Projects

The role of neural networks in motor recovery: paving the road to post-stroke rehabilitation

Performance of a motor task results in widespread activation of a number of brain regions; even simple movements such as finger-tapping activate a large sensorimotor network involving cortical and sub-cortical regions. Importantly, damage to regions within the sensorimotor network following neurologic insult (e.g., stroke) does not render the system incapable of executing movement. Rather, activation of regions previously not required for a given movement compensates for damaged brain regions, enabling some functional ability. While this compensatory activity is beneficial, reorganization of the sensorimotor network in this manner deviates from what is considered optimal network architecture, and may impede the degree of achievable recovery. In fact, it is believed that the degree of recovery is related to the pattern of connectivity observed among brain regions in the network, such that greater recovery is linked to the return of the typical motor network seen in an intact brain. Unfortunately, complete recovery following stroke is elusive. Despite the use of current, evidence-based treatments in stroke rehabilitation, upper limb functional deficits are common and chronic: less than 5% of stroke survivors recover full function of the affected upper limb, while 20% regain no functional use. So how can we promote recovery? If improved function is associated with a particular pattern of network connectivity, therapeutic interventions should be directed towards re-establishing the sensorimotor network post-stroke. To achieve this goal, we must first establish a fundamental understanding of the organization of the ‘normal’ sensorimotor network and how its architecture is altered following neurologic insult.

This research project will capitalize on novel neuroimaging technology, magnetoencephalography (MEG), to examine changes in connectivity within the sensorimotor network of individuals post-stroke. Specifically, the goal of this research is to ‘pave the road to post-stroke rehabilitation’; identifying the ‘normal’ sensorimotor network and establishing the relationship between network re-organization and functional recovery will permit the development and implementation of treatments to direct brain recovery. With regard to the proposed research project, and in groups of non-disabled controls and patients post-stroke, our objectives include:
1. To establish the connectivity pattern of the ‘normal’ sensorimotor network and demonstrate the ability to detect changes within the network using an established motor learning paradigm
2. Using clinical measures of upper limb function, we will establish the relationship between the pattern of sensorimotor network connectivity and functional recovery in well and poorly recovered patients

Funded positions associated with this study are currently open:
1. MSc or PhD students

This study is being conducted in collaboration with scientists at the National Research Council Institute for Biodiagnostics.

Funding Agency:

Atlantic Canada Modified Constraint Induced Movement Therapy Trial

Constraint induced movement therapy is effective at improving upper limb function and use in patients with moderate-mild hemiparesis resulting from stroke. However, this evidence was generated largely within the context of a privately funded health care system and thus does not readily translate to the publically funded model of health care delivery utilized in Canada. Specifically, CIMT is not routinely used clinically in Canada due to a lack of funding to provide the intensity of resources and time required to perform it. Owing to these resource limitations, alternative, yet equally effective treatments of upper limb dysfunction are needed. To address this, our research group is studying a modified CIMT (mCIMT) intervention that has shown potential in improving upper limb functional abilities post-stroke. Due to the decreased frequency and duration of therapy sessions, the mCIMT intervention has promise as a clinically feasible treatment for use in rehabilitation programs within the Canadian health care system.

In this study, we will examine the effectiveness and feasibility of an mCIMT intervention on upper limb function in two groups of individuals acutely post-stroke;
1) an experimental group that will participate in a 10 week mCIMT intervention designed to improve UL function, in addition to usual care, and
2) a control group that will participate in a program of usual care consisting of a rehabilitation intervention for the affected UL that is dose-matched to the experimental group.
In addition to assessing functional abilities to determine treatment effectiveness, data pertaining to the neural mechanisms influencing recovery [specifically corticospinal tract (CST) integrity] will be investigated.

Funding Agencies:

The Role of Neural Networks in Motor Learning: A Road Map for Rehabilitation

A number of different brain areas work together to allow movement to occur. After a brain injury (like a stroke), the way these brain areas work together changes. New areas take over for those that were damaged by the stroke. Although these changes can be helpful in recovering some movement, they can also prevent full recovery from occurring. To improve recovery, treatment should try to restore the way the brain areas work together, like they did before the stroke. To do this we first need to study how areas of the brain work together to produce movement when it isn’t damaged. We also need to find out how a non-injured brain changes as a person learns how to do a new task. This is important as the ability to learn is a main part of the treatment used with people who have had a stroke. In this study, we will look at how the areas of the brain work together to learn a new movement with a new brain imaging tool called magnetoencephalography or MEG. MEG will allow us to study how the many areas of the brain are connected, and how these connections change during motor learning.

This study is being conducted in conjunction with scientists at the National Research Council Institute for Biodiagnostics.

Funding Agencies: