Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

Alkinoos Athanasiou, Nikos Terzopoulos, Niki Pandria, Ioannis Xygonakis, Nicolas Foroglou, Konstantinos Polyzoidis, Panagiotis D Bamidis, Alkinoos Athanasiou, Nikos Terzopoulos, Niki Pandria, Ioannis Xygonakis, Nicolas Foroglou, Konstantinos Polyzoidis, Panagiotis D Bamidis

Abstract

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

Figures

Figure 1
Figure 1
Flow diagram of the experimental procedure of one part of the visual motor imagery presentation. Each presented video lasted 5 seconds and was followed by 4 seconds of black resting screen. The videos were presented 9 times each, in a random order. The presentation was divided into three parts, lasting approximately 17 minutes each, with an intermission between them.
Figure 2
Figure 2
Regions of interest (ROIs) of the sensorimotor cortex and the overlying subset of electrodes that was used for signal analysis in our study. 1: presupplementary motor area (pSMA); 2: supplementary motor area (SMA); 3: dorsal premotor area (PMd); 4: ventral premotor area (PMv); 5: cingulate motor area (CMA); 6: primary foot motor area (M1F); 7: primary hand motor area (M1H); 8: primary lip motor area (M1L); 9: primary foot somatosensory area (S1F); 10: primary hand somatosensory area (S1H); 11: secondary somatosensory area (S2); 12: somatosensory association area (SAC).
Figure 3
Figure 3
Activation time series of all regions of interest (ROIs) of the sensorimotor cortex during a random epoch and definition of time intervals around the trigger (onset of the video presenting the motor imagery task).
Figure 4
Figure 4
Average information transfer (calculated by directed transfer function) of healthy and SCI groups calculated for alpha (left) and beta (right) rhythm networks during the early imagery time interval, for the hands motor imagery category. Connections between the bilateral cingulate motor areas (CMA_R ← → CMA_L) presented the highest information transfer. Only connections with at least 25% of max information transfer among all statistically significant connections are displayed.
Figure 5
Figure 5
Nodal strengths (a: out-strength, b: in-strength) of bilateral cingulate motor areas for both subject groups. Left cingulate motor area (CMA_L) showed the highest out-strength and right cingulate motor area (CMA_R) showed the highest in-strength in the network. SCI subjects presented significantly higher CMA_R out-strength and CMA_L in-strength than healthy subjects. “∗” represent extreme values and “o” represent outliers.
Figure 6
Figure 6
Differences of group averages (healthy-SCI) of in-strength (IS) and out-strength (OS) of cingulate motor areas (CMAs) during all motor imagery categories. A trend was revealed, in which OS of CMA_R was consistently higher in the healthy than the SCI group. OS of CMA_L was consistently lower in the healthy than the SCI group. The opposite held true for IS of those nodes. This trend reached statistical significance only for early alpha walking (p = 0.002) and late beta walking (p = 0.006) tasks.
Figure 7
Figure 7
From the comparison of the networks of healthy and SCI subjects, a subset of network connections emerged as significantly stronger in the healthy group than in the SCI group for both the alpha and beta networks of “hands” motor imagery category, as calculated by network-based statistics—false discovery rate methodology.
Figure 8
Figure 8
Network density was significantly higher in beta than alpha networks (all p values < 0.001) of the “hands” motor imagery category during both early and late time intervals for all subjects of both the SCI and the healthy group (all p values < 0.001). This finding was also consistent across every other motor imagery categories in both groups (all p values < 0.001).

References

    1. Anderson K. D., Acuff M. E., Arp B. G., et al. United States (US) multi-center study to assess the validity and reliability of the spinal cord independence measure (SCIM III) 2011;49(8):880–885. doi: 10.1038/sc.2011.20.
    1. Nardone R., Höller Y., Brigo F., et al. Descending motor pathways and cortical physiology after spinal cord injury assessed by transcranial magnetic stimulation: a systematic review. 2015;1619:139–154. doi: 10.1016/j.brainres.2014.09.036.
    1. Athanasiou A., Klados M. A., Pandria N., et al. A systematic review of investigations into functional brain connectivity following spinal cord injury. 2017;11:p. 517. doi: 10.3389/fnhum.2017.00517.
