The evolution of cerebral reorganisation after stroke

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It appears that in the chronic setting a damaged brain will utilise those remaining structures and networks that can generate some form of motor signal to spinal cord motor neurons, and that in addition some areas such as PMd take on a new role in motor performance. What such studies do not tell us is how this reorganised state evolved from the time of infarction. Two early longitudinal studies with early and late time points demonstrated initial task-related over-activations in motor-related brain regions followed by a reduction over time in patients said to recover fully (Marshall et al., 2000; Calautti et al., 2001). Feydy and co-workers (2002), found no relationship between longitudinal changes and recovery scores and another study found no correlates of functional improvement outside the ipsilateral (CL) cerebellum, in which increases in task-related activation were seen with recovery (Small et al., 2002). A more detailed longitudinal fMRI study of patients with infarcts not involving M1 indicated an initial overactivation in many primary and non-primary motor regions that was more extensive in those with greatest clinical deficit (Ward et al., 2003b). The subsequent longitudinal changes were predictably different in each patient but there were changes common to all. After early overactivation, a negative correlation between size of activation and recovery scores was observed in all patients throughout IL M1, and in inferior CL M1, as well as in anterior and posterior PMd, bilaterally (BA 6 and BA 8), CL PMv, and IL SMA-proper, pre-SMA, prefrontal cortex (superior frontal sulcus), and caudal cingulate sulcus (Fig. 5.3). A positive correlation between size of activation and recovery across sessions was seen in some brain regions in four patients (including IL M1 in one patient), but there were no consistent increases in a group analysis (Ward et al., 2003b).

These longitudinal recovery-related changes are reminiscent of those seen during motor learning experiments in normal people. One model of motor learning suggests that during early motor learning movements are encoded in terms of spatial coordinates, a process requiring high levels of attention (Hikosaka et al., 2002). Encoding is performed within a frontoparietal network and in parts of the basal ganglia and cerebellum. Once learning has occurred and a task has become automatic, movement is encoded in terms of a kinematic system of joints, muscles, limb trajectories, etc., by a network involving primary motor cortex, and parts of the basal ganglia and cerebellum that are different from those involved in early learning (Hikosaka et al., 2002). Interaction between these parallel systems and the transfer of reliance from one to the other, occurs not only in cerebellum and basal ganglia but also via intracortical connections involving particularly premotor cortex and pre-SMA (Hikosaka et al., 1999, 2002). An important aspect of any motor learning model is the generation of an error signal. Attempted movements by hemiparetic patients will result in significant discrepancies between predicted and actual performance. The error signals thus generated in normal subjects are used by the cerebellum to optimise subsequent sensorimotor accuracy (Blakemore et al., 2001). Thus the longitudinal recovery-related changes described above are in part similar to those seen when normal subjects learn a motor sequence. The need to re-learn simple motor tasks after stroke is likely to engage such a mechanism, but the degree to which this is successful will depend on the degree of overall damage to the motor network. The role of error signal generation in a damaged motor system is clearly of interest, particularly as it may diminish with chronicity of impairment (Ward et al., in press Ward et al 2004). These important issues remain to be explored, and may have significant implications for rehabilitative interventions.

In summary, the goal of cerebral reorganisation after focal damage to the motor system appears to be to re-establish a connection between IL M1 and spinal cord motor neurons via fast direct CMN pathways if possible. This allows recovery of fractionated finger movements. In the face of partial CMN damage, one potential strategy is to re-map the somato-topic representations in M1 and in those parts of the motor system that project to M1. If CMN pathways are completely lost then parallel motor loops become useful only for their projections to spinal cord, not by virtue of their projections to M1. Studies in adults with damage to the entire middle cerebral artery territory are scarce. However, data from young adults who suffer unilateral brain damage in the perinatal period suggest that the ipsilateral hemisphere becomes the main source of motor output in these circumstances (Cao et al., 1998).

A process such as cerebral reorganisation during motor learning occurs in the normal brain and is a reflection of changes occurring at synaptic and systems levels. The degree to which such "normal processes" are successfully employed in the recovery process will depend on a number of variables, not least the precise amount and site of anatomical damage caused by an infarct and the amount of retraining available to the patient. However, the evidence from animal models suggests that the lesioned brain has an increased capacity for plastic change, at least early after damage. For example, widespread areas of cortical hyperexcitability appear immediately after cerebral infarction in animal brains, changes which subside over subsequent months (Buchkremer-Ratzmann et al., 1996). These changes occur in regions structurally connected to the lesion in both hemispheres as a consequence of

