Edited by: Jean-Claude Baron, University of Cambridge, UK
Reviewed by: Martin Lotze, University of Greifswald, Germany; Emmanuel Carrera, University of Geneva, Switzerland; Christian Gerloff, University Medical Center Hamburg-Eppendorf, Germany
Specialty section: This article was submitted to Stroke, a section of the journal Frontiers in Neurology
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To describe structural covariance networks of gray matter volume (GMV) change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change.
Tensor-based morphometry maps derived from high-resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function, we performed an additional multivariate regression approach.
Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md) thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex, and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely, network expression is limited in patients with large lesion volumes.
Chronic phase of sensorimotor cortical stroke has been characterized by a large scale co-varying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.
As both cross-sectional and a few longitudinal observational studies have demonstrated, behavioral recovery from hemiparesis after ischemic stroke shows marked between-subject variability (
Activation studies performed with fMRI have shown that successfully recovered subjects show almost normal cerebral patterns, exhibiting change during recovery from attention demanding controlled processing of motor performance in the subacute stage to more fluent and automatic processing in the late chronic stage (
In the following, we utilize structural MRI to study stroke recovery in a patient cohort of 28 patients selected for first cortical sensorimotor stroke and associated initial hand paresis or plegia. The analysis employs a relatively new method, tensor-based morphometry (TBM), to quantify gray matter volume (GMV) changes during recovery (
Accompanying the imaging was an array of clinical, motor and sensory assessments performed regularly during the 9-month study. Of the behavioral assessments, picking small objects (PSO), a lateralized motor skill requiring a particular precision grip, showed the greatest variance over the 9-month trial period (
A previous mass-univariate analysis of these TBM images yielded three findings: (i) most striking, impaired patients with chronic disturbed hand motor skills showed the most prominent GMV increase in the ipsilesional medio-dorsal (md) thalamus, including also the head of the caudate nucleus; (ii) all patients evidenced GMV decreases within the contralesional anterior cerebellum at a location typical of cerebellar diaschisis after sensorimotor cortical stroke; and (iii) patients showing fast recovery exhibited a slight GMV increase in the perilesional premotor cortex (PMC). These results stimulated several questions: Does the significant GMV increase of md thalamus in these patients represent an isolated, local effect or does it implicate an extended gray matter network involved in recovery after a sensorimotor cortical stroke? Does the extended network show a structural covariance pattern that discriminates among classes of recovery process? How does the network relate to the initial lesion pattern?
These questions led to the hypotheses examined in the current study: the prominent GMV changes in the md thalamus relate to the dorsolateral prefrontal circuit of Alexander et al. (
We prospectively recruited patients at two comprehensive stroke centers (Departments of Neurology, University Hospital Bern and Kantonsspital St. Gallen, Switzerland) from January 01, 2008 through July 31, 2010. Inclusion criteria were (1) first-ever stroke, (2) clinically significant contralesional sensorimotor hand function impairment as leading symptom, and (3) inclusion of the pre- and/or post-central gyri within the ischemic lesion confirmed on acute diffusion-weighted (DWI) and fluid attenuated inversion recovery (FLAIR) MRI scans. Patients were excluded if they presented (1) aphasia or cognitive deficits that precluded understanding the study purposes or task instructions, (2) prior cerebrovascular events, (3) occlusion or stenosis >70% of the carotid arteries in MR–angiography, (4) purely subcortical stroke, and (5) other medical conditions interfering with task performance. We recruited 36 patients, seven of which dropped out (three withdrew consent, two were too frail for repeated testing, one was shown to have no cortical stroke after enrollment, one was lost to follow-up). The final sample consisted of 29 patients (five female). As a control group for the analyses of behavioral and clinical data, we recruited 22 healthy older adults (11 female) from the local community. Groups were matched for age (unpaired two-tailed
We performed a baseline examination within the first 2 weeks after stroke (median 5 days, range 1–18 days) with extended measurements of clinical and behavioral data (see below). The same measurements were taken 3 months (91 days, 80–121 days) and 9 months (277 days, 154–303 days) after stroke. During each of these two visits, we acquired high-resolution anatomical imaging data. Patients were additionally seen at monthly intervals in-between these examinations to evaluate recovery of dexterous hand function.
