logo

SCIENTIA SINICA Informationis, Volume 51 , Issue 6 : 940(2021) https://doi.org/10.1360/SSI-2020-0010

Brain function evaluation in the minimally conscious state using cross-sample entropy based on brain network measure under the spinal cord stimulation

More info
  • ReceivedJan 11, 2020
  • AcceptedMar 12, 2020
  • PublishedApr 20, 2021

Abstract


Funded by

国家自然科学基金(61673333,81230023,81771359)

河北省自然科学基金优秀青年基金(F2018203281)


Author information












References

[1] M?ki-Marttunen V, Diez I, Cortes J M. Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness. Front Neuroinform, 2013, 7 CrossRef Google Scholar

[2] Vegetative versus Minimally Conscious States: A Study Using TMS-EEG, Sensory and Event-Related Potentials. PLoS ONE, 2013, 8: e57069 CrossRef ADS Google Scholar

[3] Riganello F, Cortese M D, Arcuri F. How Can Music Influence the Autonomic Nervous System Response in Patients with Severe Disorder of Consciousness?. Front Neurosci, 2015, 9 CrossRef Google Scholar

[4] Giacino J T, Trott C T. Rehabilitative Management of Patients With Disorders of Consciousness. J Head Trauma Rehabilitation, 2004, 19: 254-265 CrossRef Google Scholar

[5] Giacino J, Fins J J, Machado A. Central Thalamic Deep Brain Stimulation to Promote Recovery from Chronic Posttraumatic Minimally Conscious State: Challenges and Opportunities. Neuromodulation-Tech at Neural Interface, 2012, 15: 339-349 CrossRef Google Scholar

[6] Yamamoto T, Watanabe M, Obuchi T, et al. Spinal Cord Stimulation for Vegetative State and Minimally Conscious State: Changes in Consciousness Level and Motor Function. Acta Neurochir Suppl, 2017, 124: 37-42 DOI: 10.1007/978-3-319-39546-3_6. Google Scholar

[7] Shimizu T, Hosomi K, Maruo T. Repetitive transcranial magnetic stimulation accuracy as a spinal cord stimulation outcome predictor in patients with neuropathic pain. J Clin Neuroscience, 2018, 53: 100-105 CrossRef Google Scholar

[8] Xia X Y, Yang Y, He J H. Treatment of consciousness disorders with electrical stimulation of spinal cord. Chin J Neurotrauma Surgery, 2015, 1: 61--62. Google Scholar

[9] Bai Y, Xia X, Liang Z. Frontal Connectivity in EEG Gamma (30-45 Hz) Respond to Spinal Cord Stimulation in Minimally Conscious State Patients. Front Cell Neurosci, 2017, 11: 177 CrossRef Google Scholar

[10] Kanno T, Morita I, Yamaguchi S. Dorsal Column Stimulation in Persistent Vegetative State. Neuromodulation-Tech at Neural Interface, 2009, 12: 33-38 CrossRef Google Scholar

[11] Darmani G, Bergmann T O, Zipser C. Effects of antiepileptic drugs on cortical excitability in humans: A TMS?EMG and TMS?EEG study. Hum Brain Mapp, 2019, 40: 1276-1289 CrossRef Google Scholar

[12] Bergmann T O, Karabanov A, Hartwigsen G. Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives. NeuroImage, 2016, 140: 4-19 CrossRef Google Scholar

[13] Liang Z, Li D, Ouyang G. Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia. Clin NeuroPhysiol, 2012, 123: 681-688 CrossRef Google Scholar

[14] Rosso O A. Entropy changes in brain function. Int J PsychoPhysiol, 2007, 64: 75-80 CrossRef Google Scholar

[15] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol-Heart Circulatory Physiol, 2000, 278: H2039-H2049 CrossRef Google Scholar

[16] Zhang T, Yang Z, Coote J H. Cross-sample entropy statistic as a measure of complexity and regularity of renal sympathetic nerve activity in the rat. Exp Physiol, 2007, 92: 659-669 CrossRef Google Scholar

[17] Achard S. A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. J Neuroscience, 2006, 26: 63-72 CrossRef Google Scholar

[18] Brain's functional network clustering coefficient changes in response to instruction (RTI) in students with and without reading disabilities: Multi-leveled reading brain's RTI. Cogent Psychology, 2018, 5 CrossRef Google Scholar

