www.elsevier.com/locate/ynimg

NeuroImage 34 (2007) 322–331

The relationship between motor deficit and hemisphere activationbalance after stroke: A 3T fMRI study

Cinzia Calautti, Marcello Naccarato, Peter S. Jones, Nikhil Sharma, Diana D. Day,Adrian T. Carpenter, Edward T. Bullmore, Elizabeth A. Warburton, and Jean-Claude Baron⁎

University of Cambridge, Department of Clinical Neurosciences, Addenbrooke’s Hospital Box 83 Cambridge CB2 2QQ, UK

Received 26 January 2006; revised 18 July 2006; accepted 23 August 2006Available online 10 October 2006

Functional imaging during movement of the hand affected by a strokehas shown excess activation of the contralesional motor network,implying less physiological hemisphere activation balance. Althoughthis may be adaptive, the relationship between the severity of motordeficit and the hemisphere activation balance for the four majorcortical motor areas has not been systematically studied. Weprospectively studied 19 right-handed patients with first-ever stroke(age range 61± 10 years) in the stable phase of recovery (>3 monthsafter onset), using auditory-paced index–thumb (IT) tapping of theaffected hand at 1.25 Hz as the fMRI paradigm. The hemisphereactivation balance for the primary motor (M1), primary somatosen-sory (S1), supplementary motor (SMA) and dorsal premotor (PMd)areas was measured by a modified weighted laterality index (wLI), andcorrelations with motor performance (assessed by the affected/unaffected ratio of maximum IT taps in 15 s, termed IT-R) werecomputed. There were statistically significant negative correlationsbetween IT-R and the wLI for M1 and S1, such that the more thehemispheric balance shifted contralesionally, the worse the perfor-mance. Furthermore, worse performance was related to a greateramount of contralesional, but not ipsilesional, activation. No significantcorrelation between IT-R and the wLI was obtained for the SMA andPMd, which functionally have stronger bilateral organization. Thesefindings suggest that the degree of recovery of fine finger motion afterstroke is determined by the extent to which activation balance in theprimary sensory motor areas – where most corticospinal fibersoriginate – departs from normality. This observation may haveimplications for therapy.© 2006 Elsevier Inc. All rights reserved.

Introduction

Stroke is a leading cause of long-term physical, especiallymotor, disability, so to better understand the mechanisms under-lying recovery is important as improved rehabilitative and/orpharmacological strategies might derive. Functional imaging

⁎ Corresponding author. Fax: +44 1223 217909.E-mail address: jcb54@cam.ac.uk (J.-C. Baron).Available online on ScienceDirect (www.sciencedirect.com).

1053-8119/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.neuroimage.2006.08.026

techniques offer the opportunity to investigate neuronal reorgani-zation directly in stroke victims (Chollet et al., 1991). In partiallyrecovered stroke patients, one consistently reported and intriguingfinding with affected hand movement is enhanced activation of thecontralesional motor network, including the primary sensorimotor(SM1) cortex, resulting in a shift of activation balance towards theunaffected side (Baron et al., 2004; Calautti and Baron, 2003).

One extensively used approach to assess the hemisphereactivation balance, in both motor and language studies is thelaterality index (LI) (Cramer et al., 1997). The LI ranges from +1(activation only on the side contralateral to hand movement) to −1(only ipsilateral activation). After stroke, therefore, enhancedcontralesional activation translates as reduced or even negative LI,and accordingly the LI for SM1 is on average significantly lower inchronic stroke patients than in normal subjects (Calautti and Baron,2003; Foltys et al., 2003). However, the range of LI values is wideand strikingly overlaps with that of healthy age-matched subjects,and a close look at the literature suggests that this enlargedvariance in the LI after stroke may be subtended by differences inseverity of motor deficit. For instance, Cramer et al. (1997) andPineiro et al. (2001) reported higher LI values for SM1 than Careyet al. (2002), and their patients had less severe residual deficit(Calautti and Baron, 2003). Accordingly, Johansen-Berg et al.(2002) reported a significant negative though weak, correlationbetween motor impairment (assessed by the reaction time in atapping task) and the LI for the primary motor cortex (M1), with asimilar trend for the dorsal premotor cortex (PMd), in 10 righthemiparetic patients studied with fMRI 4–40 months aftersubcortical stroke. Confirming and expanding these findingswould constitute key information about a potentially importantaspect of plasticity, and in turn potential interventions. Further-more, the relationship between hemisphere activation balance andmotor deficit for two other major cortical areas, namely the primarysensory cortex (S1), and the supplementary motor area (SMA) hasnot been studied. Although all four areas contribute fibers to thecorticospinal tract (CST), the PMd and SMA have very densetranscallosal connections and operate under a bilateral functionalmode (Rouiller et al., 1994; Schubotz and von Cramon, 2003), as

323C. Calautti et al. / NeuroImage 34 (2007) 322–331

compared to M1 and S1. Thus, any relationship between motordeficit and the LI would be expected to be much stronger for thelatter two areas.

In the present prospective fMRI investigation, we system-atically assessed, in a large sample of chronic stroke patients,the relationship between motor performance and the LI for eachof these four areas using, for both the activation paradigm andthe clinical assessment of performance, an active task highlydependent on CST integrity, namely index finger–thumb tapping(Chollet et al., 1991). We tested the hypothesis that the moresevere the deficit the more reduced the LI would be for bothM1 and S1, but not for the PMd and SMA.

Patients and methods

Patients

Patients for this study were prospectively selected based onstrict criteria. Inclusion criteria were a single ischemic orhemorrhagic stroke more than 3 months before, with initialmotor deficit lasting at least 2 weeks; SM1 hand area intact orlargely preserved on structural imaging; ability to perform thefMRI activation task; right-handedness (based on the LateralityQuotient [L.Q.] of the Edinburgh Inventory test (Oldfield,1971)); and age over 40 years. Exclusion criteria were asfollows: aphasia, apraxia, neglect or any cognitive impairmentor depression impairing full cooperation into the activationparadigm; previous clinical stroke, silent infarct or evidence ofsignificant small vessel disease on admission CT; ipsilateralsynkinesia; proprioceptive loss associated with the motor deficit;and current intake of drugs that may interfere with recovery,such as benzodiazepine, antidepressant, and anticonvulsants. Allpatients received standard physiotherapy only.

