examined g K downmodulation in biophysical models of cortical networks that included different neuron types organized in layers, potassium diffusion in interstitial and larger extracellular spaces, and glial buffering. Our findings are fourfold. First, g K downmodulation in pyramidal and fast-spiking inhibitory interneurons led to differential effects, making the latter much more likely to enter depolarization block. Second, both neuron types showed an increase in the duration and amplitude of action potentials, with more pronounced effects in pyramidal neurons. Third, a sufficiently strong g K reduction dramatically increased network synchrony, resulting in seizure-like dynamics. Fourth, we hypothesized that broader action potentials were likely to not only improve their propagation, as in 4-AP therapeutic uses, but also to increase synaptic coupling. Notably, graded-synapses incorporating this effect further amplified network synchronization and seizure-like dynamics. Overall, our findings elucidate different effects that g K downmodulation may have in cortical networks, explaining its potential role in both pathological neural dynamics and therapeutic applications.
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Controllability of nonlinear epileptic-seizure spreading dynamics in large-scale subject-specific brain networks
Scientific Reports · 2025-02-22 · 3 citations
articleOpen accessSenior authorCorresponding
Closed-loop electrical stimulation has become an important alternative to resective surgery for control of pharmacologically-resistant focal epileptic seizures. Seizure spread across large-scale brain networks, rather than its focal onset, is what commonly leads to major disruptions in sensorimotor and cognitive processing, as well as loss-of-consciousness, one of the main impairing aspects of the disorder. Electrical stimulation, triggered by early detection of seizure onset in epileptogenic zones (EZs), has been applied to prevent spread and its subsequent effects. Here, we show how linear feedback seizure-spread controllability in subject-specific (white-matter tractography) Epileptor network models is affected by variations in brain excitability, network coupling strength, control latency and gain, and actuation targets. Feedback control can qualitatively change the nonlinear seizure dynamics, and the paths to seizure termination and spread prevention. Notably, control onset latency is a critical parameter leading to a phase transition in spread controllability. Consequently, the efficacy of EZ-only actuation is limited depending on network excitability, coupling strength, and practical latencies for detection and actuation. Additional feedback-stabilization control of theoretically-derived optimal node subsets in the network are necessary for spread prevention. Finally, we contrast our linear-feedback controllability assessment with other measures based on minimum-energy (Gramian) controllability and nonlinear pulse-perturbation approaches.
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
ArXiv.org · 2025-12-01
preprintOpen access
High-definition transcranial direct current stimulation (HD-tDCS) dosing in children remains largely empirical, relying on one-size-fits-all protocols despite rapid developmental changes in head anatomy and tissue properties that strongly modulate how currents reach the developing brain. Using 70 pediatric head models and commonly used cortical targets, our forward simulations find that standard montages produce marked age-dependent reductions in target electric-field intensity and systematic sex differences linked to tissue-volume covariation, underscoring the profound limitations of conventional uniform montages. To overcome these limitations, we introduce a developmentally informed, dual-objective optimization framework designed to generate personalized Pareto fronts summarizing the trade-off between electric-field intensity and focality. From these optimized solutions, we derive two practical dosing prescriptions: a dose-consistency strategy that, for the first time, enforces fixed target intensity across individuals to implicitly mitigate demographic effects, and a target-engagement strategy that maximizes target intensity under safety limits. Both strategies remain robust to large conductivity variations, and we further show that dense HD-tDCS solutions admit sparse equivalents without performance loss under the target-engagement strategy. We also find that tissue conductivity sensitivity is depth-dependent, with Pareto-front distributions for superficial cortical targets most influenced by gray matter, scalp, and bone conductivities, and those for a deep target predominantly shaped by gray and white matter conductivities. Together, these results establish a principled framework for pediatric HD-tDCS planning that explicitly accounts for developmental anatomy and physiological uncertainty, enabling reliable and individualized neuromodulation dosing in pediatric populations.
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From spiking neuronal networks to interpretable dynamics: a diffusion-approximation framework
bioRxiv (Cold Spring Harbor Laboratory) · 2024-12-17 · 1 citations
preprintOpen access
Abstract Modeling and interpreting the complex recurrent dynamics of neuronal spiking activity is essential to understanding how networks implement behavior and cognition. Nonlinear Hawkes process models can capture a large range of spiking dynamics, but remain difficult to interpret, due to their discontinuous and stochastic nature. To address this challenge, we introduce a novel framework based on a piecewise deterministic Markov process representation of the nonlinear Hawkes process (NH-PDMP) followed by a diffusion approximation. We analytically derive stability conditions and dynamical properties of the obtained diffusion processes for single-neuron and network models. We established the accuracy of the diffusion approximation framework by comparing it with exact continuous-time simulations of the original neuronal NH-PDMP models. Our framework offers an analytical and geometric account of the neuronal dynamics repertoire captured by nonlinear Hawkes process models, both for the canonical responses of single-neurons and neuronal-network dynamics, such as winner-take-all and traveling wave phenomena. Applied to human and nonhuman primate recordings of neuronal spiking activity during speech processing and motor tasks, respectively, our approach revealed that task features can be retrieved from the dynamical landscape of the fitted models. The combination of NH-PDMP representations and diffusion approximations thus provides a novel dynamical analysis framework to reveal single-neuron and neuronal-population dynamics directly from models fitted to spiking data.
