Biology subjects

Micera, S.

Publications and source records attributed to Micera, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A Unified Neurocomputational Framework for Closed-Loop Motor Control and Sense of Agency

Motor control relies on the closed-loop comparison of motor commands and sensory feedback to correct errors and adapt to perturbations. Relevant features must be selected from a rich stream of sensory inputs and bound to the appropriate motor commands. This process remains poorly understood. Closed-loop control is accompanied by the experience of causing the observed feedback, sense of agency (SoA). SoA grounds self-identification, and its impairment is associated with lower prosthesis acceptance and disorders like autism and schizophrenia. Like closed-loop motor control, it relies on comparing desired and observed action outcomes in fronto-parietal circuits. Yet, these two phenomena have been studied independently, leaving SoA without a functional meaning and disregarding subjective aspects in motor control models. We propose that SoA is the subjective correlate of selecting self-caused sensory features for closed-loop control. We tested this in a visuomotor task where SoA was manipulated through temporal delays and adaptation to spatial perturbations served as a proxy for closed-loop integration. Delays similarly modulated SoA and closed-loop integration, suggesting these may emerge from the same phenomenon. We modelled our results in a Bayesian framework in which self-causation probability is inferred from temporal congruence, jointly modulating SoA and the weight attributed to visual feedback.

neuroscience↗

bioRxiv2026

Decoding peripheral stimulation from cortical and spinal recordings reveals complementary sensorimotor information

The somatosensory system encodes peripheral inputs through a sequence of ascending neural relays spanning spinal, subcortical and cortical levels. While multivariate decoding of electroencephalography (EEG) signals has demonstrated that cortical activity contains fine-grained information about somatosensory stimuli, the extent to which earlier processing stages contribute additional, non-redundant information remains unclear. To address this gap, we investigated a dataset comprising peripheral sensory stimulation and mixed stimulation (i.e., stimulation engaging both sensory and motor fibers). We assessed whether stimulation characteristics can be decoded from spinal recordings using high-density electrospinography (ESG), and whether combining ESG with EEG enhances decoding performance. Decoding accuracy varied systematically with both stimulation type and signal modality. ESG was the most informative signal for mixed and mixed vs sensory discrimination, reaching an average accuracy of [~]98%, while EEG provided a relative advantage for purely sensory tasks, though absolute accuracy remained more modest for both modalities. Critically, combining the two modalities together consistently matched or outperformed either one, used alone, across all conditions, with gains most pronounced for mixed vs sensory discrimination. Multi-subject generalization improved progressively with training-set size, rising to [~]88% with 15 training subjects for mixed classification, suggesting that subject-independent decoding of motor intent may be achievable when models are trained on a larger number of subjects. Taken together, these results establish that spinal ESG signals carry decodable information about peripheral stimulation that is complementary to and not redundant with cortical EEG. This finding supports a multilevel framework for decoding sensorimotor processing in humans and motivates the development of dual-modality brain-machine interfaces that leverage both cortical and spinal signals to improve the control of neurostimulation and assistive devices.

bioengineering↗

bioRxiv2025

Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations

The brain coordinates multiple parallel motor programs, ensuring synergy and preventing interference during movements. Yet, performance often degrades when brain-machine interfaces are used during concurrent tasks or ongoing movements. We suggest that latent neural representations may represent a strategy to solve this issue. In this study, we addressed this question using neural signals from a tetraplegic individual with partial residual motor function, implanted with a wireless epidural electrocorticography (ECoG) device. By adapting dimensionality reduction techniques, we found that motor execution and motor imagery span partially overlapping subspaces in mesoscale neural signals, shaped by specific frequency band contributions. Despite substantial shared variance, we show that identifying orthogonal, condition-specific dimensions enables successful decoding of executed and imagined movements, even when performed simultaneously. These findings show that ECoG signals can expose separable neural subspaces, allowing executed and imagined actions to be harnessed independently and in concert. This opens a promising avenue to develop brain-machine interfaces that can simultaneously control multiple external devices or operate alongside natural movements.

