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Dominance of the Unaffected Hemisphere Motor Network and Its Role in the Behavior of Chronic Stroke Survivors
Sahil Bajaj1,2*, 
Stephen N. Housley3, 
David Wu3, 
Mukesh Dhamala1,4,5, 
G. A. James6 and 
Andrew J. Butler3,5,7- 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
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.