    1. Nardone R., Höller Y., Brigo F., et al. Functional brain reorganization after spinal cord injury: systematic review of animal and human studies. 2013;1504:58–73. doi: 10.1016/j.brainres.2012.12.034.
    1. Freund P., Weiskopf N., Ashburner J., et al. MRI investigation of the sensorimotor cortex and the corticospinal tract after acute spinal cord injury: a prospective longitudinal study. 2013;12(9):873–881. doi: 10.1016/S1474-4422(13)70146-7.
    1. Hou J., Xiang Z., Yan R., et al. Motor recovery at 6 months after admission is related to structural and functional reorganization of the spine and brain in patients with spinal cord injury. 2016;37(6):2195–2209. doi: 10.1002/hbm.23163.
    1. Zheng W., Chen Q., Chen X., et al. Brain white matter impairment in patients with spinal cord injury. 2017;2017:8. doi: 10.1155/2017/4671607.4671607
    1. Ilvesmäki T., Koskinen E., Brander A., Luoto T., Öhman J., Eskola H. Spinal cord injury induces widespread chronic changes in cerebral white matter. 2017;38(7):3637–3647. doi: 10.1002/hbm.23619.
    1. Astolfi L., Bakardjian H., Cincotti F., et al. Estimate of causality between independent cortical spatial patterns during movement volition in spinal cord injured patients. 2007;19(3):107–123. doi: 10.1007/s10548-007-0018-1.
    1. Fallani F. D. V., Astolfi L., Cincotti F., et al. Cortical functional connectivity networks in normal and spinal cord injured patients: evaluation by graph analysis. 2007;28(12):1334–1346. doi: 10.1002/hbm.20353.
    1. De Vico Fallani F., Astolfi L., Cincotti F., et al. Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach. 2007;19(3):125–136. doi: 10.1007/s10548-007-0019-0.
    1. Mattia D., Cincotti F., Astolfi L., et al. Motor cortical responsiveness to attempted movements in tetraplegia: evidence from neuroelectrical imaging. 2009;120(1):181–189. doi: 10.1016/j.clinph.2008.09.023.
    1. Sinatra R., de Vico Fallani F., Astolfi L., et al. Cluster structure of functional networks estimated from high-resolution EEG data. 2009;19(2):665–676. doi: 10.1142/S0218127409023020.
    1. Astolfi L., Cincotti F., Mattia D., et al. Time-varying cortical connectivity estimation from noninvasive, high-resolution EEG recordings. 2010;24(2):83–90. doi: 10.1027/0269-8803/a000017.
    1. De Vico Fallani F., Rodrigues F. A., da Fontoura Costa L., et al. Multiple pathways analysis of brain functional networks from EEG signals: an application to real data. 2011;23(4):344–354. doi: 10.1007/s10548-010-0152-z.
    1. Hou J.-M., Sun T.-S., Xiang Z.-M., et al. Alterations of resting-state regional and network-level neural function after acute spinal cord injury. 2014;277:446–454. doi: 10.1016/j.neuroscience.2014.07.045.
    1. Min Y.-S., Park J. W., Jin S. U., et al. Alteration of resting-state brain sensorimotor connectivity following spinal cord injury: a resting-state functional magnetic resonance imaging study. 2015;32(18):1422–1427. doi: 10.1089/neu.2014.3661.
    1. Min Y.-S., Chang Y., Park J. W., et al. Change of brain functional connectivity in patients with spinal cord injury: graph theory based approach. 2015;39(3):374–383. doi: 10.5535/arm.2015.39.3.374.
    1. Oni-Orisan A., Kaushal M., Li W., et al. Alterations in cortical sensorimotor connectivity following complete cervical spinal cord injury: a prospective resting-state fMRI study. 2016;11(3, article e0150351) doi: 10.1371/journal.pone.0150351.
    1. Kaushal M., Oni-Orisan A., Chen G., et al. Evaluation of whole-brain resting-state functional connectivity in spinal cord injury: a large-scale network analysis using network-based statistic. 2017;34(6):1278–1282. doi: 10.1089/neu.2016.4649.