The Addenbrooke’s NHS Trust Local Research EthicsCommittee approved the study and written informed consent wasobtained from all subjects.

Nineteen patients (age: 61±10 years, mean±1SD; 5 womenand 14 men; L.Q.=94±10) were studied. They had either right(n=5) or left (n=14) hemiparesis. As documented by structuralimaging, 15 had purely subcortical stroke and 4 had corticalinvolvement (see Fig. 1 for an illustration). Of the four patientswith cortical involvement, Patient #8 had a small lesion of M1(hand area only) with no involvement of S1, PMd or SMA.Patient #17 had a lesion partially involving M1 and PMd. Patient#16 had a lesion of the insular cortex only and likewise Patient#6 had no involvement of any of the four cortical motor areas ofinterest for this study. One patient (#3) had internal carotid arteryocclusion. Demographics, lesion topography, general stroke scalescores and motor performance (see below) are shown in Table 1.Two patients (#1 and #3) had clinically present associatedcontralateral movements during index finger tapping.

Motor performance

On the day of scanning, the maximum number of index finger-to-thumb taps in 15 s (to be referred to as IT-Max) was visuallycounted for both the affected and unaffected hand. Subjects wereinstructed to tap as fast as possible while keeping the rate as regularas possible; the amplitude was instructed to be around 1 cm butwas not strictly enforced. An “index-tapping ratio” (IT-R) wascalculated as IT-Maxunaffected/IT-Maxaffected.

fMRI scanning paradigm

Block design fMRI was obtained under two conditions, eachreplicated 4 times and performed in pseudo-random and balancedorder: (1) affected side index–thumb tapping, auditory cued at afrequency of 1.25 Hz; and (2) rest, but with the auditory tonesdelivered at the same frequency. Performance of the task wasmonitored visually. For both conditions, the subject kept theireyes closed with the head gently immobilized in a dedicatedheadrest. Brief instructions to “Move” or “Rest” were also aurallygiven. This tapping frequency was chosen because previousfunctional imaging studies have shown it results in the mostrobust and consistent motor network activation in normal subjects(Rao et al., 1996). Before the experiment, all subjects wereinstructed into the task and trained using the same stimulationsoftware until they were able to adequately follow the aurallygiven cues and instructions, which took up to 10 min.

Data acquisition

Data were acquired using a Bruker Medspec (Ettlingen,Germany) scanner operating at 3 T. T2*-weighted, echo-planarimages (EPI), depicting BOLD contrast, were acquired in a singlesession. The TR was of 3 s. The activation block design lasted 24 s,so that 8 EPI volumes were acquired, while the Rest period lasted12 s with 4 EPI volumes acquired. EPI was acquired from 21 non-contiguous near axial planes (thickness=5 mm; matrix size 64×64),parallel to the AC–PC line. In total 54 images were acquired for eachsession, the first six of which were discarded as “dummy scans” toallow for T1 equilibration effects, leaving a total of 48 images persession. T2-weighted structural images (3×3×4-mm voxels) werealso obtained for anatomical orientation.

Image processing

All calculations and image transformations were performed onUNIX SYSTEM workstations. Data processing and analysis wereaccomplished using Statistical Parametric Mapping (SPM99;Wellcome Department of Cognitive Neurology, London, UK).Pre-processing steps comprised slice acquisition time correction,within-subject image realignment, spatial normalization to the MNItemplate (voxel size: 4×4×4 mm) and spatial smoothing using aGaussian kernel (12-mm full-width at half-maximum). Maskingwas not necessary as the lesions were not large and did not causecavitation and/or brain distortion in any patient. Data sets wererejected if head displacement was greater than 5 mm in anydirection. The time series was high-pass filtered to remove low-frequency noise, and the default SPM99 temporal frequency cut-off was used. The average hemodynamic response to each blockwas modeled using a canonical, synthetic hemodynamic responsefunction (Friston, 1998).

Regions of interest

In this study, regions of interest (ROIs) for M1, PMd, S1, andSMA were automatically defined on each subject’s spatiallynormalized data set, using freely accessible ROI templates. TheM1, S1 and PMd ROIs were extracted from the wfu_Pickatlastemplate (Maldjian et al., 2003, 2004), with slight modificationsfor M1 and PMd accounting for structural and functional anatomy(Brodmann, 1999; Johansen-Berg et al., 2002; Rizzolatti et al.,

Fig. 1. This figure illustrates the localization of the stroke in each patient. The data are based on the structural MR obtained in each patient. Pt #1 and pt #5 had amedulla and pons lesion, respectively, the topography of which is not reproduced in detail in this graph.

324 C. Calautti et al. / NeuroImage 34 (2007) 322–331

1998), while the SMA ROI was extracted unchanged from theAAL template (Tzourio-Mazoyer et al., 2002). The boundaries forM1 were the central sulcus posteriorly, z=24 mm inferiorly, andthe middle of the precentral gyrus anteriorly (except in its cranialpart where it occupies most of the precentral gyrus), and theboundaries for PMd were M1 posteriorly, z=51 mm inferiorly, andthe anterior limit of the premotor ROI anteriorly. The ROIs areillustrated in Fig. 2. Adequate anatomical accuracy of the ROIs sodefined was verified in each subject in native space using reversenormalization parameters.