Clinical stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) (
All patients underwent acute phase imaging at admission according to local stroke imaging protocols. This included a diffusion-weighted imaging (DWI) scan and T1-weighted (T1w) anatomical image. At 3 and 9 months after stroke, each patient underwent high-resolution T1w imaging using a 3D-MDEFT with following imaging parameters (
Longitudinal clinical and behavioral data were analyzed with a variant of RFA (
First, each patient’s PSO task data were transformed to
Lesions were manually traced on DWI images using MRIcron,
Tensor-based morphometry maps were calculated as described in Ref. (
The PCA of the TBM images was performed on a subset of 28 patients representing the volume changes between months 3 and 9 (of the 29 patients retained for the study 1 had to be excluded because of MR motion artifacts). PCA was executed on the images data using in house software written in MATLAB based on the algorithm described by Alexander et al. and Moeller et al. (
We used median and range for descriptive statistics. We first assessed the relationship of structural covariance component expression, clinical and structural variables, e.g., lesion volume and regional GMV change, using Pearson’s correlation coefficient in order to identify the network related to hand function recovery. Next, we assessed differences with respect to subgroups in network expression and behavioral variables. To do so, we first applied the Shapiro–Wilk test and inspected Q–Q plots for each variable to assess deviations from normality. We used then non-parametric tests to compare scalar variables where appropriate, i.e., the Kruskal–Wallis one-way analysis of variance by ranks to assess differences in the central tendency among any of the three subgroups, and the Mann–Whitney
Clinical characteristics of the patient cohort are summarized in Table
No. | Id | Age | Gender | Side | Etiology | NIH B | NIH M3 | NIH M9 | mRS B | mRS M3 | mRS M9 | HD B | HD M3 | HD M9 | PSO B | PSO M3 | PSO M9 | TOR B | TOR M3 | TOR M9 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | p01 | 77 | M | L | UN | 4 | 2 | 1 | 2 | 1 | 1 | 31 | 40 | 41 | 9.7 | 7.9 | 5.7 | 30 | 30 | 30 |
2 | p02 | 50 | M | R | OC | 7 | 1 | 0 | 4 | 1 | 0 | 6 | 54 | 63 | 0.0 | 6.0 | 6.2 | 25 | 28 | 30 |
3 | p03 | 78 | M | R | LAD | 5 | 5 | 3 | 3 | 2 | 2 | 15 | 17 | 42 | 13.5 | 11.1 | 9.1 | 28 | 29 | 27 |
4 | p05 | 80 | M | L | LAD | 2 | 3 | 1 | 2 | 1 | 1 | 42 | 42 | 37 | 10.6 | 6.5 | 8.4 | 30 | 30 | 30 |
5 | p06 | 53 | F | R | LAD | 6 | 3 | 3 | 3 | 2 | 1 | 11 | 9 | 19 | 29.9 | 10.1 | 14.9 | 0 | 0 | 0 |
6 | p07 | 78 | F | R | CE | 4 | 2 | 2 | 2 | 1 | 1 | 18 | 21 | 21 | 14.0 | 7.5 | 7.1 | 0 | 12 | 24 |
