Outcomes measurement: basic principles and applications in stroke rehabilitation
1.5 The use of laboratory-based gait assessments and measures of brain
reorganization help explain changes in clinical scales and performance-based measures
Laboratory outcome measures
This section will briefly illustrate how laboratory outcome measures can be used to:
1 validate clinical measures,
2 explain the results of clinical outcomes, 3 develop new measures,
4 guide therapy.
In-depth gait studies (see Volume II, chapter 19) have elucidated the disturbed motor control during gait in persons after chronic stroke. Low muscle activations (paresis), hyperactive stretch reflexes, excessive coac-tivation of antagonist muscles and hypoextensibility of muscles and tendons (Knutsson and Richards, 1979; Dietz et al., 1981; Lamontagne et al., 2000a, b, 2001, 2002) may be present to a different extent across subjects. While analysis of gait movements and muscle activations recorded concomitantly during a laboratory gait study allow for the differentiation of the salient motor disturbance (Knutsson and Richards, 1979) to guide therapy, such analyses are not available to usual clinical practice. From studies of moments of force and mechanical power produced by the muscle activations during walking, we know that the main propulsive force comes from the “push-off”
contraction of the ankle plantarflexors (generation of power) at the end of the stance phase aided by the
“pull-off” contraction of the hip flexors (generation of power) at swing initiation and the contraction of the hip extensors in early stance (Olney et al., 1991;
Winter, 1991; Olney and Richards, 1996). Moreover, in persons with chronic stroke, Olney et al. (1994) found the power generated by the hip flexors and ankle plantarflexors of the paretic lower extremity to be the best predictors of walking speed. These labo-ratory results point to the ankle plantarflexors and hip flexors as muscles to be targeted in therapy to promote better walking speed (Olney et al., 1997;
Richards et al., 1998; Dean et al., 2000; Teixeira-Salmela et al., 2001; Richards et al., 2004).
While gait speed can be used to discern a change in functional status, it does not explain why the person walks faster. An analysis of gait kinetics (muscle acti-vations, moments, powers, work) is needed to pin-point the source of the increased speed. For example, in a recent trial evaluating the effects of task-oriented physical therapy, Richards et al. (2004) were able to attribute 27% of the improvement in walking speed to a better plantarflexor power burst.
Many new therapeutic approaches and outcome measures are first developed in the laboratory. Much work, for example, has been done on obstacle avoid-ance in healthy persons (McFadyen and Winter, 1991;
Gérin-Lajoie et al., 2002) and persons with stroke (Said et al., 1999, 2001). Virtual reality technology is now being applied to the development of training paradigms to enable persons with stroke to practice navigational skills safely in changing contextual environments (Comeau et al., 2003; McFadyen et al., 2004).
An example of the use of laboratory data to vali-date a clinical measure is recent work related to the rise-to-walk test. The clinical fluidity scale of the rise-to-walk test was validated by comparing clini-cal decisions to the smoothness of the momentum curve derived from a biomechanical analysis made in the laboratory (Dion et al., 2003) and led to the development of a fluidity scale (Malouin et al., 2003).
Neuroimaging techniques for studying changes in brain activation patterns and their relation-ship with functional recovery
With the rapid development of neuroimaging techniques (Volume II, chapter 5), such as positron-emitted tomography (PET), functional magnetic res-onance imaging (fMRI) and transcranial magnetic stimulations (TMS; Volume I, Chapter 15), it has become possible to study neural organization associ-ated with motor recovery after brain damage.
Numerous studies have looked at the predictive value of TMS (Hendricks et al., 2002; Liepert, 2003).
It provides important prognostic information in the early stage after stroke. For instance, the persistence of motor evoked potentials (MEPs) in paretic mus-cles has been correlated with good motor recovery, whereas the lack of TMS responses is predictive of poor motor recovery. Patterns of brain activation can also be used early after stroke for predicting functional outcomes. In a longitudinal fMRI study, where hand motor scores were compared to the whole sensorimotor network activation, the early recruitment and high activation of the sup-plementary motor area (SMA) was correlated with faster or better recovery (Loubinoux et al., 2003). Based on findings from a study combining fMRI and TMS, it has been proposed that the early bilateral activation of the motor networks seen in patients with rapid and good recovery may be a pre-requisite to regain motor function rapidly, and thus, may be predictive of motor recovery (Foltys et al., 2003).
