scholarly journals Enhanced Information Flow From Cerebellum to Secondary Visual Cortices Leads to Better Surgery Outcome in Degenerative Cervical Myelopathy Patients: A Stochastic Dynamic Causal Modeling Study With Functional Magnetic Resonance Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Rui Zhao ◽  
Yingchao Song ◽  
Xing Guo ◽  
Xiaotian Yang ◽  
Haoran Sun ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Motor Function ◽  
Information Flow ◽  
Information Integration ◽  
Direct Evidence ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Visual Cortices ◽  
Degenerative Cervical Myelopathy ◽  
The Relationship

Degenerative cervical myelopathy (DCM) damages the spinal cord, resulting in long-term neurological impairment including motor and visual deficits. Given that visual feedback is crucial in guiding movements, the visual disorder may be a cause of motor deficits in patients with DCM. It has been shown that increased functional connectivity between secondary visual cortices and cerebellum, which are functionally related to the visually guided movements, was correlated with motor function in patients with DCM. One possible explanation is that the information integration between these regions was increased to compensate for impaired visual acuity in patients with DCM and resulted in better visual feedback during motor function. However, direct evidence supporting this hypothesis is lacking. To test this hypothesis and explore in more detail the information flow within the “visual-cerebellum” system, we measured the effective connectivity (EC) among the “visual-cerebellum” system via dynamic causal modeling and then tested the relationship between the EC and visual ability in patients with DCM. Furthermore, the multivariate pattern analysis was performed to detect the relationship between the pattern of EC and motor function in patients with DCM. We found (1) significant increases of the bidirectional connections between bilateral secondary visual cortices and cerebellum were observed in patients with DCM; (2) the increased self-connection of the cerebellum was positively correlated with the impaired visual acuity in patients; (3) the amplitude of effectivity from the cerebellum to secondary visual cortices was positively correlated with better visual recovery following spinal cord decompression surgery; and (4) the pattern of EC among the visual-cerebellum system could be used to predict the pre-operative motor function. In conclusion, this study provided direct evidence that the increased information integration within the “visual-cerebellum” system compensated for visual impairments, which might have importance for sustaining better motor function in patients with DCM.

scholarly journals BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study

2021 ◽  
Vol 11 (1) ◽  
pp. 56
Author(s):  
Kai Yuan ◽  
Cheng Chen ◽  
Xin Wang ◽  
Winnie Chiu-wing Chu ◽  
Raymond Kai-yu Tong
Keyword(s):  
Functional Connectivity ◽  
Motor Function ◽  
Information Flow ◽  
Brain Regions ◽  
Chronic Stroke ◽  
Training Effect ◽  
Functional Reorganization ◽  
Flow Change ◽  
The Relationship ◽  
Training Therapy

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 multimodality 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. Fourteen 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 was also investigated. This work suggested that (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, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. 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.

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

2020 ◽  
Author(s):  
Kai Yu Tong ◽  
Kai Yuan ◽  
Cheng Chen ◽  
Xin Wang ◽  
Winnie Chiu-wing Chu
Keyword(s):  
Functional Connectivity ◽  
Motor Function ◽  
Information Flow ◽  
Brain Regions ◽  
Chronic Stroke ◽  
Training Effect ◽  
Functional Reorganization ◽  
Flow Change ◽  
The Relationship ◽  
Training Therapy

Abstract 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.

scholarly journals Inferring neural signalling directionality from undirected structural connectomes

2019 ◽  
Author(s):  
Caio Seguin ◽  
Adeel Razi ◽  
Andrew Zalesky
Keyword(s):  
Information Flow ◽  
Large Scale ◽  
Effective Connectivity ◽  
Fruit Fly ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Non Invasive ◽  
Cortical Hierarchy ◽  
Neural Information ◽  
Cortical Regions

Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can predict putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

The Relationship Between Preoperative Clinical Presentation and Quantitative Magnetic Resonance Imaging Features in Patients With Degenerative Cervical Myelopathy

