Motor Practice Promotes Increased Activity in Brain Regions Structurally Disconnected After Subcortical Stroke

Participants

Participants were 10 right-handed patients who had previously suffered a first stroke (Table 1) and 18 age-matched right-handed controls (mean age, 58 years; range, 30-81 years; 7 women, 11 men); they gave written informed consent to participate in accordance with the Declaration of Helsinki and local Research Ethics Committee approval (05/Q1607/63). All patients were at least 6 months post a first ischemic or hemorrhagic left-hemisphere subcortical stroke affecting motor function in the right hand and were without a history or signs and symptoms of any other neurological conditions. Patients had intact sensation to light touch in affected limbs, were able to give informed consent, and did not have aphasia significantly limiting communication (National Institute of Health Stroke Scale rating >2). Patients all demonstrated visual acuity (corrected or uncorrected) that was sufficient to follow movement of the visual target on the MRI projection screen (during fMRI scanning) and on the laptop computer screen used for home training and could generate sufficient grip force for visuomotor responses to be tracked consistently by the computer.

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Table 1.

Patient Demographics

	Age, y	Gender	Fugl-Meyer (FM) Score a	Grip Strength b (%)	Time Poststroke, mos	Lesion Volume, cm3
	61	F	24	8	7	7.0
	59	M	42	29	22	9.1
	67	M	61	67	36	3.5
	68	M	59	68	43	32.4
	69	M	51	81	21	72.9
	54	M	50	71	23	1.3
	83	M	45	36	18	9.0
	41	M	64	85	8	40.73
	70	M	61	85	37	6.6
	50	M	64	52	34	152.1
Median	64		55	67.5	22.5	9.05

a

Upper-extremity FM score ¹⁶ ranges from 0 to 66; higher values reflect better function.

b

Grip strength for the paretic limb as a percentage of the maximum grip strength for the contralateral, unimpaired hand in each patient.

Visuomotor Tracking Task

Participants viewed a computer screen displaying 2 moving bars: a computer-controlled target bar indicating the required relative force (in green) and an adjacent response bar showing the instantaneous force generated by the participant (in blue if the correct force was generated or in red if the force deviated from the target force by more than 40 pixels in the visual display). The task for participants was to match the height of the response bar to that of the target bar by altering the grip force applied to an isometric pressure-sensing device held in their right hand. ¹⁷ The force required to move the bar to its maximum amplitude was calibrated to 80% of the participant’s maximum grip strength.

During fMRI sessions, the task included alternating 38-s blocks of rest and task conditions. In the task condition of interest, blocks consisted of 2 repeats of a specific sequence of sinusoidally varying target bar movements. Participants were informed that there was a repeating sequence within the block, so this would be learned explicitly. A second task condition required them to track random bar movements, but activation during this task is not further considered here. During rest blocks, participants did not move and passively viewed 2 bars, the relative movements of which were intended to mimic those during the tracking periods (a “target” bar moved to trace a sinusoidal trajectory slightly ahead of the second, “response” bar). Participants practiced the visuomotor tracking task for 4 blocks outside the scanner before their first recorded trial.

Performance gains with practice of the visuomotor tracking task were assessed using 15 days of daily home practice (10 blocks, each containing 2 repeats of the same sequence used during fMRI, with alternating 5-s rest periods) using a laptop and identical grip device.

Behavioral Analysis of Practice Effects

For task performance during fMRI, the mean error (calculated as the absolute difference in height between the target and response bars) per block was assessed using a repeated-measures ANOVA with within-subject factors of block (1-10) and condition (random, sequence) and the between-subject factor of group (patients, controls). A short-term practice score for each participant was calculated as (Error_BLOCK1/Error_BLOCKS7-10). The mean error per day from the home practice sessions was assessed similarly using a repeated-measures analysis of variance (ANOVA) with within-subject factor of day (1-15) and between-subject factor of group (patients, controls). A long-term practice score for each participant was calculated as (Error_DAY1/Error_DAYS10-15).

