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Participants were recruited from 2014(functional groups) through 2017 as part of the Hemispheric Reorganisation (HERO) study48, approved by the Research Ethics Committee of Bern, Switzerland. Patients were identified by the Swiss Neuropaediatric Stroke Registry (SNPSR)—a multicenter, prospective, and population-based registry that includes children diagnosed with AIS under the age of 16 years1. Patients met the following inclusion criteria: Diagnosis of AIS (confirmed by MRI or computed tomography) before the age of 16 years and at least two years prior to recruitment in the chronic phase, and older than five years of age to enable adequate compliance, and contralateral corticospinal tract wiring. For more detailed information see the previously published study protocol48. The control group, a sample of typically developing peers comparable in age and sex to the patients’ groups, was recruited through advertisements on the hospital intranet and flyers. Participants were excluded if they had neurological disorders unrelated to AIS, ferrous implants, active epilepsy, claustrophobia, developmental delay, or behavioral problems that could affect their ability to comply with study requirements (see Supplementary Fig. S1).
Of the 379 patients identified from the SNPSR who met the inclusion criteria, 96 were not contacted: 20 had died, 7 had either trisomy 21, epilepsy, other severe handicaps, or severe behavioral problems, 12 were living abroad, and for 57 consent for SNPSR or follow-up studies was lacking. Of the remaining 283 patients who were contacted personally by mail and subsequently by phone, 120 did not respond and 135 reported a lack of motivation or felt that the duration and type of assessment (MRI, TMS) would be inconvenient. Of the remaining 28 patients, 2 had to be excluded because of developmental delay or behavioral problems interfering with compliance with the test conditions, 2 had an erroneous fMRI sequence, and 2 retainer artifacts. Another 4 were excluded because of the lesion being bilateral. Thus, the final sample consisted of 18 patients diagnosed with chronic AIS (see Supplementary Fig. 1). We matched the control group of typically developing peers using a 1:1 ratio by age and gender (n = 18).
All participants, or their parent or guardian if they were younger than 18 years, gave written informed consent, according to the Code of Ethics of the World Medical Association (Declaration of Helsinki).
Clinical outcome assessment
All participants underwent a standardized neurological examination performed by a research physician (J.D and S.G) at the Children’s University Hospital, Inselspital, Bern, Switzerland. Details of data collection and study design have been previously reported48. To study motor outcome, an extended and standardized test battery was adopted to investigate several domains as proposed by the International Classification of Functioning, Disability and Health framework45, including body structure (anatomical structure of the body), and body function (a physiological function of the body), activity (execution of a task or action), and participation (involvement in everyday life situations).
Disease-specific outcome
The Pediatric Stroke Outcome Measure (PSOM)49 was used to measure disease-specific outcomes at the time of MRI scanning. The PSOM assesses neurological deficits and consists of five subscales for right and left sensorimotor functioning, language production, language comprehension, and cognition/behavior. We used the sensorimotor subscale to classify the presence of hemiparesis (0 = no sensorimotor deficit; 0.5 = mild deficit, with normal function; 1 = moderate deficit, with decreased function; 2 = severe deficit with no function). Patients with scores greater than 0.5 on the sensorimotor subscale were classified as having hemiparesis50,51. Patients with a score of zero on all subscales were classified as having a good clinical outcome. Detailed information on each participant is provided in Supplementary Table S2.
Upper limb function
Hand strength. Palmar grasp strength and thumb–forefinger pinch strength was measured with a dynamometer (30 Psi pneumatic dynamometer Baseline, USA and a 30 lb mechanical pinch gauge, Baseline, USA). Participants were instructed to perform each task with maximal effort (repeating it three times with a 30-s break between attempts). After performing the task with each hand separately, the maximum values for both hands were averaged to yield a total hand strength score (HSS).
Quality of upper limb function. Quality of upper limb function was assessed using the Melbourne Assessment of Unilateral Upper Limb Function, version 252,53. This test contains 14 tasks (e.g. grasping and releasing, manipulating, reaching) that cover four basic upper limb functions, namely, range of movement, the accuracy of reaching and pointing, dexterity of reaching and manipulating, and fluency of movement. The assessment was performed for both the left and the right sides. The values of all items were summarized to yield a total Upper Limb Movement Quality Score (ULMQS) for each side.
