1,720,984 research outputs found
Machine-learning predictor of nerve fiber firing rate allows the automatic optimization of electrical stimulation protocols
No full textComputational models have been widely employed to study the electrical stimulation of the nervous system. Still, most applications either study fundamental mechanisms underlying stimulation, or address qualitative scientific questions. When quantitative questions are posed, they are mostly evaluated on a small, regular grid of parameter values, thus greatly reducing the wealth of admissible possibilities. The main obstacle to the use of computational models is their very high computational complexity, which prevents testing a large number of parameter values. Here, we show that it is possible to train a regressor that predicts the firing rate of nerve fibers stimulated according to a given multipolar electrical stimulation protocol, and show its possible application to a simplified model of optic nerve stimulation. Our results show that it is possible to build a very accurate surrogate model of nerve fiber stimulation, and that its reduced computational cost allows to perform automatic optimization of multipolar electrical stimulation protocols via evolutionary heuristics
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A computational model to design wide field-of-view optic nerve neuroprostheses
No full textSummary: Retinal stimulation (RS) allows restoring vision in blind patients, but it covers only a narrow region of the visual field. Optic nerve stimulation (ONS) has the potential to produce visual perceptions spanning the whole visual field, but it produces very irregular phosphenes. We introduced a geometrical model converting retinal and optic nerve firing rates into visual perceptions and vice versa and a method to estimate the best perceptions elicitable through an electrode configuration. We then compared in silico ONS and RS through simulated prosthetic vision of static and dynamic visual scenes. Both simulations and SPV experiments showed that it might be possible to reconstruct natural visual scenes with ONS and RS, and that ONS wide field-of-view allows the perception of more detail in dynamic scenarios than RS. Our findings suggest that ONS could represent an interesting approach for vision restoration and that our model can be used to optimize it
Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes
No full textAbstract Objective. Peripheral 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 subject’s 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. Approach. We 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 to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost. Main results. Our method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode 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. Significance. We 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.Fondation Bertarelli http://dx.doi.org/10.13039/10000915
Computational models of spinal sensorimotor pathways under epidural electrical stimulation
No full textSpinal cord injury (SCI) disrupts neuromuscular control, leading to motor impairments such as spasticity and abnormal reflex modulation, posing significant challenges for rehabilitation.
This thesis presents a computational modeling framework, developed through a systematic reimplementation and refinement of existing methodologies described in literature, which integrates biomechanical modeling, spiking neural networks, and muscle activation dynamics to investigate spinal sensorimotor pathways and neuromuscular function.
The core modeling approach involves the integration of a musculoskeletal model, responsible for generating movement and estimating sensory feedback, a spiking neural network, simulating spinal circuits that regulate motor output, and muscle activation dynamics modeling.
This framework enables the study of afferent-driven motoneuron activation, proprioceptive feedback mechanisms, and neuromuscular dynamics under both physiological and pathological conditions, such as spasticity and clonus. Additionally, it serves as a promising tool for investigating neuromuscular function and rehabilitation, with the potential to integrate EES simulations and explore its modulation of spinal circuits and motor control, advancing neuromechanical research and neuromodulation-based interventions for SCI rehabilitation
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation
No full textElectrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system seems to be a good trade-off between selectivity and invasiveness, and is thus a candidate to perform motor, sensory and autonomic function restoration. More and more sophisticated computational models of neuromodulation have been developed in the last fifty years, allowing the assessment of the expected performance of various neuroprosthetic devices and electrical stimulation protocols and the explanation of the fundamental mechanisms behind the success of neuromodulation interfaces. The complexity of such models, though, has prevented their use inside of true model-based optimization routines, since repeated computation of stimulation outcomes under different stimulation conditions requires a prohibitive amount of simulation time. The main contribution of the present work is the development of surrogate models to accelerate the evaluation of the neural variable of interest under given stimulation protocols, thus allowing the formulation of neuroprosthesis optimization frameworks. The possibility to perform model-based optimization, enabled by the use of surrogate models, leads to the need to personalize the models to the functional anatomy of the target subject. In a clinical setting, this needs to be done without any additional invasive procedure, which would negatively affect the patient's wellbeing. Here, frameworks for the determination of the functional organization of the target neural structures in the implanted patient are presented and characterized in silico, to accommodate both cases of acceptable and unacceptable off-target stimulation. The methods introduced in this work are modular and considerations on the development and use of these computational tools for different impairments, to target different parts of the nervous system and in different stages of research (fundamental, preclinical, and clinical) are outlined. The present thesis work paves the way for the development of model-based optimization of neuroprosthetic devices, with the promise to increase the effectiveness of electrical stimulation applications and at the same time reducing the amount of experimental data and resources needed to reinstate lost bodily functions in different cohorts of patients.TN
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Automatic monitoring of rehabilitation sessions through inertial measurements
No full textThe analysis of human movement is crucial in medical and biomechanical research, particularly for assessing motor impairments and rehabilitation strategies. In recent years, the use of sensors in physiotherapy has enabled objective evaluation of patient performance.
Inertial measurement units (IMUs) offer a portable and reliable solution for movement analysis, allowing the extraction of joint angles, acceleration, and velocity when placed in key body segments.
This thesis presents the design and implementation of algorithms to isolate and classify exercises performed during physiotherapy sessions. A dataset of healthy subjects and patients was collected using IMUs placed on key body segments. Data processing techniques, including autocorrelation analysis and decision trees, were applied for robust activity classification.
Additionally, lateral and backward gait were analyzed to automatically segment the gait cycle, testing machine learning and rule-based approaches.
Preliminary results indicate that IMUs can effectively characterize rehabilitation sessions and provide unbiased insights into exercise performance. These findings have potential applications in clinical assessments, rehabilitation monitoring, and movement disorder analysis, offering valuable support to healthcare professionals and researchers
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