INNOVATIVE EEG BIOMARKERS AND AI ALGORITHMS FOR THE EVALUATION OF PHYSIOLOGICAL AND PATHOLOGICAL AGING

Aging is a biologically natural process that changes the structure and functions gradually at different levels, from the cells to the large-scale brain networks. Usually, physiological aging is associated with mild cognitive change; however, in a significant number of people, the process can become pathological and eventually result in Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). Differentiating healthy from pathologic aging is still a hard scientific and clinical problem, especially in the preclinical and prodromal phases of neurodegenerative diseases. One method to study age-related brain changes is the electroencephalography (EEG), a non-invasive technique with a very high temporal resolution. Initial EEG studies have already contributed significantly to the understanding of age-related changes in brain oscillatory dynamics, but the combination of next-generation brain analytic methods, such as graph theory and entropy analysis, has the potential to reveal new biomarkers that can indicate not only physiological adaptations but also pathological deviations. Besides that, the new implementation of machine learning and artificial intelligence (AI) in the neurophysiological field represents a powerful tool for predictive and individualization purposes. The current PhD project, titled "Identification of innovative EEG biomarkers and AI algorithms for physiological and pathological aging evaluation", aims to identify novel EEG biomarkers and implement AI models to distinguish between age groups and cognitive states. Specifically, the project is meant to achieve three main goals: (i) through connectivity and entropy analysis to validate novel EEG features that show statistically significant differences not only between young, adult, and elderly subjects but also between healthy elderly controls, MCI subjects, and AD patients; (ii) to implement AI algorithms that can track brain age changes among healthy people and differentiate healthy elderly people from pathological group, with the ultimate goal of facilitating early detection, prognostic assessment, and treatment planning. 120 healthy volunteers equally spread across three age groups (young, adult, and old) and 40 MCI as well as 40 AD patients were enrolled. Additionally, data from databases were included, for a total of 109 healthy elderly subjects, 75 MCI subjects, and 85 AD patients. Resting-state EEG in eyes-closed condition will be recorded from all participants. Healthy elderly, MCI subjects, and AD patients will be evaluated through a battery of neuropsychological tests. Functional connectivity will be computed along with graph theoretic measures, and signal complexity will be described by Approximate Entropy. Additionally, to both brain age estimation and physiological 3 versus pathological aging classification, optimization and feature selection methods will be used to pinpoint the most discriminative EEG-derived predictors. These analyses allow to significantly raise the classification's precision and reliability and reveal the neurophysiological features with the most significant diagnostic and prognostic value. The research project would be a significant contribution to the emerging field of computational neurophysiology by facilitating the further application of EEG biomarkers and AI models. It would potentially lead to the implementation of robust biomarkers of aging which could be used for early detection of neurodegenerative processes, prognostic refinement, and, ultimately, the advancement of personalized therapy in the domain of age-related cognitive decline

Vastus Lateralis Muscle Fiber Conduction Velocity During Heavy and Severe Cycling Exercise

High-density surface electromyography has recently emerged as a tool to investigate neuromuscular function across different motor tasks. Muscle fiber conduction velocity, a biophysical marker estimated from high-density surface electromyography signals, quantifies the speed of action potential propagation along muscle fibers. Currently, little information is available on the responses of muscle fiber conduction velocity during dynamic exercise. We therefore explored how cycling within two distinct exercise-intensity domains (heavy and severe) elicited substantial changes in vastus lateralis muscle fiber conduction velocity. Furthermore, we compared muscle fiber conduction velocity with averaged rectified values obtained from the same high-density surface electromyography signals, and we tested differences in their respective intra-individual variability. In the severe-intensity domain, muscle fiber conduction velocity decreased from 4.44 ± 0.22 to 4.03 ± 0.22 m∙s − 1, whereas in the heavy-intensity domain, it increased from 4.56 ± 0.33 to 5.02 ± 0.42 m∙s − 1 (p < 0.001). Although both muscle fiber conduction velocity and averaged rectified values changed significantly over time, indicating the myoelectric manifestation of fatigue in the severe domain, muscle fiber conduction velocity showed less variability within single time points. These findings suggest that muscle fiber conduction velocity is a more robust indicator of myoelectric fatigue than averaged rectified values, even in highly dynamic contractions like cycling

Estimation of V̇O2peak and lactate threshold in recreationally active persons through new equations for a modified Åstrand’s maximal incremental treadmill test

Background The accurate and safe assessment of physiological parameters, such as the highest oxygen consumption (V̇O2peak) and the lactate threshold (LT), plays a crucial role in optimizing training intensity and monitoring long-term progress, ensuring the safety of the trainees. Sources of data Data were obtained during tests performed by 28 recreationally active young adults with varying levels and types of training. These individuals underwent a modified incremental Åstrand protocol for a treadmill test to exhaustion, during which their V̇O2peak and lactate threshold (Onset of Blood Lactate Accumulation method) were directly assessed. Areas of agreement Tests to exhaustion are considered valid methods for directly measuring V̇O2peak and determining LT. However, the direct assessment of V̇O2peak and LT requires specific equipment and raises various practical issues. Areas of controversy Indirect methods for estimating of V̇O2peak and lactate threshold are simpler to use, but these methods are often difficult to generalize or have a high margin of error from the many variables that can affect a subject’s V̇O2peak and lactate threshold. Growing points These results enable estimation of V̇O2peak and LT in apparently healthy young adults engaged in regular physical activity, regardless of their specific training backgrounds. Areas timely for developing research Future research should focus on developing more precise field-based methods for estimating V̇O2peak and lactate threshold, with lactate threshold presenting particular challenges due to the need for blood sampling equipment

La scelta del contratto collettivo da applicare nell'impresa: dall'art. 39 all'art. 41 della Costituzione?

Il contributo si pone l’obiettivo di individuare nell’art. 41 della Costituzione un solido riferimento normativo volto a garantire che il datore di lavoro non sindacalizzato, quando decide di farvi ricorso, applichi un contratto collettivo conferente all’attività di impresa effettivamente svolta.This contribution aims to identify a solid regulatory framework in Article 41 of Italian Constitution, to ensure that non-unionized employers, when he decides to resort to collective bargaining, apply a collective bargaining agreement that specifically addresses the business activity actually performed