Giacomo Valle

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Valle

First Name

Giacomo

ORCID

0000-0002-2637-8007

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Publications1 - 10 of 53

Morphological Neural Computation Restores Discrimination of Naturalistic Textures in Trans-radial Amputees
Mazzoni, Alberto; Oddo, Calogero M.; Valle, Giacomo; et al. (2020)
Scientific Reports
Humans rely on their sense of touch to interact with the environment. Thus, restoring lost tactile sensory capabilities in amputees would advance their quality of life. In particular, texture discrimination is an important component for the interaction with the environment, but its restoration in amputees has been so far limited to simplified gratings. Here we show that naturalistic textures can be discriminated by trans-radial amputees using intraneural peripheral stimulation and tactile sensors located close to the outer layer of the artificial skin. These sensors exploit the morphological neural computation (MNC) approach, i.e., the embodiment of neural computational functions into the physical structure of the device, encoding normal and shear stress to guarantee a faithful neural temporal representation of stimulus spatial structure. Two trans-radial amputees successfully discriminated naturalistic textures via the MNC-based tactile feedback. The results also allowed to shed light on the relevance of spike temporal encoding in the mechanisms used to discriminate naturalistic textures. Our findings pave the way to the development of more natural bionic limbs.
Brain reactions to the use of sensorized hand prosthesis in amputees
Granata, Giuseppe; Di Iorio, Riccardo; Miraglia, Francesca; et al. (2020)
Brain and Behavior
Objective We investigated for the first time the presence of chronic changes in the functional organization of sensorimotor brain areas induced by prolonged training with a bidirectional hand prosthesis. Methods A multimodal neurophysiological and neuroimaging evaluation of brain functional changes occurring during training in five consecutive amputees participating to experimental trials with robotic hands over a period of 10 years was carried out. In particular, modifications to the functional anatomy of sensorimotor brain areas under resting conditions were explored in order to check for eventual changes with respect to baseline. Results Full evidence is provided to demonstrate brain functional changes, and some of them in both the hemispheres and others restricted to the hemisphere contralateral to the amputation/prosthetic hand. Conclusions The study describes a unique experimental experience showing that brain reactions to the prolonged use of an artificial hand can be tracked for a tailored approach to a fully embedded artificial upper limb for future chronic uses in daily activities.
A Psychometric Platform to Collect Somatosensory Sensations for Neuroprosthetic Use
Valle, Giacomo; Iberite, Francesco; Strauss, Ivo; et al. (2021)
Frontiers in Medical Technology
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 and 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. The 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.
Plastic changes in the brain after a neuro-prosthetic leg use
Petrusic, Igor; Valle, Giacomo; Dakovic, Marko; et al. (2022)
Clinical Neurophysiology
Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization
Ciotti, Federico; Cimolato, Andrea; Valle, Giacomo; et al. (2023)
PLoS Computational Biology
Peripheral nerve stimulation is being investigated as a therapeutic tool in several clinical scenarios. However, the adopted devices have restricted ability to obtain desired outcomes with tolerable off-target effects. Recent promising solutions are not yet employed in clinical practice due to complex required surgeries, lack of long-term stability, and implant invasiveness. Here, we aimed to design a neural interface to address these issues, specifically dimensioned for pudendal and sacral nerves to potentially target sexual, bladder, or bowel dysfunctions. We designed the adaptable intrafascicular radial electrode (AIR) through realistic computational models. They account for detailed human anatomy, inhomogeneous anisotropic conductance, following the trajectories of axons along curving and branching fascicles, and detailed biophysics of axons. The model was validated against available experimental data. Thanks to computationally efficient geometry-based selectivity estimations we informed the electrode design, optimizing its dimensions to obtain the highest selectivity while maintaining low invasiveness. We then compared the AIR with state-of-the-art electrodes, namely InterStim leads, multipolar cuffs and transversal intrafascicular multichannel electrodes (TIME). AIR, comprising a flexible substrate, surface active sites, and radially inserted intrafascicular needles, is designed to be implanted in a few standard steps, potentially enabling fast implants. It holds potential for repeatable stimulation outcomes thanks to its radial structural symmetry. When compared in-silico, AIR consistently outperformed cuff electrodes and InterStim leads in terms of recruitment threshold and stimulation selectivity. AIR performed similarly or better than a TIME, with quantified less invasiveness. Finally, we showed how AIR can adapt to different nerve sizes and varying shapes while maintaining high selectivity. The AIR electrode shows the potential to fill a clinical need for an effective peripheral nerve interface. Its high predicted performance in all the identified requirements was enabled by a model-based approach, readily applicable for the optimization of electrode parameters in any peripheral nerve stimulation scenario.
A physical emulation of somatosensory cortex as a Neuromorphic Twin for neural prostheses
Ramirez, Hector; Donati, Elisa; von der Behrens, Wolfger; et al. (2024)
Research Square
Loss of tactile feedback in patients with paralysis or limb loss prevents the integration of sensory-motor signals, limiting dexterous motor control. Recent advances in the neurostimulation of the human somatosensory cortex offer the possibility of restoring touch in these patients. Brain-computer interfaces and bidirectional prostheses with implantable electrodes can restore feedback, improving control, awareness, and quality of life. However, current electrical stimulation methods lack the fidelity to consistently and reliably reproduce natural sensations, and the impact of this stimulation on the somatosensory cortex remains unclear. Here we propose to use a mixed-signal neuromorphic processing system to construct a “neuromorphic twin” of the somatosensory cortex, following a bottom-up approach for emulating and reproducing its intricate dynamics. We use the analog properties of electronic neuron and synapse circuits to faithfully replicate neural and synaptic response properties. To reproduce effects at the network level, we configure the neuromorphic processor to implement the recurrent connectivity patterns of the somatosensory cortex among different populations of excitatory and inhibitory silicon neurons. Our findings demonstrate the twin’s ability to reproduce the spatiotemporal firing patterns of the native neuronal population receiving afferent fiber inputs, resulting in a highly bio-realistic model that can be used to investigate the effect of different stimulation patterns. This physical emulation of the cortical circuits represents therefore an additional tool to understand and predict the somatosensory cortex behavior under neurostimulation. Using this system, neurostimulation patterns can be optimized to produce more natural sensations and ultimately improve the quality of restored feedback, leading to a better quality of life for affected patients.