    1. Kaushal M., Oni-Orisan A., Chen G., et al. Large-scale network analysis of whole-brain resting-state functional connectivity in spinal cord injury: a comparative study. 2017;7(7):413–423. doi: 10.1089/brain.2016.0468.
    1. Hawasli A. H., Rutlin J., Roland J. L., et al. Spinal cord injury disrupts resting-state networks in the human brain. 2018;35(6):864–873. doi: 10.1089/neu.2017.5212.
    1. Luo H., Dou W., Pan Y., et al. Joint analysis of multi-level functional brain networks. 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI); 2016; Datong, China. pp. 1521–1526.
    1. Pan Y., Dou W., Wang Y., et al. Non-concomitant cortical structural and functional alterations in sensorimotor areas following incomplete spinal cord injury. 2017;12(12):2059–2066. doi: 10.4103/1673-5374.221165.
    1. Hétu S., Grégoire M., Saimpont A., et al. The neural network of motor imagery: an ALE meta-analysis. 2013;37(5):930–949. doi: 10.1016/j.neubiorev.2013.03.017.
    1. Lotze M., Halsband U. Motor imagery. 2006;99(4–6):386–395. doi: 10.1016/j.jphysparis.2006.03.012.
    1. Kraeutner S., Gionfriddo A., Bardouille T., Boe S. Motor imagery-based brain activity parallels that of motor execution: evidence from magnetic source imaging of cortical oscillations. 2014;1588:81–91. doi: 10.1016/j.brainres.2014.09.001.
    1. Xu L., Zhang H., Hui M., et al. Motor execution and motor imagery: a comparison of functional connectivity patterns based on graph theory. 2014;261:184–194. doi: 10.1016/j.neuroscience.2013.12.005.
    1. Guillot A., Collet C., Nguyen V. A., Malouin F., Richards C., Doyon J. Brain activity during visual versus kinesthetic imagery: an fMRI study. 2009;30(7):2157–2172. doi: 10.1002/hbm.20658.
    1. Mizuguchi N., Nakamura M., Kanosue K. Task-dependent engagements of the primary visual cortex during kinesthetic and visual motor imagery. 2017;636:108–112. doi: 10.1016/j.neulet.2016.10.064.
    1. Harris J., Hebert A. Utilization of motor imagery in upper limb rehabilitation: a systematic scoping review. 2015;29(11):1092–1107. doi: 10.1177/0269215514566248.
    1. Bunketorp Käll L., Cooper R. J., Wangdell J., Fridén J., Björnsdotter M. Adaptive motor cortex plasticity following grip reconstruction in individuals with tetraplegia. 2018;36(1):73–82. doi: 10.3233/RNN-170775.
    1. Tung S. W., Guan C., Ang K. K., et al. Motor imagery BCI for upper limb stroke rehabilitation: an evaluation of the EEG recordings using coherence analysis. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2013; Osaka, Japan. pp. 261–264.
    1. Donati A. R. C., Shokur S., Morya E., et al. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. 2016;6(1, article 30383) doi: 10.1038/srep30383.
    1. Rajasekaran V., López-Larraz E., Trincado-Alonso F., et al. Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals. 2018;15(1):p. 4. doi: 10.1186/s12984-017-0345-8.
    1. Athanasiou A., Xygonakis I., Pandria N., et al. Towards rehabilitation robotics: off-the-shelf BCI control of anthropomorphic robotic arms. 2017;2017:17. doi: 10.1155/2017/5708937.5708937
    1. Arfaras G., Athanasiou A., Niki P., et al. Visual versus kinesthetic motor imagery for BCI control of robotic arms (Mercury 2.0). 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS); 2017; Thessaloniki, Greece. pp. 440–445.
    1. Athanasiou A., Arfaras G., Xygonakis I., et al. Commercial BCI control and functional brain networks in spinal cord injury: a proof-of-concept. 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS); 2017; Thessaloniki, Greece. pp. 262–267.
    1. Athanasiou A., Arfaras G., Pandria N., et al. Wireless brain-robot interface: user perception and performance assessment of spinal cord injury patients. 2017;2017:16. doi: 10.1155/2017/2986423.2986423
    1. Brainwave control of a wearable robotic arm for rehabilitation and neurophysiological study in cervical spine injury (CSI:Brainwave) .