[19] Bai Y, Xia X, Li X. Spinal cord stimulation modulates frontal delta and gamma in patients of minimally consciousness state. Neuroscience, 2017, 346: 247-254 CrossRef Google Scholar

[20] Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J NeuroSci Methods, 2004, 134: 9-21 CrossRef Google Scholar

[21] Zhao D, Jiang J, Chang W, et al. FPGA Implementation of FastICA Algorithm for On-line EEG Signal Separation. Communications in Computer & Information Science, 2014, 491: 59-68 DOI: 10.1007/978-3-662-45815-0_6. Google Scholar

[22] Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization. PLoS ONE, 2014, 9: e87253 CrossRef ADS Google Scholar

[23] Pincus S, Singer B H. Randomness and degrees of irregularity.. Proc Natl Acad Sci USA, 1996, 93: 2083-2088 CrossRef ADS Google Scholar

[24] Mahajan R, Morshed B I. Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA. IEEE J Biomed Health Inform, 2015, 19: 158-165 CrossRef Google Scholar

[25] Yan N, Wang Y, Wei N, et al. Feature exaction and classification of attention related electroencephalographic signals based on sample entropy. J Xi'an Jiaotong Univ, 2007, 41: 1237--1241. Google Scholar

[26] Stam C, Jones B, Nolte G. Small-World Networks and Functional Connectivity in Alzheimer's Disease. Cerebral Cortex, 2006, 17: 92-99 CrossRef Google Scholar

[27] Gaál Z A, Boha R, Stam C J. Age-dependent features of EEG-reactivity-Spectral, complexity, and network characteristics. NeuroSci Lett, 2010, 479: 79-84 CrossRef Google Scholar

[28] Bokil H, Andrews P, Kulkarni J E. Chronux: A platform for analyzing neural signals. J NeuroSci Methods, 2010, 192: 146-151 CrossRef Google Scholar

[29] Liang Z, Huang C, Li Y. Emergence EEG pattern classification in sevoflurane anesthesia. Physiol Meas, 2018, 39: 045006 CrossRef ADS Google Scholar

[30] Bassett D S, Bullmore E, Verchinski B A. Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia. J Neuroscience, 2008, 28: 9239-9248 CrossRef Google Scholar

[31] Xia X Y, Huang Y Z, Bai Y, et al. Effect of spinal cord electrical stimulation frequency on EEG in patients with disturbance of consciousness (report of four cases). Chin J Neurosurgery, 2016, 32: 566--568. Google Scholar

[32] He J H, Yang Y, Xie Q Y, et al. Application status and progress of nerve regulation in the treatment of consciousness disorders. Chin J Neurol, 2015, 14: 1290--1292. Google Scholar

[33] Liang Z, Wang Y, Sun X. EEG entropy measures in anesthesia. Front Comput Neurosci, 2015, 9 CrossRef Google Scholar

[34] Huang J R, Fan S Z, Abbod M. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia. Entropy, 2013, 15: 3325-3339 CrossRef ADS Google Scholar

[35] Asyali M H, Berry R B, Khoo M C K. Determining a continuous marker for sleep depth. Comput Biol Med, 2007, 37: 1600-1609 CrossRef Google Scholar

[36] Miyara T. A Study of EEG Activities during Sleep-Wakefulness States in Rabbits by Autocorrelation and Power Spectrum Analyses. Psychiatry Clin Neurosci, 1985, 39: 571-580 CrossRef Google Scholar

[37] Gomez C, Poza J, Gomez-Pilar J, et al. Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory. In: Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016. 2830--2833. Google Scholar

[38] Liu T, Chen Y, Lin P. Small-World Brain Functional Networks in Children With Attention-Deficit/Hyperactivity Disorder Revealed by EEG Synchrony. Clin EEG Neurosci, 2015, 46: 183-191 CrossRef Google Scholar

[39] Fernández-Espejo D, Owen A M. Detecting awareness after severe brain injury. Nat Rev Neurosci, 2013, 14: 801-809 CrossRef Google Scholar

[40] Lee H, Mashour G A, Noh G J. Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness. Anesthesiology, 2013, 119: 1347-1359 CrossRef Google Scholar

  • Figure 1

    (Color online)The parameter selection of SE

  • Figure 1

    (Color online)The parameter selection of SE

  • Figure 2

    (Color online) The power spectrum of 32 channels in the corresponding position of one MCS patient

