Handbook of Clinical Neurology, Vol. 110 (3rd series) Neurological Rehabilitation M.P. Barnes and D.C. Good, Editors # 2013 Elsevier B.V. All rights reserved
Functional neuroimaging NICK S. WARD* Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London,UK
INTRODUCTION Functional neuroimaging techniques allow examination of human brain function and have revolutionized the way we study neuroscience in humans. It is now possible to probe how the brain works under highly controlled experimental conditions. In the context of stroke, functional brain imaging provides a way of assessing how focal damage to cortical or subcortical regions alters the way surviving neural networks operate, and how these changes are related to impairment and recovery. For basic scientists, lesion-induced reorganization provides an intriguing insight into how the human brain works in a way that is rarely possible by studying normal brain function alone. For clinicians, the prospect of understanding how surviving brain networks are altered by focal damage and by subsequent treatments is exciting as it suggests a way of studying rehabilitation from a mechanistic viewpoint. Here, studies that contribute to these views will be discussed, particularly from the perspective of the motor system, which has been particularly well characterized. Detailed descriptions of the methodology underpinning functional brain imaging has been described in detail elsewhere (Ward, 2009).
STROKE ALTERS BRAIN ACTIVATION PATTERNS A number of early studies demonstrated that stroke patients exhibited different activation patterns during attempted movements with the affected hand compared to those seen in healthy controls. In general, group studies using either positron emission tomography (PET) (Chollet et al., 1991; Weiller et al., 1992, 1993; Seitz et al., 1998; Calautti et al., 2001) or functional magnetic resonance imaging (fMRI) (Cramer et al., 1997; Cao et al., 1998) found that well recovered patients with subcortical lesions had greater activation during finger tapping than
controls, particularly in a number of motor-related cortical regions in both hemispheres. These motor-related cortical regions are often referred to as secondary motor areas and include the premotor cortices (both ventral and dorsal), supplementary motor area, and cingulate motor areas, as well as prefrontal and parietal cortices. The second main finding from these studies was that the somatotopic organization within these areas was altered. For example, in one study reported alterations in topography of sensorimotor representations in two patients with good recovery following cortical strokes involving either precentral or postcentral gyrus (Cramer et al., 2000). Others have also reported this shift of cortical hand representation using fMRI, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) in a patient with recovered hand function following cortical stroke (Rossini et al., 1998). Furthermore, Weiller et al. (1993) described a ventral shift in peak sensorimotor cortex activation and others have reported an overall caudal shift in a group of recovered stroke patients (Pineiro et al., 2001). It may be that there is no consistent direction of shift and the phenomenon could be a reflection of the finding that the hand, for example, has several spatially distinct representations in primary motor cortex (Sanes and Donoghue, 2000). A recent study has, however, shown that the peak of the sensorimotor cortex activity is found more posteriorly in patients with more motor impairment (Bestmann et al., 2010), suggesting a predictable alteration in somatotopy. The third finding points to surviving peri-infarct cortical tissue being helpful to the recovering patient. Cao et al. (1994) studied patients with prior perinatal infarctions whilst performing sequential finger movements of the affected hand and found bilateral activation in motor-related regions as well as peri-infarct cortical rim activations. Cramer et al. (1997) also reported similar peri-infarct activations in recovered stroke patients.
*Correspondence to: Dr. N.S. Ward, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, 33 Queen Square, London WC1N 3BG, UK. Tel: þ 44 (0) 20 3108 0049, Fax: þ 44 (0) 20 7278 9836, E-mail: [email protected]
Taken together, these studies suggested that stroke is followed by some form of reorganization both within and between regions in a distributed motor network. Until now, imaging analysis techniques had often meant that stroke patients were treated as a homogeneous group and many of the first functional imaging studies after stroke were performed in reasonably well recovered patients. It had been noted that there was some variability in activation patterns but it was not clear whether this variability was biological in origin and it had not been examined systematically. In order to address this question, the next important advance was to include less well recovered patients in these studies. This required a change in the motor task and so hand grip was used in order to allow patients without return of fractionated finger movements to be studied. In order to avoid the problem of “performance confound,” i.e., differences in activation patterns resulting from differences in the effort required to perform the task, the target forces used were always a proportion of each subject’s own maximum grip force. The issue of dealing with performance confounds has been dealt with extensively elsewhere (Baron et al., 2004). In the first such crosssectional study using fMRI, subcortical stroke patients with greater motor impairment had increased taskrelated activity in secondary motor regions in both affected and unaffected hemispheres (Fig. 11.1), whereas patients with little residual impairment had activation patterns that were no different from those in healthy age-matched volunteers (Ward et al., 2003b). A similar result was observed in a group of patients with different levels of impairment studied at approximately 10 days poststroke (Ward et al., 2004), illustrating that lesion-induced reorganization occurs quickly. It was considered likely that damage to motor outflow tracts was driving this early alteration in motor maps. A subsequent study used TMS to quantify the “functional integrity” of the corticospinal system. Patients with more corticospinal system damage exhibited greater task-related activity in secondary motor areas in both hemispheres and less activity in ipsilesional M1 (hand area) (Ward et al., 2006). These results point to a progressive shift away from primary to secondary motor areas with increasing disruption to the corticospinal system, presumably because in some patients ipsilesional M1 is less able to influence motor output. However, this is highly likely to depend on the exact pattern of disruption to the descending pathways. The relationship between reorganization and recovery can also be explored by studying individual patients longitudinally during a period of clinical improvement (Marshall et al., 2000; Calautti et al., 2001; Feydy et al., 2002; Small et al., 2002; Ward et al., 2003a). One study scanned subcortical stroke patients on
