Biology subjects

Dalla Bella, S.

Publications and source records attributed to Dalla Bella, S..

6 recordsLinked to original sources

bioRxiv2026

Distinct Neural Signatures Underlie Finger Tapping and Walking to Auditory Rhythms

Neural oscillations synchronize with rhythmic stimuli, shaping perception and action. Auditory-motor synchronization (AMS) enhances this process but most evidence comes from finger tapping, an attentionally demanding voluntary behaviour. Whether motor-driven modulation of neural synchronization generalizes to more automatic movements like walking remains unclear. In this study, we used mobile EEG to compare neural synchronization to auditory rhythms during tapping and walking in 40 older male and female participants, manipulating cognitive load (single vs. dual task) and task instructions (synchronize with vs. ignore metronome). EEG components attuned to metronome frequencies were extracted and complementary measures of neural synchronization were computed (phase coupling to the stimulus, power at the stimulus frequency, and the stability of instantaneous frequencies). Both movements enhanced neural synchronization relative to passive listening. Neural synchronization was further increased by instructions to synchronize, but only during tapping. This instruction-facilitating effect was lost in dual tasks. These findings suggest that AMS recruits movement-dependent neural and cognitive mechanisms, depending on where AMS lies on a continuum from voluntary-controlled to automatic forms of coordination, with implications for rhythm-based interventions in aging. Significance statementNeural coupling to stimuli in the environment shape perception and action, but its dependence on movement type and cognitive control is poorly understood. Using mobile EEG, we show that both finger tapping and walking enhance neural coupling to the auditory stimuli, but tapping is uniquely modulated by task factors. Our findings suggest that cortical involvement during rhythmic motor tasks differs for voluntary (tapping) and automatic (walking) behaviours. As gait can move from being mostly automatic to voluntary-controlled (e.g., under heightened cognitive load, in motor disorders), it offers a unique opportunity to study the dynamic interactions between sensory, motor, and cognitive processes supporting neural coupling to auditory stimuli.

neuroscience↗

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↗

bioRxiv2024

The Ramp protocol: uncovering individual differences in walking to an auditory beat using TeensyStep

Intentionally walking to the beat of an auditory stimulus seems effortless for most humans. However, studies have revealed significant individual differences in the spontaneous tendency to synchronize. Some individuals tend to adapt their walking pace to the beat, while others show little or no adjustment. To fill this gap we introduce the Ramp protocol, which measures spontaneous adaptation to a change in an auditory rhythmic stimulus in a gait task. First, participants walk at their preferred cadence without stimulation. After several steps, a metronome is presented, timed to match the participants heel-strike. Then, the metronome tempo progressively departs from the participants cadence by either accelerating or decelerating. The implementation of the Ramp protocol required real-time detection of heel-strike and auditory stimuli aligned with participants preferred cadence. To achieve this, we developed the TeensyStep device, which we validated compared to a gold standard for step detection. We also demonstrated the sensitivity of the Ramp protocol to individual differences in the spontaneous response to a tempo-changing rhythmic stimulus by introducing a new measure: the Response Score. This new method and quantification of spontaneous response to rhythmic stimuli holds promise for highlighting and distinguishing different profiles of adaptation in a gait task.

neuroscience↗

bioRxiv2023

Mobile version of the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA): Implementation and adult norms

Timing and rhythm abilities are complex and multidimensional skills that are highly widespread in the general population. This complexity can be partly captured by the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA). The battery, consisting of 4 perceptual and 5 sensorimotor tests (finger tapping), has been used in healthy adults and in clinical populations (e.g., Parkinsons disease, ADHD, developmental dyslexia, stuttering), and shows sensitivity to individual differences and impairment. However, major limitations for the generalized use of this tool are the lack of reliable and standardized norms and of a version of the battery that can be used outside the lab. To circumvent these caveats we put forward a new version of BAASTA on a tablet device capable of ensuring lab-equivalent measurements of timing and rhythm abilities. We present normative data obtained with this version of BAASTA from over 100 healthy adults between the ages of 18 and 87 years in a test-retest protocol. Moreover, we propose a new composite score to summarize beat-based rhythm capacities, the Beat Tracking Index (BTI), with close to excellent test-retest reliability. BTI derives from two BAASTA tests (beat alignment, paced tapping), and offers a swift and practical way of measuring rhythmic abilities when research imposes strong time constraints. This mobile BAASTA implementation is more inclusive and far-reaching, while opening new possibilities for reliable remote testing of rhythmic abilities by leveraging accessible and cost-efficient technologies.

neuroscience↗

bioRxiv2023

Modeling EEG Resting-State Brain Dynamics: A proof of concept for clinical studies

Functional brain imaging has shown that the awake brain, independent of a task, spontaneously switches between a small set of functional networks. How useful this dynamical view of brain activity is for clinical studies, e.g., as early markers of subsequent structural and/or functional change or for assessing successful training or intervention effects, remains unclear. Core to addressing this question is to assess the robustness and reproducibility of the analysis methods that model, characterize, or infer the features of brain dynamics, and the accuracy by which these features represent and classify specific cognitive or altered cognitive states. This is particularly key given inter- and intra-individual variability and measurement noise. Here we used resting-state EEG from persons with Parkinsons Disease (PD) and healthy matched controls to systematically assess the reliability, robustness, and sensitivity of Hidden semi-Markov models (HsMM). These models are an example of model-based probabilistic methods for Brain-State allocations that are estimated from observed data. The method estimates model parameters, if the M/EEG recording or observations, over the scale of minutes, are emissions from hidden states that persist over short durations, before switching or transitioning to other states. We introduce an analysis pipeline that leads to sets of reproducible features of neurophysiological dynamics at the individual level. These features can be used as discriminatory variables to classify individuals and to evaluate the effect of non-pharmacological training schemes like in the current example a music-gait exercise program for Parkinsons Disease. Given the method stochasticity and the data variability, we emphasize the importance of repeating the analysis to reliably identify brain states and their dynamical trajectories that subsequently can be related to individualized variables.

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↗