State of art of mobility medicine: some more abstracts and evidence that the success of Pdm3 is based on extra-session relationships.

Scientific conferences increasingly suffer from the need for short presentations in which speakers like to dwell on the details of their work. A mitigating factor is to encourage discussion and planning of collaborations by organizing small meetings in a hotel large enough to host all attendees. This extends discussions' opportunities during morning breakfasts, lunches, dinners and long evenings together. Even if the vast majority of participants will not stay for the entire duration of the Conference, the possibilities for specialists to interact with specialists who are even very distant in terms of knowledge increase enormously. In any case, the results in terms of new job opportunities for young participants outweigh the costs for the organizers. Thirty years of Padova Muscle Days offer many examples, but the authors of this report on the state of the art of Mobility Medicine testify that this also happened in the 2024 Five Days of Muscle and Mobility Medicine (2024Pdm3) hosted at the Hotel Petrarca, Thermae of Euganea Hills and Padua, Italy which is in fact a valid countermeasure to the inevitable tendencies towards hyperspecialization that the explosive increase in scientific progress brings with it.

Development of soft tissue asymmetry indicators to characterize aging and functional mobility.

Introduction: The aging population poses significant challenges to healthcare systems globally, necessitating a comprehensive understanding of age-related changes affecting physical function. Age-related functional decline highlights the urgency of understanding how tissue composition changes impact mobility, independence, and quality of life in older adults. Previous research has emphasized the influence of muscle quality, but the role of tissue composition asymmetry across various tissue types remains understudied. This work develops asymmetry indicators based on muscle, connective and fat tissue extracted from cross-sectional CT scans, and shows their interplay with BMI and lower extremity function among community-dwelling older adults. Methods: We used data from 3157 older adults from 71 to 98 years of age (mean: 80.06). Tissue composition asymmetry was defined by the differences between the right and left sides using CT scans and the non-Linear Trimodal Regression Analysis (NTRA) parameters. Functional mobility was measured through a 6-meter gait (Normal-GAIT and Fast-GAIT) and the Timed Up and Go (TUG) performance test. Statistical analysis included paired t-tests, polynomial fitting curves, and regression analysis to uncover relationships between tissue asymmetry, age, and functional mobility. Results: Findings revealed an increase in tissue composition asymmetry with age. Notably, muscle and connective tissue width asymmetry showed significant variation across age groups. BMI classifications and gait tasks also influenced tissue asymmetry. The Fast-GAIT task demonstrated a substantial separation in tissue asymmetry between normal and slow groups, whereas the Normal-GAIT and the TUG task did not exhibit such distinction. Muscle quality, as reflected by asymmetry indicators, appears crucial in understanding age-related changes in muscle function, while fat and connective tissue play roles in body composition and mobility. Discussion: Our study emphasizes the importance of tissue asymmetry indicators in understanding how muscle function changes with age in older individuals, demonstrating their role as risk factor and their potential employment in clinical assessment. We also identified the influence of fat and connective tissue on body composition and functional mobility. Incorporating the NTRA technology into clinical evaluations could enable personalized interventions for older adults, promoting healthier aging and maintaining physical function.

Five Padua days on muscle and mobility medicine (2024Pdm3) 27 February - 2 March, 2024 at Hotel Petrarca, Thermae of Euganean Hills, Padua, and San Luca Hall, Prato della Valle, Padua, Italy.

At the end of the 2023 Padua Days of Muscle and Mobility Medicine the next year's meeting was scheduled from 27 February to 2 March 2024 (2024Pdm3). During the summer and autumn the program was confirmed with Scientific Sessions that will take place over five days, starting in the afternoon of February 27, 2024 at the Conference Room of the Hotel Petrarca, Thermae of Euganean Hills (Padua), Italy. As usual, the next day will be spent in Padua, in this occasion at the San Luca Hall of the Santa Giustina monastery in Prato della Valle, Padua, Italy. Collected during Autumn 2023, many more titles and abstracts than expected were submitted, forcing the organization of parallel sessions both on March 1 and March 2 2024 confirming attractiveness of the 2024 Pdm3. The five days will include oral presentations of scientists and clinicians from Argentina, Austria, Belgium, Brazil, Canada, Denmark, Egypt, France, Germany, Iceland, Ireland, Italy, Romania, Russia, Slovenia, Switzerland, UK and USA. Together with the preliminary Program at December 1, 2023, the early submitted Abstracts is e-published in this Issue 33 (4) 2023 of the European Journal of Translational Myology (EJTM). You are invited to join, submitting your Last Minute Abstracts to ugo.carraro@unipd.it by February 1, 2024. Furthermore, with the more generous deadline of May 20, 2024, submit please "Communications" to the European Journal of Translational Myology (Clarivate's ESCI Impact factor 2.2; SCOPUS Cite Score: 3.2). See you soon at the Hotel Petrarca in Montegrotto Terme, Padua, on February 27, 2024, but the complete program can be followed from home via zoom connection.

