Restorative Neurology and Neuroscience 14 (1999) 25–33
Visual network activation in recovery from sensorimotor stroke Rüdiger J. Seitz*, Uwe Knorr**, Nina P. Azari, Hans Herzog***, Hans-Joachim Freund* * Department of Neurology, Heinrich-Heine-University Düsseldorf, Moorenstraße 5, D–40225 Düsseldorf Present address: Department of Neurology, Johann-Wolfgang-Goethe-University Frankfurt, D–60528 Frankfurt ***Institute of Medicine, Research Center Jülich, D–54525 Jülich
**
Received 19 June 1998; revised 29 September 1998; accepted 29 September 1998
Abstract Recovery of finger movements after hemiparetic stroke has been shown to involve sensorimotor brain areas in perilesional and remote locations. Hand use, however, critically depends on visual guidance in such patients with stroke lesions in the middle cerebral artery territory. Using regional cerebral blood flow measurements, we wished to identify interrelated brain areas that are engaged in relation to manual activity in seven patients after their first hemiparetic brain infarction. During the blind-folded performance of sequential finger movements, the patients differed significantly from healthy controls (n = 7) by the recruitment of a predominantly contralesional network involving visual cortical areas, prefrontal cortex, thalamus, hippocampus, and cerebellum. Greater expression of this cortical-subcortical network correlated with a more severe sensorimotor deficit in the acute stage after stroke reflecting its role for post-stroke recovery. Patients also differed from controls on a lesion-related pattern expressed during rest. A third differentiating pattern involved the ipsilesional supplementary motor area and the contralesional premotor cortex. Our results suggest that post-stroke recovery from impaired sensorimotor integration utilizes crossmodal plasticity of a visual network. Keywords: Stroke, hemiparesis, sensorimotor integration, motor recovery, plasticity, functional imaging
1. Introduction Restitution of function after brain damage has been reported to involve adaptive reorganization of both, perilesional and remote neural circuitries [38,47,55]. While early brain damage is compensated by recruiting atypical cortical areas, inclusive of the contralateral hemisphere [3,52], motor recovery-related plasticity in the adult brain seems to be limited to the perilesional cortex [9,41,77] and the affected motor cortical output system [5,22]. Thus, in the adult human brain restitution of sensorimotor functions appeared to rely on within system recovery or on the substitutional recruitment Correspondence to: Dr. Rüdiger J. Seitz, M.D., Department of Neurology, Heinrich-Heine-University Düsseldorf, Moorenstraße 5, D–40225 Düsseldorf, Telephone: +49-211-81-18974, Fax: +49-211-81-18485, e-mail:
[email protected] 0922-6028/99/$8.00 © 1999, IOS Press
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of functionally related systems [59]. Clinical studies, however, suggest that other functional systems may also come into play for successful sensorimotor recovery. Specifically, visual guidance of movement has been shown to contribute markedly to the post-ischemic restitution of hand function [2,34]. Motor acts involve a number of specialized and temporally well structured computations mediating sensorimotor integration [31,48]. In healthy subjects the internal generation of voluntary movements necessitates the coherent activation of motor, somatosensory, cingulate, and subcortical brain areas [15,46,58,65]. As yet, it is unclear how this essential and widespread recruitment related to planning, initiation, and execution of movement is accomplished in patients with impaired sensorimotor integration after brain damage. Using regional cerebral blood flow (rCBF) measurements, we ex-
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amined functional interactions in the human brain after recovery from hemiparetic brain infarction. To this end, we applied to the rCBF images a principal component analysis (PCA) that, in contrast to the widely used categorical comparisons, does not demonstrate topographic rCBF differences between groups of subjects or test conditions but rather distributed systems of functionally connected brain areas [20]. Specifically, PCA describes without a priori assumption functional interrelations in large data sets, such as rCBF images, asigning a numerical value to the expression of a principal component in each subject. Thereby, PCA allows to formally compare subgroups of patients and test conditions with respect to functional brain systems using inferential statistics. Here we report that motor activity after hemiparetic stroke is accompanied by the abnormal recruitment of a cortical-subcortical network involving visual cortical areas. Preliminary data were presented in abstract form [63]. 