  • Figure 2

    (Color online) The power spectrum of 32 channels in the corresponding position of one MCS patient

  • Figure 3

    (Color online) The relative power spectral density of different frequency bands in different brain regions for Pre-SCS and Post-SCS

  • Figure 3

    (Color online) The relative power spectral density of different frequency bands in different brain regions for Pre-SCS and Post-SCS

  • Figure 4

    (Color online) The spatial distribution topographic of the SE in an MCS patient and a normal healthy subject. (a) and (b) are the topographic map of the five frequency bands before and after the stimulation, respectively. (c) is the topographic map of the five frequency bands for normal resting state

  • Figure 4

    (Color online) The spatial distribution topographic of the SE in an MCS patient and a normal healthy subject. (a) and (b) are the topographic map of the five frequency bands before and after the stimulation, respectively. (c) is the topographic map of the five frequency bands for normal resting state

  • Figure 5

    (Color online) The statistical results of the SE. The boxplot of (a) $\delta$, (b) $\theta$, (c) $\alpha$, (d) $\beta$ and protectłinebreak (e) $\gamma$ band respectively. Each figure includes the boxplot and the significant results of the four brain regions before and after stimulation, and normal resting-state

  • Figure 5

    (Color online) The statistical results of the SE. The boxplot of (a) $\delta$, (b) $\theta$, (c) $\alpha$, (d) $\beta$ and protectłinebreak (e) $\gamma$ band respectively. Each figure includes the boxplot and the significant results of the four brain regions before and after stimulation, and normal resting-state

  • Figure 6

    (Color online)The cSE matrixes of each frequency band. The cSE matrixes of the five frequency bands (a) before and (b) after the stimulation, (c) normal resting-state respectively

  • Figure 6

    (Color online)The cSE matrixes of each frequency band. The cSE matrixes of the five frequency bands (a) before and (b) after the stimulation, (c) normal resting-state respectively

  • Figure 7

    (Color online)The functional connection of each frequency band. The functional connection was based on cSE of the five frequency bands (a) before and (b) after the stimulation, (c) normal resting-state respectively. The thresholds of the connections for the presentation were set to 80% of the maximum in each frequency band

  • Figure 7

    (Color online)The functional connection of each frequency band. The functional connection was based on cSE of the five frequency bands (a) before and (b) after the stimulation, (c) normal resting-state respectively. The thresholds of the connections for the presentation were set to 80% of the maximum in each frequency band

  • Figure 8

    (Color online) The statistical results of the cSE in each region of (a) $\delta$, (b) $\theta$, (c) $\alpha$,(d) $\beta$, (e) $\gamma$ band

  • Figure 8

    (Color online) The statistical results of the cSE in each region of (a) $\delta$, (b) $\theta$, (c) $\alpha$,(d) $\beta$, (e) $\gamma$ band

  • Figure 9

    (Color online) The statistical results of thecSE between the regions of(a) $\delta$, (b) $\theta$, (c)$\alpha$, (d) $\beta$, (e) $\gamma$band

  • Figure 9

    (Color online) The statistical results of thecSE between the regions of(a) $\delta$, (b) $\theta$, (c)$\alpha$, (d) $\beta$, (e) $\gamma$band

  • Figure 10

    (Color online)The changes of brain network parameters under different threshold parameters

  • Figure 10

    (Color online)The changes of brain network parameters under different threshold parameters

  • Figure 11

    (Color online) The statistical results of the parameters of the network. (a) The average clustering coefficient; (b) the average characteristics path length; (c) the small-world parameter

  • Figure 11

    (Color online) The statistical results of the parameters of the network. (a) The average clustering coefficient; (b) the average characteristics path length; (c) the small-world parameter

