Biology subjects

Barlaam, F.

Publications and source records attributed to Barlaam, F..

3 recordsLinked to original sources

bioRxiv2026

Distinct SMA beta bursts support the development of anticipatory postural control in children

To produce smooth and coordinated movements, the brain generates anticipatory postural adjustments (APA) - feedforward motor commands that counteract predicted postural disturbances before they occur. Although APA emerge early in life, their maturation extends into early adulthood, yet little is known about the underlying neural mechanisms. Using magnetoencephalography during a naturalistic bimanual load-lifting task, we investigated anticipatory postural control in children aged 7-12 years. In this task, lifting a load from the contralateral postural forearm induces upward elbow rotation, which is anticipatorily counteracted by inhibition of Biceps brachii postural activity, stabilizing forearm posture. Surprisingly, despite greater variability in inhibition timing and less efficient forearm stabilization in children, the mechanism of anticipatory inhibition was consistent with that recently described in adults: it was mediated by transient oscillatory beta bursts (19-24 Hz), associated with reduced supplementary motor area (SMA) excitability indexed by high-gamma (90-130 Hz) power suppression, and preceded by directed influences from lateral prefrontal and ventral premotor cortex. However, burst frequency was lower in children, and an additional APA mechanism was recruited: while 19-24 Hz bursts were linked to immediate SMA suppression and stronger muscle inhibition, higher-frequency bursts (24-29 Hz) were associated with delayed ([~]100 ms) SMA suppression mediated by alpha activity (8 Hz), which attenuated post-inhibition forearm instability. These results reveal that children in middle childhood already engage adult-like neural mechanisms for anticipatory inhibition, while additionally recruiting a compensatory mechanism to counteract postural imprecision, providing new insights into the neural basis of APA maturation and its alteration in neurodevelopmental conditions.

neuroscience↗

bioRxiv2025

High-beta bursts in SMA mediate anticipatory muscle inhibition in a bimanual motor task

In motor networks, inhibition has been associated with oscillatory activity in the mu (8-12 Hz) or beta (13-30 Hz) bands, yet how these rhythms ultimately influence muscle activity remains unclear. The Bimanual Load-Lifting Task (BLLT) elicits anticipatory inhibition of the elbow flexors in the load-supporting arm during voluntary load-lifting, providing a suitable model to study oscillatory mechanisms of muscle inhibition. We recorded magnetoencephalography (MEG) in adult participants performing the BLLT. Optimal postural stabilization occurred when Biceps brachii inhibition preceded unloading by [~]10-40 ms. Stronger muscle inhibition within this time window was associated with reduced high-gamma (90-130 Hz) power, potentially reflecting reduced excitability, and increased high-beta power in the contralateral Supplementary Motor Area (SMA), with high-gamma power reduction partially mediating the effect of high-beta power on anticipatory inhibition. Furthermore, trials containing beta bursts (22-28 Hz) exhibited both optimally timed anticipatory inhibition and concurrent reduction in high-gamma power. These findings suggest that optimal anticipatory inhibition in the load-supporting elbow flexor is linked to reduced SMA excitability mediated by inhibitory beta bursts. More broadly, they provide new evidence linking beta bursts to the inhibitory control of muscle function, and contribute to a better understanding of motor anticipation.

neuroscience↗

bioRxiv2019

NeuroPycon: A free Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

neuroscience↗