B Unaffected hand
Fig. 11.1. Overactivations seen in stroke patients during affected (left) hand grip. (A) Overactivity more likely to be seen in patients with greater impairment in bilateral premotor, prefrontal and parietal cortices, supplementary motor areas, as well as contralesional primary motor cortex. Results rendered onto canonical brain (front of brain at top, left side on left). (B) Brain activity during both affected (left) and unaffected (right) hand grip in a single patient with large subcortical infarction (lenticulostriate). On left, activity seen in “hand knob” of left primary motor cortex during unaffected (right) hand grip. On right, activity seen in distributed brain regions, including contralesional (left) primary motor cortex during affected (left) hand grip. Note absence of activity in ipsilesional (right) “hand knob.” (Adapted from Ward et al., 2003b)
average eight times over the first 6 months after stroke (Ward et al., 2003a) and demonstrated early overactivation in mainly secondary motor regions. Thereafter functional recovery was associated with a focusing of task-related brain activation patterns towards a “normal” lateralized pattern. These patients had variable degrees of motor impairment early after stroke, but all made excellent recoveries. Whether this pattern of longitudinal change occurs in all patients is still not clear. In general, longitudinal studies have demonstrated a focusing of activity towards the lesioned hemisphere motor regions that is associated with improvement in motor function (Marshall et al., 2000; Calautti et al., 2001). However, cross-sectional studies tell us that chronic stroke patients with more impairment have a less “normal” pattern of activation, with examples of patients showing persistent bilateral recruitment (Feydy et al., 2002). Thus, it seems unlikely that all patients will follow this same longitudinal evolution of changes in task-related brain activation. Studies that have examined structural brain scans to find areas where damage is most likely to lead to motor impairment have demonstrated that the corona radiata,
FUNCTIONAL NEUROIMAGING at the point where descending white matter tracts converge to form the corticospinal tract, is a critical region (Lo et al., 2010). These descending pathways arise from all the secondary motor areas, and although primary motor cortex is the major contributor, there is certainly evidence that damage to non-M1 pathways is associated with alterations in motor activation patterns (Newton et al., 2006) and contributes to motor impairment (Lindenberg et al., 2010). It is overly simplistic, therefore, to view the descending motor pathways purely as a connection between M1 and spinal cord motor neurons.
ANATOMICAL SUBSTRATES OF MOTOR SYSTEM REORGANIZATION AFTER STROKE The cortical motor system is made up of four main regions: primary motor cortex (M1), premotor cortex, supplementary motor area, and cingulate motor area (Porter and Lemon, 1993). Premotor cortex has dorsal (PMd) and ventral (PMv) regions, each with different anatomical connectivity profiles (Tomassini et al., 2007). These cortical motor regions can be further subdivided based on topographic organization and the demands of the task. For example, primary motor cortex is divided into an anterior (Brodmann area 4a) and posterior (Brodmann area 4p) region, with activity in BA 4p modulated by attention to the task (Johansen-Berg and Matthews, 2002). Many of these areas contribute fibers to the descending corticofugal motor pathways, some of which project to the ventral horn of the spinal cord (corticospinal pathway) (Dum and Strick, 1991; He et al., 1993, 1995) and others that project to brainstem nuclei. Cortical regions including the primary sensory cortex (S1), posterior parietal cortex, and insula also contribute to these pathways. Although descending motor pathways from M1 are of critical importance, it is clear that several structures contribute to motor control and may potentially be useful in supporting recovery of movement after stroke (Strick, 1988). In primates, projections from secondary motor areas to spinal cord motor neurons are usually less numerous and less efficient at exiting spinal cord motoneurons than those from M1 (Maier et al., 2002; Boudrias et al., 2006). Two recent studies have shed more light on these important anatomical considerations (Boudrias et al., 2010a, b; Lindenberg et al., 2010). They compared the forelimb organizations and output properties of secondary motor areas and M1 in rhesus monkeys. Stimulations were performed in layer V cortical neurons and stimulustriggered averages of electromyographic activity were measured from forelimb muscles during a reach-to-grasp task. The onset latency and magnitude of facilitation effects from PMd, PMv, SMA, and dorsal cingulate motor area (CMAd) were significantly longer and
approximately 10 times weaker than those from M1, suggesting that the vast majority are likely to have a more indirect influence on motoneurons through either corticocortical connections with M1 and/or interneurons in the spinal cord. However, there was evidence of a small number of projections to motoneurons, at least as fast as those from M1, from each of the secondary motor areas. Finally, it is often cited that secondary motor areas have meaningful projections only to proximal rather than distal muscles. In these studies, proximal muscles were predominantly represented in PMd and PMv, but for both SMA and CMAd facilitation effects were more common in distal compared to proximal muscles. Thus, it seems that the anatomical substrate to support some improvement in hand function after stroke is likely to exist. Another possibility is that signals descend via the reticulospinal projections to cervical propriospinal premotoneurons (Mazevet et al., 2003; Stinear and Byblow, 2004). Propriospinal projections have divergent projections to muscle groups operating at multiple joints (Mazevet and Pierrot-Deseilligny, 1994; PierrotDeseilligny, 1996). This solution might account for the multijoint “associated” movements such as the synergistic flexion seen when patients with only poor or moderate recovery attempt isolated hand movements. Overall, it is feasible that a number of motor networks acting in parallel could generate an output to the spinal cord necessary for movement, and that damage in one of these networks could be at least partially compensated for by activity in another (Dum and Strick, 1996; Rouiller et al., 1996).