Laterality index (LI)

In this study, the LI was calculated according to Fernandez et al.(2001), with slight modifications. To control for individual variationin activation level, these authors introduced a normalized or

“weighted LI” (to be referred to as wLI herein). The wLI for agiven pair of ROIs is calculated from the sum of t values (to bereferred to as∑t herein) across all voxels in each ROI whose t valueslie above a threshold adjusted to the subject’s global activation level.This contrasts with the classic LI (Cramer et al., 1997) which usesthe number of voxels above an arbitrary t cut-off uniformly appliedto all subjects, which may occasionally result in no voxels above thecut-off, and hence in ceiling, floor or undefined LIs, particularly forbrain regions with low levels of activation. In their original(language) studies, Fernandez et al. (2001, 2003a,b) calculated theindividual threshold for each pair of ROIs separately, but we foundthat this was not optimal for the motor areas because even withextremely low t values in the ROIs an wLI is always generated,which could be spurious. To avoid this problem, we derived for eachsubject a single global threshold (to be applied onto all ROIs)calculated from all voxels in the SPM t-maps. To access the voxel t

Table 1Demographic data, lesion topography and clinical scores

Pt Sex Age Hemiparesis (side) Days from stroke Lesion topography ESSb IT affected hand IT unaffected hand IT-R

#1 M 50 L 112 R medulla NA 36 50 1.390#2 F 45 L 230 R striatocapsular 89 40 48 1.200#3 M 80 L 191 R striatocapsular 80 30 46 1.530#4 M 66 R 206 L corona radiata 81 30 45 1.500#5 M 49 R 270 L pons 96 46 49 0.938#6 M 71 L 91 R cortical 96 38 46 1.211#7 F 59 L 210 R striatocapsular 79 32 48 1.500#8 F 62 L 168 R cortico-subcortical 88 32 40 1.250#9 M 52 R 172 L corona radiata 96 50 50 1.000#10 M 52 R 167 L striatocapsular 100 44 33 0.750#11 M 54 L 150 R corona radiata 81 38 46 1.210#12 M 74 L 128 R internal capsule 94 46 54 1.170#13 M 60 L 180 R striatocapsulara 84 42 52 1.240#14 M 73 L 158 R striatocapsular 87 32 44 1.380#15 M 65 L 148 R thalamus/internal capsulaa 80 42 54 1.280#16 F 65 L 90 R striatocapsular+ insula 100 42 50 1.190#17 M 60 L 1380 R cortico-subcortical 95 43 62 1.440#18 F 51 L 166 R striatocapsular 85 32 54 1.687#19 M 64 R 160 L striatocapsular 84 32 48 1.500

a: Hemorrhage; b: normal value 100; ESS: European Stroke Scale; IT: maximum number of index–thumb taps in 15 s; IT-R: IT ratio.

325C. Calautti et al. / NeuroImage 34 (2007) 322–331

values, single-subject SPM t-maps (comparing the BOLD signal inTask vs. Rest) were computed, using the p<0.001 uncorrecteddefault thresholding of SPM maps (note that the actual threshold isirrelevant for the present analysis). The global t threshold wascalculated according to Fernandez et al. (2001), as half of the meanof the top 5% values (excluding negative values), to be referred to asT/2. Within each ROI, the ∑t across all voxels above T/2 was thenobtained using Matlab, and the wLI was calculated according to theclassic formula:

wLI ¼X

tC �X

tI� �

=X

tC þX

tI� �

where C and I refer to contralateral and ipsilateral, respectively(corresponding respectively to ipsilesional and contralesional withstroke-affected hand tasks). With this method, the number of voxels

Fig. 2. This figure illustrates the four ROIs used in this study. See Patientsand methods for details.

above T/2 typically ranged above 100 and 30 in ROIs contralateraland ipsilateral to movement, respectively.

Control subjects

The fMRI data from 12 right-handed control subjects of similargender ratio and age range as the patients (54±12 years) whounderwent the same paradigm were analyzed exactly in the sameway. They were all healthy and free of any medication, with noknown vascular risk factors and no history of neurological orpsychiatric disease, head trauma, or alcohol or substance abuse.Their head MRI showed no significant abnormality in excess ofwhat would be expected with normal aging.

Statistical analysis

The correlation between the wLI and IT-R values was computedseparately for M1, S1, PMd and SMA. Because of the nature of theLI, which is constrained between −1 and +1, all correlations wereassessed using non-parametric Spearman’s rho. Based on our a priorihypothesis (see Introduction), we considered negative correlationsfor M1 and S1 as significant if they reached the p<0.05, one-tailedcut-off. We expect similar results for these two areas because S1 alsocontributes CST fibers. For PMd and SMA, the two-tailed p<0.05cut off was considered significant.

To inform on the underlying nature of the wLI/IT-R relation-ship, correlations were also computed between the IT-R and the ∑tvalues separately for the contralesional and ipsilesional ROI ofeach pair.

Results

Motor performance

The IT-max for each patient is shown in Table 1. The medianand range of the IT-max in the group of age-matched healthysubjects was 52 (38–58) and 48 (32–62) for the right and left

Table 3Median (and range) wLI values obtained in 12 control subjects (age: 54±12 years)

M1 S1 PMd SMA

Right handMedian 0.786 0.883 0.590 0.369Range [0.378–1] [0.190–0.997] [−0.276–1] [−0.887–1]

Left handMedian 0.808 0.651 0.274 −0.201Range [0.306–1] [−0.030–1] [−0.727–1] [−0.539–0.448]

No significant difference was found between Right hand and Left hand dataexcept for SMA (p=0.02; Wilcoxon test).

326 C. Calautti et al. / NeuroImage 34 (2007) 322–331

hands, respectively. A comparison between the IT-max of theaffected hand in patients and the IT-max for the same proportion ofright and left hand values in controls showed a statisticallysignificant decrease in patients (p=0.004, Mann–Whitney test), butno significant difference for the unaffected hand.

wLI results

The wLI values for each patient and each ROI are shown inTable 2. The wLI value for the SMAwas undefined in pt #5 due tono voxels above threshold both ipsi- and contralesionally.

For comparison, Table 3 lists the median and range for the wLIvalues obtained in the age-matched healthy controls. In controls,the wLI was generally lower for the PMd and SMA than for M1and S1, and was significantly lower for the left than the right handfor the SMA, with a similar but non-significant trend for PMd. Toaccount for this in the between-group comparison, the proportionof right and left hands was matched, taking the wLI values for 12left hands and 4 right hands in controls to match the 14 left handsand 5 right hands of the patients. Using these matched samples, thewLI for S1 was significantly lower in patients vs. controls(p=0.04, Mann–Whitney), with a similar but non-significant trendfor the M1 and SMA.