7 | p09 | 70 | F | R | CE | 3 | 2 | 0 | 2 | 1 | 0 | 21 | 31 | 34 | 9.1 | 8.5 | 6.0 | 29 | 30 | 30 |
8 | p11 | 41 | F | L | LAD | 3 | 2 | 0 | 1 | 0 | 0 | 32 | 37 | 39 | 5.6 | 4.0 | 5.11 | 24 | 30 | 30 |
9 | p12 | 54 | M | R | UN | 4 | 2 | 1 | 3 | 1 | 0 | 14 | 33 | 38 | 8.5 | 5.5 | 5.2 | 30 | 30 | 30 |
10 | p15 | 54 | M | L | LAD | 6 | 4 | 1 | 3 | 1 | 1 | 10 | 24 | 33 | 38.8 | 13.1 | 11.1 | 0 | 6 | 10 |
11 | p16 | 73 | M | R | OC | 4 | 2 | 0 | 2 | 1 | 0 | 51 | 55 | 55 | 7.3 | 4.9 | 5.3 | 26 | 29 | 30 |
12 | p17 | 58 | M | L | CE | 4 | 2 | 0 | 3 | 0 | 0 | 20 | 39 | 48 | 11.5 | 4.3 | 4.7 | 30 | 29 | 30 |
13 | p20 | 70 | M | L | CE | 6 | 4 | 2 | 3 | 1 | 1 | 24 | 35 | 42 | 12.9 | 9.7 | 9.3 | 0 | 6 | 10 |
14 | p24 | 74 | M | R | CE | 4 | 1 | 0 | 1 | 0 | 0 | 34 | 49 | 50 | 14.3 | 6.9 | 5.1 | 28 | 30 | 30 |
15 | p25 | 49 | M | R | CE | 3 | 2 | 1 | 2 | 1 | 0 | 49 | 59 | 67 | 12.3 | 5.3 | 5.9 | 0 | 6 | 10 |
16 | p26 | 44 | M | L | CE | 3 | 1 | 0 | 1 | 0 | 0 | 9 | 33 | 50 | 11.5 | 6.0 | 5.1 | 30 | 30 | 30 |
17 | p30 | 63 | M | L | CE | 4 | 1 | 1 | 3 | 0 | 0 | 43 | 41 | 45 | 10.6 | 6.3 | 6.3 | 30 | 30 | 30 |
18 | p31 | 63 | M | L | UN | 5 | 0 | 0 | 2 | 0 | 0 | 30 | 48 | 44 | 5.3 | 4.2 | 4.7 | 30 | 30 | 30 |
19 | p33 | 75 | M | R | LAD | 3 | 2 | 2 | 2 | 1 | 1 | 3 | 14 | 22 | 0.0 | 18.8 | 11.5 | 12 | 28 | 30 |
20 | p35 | 78 | M | L | LAD | 5 | 3 | 2 | 3 | 1 | 1 | 23 | 48 | 40 | 10.1 | 6.8 | 6.1 | 30 | 30 | 30 |
21 | p36 | 60 | M | L | CE | 4 | 1 | 1 | 3 | 1 | 1 | 31 | 40 | 41 | 18.2 | 8.0 | 6.6 | 30 | 30 | 30 |
22 | p37 | 75 | M | R | OC | 4 | 2 | 1 | 2 | 1 | 1 | 0 | 27 | 32 | 0.0 | 8.6 | 10.4 | 4 | 23 | 25 |
23 | p38 | 77 | M | L | LAD | 5 | 2 | 2 | 3 | 1 | 1 | 10 | 21 | 23 | 26.9 | 10.9 | 8.3 | 29 | 30 | 30 |
24 | p41 | 51 | M | R | CE | 2 | 1 | 0 | 2 | 1 | 1 | 36 | 41 | 52 | 7.1 | 5.1 | 4.8 | 30 | 30 | 30 |
25 | p42 | 64 | M | R | LAD | 1 | 0 | 0 | 2 | 0 | 0 | 14 | 33 | 35 | 18.9 | 7.1 | 7.4 | 29 | 30 | 30 |
26 | p43 | 82 | M | L | LAD | 3 | 3 | 2 | 2 | 2 | 1 | 17 | 10 | 18 | 16.8 | 21.4 | 13.9 | 20 | 22 | 25 |
27 | p44 | 67 | M | R | UN | 11 | 10 | 9 | 4 | 3 | 3 | 15 | 15 | 41 | 52.3 | 45.1 | 12.3 | 3 | 4 | 2 |
28 | p45 | 53 | M | R | LAD | 11 | 9 | 4 | 5 | 3 | 2 | 0 | 10 | 17 | 0.0 | 45.5 | 19.9 | 0 | 1 | 3 |
Median | 65.5 | 24 M | 13 L | 11 LAD, 10 CE, 4 UN, 3 OC | 4 | 2 | 1 | 2 | 1 | 1 | 20 | 35 | 41 | 11.0 | 7.3 | 6.5 | 28 | 29 | 30 | |
Range | 41, 82 | 4 F | 15 R | 1, 11 | 0, 10 | 0, 9 | 1, 4 | 0, 3 | 0, 3 | 0, 51 | 9, 59 | 17, 67 | 0.0, 52.3 | 4.0, 45.5 | 4.7, 19.9 | 0, 30 | 0, 30 | 0, 30 | ||
Median ( |
−1.3 | −0.2 | 0.4 | −5.0 | −1.2 | −0.4 | 0.6 | 0.6 | 0.6 | |||||||||||
Range ( |
−2.8, 1.3 | −2.3, 1.9 | 1.6, 2.6 | −38.9, 0.5 | −33.2, 1.6 | −11.7, 1.0 | −7.5, 0.6 | −6.5, 0.6 | −4.5, 0.6 |
Table