THE FUNCTIONAL RELEVANCE OF LESION-INDUCED BRAIN REORGANIZATION Over the last few years the debate has moved on from asking whether poststroke brain reorganization occurs to considering what this reorganization might mean for rehabilitation. This has mainly focused on the functional relevance of contralesional hemisphere activity seen in patients but not healthy controls, and whether this affords a target for therapeutic manipulation. Strategies to “normalize” the more bilateral poststroke motor cortex activity towards the ipsilesional hemisphere (as in healthy brains) have become a major focus of attempts to reduce upper limb impairment after stroke (Ward and Cohen, 2004). These approaches are based on the finding that, in the studies described so far, patients with minimal impairment tend to have a more normal activation pattern. But, as discussed above, it is clear that the pattern of organization in chronic stroke patients may be predominantly either ipsilesional, contralesional, or bilateral. Although a more “normal” pattern of brain
activation is associated with the best motor performance, it seems reasonable to assume that for some patients the anatomy of the damage will prevent a return to normal activation patterns. The question remains whether the brain regions engaged in the poststroke functional architecture are contributing to what recovered function there is. Work in animal models has demonstrated contralesional structural changes in homotopic cortical areas, possibly as a result of alterations in the pattern of use of the unaffected forelimb (Jones and Schallert, 1992; Jones et al., 1996). More recently work in mice has convincingly demonstrated that contralesional structural changes occurring within days of a cortical lesion are related to functional recovery, at least in the sensory domain (Takatsuru et al., 2009). Many functional imaging studies have observed motor task-related brain activity in the contralesional hemisphere of stroke patients as described above (Weiller et al., 1993; Cramer et al., 1997; Cao et al., 1998; Newton et al., 2002; Ward et al., 2003b, 2006), probably more so in patients with residual impairment (Ward et al., 2003b, 2004). The debate about the functional role of this increased activity is ongoing, but is likely to focus on whether “new” functional roles for cortical motor areas may vary depending on factors such as lesion site. It is worth reviewing the evidence regarding functionally relevant reorganization below. The timing of the task-related activity has been used to infer causal involvement in the motor task. For example, in an event-related fMRI design, Newton et al. (2002) demonstrated that contralesional M1 activity peaked seconds before ipsilesional M1 in stroke patients, in comparison to controls in whom the opposite relationship was observed. Contralesional hemisphere activity has also been demonstrated in stroke patients using the fine temporal resolution of electroencephalography (EEG) (Verleger et al., 2003). In this study, contralesional hemisphere activity was detected after the motor response had been made suggesting that it was not related to movement initiation in these patients. However, despite its temporal resolution, EEG lacks fine spatial resolution, and so it is not certain which contralesional brain region this result related to, M1 or premotor cortex for example. Serrien et al. (2004) used directed EEG coherence to investigate whether there is increased flow of information from the ipsilateral motor cortex following motor stroke. They found that in less well recovered patients most taskrelated flow of information between the sensorimotor cortices in the low beta band of the EEG came from the contralesional hemisphere during grip with the affected hand. This was not the case in recovered patients and controls when cortical activity was driven from the contralateral sensorimotor cortex.
Another approach is to measure how task-related activity covaries with modulation of task parameters. Riecker et al. (2010) were able to demonstrate that activity in contralesional sensorimotor and premotor cortices increased in proportion to the frequency of finger movements in well recovered stroke patients in comparison to control subjects. Ward et al. (2007) looked for regional changes in the control of force modulation after stroke and, specifically, how these changes were altered by variations in corticospinal system damage. In healthy humans increasing force production is associated with linear increases in BOLD signal in contralateral M1 and medial motor regions, implying that they have a functional role in force production (Dettmers et al., 1995; Thickbroom et al., 1999; Ward and Frackowiak, 2003). In patients with minimal corticospinal system damage and excellent recovery, the cortical motor system behaved in a way that was similar to younger healthy controls. However, in patients with greater corticospinal system damage, force-related signal changes were seen mainly in contralesional dorsolateral premotor cortex, bilateral ventrolateral premotor cortices and contralesional cerebellum, but not ipsilesional primary motor cortex. Interestingly a qualitatively similar result was found in healthy volunteers with increasing age, suggesting that this “reorganization” might be a generic property of the cortical motor system in response to a variety of insults (Ward et al., 2008). Thus not only do premotor cortices become increasingly active as corticospinal system integrity diminishes (Ward et al., 2006), but they can take on a new “M1-like” role during modulation of force output, which implies a new and functionally relevant role in motor control. At rest, it seems that the influence of contralesional premotor cortex on ipsilesional motor cortex is inhibitory in well recovered patients, but becomes more in those with greater clinical impairment (Bestmann et al., 2010). By using concurrent TMS-fMRI, it was also possible to see that during affected hand movement greater clinical impairment was associated with a stronger influence of contralesional premotor cortex on two posterior parts of the ipsilesional sensorimotor cortex parts of the ipsilesional hemisphere most likely to be able to generate descending motor signals to the spinal cord (Fig. 11.2) (Bestmann et al., 2010). This work points to the possible mechanism by which contralesional premotor cortex might exert its state-dependent influence over the surviving cortical motor system in a way that might support recovered motor function. Another view is that cortical motor areas in the contralesional hemisphere, in particular M1, are pathologically overactive in some stroke patients. The case for contralesional M1 hindering motor performance comes from both TMS (Murase et al., 2004) and fMRI
Facilitation from PMd during grip
4 3 2 1 0
20 40 60 80 Motor performance
Fig. 11.2. Transcranial magnetic stimulation was delivered to contralesional PMd (red symbol) during hand grip. The influence of contralesional PMd on ipsilesional sensorimotor cortex (blue arrow) is facilitatory during hand grip in all patients. The graph shows that this facilitatory influence is greater in patients with more impairment. AH, affected hemisphere. (Adapted from Bestmann et al., 2010.)