  • Table 1   The descriptive statistics for the relative power spectral density (mean$\pm$std)
    $\delta$$\theta$$\alpha$$\beta$$\gamma$
    2*F Pre-SCS0.401$\pm$0.0570.204$\pm$0.0550.110$\pm$0.0250.136$\pm$0.0260.144$\pm$0.049
    Post-SCS0.405$\pm$0.0690.214$\pm$0.0500.097$\pm$0.0210.130$\pm$0.0260.149$\pm$0.049
    2*C Pre-SCS0.397$\pm$0.0660.233$\pm$0.0370.130$\pm$0.0240.125$\pm$0.0290.112$\pm$0.021
    Post-SCS0.382$\pm$0.0570.247$\pm$0.0410.120$\pm$0.0220.122$\pm$0.0190.125$\pm$0.023
    2*P Pre-SCS0.398$\pm$0.0630.225$\pm$0.0310.137$\pm$0.0290.123$\pm$0.0220.114$\pm$0.022
    Post-SCS0.379$\pm$0.0780.224$\pm$0.0310.128$\pm$0.0330.128$\pm$0.0210.136$\pm$0.034
    2*O Pre-SCS0.386$\pm$0.0590.226$\pm$0.0360.135$\pm$0.0260.127$\pm$0.0190.122$\pm$0.033
    Post-SCS0.346$\pm$0.0920.199$\pm$0.0340.114$\pm$0.0290.140$\pm$0.0310.196$\pm$0.063
  • Table 2   The descriptive statistics for the SE (median (min$\sim$max))
    $\delta$$\theta$$\alpha$$\beta$$\gamma$
    Pre-SCS0.49 (0.38$\sim$0.58)0.64 (0.59$\sim$0.67)0.64 (0.59$\sim$0.75)1.79 (1.64$\sim$1.94)1.48 (1.36$\sim$1.56)
    FPost-SCS0.52 (0.45$\sim$0.58)0.64 (0.60$\sim$0.68)0.67 (0.60$\sim$0.74)1.88 (1.71$\sim$2.03)1.55 (1.37$\sim$1.65)
    Resting-normal0.46 (0.30$\sim$0.63)0.64 (0.56$\sim$0.70)0.67 (0.48$\sim$0.78)1.88 (1.51$\sim$2.11)1.55 (1.41$\sim$1.68)
    Pre-SCS0.50 (0.40$\sim$0.58)0.64 (0.59$\sim$0.68)0.64 (0.57$\sim$0.71)1.78 (1.58$\sim$1.94)1.49 (1.36$\sim$1.63)
    CPost-SCS0.52 (0.44$\sim$0.59)0.64 (0.60$\sim$0.68)0.66 (0.59$\sim$0.72)1.84 (1.68$\sim$2.01)1.55 (1.41$\sim$1.66)
    Resting-normal0.52 (0.32$\sim$0.67)0.63 (0.55$\sim$0.68)0.66 (0.57$\sim$0.74)1.85 (1.61$\sim$2.08)1.57 (1.37$\sim$1.71)
    Pre-SCS0.50 (0.41$\sim$0.57)0.63 (0.58$\sim$0.67)0.64 (0.57$\sim$0.72)1.82 (1.62$\sim$2.02)1.51 (1.37$\sim$1.62)
    PPost-SCS0.52 (0.45$\sim$0.57)0.64 (0.59$\sim$0.67)0.65 (0.58$\sim$0.72)1.81 (1.65$\sim$1.96)1.54 (1.41$\sim$1.66)
    Resting-normal0.53 (0.39$\sim$0.60)0.65 (0.59$\sim$0.68)0.67 (0.57$\sim$0.76)1.83 (1.55$\sim$2.03)1.53 (1.36$\sim$1.68)
    Pre-SCS0.51 (0.40$\sim$0.58)0.64 (0.60$\sim$0.67)0.65 (0.57$\sim$0.72)1.83 (1.66$\sim$2.02)1.51 (1.32$\sim$1.67)
    OPost-SCS0.52 (0.43$\sim$0.59)0.64 (0.59$\sim$0.69)0.66 (0.58$\sim$0.73)1.82 (1.69$\sim$1.97)1.53 (1.42$\sim$1.64)
    Resting-normal0.52 (0.34$\sim$0.61)0.63 (0.58$\sim$0.67)0.69 (0.62$\sim$0.76)1.83 (1.25$\sim$2.13)1.52 (1.39$\sim$1.66)