    1. Marks D. F. New directions in mental imagery research. 1995;19(3-4):153–167.
    1. Oostenveld R., Praamstra P. The five percent electrode system for high-resolution EEG and ERP measurements. 2001;112(4):713–719. doi: 10.1016/S1388-2457(00)00527-7.
    1. Christ O., Reiner M. Perspectives and possible applications of the rubber hand and virtual hand illusion in non-invasive rehabilitation: technological improvements and their consequences. 2014;44:33–44. doi: 10.1016/j.neubiorev.2014.02.013.
    1. Athanasiou A., Klados M. A., Styliadis C., Foroglou N., Polyzoidis K., Bamidis P. D. Investigating the role of alpha and beta rhythms in functional motor networks. 2016 doi: 10.1016/j.neuroscience.2016.05.044. In press.
    1. Qin L., Ding L., He B. Motor imagery classification by means of source analysis for brain–computer interface applications. 2004;1(3):135–141. doi: 10.1088/1741-2560/1/3/002.
    1. Kamousi B., Amini A. N., He B. Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy. 2007;4(2):17–25. doi: 10.1088/1741-2560/4/2/002.
    1. Kamousi B., Liu Z., He B. Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. 2005;13(2):166–171. doi: 10.1109/TNSRE.2005.847386.
    1. Edelman B. J., Baxter B., He B. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. 2016;63(1):4–14. doi: 10.1109/TBME.2015.2467312.
    1. Oostenveld R., Fries P., Maris E., Schoffelen J.-M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. 2011;2011:9. doi: 10.1155/2011/156869.156869
    1. Belouchrani A., Cichocki A. Robust whitening procedure in blind source separation context. 2000;36(24):p. 2050. doi: 10.1049/el:20001436.
    1. Perrin F., Pernier J., Bertrand O., Echallier J. F. Spherical splines for scalp potential and current density mapping. 1989;72(2):184–187. doi: 10.1016/0013-4694(89)90180-6.
    1. Hamedi M., Salleh S.-H., Noor A. M. Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. 2016;28(6):999–1041. doi: 10.1162/NECO_a_00838.
    1. Ginter J., Jr., Blinowska K. J., Kamiński M., Durka P. J., Pfurtscheller G., Neuper C. Propagation of EEG activity in the beta and gamma band during movement imagery in humans. 2005;44(1):106–113. doi: 10.1055/s-0038-1633932.
    1. Avanzini P., Fabbri-Destro M., Dalla Volta R., Daprati E., Rizzolatti G., Cantalupo G. The dynamics of sensorimotor cortical oscillations during the observation of hand movements: an EEG study. 2012;7(5, article e37534) doi: 10.1371/journal.pone.0037534.
    1. Sakihara K., Inagaki M. Mu rhythm desynchronization by tongue thrust observation. 2015;9:p. 501. doi: 10.3389/fnhum.2015.00501.
    1. Grech R., Cassar T., Muscat J., et al. Review on solving the inverse problem in EEG source analysis. 2008;5(1):p. 25. doi: 10.1186/1743-0003-5-25.
    1. He B., Dai Y., Astolfi L., Babiloni F., Yuan H., Yang L. eConnectome: a MATLAB toolbox for mapping and imaging of brain functional connectivity. 2011;195(2):261–269. doi: 10.1016/j.jneumeth.2010.11.015.
    1. Babiloni F., Cincotti F., Babiloni C., et al. Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function. 2005;24(1):118–131. doi: 10.1016/j.neuroimage.2004.09.036.
    1. Evans A. C., Collins D. L., Mills S. R., Brown E. D., Kelly R. L., Peters T. M. 3D statistical neuroanatomical models from 305 MRI volumes. 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference; 1993; San Francisco, CA, USA. pp. 1813–1817.
    1. Buzsáki G., Anastassiou C. A., Koch C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. 2012;13(6):407–420. doi: 10.1038/nrn3241.
    1. Becker H., Albera L., Comon P., Gribonval R., Wendling F., Merlet I. Brain-source imaging: from sparse to tensor models. 2015;32(6):100–112. doi: 10.1109/MSP.2015.2413711.
    1. Hansen P. C., Jensen T. K., Rodriguez G. An adaptive pruning algorithm for the discrete L-curve criterion. 2007;198(2):483–492. doi: 10.1016/j.cam.2005.09.026.
