Biology subjects

Begel, V.

Publications and source records attributed to Begel, V..

4 recordsLinked to original sources

bioRxiv2025

Fine Motor Serious Game Training Improves Gait in Parkinson s Disease: A Pilot Randomized Controlled Trial

Crucial functions for human behavior such as gross motor skills (e.g., walking), cognitive, rhythmic, and fine motor processes are mostly considered unrelated. However, these functions may interact, but their relations are still poorly understood. Moreover, evidence of causal links between them is scarce. Neurological disorders such as Parkinsons Disease affect all these functions and thus provide a model to study the interplay between them. We tested the effect of a training delivered using serious games on tablet, involving upper limb rhythmic and fine motor functions, on walking capacities in patients with Parkinsons Disease (PwPD). PwPD gathered into an Intervention group played either a rhythm game, or an adaptation of Tetris, four times a week for six weeks. Before and after the training, gait was evaluated in spontaneous walking and a dual task (counting backward while walking). A Control group of participants did not receive any training. Gait speed, stride length and cadence improved in the Intervention group after the training in comparison with the Control group. The improvement was observed in both Intervention groups, in the spontaneous and in the dual-task conditions. These findings support the hypothesis that gross (axial) motor functions can be trained by fine (lateralized) motor training administered via serious games, possibly by stimulating the rhythmic, perceptual, and cognitive resources sensory systems. This opening promising perspectives for telerehabilitation. Patients with movement disorders could benefit from this promising low-cost and engaging training method, fostering inclusivity and autonomy for people who have reduced access to the clinic.

neuroscience↗

bioRxiv2023

Stability of Neural Oscillations Supports Auditory-Motor Synchronization

Previous findings suggest that musical training leads to increased coactivation of auditory and motor brain networks, as well as enhanced auditory-motor synchronization. Less is known about the temporal dynamics of auditory-motor network interactions and how these temporal dynamics are shaped by musical training. The current study applied Recurrence Quantification Analysis, a nonlinear technique for characterizing the temporal dynamics of complex systems, to participants neurophysiological activity recorded via electroencephalography (EEG) during an auditory-motor synchronization task. We investigated changes in neural predictability and stability with musical training, and how these changes were related to synchronization accuracy and consistency. EEG was recorded while musicians and nonmusicians first tapped a familiar melody at a comfortable rate, called Spontaneous Production Rate (SPR). Then participants synchronized their taps with an auditory metronome presented at each participants SPR and at rates 15% and 30% slower than their SPR. EEG-based outcomes of determinism (predictability) and meanline (stability) were compared with behavioral synchronization measures. Musicians synchronized more consistently overall than nonmusicians. Both groups of participants showed decreased synchronization accuracy at slower rates, and higher EEG-based determinism (predictability) at slower rates. Furthermore, neural meanline (stability) measures correlated with synchronization consistency across all participants and stimulus rates; as neural stability increased, so did synchronization consistency. Neural stability may be a general mechanism supporting the maintenance of synchronization across rates, which may improve with musical training.

neuroscience↗

bioRxiv2023

Unravelling individual rhythmic abilities using machine learning

Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, for example in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp with single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on the behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that the variability of rhythmic abilities, and their link with formal and informal music experience, can be successfully captured by profiles including a minimal set of behavioral measures. These profiles can shed light on individual variability in healthy and clinical populations, and provide guidelines for personalizing rhythm-based interventions.

bioinformatics↗

bioRxiv2022

Tango for Two: Modeling Synchronization and Social Interaction in Turn-Taking

Important individual differences are observed in peoples abilities to synchronize their body movements with regular auditory rhythms. We investigate whether synchronizing with a regular auditory cue is affected by each persons spontaneous production rate (SPR) and by hearing a partners synchronization in a social context. Musically trained and untrained participants synchronized their tapping with an auditory cue presented at different rates (their own SPR or their partners SPR) and in a Solo or Joint (turn-taking) condition. Linear and nonlinear oscillator models were fit to participants mean asynchronies (signed timing differences between the cued onsets and taps). In Joint turn-taking, participants synchrony was increased when the auditory signal was cued at the participants own SPR, compared with their partners SPR; in contrast, synchronization did not differ across rates in the Solo condition. Asynchronies in the Joint task became larger as the difference between partners spontaneous rates increased; the increased asynchronies were driven by the faster partner who did not slow down to match the rate of their slower partner. Nonlinear delay-coupled models (with time delay, coupling strength, and intrinsic frequency) outperformed linear models (intrinsic frequency only) in accounting for tappers synchronization adjustments. The nonlinear models coupling value increased for musically trained participants, relative to untrained participants. Overall, these findings suggest that both intrinsic differences in partners spontaneous rates and the social turn-taking context contribute to the range of synchrony in the general population. Delay-coupled models are capable of capturing the wide range of individual differences in auditory-motor synchronization.

neuroscience↗