(Grefkes et al., 2008) studies, which suggest that in some subcortical stroke patients contralesional M1, although “active,” may exert an abnormally high degree of interhemispheric inhibitory drive towards ipsilesional M1 during attempted voluntary movement of the affected hand. This led to suggestions that contralesional M1 overactivity somehow suppresses ipsilesional M1 activity, motor performance, and therefore recovery. Others have used this concept to try to transiently improve motor function after stroke by suppressing excitability in contralesional M1 (Fregni et al., 2005, 2006; Mansur et al., 2005; Takeuchi et al., 2005; Liepert et al., 2007; Nowak et al., 2008). Proof-of-principle studies in chronic, mildly impaired, subcortical stroke patients are encouraging (Talelli and Rothwell, 2006) but a critical question remains whether this normalization is appropriate for all patients. There are two, possibly contradictory, sets of results here. On the one hand, the influence of premotor cortex becomes increasingly facilitatory towards ipsilesional sensorimotor cortex in patients with greater impairment
(Bestmann et al., 2010). On the other, the influence of contralesional M1 becomes increasingly inhibitory in the same types of patient (Murase et al., 2004). One possibility is that control of inhibition from contralesional M1 is normally managed by circuits in ipsilesional M1 that suppress inputs prior to movement. If these are damaged by stroke, then the influence of contralesional M1 will appear negative. Conversely inputs from contralesional premotor cortex may normally assist production of certain types of movement and this facilitation may increase after damage to the lesioned hemisphere. Although the physiological signatures for the interhemispheric influences of contralesional M1 and premotor cortex appear very different, the commonality for both sets of observations is that interhemispheric influences from contralesional to ipsilesional motor regions are systematically more abnormal in patients with more impaired clinical motor function. Investigations into the mechanistic aspects of poststroke cortical reorganization continue. One way of testing directly whether a cortical region is contributing to the performance of a particular task is to disrupt it transiently with TMS and measure whether there is a behavioral effect. In studies involving adult patients with small subcortical infarcts, the effect of disruption of contralesional M1 function by TMS depends on whether the motor task is a simple react time task (no disruption) (Johansen-Berg et al., 2002; Werhahn et al., 2003) or involves pressing sequences of buttons (disruption of timing) (Lotze et al., 2006), supporting a role for contralesional M1 in some patients and in some tasks. These studies have addressed the issue of whether contralesional M1 contributes to recovered function, but what about secondary motor areas? First, transiently disrupting activity in either ipsilesional or contralesional PMd with TMS can lead to worsening of recovered motor behaviors in some chronic subcortical stroke patients in a way that has no effect in healthy volunteers (Johansen-Berg et al., 2002; Fridman et al., 2004; Lotze et al., 2006). However, once again there is evidence that the effect is dependent on residual impairment (and therefore on the anatomy of the damage). TMS to contralesional PMd is more disruptive in patients with greater impairment (Johansen-Berg et al., 2002), whereas TMS to ipsilesional PMd is more disruptive in less impaired patients (Fridman et al., 2004), implying a contralesional shift in balance of functionally relevant activity with greater impairment. These findings again suggest a functional role for the contralesional hemisphere in organizing movement of the impaired limb following stroke, but only in those patients who do not make a good functional recovery. Patients making a fuller recovery organize movement-related cortical activity from the hemisphere contralateral to movement.
These results are important because they tell us that the response to focal injury does not involve simple up- or downregulation of the motor network as a whole. It is clear that nodes within remaining motor networks can take on new functional roles and that the poststroke motor system is organized differently to that in the normal brain. However, there are now several lines of evidence to suggest that the mechanisms of reorganization are lesion-specific. If restoration of function is dependent on the interaction of the treatment and the residual motor network, it therefore follows that treatments will have different effects in different patients. Furthermore, individual patients may require different rehabilitative strategies in order to “interact” with motor systems that are organized differently. An interesting line of research to emerge from this line of thinking is whether it is possible to predict the likelihood of improvement with a particular treatment, based on a careful study of the residual poststroke structural and functional anatomy. A number of studies have examined changes in brain activity before and after therapeutic intervention in chronic stroke patients (Hodics et al., 2006). Many studies found treatment-associated increases in ipsilesional activity in keeping with the previous longitudinal studies, but some saw a shift in the balance of activation in the opposite direction (Schaechter et al., 2002; Luft et al., 2004), more in keeping with the kinds of change described above. The evidence suggests that the contribution of contralesional motor regions varies, but it is not clear what baseline characteristics might predict such shifts. None of these studies included baseline characteristics of the patients in order to determine who was most likely to improve.