Fragmented Dermo-Epidermal Units (FdeU) as an Emerging Strategy to Improve Wound Healing Process: An In Vitro Evaluation and a Pilot Clinical Study.

Innovative strategies have shown beneficial effects in healing wound management involving, however, a time-consuming and arduous process in clinical contexts. Micro-fragmented skin tissue acts as a slow-released natural scaffold and continuously delivers growth factors, and much other modulatory information, into the microenvironment surrounding damaged wounds by a paracrine function on the resident cells which supports the regenerative process. In this study, in vitro and in vivo investigations were conducted to ascertain improved effectiveness and velocity of the wound healing process with the application of fragmented dermo-epidermal units (FdeU), acquired via a novel medical device (Hy-Tissue® Micrograft Technology). MTT test; LDH test; ELISA for growth factor investigation (IL) IL-2, IL-6, IL-7 IL-8, IL-10; IGF-1; adiponectin; Fibroblast Growth Factor (FGF); Vascular Endothelial Growth Factor (VEGF); and Tumor Necrosis Factor (TNF) were assessed. Therefore, clinical evaluation in 11 patients affected by Chronic Wounds (CW) and treated with FdeU were investigated. Functional outcome was assessed pre-operatory, 2 months after treatment (T0), and 6 months after treatment (T1) using the Wound Bed Score (WBS) and Vancouver Scar Scale (VSS). In this current study, we demonstrate the potential of resident cells to proliferate from the clusters of FdeU seeded in a monolayer that efficiently propagate the chronic wound. Furthermore, in this study we report how the discharge of trophic/reparative proteins are able to mediate the in vitro paracrine function of proliferation, migration, and contraction rate in fibroblasts and keratinocytes. Our investigations recommend FdeU as a favorable tool in wound healing, displaying in vitro growth-promoting potential to enhance current therapeutic mechanisms.

Human Bone-Marrow-Derived Stem-Cell-Seeded 3D Chitosan-Gelatin-Genipin Scaffolds Show Enhanced Extracellular Matrix Mineralization When Cultured under a Perfusion Flow in Osteogenic Medium.

Tissue-engineered bone tissue grafts are a promising alternative to the more conventional use of natural donor bone grafts. However, choosing an appropriate biomaterial/scaffold to sustain cell survival, proliferation, and differentiation in a 3D environment remains one of the most critical issues in this domain. Recently, chitosan/gelatin/genipin (CGG) hybrid scaffolds have been proven as a more suitable environment to induce osteogenic commitment in undifferentiated cells when doped with graphene oxide (GO). Some concern is, however, raised towards the use of graphene and graphene-related material in medical applications. The purpose of this work was thus to check if the osteogenic potential of CGG scaffolds without added GO could be increased by improving the medium diffusion in a 3D culture of differentiating cells. To this aim, the level of extracellular matrix (ECM) mineralization was evaluated in human bone-marrow-derived stem cell (hBMSC)-seeded 3D CGG scaffolds upon culture under a perfusion flow in a dedicated custom-made bioreactor system. One week after initiating dynamic culture, histological/histochemical evaluations of CGG scaffolds were carried out to analyze the early osteogenic commitment of the culture. The analyses show the enhanced ECM mineralization of the 3D perfused culture compared to the static counterpart. The results of this investigation reveal a new perspective on more efficient clinical applications of CGG scaffolds without added GO.

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings.

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Adaptation strategies and neurophysiological response in early-stage Parkinson's disease: BioVRSea approach.

Introduction: There is accumulating evidence that many pathological conditions affecting human balance are consequence of postural control (PC) failure or overstimulation such as in motion sickness. Our research shows the potential of using the response to a complex postural control task to assess patients with early-stage Parkinson's Disease (PD). Methods: We developed a unique measurement model, where the PC task is triggered by a moving platform in a virtual reality environment while simultaneously recording EEG, EMG and CoP signals. This novel paradigm of assessment is called BioVRSea. We studied the interplay between biosignals and their differences in healthy subjects and with early-stage PD. Results: Despite the limited number of subjects (29 healthy and nine PD) the results of our work show significant differences in several biosignals features, demonstrating that the combined output of posturography, muscle activation and cortical response is capable of distinguishing healthy from pathological. Discussion: The differences measured following the end of the platform movement are remarkable, as the induced sway is different between the two groups and triggers statistically relevant cortical activities in α and θ bands. This is a first important step to develop a multi-metric signature able to quantify PC and distinguish healthy from pathological response.

Predicting stroke, neurological and movement disorders using single and dual-task gait in Korean older population.

