Relationship between functional connectivity and motor function assessment in stroke patients with hemiplegia: a resting-state functional MRI study

Zhang, Ye; Liu, Hongliang; Wang, Li; Yang, Jun; Yan, Rubing; Zhang, Jingna; Sang, Linqiong; Li, Pengyue; Wang, Jian; Qiu, Mingguo

doi:10.1007/s00234-016-1646-5

Cited by 79 publications

(87 citation statements)

References 26 publications

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“…A more recent analysis of hemiplegic stroke patients examined the functional connectivity between ipsilesional M1 and the entire brain using resting state fMRI [53]. As expected, stroke altered functional connectivity and disrupted motor pathways (as compared to healthy controls).…”

Section: Neural Correlates Of MI and Clinical Implicationsmentioning

confidence: 98%

Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke

Tong¹,

Pendy²,

Li³

et al. 2017

Aging and disease

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Motor imagery (MI), defined as the mental implementation of an action in the absence of movement or muscle activation, is a rehabilitation technique that offers a means to replace or restore lost motor function in stroke patients when used in conjunction with conventional physiotherapy procedures. This article briefly reviews the concepts and neural correlates of MI in order to promote improved understanding, as well as to enhance the clinical utility of MI-based rehabilitation regimens. We specifically highlight the role of the cerebellum and basal ganglia, premotor, supplementary motor, and prefrontal areas, primary motor cortex, and parietal cortex. Additionally, we examine the recent literature related to MI and its potential as a therapeutic technique in both upper and lower limb stroke rehabilitation.

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Section: Neural Correlates Of MI and Clinical Implicationsmentioning

confidence: 98%

Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke

Tong¹,

Pendy²,

Li³

et al. 2017

Aging and disease

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“…[Insert Table 1 here] Among the studies observing recovery without additional training, the dominant technique of connectivity measurement was fMRI (Cheng et al, 2012;Cheng et al, 2015;Golestani et al, 2013;Lee et al, 2015;Liu et al, 2016;Ovadia-Caro et al, 2013;Park et al, 2011;Rehme et al, 2011;Rosso et al, 2013;Wang et al, 2010;Xu et al, 2014;Yourganov et al, 2010). One study used MEG (Westlake et al, 2012) and one used EEG (Nicolo et al, 2015).…”

Section: Recovery Studiesmentioning

confidence: 99%

“…Five studies assessed connectivity during hand movement (Cheng et al, 2012;Cheng et al, 2015;Rehme et al, 2011;Rosso et al, 2013;Yourganov et al, 2010) and the remainder utilised the resting state paradigm (Golestani et al, 2013;Lee et al, 2015;Liu et al, 2016;Nicolo et al, 2015;Ovadia-Caro et al, 2013;Park et al, 2011;Wang et al, 2010;Westlake et al, 2012;Xu et al, 2014). The fMRI studies analysed FC based on the time series correlation matrix and reported specific regions, change, lateralization and FC density of regions connected with the region of interest (ROI) except Yourganov and colleagues https://mc.manuscriptcentral.com/revneuro Neurosciences 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 F o r P r e v i e w O n l y 9 (2010), who assessed the complexity measures of a covariance matrix basing on principal component analysis (PCA).…”

Section: Reported Measuresmentioning

confidence: 99%

“…All studies included M1 in their ROIs (Cheng et al, 2012;Cheng et al, 2015;Golestani et al, 2013;Lee et al, 2015;Liu et al, 2016;Nicolo et al, 2015;Ovadia-Caro et al, 2013;Park et al, 2011;Rehme et al, 2011;Rosso et al, 2013;Wang et al, 2010;Westlake et al, 2012;Xu et al, 2014), except for one study analysing only the perilesional activity (Westlake et al, 2012). The ROIs were defined a priori, except of studies which were based on measures derived from whole-brain analysis: 264 functional areas (Cheng et al, 2012); regions based on whole-brain automated segmentation (Lee et al, 2015); whole-brain FC density followed by FC strength analysis in selected ROIs (Liu et al, 2016); regions generated from activation maps of action versus rest data in half of the healthy voluneers group (Rosso et al, 2013) and whole-brain analysis (Yourganov et al, 2010). In one study (Ovadia-Caro et al, 2013), longitudinal changes of FC in resting state networks were derived from independent component analysis (ICA) and analysed based on an eight network template (Beckmann et al, 2005).…”

Section: Regions Of Interestmentioning

confidence: 99%

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Dynamics of brain connectivity after stroke

Desowska

Turner

2019

Reviews in the Neurosciences

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Recovery from a stroke is a dynamic time-dependent process, in which the central nervous system reorganises to accommodate for the impact of the injury. The purpose of this paper is to review recent longitudinal studies of changes in brain connectivity after stroke. A systematic review of research papers reporting functional or effective connectivity at two or more time points in stroke patients was conducted. Stroke leads to an early reduction of connectivity in the motor network. With recovery time, the connectivity increases and can reach the same levels as in healthy participants. The increase in connectivity is correlated with functional motor gains. A new, more randomised pattern of connectivity may then emerge in the longer term. In some instances, a pattern of increased connectivity even higher than in healthy controls can be observed, and is related either to a specific time point or to a specific neural structure. Rehabilitation interventions can help improve connectivity between specific regions. Moreover, motor network connectivity undergoes reorganisation during recovery from a stroke and can be related to behavioural recovery. A detailed analysis of changes in connectivity pattern may enable a better understanding of adaptation to a stroke and how compensatory mechanisms in the brain may be supported by rehabilitation.

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“…We have the assumption that the modulation of 11 neuroplasticity is one of the underlying reasons of motor function improvement. FC 12 could serve as a biomarker of motor function recovery in stroke [83], which depicts 13 the functional organization of the brain and neuroplasticity. The significant corre- 14 lation between motor function improvement and FC change indicated that iM1 and 15 contralesional BA6 could be regarded as the related areas to facilitate upper-limb 16 motor recovery after stroke.…”

mentioning

confidence: 99%

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

Tong

Yuan

Chen

et al. 2020

Preprint

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Background: Brain-computer interface (BCI) guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multi-modality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement.Method: 14 chronic stroke subjects received 20 sessions of BCI-guided robot hand training. Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes and electrophysical changes derived from the two neuroimaging modalities were also investigated. Results: This work suggested: (a) significant motor function improvement could be obtained after BCI training therapy; (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI; (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG; (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change.Conclusions: Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change. This study was registered at https://clinicaltrials.gov (NCT02323061) on 23 December 2014.

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Relationship between functional connectivity and motor function assessment in stroke patients with hemiplegia: a resting-state functional MRI study

Abstract: Our results revealed an abnormal motor network after stroke and suggested that the FC could serve as a biomarker of motor function recovery in stroke patients with hemiplegia.

Cited by 79 publications

References 26 publications

Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke

Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke

Dynamics of brain connectivity after stroke

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

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