Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Filatova, Olena G.; Yang, Yuan; Dewald, Julius P. A.; Tian, Runfeng; Maceira-Elvira, Pablo; Takeda, Yusuke; Kwakkel, Gert; Yamashita, Okito; Helm, F.C.T. van der

doi:10.3389/fncir.2018.00079

Cited by 18 publications

(17 citation statements)

References 73 publications

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“…Indeed, preliminary evidence has shown that sensory information travels from the contralateral somatosensory cortex to the ipsilateral somatosensory cortex via the corpus callosum post‐stroke (Filatova et al . 2018). Additionally, sensory recovery post‐stroke has been associated with changes in both the contralateral and ipsilateral somatosensory cortex (Dechaumont‐Palacin et al .…”

Section: Discussionmentioning

confidence: 99%

Limited capacity for ipsilateral secondary motor areas to support hand function post‐stroke

Wilkins

Yao

Owen

et al. 2020

The Journal of Physiology

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Key points Ipsilateral‐projecting corticobulbar pathways, originating primarily from secondary motor areas, innervate the proximal and even distal portions, although they branch more extensively at the spinal cord. It is currently unclear to what extent these ipsilateral secondary motor areas and subsequent cortical projections may contribute to hand function following stroke‐induced damage to one hemisphere. In the present study, we provide both structural and functional evidence indicating that individuals increasingly rely on ipsilateral secondary motor areas, although at the detriment of hand function. Increased activity in ipsilateral secondary motor areas was associated with increased involuntary coupling between shoulder abduction and finger flexion, most probably as a result of the low resolution of these pathways, making it increasingly difficult to open the hand. These findings suggest that, although ipsilateral secondary motor areas may support proximal movements, they do not have the capacity to support distal hand function, particularly for hand opening. Abstract Recent findings have shown connections of ipsilateral cortico‐reticulospinal tract (CRST), predominantly originating from secondary motor areas to not only proximal, but also distal muscles of the arm. Following a unilateral stroke, CRST from the ipsilateral side remains intact and thus has been proposed as a possible backup system for post‐stroke rehabilitation even for the hand. We argue that, although CRST from ipsilateral secondary motor areas can provide control for proximal joints, it is insufficient to control either hand or coordinated shoulder and hand movements as a result of its extensive spinal branching compared to contralateral corticospinal tract. To address this issue, we combined magnetic resonance imaging, high‐density EEG, and robotics in 17 individuals with severe chronic hemiparetic stroke and 12 age‐matched controls. We tested for changes in structural morphometry of the sensorimotor cortex and found that individuals with stroke demonstrated higher grey matter density in secondary motor areas ipsilateral to the paretic arm compared to controls. We then measured cortical activity when participants were attempting to generate hand opening either supported on a table or when lifting against a shoulder abduction load. The addition of shoulder abduction during hand opening increased reliance on ipsilateral secondary motor areas in stroke, but not controls. Crucially, the increased use of ipsilateral secondary motor areas was associated with decreased hand opening ability when lifting the arm as a result of involuntary coupling between the shoulder and wrist/finger flexors. Taken together, this evidence implicates a compensatory role for ipsilateral (i.e. contralesional) secondary motor areas post‐stroke, although with no apparent capacity to support hand function.

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Section: Discussionmentioning

confidence: 99%

Limited capacity for ipsilateral secondary motor areas to support hand function post‐stroke

Wilkins

Yao

Owen

et al. 2020

The Journal of Physiology

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“…Recently, VBMEG was extended to perform a connectome dynamics estimation proposed by Fukushima et al (2015), and its second version was released in 2017. Its usefulness was also confirmed by Filatova et al (2018).…”

Section: Introductionmentioning

confidence: 54%

“…Without assuming region of interests (ROIs), it estimates a whole-brain linear dynamics model by only assuming connectivity coefficients between anatomically connected regions. This drastically reduces the connectivity coefficients to estimate and suppress false positive connectivities (Filatova et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

MEG Source Imaging and Group Analysis Using VBMEG

et al. 2019

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Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) ( https://vbmeg.atr.jp/ ). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page ( https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html ), we show its full pipeline using an open dataset recorded by Wakeman and Henson ( 2015 ). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG.

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“…During the supervised training, the inverse dynamics moment is used as the target value of the training samples. The variance accounted for (VAF) [51] is used to evaluate the accuracy of the ELM, its expression is as follows:…”

Section: Prediction Evaluationmentioning

confidence: 99%

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Xiong

Zeng

et al. 2020

Sensors

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Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e., muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

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Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Cited by 18 publications

References 73 publications

Limited capacity for ipsilateral secondary motor areas to support hand function post‐stroke

Limited capacity for ipsilateral secondary motor areas to support hand function post‐stroke

MEG Source Imaging and Group Analysis Using VBMEG

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

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