Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression Recognition.

Facial expression recognition (FER) poses a complex challenge due to diverse factors such as facial morphology variations, lighting conditions, and cultural nuances in emotion representation. To address these hurdles, specific FER algorithms leverage advanced data analysis for inferring emotional states from facial expressions. In this study, we introduce a universal validation methodology assessing any FER algorithm's performance through a web application where subjects respond to emotive images. We present the labelled data database, FeelPix, generated from facial landmark coordinates during FER algorithm validation. FeelPix is available to train and test generic FER algorithms, accurately identifying users' facial expressions. A testing algorithm classifies emotions based on FeelPix data, ensuring its reliability. Designed as a computationally lightweight solution, it finds applications in online systems. Our contribution improves facial expression recognition, enabling the identification and interpretation of emotions associated with facial expressions, offering profound insights into individuals' emotional reactions. This contribution has implications for healthcare, security, human-computer interaction, and entertainment.

A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems.

The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data are collected. Edge computing, indeed, mitigates the limitations of cloud computing, implementing artificial intelligence algorithms directly on the embedded devices enabling low latency responses without network overhead or high costs, and improving solution scalability. Today, the hardware improvements of the edge devices make them capable of performing, even if with some constraints, complex computations, such as those required by Deep Neural Networks. Nevertheless, to efficiently implement deep learning algorithms on devices with limited computing power, it is necessary to minimize the production time and to quickly identify, deploy, and, if necessary, optimize the best Neural Network solution. This study focuses on developing a universal method to identify and port the best Neural Network on an edge system, valid regardless of the device, Neural Network, and task typology. The method is based on three steps: a trade-off step to obtain the best Neural Network within different solutions under investigation; an optimization step to find the best configurations of parameters under different acceleration techniques; eventually, an explainability step using local interpretable model-agnostic explanations (LIME), which provides a global approach to quantify the goodness of the classifier decision criteria. We evaluated several MobileNets on the Fudan Shangai-Tech dataset to test the proposed approach.

A Multi-Sensor System for Sea Water Iodide Monitoring and Seafood Quality Assurance: Proof-of-Concept Study.

Iodine is a trace chemical element fundamental for a healthy human organism. Iodine deficiency affects about 2 billion people worldwide causing from mild to severe neurological impairment, especially in children. Nevertheless, an adequate nutritional intake is considered the best approach to prevent such disorders. Iodine is present in seawater and seafood, and its common forms in the diet are iodide and iodate; most iodide in seawater is caused by the biological reduction of the thermodynamically stable iodate species. On this basis, a multisensor instrument which is able to perform a multidimensional assessment, evaluating iodide content in seawater and seafood (via an electrochemical sensor) and discriminating when the seafood is fresh or defrosted quality (via a Quartz Micro balance (QMB)-based volatile and gas sensor), is strategic for seafood quality assurance. Moreover, an electronic interface has been opportunely designed and simulated for a low-power portable release of the device, which should be able to identify seafood over or under an iodide threshold previously selected. The electrochemical sensor has been successfully calibrated in the range 10-640 µg/L, obtaining a root mean square error in cross validation (RMSECV) of only 1.6 µg/L. Fresh and defrosted samples of cod, sea bream and blue whiting fish have been correctly discriminated. This proof-of-concept work has demonstrated the feasibility of the proposed application which must be replicated in a real scenario.

Essential elements in determining patient's complexity of nursing care.

BACKGROUND: Nurses dedicate majority of working time to satisfy patients' needs, which are oftentimes complex. Although the concept of patient's complexity of care (PCC) has been extensively studied, it remains undefined in its essential characteristics. Various tools for assessing PCC have been developed, yet, there is no gold standard of reference. AIM: This study intends to explore the elements that determine PCC focusing on the patient's needs. METHODS: The bed management team of a University Hospital developed in 2010 a PCC measurement instrument, similar to a triage system, to classify rapidly patients into macro-levels of care complexity (low, medium, high, intensive). Descriptive analysis of prospectively collected data registered in nursing records during 2016-2017 are presented in this study. The incidence of individual metrics on the value assigned to the overall PCC level was calculated. RESULTS: 26593 patients' records were analyzed. The final level of PCC was equal to patient's level of autonomy in 92,2% of cases, to clinical stability in 74,4% and to cognitive status in 58,4%. CONCLUSIONS: Our finding indicate that essential elements to determine PCC are patient's autonomy and clinical stability. Therefore, nurses can use these metrics to classify quickly PCC in their work settings. NURSING IMPLICATIONS: Identifying and measuring PCC is very important for nurses. It can facilitate and support nurse decision-making in design, implementation and evaluation of activities aimed at improving patient care. Moreover, it can help in the distribution of care loads in the shift, and from an organizational point of view, it can allow defining staffing assets.

