Neural substrates underlying motor skill learning in chronic hemiparetic stroke patients

Lots of big words used but nothing on how this could be used in protocols to help survivors. Whomever sponsors stroke research should require an 8th grade reading level and protocols to help survivors.
http://journal.frontiersin.org/article/10.3389/fnhum.2015.00320/full?utm_source=newsletter&utm_medium=email&utm_campaign=Neuroscience-w33-2015

• U Dinant-Godinne UCL Namur, Université Catholique de Louvain, Yvoir, Belgium
• ³Imagilys, Brussels, Belgium
• ⁴Faculty of Electronics and Information Technology, Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland
• ⁵Neurology Department, Site Saint-Joseph, CHC, Liège, Belgium
• ⁶Neurology Department, Clinique Saint-Pierre, Ottignies, Belgium
• ⁷Service de Neurologie, Unité Neuro-Vasculaire, Cliniques Universitaires Saint Luc UCL, Université Catholique de Louvain, Brussels, Belgium
• ⁸Scientific Support Unit, CHU Dinant-Godinne UCL Namur, Université Catholique de Louvain, Yvoir, Belgium
• ⁹Louvain Bionics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Motor skill learning is critical in post-stroke motor recovery, but little is known about its underlying neural substrates. Recently, using a new visuomotor skill learning paradigm involving a speed/accuracy trade-off in healthy individuals we identified three subpopulations based on their behavioral trajectories: fitters (in whom improvement in speed or accuracy coincided with deterioration in the other parameter), shifters (in whom speed and/or accuracy improved without degradation of the other parameter), and non-learners. We aimed to identify the neural substrates underlying the first stages of motor skill learning in chronic hemiparetic stroke patients and to determine whether specific neural substrates were recruited in shifters versus fitters. During functional magnetic resonance imaging (fMRI), 23 patients learned the visuomotor skill with their paretic upper limb. In the whole-group analysis, correlation between activation and motor skill learning was restricted to the dorsal prefrontal cortex of the damaged hemisphere (DLPFC_damh: r = −0.82) and the dorsal premotor cortex (PMd_damh: r = 0.70); the correlations was much lesser (−0.16 < r > 0.25) in the other regions of interest. In a subgroup analysis, significant activation was restricted to bilateral posterior parietal cortices of the fitters and did not correlate with motor skill learning. Conversely, in shifters significant activation occurred in the primary sensorimotor cortex_damh and supplementary motor area_damh and in bilateral PMd where activation changes correlated significantly with motor skill learning (r = 0.91). Finally, resting-state activity acquired before learning showed a higher functional connectivity in the salience network of shifters compared with fitters (qFDR < 0.05). These data suggest a neuroplastic compensatory reorganization of brain activity underlying the first stages of motor skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of bilateral PMd.

Introduction

Stroke is a devastating disorder that causes life-long upper limb hemiparesis in 30–70% of survivors (Lai et al., 2002; Kwakkel et al., 2003). The biochemical mechanisms triggered by acute stroke (e.g., edema resolution, inflammation, up- and down-regulation of neurotransmitters) play a prominent role in early recovery (Kreisel et al., 2006; Carey and Seitz, 2007). Beyond these biochemical cascades, recovery of motor function also relies on neuroplastic reconfiguration of the cortical motor network and its descending projections, which support transfer of impaired functions toward undamaged areas of the brain (Feydy et al., 2002; Johansen-Berg et al., 2002; Lotze et al., 2006; Lindenberg et al., 2010; Schulz et al., 2012). Although this neuroplastic reorganization may reflect a simple re-routing of information flow through pre-existing, undamaged pathways, stroke patients must learn how to recruit these neuronal resources. To some extent, recovering from hemiparesis might be conceptualized as a particular form of motor skill learning, in other words, learning to use the reconfigured motor network to optimize planning, execution and movement control of the paretic upper limb. Indeed, the idea that motor skill learning plays a central role in post-stroke motor recovery is becoming a major focus in neurorehabilitation (Matthews et al., 2004; Krakauer, 2006; Dipietro et al., 2012; Kitago and Krakauer, 2013).

The neural substrates of motor skill learning are relatively well elucidated in healthy individuals. Functional magnetic resonance imaging (fMRI) studies demonstrated that motor skill learning relies on a network encompassing the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PM), dorsolateral prefrontal cortex (DLPFC), cerebellum and basal ganglia (Ghilardi et al., 2000; Halsband and Lange, 2006; Debas et al., 2010; Hardwick et al., 2013). Recently, the definition of motor skill learning has been refined to a training-induced acquisition and improvement of motor performance (i.e., skills), persisting over time and characterized by a shift of the speed/accuracy trade-off (SAT), automatisation and reduction of performance variability (Reis et al., 2009; Dayan and Cohen, 2011; Krakauer and Mazzoni, 2011). Using a visuomotor skill learning paradigm involving a SAT, we demonstrated that different that different behavioral trajectories can be observed in healthy individuals during the first stages of motor skill learning (Lefebvre et al., 2012). The first one was characterized either by improvement in both speed and accuracy or by improvement of one parameter without a concomitant worsening of the other one; this resulted in a shift of the SAT, which suggests a rapid and successful motor skill learning. The second behavioral trajectory was characterized by opposite changes in speed and accuracy over time, resulting in less efficient motor skill learning. E.g., when speed improved, accuracy worsened, resulting in slight improvement in the SAT. Finally, the third behavioral trajectory was characterized by a deterioration of both speed and accuracy or a lack of any improvement. According to their behavioral trajectory, the subjects were refereed as shifters, fitters and non-learners respectively. These different behavioral trajectories were observed despite identical instructions and experimental conditions, and they were associated with specific brain activation patterns. Specifically, in the efficient shifters, activation was found in the M1, cerebellum and the SMA where the activation changes correlated with performance improvement, suggesting that the SMA plays a key role in early motor skill learning involving a SAT. In the less efficient fitters, there was only a non-significant correlation in the cerebellum (Lefebvre et al., 2012).