2. Materials and Methods 2.1. Subjects Seven patients (54 ± 8 (SD) years, 1 female, 6 males) with their first brain infarction were investigated. Patients were referred to our clinic because of first completed ischemic stroke. Inclusion criteria for this study were acute hemiplegia or severe hemiparesis with complete loss of fractionated movements of the affected hand, and presence of only one brain lesion as evident from magnetic resonance images (MRI) and right handed as assessed with the Edingburgh questionnaire [43]. Patients with only moderate or slight hemiparesis and those who did not recover were excluded from the study. The study was approved by the Ethics Committee of the Heinrich-Heine-University Düsseldorf. Motor impairment was assessed by the Barthel Index [37] and a multifactorial score that was specifically designed for examining arm and hand function by separate assessment of various components contributing to the motor disturbance [36]. Testing was performed in the acute stage (1–3 days) and within two days before or after PET scanning. PET scanning was performed after significant recovery which was on average six months after brain infarction. Seven healthy, right-handed volunteers (age range 26 to 29 years, 1 female, 6 males) without a neurological abnormality on history and examination served as controls. Although younger than the patients, they were chosen because at this age they had no neurological abnormality and presented with a normal MRI of their brains and normal rCBF scans. 2.2. Lesion assessment The stroke lesions were outlined in spatially standardized [60], proton weighted MRI taken at the chronic stage after infarction (3 days before or after PET scanning) and plotted in stereotaxic space [66]. Five patients had right and two had left hemispheric infarctions. Functions of the cortico-spinal tract and of the afferent somatosensory tract were determined elec-
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trophysiologically and compared to the non-affected ipsilesional side as reported in detail elsewhere [5,60]. Specifically, motor evoked potentials (MEP) were recorded after transcranial magnetic stimulation in the first dorsal interosseus muscles and somatosensory evoked potentials (SEP) after electrical stimulation of the median nerves on the scalp. For both methods, amplitude ratios were assessed as the percentage of the amplitude (%) on the affected side in comparison with the amplitude on the nonaffected side (100 %) as described previously [5]. 3. RCBF-measurements An eight-ring GE/SCANDITRONIX PC4096 plus PET camera was used to measure the rCBF following intravenous bolus injection of [15O]-butanol. This PET camera had an optimal spatial resolution of 4.9 mm in plane, and a slice distance of 6.4 mm [50]. A transmission scan using a rotating 68 Ge pin source was obtained prior to the emission scans to correct for attenuation. The 15 PET image slices were reconstructed with a Hanning filter to an effective image resolution (FWHM) of 8 mm. PET scanning started at the time of the intravenous bolus injection of 40 mCi [15O]-butanol into the right brachial vein. Quantitation of the rCBF was performed with a combined dynamic-autoradiographic approach [27] using a common look-up table as detailed elsewhere [67]. 3.1. Sensorimotor activation The patients and controls were blind-folded during the rCBF measurements. In one rCBF scan, the patients had to perform finger movement sequences of the recovered hand as accurately and fast as they could. The control subjects had to perform the finger movement sequences with their right hand. Prior to scanning, the patients and controls were instructed to sequentially touch the index, long, ring, and little finger with the thumb of the recovered hand and were trained until they exactly knew what to do. In a second scan, the patients performed the same sequence, however, with the unaffected ipsilesional hand. A further scan was a rest condition, which was taken either as the first or the last scan both in patients and controls. The sequence of tasks was randomized across the patients and controls. One of the investigators observed the subjects and registered the number of finger movements. 3.2. Data analysis The rCBF images were spatially standardized as detailed elsewhere [60,61]. Standardization yielded 21 axial image slices that were 6.43 mm apart with a matrix of 128 × 128 pixels, each of 2.55 × 2.55 mm. After image standardization, the rCBF images obtained during rest and activation in the patients were compared with those in the healthy control subjects pixel-by-pixel. Since the correlated changes in the complex functional networks of the human brain cannot be assessed by categori-
