Compensatory motor network connectivity is associated with motor sequence learning after subcortical stroke

Abstract

Following stroke, functional networks reorganize and the brain demonstrates widespread alterations in cortical activity. Implicit motor learning is preserved after stroke. However the manner in which brain reorganization occurs, and how it supports behaviour within the damaged brain remains unclear. In this functional magnetic resonance imaging (fMRI) study, we evaluated whole brain patterns of functional connectivity during the performance of an implicit tracking task at baseline and retention, following 5 days of practice. Following motor practice, a significant difference in connectivity within a motor network, consisting of bihemispheric activation of the sensory and motor cortices, parietal lobules, cerebellar and occipital lobules, was observed at retention. Healthy subjects demonstrated greater activity within this motor network during sequence learning compared to random practice. The stroke group did not show the same level of functional network integration, presumably due to the heterogeneity of functional reorganization following stroke. In a secondary analysis, a binary mask of the functional network activated from the aforementioned whole brain analyses was created to assess within-network connectivity, decreasing the spatial distribution and large variability of activation that exists within the lesioned brain. The stroke group demonstrated reduced clusters of connectivity within the masked brain regions as compared to the whole brain approach. Connectivity within this smaller motor network correlated with repeated sequence performance on the retention test. Increased functional integration within the motor network may be an important neurophysiological predictor of motor learning-related change in individuals with stroke.

The importance of different learning stages for motor sequence learning after stroke

“…Without providing exact lesion information (cortical/subcortical), a further study demonstrated improved sleep-dependent offline learning in stroke patients for explicit and implicit tasks compared to healthy subjects (Meehan et al, 2011;Wadden et al, 2015;Wadden et al, 2017).…”

“…Concerning the affected (contralesional) hand, the results are heterogeneous. Some studies conclude that motor sequence learning is preserved (Meehan et al, 2011;Shin et al, 2005;Wadden et al, 2015), while other studies find an impairment of motor sequence learning for the affected hand after stroke (Dirnberger et al, 2013;Exner et al, 2001;Fleming et al, 2018;Gomez Beldarrain, 1999;Gomez Beldarrain et al, 2002;Vakil et al, 2000;Wadden et al, 2017). All available examples of these studies investigated patients in the chronic phase of stroke.…”

“…Regarding implicit sequence learning, the results are more divergent than those for explicit learning. Six studies demonstrated an impairment in implicit sequence learning for the affected hand (Dirnberger et al, 2013;Exner et al, 2001;Gomez Beldarrain, 1999;Gomez Beldarrain et al, 2002;Vakil et al, 2000;Wadden et al, 2017), while three others reported that implicit motor learning was preserved after stroke (Meehan et al, 2011;Shin et al, 2005;Wadden et al, 2015). Additionally, a recent meta-analysis from 2017 investigated the question of whether implicit motor learning is preserved after stroke.…”

Spontaneous and Therapeutic-Induced Mechanisms of Functional Recovery After Stroke

“…Research has demonstrated disruptions in resting-state functional connectivity involving regions of motor [139, 137, 140], sensory, attention, and language [141] early after stroke. These disruptions often relate to the extent of white matter damage [142, 143].…”

“…Additional work is warranted to substantiate these findings. Other examples of potential stroke biomarkers include total infarct volume [195], white matter tract injury [20–22, 138, 196], cortical activation [42, 52, 91, 175, 138, 197, 198] and connectivity [139, 137, 145, 147, 177, 138, 199], and genetic polymorphisms derived from simple blood tests [15, 16, 200]. These measures may serve an important role in guiding treatment, stratifying subjects in intervention studies, and ultimately predicting functional outcome and response to therapy.…”