Asymmetry of upper limb function. Using the assessment of HSS and ULMQS for both upper limbs allowed us to calculate the asymmetry between the dominant and non-dominant hand51 with the following Eq. (1):
$$Asymmetry index=\frac{scor{e}_{dominant side}-scor{e}_{nondominant side }}{scor{e}_{dominant side}+scor{e}_{nondominant side}}100$$(1)
An asymmetry index of zero represents perfect symmetry between the dominant and non-dominant side, whereas an index larger than zero represents asymmetry towards the dominant side.
Manual ability in everyday life
Manual ability in everyday life situations was assessed using the ABILHAND-Kids questionnaire54. This is a parent-reported outcome measure assessing the use of the upper limbs in everyday situations (0 = impossible, 1 = difficult, or 2 = easy to perform). The total score ranges from + 6.68 (all items easy to perform) to − 6.75 (all items impossible to perform).
Cortical Reorganization
Transcranial magnetic stimulation (TMS) was performed to determine the type of cortical reorganization after AIS11. For this purpose, silver-silver chloride surface electrodes (ALPINE, Biomed) were mounted in a tendon-belly arrangement over the Abductor Pollicis muscle on both hands55. A Neurodata amplifier system connected to an IPS230 Isolated Power System (Grass-Telefactor, Braintree, MA, USA) was used for pre-amplification (1000x) and as a bandpass filter (10–1000 Hz) of the EMG signals. The inputs were entered into a computer-assisted data acquisition system (sampling rate 5 kHz)56. The EMG signal peak-to-peak amplitudes were calculated for all derived muscles in a 65 ms time window. Single-pulse monophasic TMS pulses were delivered over both hemispheres. Both hemispheres were examined according to the stimulation response in the contralateral and/or ipsilateral upper extremity.
The cortical reorganization was defined according to the stimulation response in the ipsilateral or contralateral Abductor Pollicis Brevis muscle11: a stimulation response only in the contralateral Abductor Pollicis Brevis muscle were defined as contralateral reorganized, stimulation response only in the ipsilateral Abductor Pollicis Brevis muscle was defined as ipsilateral reorganized, and stimulation response in both the ipsilateral and contralateral Pollicis Brevis muscle was defined as mixed. The methodology was carried out in accordance with the safety regulations and guidelines57 and is described in detail in the Supplementary Material.
Neuroimaging
The MRI protocol was carried out in accordance with the safety regulations and guidelines48,58. All MRI recordings were performed on a 3 T scanner (Magnetom Verio, Siemens, Erlangen, Germany) equipped with a 32-channel phased-array head coil at the Inselspital, Bern University Hospital, Switzerland.
Structural MRI data
High-resolution anatomical T1-weighted images were acquired with a magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) sequence with the following parameters: repetition time (TR) = 2530 ms; echo time (TE) = 2.92 ms; inversion time (Ti) = 1100 ms; flip angle (FA) = 9°; field-of-view (FOV) = 256 mm × 256 mm; matrix dimension = 256 × 256; isotropic voxel resolution = 1 mm3; with a total of 160 sagittal slices.
Lesion characteristics such as location, size, and side affected were obtained from anatomical images (T1) by a board-certified neuroradiologist (N.S.). Lesions were classified according to the hemisphere affected (left, right, or bilateral) and anatomical location (cortical, subcortical, or both cortical and subcortical). To calculate the volume of affected brain tissue in the chronic stage, ischemic lesions were manually traced on T1 weighted images acquired the same day as functional imaging. Lesion size was defined as the affected brain tissue in relation to the total brain volume (lesion volume [cm3]/total brain volume [cm3]). Total brain volume was calculated using the statistical parametric mapping toolbox (SPM12, Wellcome Department of Imaging Neuroscience, London, England).