Correlations between wLI and IT-R

There was a significant negative correlation between the IT-Rand the wLI values for M1 (rho=−0.612, one-tailed p<0.01) andS1 (rho=−0.418, one-tailed p<0.05), but not even a trend for PMdand SMA (rho=−0.130, p=0.57 and rho=+0.117, p=0.63,respectively) (Fig. 3). There was no effect of patient age and timeelapsed since stroke onset on these correlations.

The results did not substantially change when considering onlythe subgroup of patients affected by subcortical stroke (n=15). TheSpearman one-tailed p value for M1 and S1 were p=0.018 andp=0.07, respectively; the correlations were not significant for PMdand SMA.

Table 2wLI values for each patient and each ROI

Pt# M1 S1 PMd SMA

1 0.757 0.746 0.275 0.0502 0.767 0.863 0.553 0.1553 0.691 −0.091 0.409 −0.4524 0.588 0.831 −0.347 1.0005 0.832 0.983 −0.257 *6 0.759 0.623 1.000 −0.5547 0.212 0.612 −0.405 −0.7858 −0.272 −0.352 −1.000 −0.4049 0.380 0.770 −0.827 −0.41310 1.000 0.639 0.847 0.70411 0.983 0.218 1.000 −0.33612 0.787 0.910 −0.035 −0.67713 0.982 −0.599 0.235 −1.00014 0.105 0.113 −0.086 −0.20315 0.828 0.990 0.672 −0.68616 0.933 0.602 1.000 −0.20117 −0.630 −0.835 −0.137 −0.13218 0.152 0.114 0.988 0.22119 0.674 0.616 −0.138 −0.083

*No voxel was above the t threshold for this ROI in either hemisphere.

The two patients with contralateral associated movements werenot different from the rest of the sample (see figures), andremoving them from the overall analysis did not change theresults.

Correlation between ∑t and IT-R

Fig. 4 shows the relationship between the IT-R and the ∑tvalues for the ipsilesional and contralesional M1 and SMA. Therewas a significant positive correlation for the contralesional M1(rho=+0. 621, two-tailed p<0.01), but not for the ipsilesional M1(rho=+0. 040). Similar results were found for S1 (rho=0.454,p=0.05 and rho=0.07, respectively). Regarding the SMA, therewas a significant correlation for the contralesional hemisphere anda trend for the ipsilesional hemisphere (rho=0.483, p=0.03; andrho=0.382, p=0.11, respectively). No significant correlation wasfound for PMd for either hemisphere.

Classic LI

For the sake of comparison, we also calculated the correlationsbetween the laterality index and the IT-R using the classic LI(obtained as described above using the default SPM threshold ofp<0.001 uncorrected), instead of the wLI. There was a significantnegative correlation for M1 (rho=−0.517, one-tailed p<0.01),with a similar trend for S1 (rho=−0.294), but not even a trend forPMd and SMA.

Discussion

In agreement with our hypothesis, we found that the more thehemispheric balance for M1 and S1 shifted towards the contrale-sional hemisphere the worse the finger-tapping deficit, while nosuch relationship existed for the PMd and SMA.

General considerations

ROI- versus voxel-based analysisOur primary interest in this study was to assess the hemispheric

balance of activation for the four main cortical motor areas. To thisend, we used a ROI-based approach, consistent with previous work(Cramer et al., 1997; Pineiro et al., 2001; Carey et al., 2002;Johansen-Berg et al., 2002; Foltys et al., 2003). Although inprinciple mapping the LI voxel-wise would not be impossible,matching functional voxels on both hemispheres is not validatedand is likely unrealistic considering minute anatomical asymme-tries. However, to try and determine more precisely which area

Fig. 3. Correlation between the IT-R and the wLI for the four regions. The correlation was significant for M1 (Spearman's rho=−0.612, p<0.01) and S1 (rho=−0.418, p<0.05), but not for the PMd or SMA (rho=−0.130 and +0.117, respectively). For completeness, the cortical and subcortical strokes are shown withdifferent symbols, and the two patients with contralateral associated movements are also individualized.

327C. Calautti et al. / NeuroImage 34 (2007) 322–331

within the ROIs was most related to the IT-R, we have also run avoxel-based analysis on our series of patients, applying SPM99within each of the four ROIs, after flipping all the brain images sothat all patients had their lesion on the right side and using the IT-Ras covariate. Looking only at positive correlations based on theROI data (see Fig. 4), and using the liberal p<0.01 threshold in anexploratory manner, small clusters (1–16 voxels) were foundipsilesionally for M1 (44, −8, 56, p=0.008) and SMA (4, −20, 68;p=0.002), and contralesionally for M1 (hand area at −28, −12, 68,p=0.01), PMd (−28, −8, 68, p+0.008) and SMA (−4, −12, 68;p<0.001). These results are largely consistent with previousreports (Ward et al., 2003).

Weighted LIIn this study, we elected to use a weighted, rather than the

classic, LI to assess the hemisphere activation balance. As detailedin the Patients and methods section, the main advantage of the wLIis that it normalized for individual variations in global activationlevel (Fernandez et al., 2001). Accordingly, only one wLI valuewas undefined and seven were ceiling or floor (see Table 2).Running the same data to obtain the classic LI, there were 19ceiling and 28 floor values (data not shown). Although testing the

correlations between the classic LI and the IT-R resulted in thesame pattern as with the wLI (see Results), the correlations wereclearly less significant and the values were better defined with thewLI.