CO2 and O2 Detection by Electric Field Sensors.

In this work an array of chemical sensors for gas detection has been developed, starting with a commercial sensor platform developed by Microchip (GestIC), which is normally used to detect, trace, and classify hand movements in space. The system is based on electric field changes, and in this work, it has been used as mechanism revealing the adsorption of chemical species CO2 and O2. The system is composed of five electrodes, and their responses were obtained by interfacing the sensors with an acquisition board based on an ATMEGA 328 microprocessor (Atmel MEGA AVR microcontroller). A dedicated measurement chamber was designed and prototyped in acrylonitrile butadiene styrene (ABS) using an Ultimaker3 3D printer. The measurement cell size is 120 × 85 mm. Anthocyanins (red rose) were used as a sensing material in order to functionalize the sensor surface. The sensor was calibrated using different concentrations of oxygen and carbon dioxide, ranging from 5% to 25%, mixed with water vapor in the range from 50% to 90%. The sensor exhibits good repeatability for CO2 concentrations. To better understand the sensor response characteristics, sensitivity and resolution were calculated from the response curves at different working points. The sensitivity is in the order of magnitude of tens to hundreds of µV/% for CO2, and of µV/% in the case of O2. The resolution is in the range of 10-1%-10-3% for CO2, and it is around 10-1% for O2. The system could be specialized for different fields, for environmental, medical, and food applications.

VENUS BY BOTTICELLI AND HER PITUITARY ADENOMA.

Objective: Simonetta Vespucci, considered the most beautiful woman of the Renaissance, is the inspiration and face of one of the most famous paintings of all times, "The Birth of Venus," by Botticelli. She died in 1476 at the age of 23 years. We postulate she suffered from a pituitary-secreting tumor progressing to pituitary apoplexy. The goals of this study were 3-fold: (i) verify that the subject depicted by Botticelli in different paintings represents the same woman; (ii) identify the facial traits affected by the progression of a growth hormone- and prolactin-secreting tumor; and (iii) confirm that the observed changes of the face traits observed in the portraits of Simonetta Vespucci are compatible with the facial traits changes identified earlier. Methods: Comparison among face traits was based on the analysis of the face regions measured by means of fiducial points and their distances, and after pose compensation based on three-dimensional head modelling. Results: In favor of the hypothesis that Simonetta suffered from a pituitary growth hormone- and prolactin-secreting tumor stands changes of her lineaments, a feature which becomes evident over the years and particularly manifest in the Allegorical Lady, where galactorrhea is depicted. Conclusion: We conclude that sufficient evidence is presented to suggest that Simonetta Vespucci, the Venus depicted by Botticelli, suffered from pituitary adenoma secreting prolactin and growth hormon with parasellar expansion. The current interpretation of the Venus strabism should be revisited according to this finding. Abbreviation: GH = growth hormone.

Sensorimotor cortex excitability and connectivity in Alzheimer's disease: A TMS-EEG Co-registration study.

Several studies have shown that, in spite of the fact that motor symptoms manifest late in the course of Alzheimer's disease (AD), neuropathological progression in the motor cortex parallels that in other brain areas generally considered more specific targets of the neurodegenerative process. It has been suggested that motor cortex excitability is enhanced in AD from the early stages, and that this is related to disease's severity and progression. To investigate the neurophysiological hallmarks of motor cortex functionality in early AD we combined transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We demonstrated that in mild AD the sensorimotor system is hyperexcitable, despite the lack of clinically evident motor manifestations. This phenomenon causes a stronger response to stimulation in a specific time window, possibly due to locally acting reinforcing circuits, while network activity and connectivity is reduced. These changes could be interpreted as a compensatory mechanism allowing for the preservation of sensorimotor programming and execution over a long period of time, regardless of the disease's progression. Hum Brain Mapp 37:2083-2096, 2016. © 2016 Wiley Periodicals, Inc.

Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application.

BACKGROUND: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population. METHODS: A Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) Recurrent Neural Network were optimized on glucose, insulin, and meal data of 10 virtual pediatric patients. The trained models were then implemented on two edge-computing boards to evaluate the feasibility of an edge system for glucose forecasting in terms of prediction accuracy and inference time. RESULTS: The LSTM model achieved the best numeric and clinical accuracy when tested in the .tflite format, whereas the CNN achieved the best clinical accuracy in uint8. The inference time for each prediction was far under the limit represented by the sampling period. CONCLUSION: Both models effectively predict glucose in pediatric patients in terms of numerical and clinical accuracy. The edge implementation did not show a significant performance decrease, and the inference time was largely adequate for a real-time application.

Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review.

Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Machine Learning", "Deep Learning", "Neural Network", "Computer Aided Diagnosis", "Low Back Pain", "Lumbar", "Intervertebral Disc Degeneration", "Spine Surgery", etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.

Natural language processing in low back pain and spine diseases: A systematic review.

Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models' points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.

Measuring the effects of first antiepileptic medication in Temporal Lobe Epilepsy: Predictive value of quantitative-EEG analysis.

OBJECTIVE: To determine the quantitative EEG responses in a population of drug-naïve patients with Temporal Lobe Epilepsy (TLE) after Levetiracetam (LEV) initiation as first antiepileptic drug (AED). We hypothesized that the outcome of AED treatment can be predicted from EEG data in patients with TLE. METHODS: Twenty-three patients with TLE and twenty-five healthy controls were examined. Clinical outcome was dichotomized into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG parameters were compared between healthy controls and patients with TLE at baseline (EEGpre) and after three months of AED therapy (EEGpre-post) and between SF and NSF patients. Receiver Operating Characteristic curves models were built to test whether EEG parameters predicted outcome. RESULTS: AED therapy induces an increase in EEG power for Alpha (p = 0.06) and a decrease in Theta (p < 0.05). Connectivity values were lower in SF compared to NSF patients (p < 0.001). Quantitative EEG predicted outcome after LEV treatment with an estimated accuracy varying from 65.2% to 91.3% (area under the curve [AUC] = 0.56-0.93) for EEGpre and from 69.9% to 86.9% (AUC = 0.69-0.94) for EEGpre-post. CONCLUSIONS: AED therapy induces EEG modifications in TLE patients, and such modifications are predictive of clinical outcome. SIGNIFICANCE: Quantitative EEG may help understanding the effect of AEDs in the central nervous system and offer new prognostic biomarkers for patients with epilepsy.

TMS-EEG Biomarkers of Amnestic Mild Cognitive Impairment Due to Alzheimer's Disease: A Proof-of-Concept Six Years Prospective Study.

Background: Early and affordable identification of subjects with amnestic mild cognitive impairment (aMCI) who will convert to Alzheimer's disease (AD) is a major scientific challenge. Objective: To investigate the neurophysiological hallmarks of sensorimotor cortex function in aMCI under the hypothesis that some may represent the plastic rearrangements induced by neurodegeneration, hence predictors of future conversion to AD. We sought to determine (1) whether the sensorimotor network shows peculiar alterations in patients with aMCI and (2) if sensorimotor network alterations predict long-term disease progression at the individual level. Methods: We studied several transcranial magnetic stimulation (TMS)-electroencephalogram (EEG) parameters of the sensorimotor cortex in a group of patients with aMCI and followed them for 6 years. We then identified aMCI who clinically converted to AD [prodromal to AD-MCI (pAD-MCI)] and those who remained cognitively stable [non-prodromal to AD-MCI (npAD-MCI)]. Results: Patients with aMCI showed reduced motor cortex (M1) excitability and disrupted EEG synchronization [decreased intertrial coherence (ITC)] in alpha, beta and gamma frequency bands compared to the control subjects. The degree of alteration in M1 excitability and alpha ITC was comparable between pAD-MCI and npAD-MCI. Importantly, beta and gamma ITC impairment in the stimulated M1 was greater in pAD-MCI than npAD-MCI. Furthermore, an additional parameter related to the waveform shape of scalp signals, reflecting time-specific alterations in global TMS-induced activity [stability of the dipolar activity (sDA)], discriminated npAD-MCI from MCI who will convert to AD. Discussion: The above mentioned specific cortical changes, reflecting deficit of synchronization within the cortico-basal ganglia-thalamo-cortical loop in aMCI, may reflect the pathological processes underlying AD. These changes could be tested in larger cohorts as neurophysiological biomarkers of AD.