Functional imaging and electrophysiologic brain imaging techniques have provided substantial information about adaptive changes of cerebral net-works associated with recovery from brain damage (Calautti and Baron, 2003). For example, in two rigorously controlled studies, the effects of task-oriented training for the upper limb on brain activa-tion patterns were studied using fMRI (Carey et al., 2002) and PET (Nelles et al., 2002). Both studies found that, in contrast to patients in control groups whose brain activation patterns remained unchanged, patients in the treatment groups displayed enhanced activations in the lesioned sensorimotor cortex in parallel with improved motor function. Similar cor-relations between changes in brain activation pat-terns and motor recovery have also been reported after a single dose of fluoxetine (Pariente et al., 2001). TMS mapping studies (Liepert, 2003) provide further evidence of a relationship between training-induced cerebral changes and motor recovery. In these studies, TMS was used to map the motor out-put area (motor representation) of targeted muscles.
Increased cortical excitability and a shift in the motor maps after active rehabilitation (Traversa et al., 1997) or constraint-induced therapy (Liepert et al.,
2000) are associated with improved motor function suggesting treatment-induced reorganization in the affected hemisphere (Liepert et al., 2000).
Recently, the laterality index (LI) has been pro-posed to quantify changes of brain activation pat-terns observed in functional neuroimaging studies of recovery post-stroke (Cramer et al., 1997). LI pro-vides an estimate of the relative hemispheric activa-tion in motor cortices. LI values range from1 (activation exclusively ipsilesional or affected hemi-sphere) to1 (activation exclusively contralesional or unaffected hemisphere). These LIs are generally lower in patients, especially in poorly recovered chronic patients, indicating a relatively greater acti-vation of the unaffected hemisphere consistent with the aforementioned general patterns of changes (Calautti and Baron, 2003). Dynamic changes in LI values over time have also been reported in a longi-tudinal study (Marshall et al., 2000; Calautti et al., 2001). After specific finger-tracking training, Carey
et al. (2002) found increases in LI values correspon-ding to a switch of activation to the affected hemi-sphere to be related to improved hand function, suggesting that the LI is a good marker of brain reorganization.
Likewise, inter-hemispheric motor reorganization can be quantified using TMS input/output (i/o) curves. The i/o curves, provide a reliable measure (Carroll et al., 2001) of the increase of MEP ampli-tudes against incrementing levels of TMS intensity (Devanne et al., 1997). Comparisons of the excitabil-ity of the motor cortex of the two hemispheres (Fig. 1.2), indicate that in a patient with good motor recovery, the excitability of the motor cortex con-tralateral to the paretic tibialis anterior (TA) muscle (LI of motor threshold 0.78) is greater (lower motor threshold, steeper slope and higher plateau of MEP amplitude) compared to the ipsilateral motor cortex and resembles the pattern seen in a healthy individual (LI of motor threshold 1.0). In contrast,
20 30 40 50 60 70 80 90
1.50 1.25 1.00 0.75
0.25 0.50
MEPs from paretic TA (mV) 0.00
i/o contralateral i/o ipsilateral
TMS (%)
50 55 60 65 70 75 80 85 90 95 100
1.50 1.25 1.00 0.75
0.25 0.50
MEPs from paretic TA (mV) 0.00
i/o contralateral i/o ipsilateral
Motor threshold Slope Plateau
TMS (%)
(a) (b)
1.50 1.25 1.00 0.75
0.25 0.50
0.00
i/o contralateral i/o ipsilateral
20 30 40 50 60 70 80 90
TMS (%)
TA MEPs (mV)
(c)
Figure 1.2. Examples of the contralateral and ipsilateral i/o curves of the paretic TA in a patient with (a) poor motor recovery (LI:0.28) and (b) good recovery (LI 0.78) compared to normal curves from a (c) healthy subject (LI 1.0). Each symbol represents the mean of 4 MEPs (1 SD). See text for more details (Schneider and Malouin, unpublished data).
in a person with poor recovery, a greater excitability is observed in the motor cortex ipsilateral to the paretic TA (LI of motor threshold 0.28), corre-sponding to a relatively greater activation from the unaffected hemisphere (Schneider and Malouin, unpublished data). An LI can be calculated for each parameter (motor threshold, slope and MEP plateau) and for the ensemble (Fig. 1.2).
These examples show the potential of combining clinical, laboratory and brain imaging measures to better understand the recovery of locomotor function after stroke.
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