Component | Variance (%) | Parameters with significant correlations |
Values of parameters |
Correlation coefficient ( |
---|---|---|---|---|
PC1TBM | 19.9 | GMV change ant. cerebellum | −0.2 (−1.3, 0.6) % | −0.57 |
PC2TBM | 9.1 | Lesion volume | 9.0 (0.6, 141.7) cc | 0.61 |
PC1NIHSS expression | −3.65 to 11.9 | 0.61 | ||
PC1PSO expression | −20.99 to 64.26 | 0.51 | ||
GMV change md Thalamus | 0.4 (−0.6, 4.0) cc | 0.72 | ||
PC4TBM | 8.1 | PC1NIHSS expression | 0 (−3.65, 11.9) | 0.54 |
Cumulative Variance | 37.1 |
Since the second structural covariance network PC2TBM correlated with our specific measure of hand function recovery, we focused further analysis on its critical clusters (or nodes, Figure
Cluster | Size ( |
MNI (max.) | Anatomical area | Cytoarchitectonic area | Functional correlate (references in brackets) |
---|---|---|---|---|---|
1+ | 1362 | 38/−28/16 | R. parietal operculum | OP1, OP2, OP3 | Tactile working memory, stimulus discrimination and perceptual learning ( |
R. insula | Ig1, Ig2 | Multisensory processing ( |
|||
54/−26/28 | R. inferior parietal lobule | PFcm, PFop, PFt | Action observation and imitation ( |
||
2+ | 653 | 43/26/26 |
R. DLPFC (dorsal-posterior part) |
n.a. |
Action execution and working memory ( |
3+ | 502 | 10/−20/6 | R. thalamus | Thal: prefontal |
MD nucleus to prefrontal cortex ( |
4+ | 408 | 42/−38/42 | R. intraparietal sulcus |
hIp1, hIp2, hIp3 |
Spatial attention, visuomotor transformation ( |
5+ | 271 | 52/−48/2 | R. superior (and middle temporal) gyrus | n.a. | Spatial awareness ( |
6+ | 158 | 30/−62/36 | R. middle occipital gyrus | n.a. | Spatial processing of tactile stimuli ( |
1− | 179 | 54/−14/38 | Pre- and post-central gyrus | BA 4p, 3b, 1, 2 | Voluntary and passive finger motion (BA 4p) ( |
Having identified a structural network related to hand function recovery (Table
Fast recovery |
Slow recovery |
Impaired recovery |
Kruskal–Wallis, |
Mann–Whitney, impaired versus recovered |
|
---|---|---|---|---|---|
PC2TBM expression coeff. | −0.079 (−0.117, 0.025) | −0.04 (−0.23, 0.52) | 0.06 (−0.22, 0.51) | 0.55 | n.a. |
Age | 63 (41, 73) | 75 (49, 80) | 68.5 (53, 82) | 0.31 | n.a. |
Lesion size (cc) |
7.80 (0.76, 75.52) | 3.48 (0.57, 70.39) | 42.84 (2.72, 141.71) | 0.08 | <0.05 |
PC1 (NIH) expression coeff. |
−2.18 (−3.07, 0.61) | −1.31 (−3.65, 2.12) | 1.33 (−1.40, 11.90) | <0.01 | <0.01 |
PC1 (PSO) expression coeff. |
−15.3 (−21.0, 6.7) | −10.8 (−16.8, 3.1) | 16.5 (−5.6, 64.3) | <0.0001 | <0.0001 |
PC1 (TOR) expression coeff. | 13.9 (−0.45, 14.3) | 13.9 (11.0, 14.3) | −29.3 (−34.3, 13.7) | <0.001 | <0.001 |
GMV premotor area | 0.0043 (−0.0010, 0.0092) | 0.0017 (−0.0013, 0.0078) | 0.0011 (−0.0020, 0.0083) | 0.69 | n.a. |
GMV thalamus |
