This review addresses what is currently known about the time course of skill reacquisition after stroke. There is growing evidence that the natural logarithmic pattern of functional recovery can be modified by intensive task-oriented practice preferably initiated within 6 months after stroke. However, the impact of practice on the learning-dependent and intrinsic spontaneous mechanisms of neurological recovery is poorly understood. At least four probably interrelated mechanisms have been identified that drive motor and recovery after stroke: (1) salvation of penumbral tissue in the first days to weeks after stroke; (2) alleviation of diaschisis; (3) homeostatic and learning-dependent (Hebbian) neuroplasticity; (4) behavioral compensation strategies. These mechanisms underlying recovery are highly interactive, and operate in different, sometimes limited, time-windows after stroke onset. In line with these mechanisms of improvement after stroke, we present a hypothetical phenomenological model for understanding skill reacquisition after stroke. Translational research is important at this point to improve our knowledge about the neural correlates of what and how patients learn when they show functional improvement after stroke. This knowledge should serve as a basis to optimize the timing, focus and intensity of evidence-based rehabilitation interventions and to design innovative strategies to enhance motor recovery after stroke. 10 11 12 13 14 15 16 17 18 19 20 21 a Barthel Index (BI) below 12 points at 3 months 29 after after stroke (Heuschmann et al., 2011). In the 30 United States, stroke has a mortality rate of 15%, and 31 26% of stroke survivors aged 65 years and older are 32 institutionalized at 6 months after stroke, while 50% 33 suffer from hemiparesis and 30% cannot walk with-34 out assistance (Kelly-Hayes et al., 2003; Lloyd-Jones 35 et al., 2009). Although individual recovery patterns 36 and outcome differ between patients, several prognos-37 tic studies have shown that outcome at 3 or 6 months 38 is highly predictable for upper (Nijland et al., 2010; 39 0922-6028/13/$27.50 © 2013 -IOS Press and the authors. All rights reserved U n c o r r e c t e d A u t h o r P r o o f 2 F. Buma et al. / Understanding upper limb recovery after stroke Stinear et al., 2012), and lower limb (Veerbeek et 40 al., 2011) as well as basic activities of daily living 41 (ADLs)in general (Kwakkel et al., 2006; Prabhakaran 42 et al., 2008). Almost all patients show a certain degree 43 of spontaneous neurological recovery, following a nat-44 ural logarithmic pattern (Langhorne et al., 2011). The 45 recovery rate is highest in the first months after stroke, 46 after which recovery levels of and reaches a plateau 47 (Kwakkel et al., 2006; Langhorne et al, 2009; Ng et 48 al., 2007). Unfortunately, the underlying mechanisms 49 responsible for these spontaneous, natural logarith-50 mic changes in impairment in the first months after 51 stroke are poorly understood and the subject of the 52 present review. We first introduce the theoreti...

Despite methodological shortcomings and heterogeneity, trends can be discerned. However, statistically sound associations with recovery are not consistent. The challenges in future research will be, controlling for confounding factors, finding outcomes that specifically measure dexterity of the paretic limb, to control for the extent of white matter damage and changes in perfusion in order to establish the longitudinal construct validity of fMRI and PET with regard to upper limb recovery after stroke.

It is unclear whether additionally recruited sensorimotor areas in the ipsilesional and contralesional hemisphere and the cerebellum can compensate for lost neuronal functions after stroke. The objective of this study was to investigate how increased recruitment of secondary sensorimotor areas is associated with quality of motor control after stroke. In seventeen patients (three females, fourteen males; age: 59.9 ± 12.6 years), cortical activation levels were determined with functional magnetic resonance imaging (fMRI) in 12 regions of interest during a finger flexion–extension task in weeks 6 and 29 after stroke. At the same time points and by using 3D kinematics, the quality of motor control was assessed by smoothness of the grasp aperture during a reach-to-grasp task, quantified by normalized jerk. Ipsilesional premotor cortex, insula and cerebellum, as well as the contralesional supplementary motor area, insula and cerebellum, correlated significantly and positively with the normalized jerk of grasp aperture at week 6 after stroke. A positive trend towards this correlation was observed in week 29. This study suggests that recruitment of secondary motor areas at 6 weeks after stroke is highly associated with increased jerk during reaching and grasping. As jerk represents the change in acceleration, the recruitment of additional sensorimotor areas seems to reflect a type of control in which deviations from an optimal movement pattern are continuously corrected. This relationship suggests that additional recruitment of sensorimotor areas after stroke may not correspond to restitution of motor function, but more likely to adaptive motor learning strategies to compensate for motor impairments.