BACKGROUND: Single and motor or cognitive dual-gait analysis is often used in clinical settings to evaluate older adults affected by neurological and movement disorders or with a stroke history. Gait features are frequently investigated using Machine Learning (ML) with significant results that can help clinicians in diagnosis and rehabilitation. The present study aims to classify patients with stroke, neurological and movement disorders using ML to analyze gait characteristics and to understand the importance of the single and dual-task features among Korean older adults. METHODS: A cohort of 122 non-hospitalized Korean older adult participated in a single and a cognitive dual-task gait performance analysis. The extracted temporal and spatial features, together with clinical data, were used as input for the binary classification using tree-based ML algorithms. A repeated-stratified 10-fold cross-validation was performed to better evaluate multiple classification metrics with a final feature importance analysis. RESULTS AND SIGNIFICANCE: The best accuracy - maximum >90 % - for gait and neurological disorders classification was obtained with Random Forest. In the stroke classification a 91.7 % of maximum accuracy was reached, with a significant recall of 92 %. The feature importance analysis showed a substantial balance between single and dual-task, while clinical data did not show elevated importance. The current findings indicate that a cognitive dual-task gait performance is highly recommendable together with a single-task in the analysis of older population, particularly for patients with a history of stroke. The results could be useful to medical professionals in treating and diagnosing motor and neurological disorders, and to improve rehabilitation strategies for stroke patients. Furthermore, the results confirm the proficiency of the tree-based ML algorithms in biomedical data analysis. Finally, in the future, this research could be replicated with a non-Asian population dataset to deepen the understanding of gait differences between Asian-Korean population and other ethnicities.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings.

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Abstracts of the 2023 Padua Days of Muscle and Mobility Medicine (2023Pdm3) to be held March 29 - April 1 at the Galileian Academy of Padua and at the Petrarca Hotel, Thermae of Euganean Hills, Padua, Italy.

At the end of the 2022 Padua Days of Muscle and Mobility Medicine (Pdm3) the next year's meeting was scheduled from 29 March to 1 April 2023. Despite the worsening evolution of the crisis in Eastern Europe, the program was confirmed in autumn 2022 with Scientific Sessions that will take place over three full days in the Aula Guariento of the Galileian Academy of Arts, Letters and Sciences of Padua (March 29, 2023) and then at the Conference Room of the Hotel Petrarca, Thermae of Euganean Hills (Padua), Italy. Collected during autumn and early winter, many titles and abstracts where submitted (about 100 Oral presentations are listed in the preliminary Program by January 31, 2023) confirming attractiveness of the 2023 Pdm3. The four days will include oral presentations of scientists and clinicians from Austria, Bulgaria, Canada, Denmark, France, Georgia, Germany, Iceland, Ireland, Italy, Mongolia, Norway, Russia, Slovakia, Slovenia, Spain, Switzerland, The Netherlands and USA. Together with the preliminary Program at January 31, 2023, the Collection of Abstracts is e-published in this Issue 33 (1) 2023 of the European Journal of Translational Myology (EJTM). You are invited to join, submitting your Last Minute Abstracts to ugo.carraro@unipd.it by March 15, 2023. Furthermore, with the more generous deadline of May 20, 2023, submit please "Communications" to the European Journal of Translational Myology (SCOPUS Cite Score Tracker 2023: 3.2 by January 5, 2023) and/or to the 2023 Special Issue: "Pdm3" of the Journal Diagnostics, MDPI, Basel (I.F. near to 4.0) with deadline September 30, 2023. Both journals will provide discounts to the first accepted typescripts. See you soon at the Hotel Petrarca of Montegrotto Terme, Padua, Italy. For a promo of the 2023 Pdm3 link to: https://www.youtube.com/watch?v=zC02D4uPWRg.

Toward New Assessment of Knee Cartilage Degeneration.

OBJECTIVE: Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. DESIGN: We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. RESULTS: Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. CONCLUSION: The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.

Motion sickness susceptibility and visually induced motion sickness as diagnostic signs in Parkinson's disease.

Postural instability and loss of vestibular and somatosensory acuity can be part of the signs encountered in Parkinson's Disease (PD). Visual dependency is described in PD. These modifications of sensory input hierarchy are predictors of motion sickness (MS). The aim of this study was to assess MS susceptibility and effects of real induced MS in posture. 63 PD patients, whose medication levels (levodopa) reflected the pathology were evaluated, and 27 healthy controls, filled a MS questionnaire; 9 PD patients and 43 healthy controls were assessed by posturography using virtual reality. Drug amount predicted visual MS (p=0.01), but not real induced MS susceptibility. PD patients did not experience postural instability in virtual reality, contrary to healthy controls. Since PD patients do not seem to feel vestibular stimulated MS, they may not rely on vestibular and somatosensory inputs during the stimulation. However, they feel visually induced MS more with increased levodopa drug effect. Levodopa amount can increase visual dependency. The strongest MS predictors must be studied in PD to better understand the effect of visual stimulation and its absence in vestibular stimulation.