A Mobile App for the Remote Monitoring and Assistance of Patients with Parkinson's Disease and their Caregivers.

The remote monitoring of patients is based on digital systems that enable the remote collection, usually at home, of health data and its transmission to health centers. The telemedicine paradigm is of particular interest in chronic diseases, fragile population and elderly monitoring. Parkinson's disease (PD) is a neurodegenerative disorder having high impact on the lives of patients and their families. Such a disease impacts on the physical and psychological abilities of the patient and may have an effect on the relationship among family members. The strict monitoring of PD patients and their caregivers is of paramount importance in the implementation of prompt actions counteracting the worsening of the disease or that of the caring process. In this paper we present a mobile App developed for PD patients and their caregiver. The App aims at improving the communication among the patient/caregiver and the specialists, covering aspects related to both the disease symptoms and the caring process. In the paper we describe the App along with results collected during a one year experimentation on a cohort of 10 patients and 7 caregivers. The results show that the approach is accepted by patients and caregivers. Furthermore, obtained results demonstrate that the monitoring system is effective in the identification of dangerous conditions for the patient and useful in the implementation of reactive health management strategies.

Advances in the Electronics for Cyclic Voltammetry: the Case of Gas Detection by Using Microfabricated Electrodes.

This paper presents an advanced voltammetric system to be used as electronic tongue for liquid and gas analysis. It has been designed to be more flexible and accurate with respect to other existing and similar systems. It features improved electronics and additional operative conditions. Among others these include the possibility to optically excite the solution and to treat the output signal by a differentiation process in order to better evidence the existence of small details in the response curve. Finally by the same type of tongue preliminary results are shown dealing with O2 and CO2 concentration measurements in appropriate solutions.

An integrated system for the monitoring of therapy and drug's side effects in Lymphoproliferative disorders.

The wide diffusion of telecommunication systems and the availability of cheap computational devices, such as smart wearable, PDA and smartphones, is multiplying the number of collaborative and remote-monitored applications accessible to a large mass of people. In particular, this scenario makes it possible the implementation of specific systems that improve the control of patients with minimal impact on the quality of their lives. This paper moves in this context and presents a general system for the continuous monitoring at home of therapy and disease symptoms. Indeed, starting from a specific application aiming at monitoring patients with Lymphoproliferative disorders and the side effects related to specific drugs used in treatment of these diseases, in this paper we present a more general framework easy customizable to the requirements of different applications. In particular, the proposed system has been designed to be easily tuned to a larger class of disorders and, in our opinion, it can be applied in almost all the scenarios where patients require a strict monitoring of their conditions in their home environment. The paper presents the model, the architecture and the implementation of the system.

Age-related changes of cortical excitability and connectivity in healthy humans: non-invasive evaluation of sensorimotor network by means of TMS-EEG.

The sensorimotor cortical system undergoes structural and functional changes across its lifespan. Some of these changes are physiological and parallel the normal aging process, while others might represent pathophysiological mechanisms underlying neurodegenerative disorders. In the last years, the study of possible age-related modifications in brain sensorimotor functional characteristics has been the focus of several research projects. Here we have used the transcranial magnetic stimulation (TMS)-electroencephalography (EEG) navigated co-registration to investigate the influence of physiological aging on the excitability and connectivity of the human sensorimotor cortical system. To this end, we compared the TMS-evoked EEG potentials (TEPs) collected after stimulating the dominant primary motor cortex (M1) in healthy young subjects (mean age 24.5years) with those collected in healthy older adults (mean age 67.6years). We have shown that, after stimulation of the left motor cortex, TEPs are significantly affected by physiological aging. This phenomenon has a clear spatio-temporal specificity and we speculate that normal aging per se leads to some changes in the excitability of specific cortical neural assemblies whereas other alterations could reflect compensatory mechanisms to such changes.