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cal comparisons, we employed a PCA for data evaluation [20]. The PCA was applied to the rCBF data of the patients and controls after normalization across groups and conditions, which standardized the variances across the PET images. The PCA extracted the important features of the covariance matrix in terms of principal components (PCs) or eigenvectors without a priori assumptions. These vectors were the linear combinations that accounted for independent or orthogonal amounts of variance in the observed data. Only a few PCs are usually required to explain the majority of observed variance. In terms of functional connectivity, a principal component (PC) represented a truly distributed brain system within which there were high intercorrelations. Because any of these PCs is orthogonal to the remaining, these systems were functionally unconnected from each other [20]. The expression of such a pattern in each subject was given by a single number, the so-called factor score or PCvalue. The coupling of a single brain voxel with such a PC was given by a number, the PC-load. Thus, a pattern of functional connectivity networks could be visualized as an image in which each voxel showed the PC-load. Since each individual subject was given a numerical value for each PC, groups and conditions were formally compared on these PC-values (two-tailed t-test of independent means) to determine which pattern of functional connectivity networks were differentially expressed (significance p < 0.05). Of these, eight PCs accounted for 80 % of the
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variance. PC1, PC3, and PC8 showed significant group differences. Constituting areas (loading factors > 0.5) were localized in stereotaxic coordinates [66]. The loads of the group-differentiating PCs were displayed in pseudocolor scale and superimposed onto the spatially standardized MRI of the patient with the largest stroke lesion. Also, linear regressions of the PC-values and external measures were calculated; R denotes the regression coefficient; p-values correspond to testing if the slope was different from zero. In addition, an omnibus testing of the mean rCBF differences between the patients and the controls was performed. The calculated descriptive t-maps were thresholded at a tvalue of 2.681. To correct for the autocorrelation of adjacent pixels that is limited by the spatial resolution (FWHM) of the reconstructed PET images, only clusters of at least 17 spatially contiguous suprathreshold pixels in the PET image slices were accepted corresponding to p < 0.01 [76]. This descriptive analysis is similar to theoretical cluster analysis approaches that incorporate the degree of smoothness in the images [21,75]. In addition, the stroke lesions as defined in MRI were excluded for the comparison of the PET images recorded at rest. 4. Results The patients had all recovered remarkably well from their first, severely disabling hemiparetic stroke in spite of the in-
Fig. 1: Stroke induced changes. a) Largest extent of the stroke lesions in axial planes dorsal to the intercommissural line (z, mm) in stereotaxic space [66]. b) Impairment after stroke and recovery (p < 0.0001) as assessed by the Barthel Index [37]. c) Persistent reduction (p < 0.02) of somatosensory evoked potentials (SEP) of the median nerve compared to almost normalized motor evoked potentials (MEP) of small hand muscle after recovery. d) Lesion-related (PC1) pattern affecting cortical areas and subcortical structures ipsilateral to the infarction (white) and with opposite relation in the contralesional cerebral hemisphere (grey). e) Positive relation of lesion volume and lesion-related (PC1) pattern.
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volvement of large portions of the motor and somatosensory cortex around the central sulcus (Fig. 1a). The stroke lesions also affected the parietal cortex and the striatum but spared the motor hand area, inclusive of the dorsolateral precentral gyrus, the internal capsule, and the thalamus. The largest extent of the stroke lesions is depicted in Fig. 1a. In the acute stage, the patients were severely disabled by dense hemiparesis including absolute inability to move their hand or fingers. However after recovery, the patients were unsupported ambulatory again and had regained their ability to perform individual finger movements with the contralesional hand (Fig. 1b). At the time of PET scanning, the MEPs were slightly reduced in the recovered hand, while the SEPs were still significantly affected (Fig. 1c). When performing the simple finger movement sequences while being blind-folded, the patients were significantly impaired as compared to healthy controls in both the recovered contralesional arm, as well as in the non-affected ipsilesional arm (Fig 2a,b).