Patient sampleThere are several points that need to be addressed regarding our

sample. Firstly, it included mainly isolated subcortical strokes butalso four strokes that involved the cortex (though not completelydestroying any of the four regions assessed in this study). However,the cortical strokes fell well within the overall wLI-ITR relationship(see Figs. 3 and 4). Thus, our data would suggest that with respect toamount of activation and hemispheric balance for the four motorcortical areas studied, the cortical strokes behave similarly tosubcortical strokes, although tend to result in both more severechronic deficit and more negative LIs. However, the number ofcortical strokes studied here was small and further studies arewarranted to confirm this point. Regarding subcortical strokes, thecorrelations between wLI and IT-R did not substantially changewhen considering only this subgroup. Of note, however, the wLI forS1 and M1 was positive in almost all subcortical cases, indicatingthat the contribution of the contralesional hemisphere, though

Fig. 4. Top. Relationship between the IT-R and ∑t values for the ipsilesional and contralesional M1. There was a significant positive correlation for the latter(Spearman's rho=+0. 0.621, p<0.01) but not for the former (Spearman's rho=0.04). For the sake of comparison, corresponding ranges for ∑t in 12 healthyage-matched controls were 118–1460 (median=500) and 0–455 (median=67) for the M1 contralateral and ipsilateral to tapping, respectively. Bottom.Relationship between the IT-R and ∑t values for the ipsilesional and contralesional SMA. There was a significant positive correlation for the contralesional(Spearman's rho=+0.483, p<0.03) and a trend for the ipsilesional (rho=0.382, p=0.107) SMA. Symbols as in Fig. 3.

328 C. Calautti et al. / NeuroImage 34 (2007) 322–331

elevated, is generally less than that of the ipsilesional hemisphere,i.e., the hemispheric balance is usually not inverted. A secondpoint is that in this study, patients with either right or lefthemiparesis were included, which might constitute a confound.However, the correlations computed separately for the right andleft hemiparesis subgroups were not significantly different bydirect comparison of the correlation coefficients (Snedecor andCochran, 1989), be it for M1 or for any other ROI includingPMd. This suggests that the relationship between chronic-stagemotor deficit and hemispheric balance does not depend onwhether the dominant or non-dominant side is affected. Thirdly,two patients had associated movements during the execution ofthe motor task, but removing them from the analysis did not alterthe overall results. Finally, one patient had carotid arteryocclusion, which might affect the BOLD signal (Baron et al.,2004), but excluding him from the correlation analysis also didnot change the results.

fMRI paradigmIn this study we chose to use a fixed-performance paradigm in

functional imaging, rather than a paradigm attempting to adjustperformance to individual maximal performance. The issue ofwhich paradigm is best suited to address brain reorganization hasbeen the topic of considerable discussion, without any clear

consensus emerging (Baron et al., 2004), while the single studythat compared both in the same set of patients found no substantialdifference in the findings (Ward et al., 2003).

Motor performanceWe assessed clinical motor function with the finger-tapping task

(maximum index–thumb taps in 15 s), a widely accepted measureof cortico-spinal tract deficit (Chollet et al., 1991; Johansen-Berg etal., 2002), and expressed the results by normalizing to theunaffected hand (Johansen-Berg et al., 2002). The tapping task wasperformed only once to avoid fatigue with potentially unstableperformance in stroke patients. Although the performance ofpatient #10, who performed 25% better with the paretic right hand,is unusual, it was checked several times without significant change,and the patient stated that he felt more uncomfortable performingthe task with his unaffected (non-dominant) hand. We haveobserved similar hand-to-hand differences in normal subjects too(C. Calautti, unpublished data).

Interpretation of the findings

What is the significance of a decrease in laterality index withincreasing tapping deficit? The relevance of hemispheric balancefor the motor cortical areas is underpinned by an extensive human

329C. Calautti et al. / NeuroImage 34 (2007) 322–331

and monkey physiological literature documenting effective trans-callosal connections (Rizzolatti et al., 1998; Rouiller et al., 1994).Additionally, TMS studies document prominent effects ofunilateral intervention on function of the homologous motor area(Kobayashi et al., 2003; Murase et al., 2004). As an expression ofthis balance in brain-damaged human subjects, the LI has beenshown to correlate with MRI-defined lesion load in multiplesclerosis (Lee et al., 2000) as well as with rTMS-induced impairedmotor function in chronic stroke (Johansen-Berg et al., 2002).

An LI lower than unity reflects bilateral activation, which forSM1 is seen in normal subjects performing complex motor tasks(Calautti and Baron, 2003). In stroke patients the reduced LIrelative to normal subjects performing the same task reflects highercontralesional-to-ipsilesional relative activation. Our finding of adecrease in LI associated with worse deficit therefore represents anincreasingly less physiological motor recruitment pattern, whichmay reflect the brain’s attempt to perform the task despite thelesion (see below).

We will now discuss the findings for the four cortical motorareas in succession.

M1Our findings are consistent with Johansen-Berg et al. (2002)

who reported a significant negative correlation between the LI forM1 and hand motor impairment in 10 patients with righthemiparesis from left-sided chronic subcortical stroke. Theseauthors used reaction time during a visually cued index finger keypress task to assess hand motor impairment, while in the presentstudy we used motor speed. Thus, the relationship between the LIfor M1 and hand motor impairment would seem to apply toapparently distinct measures of finger motor impairment. Anotherdifference with the Johansen-Berg study is that their study involvedonly patients with right hemiparesis, while our sample comprised14 patients with left hemiparesis. A post hoc analysis of our dataset found also a significant correlation for M1 for this subgroup(data not shown), which together with the Johansen-Berg datasuggests that this relationship holds true for both dominant andnon-dominant hemisphere strokes. Both our and Johansen-Berg etal.’s (2002) cross-sectional findings are also consistent withMarshall et al. (2000) who reported an increasing LI for M1paralleling recovery following stroke.