Biomaterials for Regenerative Medicine in Italy: Brief State of the Art of the Principal Research Centers.

Regenerative medicine is the branch of medicine that effectively uses stem cell therapy and tissue engineering strategies to guide the healing or replacement of damaged tissues or organs. A crucial element is undoubtedly the biomaterial that guides biological events to restore tissue continuity. The polymers, natural or synthetic, find wide application thanks to their great adaptability. In fact, they can be used as principal components, coatings or vehicles to functionalize several biomaterials. There are many leading centers for the research and development of biomaterials in Italy. The aim of this review is to provide an overview of the current state of the art on polymer research for regenerative medicine purposes. The last five years of scientific production of the main Italian research centers has been screened to analyze the current advancement in tissue engineering in order to highlight inputs for the development of novel biomaterials and strategies.

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration.

Knee Osteoarthritis (OA) is a highly prevalent condition affecting knee joint that causes loss of physical function and pain. Clinical treatments are mainly focused on pain relief and limitation of disabilities; therefore, it is crucial to find new paradigms assessing cartilage conditions for detecting and monitoring the progression of OA. The goal of this paper is to highlight the predictive power of several features, such as cartilage density, volume and surface. These features were extracted from the 3D reconstruction of knee joint of forty-seven different patients, subdivided into two categories: degenerative and non-degenerative. The most influent parameters for the degeneration of the knee cartilage were determined using two machine learning classification algorithms (logistic regression and support vector machine); later, box plots, which depicted differences between the classes by gender, were presented to analyze several of the key features' trend. This work is part of a strategy that aims to find a new solution to assess cartilage condition based on new-investigated features.

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea).

Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior-posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

Propionibacterium acnes in urine and semen samples from men with urinary infection.

OBJECTIVE: Propionibacterium acnes has been implicated in the pathogenesis of prostate disease as acute and chronic prostatic inflammation, benign prostatic hyperplasia and prostate cancer although it should still be clarified if Propionibacterium acnes (P. acnes) is a commensal or accidental prostate pathogen. Aiming to evaluate the pathogenic potential for genitourinary tract of Propionibacterium acnes, we investigated the frequency of P. acnes genome in urine or semen samples from men with recurrent symptoms of urinary infection and negative testing for the most common urinary tract pathogens and sexually transmitted infections (STI) agents as Chlamydia trachomatis, Mycoplasma genitalium, Mycoplasma hominis, Ureaplasma parvum and Ureaplasma urealyticum. MATERIALS AND METHODS: The DNA extracted from urine and semen samples was analyzed for evaluating the P. acnes genome presence by real-time polymerase chain reaction (PCR). Infections were treated with vancomycin and cephalosporins antibiotics and then the search for the P.acnes genome by realtime PCR was repeated. RESULTS: The P. acnes qualitative real-time PCR revealed the genome in 73 out of 159 samples examined (108 urine and 51 semen). After antibiotic therapy, P. acnes was never detected. CONCLUSIONS: These results suggested that P. acnes genome determination should be performed in cases of chronic inflammation in the urinary tract to identify an unknown potential pathogen of genitourinary tract.

Active Materials for 3D Printing in Small Animals: Current Modalities and Future Directions for Orthopedic Applications.

The successful clinical application of bone tissue engineering requires customized implants based on the receiver's bone anatomy and defect characteristics. Three-dimensional (3D) printing in small animal orthopedics has recently emerged as a valuable approach in fabricating individualized implants for receiver-specific needs. In veterinary medicine, because of the wide range of dimensions and anatomical variances, receiver-specific diagnosis and therapy are even more critical. The ability to generate 3D anatomical models and customize orthopedic instruments, implants, and scaffolds are advantages of 3D printing in small animal orthopedics. Furthermore, this technology provides veterinary medicine with a powerful tool that improves performance, precision, and cost-effectiveness. Nonetheless, the individualized 3D-printed implants have benefited several complex orthopedic procedures in small animals, including joint replacement surgeries, critical size bone defects, tibial tuberosity advancement, patellar groove replacement, limb-sparing surgeries, and other complex orthopedic procedures. The main purpose of this review is to discuss the application of 3D printing in small animal orthopedics based on already published papers as well as the techniques and materials used to fabricate 3D-printed objects. Finally, the advantages, current limitations, and future directions of 3D printing in small animal orthopedics have been addressed.

CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Cortical Pathways During Postural Control: New Insights From Functional EEG Source Connectivity.

Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.

Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes: BioVRSea paradigm.

Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task. Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses. Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML). Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.