Sensory feedback for limb prostheses in amputees
Raspopovic, Stanisa; Valle, Giacomo; Petrini, Francesco M. (2021)
Nature Materials
Commercial prosthetic devices currently do not provide natural sensory information on the interaction with objects or movements. The subsequent disadvantages include unphysiological walking with a prosthetic leg and difficulty in controlling the force exerted with a prosthetic hand, thus creating health issues. Restoring natural sensory feedback from the prosthesis to amputees is an unmet clinical need. An optimal device should be able to elicit natural sensations of touch or proprioception, by delivering the complex signals to the nervous system that would be produced by skin, muscles and joints receptors. This Review covers the various neurotechnological approaches that have been proposed for the development of the optimal sensory feedback restoration device for arm and leg amputees.
Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain
Petrini, Francesco M.; Bumbasirevic, Marko; Valle, Giacomo; et al. (2019)
Nature Medicine
Despite advances in the development of lower-limb prosthetics, the potential benefits of restoring sensory feedback from such devices to transfemoral (above-knee) or transtibial (below-knee) amputees has not been investigated. Most surgery techniques and noninvasive methods to restore sensory feedback have been tested only in transtibial amputations, which produce a less disabling clinical condition than transfemoral amputation. Direct neural stimulation through transversal intrafascicular multichannel electrodes (TIMEs) has enabled upper-limb amputees to feel touch sensations from the missing hand and to exploit them for long-term prosthesis control. Only a few trials with direct nerve stimulation that did not show clear benefits for the leg amputees have been conducted. Restoring sensory feedback from the phantom hand of upper-limb amputees through neural stimulation has been shown to decrease phantom limb pain (PLP). However, the efficacy of low-frequency nerve stimulation has never been investigated for treating PLP in leg amputees. In this study, we recruited two volunteers with transfemoral amputation as a consequence of traumatic events (Supplementary Table 1). These volunteers were implanted with four TIMEs in the nearest portion of the residual tibial nerve to the amputation for more than 90 d each (top right in Fig. 1 and Extended Data Fig. 1).
Modeling of the Peripheral Nerve to Investigate Advanced Neural Stimulation (Sensory Neural Prosthesis)
Katic, Natalija; Valle, Giacomo; Raspopovic, Stanisa (2022)
Handbook of Neuroengineering
Somatosensory neuroprostheses are devices used for restoring functions in patients with sensory or motor disabilities that potentially could improve their health and quality of life. Their key element is a neural interface: the device that establishes an intimate contact with residual neural structures. Since different geometries of implantable electrodes and their policies of use to target the peripheral nerves exist, there is a need for choosing the optimal ones to implement. That led to the development of computational models of nerve stimulation. In this way, the optimization of the stimulation strategies to encode more natural and effective sensations can be tested and implemented. Models can be used to define the optimal design, dimensions, and number of active sites of electrodes for targeting a specific sensory-motor nerve, and to give surgical indications as the number of electrodes to implant. Developing a valid computational model is not only an efficient option for neuroprostheses optimization but also reduces the number of animal experiments in development process even at early stages.
Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian optimization
Aiello, Giovanna; Valle, Giacomo; Raspopovic, Stanisa (2023)
Journal of Neural Engineering
Objective. Neuromodulation technology holds promise for treating conditions where physiological mechanisms of neural activity have been affected. To make treatments efficient and devices highly effective, neurostimulation protocols must be personalized. The interface between the targeted nervous tissue and the neurotechnology (i.e. human-machine link or neural interface) usually requires constant re-calibration of neuromodulation parameters, due to many different biological and microscale phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with corresponding economic burden, compromised every-day usability and efficacy of the device, and consequent loss of time and increased discomfort of patients going back to clinics to get the device tuned. We aim to construct an adaptable AI-based system, able to compensate for these changes autonomously.Approach. We exploited Gaussian process-based Bayesian optimization (GPBO) methods to re-adjust the neurostimulation parameters in realistic neuroprosthetic data by integrating temporal information into the process to tackle the issue of time variability. To this aim, we built a predictive model able to tune the neuromodulation parameters in two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites in a multichannel electrode, i.e. able to cover a certain function for a neuroprosthesis, which in this specific case was the evoked-sensation location variability. In the second one, we propose an algorithm able to adapt the injected charge required to obtain a functional neural activation (e.g. perceptual threshold variability). By retrospectively collecting the outcomes from the calibration experiments in a human clinical trial utilizing implantable neuromodulation devices, we were able to quantitatively assess our GPBO-based approach in an offline setting.Main results.Our automatic algorithm can successfully adapt neurostimulation parameters to evoked-sensation location changes and to perceptual threshold changes over-time. These findings propose a quick, automatic way to tackle the inevitable variability of neurostimulation parameters over time. Upon validation in other frameworks it increases the usability of this technology through decreasing the time and the cost of the treatment supporting the potential for future widespread use. This work suggests the exploitation of AI-based methods for developing the next generation of 'smart' neuromodulation devices.

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Giacomo Valle

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