Figure 5.3. Results of single subject longitudinal analysis examining for linear changes in task-related brain activations over sessions as a function of recovery. The patient suffered from a left sided pontine infarct resulting in right hemiparesis. (a) Results are surface rendered onto a canonical brain; red areas represent recovery-related decreases in task-related activation across sessions, and green areas represent the equivalent recovery related increases. All voxels are significant at P < 0.001 (uncorrected for multiple comparisons) for display purposes. The brain is shown (from left to right) from the left ipsilesional (IL) side, from above (left hemisphere on the left), and from the right contralesional (CL). (b) Results are displayed on patients own normalised T1-weighted anatomical images (voxels significant at P < 0.05, corrected for multiple comparisons across the whole brain), with corresponding plots of size of effect against overall recovery score (normalised), for selected brain regions. Coordinates of peak voxel in each region are followed by the correlation coefficient and the associated P value: (1) ipsilesional (IL) cerebellum (x = —26, y = —84, z = —22) (r2 = 0.77, P < 0.01), (2) contralesional (CL) PMd (x = 38, y = 0, z = 58) (r2 = 0.85, P < 0.01), (3) contralesional (CL) M1 (x = 28, y = —14, z = 70) (r2 = 0.74, P < 0.01), (4) ipsilesional (IL) SMA (x = —2, y = —2, z = 60) (r2 = 0.53, P = 0.02), (5) ipsilesional (IL) M1 (x = —30, y = —14, z = 58) (r2 = 0.80, P < 0.01), (6) contralesional (CL) PMd (x = —18, y = —10, z = 74) (r2 = 0.63, P = 0.01) (from Ward et al. Brain, 2003; 126: 2476-2496, by permission of Oxford University Press).

Recovery score (normalised)

Figure 5.3. Results of single subject longitudinal analysis examining for linear changes in task-related brain activations over sessions as a function of recovery. The patient suffered from a left sided pontine infarct resulting in right hemiparesis. (a) Results are surface rendered onto a canonical brain; red areas represent recovery-related decreases in task-related activation across sessions, and green areas represent the equivalent recovery related increases. All voxels are significant at P < 0.001 (uncorrected for multiple comparisons) for display purposes. The brain is shown (from left to right) from the left ipsilesional (IL) side, from above (left hemisphere on the left), and from the right contralesional (CL). (b) Results are displayed on patients own normalised T1-weighted anatomical images (voxels significant at P < 0.05, corrected for multiple comparisons across the whole brain), with corresponding plots of size of effect against overall recovery score (normalised), for selected brain regions. Coordinates of peak voxel in each region are followed by the correlation coefficient and the associated P value: (1) ipsilesional (IL) cerebellum (x = —26, y = —84, z = —22) (r2 = 0.77, P < 0.01), (2) contralesional (CL) PMd (x = 38, y = 0, z = 58) (r2 = 0.85, P < 0.01), (3) contralesional (CL) M1 (x = 28, y = —14, z = 70) (r2 = 0.74, P < 0.01), (4) ipsilesional (IL) SMA (x = —2, y = —2, z = 60) (r2 = 0.53, P = 0.02), (5) ipsilesional (IL) M1 (x = —30, y = —14, z = 58) (r2 = 0.80, P < 0.01), (6) contralesional (CL) PMd (x = —18, y = —10, z = 74) (r2 = 0.63, P = 0.01) (from Ward et al. Brain, 2003; 126: 2476-2496, by permission of Oxford University Press).

downregulation of the a1-gamma-aminobutyric acid (GABA) receptor subunit and a decrease in GABAergic inhibition (Neumann-Haefelin et al., 1998) (see Chapter 14 of Volume I, pp. 18-21 for further discussion of these phenomena). This would be of particular interest to clinicians as it is easier to induce long-term potentiation (LTP) in hyperex-citable cortex, that is the cortex is more responsive to afferent input. In humans, acute limb deaf-ferentation leads to reduced levels of GABA within minutes (Levy et al., 2002). It is tempting to think that the same thing may happen in areas of partially disconnected cortex after stroke. There is evidence of hyperexcitability in the CL motor cortex after both cortical and subcortical stroke in humans with at least moderate recovery (Butefisch et al., 2003). Such hyperexcitability decreases with time after infarction, in keeping with data from animals (Buchkremer-Ratzmann et al., 1996; Witte, 1998; Shimizu et al., 2002). This window of opportunity, if therapeutically useful, may last only for a limited time.

One final caveat should be mentioned. After injury-induced reorganisation of the brain the capacity for subsequent adaptive change is reduced (Kolb et al., 1998). Adaptive changes in older brains have been described (Ward and Frackowiak, 2003) and these may themselves limit the capacity for further reorganisation after injury in older patients. This may have implications for what can be expected from therapy designed to promote cerebral reorganisation after stroke in older subjects.

Cerebral reorganisation undoubtedly contributes to functional recovery after stroke, but it is clear that a more detailed understanding of the natural history of these processes is required, together with the factors that influence them, before they can be utilised to rationalise therapeutical strategies in individual patients or groups.

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