To study the brain areas participating in movement activity, the rCBF was measured. The rCBF images comprised three functional connectivity patterns that separated the patients from controls as well as the activation and the rest conditions.
Fig. 2: Motor performance. a) Simplified finger movement sequence to be performed while blindfolded during the rCBF-measurements. b) Reduced finger movement rate of the recovered (Recover, p < 0.001) and non-affected ipsilesional (Ipsi, p < 0.01) hand as compared to healthy controls.
TABLE I: Group differentiating principal components PC Constituting Areas
Loads
Coordinates x
PC1
PC3
PC8
y
Group comparisons
Functional Implication
NORM-REST vs. STROKE-REST
Lesion-related pattern
z
– Lesion extent – Mid. frontal gyrus IL
+
−39
− 3
−47
Precentral gyrus CL
−
−46
− 5
−32
Precuneus CL
−
−12
−53
−38
Post. cingulate gyrus CL
−
− 7
−54
-16
Mid. occipital gyrus IL
−
−25
−85
-06
Mid. occipital gyrus CL
−
−32
−82 −12 − 6 − 0
Basal ganglia CL
−
−25
Thalamus IL
+
−15 −17 − 8
Thalamus CL
−
−18
Cuneus IL
+
− 4 −77 −31
Cuneus CL
+
− 9
Sup. frontal gyrus IL
−
− 7 −47 − 3
Sup. frontal gyrus CL
+
− 7
−50 − 3
Postcentral gyrus CL
+
−57
−15
Parietal operculum CL
+
−52
−15 −16
Ant. cingulate gyrus CL
+
− 5
−25
−25
Hippocalamus CL
+
−27
−28
− 4
Lateral thalamus CL
+
−18
−20
− 8
Pulvinar thalami CL
−
−22
−20
−14
Ant. cerebellum IL
−
−20 −76 −10
Ant. cerebellum CL
−
−20
−76 −10
Sup. frontal gyrus IL
+
− 6
- 2
-43
Mid.Frontal gryrus CL
+
−27
- 6
-43
−20 − 4 NORM-ACTIVATION vs. STROKE-ACTIVATION Recovery-related pattern
−73 −21
−43
NORM-REST vs. NORM-ACTIVATION NORM-REST vs. STROKE-ACTIVATIONi NORM-REST vs. STROKE-ACTIVATION STROKE-REST vs. STROKE-ACTIVATIONi
Motor learning pattern
Principal component, PC; coordinates of stereotaxis space (mm)66; Stroke patients,STROKE; Controls, NORM; Resting condition, REST; recovered hand movements, ACTIVATION; non-affected ipsilesional hand movements, ACTIVATIONi; hand movements in controls are ACTIVATION. IL: ipsilesional, CL: contralesional. Loads: +, loading factors > 0.5; −, loading factors < 0.5. Group separation was significant at p < 0.05, two-tailed t-test of independent means.
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a
c
b Two functional connectivity patterns were differently expressed in the patients and controls (Table 1). The first pattern (PC1) was characterized by interactions during the resting state involving the core and perilesional stroke area, the contralesional motor, medial parietal and lateral occipital cortex, as well as the bilateral basal ganglia and thalamus. This pattern was related to lesion volume showing positive effects for ip-
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Fig. 3: Plasticity-related changes. a) Recovery-related pattern (PC3) with functional interaction of bilateral visual association areas, cingulate cortex, hippocampal formation, and bilateral cerebellum; red = positive PC-load, blue = negative PC-load. b) Relation of poor motor score initially after stroke as assessed by a dedicated motor score [36] and greater expression of the recovery-related pattern (PC3) during finger movements of the recovered hand suggesting compensatory reorganization. c) Motor learning pattern (PC8) involving contralesional dorsolateral premotor cortex and ipsilesional supplementary motor area.
silesional and negative effects for contralesional brain areas (Fig. 1d,e). That is, the larger the effective brain damage, the more PC1 was expressed. The categorical comparison of the rCBF images obtained during rest demonstrated significant mean rCBF depressions in the patients only in the ipsilesional cerebral hemisphere. Apart from the infarction they occurred in the premotor and parietal cortex but spared the thalamus.
