Dataset Information

Patterns in cortical connectivity for determining outcomes in hand function after subcortical stroke.

ABSTRACT:

Background and purpose

Previous studies have noted changes in resting-state functional connectivity during motor recovery following stroke. However, these studies always uncover various patterns of motor recovery. Moreover, subgroups of stroke patients with different outcomes in hand function have rarely been studied.

Materials and methods

We selected 24 patients who had a subcortical stroke in the left motor pathway and displayed only motor deficits. The patients were divided into two subgroups: completely paralyzed hands (CPH) (12 patients) and partially paralyzed hands (PPH) (12 patients). Twenty-four healthy controls (HC) were also recruited. We performed functional connectivity analysis in both the ipsilesional and contralesional primary motor cortex (M1) to explore the differences in the patterns between each pair of the three diagnostic groups.

Results

Compared with the HC, the PPH group displays reduced connectivity of both the ipsilesional and contralesional M1 with bilateral prefrontal gyrus and contralesional cerebellum posterior lobe. The connectivity of both the ipsilesional and contralesional M1 with contralateral primary sensorimotor cortex was reduced in the CPH group. Additionally, the connectivity of the ipsilesional M1 with contralesional postcentral gyrus, superior parietal lobule and ipsilesional inferior parietal lobule was reduced in the CPH group compared with the PPH group. Moreover, the connectivity of these regions was positively correlated with the Fugl-Meyer Assessment scores (hand+wrist) across all stroke patients.

Conclusions

Patterns in cortical connectivity may serve as a potential biomarker for the neural substratum associated with outcomes in hand function after subcortical stroke.

SUBMITTER: Yin D

PROVIDER: S-EPMC3527607 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Publications

Patterns in cortical connectivity for determining outcomes in hand function after subcortical stroke.

Yin Dazhi D Song Fan F Xu Dongrong D Peterson Bradley S BS Sun Limin L Men Weiwei W Yan Xu X Fan Mingxia M

PloS one 20121220 12

<h4>Background and purpose</h4>Previous studies have noted changes in resting-state functional connectivity during motor recovery following stroke. However, these studies always uncover various patterns of motor recovery. Moreover, subgroups of stroke patients with different outcomes in hand function have rarely been studied.<h4>Materials and methods</h4>We selected 24 patients who had a subcortical stroke in the left motor pathway and displayed only motor deficits. The patients were divided int ...[more]

PMID: 23285171

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Project description:ObjectiveFunctional near-infrared spectroscopy (fNIRS) is a non-invasive and promising tool to map the brain functional networks in stroke recovery. Our study mainly aimed to use fNIRS to detect the different patterns of resting-state functional connectivity (RSFC) in subacute stroke patients with different degrees of upper extremity motor impairment defined by Fugl-Meyer motor assessment of upper extremity (FMA-UE). The second aim was to investigate the association between FMA-UE scores and fNIRS-RSFC among different regions of interest (ROIs) in stroke patients.MethodsForty-nine subacute (2 weeks-6 months) stroke patients with subcortical lesions were enrolled and were classified into three groups based on FMA-UE scores: mild impairment (n = 17), moderate impairment (n = 13), and severe impairment (n = 19). All patients received FMA-UE assessment and 10-min resting-state fNIRS monitoring. The fNIRS signals were recorded over seven ROIs: bilateral dorsolateral prefrontal cortex (DLPFC), middle prefrontal cortex (MPFC), bilateral primary motor cortex (M1), and bilateral primary somatosensory cortex (S1). Functional connectivity (FC) was calculated by correlation coefficients between each channel and each ROI pair. To reveal the comprehensive differences in FC among three groups, we compared FC on the group level and ROI level. In addition, to determine the associations between FMA-UE scores and RSFC among different ROIs, Spearman's correlation analyses were performed with a significance threshold of p < 0.05. For easy comparison, we defined the left hemisphere as the ipsilesional hemisphere and flipped the lesional right hemisphere in MATLAB R2013b.ResultsFor the group-level comparison, the one-way ANOVA and post-hoc t-tests (mild vs. moderate; mild vs. severe; moderate vs. severe) showed that there was a significant difference among three groups (F = 3.42, p = 0.04) and the group-averaged FC in the mild group (0.64 ± 0.14) was significantly higher than that in the severe group (0.53 ± 0.14, p = 0.013). However, there were no significant differences between the mild and moderate group (MD ± SE = 0.05 ± 0.05, p = 0.35) and between the moderate and severe group (MD ± SE = 0.07 ± 0.05, p = 0.16). For the ROI-level comparison, the severe group had significantly lower FC of ipsilesional DLPFC-ipsilesional M1 [p = 0.015, false discovery rate (FDR)-corrected] and ipsilesional DLPFC-contralesional M1 (p = 0.035, FDR-corrected) than those in the mild group. Moreover, the result of Spearman's correlation analyses showed that there were significant correlations between FMA-UE scores and FC of the ipsilesional DLPFC-ipsilesional M1 (r = 0.430, p = 0.002), ipsilesional DLPFC-contralesional M1 (r = 0.388, p = 0.006), ipsilesional DLPFC-MPFC (r = 0.365, p = 0.01), and ipsilesional DLPFC-contralesional DLPFC (r = 0.330, p = 0.021).ConclusionOur findings indicate that different degrees of post-stroke upper extremity impairment reflect different RSFC patterns, mainly in the connection between DLPFC and bilateral M1. The association between FMA-UE scores and the FC of ipsilesional DLPFC-associated ROIs suggests that the ipsilesional DLPFC may play an important role in motor-related plasticity. These findings can help us better understand the neurophysiological mechanisms of upper extremity motor impairment and recovery in subacute stroke patients from different perspectives. Furthermore, it sheds light on the ipsilesional DLPFC-bilateral M1 as a possible neuromodulation target.

Project description:IMPORTANCE:Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. OBJECTIVES:To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. DESIGN, SETTING, AND PARTICIPANTS:We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. MAIN OUTCOMES AND MEASURES:We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. RESULTS:Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t???3.13, P?<?.001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r???0.21, P?<?.0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r???-0.09, P?<?.012 corrected), although this association was significant only in typically developing participants (all r???-0.13, P?<?.009 corrected). CONCLUSIONS AND RELEVANCE:Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Dataset Information

Patterns in cortical connectivity for determining outcomes in hand function after subcortical stroke.

Background and purpose

Materials and methods

Results

Conclusions

Publications

Patterns in cortical connectivity for determining outcomes in hand function after subcortical stroke.

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