    1. Hansen P. C. Regularization tools version 4.0 for Matlab 7.3. 2007;46(2):189–194. doi: 10.1007/s11075-007-9136-9.
    1. Wilke C., Ding L., He B. Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function. 2008;55(11):2557–2564. doi: 10.1109/TBME.2008.919885.
    1. Kugiumtzis D. On the reliability of the surrogate data test for nonlinearity in the analysis of noisy time series. 2001;11(7):1881–1896. doi: 10.1142/S0218127401003061.
    1. Kugiumtzis D. Boston, MA, USA: Springer; 2002. Surrogate data test on time series; pp. 267–282.
    1. Kamiński M., Ding M., Truccolo W. A., Bressler S. L. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. 2001;85(2):145–157. doi: 10.1007/s004220000235.
    1. Granger C. W. J. Investigating causal relations by econometric models and cross-spectral methods. 1969;37(3):p. 424. doi: 10.2307/1912791.
    1. Schneider T., Neumaier A. Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. 2001;27(1):58–65. doi: 10.1145/382043.382316.
    1. Delorme A., Mullen T., Kothe C., et al. EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. 2011;2011:12. doi: 10.1155/2011/130714.130714
    1. Jansen B. H., Bourne J. R., Ward J. W. Spectral decomposition of EEG intervals using Walsh and Fourier transforms. 1981;28(12):836–838. doi: 10.1109/TBME.1981.324686.
    1. Florian G., Pfurtscheller G. Dynamic spectral analysis of event-related EEG data. 1995;95(5):393–396. doi: 10.1016/0013-4694(95)00198-8.
    1. Sabate M., Llanos C., Enriquez E., Gonzalez B., Rodriguez M. Fast modulation of alpha activity during visual processing and motor control. 2011;189:236–249. doi: 10.1016/j.neuroscience.2011.05.011.
    1. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. 2010;52(3):1059–1069. doi: 10.1016/j.neuroimage.2009.10.003.
    1. Sporns O. Graph theory methods for the analysis of neural connectivity patterns. In: Kötter R., editor. Boston, MA, USA: Springer; 2003. pp. 171–185.
    1. Sporns O., Kötter R. Motifs in brain networks. 2004;2(11, article e369) doi: 10.1371/journal.pbio.0020369.
    1. Floyd R. W. Algorithm 97: shortest path. 1962;5(6):p. 345. doi: 10.1145/367766.368168.
    1. Goni J., van den Heuvel M. P., Avena-Koenigsberger A., et al. Resting-brain functional connectivity predicted by analytic measures of network communication. 2014;111(2):833–838. doi: 10.1073/pnas.1315529111.
    1. Onnela J.-P., Saramäki J., Kertész J., Kaski K. Intensity and coherence of motifs in weighted complex networks. 2005;71(6, article 065103) doi: 10.1103/PhysRevE.71.065103.
    1. Fagiolo G. Clustering in complex directed networks. 2007;76(2, article 026107) doi: 10.1103/PhysRevE.76.026107.
    1. Watts D. J., Strogatz S. H. Collective dynamics of ‘small-world’ networks. 1998;393(6684):440–442. doi: 10.1038/30918.
    1. Achard S., Salvador R., Whitcher B., Suckling J., Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. 2006;26(1):63–72. doi: 10.1523/JNEUROSCI.3874-05.2006.
    1. Humphries M. D., Gurney K. Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. 2008;3(4, article e0002051) doi: 10.1371/journal.pone.0002051.
    1. Sporns O., Honey C. J., Kötter R. Identification and classification of hubs in brain networks. 2007;2(10, article e1049) doi: 10.1371/journal.pone.0001049.
    1. Zalesky A., Fornito A., Bullmore E. T. Network-based statistic: identifying differences in brain networks. 2010;53(4):1197–1207. doi: 10.1016/j.neuroimage.2010.06.041.
    1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. 1995;57(1):289–300.
    1. Xia M., Wang J., He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. 2013;8(7, article e68910) doi: 10.1371/journal.pone.0068910.