As stated above, an LI lower than unity reflects bilateralactivation. The M1 cortex has anatomical (Lawrence and Kuypers,1968) and functional transcallosal connections, although less densethan the PMd and SMA (Rouiller et al., 1994). To determine howeach hemisphere accounted for the observed correlation betweenthe wLI and motor deficit, we assessed the relationship between ∑tand IT-R for each side separately. This disclosed a significantincrease in contralesional ∑t with worse deficit, not present foripsilesional ∑t. Thus, an increasing contralesional ∑t was the maincontributor to the observed correlation between wLI and IT-R. Intheir cross-sectional adjusted-force paradigm voxel-based fMRIstudy, Ward et al. (2003) reported clusters of significant correlationwith clinical motor deficit within contralesional M1, in the samedirection as here; our ancillary voxel-based analysis was consistentwith this previous work and interestingly showed the mostsignificant cluster to lie within the hand area. Physiologicalhyperexcitability of contralesional M1 is consistently observedwith both TMS and magneto-encephalography after stroke(Rossini et al., 2003). Also consistent with our finding, contrale-sional M1 overactivation tends to decrease as recovery proceeds

after stroke (Calautti et al., 2001). Finally, the decrease in LI withgreater white matter damage in multiple sclerosis is also driven bygreater activation of ipsilateral SM1 (Lee et al., 2000). But doesthis increase in contralesional M1 activation with more severedeficit reflect useful reorganization? A number of studies usingTMS (Johansen-Berg et al., 2002; Murase et al., 2004; Turton etal., 1996; Werhahn et al., 2003a) or EEG coherence (Mima et al.,2001) found no direct role of contralesional M1 in motor functionof the paretic hand, although there might be a minor role on timingonly for complex learned sequential finger tasks (Lotze et al.,2006). Some argue that contralesional M1 activation may merelyreflect the co-occurrence of mirror movements (clinically presentin only two patients of this series) or the recruitment of theuncrossed CST—both interpretations being possibly related(Calautti and Baron, 2003; Nelles et al., 1998; Turton et al.,1996; Weiller et al., 1993). However, there is no systematicrelationship between contralesional activation and mirror move-ments (Butefisch et al., 2003; Calautti and Baron, 2003).Furthermore, there is no evidence from combined TMS studiesthat contralesional activation represents firing of uncrossed CSTfibers—which would anyway be expected to involve proximalrather than distal movement (Foltys et al., 2003). Overactivation ofcontralesional M1 might in fact largely represent an epiphenome-non reflecting diffuse recruitment of the motor networks driven byhigher order areas when attempting to perform the task, i.e., aglobal “brain effort” is needed to send enough inputs down theremaining CST fibers (Calautti and Baron, 2003); it is relevant thatthe fMRI paradigm chosen in this investigation did not control foreffort (see above). However, overactivation of the contralesionalM1 could also reflect dendritic overgrowth due to overuse of thehealthy hand that would be unmasked by lack of transcallosalinhibition from the damaged M1, again an epiphenomenon withoutfunctionality.

In contrast, only a weak positive trend of correlation wasfound between ∑t and IT-R for ipsilesional M1. It is widelyaccepted that well-recovered hand function relies predominantlyon ipsilesional activity (Calautti and Baron, 2003; Rossini et al.,2003; Ward et al., 2003). TMS studies show that amplitude of themotor-evoked potential after stimulation of ipsilesional M1correlates with the extent of hand motor recovery (Rapisarda etal., 1996), while inhibition of ipsilesional M1 results in delayedreaction time in a simple finger-tapping task (Werhahn et al.,2003b). However, our data suggest that, contrary to contralesionalM1, the recruitment of ipsilesional M1 needed to perform thefMRI task varies across patients in a manner largely independentof the actual deficit—although the ancillary voxel-based analysisrevealed a marginally significant correlation within ipsilesionalM1 (see above). These findings may be related to the complextranscallosal connections for M1, which although not dense seemto involve bi-directional inhibitory drive (Mansur et al., 2005;Murase et al., 2004). Using TMS, Murase et al. (2004)demonstrated an abnormal inhibitory drive from the M1 of thenon-lesioned onto the M1 of the lesioned hemisphere duringmovement of the affected hand. By constraining ipsilesional M1activity, this could hinder motor recovery, and perhaps alsoexplain the lack of correlation between ∑t for ipsilesional M1 andmotor impairment in our study. The converse hypothesis, thatcontralesional M1 hyperactivity in poorly recovered patients issimply secondary to reduced inhibitory drive from ipsilesionalM1, is not supported by our finding of no significant correlationbetween the latter and motor deficit.

330 C. Calautti et al. / NeuroImage 34 (2007) 322–331

S1We also found a significant correlation between the LI for S1

and motor deficit, which was however weaker than for M1. Froman anatomical point of view, S1 contributes to the CST, so thisfinding is not surprising in the first approach. Accordingly, anumber of studies on motor recovery have merged S1 and M1 in asingle ROI, and when doing so in the present series the correlationbetween wLI and IT-R remained statistically significant. Thereason why we separated S1 and M1 in this study was to assess ifsensory feedback, which was clinically preserved in all ourpatients, would dominate fMRI signal in S1 and show dissociationwith M1, which was not the case. Furthermore, as with M1, therewas a significant positive correlation between motor performanceand the ∑t for contralesional but not ipsilesional S1.

PMdConsistent with our hypothesis, but at variance with the trend

previously reported by Johansen-Berg et al. (2002), we found nocorrelation between the wLI for PMd and motor performance. Onepossible cause for this difference with the Johansen-Berg study isthe measure used to assess motor impairment, namely reactiontimes during a visually cued index finger key press task inJohansen-Berg, as compared to index–thumb tapping speed here. Ifthis difference is the cause of the discrepancy, this would suggestthat, in contrast with M1, the relationship between PMd activationbalance and motor function after stroke depends on the precisemotor function assessed. Although the PMd significantly con-tributes to the CST, this is mainly via the bilaterally organizedcortico-reticular tract, which is particularly involved in proximalmovement with less contribution to hand function (Rizzolatti et al.,1998; Rouiller et al., 1994). Because of its more bilateralfunctioning mode, especially for distal movement, the balance ofactivation for PMd would less strongly relate to finger motion thanit does for M1. In support to this interpretation, the wLI for PMdwas lower than for M1 or S1 in the control subjects (see Table 3),consistent with previous reports (Johansen-Berg et al., 2002). Thislack of involvement of the hemispheric balance for PMd in affectedhand movement does not however deny a potential role for thiscortical area in post-stroke recovery (Fridman et al., 2004;Johansen-Berg et al., 2002; Nelles et al., 1999).

