Explaining Individual Differences in Motor Behavior by Intrinsic Functional Connectivity and Corticospinal Excitability

Abstract

Motor performance varies substantially between individuals. This variance is rooted in individuals' innate motor abilities, and should thus have a neural signature underlying these differences in behavior. Could these individual differences be detectable with neural measurements acquired at rest? Here, we tested the hypothesis that motor performance can be predicted by resting motor-system functional connectivity and motor-evoked-potentials (MEPs) induced by non-invasive brain stimulation. Twenty healthy right handed subjects performed structural and resting-state fMRI scans. On a separate day, MEPs were measured using transcranial magnetic stimulation (TMS) over the contrateral primary motor cortex (M1). At the end of the session, participants performed a finger-tapping task using their left non-dominant hand. Resting-state functional connectivity between the contralateral M1 and the supplementary motor area (SMA) predicted motor task performance, indicating that individuals with stronger resting M1-SMA functional connectivity exhibit better motor performance. This prediction was neither improved nor reduced by the addition of corticospinal excitability to the model. These results confirm that motor behavior can be predicted from neural measurements acquired prior to task performance, primarily relying on resting functional connectivity rather than corticospinal excitability. The ability to predict motor performance from resting neural markers, provides an opportunity to identify the extent of successful rehabilitation following neurological damage.

Meta-analytic maps of brain activity evoked by cognitive function diverge from resting state networks

“…It has been hypothesized that RSN originate from spontaneous cognition taking place during conscious wakefulness (also known as mind-wandering) (Gonzalez-Castillo et al, 2021). This hypothesis is compatible with the small increase in metabolism seen during effortful tasks, since spontaneous brain activity would already reflect ongoing cognitive processing; furthermore, it is also consistent with studies showing that resting state activity can be used to decode mental states (Richiardi et al, 2011;Gonzalez-Castillo et al, 2015), as well as to predict performance in motor and perceptual tasks (Boly et al, 2007;Hesselmann et al, 2008;Kannurpatti et al, 2012;Sadaghiani et al, 2015;Herszage et al, 2020). A seminal study published by Smith and colleagues demonstrated a direct correspondence between the spatial configuration of RSN and task-evoked activity patterns, obtained by applying ICA to a large database of brain activation maps (www.brainmap.org) (Smith et al, 2009).…”

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

“…There have been several previous attempts to quantify neurophysiological markers of motor learning and performance (Baetu et al, 2015;Andreska et al, 2020;Herszage et al, 2020). Such attempts have included resting-state electroencephalography (EEG) activity, which has been shown to pre-dict visuomotor performance with the rotary pursuit task (Wu et al, 2014).…”

Multiple bouts of high-intensity interval exercise reverse age-related functional connectivity disruptions without affecting motor learning in older adults

“…It is well established that regions including primary motor cortex and supplementary motor area (SMA) are critical for the formation of motor plans, including the relay of kinematic transformation to the task specific effector [72][73][74][75][76] . Increased resting state FC between the cerebellum and both the primary motor cortex and SMA is associated with learning a finger-opposition sequence task 77,78 . Additionally, our previous work showed that high-intensity interval exercise led to a release of cerebellar inhibition 79 .…”