Functional MRI data
The BOLD rs-fMRI images were recorded with a multiband echo planar imaging T2*-weighted sequence “MB-EPI” (Feinberg et al., 2013) and had the following parameters: TR = 300 ms; TE = 30 ms; FA = 90°; FOV = 230 × 230 mm; pixel size = 3.6 × 3.6 mm; matrix dimension = 64 × 64; 32 slices positioned in the line between the anterior and posterior commissure (interleaved ascending acquisition order); slice thickness = 3.6 mm; isotropic voxel resolution of 3.6 mm3; and a total of 1000 images were recorded. The fMRI time-series were acquired with a 2 GRAPPA acceleration factor and a 3D prospective acquisition correction mode.
Data were pre-processed using the functional connectivity toolbox (CONN, version 17)59 as implemented on the platform MATLAB (R2017; MathWorks, Natick, MA, USA). We used the standard preprocessing pipeline59. First, functional images from patients with lesions in the right hemisphere (n = 3) were flipped along the midsagittal plane, so that the affected hemisphere corresponded to the left hemisphere in the whole patient sample. Second, by visual inspection, we verified that none of the patients with lesions involving the cortex (n = 3) had overlapped with our predefined ROIs in the motor network. There were no cortical lesions overlapping with regions included in the analysis. Third, the structural images were segmented to allow the creation of white matter and cerebrospinal fluid (CSF) masks. The spatial preprocessing of the functional images included the correction of slice time, realignment, normalization, and smoothing (applying the Gaussian filter kernel, FWH = 8 mm). The quality of registration and parcellation was assessed by visual inspection of each subjects’ data. Fourth, the temporal processing of the functional images took into account potential confounding factors, such as movement parameters and artifacts. BOLD signals obtained from white matter and CSF masks were also included. All these temporal confounding factors were regressed out from the functional images using a generalized linear model framework. Finally, the functional images were filtered using a “band-pass filter” (0.01–0.1 Hz).
Functional connectivity was assessed in predefined ROIs of an extended model of the motor network (Fig. 4). This extended model was based on the previous literature on stroke recovery in humans and animals32 and included the bilateral areas of M1, prefrontal cortex (PFC), dorsal premotor cortex (PMC), supplementary motor area (SMA), and superior parietal lobe (SPL). Together, these five ROIs represent a motor network with 14 intrahemispheric and two interhemispheric connections (Fig. 4). For data extraction from those ROIs, we used the brain parcellation atlas from the CONN toolbox59. For each participant, we extracted the mean time-series by averaging across all voxels in each ROI and computed bivariate correlation coefficients for each pair of ROIs. For further analyses, we Fisher z-transformed the correlation coefficients.
Figure 4
The regions of interest (ROI) of the motor network. The functional connectivity analyses included the following ROIs: the prefrontal cortex (PFC), dorsal premotor cortex (PMC), the primary motor cortex (M1), supplementary motor area (SMA), superior parietal lobe (SPL) (adapted from Sharma et al.32). Altogether, this motor network consists of 14 intrahemispheric and 2 interhemispheric connections.
Related to our hypotheses on alterations in intra- and interhemispheric connectivity, we calculated the indices of average network connectivity: (1) ROI-to-ROI correlation coefficients of all connections in the ipsilesional (7 connections) and contralesional hemisphere (7 connections) were averaged for each participant to obtain average intrahemispheric network connectivity of the ipsilesional and contralesional hemisphere, (2) ROI-to-ROI correlation coefficients between homologous regions (e.g. M1–M1 and PMC–PMC; two connections) were averaged for each participant to obtain average interhemispheric network connectivity.
Statistical analyses
To test our primary hypothesis that patients after AIS with hemiparesis have lower inter-and intrahemispheric functional connectivity compared with patients with good motor outcomes and typically developing peers, we used the non-parametric Kruskal–Wallis test, and the Mann–Whitney U-test for post hoc pairwise comparison.
To test our second hypothesis that asymmetry of upper limb function and manual ability (assessed by HSS, ULMQS, and ABILHAND-Kids) is related to average inter-and intrahemispheric network connectivity, we used partial Spearman correlation analyses with age at assessment, age at a stroke, and lesion size as covariates. For visualization of the results, we extracted the partial correlations’ residuals.
All analyses were performed using the statistical software package R 3.6.060. To account for the effects of multiple hypothesis testing (type I error), false discovery rate (FDR) correction was employed. Results of P < 0.05 FDR-corrected were considered significant.