Static and dynamic cortical activity underlying the negative impact of lifting on hand opening post-stroke

Impaired hand opening ability is one of the most debilitating deficits following stroke, compromising individuals¿ ability to participate in everyday tasks. Unfortunately, lifting the paretic arm during attempted hand opening significantly reduces opening ability even further. Consequently, functionally using the hand is often impossible in this population. The underlying neural mechanisms for the negative impact of lifting on hand opening are currently unknown since studies investigating cortical activity during movement typically use MRI scanners in which multi-joint movements cannot be performed. I propose to use a robotic device that can modulate shoulder abduction load during 3D movements combined with high-density EEG to investigate cortical activity related to hand opening with and without lifting at the shoulder. Additionally, previous studies have focused primarily on static activity within motor regions related to motor output, but it is likely that the stroke-induced lesion may also impact the motor planning phase that shapes this motor output. In addition to cortical activity related to motor output, I will investigate how motor planning changes post-stroke for these 2 movements by examining dynamic connectivity between regions to provide a comprehensive understanding of the neural mechanisms underlying the observed behavior. Specifically, I will determine the effect of lifting on cortical activity during motor output (Aim 1) and cortical connectivity during motor planning (Aim 2) related to hand opening in chronic stroke. I will accomplish these aims by using a robotic setup that can modulate shoulder abduction load in a 3D environment while simultaneously measuring high-density EEG and hand kinetics in stroke individuals and age-matched controls. I will then quantify static activity within motor regions related to movement onset (i.e., the motor output), as well as the dynamic connectivity between regions within a motor network in the time leading up to movement onset (i.e., motor planning phase). In order to quantify connectivity, I will use a form of Dynamic Causal Modeling that will allow me to investigate both linear and nonlinear frequency interactions within a network. Results from this proposal will provide a comprehensive view of both the static activity within regions related to motor output (Aim 1) and dynamic connectivity between regions related to motor planning (Aim 2) that contribute to the observed hand impairment.