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Patients and controls also differed on a second pattern (PC3) expressed during sequential finger movements. This pattern was characterized by interactions among bilateral occipital and prefrontal cortical areas, contralesional cingulate, hippocampal formation and pulvinar, as well as the bilateral cerebellum (Table 1, Fig. 3a). Greater expression of this pattern in the patients correlated with lower motor scores initially after stroke (Fig. 3b). This meant that less motor impairment was positively related to the cerebellum, while a severe hemiparesis was related to occipital, parietal, prefrontal, and hippocampal interactions. Because the patients were blindfolded during the rCBF-measurements, this second pattern probably reflected a recovery-related reorganization involving a compensatory recruitment of a visual network. Comparing the rCBF images obtained during finger movements in a categorical manner, the healthy subjects showed a greater mean rCBF only in the contralateral motor cortex and the ipsilateral anterior cerebellum than the patients. In contrast, there was no greater mean rCBF in the patients compared to control. The third differentiating pattern (PC8) separated movement activity from resting conditions in both, the patients and controls. This pattern involved the contralesional dorsolateral premotor cortex and the ipsilesional frontomesial cortex probably inclusive of the supplementary motor area (Table 1, Fig. 3c). 5. Discussion The clinically recovered patients had regained the ability to generate individual finger movements of the contralesional recovered hand, while their somatosensory cortex was damaged by the stroke lesion (Fig. 1). Still, the finger movement rate during the blind-folded execution of the simplified finger movement sequences was reduced in the recovered arm compared to healthy controls (Fig 2a,b). As became evident from our study, the stroke lesion had a widespread effect on the rCBF in the patients. The first pattern successfully identified the stroke lesions explaining the variance in the image data related to this pathology. Moreover, PC1 differentiated patients and controls reflecting also the spatial extent and distribution of the remote stroke effects in the affected and contralesional hemisphere [1,60]. While the categorical comparisons showed that the mean rCBF depressions in the patients were restricted to the affected cerebral hemisphere, the pattern of functional interrelations revealed that also the contralesional hemisphere was markedly involved (Fig. 1d). Interestingly, however, there was no significant mean rCBF depression in the ipsilesional thalamus which was in line with the critical role of the thalamus for motor recovery after hemiparetic stroke [1,5]. While being blind-folded, the patients not only had a reduced finger movement rate but also failed to perform a more complicated finger movement sequence that they could perform under visual guidance outside the PET scanner and that can be performed by naive healthy subjects [58]. Corre-
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spondingly, the mean rCBF in the affected motor cortex and the ipsilateral anterior cerebellum were lower in the patients compared to controls probably due to the known movement rate effect on rCBF [6,30,53,56,58,61]. In addition, movement performance was similarly reduced in the non-affected ipsilesional arm. Both, the residual deficits in the recovered arm and the non-affected ipsilateral arm were not unexpected [12,44,74]. Although a slight reduction of finger movement frequency occurs in proportion with age [25], the significant impairment in our patients indicated that they suffered a more generalized impairment of sensorimotor integration. This impairment affected the dominant as well as the non-dominant hand in our patients (Fig. 1). Therefore, sensorimotor conception and sensory guidance of movement normally subserved by the parietal cortex [15,35,58,61,65] was impaired in our patients. It is tempting to speculate that the lesion-related rCBF pattern involving both cerebral hemispheres was the cerebral counterpart to this bilateral behavioral deficit. Since sensorimotor guidance was impaired in our patients, they had to use a new strategy to create a mental representation of movement to successfully perform the finger movement sequence task with the recovered hand during the 60 seconds of the rCBF measurements [4,31]. It is a well established clinical observation that patients with sensorimotor deficits after cortical lesions and peripheral deafferentation employ visual