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Barnes, G.

Publications and source records attributed to Barnes, G..

3 recordsLinked to original sources

Motor cortical beta transients delay movement initiation and track errors

Motor cortical activity in the beta range (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Recent work in primates and human sensory cortex suggests that sustained oscillatory beta activity observed on average, may arise from the summation of underlying short-lasting, high-amplitude bursts of activity. Classical human movement-related event-related beta desynchronisation (ERD) and synchronization (ERS) may thus provide insufficient, non-dynamic, summaries of underlying focal spatio-temporal burst activity, limiting insight into their functional role during healthy and pathological movement.\n\nHere we directly investigate this transient beta burst activity and its putative behavioural relevance for movement control, using high-precision magnetoencephalography (MEG). We quantified the subject-specific (n=8), trial-wise (n>12,000) dynamics of beta bursts, before and after movement. We show that beta activity on individual trials is dominated by high amplitude, short lasting bursts. While average beta changes generally manifest as bilaterally distributed activity (FWHM = 25mm), individual bursts are spatially more focal (FWHM = 6 mm), sporadic (1.3 -1.5/s), and transient (mean: 96 ms).\n\nPrior to movement (the period of the classical ERD), the timing of the last pre-movement burst predicts movement onset, suggesting a role in the specification of the goal of movement. After movement (the period of the classical ERS), the first beta burst is delayed by ~100ms after a response error occurs, intimating a role in error monitoring and evaluation.\n\nMovement-related beta activity is therefore dominated by a spatially dispersed summation of short lasting, sporadic and focal bursts. Movement-related beta bursts coordinate the retrieval and updating of movement goals in the pre- and post-movement periods, respectively.

neuroscience

Neural competitive queuing of ordinal structure underlies skilled sequential action

The fluent retrieval and production of movement sequences is essential for a variety of daily activities such as speech, tool-use, musical and athletic performance, but the neural mechanisms underlying sequence planning remain elusive. Here, participants learned sequences of finger presses with different timings and different finger orders, and reproduced them in a magneto-encephalography (MEG) scanner. We classified the MEG patterns immediately preceding each press in the sequence, and examined their dynamics over the production of the whole sequence. Our results confirm a role for the competitive queuing of upcoming action representations in the production of learned motor sequences, extending previous computational and non-human primate recording studies to non-invasive measures in humans. In addition, we show that competitive queuing does not simply reflect specific motor actions, but representations of higher-level sequential order that generalise across different motor sequences. Finally, we show that the quality of competitive queuing predicts participants production accuracy, and originates from parahippocampal and cerebellar sources. These results suggest that the brain learns and produces multiple behavioural sequences by flexibly combining representations of specific actions with more abstract, parallel representations of sequential structure.

neuroscience

Quantifying the performance of MEG source reconstruction using resting state data

Resting state networks measured with magnetoencephalography (MEG) form transiently stable spatio-temporal patterns in the subsecond range, and therefore fluctuate more rapidly than previously appreciated. These states populate and interact across the whole brain, are simple to record, and possess the same dynamic structure of task related changes. They therefore provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. Here we validate a non-invasive method for quantifying the resolution of different inversion assumptions under different recording regimes (with and without head-casts) based on resting state MEG. Spatio-temporally partitioning of data into self-similar periods confirmed a rich and rapidly dynamic temporal structure with a small number of regularly reoccurring states. To test the anatomical precision that could be resolved through these transient states we then inverted these data onto libraries of systematically distorted, subject specific, cortical meshes and compared the quality of the fit using Cross Validation and a Free Energy metric. This revealed which inversion scheme was able to best support the least distorted (most accurate) anatomical models. Both datasets showed an increase in model fit as anatomical models moved towards the true cortical surface. In the head-cast MEG data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm / LORETA (6.0 mm) and Multiple Sparse priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods or recording paradigms.

neuroscience