Spontaneous motor recovery after stroke appears to be associated with structural and functional changes in the motor network. The aim of the current study was to explore time-dependent changes in resting-state (rs) functional connectivity in motor-impaired stroke patients, using rs-functional MRI at 5 weeks and 26 weeks post-stroke onset. For this aim, 13 stroke patients from the EXPLICIT-stroke Trial and age and gender-matched healthy control subjects were included. Patients’ synergistic motor control of the paretic upper-limb was assessed with the upper extremity section of the Fugl-Meyer Assessment (FMA-UE) within 2 weeks, and at 5 and 26 weeks post-stroke onset. Results showed that the ipsilesional rs-functional connectivity between motor areas was lower compared to the contralesional rs-functional connectivity, but this difference did not change significantly over time. No relations were observed between changes in rs-functional connectivity and upper-limb motor recovery, despite changes in upper-limb function as measured with the FMA-UE. Last, overall rs-functional connectivity was comparable for patients and healthy control subjects. To conclude, the current findings did not provide evidence that in moderately impaired stroke patients the lower rs-functional connectivity of the ipsilesional hemisphere changed over time.

Constraint-induced movement therapy (CIMT) is a commonly used rehabilitation intervention to improve upper limb function after stroke. CIMT was originally developed for patients with a chronic upper limb paresis. Although there are indications that exercise interventions should start as early as possible after stroke, only a few randomized controlled trials have been published on either CIMT or modified forms of CIMT (mCIMT) during the acute phase after stroke. The implementation of (m)CIMT in published studies is very heterogeneous in terms of content, timing and intensity of therapy. Moreover, mCIMT studies often fail to provide a detailed description of the protocol applied. The purpose of the present paper is therefore to describe the essential elements of the mCIMT protocol as developed for the EXplaining PLastICITy after stroke (EXPLICIT-stroke) study. The EXPLICIT-stroke mCIMT protocol emphasizes restoring body functions, while preventing the development of compensatory movement strategies. More specifically, the intervention aims to improve active wrist -and finger extension, which is assumed to be a key factor for upper limb function. The intervention starts within 2 weeks after stroke onset. The protocol retains two of the three key elements of the original CIMT protocol, that is, repetitive training and the constraining element. Repetitive task training is applied for 1 hour per working day, and the patients wear a mitt for at least 3 hours per day for three consecutive weeks.

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ObjectiveThe nature of changes in brain activation related to good recovery of arm function after stroke is still unclear. While the notion that this is a reflection of neuronal plasticity has gained much support, confounding by compensatory strategies cannot be ruled out. We address this issue by comparing brain activity in recovered patients 6 months after stroke with healthy controls.MethodsWe included 20 patients with upper limb paresis due to ischemic stroke and 15 controls. We measured brain activation during a finger flexion-extension task with functional MRI, and the relationship between brain activation and hand function. Patients exhibited various levels of recovery, but all were able to perform the task.ResultsComparison between patients and controls with voxel-wise whole-brain analysis failed to reveal significant differences in brain activation. Equally, a region of interest analysis constrained to the motor network to optimize statistical power, failed to yield any differences. Finally, no significant relationship between brain activation and hand function was found in patients. Patients and controls performed scanner task equally well.ConclusionBrain activation and behavioral performance during finger flexion-extensions in (moderately) well recovered patients seems normal. The absence of significant differences in brain activity even in patients with a residual impairment may suggest that infarcts do not necessarily induce reorganization of motor function. While brain activity could be abnormal with higher task demands, this may also introduce performance confounds. It is thus still uncertain to what extent capacity for true neuronal repair after stroke exists.

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