neuroscience↗

bioRxiv2024

Simulation Insights on the Compound Action Potential in Multifascicular Nerves

ObjectiveDevelop an efficient method for simulating evoked compound action potential (eCAP) signals from complex nerves to help optimize and interpret eCAP recordings; validate it through comparison with measured vagus nerve eCAP recordings; elucidate the subtle interplay giving rise to specific eCAP signal shapes and magnitudes. ApproachWe developed an extended reciprocity theorem approach to model neuron signals in heterogeneous environments, and use it to study analytically the single fibre action potential. We then established a semi-analytic model that also uses hybrid electromagnetic-electrophysiological simulations to model eCAP signals from complex nerves populated with heterogeneous fiber populations of fibers. A cuff electrode was used to measure activity induced by vagus nerve stimulation in in vivo porcine experiments; these measurements were compared with signals produced by the model. Main ResultsThe semi-analytic model produces signals that approximate the shape and amplitude of in vivo measurements. Partially activated fascicles contribute substantially to the signal, as eCAP contributions from smoothly varying fiber calibers in fully activated ones partially cancel. As a result, eCAP magnitude does not depend monotonically on the stimulation current and recruitment level. Because the eCAP is sensitive to the degree of activation in individual fascicles, and to the location of the recording electrodes with respect to individual fascicles, the contributions of different fascicles to the recorded eCAP signals vary significantly with changes in the shape and placement of the stimulus and the recording electrodes. SignificanceOur method can be used to rapidly assess new stimulation and recording setups involving complex nerves and neurovascular bundles, e.g., to maximize signal information content, for closed-loop control in bioelectronic medicine applications, and potentially to non-destructively reconstruct structural and functional nerve topologies through inverse problem solving.

neuroscience↗

bioRxiv2024

Combining Gamma Neuromodulation and Robotic Rehabilitation Restores Parvalbimin-mediated Gamma Function and Boosts Motor Recovery in Stroke Mice

Stroke is a leading cause of long-term disability, often characterized by compromised motor function. Gamma band is known to be related to Parvalbumin interneurons (PV-IN) synchronous discharge and it has been found to be affected after stroke in humans and animals. Both Gamma band and PV-IN also play a key role in motor function, thus representing a promising target for post-stroke neurorehabilitation. Non-Invasive neuromodulatory approaches are considered a safe intervention and can be used for this purpose. This study presents a novel, clinically relevant, non-invasive and well-tolerated sub-acute treatment combining robotic rehabilitation with advanced neuromodulation techniques, validated in a mouse model of ischemic injury. In the sub-acute phase after stroke, we scored profound deficits in motor-related Gamma band regulation on the perilesional cortex. Accordingly, both at the perilesional and at the whole-cortex levels, the damage results in impaired PV-IN activity, with reduced firing rate and increased functional connectivity levels. Therefore, we tested the therapeutic potential of coupling robotic rehabilitation with optogenetic PV-driven Gamma band stimulation in a subacute post-stroke phase during motor training to reinforce the efficacy of the treatment. Frequency-specific movement-related Gamma band stimulation, when combined with physical training, significantly improved forelimb motor function. More importantly, by pairing robotic rehabilitation with a clinical-like non-invasive 40 Hz transcranial Alternating Current Stimulation, we achieved similar motor improvements mediated by the effective restoring of movement-related Gamma band power and increased PV-IN connections in premotor cortex. Our research introduces a new understanding of the role of parvalbumin-interneurons in post-stroke impairment and recovery. These results highlight the synergistic potential of combining perilesional Gamma band stimulation with robotic rehabilitation as a promising and realistic therapeutic approach for stroke patients. SummaryStroke-induced motor deficits are accompanied by alteration of Gamma modulation and PV-interneurons activity and restored by a combination of non-invasive Gamma stimulation and robotic therapy.

neuroscience↗

bioRxiv2024

Brain and grammar: revealing electrophysiological basic structures with competing statistical models

Acoustic, lexical and syntactic information is simultaneously processed in the brain. Therefore, distinguishing the electrophysiological activity pertaining to these components requires complex and indirect strategies. Capitalizing on previous works which factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited EEG recordings and compared different surprisal models selectively involving lexical information, part of speech or syntactic structures in various combinations. EEG responses were recorded in 32 participants during listening to affirmative active declarative sentences and compared the activation corresponding to basic syntactic structures, such as noun phrases vs verb phrases. Lexical and syntactic processing activates different frequency bands, different time windows and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. Finally, we confirm previous measures obtained with intracortical recordings independently supporting the original hypothesis addressed here in a robust way.