  • Table 3   The descriptive statistics for the cSE in each region (median (min$\sim$max))
    $\delta$$\theta$$\alpha$$\beta$$\gamma$
    Pre-SCS0.45 (0.34$\sim$0.56)0.61 (0.56$\sim$0.65)0.58 (0.53$\sim$0.64)1.52 (1.34$\sim$1.68)1.24 (1.08$\sim$1.38)
    FPost-SCS0.48 (0.38$\sim$0.57)0.61 (0.58$\sim$0.65)0.60 (0.55$\sim$0.65)1.59 (1.46$\sim$1.73)1.29 (1.15$\sim$1.43)
    Resting-normal0.47 (0.24$\sim$0.66)0.61 (0.51$\sim$0.68)0.61 (0.56$\sim$0.6)1.60 (1.28$\sim$1.89)1.29 (0.97$\sim$1.60)
    Pre-SCS0.46 (0.33$\sim$0.58)0.61 (0.56$\sim$0.66)0.58 (0.54$\sim$0.63)1.51 (1.29$\sim$1.73)1.24 (1.08$\sim$1.40)
    CPost-SCS0.47 (0.37$\sim$0.58)0.61 (0.58$\sim$0.65)0.60 (0.55$\sim$0.66)1.57 (1.39$\sim$1.74)1.29 (1.16$\sim$1.42)
    Resting-normal0.47 (0.25$\sim$0.66)0.62 (0.52$\sim$0.67)0.61 (0.54$\sim$0.64)1.56 (1.27$\sim$1.85)1.26 (0.87$\sim$1.61)
    Pre-SCS0.44 (0.32$\sim$0.57)0.60 (0.56$\sim$0.65)0.58 (0.54$\sim$0.63)1.55 (1.38$\sim$1.73)1.27 (1.13$\sim$1.40)
    PPost-SCS0.47 (0.38$\sim$0.57)0.61 (0.56$\sim$0.66)0.59 (0.53$\sim$0.65)1.54 (1.40$\sim$1.67)1.29 (1.18$\sim$1.40)
    Resting-normal0.46 (0.24$\sim$0.58)0.60 (0.56$\sim$0.65)0.60 (0.48$\sim$0.66)1.47 (1.15$\sim$1.79)1.27 (1.02$\sim$1.49)
    Pre-SCS0.45 (0.31$\sim$0.56)0.61 (0.57$\sim$0.65)0.57 (0.53$\sim$0.62)1.56 (1.35$\sim$1.75)1.23 (1.10$\sim$1.39)
    OPost-SCS0.47 (0.37$\sim$0.57)0.62 (0.57$\sim$0.66)0.60 (0.51$\sim$0.68)1.54 (1.40$\sim$1.67)1.27 (1.18$\sim$1.39)
    Resting-normal0.42 (0.21$\sim$0.65)0.61 (0.58$\sim$0.66)0.61 (0.54$\sim$0.67)1.48 (1.20$\sim$1.79)1.26 (1.01$\sim$1.50)
  • Table 4   The descriptive statistics for the cSE between the regions (median (min$\sim$max))
    $\delta$$\theta$$\alpha$$\beta$$\gamma$
    Pre-SCS 0.46 (0.34$\sim$0.57)0.60 (0.56$\sim$0.65)0.58 (0.53$\sim$0.64)1.52 (1.30$\sim$1.70)1.24 (1.10$\sim$1.39)
    F-CPost-SCS 0.47 (0.37$\sim$0.57)0.61 (0.57$\sim$0.66)0.60 (0.55$\sim$0.65)1.59 (1.45$\sim$1.73)1.29 (1.17$\sim$1.43)
    Resting-normal 0.45 (0.20$\sim$0.63)0.60 (0.51$\sim$0.67)0.61 (0.55$\sim$0.66)1.60 (1.30$\sim$1.88)1.35 (0.96$\sim$1.51)
    Pre-SCS 0.45 (0.32$\sim$0.58)0.61 (0.56$\sim$0.65)0.59 (0.52$\sim$0.63)1.53 (1.37$\sim$1.71)1.25 (1.11$\sim$1.38)
    F-PPost-SCS 0.47 (0.36$\sim$0.55)0.61 (0.56$\sim$0.66)0.60 (0.54$\sim$0.65)1.58 (1.42$\sim$1.76)1.29 (1.15$\sim$1.47)
    Resting-normal 0.44 (0.20$\sim$0.64)0.70 (0.52$\sim$0.67)0.60 (0.55$\sim$0.66)1.58 (1.26$\sim$1.88)1.36 (1.25$\sim$1.43)