MRI Data Acquisition

MR Image Analysis

Analysis of MRI data was carried out using tools from the FMRIB Software Library (www.fmrib.ox.ac.uk/fsl). ¹⁸ The stroke lesion volume was segmented for each patient by manually delineating the region of abnormally hypointense signal on the patient’s T1-weighted scan using FSLview and calculating the volume of this region using fslstats.

Baseline Differences Between Brain Activity in Patients and Controls While Performing the Visuomotor Tracking Task

At baseline, patients had reduced activation relative to the healthy controls in cortical regions (primary sensorimotor, premotor, and supplementary motor cortices of the lesioned hemisphere) involved in motor control ^25,26 and anatomically interconnected ^27,28 subcortical areas (ventrolateral and anterior thalamus bilaterally and contralesional cerebellum, lobules V and VI). This finding is consistent with the patterns of brain injury, characterized by varying degrees of interruption of corticothalamic-cerebellar motor pathways.

The relative reduction in motor cortical activity in patients relative to healthy controls that we observed should be contrasted with previous reports that have emphasized increased movement-associated activation in patients after a stroke. ^29,30 The differences may arise from task differences because the majority of previous studies have used simpler movement tasks than the one used here; a limited adaptive capacity or “reserve” for compensatory activation in the injured brain may be made apparent only with more difficult or complex tasks. ³¹ In addition, with a learning task of the sort used here, there may be interindividual variation in the effects of a fixed amount of short-term task practice on brain functional responses. Whereas some participants may have reached a performance plateau, others could continue to improve if allowed to practice for longer (as shown in a previous study of long-term practice effects on brain responses) ³² , and so any associated gains in brain functional responses may not be maximal. Future studies with varied practice schedules, larger groups, and a range of motor tasks could test these hypotheses directly.

Dependence of Baseline Task Performance on White Matter Microstructure

We found that the loss of white matter integrity in the PLIC explained a substantial proportion of the variance in performance error reductions with baseline short-term task practice for the patients. The PLIC region implicated includes fiber tracts projecting from frontal cortical regions, including the premotor cortex. ³³ Previous work has identified premotor regions as important for hand motor control following damage ^30,34,35 and has emphasized the importance of descending motor outputs in both the lesioned and the contralesional hemisphere. ³⁶

We believe that the association between the behavioral effects of motor practice and white matter microstructure in the contralesional PLIC most likely reflects transsynaptic changes occurring as a consequence of the strokes. ^37,38 However, it also could reflect differences in the microstructure of white matter prior to the stroke; individual differences in performance are associated with variation in FA in task-relevant pathways, even in healthy individuals. ³⁹ Although regional correlations between FA and performance were not found for the healthy controls in the current study, the dependence of performance on the microstructure of undamaged white matter regions may be greater in patients as a consequence of their brain injury. This is consistent with clinical observations of worsening motor impairment in a stroke-affected paretic limb following the development of a new lesion in the ipsilateral cortex. ⁴⁰

Effects of Motor Practice on Task-Related Brain Activity

Control of grip force demands integration of sensory input and motor output in brain regions, including the motor, somatosensory, premotor, and parietal cortices. ⁴¹ Implicit learning of a sequence of grip forces is associated with a shift of activation from cortical to subcortical activation in a thalamic–basal-ganglia–premotor network. ⁶ Whereas previous studies of the effects of motor practice following stroke have provided useful insights into changes specifically in motor cortical areas, ⁴² we assessed changes across the brain more widely and were therefore able to provide novel evidence on changes in polymodal areas and subcortical nuclei. With long-term task practice, we found a double dissociation between patterns of practice-related changes in brain activity in patients and healthy controls in cortical regions, including the inferior frontal gyrus, insular and superior temporal regions, and in the basal ganglia and thalamus subcortically. In healthy controls, task performance at follow-up was associated with significantly lower activity in these brain regions than performance of the same task at baseline, consistent with the notion of increasing efficiency of motor activity for performance of highly practiced or overlearned movements. ⁴³ However, opposite trends were seen in patients, who showed an increase in task-related activity in these regions following practice, analogous to observations of increased recruitment of task-relevant areas with short-term motor learning for healthy participants. ^6,44-46 The observation of increased brain activation with practice for patients is consistent with that in previous studies of rehabilitation interventions, which have shown that good treatment outcomes are associated with increased activation in brain regions relevant to the task. ^32,47,48