neuroscience↗

bioRxiv2023

Neural stimulation hardware for the selective intrafascicular modulation of the vagus nerve

The neural stimulation of the vagus nerve is able to modulate various functions of the parasympathetic response in different organs. The stimulation of the vagus nerve is a promising approach to treating inflammatory diseases, obesity, diabetes, heart failure, and hypertension. The complexity of the vagus nerve requires highly selective stimulation, allowing the modulation of target-specific organs without side effects. Here, we address this issue by adapting a neural stimulator and developing an intraneural electrode for the particular modulation of the vagus nerve. The neurostimulator parameters such as amplitude, pulse width, and pulse shape were modulated. Single-, and multi-channel stimulation was performed at different amplitudes. For the first time, I polyimide thin-film neural electrode was designed for the specific stimulation of the vagus nerve. In vivo experiments were performed in the adult minipig to validate to elicit electrically evoked action potentials and to modulate physiological functions selectively, validating the selectivity of intraneural stimulation. Electrochemical tests of the electrode and the neurostimulator showed that the stimulation hardware was working correctly. Stimulating the porcine vagus nerve resulted in selective modulation of the vagus nerve. Alpha, beta, and theta waves could be distinguished during single- and multi-channel stimulation. We have shown that the here presented system is able to activate the vagus nerve selectively and can therefore modulate the heart rate, diastolic pressure, and systolic pressure. The here presented system may be used to restore the cardiac loop after denervation by implementing biomimetic stimulation patterns. Presented methods may be used to develop intraneural electrodes adapted for various applications.

bioengineering↗

bioRxiv2023

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

ObjectivePeripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon the implantation procedure and the subjects anatomy. Currently, few alternatives exist for the determination of the target subject structural and functional anatomy, and statistical characterizations from cadaveric samples are limited because of their high cost. We propose an optimization workflow that can guide both the pre-surgical planning and the determination of maximally selective multisite stimulation protocols for implants consisting of several intraneural electrodes, and we characterize its performance in silico. We show that the availability of structural and functional information leads to very high performances and allows taking informed decisions on neuroprosthetic design. ApproachWe employ hybrid models (HMs) of neuromodulation in conjunction with a machine learning-based surrogate model to determine fiber activation under electrical stimulation, and two steps of optimization through particle swarm optimization (PSO) to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost. Main resultsOur method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode (TIME) implants, the optimal number of electrodes to implant, their optimal insertion, and a set of multipolar stimulation protocols that lead in silico to selective activation of all the muscles innervated by the human median nerve. SignificanceWe show how to use effectively HMs for optimizing personalized neuroprostheses for motor function restoration. We provide in-silico evidences about the potential of multipolar stimulation to increase greatly selectivity. We also show that the knowledge of structural and functional anatomies of the target subject leads to very high selectivity and motivate the development of methods for their in vivo characterization.

bioengineering↗

bioRxiv2022

Validation of manifold-based direct control for a brain-to-body neural bypass

Brain-body interfaces (BBIs) are neuroprostheses that can restore the connection between brain activity and body movements. They have emerged as a radical solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands to actuate the limb from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present the design and demonstration in a monkey of a novel brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, meant to achieve ease of learning and long-term robustness. We identified once an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkeys neural activity associated to reach-to-grasp movements. We then tested the animals ability to directly control a computer cursor using cortical activation along the manifold axes and demonstrated rapid learning and stable high performance over 16 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger hand movements. These results provide evidence that manifold-based direct control has promising characteristics for clinical applications of BBIs.

bioengineering↗

bioRxiv2022

Event-related causality in Stereo-EEG discriminates syntactic processing of noun phrases and verb phrases

Syntax involves complex neurobiological mechanisms, which are difficult to disentangle for multiple reasons. Using a protocol able to separate syntactic information from sound information we investigated the neural causal connections evoked by the processing of homophonous phrases, either verb phrases (VP) or noun phrases (NP). We used event-related causality (ERC) from stereo-electroencephalographic (SEEG) recordings in 10 epileptic patients in multiple cortical areas, including language areas and their homologous in the non-dominant hemisphere. We identified the different networks involved in the processing of these syntactic operations (faster in the dominant hemisphere) showing that VPs engage a wider cortical network. We also present a proof-of-concept for the decoding of the syntactic category of a perceived phrase based on causality measures. Our findings help unravel the neural correlates of syntactic elaboration and show how a decoding based on multiple cortical areas could contribute to the development of speech prostheses for speech impairment mitigation.