    Pre-SCS 0.45 (0.33$\sim$0.56)0.60 (0.56$\sim$0.65)0.59 (0.52$\sim$0.64)1.55 (1.31$\sim$1.79)1.23 (1.02$\sim$1.43)
    F-OPost-SCS 0.47 (0.38$\sim$0.56)0.61 (0.57$\sim$0.66)0.61 (0.54$\sim$0.66)1.58 (1.42$\sim$1.75)1.29 (1.16$\sim$1.43)
    Resting-normal 0.45 (0.21$\sim$0.64)0.70 (0.53$\sim$0.66)0.60 (0.54$\sim$0.66)1.65 (1.39$\sim$1.89)1.32 (1.26$\sim$1.42)
    Pre-SCS 0.45 (0.32$\sim$0.57)0.60 (0.56$\sim$0.65)0.57 (0.52$\sim$0.64)1.53 (1.36$\sim$1.69)1.25 (1.11$\sim$1.40)
    C-PPost-SCS 0.46 (0.36$\sim$0.56)0.61 (0.57$\sim$0.65)0.60 (0.54$\sim$0.64)1.55 (1.40$\sim$1.71)1.27 (1.16$\sim$1.38)
    Resting-normal 0.44 (0.25$\sim$0.61)0.60 (0.52$\sim$0.65)0.59 (0.52$\sim$0.65)1.54 (1.22$\sim$1.85)1.23 (0.98$\sim$1.43)
    Pre-SCS 0.45 (0.32$\sim$0.57)0.60 (0.55$\sim$0.66)0.58 (0.51$\sim$0.64)1.55 (1.29$\sim$1.78)1.23 (1.02$\sim$1.41)
    C-OPost-SCS 0.46 (0.35$\sim$0.54)0.62 (0.58$\sim$0.65)0.60 (0.54$\sim$0.66)1.56 (1.41$\sim$1.71)1.29 (1.19$\sim$1.39)
    Resting-normal 0.45 (0.21$\sim$0.63)0.60 (0.53$\sim$0.66)0.61 (0.56$\sim$0.65)1.55 (1.30$\sim$1.79)1.25 (1.01$\sim$1.41)
    Pre-SCS 0.45 (0.35$\sim$0.55)0.60 (0.56$\sim$0.65)0.59 (0.53$\sim$0.64)1.57 (1.33$\sim$1.79)1.25 (1.00$\sim$1.45)
    P-OPost-SCS 0.47 (0.38$\sim$0.56)0.61 (0.57$\sim$0.66)0.60 (0.54$\sim$0.66)1.55 (1.42$\sim$1.72)1.28 (1.17$\sim$1.39)
    Resting-normal 0.46 (0.20$\sim$0.62)0.69 (0.57$\sim$0.65)0.59 (0.54$\sim$0.65)1.53 (1.24$\sim$1.83)1.29 (1.10$\sim$1.40)
  • Table 5   The descriptive statistics for the parameters of the network (mean$\pm$std)
    $\delta$$\theta$$\alpha$$\beta$$\gamma$
    Pre-SCS0.451$\pm$0.0200.608$\pm$0.0450.585$\pm$0.0740.599$\pm$0.0250.639$\pm$0.086
    CPost-SCS0.473$\pm$0.0170.616$\pm$0.0340.699$\pm$0.0840.793$\pm$0.0540.767$\pm$0.047
    Resting-normal0.557$\pm$0.0360.700$\pm$0.0240.834$\pm$0.0310.853$\pm$0.0510.884$\pm$0.024
    Pre-SCS1.847$\pm$0.0731.152$\pm$0.1021.560$\pm$0.2071.501$\pm$0.1031.421$\pm$0.155
    LPost-SCS1.825$\pm$0.0671.148$\pm$0.1031.155$\pm$0.2051.004$\pm$0.1011.027$\pm$0.105
    Resting-normal1.643$\pm$0.0520.950$\pm$0.0790.923$\pm$0.0720.707$\pm$0.0820.733$\pm$0.112
    Pre-SCS0.244$\pm$0.0390.528$\pm$0.0400.375$\pm$0.0490.422$\pm$0.0680.426$\pm$0.071
    SPost-SCS0.259$\pm$0.0650.537$\pm$0.0670.605$\pm$0.0370.772$\pm$0.0950.763$\pm$0.052
    Resting-normal0.339$\pm$0.0550.737$\pm$0.0540.904$\pm$0.0451.164$\pm$0.0351.251$\pm$0.027
qqqq

Contact and support