Earlier studies of brain activation with simple movement have emphasized adaptive changes in secondary motor control regions such as the premotor cortex. ^29,34,35,49 A recent, well-designed study of implicit visuomotor learning with a joystick highlighted increasing prefrontal activation in patients poststroke with learning, in contrast to decreasing activity in healthy controls. ¹⁰ This difference was interpreted in terms of adaptive increases in attentional control with learning for the stroke patients.

Increased Activation of Brain Regions With Impaired Connectivity in Patients After Motor Practice

We found evidence for reduced structural connectivity not only local to the stroke but also in more distant regions, including the contralesional hemisphere. Whereas diffusion studies of FA have revealed patterns of anterograde (Wallerian) and retrograde white matter tract degeneration following focal damage, ^13-15,50 such effects are rarely reported in the contralesional hemisphere. However, observations of transhemispheric diaschisis ³⁷ support the concept that widespread interconnected regions, even in the contra lesional hemisphere, can be functionally and structurally altered after a focal lesion. Elsewhere, we have used a subset of the data reported here to characterize a novel, multivariate approach to the analysis of white matter connectivity that suggests a potential for explicitly network-based approaches for detecting this kind of distributed neuropathology. ³⁸

However, in the current report, we have assessed connectivity simply using averaged pairwise connection probabilities generated from a tractography approach and have compared the results of this analysis with fMRI findings. The regions with reduced anatomic connectivity at baseline showed a striking overlap with regions in which functional activity increased (despite general decreases in activation in healthy controls) with task practice in the stroke patients. The between-group differences in longitudinal change of brain activity suggest that motor practice plays distinct roles in patients as compared with healthy individuals. In the healthy controls, practice is associated with regionally increased functional efficiency, ⁴³ but in patients, practice enables increased functional recruitment of at least some of the regions that are structurally compromised. Although we presume that both rely on mechanisms of practice-related brain plasticity, we infer from the different directions of longitudinal activation change that the local circuit adaptations are distinct.

There is considerable overlap between the structurally impaired thalamic, basal ganglia, and superior temporal regions showing practice-related increases in patients (Figure 4) and areas showing reduced activity at baseline (Figure 3). Taken together, these findings further suggest that chronic, degenerative transsynaptic changes after a stroke contribute to impaired performance but that a behavioral intervention (eg, motor practice) can increase functional recruitment of affected areas, presumably reflecting local, activity-dependent plasticity.

We recognize clear limitations to our study. First, the number of patients tested was relatively small. Although observation of significant effects despite modest patient numbers emphasizes the magnitude of the detected relationships, other more subtle associations may have been missed. By selecting a relatively homogeneous patient population, we attempted to improve our sensitivity to detect effects, but this may limit generalization to a wider stroke population. More complete evaluation with a much larger patient group could test the relationship between differences in the anatomic distribution of impaired connectivity with different sizes or localization of stroke and individual differences in practice-related brain activation. Furthermore, we used only a single visuomotor paradigm, although we took care to ensure that this provided objective and quantitative performance measures. Future work should contrast outcomes with practice of a broader range of activities and compare their effects with appropriate control activities and explore the influence of differences in practice duration and schedules. Although longitudinal changes with practice of a single motor task provides a simple model for recovery, ² it cannot capture the range of cognitive processes that contribute to outcomes from a clinical rehabilitation intervention.