Age related differences in functional synchronization of EEG activity as evaluated by means of TMS-EEG coregistrations.

It was recently demonstrated that the characteristics of EEG rhythms preceding a transcranial magnetic stimulation (TMS) of the motor cortex (M1) influence the motor-evoked potential (MEP) amplitude with a peculiar pattern, thus reflecting the M1 functional state. As physiological aging is related to a decrease in motor performance and changes in excitability and connectivity strength within cerebral sensorimotor circuits, we aimed to explore whether aging affects EEG-MEP interactions. Using MRI-navigated TMS and multichannel EEG, we compared the EEG-MEP interactions observed in healthy aged subjects with those observed in young volunteers. We divided the MEPs amplitude into two different subgroups consisting of "high" and "low" MEPs, based on the 50th percentile of their amplitude distribution. Then we analysed the characteristics of the pre-stimulus EEG from M1 and correlated areas separately for the "high" and "low" MEPs, comparing the two conditions. In both young and old subjects, significantly larger MEPs were evoked when the stimulated M1 was coupled in the beta-2 band with the homolateral prefrontal cortex. Conversely, only in young participants was the MEP size modulated when the M1 and homolateral parieto-occipital cortices were coupled in the delta band. The elderly didn't show this kind of pattern. Importantly, this coupling was significantly higher in elderly brains than in young brains, both for high and low MEPs. Our results suggest an age-related significant influence of time-varying coupling of spatially patterned EEG rhythms on motor cortex excitability in response to TMS.

Optimal weighted averaging of event related activity from acquisitions with artifacts.

In several biomedical applications that require the signal processing of biological data, the starting procedure for noise reduction is the ensemble averaging of multiple repeated acquisitions (trials). This method is based on the assumption that each trial is composed of two additive components: (i) a time-locked activity related to some sensitive/stimulation phenomenon (ERA, Event Related Activity in the following) and (ii) a sum of several other non time-locked background activities. The averaging aims at estimating the ERA activity under very low Signal to Noise and Interference Ratio (SNIR). Although averaging is a well established tool, its performance can be improved in the presence of high-power disturbances (artifacts) by a trials classification and removal stage. In this paper we propose, model and evaluate a new approach that avoids trials removal, managing trials classified as artifact-free and artifact-prone with two different weights. Based on the model, a weights tuning is possible and through modeling and simulations we show that, when optimally configured, the proposed solution outperforms classical approaches.

Neurophysiological features of motor cortex excitability and plasticity in Subcortical Ischemic Vascular Dementia: a TMS mapping study.

OBJECTIVE: To evaluate neurophysiological features of M1 excitability and plasticity in Subcortical Ischemic Vascular Dementia (SIVD), by means of a TMS mapping study. METHODS: Seven SIVD and nine AD patients, along with nine control subjects were tested. The M1 excitability was studied by resting thresholds, area and volume of active cortical sites for forearm and hand's examined muscles. For M1 plasticity, coordinates of the hot-spot and the center of gravity (CoG) were evaluated. The correlation between the degree of hyperexcitability and the amount of M1 plastic rearrangement was also calculated. RESULTS: Multivariate analysis of excitability measures demonstrated similarly enhanced cortical excitability in AD and SIVD patients with respect to controls. SIVD patients showed a medial and frontal shift of CoG from the hot-spot, not statistically different from that observed in AD. A significant direct correlation was seen between parameters related to cortical excitability and those related to cortical plasticity. CONCLUSIONS: The results suggest the existence of common compensatory mechanisms in different kind of dementing diseases supporting the idea that cortical hyperexcitability can promote cortical plasticity. SIGNIFICANCE: This study characterizes neurophysiological features of motor cortex excitability and plasticity in SIVD, providing new insights on the correlation between cortical excitability and plasticity.