0.0017 (−0.0043, 0.0143) | 0.0023 (−0.0061, 0.0292) | 0.0083 (−0.0002, 0.0179) | 0.06 | <0.05 |
GMV cerebellum | −0.0019 (−0.0130, 0.0031) | −0.0029 (−0.0089, 0.0060) | −0.0012 (−0.0121, 0.0051) | 0.43 | n.a. |
Cluster 1+ | Cluster 2+ | Cluster 3+ | Cluster 4+ | Cluster 5+ | Cluster 6+ | Cluster 1− | ||
---|---|---|---|---|---|---|---|---|
pOP | vPMC | Thal | IPS | STG | MOG | PCG | ||
10.9 | 5.2 | 4.0 | 3.3 | 2.2 | 1.3 | 1.4 | ||
Fast | 105.7 | 7.10 | 0.3 | 0 | 1.0 | 0.3 | 0 | 0.3 |
Slow | 113.9 | 1.82 | 0.5 | 0 | 1.0 | 1.0 | 0.2 | 0.6 |
Impaired | 239.7 | 8.7 | 3.3 | 0 | 3.3 | 1.1 | 0.9 | 1.4 |
Fast | 6.7 | 0.3 | 0 | 0.9 | 0.3 | 0 | 0.3 | |
Slow | 1.6 | 0.4 | 0 | 0.9 | 0.9 | 0.2 | 0.5 | |
Impaired | 3.6 | 1.4 | 0 | 1.4 | 0.5 | 0.4 | 0.6 | |
Fast | 61.5 | 5.8 | 0 | 30.3 | 13.6 | 0 | 21.4 | |
Slow | 14.7 | 9.4 | 0 | 29.7 | 46.4 | 17.7 | 44.3 | |
Impaired | 33.0 | 62.7 | 0 | 98.8 | 51.4 | 69.2 | 100.0 |
We further compared the patients subgroups presenting normal motor performance after 9 months (fast and slow recovery) with the subgroup that did not achieve normal performance (impaired recovery). As expected from the RFA, the latter group yielded the highest expression coefficients in PC1NIHSS (
However, only the impaired recovery subgroup showed a linear relationship between the expression of structural covariance network of PC2TBM and recovery (Figure
To further test the specificity of the association between PC2TBM and hand function recovery, we calculated a multivariate linear regression of PC1PSO onto covariance network expression, age, volume, and thalamic GMV change: it showed significant effects of the model intercept (
In this study, we have identified structural covariance networks deduced from GMV changes during the recovery of patients suffering from hand paresis after ischemic sensorimotor stroke. These networks correspond to the first, second, and fourth principal components determined from a PCA of TBM images and explained 19.9, 9.1, and 8.1% of the variance, respectively. Implied by the correlation of its expression coefficients with GMV-decrease in the anterior cerebellum contralateral to pre- and post-central infarction in all patients, the first component PC1TBM appears to reflect a neuronal network caused by diaschisis from sensorimotor cortex (
Finally, a multivariate linear regression approach verified (i) the unique relationship of PC1PSO to the structural covariance network of PC2TBM; and furthermore, that this relationship is related specifically to the network expression but not to a single constituent, e.g., md thalamus. Since PC2TBM relates directly to hand function recovery and thus to our study aim, we will discuss this network in more detail in the following.