guidance for the control of even simple movements [2,19,29,51]. Since our patients were blind-folded, they could not directly observe their performance but used the mental movement representation for guidance of the required motor task. Indeed, our imaging data showed that the patients utilized a predominantly contralesional network, which may have promoted the processing, encoding, and imagery of visual and visuomotor information [14,35,49,61,65,71]. The occipital cortical areas mediate visual information processing induced by both visual stimulation and visual imagery [49,71]. The hippocampus and the prefrontal cortex participate in memory processes; the former in storage [17,64], the latter in encoding and retrieval [70]. Further, prefrontal cortical connections to the cerebellum serve to facilitate practice-induced performance improvement [33,45]. The parietal areas and the parietal operculum which were involved in the contralesional hemisphere assisted probably in guidance of accurate movements as also evident in healthy subjects [33,58,61]. Since movements were not executed in relation to external cues, there was predictably no engagement of the superior parietal cortex such as Brodmann areas 5 or 7 [15,33,35]. Further, in healthy subjects visual cortical areas are deactivated during blindfolded movement activity [58]. We therefore propose that the damaged human brain may engage a dedicated cortical-subcortical network related to mental visualization of movement to compensate for impaired somatosensory-motor integration. Interestingly, our data also indicated a non-significant trend suggesting that the older patients demonstrated the greatest motor improvement. Because such a finding is contrary to the evidence for
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age-related “impairments” in recovery from stroke [26,69], one may speculate that our older patients had a “greater” potential than younger patients for plasticity and compensatory reorganization as reflected in the PC3 pattern. The third differentiating pattern involved areas in lateral and medial premotor cortex that participate in normal motor learning [33,35,58,61,65] and become activated after recovery from striatocapsular infarction [72,73]. In accordance with recent neuroimaging studies there was a lack of sensorimotor cortex activation in the affected middle cerebral artery territory [8,13,62]. Due to their corticospinal projections [16,23], the engaged premotor cortical areas can serve, however, also as substitutional systems for the damaged motor cortex. Moreover, the dorsolateral premotor cortex plays an important role in visuomotor integration [14,35,48,61]. Normally, there is a tight anatomical and functional link between sensorimotor representations in parietal and premotor areas [24,32]. In our patients, however, the motor learning pattern in premotor cortex appeared to be independent from the recovery-related pattern that subserved the creation of a visual movement representation. Recently, network analysis approaches have been shown to be important complements to categorical analyses of neuroimaging data in identifying functionally interrelated brain systems in both health as well as disease [7,28,42]. Here we show that PCA revealed functional connectivity patterns evident in our data which differentiated patients from controls. Specifically, we were able to show that sensorimotor recovery after hemiparetic stroke seems to utilize crossmodal plasticity of visual system pathways. In our patients, a visuomotor brain system appeared to compensate the poststroke somatosensory-motor deficit during movement activity. Such crossmodal plasticity has been observed in animal models of focal brain lesions and in the developing human visual and auditory systems [10,11,40, 54]. Our findings suggest that crossmodal plasticity also may occur in the sensorimotor domain of the adult human brain. It should be pointed out that the recruited network utilized anatomical structures not affected by the stroke lesion. Thus, the interactions did not take place via newly formed structural connections as shown to occur in experimental models of postlesional repair in the central nervous system [39,57,68]. 6. Acknowledgements The authors thank W. Hamkens, Institute of Radiochemistry, Research Center Jülich, for skillful tracer production and L. Theelen and L. Tellmann for expert assistance in PET scanning and PET data pre-processing. N.P. Azari was recipient of an Alexander-von-Humboldt fellowship. References [1] ! " !#" $#"! ! "$! !!% &! $ !'% $&! ()**+,-./0-12
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