Leonardo Ariel Cano

Motor planning emerges from the integration of sensory information, effector selection, and inhibitory control within large-scale cortical networks. Although behavioral asymmetries between dominant and non-dominant hands have been widely reported, the network-level mechanisms remain poorly understood. Here, we examined whether differences in motor planning efficiency reflect distinct patterns of cortical network organization. Seventeen healthy right-handed individuals performed a visually guided hand-selection task while EEG, EMG, and kinematic signals were recorded. Motor planning time was defined as the interval between stimulus onset and the onset of agonist muscle activity. Functional coupling during planning was estimated in the beta band and characterized using graph-theoretical measures of network integration, segregation, and regional hubness. Planning time was significantly longer for dominant-hand movements, yet correlated across hands, indicating partially shared planning processes. Only dominant-hand planning showed systematic relationships between behavior and network organization. Longer planning times were associated with increased global efficiency and clustering, suggesting enhanced integration and local specialization as planning demands increased. Regional analyses revealed a left-lateralized set of temporal, sensorimotor, and parietal functional hubs selectively engaged during dominant-hand planning. In particular, planning time increases with functional coupling between a left parietal and contralateral frontal regions pointed to dynamic interhemispheric coordination. No comparable network–behavior relationships were observed during non-dominant hand planning. These findings demonstrate that motor planning efficiency is constrained by the dynamic reconfiguration of large-scale cortical networks rather than by isolated regional activations. Dominant-hand planning recruits an asymmetric parieto-frontal network whose increasing integration scales with planning demands, consistent with bounded evidence-accumulation frameworks.

High-intensity interval training (HIIT) is presented as a safe, effective, and time-efficient strategy for individuals with type 1 diabetes, offering benefits for glycemic control, cardiovascular function, and physical fitness, with a lower risk of hypoglycemia than other exercise modalities. However, substantial variability exists among protocols, and there is no consensus on optimal dosage. This study reviewed 18 investigations to identify key parameters for safe and effective implementation. Results emphasize the importance of individualized programming, adherence to protocols, frequent glucose monitoring, and professional supervision. A preliminary framework is proposed to guide personalized HIIT programs for people with type 1 diabetes.

Time–frequency (TF) characterization of electromyographic (EMG) bursts is essential for accurately assessing muscle function, particularly when the signals exhibit a high degree of nonstationarity. In this exploratory study, we investigated the temporal dynamics of the spectral components associated with short-latency EMG bursts using several TF analysis techniques. Specifically, we compared the performance and interpretability of spectrograms obtained via the short-time Fourier transform (STFT), the continuous wavelet transform (CWT), and noise-assisted multivariate empirical mode decomposition (NA-MEMD), applied to EMG signals recorded from the biceps femoris muscle of freely moving rats in an animal model of Parkinson’s disease, acquired using chronically implanted bipolar electrodes during treadmill locomotion. For each method, we evaluated its effectiveness in capturing transient variations in frequency content, the stability of extracted features across bursts, and the extent to which these features reflect physiologically meaningful aspects of muscle activation. The results show that TF approaches reveal complementary information about burst structure; NA-MEMD provides greater adaptability to nonlinear and nonstationary components, whereas STFT- and CWT-based representations offer more controlled and comparable analyses. Overall, these findings highlight the value of TF analysis as a methodological tool for evaluating muscle function and provide a solid foundation for selecting analytical strategies in studies where EMG bursts exhibit complex and highly variable spectral profiles.

Subjects with low back pain (LBP) have shown different responses to fatigue compared to healthy individuals. The aim of this study was to analyze and compare the connectivity of the multifidus muscles using intermuscular coherence (IMC) in a squat protocol between healthy subjects and a subject with a history of low back pain. The protocol consisted of three sets of squats, with the final set performed with as many repetitions as possible to induce muscular fatigue. The findings indicated that IMC decreased among healthy subjects during the last repetitions of the squat series, with percentage changes in connectivity (CC) ranging from approximately 43.3% to 67.3% when comparing the first and last 10 repetitions. In contrast, the subject with a history of low back pain (LBP) exhibited an opposite trend, showing a 41.8% increase in IMC. These results suggest a different response of IMC to fatigue related to LBP. This also establishes IMC as a potentially useful tool for characterizing fatigue and LBP during physical exercise.

This pilot study investigates the relationship between gesture intuitiveness and muscle network efficiency in human–machine interactions (HMIs), emphasizing objective neurophysiological measures. EMG signals from 16 muscles were analyzed to construct muscle networks based on synergies derived from non-negative matrix factorization (NMF) and intermuscular coherence (IMC) across three frequency bands. Metrics such as weighted global efficiency (WGE) and effective average strength (EAS) were correlated with gesture intuitiveness levels. Preliminary results suggest that WGE and EAS in synergy-based muscle networks, particularly in the Gamma 1 band, may be associated with perceived gesture intuitiveness (p = 0.10 and 0.05, respectively). These findings suggest the potential utility of neurophysiological metrics to support the assessment of intuitive gestures, warranting further investigation with broader samples.

Hamstring muscle tears are among the most prevalent sports injuries. The occurrence of muscle injuries has been demonstrated to result in alterations to the movement control system, thus prompting the implementation of compensatory strategies. The potential for in-depth study of these adaptive or compensatory strategies for injuries is made possible by the use of electrophysiological biomarkers. The aim of this study was to evaluate the current evidence concerning the analysis of electrophysiological biomarkers in research conducted on subjects with a history of hamstring injury (HSI) on sprinting tasks. A comprehensive literature review was conducted, and five articles were selected based on a rigorous set of selection criteria. The heterogeneity of the results precludes the establishment of broad generalisations regarding hamstring muscle activity during running or sprinting. Notwithstanding these discrepancies, individuals with a history of hamstring injury have been shown to consistently exhibit altered EMG patterns. In order to enhance our comprehension of the neural strategies underpinning movement, it is imperative to employ methodologies that transcend the limitations of EMG amplitude measures.