    1. Cramer D. New York, NY, USA: Routledge; 1998.
    1. Cramer D., Howitt D. London, UK: SAGE Publications Ltd; 2004.
    1. Doane D. P., Seward L. E. Measuring skewness : a forgotten statistic? 2011;19(2) doi: 10.1080/10691898.2011.11889611.
    1. Razali N. M., Wah Y. B., Sciences M. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. 2011;2(1):21–33.
    1. Shapiro S. S., Wilk M. B. An analysis of variance test for normality (complete samples) 1965;52(3-4):591–611. doi: 10.1093/biomet/52.3-4.591.
    1. Kordjazi N., Koravand A., Sveistrup H. Enhancing the representational similarity between execution and imagination of movement using network-based brain computer interfacing. 2017 doi: 10.1101/166603. In press.
    1. Birbaumer N., Sauseng P. Berlin, Heidelberg: Springer; 2009. Brain–computer interface in neurorehabilitation; pp. 155–169.
    1. De Vico Fallani F., Latora V., Astolfi L., et al. Persistent patterns of interconnection in time-varying cortical networks estimated from high-resolution EEG recordings in humans during a simple motor act. 2008;41(22, article 224014) doi: 10.1088/1751-8113/41/22/224014.
    1. Athanasiou A., Lithari C., Kalogianni K., Klados M. A., Bamidis P. D. Source detection and functional connectivity of the sensorimotor cortex during actual and imaginary limb movement: a preliminary study on the implementation of econnectome in motor imagery protocols. 2012;2012:10. doi: 10.1155/2012/127627.127627
    1. Baxter B. S., Edelman B. J., Sohrabpour A., He B. Anodal transcranial direct current stimulation increases bilateral directed brain connectivity during motor-imagery based brain-computer interface control. 2017;11:p. 691. doi: 10.3389/fnins.2017.00691.
    1. Grosse-wentrup M. Understanding brain connectivity patterns during motor imagery for brain-computer interfacing. In: Koller D., Schuurmans D., Bengio Y., Bottou L., editors. Red Hook, NY, USA: Curran Associates, Inc.; 2009. pp. 561–568.
    1. Kim Y. K., Park E., Lee A., Im C.-H., Kim Y.-H. Changes in network connectivity during motor imagery and execution. 2018;13(1, article e0190715) doi: 10.1371/journal.pone.0190715.
    1. Yi W., Qiu S., Wang K., et al. Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery. 2014;9(12, article e114853) doi: 10.1371/journal.pone.0114853.
    1. Hamedi M., Salleh S.-H., Samdin S. B., Noor A. M. Motor imagery brain functional connectivity analysis via coherence. 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA); 2015; Kuala Lumpur, Malaysia. pp. 269–273.
    1. Samdin S. B., Ting C.-M., Salleh S.-H., Hamedi M., Noor A. B. M. Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm. 2014 IEEE Workshop on Statistical Signal Processing (SSP); 2014; Gold Coast, VIC, Australia. pp. 181–184.
    1. Kang B. K., Kim J. S., Ryun S., Chung C. K. Prediction of movement intention using connectivity within motor-related network: an electrocorticography study. 2018;13(1, article e0191480) doi: 10.1371/journal.pone.0191480.
    1. Athanasiou A., Foroglou N., Polyzoidis K., Bamidis P. D. Graph analysis of sensorimotor cortex functional networks – comparison of alpha vs beta rhythm in motor imagery and execution. SAN/NIHC 2014 Meeting; 2014; Utrecht, Netherlands. pp. 64–65.
    1. Willemse R. B., de Munck J. C., Verbunt J. P. A., et al. Topographical organization of mu and beta band activity associated with hand and foot movements in patients with perirolandic lesions. 2010;4:93–99. doi: 10.2174/1874440001004010093.
    1. De Vico Fallani F., Pichiorri F., Morone G., et al. Multiscale topological properties of functional brain networks during motor imagery after stroke. 2013;83:438–449. doi: 10.1016/j.neuroimage.2013.06.039.
    1. Bastos A. M., Schoffelen J.-M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. 2016;9:p. 175. doi: 10.3389/fnsys.2015.00175.
    1. He B., editor. Boston, MA, USA: Springer; 2005.
    1. Nunez P. L., Srinivasan R. New York, NY, USA: Oxford University Press; 2006.

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