neuroscience↗

bioRxiv2021

Fast-spiking interneurons of the premotor cortex contribute to action planning

Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, the specific functional role of GABAergic cells remains only partly understood. Here, electrophysiological and computational analyses are employed to compare directly the response properties of putative pyramidal (PNs) and fast-spiking, GABAergic neurons (FSNs) during licking and forelimb retraction in mice. Recordings from anterolateral motor cortex and rostral forelimb area, reveal that FSNs fire earlier and for a longer duration than PNs, with the exception of a subset of early-modulated PNs in deep layers. Computational analysis reveals that FSNs carry vastly more information than PNs about the onset of movement. While PNs differently modulate their discharge during distinct motor acts, most FSNs respond with a stereotyped increase in firing rate. Accordingly, the informational redundancy was greater among FSNs than PNs. These data suggest that a global rise of inhibition contributes to early action planning.

physiology↗

bioRxiv2020

Mechanistic modeling suggests that low-intensity focused ultrasound can selectively recruit myelinated or unmyelinated nerve fibers

Low-Intensity Focused Ultrasound Stimulation (LIFUS) holds promise for the remote modulation of neuronal activity, but an incomplete mechanistic characterization hinders its clinical maturation. Here, we developed a computational framework to model intramembrane cavitation in multi-compartmental, morphologically-realistic neuronal representations, and used it to investigate ultrasound neuromodulation of peripheral nerves by spatially-varying pressure fields. Our findings show that LIFUS offers distinct parametric sub-spaces to selectively recruit myelinated or unmyelinated axons and modulate their spiking activity over physiologically relevant regimes and within safe exposure limits. This singular feature, explained by fiber-specific differences in membrane electromechanical coupling, consistently explains recent empirical findings and suggests that LIFUS can preferentially target nociceptive and sensory fibers to enable peripheral therapeutic applications not addressable by electric stimulation. These results open up new opportunities for the development of more selective and effective peripheral neuroprostheses. Our framework can be readily applied to other neural targets to establish application-specific LIFUS protocols.

neuroscience↗

bioRxiv2020

Immersive virtual reality alters selection of head-trunk coordination strategies in young children

The acquisition of postural control is an elaborate process, which relies on the balanced integration of multisensory inputs. Current models suggest that young children rely on an en-block control of their upper body before sequentially acquiring a segmental control around the age of 7, and that they resort to the former strategy under challenging conditions. While recent works suggest that a virtual sensory environment alters visuomotor integration in healthy adults, little is known about the effects on younger individuals. Here we show that this coordination pattern is disrupted by an immersive virtual reality framework where a steering role is assigned to the trunk, which causes 6- to 8-year-olds to employ an ill-adapted segmental strategy. These results provide an alternate trajectory of motor development and emphasize the immaturity of postural control at these ages.

neuroscience↗

bioRxiv2020

Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior

Behavior relies on continuous influx of sensory information about the body. In primates, motor cortex must integrate somatic feedback to accurately reach and manipulate objects. Yet, prior work demonstrates that motor cortex is well-described with deterministic, rather than input-driven, dynamics. Deterministic dynamics facilitate robust movement generation, but flexible motor output requires rapid responses to unexpected inputs. Here, we resolved this paradox by simultaneously recording neural population activity in motor and somatosensory cortex from four monkeys performing a naturalistic object interaction behavior resulting in occasional errors. Motor cortex was strikingly input-driven surrounding behavioral error correction. Intriguingly, input-driven dynamics were isolated to a subspace of the population activity that putatively captured somatosensory feedback. Using electrical stimulation of ascending somatosensory tracts, we causally verified that this feedback subspace captured peripheral inputs to cortex. Our results demonstrate that cortical activity is compartmentalized within distinct subspaces, enabling flexible integration of salient inputs for robust behavior.

neuroscience↗

bioRxiv2020

A psychometric platform to collect somatosensory sensations for neuroprosthetic use