PREDICTING RECOVERY WITH NEUROIMAGING There is still a long way to go before these studies influence how best to treat the impairment suffered by patients after stroke. The question is whether imaging and/or neurophysiological data can contribute to predictive models, not of outcome, but of the potential for therapy-driven improvements in function. For example, a recent study demonstrated that the beneficial effects of facilitatory repetitive TMS over ipsilesional M1 on motor function of the affected hand were seen in patients with subcortical stroke but not in those with extension of the infarct into ipsilesional M1 (Ameli et al., 2009). Furthermore, task-related activity in ipsilesional M1 measured with fMRI at baseline correlated with improvement of motor performance induced by repetitive TMS. Although this seems an obvious result, this kind of stratification based on residual functional and
structural anatomy is rarely considered, although clearly has the potential to improve trial design (Ward, 2008). Stinear and colleagues (Stinear et al., 2007) also set out to determine whether characterizing the state of the motor system of a series of chronic stroke patients would help in predicting an individual’s capacity for further functional improvement at least 6 months following stroke made in a subsequent motor practice programme. A variety of tools were used, including TMS, structural MRI, and functional MRI. The presence or absence of motor evoked potentials (MEPs) to TMS in the affected upper limb, and fractional anisotropy values were both used to assess the structural integrity of the descending white matter pathways in the posterior limb of the internal capsules. Not surprisingly, in patients with MEPs, meaningful gains with motor practice were still possible 3 years after stroke. The situation in patients without MEPs has always been more difficult to predict in the clinical setting but is often taken as a poor prognostic sign (Heald et al., 1993). Here, the functional potential in patients without MEPs was predicted by corticospinal pathway disruption as assessed with fractional anisotropy values acquired with diffusion tensor imaging (DTI). Specifically, it was stated that, below a certain threshold, little therapy-induced functional improvement was possible. Conversely, in some patients without MEPs, the DTI data suggested that functional improvement was possible. Interestingly, the patients also performed a simple motor task during fMRI, but the results as assessed by the degree of lateralization to one hemisphere or the other did not contribute to the predictive model. Nevertheless, this kind of study illustrates how multimodal imaging and neurophysiological data could be used to assess the state of the motor system and predict the potential for therapy-driven functional improvements. Such information could be very valuable in the process of goal setting during rehabilitation. In a similar approach, Cramer et al. (2007) assessed 13 baseline clinical/radiological measures and whether each was able to predict subsequent gains made during 6 weeks of rehabilitation therapy. Only two baseline measures were significant and independent predictors of clinical improvement. The first was a lower level of impairment and the second was lower motor cortex activation as measured with fMRI. This is an interesting finding, because, in general, patients with greater impairment are more likely to have less task-related ipsilesional M1 activity, although this is an inconsistent finding. Despite this, the result tells us that there is something in the imaging data that is independent of baseline clinical impairment, which predicts improvements. Lower baseline motor cortex activation was also associated with larger increases in motor cortex activation after treatment, and so it was suggested that low baseline
FUNCTIONAL NEUROIMAGING cortical activity represents underuse of surviving cortical resources. When used carefully, it appears that measures of brain function as well as structure can be important for optimal clinical decision-making in the context of a restorative intervention. Functional MRI data acquired in the first few days after stroke has also been used to try to predict a subsequent change in motor performance (Marshall et al., 2009). A particular pattern of brain activation was highly predictive of clinical change over the next 3 months, a finding that was independent of initial stroke severity and lesion volume. Although the multivariate analysis used did not allow anatomical inference to be made, it is clear that there is something about the way the function of the brain responds to injury, over and above the anatomy of the damage, that holds clues about future clinical progression. The pattern was distributed and certainly not confined to the motor system, even though clinical improvement was measured in the motor domain. The idea that motor improvement may not be solely related to the integrity of the corticospinal system but also with other characteristics of the poststroke brain is supported by the finding that motor performance at 3 months correlated only weakly with a measure of corticospinal tract integrity (using TMS) but strongly with a measure of intracortical excitability (Swayne et al., 2008). These findings suggest that the anatomy of the damage may set a limit on the extent of recovery, but that other parameters, perhaps preserved cortico-cortical connectivity, might be important when considering whether a patient has the capacity or potential to improve.
