Abstract
Oscillatory activity in the beta range, in human primary motor cortex (M1), shows interesting dynamics that are tied to behaviour and change systematically in disease. To investigate the pathophysiology underlying these changes, we must first understand how changes in beta activity are caused in healthy subjects. We therefore adapted a canonical (repeatable) microcircuit model used in dynamic causal modelling (DCM) previously used to model induced responses in visual cortex. We adapted this model to accommodate cytoarchitectural differences between visual and motor cortex. Using biologically plausible connections, we used Bayesian model selection to identify the best model of measured MEG data from 11 young healthy participants, performing a simple handgrip task. We found that the canonical M1 model had substantially more model evidence than the generic canonical microcircuit model when explaining measured MEG data. The canonical M1 model reproduced measured dynamics in humans at rest, in a manner consistent with equivalent studies performed in mice. Furthermore, the changes in excitability (self-inhibition) necessary to explain beta suppression during handgrip were consistent with the attenuation of sensory precision implied by predictive coding. These results establish the face validity of a model that can be used to explore the laminar interactions that underlie beta-oscillatory dynamics in humans in vivo. Our canonical M1 model may be useful for characterising the synaptic mechanisms that mediate pathophysiological beta dynamics associated with movement disorders, such as stroke or Parkinson's disease.
Highlights
There is increasing interest in studying oscillations as a marker of brain function
We show how a biophysical model facilitates the investigation of the laminar interactions underlying noninvasive measurements of neuronal oscillations from primary motor cortex (M1) in humans
The metric used to appraise model space was log evidence. The interpretation of such a metric is dependent on the Bayes Factor (BF), whether providing weak (BF b 3), positive (3 ≤ BF b 20), strong (20 ≤ BF b 150), or very strong (BF ≥ 150) evidence for preferring one model over another is the basis for the calculation of model evidence
Summary
There is increasing interest in studying oscillations as a marker of brain function. Neuronal oscillations in the beta frequency range (15–30 Hz) in primary motor cortex (M1) are fundamental for motor control (Engel and Fries, 2010) and are a putative biomarker of pathophysiology in conditions like Parkinson's disease.Magnetoencephalography (MEG) studies have shown that voluntary movement is associated with a systematic reduction in power of beta oscillations (movement-related betadesynchronisation, MRBD) in M1, which rebounds following movement cessation (post-movement beta rebound, PMBR) (Salmelin and Hari, 1994). We show how a biophysical (neuronal mass) model facilitates the investigation of the laminar interactions underlying noninvasive measurements of neuronal oscillations from primary motor cortex (M1) in humans. Excitation of the deep pyramidal layer (Yamawaki et al, 2008) or possibly synchronous hyperpolarisation of superficial and deep pyramidal layers (Lacey et al, 2014) gives rise to beta oscillations. In both cases, recurrent interactions with inhibitory interneurons are important, as is the case for gamma oscillations (Traub et al, 2001)
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