In stroke patients, functional brain imaging has been used extensively to explore the reorganization of the network controlling the paretic arm or hand. Grossly, early after stroke, this reorganized network is characterized by compensatory recruitment of the undamaged hemisphere, especially the motor and premotor areas (Feydy et al., 2002; Tombari et al., 2004; Jaillard et al., 2005; Ward and Frackowiak, 2006) and/or widespread activation in the damaged hemisphere with extensive activation of the somatosensory and premotor areas (Tombari et al., 2004; Ward et al., 2006). Over time, motor recovery is associated with a shift of activation back toward the damaged hemisphere (Pineiro et al., 2001; Jaillard et al., 2005; Favre et al., 2014; Grefkes and Ward, 2014) and a progressive recruitment of the cerebellum ipsilateral to the paretic hand (Small et al., 2002). In addition, changes in brain connectivity have been associated with motor function recovery after stroke (Schaechter et al., 2009; Jiang et al., 2013; Grefkes and Ward, 2014). Early after stroke, both anatomical and functional connectivity (FC) decrease within the damaged hemisphere; over time, motor function recovery is associated with gradual recovery of connectivity (Pannek et al., 2009; Westlake et al., 2012; Golestani et al., 2013). Thus, the more similar the reorganized motor network becomes to that of healthy individuals, the better the recovery. Nevertheless, the undamaged hemisphere may still play a vicarious role in recovered motor control of the paretic hand (Johansen-Berg et al., 2002; Werhahn et al., 2003; Tombari et al., 2004; Lotze et al., 2006; Bestmann et al., 2010; Grefkes and Ward, 2014). It has also been suggested that the resting-state FC correlates with the motor recovery potential (Park et al., 2011, 2014; Yin et al., 2012; Golestani et al., 2013; Ovadia-Caro et al., 2013; Dacosta-Aguayo et al., 2014).

Since functional reorganization occurs in the network supporting motor recovery of the paretic upper limb after stroke, it seems logical that similar neuroplasticity should occur in the network underlying motor skill learning. However, despite extensive fMRI studies of the functional neuroanatomy of motor skill learning in healthy individuals (Ghilardi et al., 2000; Halsband and Lange, 2006; Debas et al., 2010; Lefebvre et al., 2012; Hardwick et al., 2013), very few studies have assessed stroke patients. Using a region of interest (ROI) approach, one study with 10 chronic stroke patients performing visuomotor tracking with the paretic hand showed a bilaterally reorganized pattern with a predominance in the undamaged hemisphere during the pre-training fMRI session (Carey et al., 2002). After training, activation was partially transferred back toward the damaged hemisphere, suggesting functional reorganization (Carey et al., 2002). Another study using ROI showed decreased task-related fMRI activation in the contralesional M1 of nine chronic stroke patients after 3 days of training on a serial targeting task (Boyd et al., 2010). A recent fMRI study highlighted the differences in brain activation patterns between nine healthy individuals and nine chronic stroke patients during training over several days on an implicit sequential visuomotor tracking task (Meehan et al., 2011). Compared with healthy individuals, motor skill learning and retention in stroke patients relied on a reorganized network involving compensatory activations, especially in prefrontal attentional areas such as the DLPFC. Finally, during baseline performance of a sequential grip-force tracking task, 10 chronic stroke patients showed reduced fMRI activation in the damaged hemisphere compared with healthy controls (Bosnell et al., 2011). After repeated training, fMRI activation decreased in healthy controls but was maintained or increased in stroke patients.

These four studies involved relatively small cohorts of mostly high-functioning patients, typically with sub-cortical strokes, and they did not characterize motor skill learning through SAT. Instead, they compared fMRI activation related to motor performance pre- and post-training, and two used an ROI approach (Carey et al., 2002; Boyd et al., 2010). Since motor learning plays a key role in motor function recovery, better knowledge of the neurophysiology of motor skill learning after stroke should lead to the refinement of recovery models and translate into the development of specific neurorehabilitation methods based on the principles of motor learning.

Motor skill learning can be divided in two stages: a fast on-line learning process leading to large performance improvement over a single training session (i.e., early stages of motor skill learning as described is the present study), and a slower process involving smaller performance gains obtained through repeated training sessions (Dayan and Cohen, 2011).

This study aimed to specifically explore the early stages of motor skill learning with the paretic hand in chronic hemiparetic stroke patients, using an innovative motor skill learning paradigm with a SAT. This is a first step to understand the “recovery process” in stroke patients, whether residual motor learning aptitudes are present and which brain areas are (neuroplastically?) involved. A better knowledge about motor (skill) learning in stroke patients could help to refine neurorehabilitation protocols, in which motor learning is often imbedded as an implicit assumption but poorly recognized. The purposes of this study were: (i) to use random effect (RFX) analyses of whole-brain fMRI activation to identify the neural substrates underlying the first stages of motor skill learning involving a SAT in a larger cohort of chronic stroke patients using their paretic upper limb, (ii) to determine whether shifter and fitter stroke patients recruit specific neural substrates, and (iii) to determine whether resting-state FC acquired before training would predict the behavioral trajectory (shifter/fitter) during the first stage of motor skill learning and/or correlate with the amount of motor skill learning.

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