SMAAgain consistent with our hypothesis, there was no significant

correlation between the wLI for SMA and motor performance.Regarding the sum of t values, there was a significant correlationfor the contralesional hemisphere and a trend for the ipsilesionalhemisphere, so that the SMA seems to be bilaterally moreactivated in bad recoverers. This could in part underlie the lack ofsignificant correlation between the wLI and the motor deficit forthis area. To the best of our knowledge, the LI for SMA has notbeen previously studied in stroke. From these preliminary results apotential role of the contralesional SMA in recovery could beargued based on the correlation found between the amount ofactivation and the deficit. In this study, the SMA ROI includedboth the SMA proper and the pre-SMA, but the findings wereunchanged if the SMA proper was considered alone (data notshown). The dense transcallosal connections for the SMAprobably explain the lack of correlation between the wLI andthe motor performance. Accordingly the LI for SMA in ourcontrol subjects was the lowest of all ROIs (see Table 3).However, the SMA contributes direct CST fibers, including for

finger movements (Rouiller et al., 1994; Tanji, 1996), whichmight explain the relationship observed between ∑t and IT-R.

Conclusion

Although our patients were able to move their fingers, theirdegree of impairment ranged from severe to none, and thus theyare representative of a wide range of performance after stroke. Inthis sample, increasingly unphysiological hemispheric balancecorrelated with worse deficit, and was mainly due to contralesionalM1 recruitment—although whether the latter is adaptative,maladaptative or epiphenomenal remains unclear. Based on ourfindings, it would be worth testing the hypothesis that furtherrecovery should occur if the laterality index can be driven towardsthe affected hemisphere (Carey et al., 2002).

References

Baron, J.C., Cohen, L.G., Cramer, S.C., Dobkin, B.H., Johansen-Berg, H.,Loubinoux, I., Marshall, R.S., Ward, N.S., 2004. Neuroimaging in strokerecovery: a position paper from the first international workshop onneuroimaging and stroke recovery. Cerebrovasc. Dis. 18, 260–267.

Brodmann, K., 1999. Localisation in the Cerebral Cortex. Imperial CollegePress.

Butefisch, C.M., Netz, J., Wessling, M., Seitz, R.J., Homberg, V., 2003.Remote changes in cortical excitability after stroke. Brain 126, 470–481.

Calautti, C., Baron, J.C., 2003. Functional neuroimaging studies of motorrecovery after stroke in adults: a review. Stroke 34, 1553–1566.

Calautti, C., Leroy, F., Guincestre, J.Y., Baron, J.C., 2001. Dynamics ofmotor network overactivation after striatocapsular stroke: a longitu-dinal PET study using a fixed-performance paradigm. Stroke 32,2534–2542.

Carey, J.R., Kimberley, T.J., Lewis, S.M., Auerbach, E.J., Dorsey, L.,Rundquist, P., Ugurbil, K., 2002. Analysis of fMRI and finger trackingtraining in subjects with chronic stroke. Brain 125, 773–788.

Chollet, F., DiPiero, V., Wise, R.J., Brooks, D.J., Dolan, R.J., Frackowiak,R.S., 1991. The functional anatomy of motor recovery after stroke inhumans: a study with positron emission tomography. Ann. Neurol. 29,63–71.

Cramer, S.C., Nelles, G., Benson, R.R., Kaplan, J.D., Parker, R.A., Kwong,K.K., Kennedy, D.N., Finklestein, S.P., Rosen, B.R., 1997. A functionalMRI study of subjects recovered from hemiparetic stroke. Stroke 28,2518–2527.

Fernandez, G., de Greiff, A., von Oertzen, J., Reuber, M., Lun, S., Klaver, P.,Ruhlmann, J., Reul, J., Elger, C.E., 2001. Language mapping in less than15 minutes: real-time functional MRI during routine clinical investiga-tion. NeuroImage 14, 585–594.

Fernandez, G., Specht, K., Weis, S., Tendolkar, I., Reuber, M., Fell, J.,Klaver, P., Ruhlmann, J., Reul, J., Elger, C.E., 2003a. Intrasubjectreproducibility of presurgical language lateralization and mapping usingfMRI. Neurology 60, 969–975.

Fernandez, G., Weis, S., Stoffel-Wagner, B., Tendolkar, I., Reuber, M.,Beyenburg, S., Klaver, P., Fell, J., de Greiff, A., Ruhlmann, J., Reul, J.,Elger, C.E., 2003b. Menstrual cycle-dependent neural plasticity in theadult human brain is hormone, task, and region specific. J. Neurosci. 23,3790–3795.

Foltys, H., Krings, T., Meister, I.G., Sparing, R., Boroojerdi, B., Thron, A.,Topper, R., 2003. Motor representation in patients rapidly recoveringafter stroke: a functional magnetic resonance imaging and transcranialmagnetic stimulation study. Clin. Neurophysiol. 114, 2404–2415.

Fridman, E.A., Hanakawa, T., Chung, M., Hummel, F., Leiguarda, R.C.,Cohen, L.G., 2004. Reorganization of the human ipsilesional premotorcortex after stroke. Brain 127, 747–758.

Friston, K.J., 1998. Imaging neuroscience: principles or maps? Proc. Natl.Acad. Sci. U. S. A. 95, 796–802.

331C. Calautti et al. / NeuroImage 34 (2007) 322–331

Johansen-Berg, H., Rushworth, M.F., Bogdanovic, M.D., Kischka, U.,Wimalaratna, S., Matthews, P.M., 2002. The role of ipsilateral premotorcortex in hand movement after stroke. Proc. Natl. Acad. Sci. U. S. A. 99,14518–14523.

Kobayashi, M., Hutchinson, S., Schlaug, G., Pascual-Leone, A., 2003.Ipsilateral motor cortex activation on functional magnetic resonanceimaging during unilateral hand movements is related to interhemisphericinteractions. NeuroImage 20, 2259–2270.

