Dominance of the Unaffected Hemisphere Motor Network and Its Role in the Behavior of Chronic Stroke Survivors

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Dominance of the Unaffected Hemisphere Motor Network and Its Role in the Behavior of Chronic Stroke Survivors

Sahil Bajaj^1,2*, Stephen N. Housley³, David Wu³, Mukesh Dhamala^1,4,5, G. A. James⁶ and Andrew J. Butler^3,5,7

• ¹Department of Physics and Astronomy, Georgia State University, Atlanta, GA, USA
• ²Department of Psychiatry, College of Medicine, University of Arizona, Tucson, AZ, USA
• ³Byrdine F. Lewis School of Nursing and Health Professions, Georgia State University, Atlanta, GA, USA
• ⁴Joint Center for Advanced Brain Imaging, Center for Behavioral Neuroscience, Center for Nano-Optics, Center for Diagnostics and Therapeutics, Georgia State University, Atlanta, GA, USA
• ⁵Neuroscience Institute, Georgia State University, Atlanta, GA, USA
• ⁶Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA
• ⁷Department of Veterans Affairs, Atlanta Rehabilitation Research and Development Center of Excellence, Decatur, GA, USA

Balance of motor network activity between the two brain hemispheres after stroke is crucial for functional recovery. Several studies have extensively studied the role of the affected brain hemisphere to better understand changes in motor network activity following stroke. Very few studies have examined the role of the unaffected brain hemisphere and confirmed the test–retest reliability of connectivity measures on unaffected hemisphere. We recorded blood oxygenation level dependent functional magnetic resonance imaging (fMRI) signals from nine stroke survivors with hemiparesis of the left or right hand. Participants performed a motor execution task with affected hand, unaffected hand, and both hands simultaneously. Participants returned for a repeat fMRI scan 1 week later. Using dynamic causal modeling (DCM), we evaluated effective connectivity among three motor areas: the primary motor area (M1), the premotor cortex (PMC) and the supplementary motor area for the affected and unaffected hemispheres separately. Five participants’ manual motor ability was assessed by Fugl-Meyer Motor Assessment scores and root-mean square error of participants’ tracking ability during a robot-assisted game. We found (i) that the task performance with the affected hand resulted in strengthening of the connectivity pattern for unaffected hemisphere, (ii) an identical network of the unaffected hemisphere when participants performed the task with their unaffected hand, and (iii) the pattern of directional connectivity observed in the affected hemisphere was identical for tasks using the affected hand only or both hands. Furthermore, paired t-test comparison found no significant differences in connectivity strength for any path when compared with one-week follow-up. Brain-behavior linear correlation analysis showed that the connectivity patterns in the unaffected hemisphere more accurately reflected the behavioral conditions than the connectivity patterns in the affected hemisphere. Above findings enrich our knowledge of unaffected brain hemisphere following stroke, which further strengthens our neurobiological understanding of stroke-affected brain and can help to effectively identify and apply stroke-treatments.

Introduction

An estimated 795,000 Americans suffer a stroke annually, leading to long-term disability for an estimated 6.4 million Americans. Many stroke survivors exhibit some degree of motor impairment that limits functional status after stroke. Advances in acute care medicine have significantly reduced mortality, which has coincidentally led to rising numbers of stroke survivors that utilize rehabilitation therapies. As the body of evidence of stroke rehabilitation is expanding (Dobkin, 2004; Brewer et al., 2013; Bajaj et al., 2015b), it has become exceedingly important to explore how brain networks are influenced following stroke and the role those networks play in functional recovery. A rich neurobiological understanding of the basic principles of stroke-recovery will aid in the development of more effective stroke treatments.

Over the past several years, numerous studies have been proposed to better understand the connectivity patterns in motor network of people suffering from stroke. Most of the studies have focused on the basic motor networks directly involved after stroke (before and after stroke treatment) and compared the results with healthy controls. The primary motor area (M1), which is an integral part of basic motor network, due to its association with upper-limb recovery, is the most common target for stroke therapies. Other motor areas such as premotor cortex (PMC) and supplementary motor area (SMA) are functionally and anatomically in close association with M1 and play a crucial role to execute motor tasks (Bajaj et al., 2014, 2015a,b). Previous studies have discussed the role of the motor network in the unaffected hemisphere of stroke patients and its test–retest reliability with time. Although investigating changes in motor network connectivity strength provide important insight into brain reorganization following stroke, few studies assessing these changes are grounded by the functional ability and motor performance outcomes that are important for stroke survivors with residual upper limb impairment (Fong et al., 2001; Arya et al., 2011; Bajaj et al., 2015a). Recently, in a stroke study, Li et al. (2016) observed significant correlations between the connectivity strength and functional ability, implying that the connectivity of ipsilateral M1 may be useful in evaluating and predicting functional ability and motor performance. This is in agreement with other studies (Grefkes and Fink, 2011; Lindenberg et al., 2012; Chen and Schlaug, 2013) that have found changes in cortical network connectivity of stroke patients are associated with impaired functional ability and motor performance. This is an evolving area of research, with most studies associating clinical outcome to a single region of interest (ROI) association, and fewer studies relating outcome to more complex network models (Park et al., 2011). To our knowledge, no studies have previously compared the role that affected and unaffected hemispheres networks play in encoding stroke patients’ functional ability while simultaneously assessing time-dependent test–retest reliability of these outcomes.