CONCLUSIONS In summary, there is a reconfiguration of brain networks after focal brain damage that does not appear to be simply up- or downregulation of networks in their entirety. Residual functional networks seem to operate in a different way, with some brain regions adopting the characteristics of damaged or disconnected regions. This process varies across chronic stroke patients, but does so in a way that appears to be at least partially predictable. It is important to stress that this reorganization is often not successful in returning motor performance back to premorbid levels. It is less effective than that in the intact brain but will nevertheless support what recovered function there is. The exact configuration of this new motor system will be determined most obviously by the extent of the anatomical damage. This includes the extent to which the damage affects cortical motor regions, white matter pathways (Newton et al., 2006), and even which hemisphere is affected (Zemke et al., 2003). The potential for functionally relevant change to occur will depend on a number of other factors, not
least the biological age of the subject and the premorbid state of their brain (Talelli et al., 2008), but also current drug treatments (Goldstein, 1990). Furthermore levels of neurotransmitters and growth factors, which are able to influence the ability of the brain to respond to afferent input (i.e., how plastic it is), might be determined by their genetic status (Kleim et al., 2006). The basis of impairment-based treatment is likely to be the promotion of activity-dependent change within these surviving networks, and so understanding the factors that shape it will be critical (Ward, 2008). In future, clinical trials of restorative treatments will have to consider how the treatment interacts with the damaged poststroke brain, allowing appropriate patients to be targeted and smaller, more effective trial design (Ward, 2008). It is clear that individual differences will have a major influence on how a patient might respond to restorative therapies, and it is in this context that modern neuroimaging (together with neurophysiological) techniques may be able to shed light on poststroke functional organization in individual subjects. Future work should aim to use these kinds of approach to determine whether assessment of individual poststroke residual functional architecture can be a major predictor of outcome, opening the way for stratification of patients based on the likely response to an intervention.
REFERENCES Ameli M, Grefkes C, Kemper F et al. (2009). Differential effects of high-frequency repetitive transcranial magnetic stimulation over ipsilesional primary motor cortex in cortical and subcortical middle cerebral artery stroke. Ann Neurol 66: 298–309. Baron JC, Cohen LG, Cramer SC et al. (2004). Neuroimaging in stroke recovery: a position paper from the First International Workshop on Neuroimaging and Stroke Recovery. Cerebrovasc Dis 18: 260–267. Bestmann S, Swayne OBC, Blankenburg F et al. (2010). The role of contralesional premotor cortex after stroke. J Neurosci 30: 11926–11937. Boudrias MH, Belhaj-Saif A, Park MC et al. (2006). Contrasting properties of motor output from the supplementary motor area and primary motor cortex in rhesus macaques. Cereb Cortex 16: 632–638. Boudrias MH, Lee SP, Svojanovsky S et al. (2010a). Forelimb muscle representations and output properties of motor areas in the mesial wall of rhesus macaques. Cereb Cortex 20: 704–719. Boudrias MH, McPherson RL, Frost SB et al. (2010b). Output properties and organization of the forelimb representation of motor areas on the lateral aspect of the hemisphere in rhesus macaques. Cereb Cortex 20: 169–186. Calautti C, Leroy F, Guincestre JY et al. (2001). Dynamics of motor network overactivation after striatocapsular stroke: a longitudinal PET study using a fixed-performance paradigm. Stroke 32: 2534–2542.
Cao Y, Vikingstad EM, Huttenlocher PR et al. (1994). Functional magnetic resonance studies of the reorganization of the human hand sensorimotor area after unilateral brain injury in the perinatal period. Proc Natl Acad Sci U S A 91: 9612–9616. Cao Y, D’Olhaberriague L, Vikingstad M et al. (1998). Pilot study of functional MRI to assess cerebral activation of motor function after poststroke hemiparesis. Stroke 29: 112–122. Chollet F, DiPiero V, Wise RJ et al. (1991). The functional anatomy of motor recovery after stroke in humans: a study with positron emission tomography. Ann Neurol 29: 63–71. Cramer SC, Nelles G, Benson RR et al. (1997). A functional MRI study of subjects recovered from hemiparetic stroke. Stroke 28: 2518–2527. Cramer SC, Moore CI, Finklestein SP et al. (2000). A pilot study of somatotopic mapping after cortical infarct. Stroke 31: 668–671. Cramer SC, Parrish TB, Levy RM et al. (2007). Predicting functional gains in a stroke trial. Stroke 38: 2108–2114. Dettmers C, Fink GR, Lemon RN et al. (1995). Relation between cerebral activity and force in the motor areas of the human brain. J Neurophysiol 74: 802–815. Dum RP, Strick PL (1991). The origin of corticospinal projections from the premotor areas in the frontal lobe. J Neurosci 11: 667–689. Dum RP, Strick PL (1996). Spinal cord terminations of the medial wall motor areas in macaque monkeys. J Neurosci 16: 6513–6525. Feydy A, Carlier R, Roby-Brami A et al. (2002). Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke 33: 1610–1617. Fregni F, Boggio PS, Mansur CG et al. (2005). Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport 16: 1551–1555. Fregni F, Boggio PS, Valle AC et al. (2006). A sham-controlled trial of a 5-day course of repetitive transcranial magnetic stimulation of the unaffected hemisphere in stroke patients. Stroke 37: 2115–2122. Fridman EA, Hanakawa T, Chung M et al. (2004). Reorganization of the human ipsilesional premotor cortex after stroke. Brain 127: 747–758. Goldstein LB (1990). Pharmacology of recovery after stroke. Stroke 21: III139–III142. Grefkes C, Nowak DA, Eickhoff SB et al. (2008). Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol 63: 236–246. He SQ, Dum RP, Strick PL (1993). Topographic organization of corticospinal projections from the frontal lobe: motor areas on the lateral surface of the hemisphere. J Neurosci 13: 952–980. He SQ, Dum RP, Strick PL (1995). Topographic organization of corticospinal projections from the frontal lobe: motor areas on the medial surface of the hemisphere. J Neurosci 15: 3284–3306. Heald A, Bates D, Cartlidge NE et al. (1993). Longitudinal study of central motor conduction time following stroke. 2. Central motor conduction measured within 72 h after