Lawrence, D.G., Kuypers, H.G., 1968. The functional organization of themotor system in the monkey: II. The effects of lesions of the descendingbrain-stem pathways. Brain 91, 15–36.

Lee, M., Reddy, H., Johansen-Berg, H., Pendlebury, S., Jenkinson, M.,Smith, S., Palace, J., Matthews, P.M., 2000. The motor cortex showsadaptive functional changes to brain injury from multiple sclerosis. Ann.Neurol. 47, 606–613.

Lotze, M., Markert, J., Sauseng, P., Hoppe, J., Plewnia, C., Gerloff, C., 2006.The role of multiple contralesional motor areas for complex handmovements after internal capsular lesion. J. Neurosci. 26 (22),6096–6102 (May 31).

Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H., 2003. Anautomated method for neuroanatomic and cytoarchitectonic atlas-basedinterrogation of fMRI data sets. NeuroImage 19, 1233–1239.

Maldjian, J.A., Laurienti, P.J., Burdette, J.H., 2004. Precentral gyrusdiscrepancy in electronic versions of the Talairach atlas. NeuroImage 21,450–455.

Mansur, C.G., Fregni, F., Boggio, P.S., Riberto, M., Gallucci-Neto, J.,Santos, C.M., Wagner, T., Rigonatti, S.P., Marcolin, M.A., Pascual-Leone, A., 2005. A sham stimulation-controlled trial of rTMS of theunaffected hemisphere in stroke patients. Neurology 64, 1802–1804.

Marshall, R.S., Perera, G.M., Lazar, R.M., Krakauer, J.W., Constantine,R.C., DeLaPaz, R.L., 2000. Evolution of cortical activation duringrecovery from corticospinal tract infarction. Stroke 31, 656–661.

Mima, T., Toma, K., Koshy, B., Hallett, M., 2001. Coherence betweencortical and muscular activities after subcortical stroke. Stroke 32,2597–2601.

Murase, N., Duque, J., Mazzocchio, R., Cohen, L.G., 2004. Influence ofinterhemispheric interactions on motor function in chronic stroke. Ann.Neurol. 55, 400–409.

Nelles, G., Cramer, S.C., Schaechter, J.D., Kaplan, J.D., Finklestein, S.P.,1998. Quantitative assessment of mirror movements after stroke. Stroke29, 1182–1187.

Nelles, G., Spiekermann, G., Jueptner, M., Leonhardt, G., Muller, S.,Gerhard, H., Diener, H.C., 1999. Reorganization of sensory and motorsystems in hemiplegic stroke patients. A positron emission tomographystudy. Stroke 30, 1510–1516.

Oldfield, R.C., 1971. The assessment and analysis of handedness: theEdinburgh inventory. Neuropsychologia 9, 97–113.

Pineiro, R., Pendlebury, S., Johansen-Berg, H., Matthews, P.M., 2001.

Functional MRI detects posterior shifts in primary sensorimotor cortexactivation after stroke: evidence of local adaptive reorganization? Stroke32, 1134–1139.

Rao, S.M., Bandettini, P.A., Binder, J.R., Bobholz, J.A., Hammeke,T.A., Stein, E.A., Hyde, J.S., 1996. Relationship between fingermovement rate and functional magnetic resonance signal change inhuman primary motor cortex. J. Cereb. Blood Flow Metab. 16,1250–1254.

Rapisarda, G., Bastings, E., de Noordhout, A.M., Pennisi, G., Delwaide, P.J.,1996. Can motor recovery in stroke patients be predicted by earlytranscranial magnetic stimulation? Stroke 27, 2191–2196.

Rizzolatti, G., Luppino, G., Matelli, M., 1998. The organization of thecortical motor system: new concepts. Electroencephalogr. Clin.Neurophysiol. 106, 283–296.

Rossini, P.M., Calautti, C., Pauri, F., Baron, J.C., 2003. Post-stroke plasticreorganisation in the adult brain. Lancet Neurol. 2, 493–502.

Rouiller, E.M., Babalian, A., Kazennikov, O., Moret, V., Yu, X.H.,Wiesendanger, M., 1994. Transcallosal connections of the distalforelimb representations of the primary and supplementary motorcortical areas in macaque monkeys. Exp. Brain Res. 102, 227–243.

Schubotz, R.I., von Cramon, D.Y., 2003. Functional–anatomical concepts ofhuman premotor cortex: evidence from fMRI and PET studies.NeuroImage 20 (Suppl. 1), S120–S131.

Snedecor, G.W., Cochran, W.G., 1989. Statistical methods. Iowa StateUniversity Press, IA, USA.

Tanji, J., 1996. New concepts of the supplementary motor area. Curr. Opin.Neurobiol. 6, 782–787.

Turton, A., Wroe, S., Trepte, N., Fraser, C., Lemon, R.N., 1996.Contralateral and ipsilateral EMG responses to transcranial magneticstimulation during recovery of arm and hand function after stroke.Electroencephalogr. Clin. Neurophysiol. 101, 316–328.

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard,O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomicallabeling of activations in SPM using a macroscopic anatomicalparcellation of the MNI MRI single-subject brain. NeuroImage 15,273–289.

Ward, N.S., Brown, M.M., Thompson, A.J., Frackowiak, R.S., 2003. Neuralcorrelates of outcome after stroke: a cross-sectional fMRI study. Brain126, 1430–1448.

Weiller, C., Ramsay, S.C., Wise, R.J., Friston, K.J., Frackowiak, R.S., 1993.Individual patterns of functional reorganization in the human cerebralcortex after capsular infarction. Ann. Neurol. 33, 181–189.

Werhahn, K.J., Conforto, A.B., Kadom, N., Hallett, M., Cohen, L.G., 2003a.Contribution of the ipsilateral motor cortex to recovery after chronicstroke. Ann. Neurol. 54, 464–472.

Werhahn, K.J., Mortensen, J., Van Boven, R.W., Cohen, L.G., 2003b.Bihemispheric plasticity after acute hand deafferentation. Suppl. Clin.Neurophysiol. 56, 232–241.