Biology subjects

Rossiter, H. E.

Publications and source records attributed to Rossiter, H. E..

3 recordsLinked to original sources

bioRxiv2025

Disentangling Rhythmic and Aperiodic Neural Activity: Investigating the Role of GABA Modulation in Sensorimotor Beta Oscillations

Beta oscillations (13-30 Hz) in the sensorimotor cortex are fundamental to motor control and are disrupted in motor disorders such as Parkinsons disease and stroke. Animal studies and pharmacological research in humans have implicated GABAergic mechanisms in modulating these oscillations. Here, we explored the impact of GABAergic modulation on sensorimotor beta oscillations using magnetoencephalography (MEG) during a finger abduction task, with participants administered gaboxadol and zolpidem, two GABA-A positive allosteric modulators. We combined traditional oscillatory analyses with spectral parametrisation methods to separate periodic and aperiodic neural components. Time-frequency representations (TFRs) showed that gaboxadol induced a stronger modulation of beta dynamics than zolpidem, leading to deeper beta desynchronisation during movement and a more pronounced post-movement beta rebound. When accounting for aperiodic activity, gaboxadol showed modest effects limited to movement initiation, with increased beta power during movement onset but no significant impact during post-movement rebound. In contrast, zolpidem significantly enhanced beta power and altered aperiodic components, suggesting differential influences on GABAergic modulation and excitation-inhibition balance. These results underscore the necessity of distinguishing between periodic and aperiodic signals in spectral measures to improve the interpretation of neural oscillatory data.

neuroscience↗

bioRxiv2019

Action kinematics as an organising principle in the cortical control of human hand movement

Hand movements are controlled by neuronal networks in primary motor cortex (M1). The organising principle in M1 does not follow an anatomical body map, but rather a distributed representational structure in which motor primitives are combined to produce motor outputs. Both electrophysiological recordings in primates and human imaging data suggest that M1 encodes kinematic features of movements, such as joint position and velocity. However, M1 exhibits well-documented sensory responses to cutaneous and proprioceptive stimuli, raising questions regarding the origins of kinematic motor representations: are they relevant in top-down motor control, or are they an epiphenomenon of bottom-up sensory feedback during movement? Moreover, to what extent is information related to muscle activity encoded in motor cortex? Here we provide evidence for spatially and temporally distinct encoding of kinematic and muscle information in human M1 during the production of a wide variety of naturalistic hand movements. Using a powerful combination of high-field fMRI and MEG, a spatial and temporal multivariate representational similarity analysis revealed encoding of kinematic information from data glove recordings in more caudal regions of M1, over 200ms before movement onset. In contrast, patterns of muscle activity from EMG were encoded in more rostral motor regions later in the cycle of movement. Our spatial and temporal analysis provide compelling evidence that top-down control of dexterous movement engages kinematic representations in caudal regions of M1 prior to movement production; an area with direct cortico-motorneuronal connections. Muscle information encoded more rostrally in M1 was engaged later, suggestive of involvement in bottom-up signalling.

neuroscience↗

bioRxiv2017

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency- specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

neuroscience↗