stroke as a predictor of functional outcome at 12 months. Brain 116: 1371–1385. Hodics T, Cohen LG, Cramer SC (2006). Functional imaging of intervention effects in stroke motor rehabilitation. Arch Phys Med Rehabil 87: S36–S42. Johansen-Berg H, Matthews PM (2002). Attention to movement modulates activity in sensori-motor areas, including primary motor cortex. Exp Brain Res 142: 13–24. Johansen-Berg H, Rushworth MF, Bogdanovic MD et al. (2002). The role of ipsilateral premotor cortex in hand movement after stroke. Proc Natl Acad Sci U S A 99: 14518–14523. Jones TA, Schallert T (1992). Overgrowth and pruning of dendrites in adult rats recovering from neocortical damage. Brain Res 581: 156–160. Jones TA, Kleim JA, Greenough WT (1996). Synaptogenesis and dendritic growth in the cortex opposite unilateral sensorimotor cortex damage in adult rats: a quantitative electron microscopic examination. Brain Res 733: 142–148. Kleim JA, Chan S, Pringle E et al. (2006). BDNF Val66met polymorphism is associated with modified experiencedependent plasticity in human motor cortex. Nat Neurosci 9: 735–737. Liepert J, Zittel S, Weiller C (2007). Improvement of dexterity by single session low-frequency repetitive transcranial magnetic stimulation over the contralesional motor cortex in acute stroke: a double-blind placebo-controlled crossover trial. Restor Neurol Neurosci 25: 461–465. Lindenberg R, Renga V, Zhu LL et al. (2010). Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 74: 280–287. Lo R, Gitelman D, Levy R et al. (2010). Identification of critical areas for motor function recovery in chronic stroke subjects using voxel-based lesion symptom mapping. Neuroimage 49: 9–18. Lotze M, Markert J, Sauseng P et al. (2006). The role of multiple contralesional motor areas for complex hand movements after internal capsular lesion. J Neurosci 26: 6096–6102. Luft AR, McCombe-Waller S, Whitall J et al. (2004). Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA 292: 1853–1861. Maier MA, Armand J, Kirkwood PA et al. (2002). Differences in the corticospinal projection from primary motor cortex and supplementary motor area to macaque upper limb motoneurons: an anatomical and electrophysiological study. Cereb Cortex 12: 281–296. Mansur CG, Fregni F, Boggio PS et al. (2005). A sham stimulation-controlled trial of RTMS of the unaffected hemisphere in stroke patients. Neurology 64: 1802–1804. Marshall RS, Perera GM, Lazar RM (2000). Evolution of cortical activation during recovery from corticospinal tract infarction. Stroke 31: 656–661. Marshall RS, Zarahn E, Alon L et al. (2009). Early imaging correlates of subsequent motor recovery after stroke. Ann Neurol 65: 596–602. Mazevet D, Pierrot-Deseilligny E (1994). Pattern of descending excitation of presumed propriospinal neurones at the
FUNCTIONAL NEUROIMAGING onset of voluntary movement in humans. Acta Physiol Scand 150: 27–38. Mazevet D, Meunier S, Pradat-Diehl P et al. (2003). Changes in propriospinally mediated excitation of upper limb motoneurons in stroke patients. Brain 126: 988–1000. Murase N, Duque J, Mazzocchio R et al. (2004). Influence of interhemispheric interactions on motor function in chronic stroke. Ann Neurol 55: 400–409. Newton J, Sunderland A, Butterworth SE et al. (2002). A pilot study of event-related functional magnetic resonance imaging of monitored wrist movements in patients with partial recovery. Stroke 33: 2881–2887. Newton JM, Ward NS, Parker GJ et al. (2006). Non-invasive mapping of corticofugal fibres from multiple motor areas: relevance to stroke recovery. Brain 129: 1844–1858. Nowak DA, Grefkes C, Dafotakis M et al. (2008). Effects of low-frequency repetitive transcranial magnetic stimulation of the contralesional primary motor cortex on movement kinematics and neural activity in subcortical stroke. Arch Neurol 65: 741–747. Pierrot-Deseilligny E (1996). Transmission of the cortical command for human voluntary movement through cervical propriospinal premotoneurons. Prog Neurobiol 48: 489–517. Pineiro R, Pendlebury S, Johansen-Berg H et al. (2001). Functional MRI detects posterior shifts in primary sensorimotor cortex activation after stroke: evidence of local adaptive reorganization? Stroke 32: 1134–1139. Porter R, Lemon RN (1993). Corticospinal Function and Voluntary Movement. Oxford University Press, Oxford, UK. Riecker A, Groschel K, Ackermann H et al. (2010). The role of the unaffected hemisphere in motor recovery after stroke. Hum Brain Mapp 31: 1017–1029. Rossini PM, Caltagirone C, Castriota-Scanderbeg A et al. (1998). Hand motor cortical area reorganization in stroke: a study with fMRI, MEG and TCS maps. Neuroreport 9: 2141–2146. Rouiller EM, Moret V, Tanne J et al. (1996). Evidence for direct connections between the hand region of the supplementary motor area and cervical motoneurons in the macaque monkey. Eur J Neurosci 8: 1055–1059. Sanes JN, Donoghue JP (2000). Plasticity and primary motor cortex. Annu Rev Neurosci 23: 393–415. Schaechter JD, Kraft E, Hilliard TS et al. (2002). Motor recovery and cortical reorganization after constraint-induced movement therapy in stroke patients: a preliminary study. Neurorehabil Neural Repair 16: 326–338. Seitz RJ, Hoflich P, Binkofski F et al. (1998). Role of the premotor cortex in recovery from middle cerebral artery infarction. Arch Neurol 55: 1081–1088. Serrien DJ, Strens LH, Cassidy MJ et al. (2004). Functional significance of the ipsilateral hemisphere during movement of the affected hand after stroke. Exp Neurol 190: 425–432. Small SL, Hlustik P, Noll DC et al. (2002). Cerebellar hemispheric activation ipsilateral to the paretic hand correlates with functional recovery after stroke. Brain 125: 1544–1557. Stinear JW, Byblow WD (2004). The contribution of cervical propriospinal premotoneurons in recovering hemiparetic stroke patients. J Clin Neurophysiol 21: 426–434.