This study represents important progress following our recent paper on “Gray matter volumetric changes related to recovery from hand paresis after cortical sensorimotor stroke” (
Irrespective of the clinical and behavioral course, this PC2TBM network distinguishes clearly within the study cohort since the subgroup with positive expression coefficients is associated with large GMV increases in the md thalamus between months 3 and 9, while the subgroup with negative expression coefficients did not exhibit a recognizable GMV change. The GMV increases in the former subgroup exceed the measurement uncertainty and are consistent with the few comparable studies, e.g., in the paper of Gauthier et al. (
The salient regions of the second principal component PC2TBM are summarized in Table
Both parts of posterior medial thalamus and dorsal-posterior subarea of the dorsolateral prefrontal cortex are interconnected with the posterior parietal cortex (PPC) (
Densely interconnected structures of ventral PMC, PPC, SII and posterior insula are represented in the component image of PC2TBM, representing possible sub-networks engaged in higher order sensorimotor information processing and spatial awareness (see below). In the PPC locally functional processed information, e.g., space and action perception, is transmitted via feedback loops to ventral PMC (
A remarkable feature of the structural covariance pattern is the appearance of the parietal operculum subarea OP1 in the absence of OP4. OP4 plays a role mainly in basal sensorimotor integration processes, e.g., incorporating sensory feedback into motor actions which are the basis for information processing during tactile exploration (
As has been shown in primates, while area 2 is activated by fine grained proprioceptive sensory information obtained by transitive finger movements (
Of the salient regions of the second principal component PC2TBM, a single cortical cluster contains voxels belonging to the ninety-ninth percentile, which presumably characterizes fast and slow recovered individuals. It includes a sub-network within pre- and post-central gyrus, ventral to the center of gravity of the lesion in the slowly recovering subjects as described in our previous paper (
This study comprises a detailed evaluation and discussion of structural covariance networks associated with hand motor skill. At the outset, the number and composition of recovery subgroups in the patient cohort was unknown. Thus, the number of patients in each subgroup is relatively small. Larger cohorts would be desirable to assign subjects reliably to subgroups characterized by distinct patterns of structural reorganization associated with varying degrees of recovery. Besides subgroup specific patterns, especially in the subgroup with slow but complete recovery, the assessment of idiosyncratic aspects, e.g., exceptions to the involvement of the dorsolateral prefrontal-striato-thalamic loop, is another challenge. Meeting it would necessitate detailed protocols, including a comprehensive neuro-rehabilitation program, reporting of targeting interventions and physiological measures of movement efforts versus efficiency of motor activity. As the existence of the subgroup with fast complete recovery indicates, an earlier begins after stroke of the study might help to assess structural plasticity in the first 3 months when most recovery occurs. The incomplete gender matching must also been taken into consideration, because women have been shown to perform dexterity tasks (nine-hole peg test) faster than men depending on age, and upper limb kinesthetic asymmetries in contralateral reproduction of elbow movements, elicited by tendon vibration, were prevalent in males (
As posited in Section “
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The Supplementary Material for this article can be found online at
We are indebted to our patients and their caregivers for generously supporting our study. We thank our neuroradiological technicians for help with image acquisition and data management. This work was funded by a Swiss National Foundation grant (SNF 3200B0-118018).
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