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Linear Feedback Control of Spreading Dynamics in Stochastic Nonlinear Network Models: Epileptic Seizures
2023-04-24 · 1 citations
articleOpen accessSenior author
The development of models and approaches for controlling the spreading dynamics of epileptic seizures is an essential step towards new therapies for people with pharmacologically resistant epilepsy. Beyond resective neurosurgery, in which epileptogenic zones (EZs) are the target of surgery, closed-loop control based on intracranial electrical stimulation, applied at the very early stage of seizure evolution, has been the main alternative, e.g. the RNS system from NeuroPace (Mountain View, CA). In this approach the electrical stimulation is delivered to target brain areas after detection of seizure initiation in the EZ. Here, we examined, on model simulations, some of the closed-loop control aspects of the problem. Seizure dynamics and spread are typically modeled with highly nonlinear dynamics on complex brain networks. Despite the nonlinearity and complexity, currently available optimal feedback control approaches are mostly based on linear approximations. Alternative machine learning control approaches might require amounts of data beyond what is commonly available in the intended application. We thus examined how standard linear feedback control approaches perform when applied to nonlinear models of neural dynamics of seizure generation and spread. In particular, we considered patient-specific epileptor network models for seizure onset and spread. The models incorporate network connectivity derived from (diffusion MRI) white-matter tractography, have been shown to capture the qualitative dynamics of epileptic seizures and can be fit to patient data. For control, we considered simple linear quadratic Gaussian (LQG) regulators. The LQG control was based on a discrete-time state-space model derived from the linearization of the patient-specific epileptor network model around a stable fixed point in the regime of preictal dynamics. We show in simulations that LQG regulators acting on the EZ node during the initial seizure period tend to be unstable. The LQG solution for the control law in this case leads to global feedback to the EZ-node actuator. However, if the LQG solution is constrained to depend on only local feedback originating from the EZ node itself, the controller is stable. In this case, we demonstrate that localized LQG can easily terminate the seizure at the early stage and prevent spread. In the context of optimal feedback control based on linear approximations, our results point to the need for investigating in more detail feedback localization and additional relevant control targets beyond epileptogenic zones.
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Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures
PLoS Computational Biology · 2023-02-07 · 11 citations
articleOpen accessSenior authorCorresponding
The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.
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Top-down semantic predictions align phonetic neuronal dynamics in human superior temporal gyrus
bioRxiv (Cold Spring Harbor Laboratory) · 2023-10-31 · 4 citations
preprintOpen access
Traditional models of speech perception posit that neural activity encodes speech through a hierarchy of cognitive processes, from low-level representations of acoustic and phonetic features to high-level semantic encoding. Yet it remains unknown how neural representations are transformed across levels of the speech hierarchy. Here, we analyzed unique microelectrode array recordings of neuronal spiking activity from the human left anterior superior temporal gyrus, a brain region at the interface between phonetic and semantic speech processing, during a semantic categorization task and natural speech perception. We identified distinct neural manifolds for semantic and phonetic features, with a functional separation of the corresponding low-dimensional trajectories. Moreover, phonetic and semantic representations were encoded concurrently and reflected in power increases in the beta and low-gamma local field potentials, suggesting top-down predictive and bottom-up cumulative processes. Our results are the first to demonstrate mechanisms for hierarchical speech transformations that are specific to neuronal population dynamics.
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Grasp-squeeze adaptation to changes in object compliance leads to dynamic beta-band communication between primary somatosensory and motor cortices
Scientific Reports · 2022-04-26 · 7 citations
articleOpen access
In asking the question of how the brain adapts to changes in the softness of manipulated objects, we studied dynamic communication between the primary sensory and motor cortical areas when nonhuman primates grasp and squeeze an elastically deformable manipulandum to attain an instructed force level. We focused on local field potentials recorded from S1 and M1 via intracortical microelectrode arrays. We computed nonparametric spectral Granger Causality to assess directed cortico-cortical interactions between these two areas. We demonstrate that the time-causal relationship between M1 and S1 is bidirectional in the beta-band (15-30 Hz) and that this interareal communication develops dynamically as the subjects adjust the force of hand squeeze to reach the target level. In particular, the directed interaction is strongest when subjects are focused on maintaining the instructed force of hand squeeze in a steady state for several seconds. When the manipulandum's compliance is abruptly changed, beta-band interareal communication is interrupted for a short period (~ 1 s) and then is re-established once the subject has reached a new steady state. These results suggest that transient beta oscillations can provide a communication subspace for dynamic cortico-cortical S1-M1 interactions during maintenance of steady sensorimotor states.
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Population Encoding/Decoding
Encyclopedia of Computational Neuroscience · 2022-01-01
book-chapter1st authorCorresponding
Publisher DOI
Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions
PLoS ONE · 2022-08-23 · 26 citations
articleOpen accessSenior authorCorresponding
Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes "critical" phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian's leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.
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