Stinear CM, Barber PA, Smale PR et al. (2007). Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain 130: 170–180. Strick PL (1988). Anatomical organization of multiple motor areas in the frontal lobe: implications for recovery of function. Adv Neurol 47: 293–312. Swayne OB, Rothwell JC, Ward NS et al. (2008). Stages of motor output reorganization after hemispheric stroke suggested by longitudinal studies of cortical physiology. Cereb Cortex 18: 1909–1922. Takatsuru Y, Fukumoto D, Yoshitomo M et al. (2009). Neuronal circuit remodeling in the contralateral cortical hemisphere during functional recovery from cerebral infarction. J Neurosci 29: 10081–10086. Takeuchi N, Chuma T, Matsuo Y et al. (2005). Repetitive transcranial magnetic stimulation of contralesional primary motor cortex improves hand function after stroke. Stroke 36: 2681–2686. Talelli P, Rothwell J (2006). Does brain stimulation after stroke have a future? Curr Opin Neurol 19: 543–550. Talelli P, Ewas A, Waddingham W et al. (2008). Neural correlates of age-related changes in cortical neurophysiology. Neuroimage 40: 1772–1781. Thickbroom GW, Phillips BA, Morris I et al. (1999). Differences in functional magnetic resonance imaging of sensorimotor cortex during static and dynamic finger flexion. Exp Brain Res 126: 431–438. Tomassini V, Jbabdi S, Klein JC et al. (2007). Diffusionweighted imaging tractography-based parcellation of the human lateral premotor cortex identifies dorsal and ventral subregions with anatomical and functional specializations. J Neurosci 27: 10259–10269. Verleger R, Adam S, Rose M et al. (2003). Control of hand movements after striatocapsular stroke: high-resolution temporal analysis of the function of ipsilateral activation. Clin Neurophysiol 114: 1468–1476. Ward NS (2008). Getting lost in translation. Curr Opin Neurol 21: 625–627. Ward NS (2009). fMRI in cerebrovascular disorders. In: M Filipi (Ed.), fMRI Techniques and Protocols. Humana Press, Dordrecht, pp. 597–613. Ward NS, Cohen LG (2004). Mechanisms underlying recovery of motor function after stroke. Arch Neurol 61: 1844–1848. Ward NS, Frackowiak RS (2003). Age-related changes in the neural correlates of motor performance. Brain 126: 873–888. Ward NS, Brown MM, Thompson AJ et al. (2003a). Neural correlates of motor recovery after stroke: a longitudinal fMRI study. Brain 126: 2476–2496. Ward NS, Brown MM, Thompson AJ et al. (2003b). Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain 126: 1430–1448. Ward NS, Brown MM, Thompson AJ et al. (2004). The influence of time after stroke on brain activations during a motor task. Ann Neurol 55: 829–834. Ward NS, Newton JM, Swayne OB et al. (2006). Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain 129: 809–819.
Ward NS, Newton JM, Swayne OB et al. (2007). The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke. Eur J Neurosci 25: 1865–1873. Ward NS, Swayne OB, Newton JM (2008). Age-dependent changes in the neural correlates of force modulation: an fMRI study. Neurobiol Aging 29: 1434–1446. Weiller C, Chollet F, Friston KJ et al. (1992). Functional reorganization of the brain in recovery from striatocapsular infarction in man. Ann Neurol 31: 463–472.
Weiller C, Ramsay SC, Wise RJ et al. (1993). Individual patterns of functional reorganization in the human cerebral cortex after capsular infarction. Ann Neurol 33: 181–189. Werhahn KJ, Conforto AB, Kadom N et al. (2003). Contribution of the ipsilateral motor cortex to recovery after chronic stroke. Ann Neurol 54: 464–472. Zemke AC, Heagerty PJ, Lee C et al. (2003). Motor cortex organization after stroke is related to side of stroke and level of recovery. Stroke 34: e23–e28.