Neurosurgery ◽  
2016 ◽  
Vol 80 (1) ◽  
pp. 121-128 ◽  
Author(s):  
Aria Nouri ◽  
Lindsay Tetreault ◽  
Kristian Dalzell ◽  
Juan J. Zamorano ◽  
Michael G. Fehlings
Keyword(s):  
Magnetic Resonance Imaging ◽  
Spinal Cord ◽  
Magnetic Resonance ◽  
Lower Limb ◽  
Cervical Myelopathy ◽  
Clinical Presentation ◽  
Resonance Imaging ◽  
Mri Features ◽  
Degenerative Cervical Myelopathy ◽  
The Relationship

Abstract BACKGROUND: Degenerative cervical myelopathy encompasses a group of conditions resulting in progressive spinal cord injury through static and dynamic compression. Although a constellation of changes can present on magnetic resonance imaging (MRI), the clinical significance of these findings remains a subject of controversy and discussion. OBJECTIVE: To investigate the relationship between clinical presentation and quantitative MRI features in patients with degenerative cervical myelopathy. METHODS: A secondary analysis of MRI and clinical data from 114 patients enrolled in a prospective, multicenter study was conducted. MRIs were assessed for maximum spinal cord compression (MSCC), maximum canal compromise (MCC), signal changes, and a signal change ratio (SCR). MRI features were compared between patients with and those without myelopathy symptoms with the use of t tests. Correlations between MRI features and duration of symptoms were assessed with the Spearman ρ. RESULTS: Numb hands and Hoffmann sign were associated with greater MSCC (P < .05); broad-based, unstable gait, impairment of gait, and Hoffmann sign were associated with greater MCC (P < .05); and numb hands, Hoffmann sign, Babinski sign, lower limb spasticity, hyperreflexia, and T1 hypointensity were associated with greater SCR (P < .05). Patients with a T2 signal hyperintensity had greater MSCC and MCC (P < .001). CONCLUSION: MSCC was associated with upper limb manifestations, and SCR was associated with upper limb, lower limb, and general neurological deficits. Hoffmann sign occurred more commonly in patients with a greater MSCC, MCC and SCR. The Lhermitte phenomenon presented more commonly in patients with a lower SCR and may be an early indicator of mild spinal cord involvement. Research to validate these findings is required.

scholarly journals Causal evidence for lateral prefrontal cortex dynamics supporting cognitive control

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Derek Evan Nee ◽  
Mark D'Esposito
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Control ◽  
Information Flow ◽  
Magnetic Stimulation ◽  
Control Site ◽  
Causal Modeling ◽  
Model Fit ◽  
Dynamic Causal Modeling ◽  
Lateral Prefrontal Cortex ◽  
Theta Burst

The lateral prefrontal cortex (LPFC) is essential for higher-level cognition, but the nature of its interactions in supporting cognitive control remains elusive. Previously (Nee and D'Esposito, 2016), dynamic causal modeling (DCM) indicated that mid LPFC integrates abstract, rostral and concrete, caudal influences to inform context-appropriate action. Here, we use continuous theta-burst transcranial magnetic stimulation (cTBS) to test this model causally. cTBS was applied to three LPFC sites and a control site in counterbalanced sessions. Behavioral modulations resulting from cTBS were largely predicted by information flow within the previously estimated DCM. However, cTBS to caudal LPFC unexpectedly impaired processes that are presumed to involve rostral LPFC. Adding a pathway from caudal to mid-rostral LPFC significantly improved the model fit and accounted for the observed behavioral findings. These data provide causal evidence for LPFC dynamics supporting cognitive control and demonstrate the utility of combining DCM with causal manipulations to test and refine models of cognition.

scholarly journals Inferring neural signalling directionality from undirected structural connectomes

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Caio Seguin ◽  
Adeel Razi ◽  
Andrew Zalesky
Keyword(s):  
Information Flow ◽  
Large Scale ◽  
Effective Connectivity ◽  
Fruit Fly ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Non Invasive ◽  
Cortical Hierarchy ◽  
Neural Information ◽  
Cortical Regions

Abstract Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.

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