Somatosensory neuroprostheses exploit invasive and non-invasive feedback technologies to restore sensorimotor functions lost to disease or trauma. These devices use electrical stimulation to communicate sensory information to the brain. A sensation characterization procedure is thus necessary to determine the appropriate stimulation parameters and to establish a clear personalized map of the sensations that can be restored. Several questionnaires have been described in the literature to collect the quality, type, location and intensity of the evoked sensations, but there is still no standard psychometric platform. Here we propose a new psychometric system containing previously validated questionnaires on evoked sensations, which can be applied to any kind of somatosensory neuroprosthesis. The platform collects stimulation parameters used to elicit sensations; records subjects percepts in terms of sensation location, type, quality, perceptual threshold, and intensity. It further collects data using standardized assessment questionnaires and scales, performs measurements over time, and collects phantom limb pain syndrome data. The psychometric platform is user-friendly and provides clinicians with all the information needed to assess the sensory feedback. The psychometric platform was validated with three trans-radial amputees. They platform was used to assess intraneural sensory feedback provided through implanted peripheral nerve interfaces. The proposed platform could act as a new standardized assessment toolbox to homogenize the reporting of results obtained with different technologies in the field of somatosensory neuroprosthetics.

neuroscience↗

bioRxiv2020

Simultaneous decoding of cardiovascular and respiratory functional changes from pig intraneural vagus nerve signals

Bioelectronic medicine is opening new perspectives for the treatment of some major chronic diseases through the physical modulation of autonomic nervous system activity. Being the main peripheral route for electrical signals between central nervous system and visceral organs, the vagus nerve (VN) is one of the most promising targets. Closed-loop neuromodulation would be crucial to increase effectiveness and reduce side effects, but it depends on the possibility of extracting useful physiological information from VN electrical activity, which is currently very limited. Here, we present a new decoding algorithm properly detecting different functional changes from VN signals. They were recorded using intraneural electrodes in anaesthetized pigs during cardiovascular and respiratory challenges mimicking increases in arterial blood pressure, tidal volume and respiratory rate. A novel decoding algorithm was developed combining discrete wavelet transformation, principal component analysis, and ensemble learning made of classification trees. It robustly achieved high accuracy levels in identifying different functional changes and discriminating among them. We also introduced a new index for the characterization of recording and decoding performance of neural interfaces. Finally, by combining an anatomically validated hybrid neural model and discrimination analysis, we provided new evidence suggesting a functional topographical organization of VN fascicles. This study represents an important step towards the comprehension of VN signaling, paving the way to the development of effective closed-loop bioelectronic systems.

neuroscience↗

bioRxiv2020

Experimental and computational study on motor control and recovery after stroke: towards a constructive loop betweenexperimental and virtual embodiedneuroscience

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

neuroscience↗

bioRxiv2020

Synergic effect of optogenetic stimulation and motor training boosts recovery of motor functionality after stroke supported by segregation of motor representation

BackgroundAn ischemic stroke is followed by the remapping of motor representation and extensive changes in cortical excitability involving both hemispheres. Although stimulation of the ipsilesional motor cortex, especially when paired with motor training, facilitates plasticity and functional restoration, the mechanisms underneath the reshaping of cortical functionality are widely unknown. ObjectiveWe investigated the spatio-temporal features of motor-evoked cortical activity associated with generalized recovery after stroke, and its dependence on the type of rehabilitative treatment. MethodsWe designed a novel rehabilitative treatment that combines neuro-plasticizing intervention with motor training. Specifically, optogenetic stimulation of peri-infarct excitatory neurons expressing Channelrhodopsin-2 was associated with daily motor training on a robotic device. The effectiveness of the combined therapy was compared with spontaneous recovery and with the single treatments (i.e. individually administered optogenetic stimulation or motor training). ResultsWe found that only the combined therapy promotes generalized recovery of forelimb function and the rescue of spatio-temporal features of motor-evoked activity. Generalized recovery results from a new excitatory/inhibitory balance between hemispheres as revealed by the augmented motor response flanked by the increased expression of parvalbumin positive neurons in the peri-infarct area. ConclusionsOur findings demonstrate that though behavioral recovery is not necessarily associated with the restoration of pre-stroke motor-evoked activity, the reestablishment of pre-stroke activation transients was a distinguishing feature of the most efficient therapeutic approach, the combined therapy.

neuroscience↗