Spectral and temporal electroencephalography measures reveal distinct neural networks for the acquisition, consolidation, and interlimb transfer of motor skills in healthy young adults

Cerebellar Purkinje cells can differentially modulate coherence between sensory and motor cortex depending on region and behavior

“…This integration is particularly relevant when subjects explore their environment via active touch, requiring sensory input to be directly related to the momentary position and movement of eyes, fingertips, antennae, whiskers, or other sense organs (1)(2)(3). In vertebrates, adapting movement based upon somatosensory feedback requires collaborative action of primary somatosensory (S1) and motor (M1) cortices (4)(5)(6), which are directly and reciprocally connected (7)(8)(9)(10)(11)(12). The reciprocal connections between S1 and M1 imply that each region can differentially affect the amplitude of neuronal responses in the other region.…”

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

“…The Medical Ethical Committee of the University Medical Center Groningen approved the study protocol, which was conducted according to the declaration of Helsinki (2013) and registered in the Dutch trial register (NL5484). Data from the young participants were used in a previous study (Veldman et al, 2018). They were re-analyzed and compared with new data collected in older adults.…”

“…For the DICS analysis, artifactfree data were filtered in the beta frequency range (13-30 Hz). We limited the analyses to neural oscillations in the beta frequency range, as they are known to be involved in sensorimotor processing and motor learning (Bhatt et al, 2016;Pfurtscheller and Lopes, 1999;Pollok et al, 2014;Rossiter et al, 2014;Sugata et al, 2020;Veldman et al, 2018;Wu et al, 2014). For the definition of the source space, we used a generic magnetic resonance imaging template available in Fieldtrip with a 5 mm voxel size, which was segmented and resliced using SPM12 (Wellcome Centre for Human Neuroimaging, London, UK).…”

“…We used standard conductivity values for the three layers included in the headmodel: 0.33 S/m for the brain and scalp, and 0.0042 S/m for the skull. Because the primary aim of this study was to identify the role of connectivity in the acquisition, consolidation, and interlimb transfer of a visuomotor skill, we performed DICS beamformer analysis on the resting-state data in the beta frequency range, which is instrumental in visuomotor learning (Gross et al, 2001;Veldman et al, 2018;Wu et al, 2014). The DICS method uses adaptive spatial filters to relate the electrical activity measured at the scalp to the underlying neural activity with a high spatial resolution (Greenblatt et al, 2005;Sekihara et al, 2005).…”

Necessity of Sleep for Motor Gist Learning in Mice

“…Such gist learning, or generalization, can be applied to motor learning. The most commonly studied form of this is inter-manual transfer, where motor learning in one limb can augment subsequent motor performance on the same task in the contralateral untrained limb (Censor, 2013; Veldman et al, 2017). Such ability is thought to be augmented by a period of sleep after the initial motor learning (Witt et al, 2010), but this has not been investigated extensively, and nearly all studies of this phenomenon have occurred in human subjects.…”