Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing

Abstract

Action observation can facilitate the acquisition of novel motor skills; however, there is considerable individual variability in the extent to which observation promotes motor learning. Here we tested the hypothesis that individual differences in brain function or structure can predict subsequent observation-related gains in motor learning. Subjects underwent an anatomical MRI scan and resting-state fMRI scans to assess preobservation gray matter volume and preobservation resting-state functional connectivity (FC), respectively. On the following day, subjects observed a video of a tutor adapting her reaches to a novel force field. After observation, subjects performed reaches in a force field as a behavioral assessment of gains in motor learning resulting from observation. We found that individual differences in resting-state FC, but not gray matter volume, predicted postobservation gains in motor learning. Preobservation resting-state FC between left primary somatosensory cortex and bilateral dorsal premotor cortex, primary motor cortex, and primary somatosensory cortex and left superior parietal lobule was positively correlated with behavioral measures of postobservation motor learning. Sensory-motor resting-state FC can thus predict the extent to which observation will promote subsequent motor learning. We show that individual differences in preobservation brain function can predict subsequent observation-related gains in motor learning. Preobservation resting-state functional connectivity within a sensory-motor network may be used as a biomarker for the extent to which observation promotes motor learning. This kind of information may be useful if observation is to be used as a way to boost neuroplasticity and sensory-motor recovery for patients undergoing rehabilitation for diseases that impair movement such as stroke.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

“…The abundance of visual signals activated the primary visual cortex (BA19), while activation of the lateral occipitotemporal cortex (part of BA37) was likely related to hand-specific visual processing. In addition, the superior parietal lobule, part of BA7, which is involved in locating objects in space and in visuo-motor coordination, serves as a point of convergence between vision and proprioception in order to determine where objects are in relation to parts of the body (51). Sensory input from the UL may also play an important role in BCI training, since training involved continuous movements of the exoskeleton and routine training.…”

“…The signal from eight active electrodes was sampled at 256 Hz. EEG signals were also processed in real-time by the amplifier using a band-pass filter (2-60 Hz) and a notch filter (48)(49)(50)(51)(52) to remove artifacts and power line interference, respectively. The EEG electrodes were placed over the central area according to the International 10-20 system (FC3, FC4, C3, C4, CP3, CP4, C1, C2).…”

Surface-based analysis increases the specificity of cortical activation patterns and connectivity results

“…This finding is well in agreement with the blurring effect of the volume-based smoothing kernel that causes a mixture between somatosensory-and motor signals and leading to increased signal correlation. This result is not only of theoretical interest as it might affect results of studies that estimated the functional connectivity between the primary somatosensory and primary motor cortex [58][59][60] , but it is also generalizable to other studies investigating the functional connectedness of neighboring brain regions. Regarding two signals located in the pre-and postcentral gyrus that were explicitly designed to be uncorrelated, we found inflated functional connectivity in both the VBA and the SBA.…”

Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

“…Furthermore, although a certain profile of spectral power, namely the spectral tilt phenomenon, is thought to reflect cortical activation ( Miller et al, 2007 ; Burke et al, 2015b ), analyzing multifocal spectral activity is not a quantitative measure of regional interactions. For dynamic cognitive tasks, precisely quantifying these interactions may be more task relevant than the collective regional pattern of activity ( Varela, 1995 ; Friston, 1997 ; Tononi, 1998 ; Büchel et al, 1999 ; Sporns et al, 2004 ; Rypma et al, 2006 ; Canolty et al, 2009 ; Brancucci, 2012 ; Bassett et al, 2015 ; McGregor and Gribble, 2017 ).…”

“…The features of the functional network can be quantified using numerous statistics commonly referred to as graph statistics, network measures, or connectomic metrics. The quantification of network architecture in patterns of FC can provide robust estimates of task-dependent interregional coordination, and its relation to multisensory processing, cognition, memory, and learning ( Friston, 1997 ; Tononi, 1998 ; Sporns et al, 2004 ; Miller et al, 2007 ; Canolty et al, 2009 ; Bassett et al, 2015 ; McGregor and Gribble, 2017 ; Nicolae et al, 2017 ).…”