The role of unaffected hemisphere in motor recovery has been considered somewhat controversial (Buetefisch, 2015). It has been reported that the neural substrates in the unaffected hemisphere can mediate recovery only when such substrates in the affected hemisphere are significantly damaged (John et al., 2015). In other studies, abnormalities have been reported in the unaffected arm after stroke, which further depends on whether the infarct was in the dominant or non-dominant hemisphere (Colebatch and Gandevia, 1989; Haaland and Harrington, 1989; Jones et al., 1989; Winstein and Pohl, 1995; Haaland et al., 2004). It is hypothesized that the behavioral recovery observed after stroke is supported by the sensorimotor network in the affected hemisphere (Pineiro et al., 2002; Loubinoux et al., 2003; Calautti et al., 2007; Loubinoux, 2007), whereas it is also hypothesized that the unaffected hemisphere may support motor-recovery (O’Shea et al., 2007; Riecker et al., 2010; Rehme et al., 2011). Although a significant ipsilateral activation has been considered as a marker for poor motor recovery (Ward et al., 2003) alternatively, this has been found in motor areas of subacute and chronic stroke patients (Weiller et al., 1992; Seitz et al., 1998; Bütefisch et al., 2005; Lotze et al., 2006; Schaechter and Perdue, 2008). A lot of gobbledegook words there, assuredly so survivors can't understand and act upon this.

Reliability of functional and effective connectivity among motor areas and reliability of various neuroimaging tools over time has been another important aspect to consider when assessing cortical mechanism of recovery. The reliability of functional MRI (fMRI) during visual motor tasks in stroke patients has been tested within and between sessions. By comparing interclass correlation coefficients (ICC), within-session reliability has been reported to be higher than between session reliability, but the overall results reflect that brain activations are reproducible and such research designs could be used for stroke patients (Kimberley et al., 2008b). Using ROI seed-based and ROI correlation matrix approaches, a 1-year test–retest reliability of intrinsic connectivity network was confirmed for older adults using fMRI (Guo et al., 2012). This study was found to be consistent with other short-term reliability studies on young (Schwarz and McGonigle, 2011) as well as older controls (Telesford et al., 2010).

In order to better understand the brain connectivity pattern of the affected and unaffected hemispheres while performing the motor execution task, nine stroke survivors underwent fMRI scanning over two sessions with one-week separation. Our goals in this study were to: (a) Explore the brain connectivity pattern for: (i) affected hemisphere during tapping with affected hand only (AHem-aHand) (ii) affected hemisphere during tapping with both hands (affected and unaffected) simultaneously (AHem-bHand) (iii) unaffected hemisphere during tapping with affected hand only (UHem-aHand), and (iv) unaffected hemisphere during tapping with unaffected hand only (UHem-uHand); (b) check if bilateral tapping (i.e., tapping with both hands) strengthened the connectivity patterns more in affected hemisphere compared to unilateral tapping (i.e., tapping with affected hand only) (AHem-bHand vs. AHem-aHand); (c) check if unilateral tapping with unaffected hand better estimated the connectivity pattern on unaffected hemisphere (UHem-uHand) than the connectivity pattern on affected (AHem-aHand) and unaffected (UHem-aHand) hemispheres while tapping with affected hand; (d) check if brain connectivity parameters were reliable between two sessions of one week apart; and (e) explore the brain-behavior correlations for affected and unaffected hemispheres.

We hypothesized that the:

(1) connectivity pattern would be (a) stronger for AHem-bHand than AHem-aHand (b) stronger for UHem-uHand than for either AHem-aHand or AHem-bHand and (c) weaker and different for UHem-aHand than AHem-aHand.

(2) connectivity strength parameters would significantly (a) positively correlate with FMA scores and (b) negatively correlate with RMSE scores for UHem-uHand only.

Here higher FMA scores and lower RMSE scores represent better performance and vice-versa.