Differential modulation of motor network connectivity during movements of the upper and lower limbs

Volz, Lukas J.; Eickhoff, Simon B.; Pool, Eva-Maria; Fink, Gereon R.

doi:10.1016/j.neuroimage.2015.05.101

Cited by 52 publications

(67 citation statements)

References 62 publications

(20 reference statements)

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“…It is worth noting that our findings on between‐group commonalities in endogenous connectivity diverge from previous findings, which consisted of a network of excitatory connections among cortical motor areas apart from intra‐ and interhemispheric inhibitory influences between hand and foot M1s (Volz et al, ). We observed a mostly excitatory network, except for premotor connections originating from or targeting the vPMC.…”

Section: Discussioncontrasting

confidence: 99%

“…To address hypothesis (4), we computed the effective connectivity between key motor areas of the cortical nonmirroring network — RhM1, LhM1, RfM1, LfM1, RSMA, LSMA, RvPMC, LvPMC, using a bilinear DCM. Endogenous connectivity between the ROIs was based on previous DCM studies of upper and lower limb motor network connectivity (Grefkes, Eickhoff, Nowak, Dafotakis, & Fink, ; Pool, Rehme, Fink, Eickhoff, & Grefkes, ; Volz, Eickhoff, Pool, Fink, & Grefkes, ; Wang & Liu, ). For the full DCM, see Figure .…”

Section: Methodsmentioning

confidence: 99%

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Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

Jastrzębowska

Marquis

Melie‐García

et al. 2019

Human Brain Mapping

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The dopaminergic system has a unique gating function in the initiation and execution of movements. When the interhemispheric imbalance of dopamine inherent to the healthy brain is disrupted, as in Parkinson's disease (PD), compensatory mechanisms act to stave off behavioral changes. It has been proposed that two such compensatory mechanisms may be (a) a decrease in motor lateralization, observed in drug‐naïve PD patients and (b) reduced inhibition ‐ increased facilitation. Seeking to investigate the differential effect of dopamine depletion and subsequent substitution on compensatory mechanisms in non‐drug‐naïve PD, we studied 10 PD patients and 16 healthy controls, with patients undergoing two test sessions — “ON” and “OFF” medication. Using a simple visually‐cued motor response task and fMRI, we investigated cortical motor activation — in terms of laterality, contra‐ and ipsilateral percent BOLD signal change and effective connectivity in the parametric empirical Bayes framework. We found that decreased motor lateralization persists in non‐drug‐naïve PD and is concurrent with decreased contralateral activation in the cortical motor network. Normal lateralization is not reinstated by dopamine substitution. In terms of effective connectivity, disease‐related changes primarily affect ipsilaterally‐lateralized homotopic cortical motor connections, while medication‐related changes affect contralaterally‐lateralized homotopic connections. Our findings suggest that, in non‐drug‐naïve PD, decreased lateralization is no longer an adaptive cortical mechanism, but rather the result of maladaptive changes, related to disease progression and long‐term dopamine replacement. These findings highlight the need for the development of noninvasive therapies, which would promote the adaptive mechanisms of the PD brain.

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Section: Discussioncontrasting

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

Jastrzębowska

Marquis

Melie‐García

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…Here, we fixed the connectivity strength to 0.15 which refers to connection strengths typically found in the DCM studies (Li et al., 2014; Volza, Eickhoff, Pool, Fink, & Grefkes, 2015). …”

Section: Methodsmentioning

confidence: 99%

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

et al. 2017

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PurposeMultiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.Materials and MethodsIn this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.ResultsFirst, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state‐of‐the‐art datasets does not influence the performance of the lagged methods.ConclusionsFactors such as background scale‐free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

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“…Brain & NeuroRehabilitation 'use of hand' [42][43][44]. So among many events influencing plasticity, behavioral experience is one of the most salient determinants for recovery [42][43][44].…”

Section: Bmi and Robot Therapy For Arm After Strokementioning

confidence: 99%

“…So among many events influencing plasticity, behavioral experience is one of the most salient determinants for recovery [42][43][44]. In addition to above local neural plasticity, robotic training may have influence on the plasticity through remote interhemispheric and intrahemispheric neural network [45,46].…”

Section: Bmi and Robot Therapy For Arm After Strokementioning

confidence: 99%

Brain-machine Interface in Robot-assisted Neurorehabilitation for Patients with Stroke and Upper Extremity Weakness – the Therapeutic Turning Point

Kim

2016

Brain Neurorehabil

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Highlights• Robot with BMI therapy for arm after stroke has closed feedback and more chance of neural plasticity.• Understanding of the new rehabilitation technologies such as robot with BMI therapy for arm after stroke shall give the therapeutic turning point. ABSTRACTActivity and participation after stroke can be increased by neurorehabilitation of upper extremity. As the technology advances, a robot-assisted restorative therapy with/ without a brain-machine interface (BMI) is suggested as a promising therapeutic option. Understanding the therapeutic point of view about robots and BMIs can be linked to the patient-oriented usability of the devices. The therapeutic turning point concept of robotassisted rehabilitation with BMIs, basics of robotics for stroke and upper extremity weakness and consequent neuroplasticity/motor recovery are reviewed.

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Differential modulation of motor network connectivity during movements of the upper and lower limbs

Cited by 52 publications

References 62 publications

Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

Dopaminergic modulation of motor network compensatory mechanisms in Parkinson's disease

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

Brain-machine Interface in Robot-assisted Neurorehabilitation for Patients with Stroke and Upper Extremity Weakness – the Therapeutic Turning Point

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