Can EEG and MEG detect signals from the human cerebellum?

Abstract

The cerebellum plays a key role in the regulation of motor learning, coordination and timing, and has been implicated in sensory and cognitive processes as well. However, our current knowledge of its electrophysiological mechanisms comes primarily from direct recordings in animals, as investigations into cerebellar function in humans have instead predominantly relied on lesion, haemodynamic and metabolic imaging studies. While the latter provide fundamental insights into the contribution of the cerebellum to various cerebellar-cortical pathways mediating behaviour, they remain limited in terms of temporal and spectral resolution. In principle, this shortcoming could be overcome by monitoring the cerebellum's electrophysiological signals. Non-invasive assessment of cerebellar electrophysiology in humans, however, is hampered by the limited spatial resolution of electroencephalography (EEG) and magnetoencephalography (MEG) in subcortical structures, i.e., deep sources. Furthermore, it has been argued that the anatomical configuration of the cerebellum leads to signal cancellation in MEG and EEG. Yet, claims that MEG and EEG are unable to detect cerebellar activity have been challenged by an increasing number of studies over the last decade. Here we address this controversy and survey reports in which electrophysiological signals were successfully recorded from the human cerebellum. We argue that the detection of cerebellum activity non-invasively with MEG and EEG is indeed possible and can be enhanced with appropriate methods, in particular using connectivity analysis in source space. We provide illustrative examples of cerebellar activity detected with MEG and EEG. Furthermore, we propose practical guidelines to optimize the detection of cerebellar activity with MEG and EEG. Finally, we discuss MEG and EEG signal contamination that may lead to localizing spurious sources in the cerebellum and suggest ways of handling such artefacts. This review is to be read as a perspective review that highlights that it is indeed possible to measure cerebellum with MEG and EEG and encourages MEG and EEG researchers to do so. Its added value beyond highlighting and encouraging is that it offers useful advice for researchers aspiring to investigate the cerebellum with MEG and EEG.

Fig. 1

Similarities between Purkinje cells (cerebellum) and pyramidal cells (cerebral cortex) A) a sketch of a Purkinje cell from the human cerebellum. B) a sketch of the pyramidal cells in sensory cortex and motor cortex of an adult, showcasing the different cortical layers. Both sketches are by Ramon y Cajal and are public domain:https://en.wikipedia.org/wiki/File:Purkinje_cell_by_Cajal.png andhttps://commons.wikimedia.org/wiki/File:Cajal_cortex_drawings.png.

Fig. 2

Strength of task-based coherence with primary cortex as a reference: subjects were to counteract the unpredictable movements of a cube rotating around its centre by moving a trackball. The kinematics of the trackball movement were registered and its coupling to the neural time series were estimated, using task-related Z-transformed coherence with M1 activity (white dot) as an outcome measure (ΔZcoh), showing coherence with the cerebellum. Figure from Jerbi et al. (2007).

Fig. 3

Pre-movement beta activation in cerebellar cortices. Beta activation in ipsilateral cerebellar cortices following a flexion-extension movement. The maximum is in the inferior portions of ipsilateral cerebellum crus II. This figure is adapted from Wilson et al., (2010) with permission.

Fig. 4

Differences in cerebellar activation between expected and unexpected stimulations. Subjects had their right index finger stimulated rhythmically (every 3 ​s). Every now and then a stimulation was omitted. The contrasts shown here indicate brain regions exhibiting significantly more power for repeated stimulations (a stimulation following another stimulation) than for first stimulations (a stimulation following an omission), where 0 ​ms refers to stimulation onset. This figure is adapted from Andersen and Lundqvist (2019) under the CC BY 4.0 licence.

Fig. 5

Tilting the head to obtain better sensor coverage of the cerebellum. The upper panel shows a typical head placement in a modern MEG system, the Neuromag Triux, with its 102 sensor locations depicted in blue. While the cerebellum is partially covered with this positioning, tilting the head backwards relative to the sensor array may provide a more complete coverage of the cerebellum. Hashimoto et al. (2003) demonstrates such a positioning with a 160-channel Yokogawa MEG system, as seen in the lower panel, reproduced with permission (A ​= ​Anterior, P=Posterior, L ​= ​Left, R ​= ​Right).