Search Advanced

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 3: 2496-2515. doi: 10.3934/mbe.2020137
Previous Article Next Article
Review

A review on epileptic foci localization using resting-state functional magnetic resonance imaging

  • 1.
    College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
  • 2.
    Department of Internal Neurology, Tiantan Hospital, Beijing 100070, China
  • Received: 17 October 2019 Accepted: 13 February 2020 Published: 26 February 2020

  • Epilepsy is a brain syndrome caused by synchronous abnormal discharge of brain neurons. As an effective treatment for epilepsy, successful surgical resection requires accurate localization of epileptic foci to avoid iatrogenic disability. Previous studies have demonstrated the potential of restingstate functional magnetic resonance imaging (rs-fMRI) technique to localize epileptic foci though clinical applications of rs-fMRI are still at an early stage of development. fMRI data analysis approaches seek pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. In present study, localization strategies of epileptic foci in rs-fMRI technology were classified and summarized. To begin with, data-driven approaches attempting to determine the intrinsic structure of the data were discussed in detail. Then, as novel fMRI data analysis methods, deconvolution algorithms such as total activation (TA) and blind deconvolution were discussed, which were applied to explore the underlying activity-inducing signal of the BOLD signal. Lastly, effective connectivity approaches such as autocorrelation function method and Pearson correlation coefficient have also been proposed to identify the brain regions driving the generation of seizures within the epileptic network. In the future, fMRI technology can be used as a supplement of intraoperative subdural electrode method or combined with traditional epileptic focus localization technologies, which is one of the most attractive aspect in clinic. It may also play an important role in providing diagnostic information for epilepsy patients.

    Citation: Yue Shi, Xin Zhang, Chunlan Yang, Jiechuan Ren, Zhimei Li, Qun Wang. A review on epileptic foci localization using resting-state functional magnetic resonance imaging[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2496-2515. doi: 10.3934/mbe.2020137

    Related Papers:

  • Epilepsy is a brain syndrome caused by synchronous abnormal discharge of brain neurons. As an effective treatment for epilepsy, successful surgical resection requires accurate localization of epileptic foci to avoid iatrogenic disability. Previous studies have demonstrated the potential of restingstate functional magnetic resonance imaging (rs-fMRI) technique to localize epileptic foci though clinical applications of rs-fMRI are still at an early stage of development. fMRI data analysis approaches seek pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. In present study, localization strategies of epileptic foci in rs-fMRI technology were classified and summarized. To begin with, data-driven approaches attempting to determine the intrinsic structure of the data were discussed in detail. Then, as novel fMRI data analysis methods, deconvolution algorithms such as total activation (TA) and blind deconvolution were discussed, which were applied to explore the underlying activity-inducing signal of the BOLD signal. Lastly, effective connectivity approaches such as autocorrelation function method and Pearson correlation coefficient have also been proposed to identify the brain regions driving the generation of seizures within the epileptic network. In the future, fMRI technology can be used as a supplement of intraoperative subdural electrode method or combined with traditional epileptic focus localization technologies, which is one of the most attractive aspect in clinic. It may also play an important role in providing diagnostic information for epilepsy patients.


    加载中


    [1] N. Jetté, J. W. Sander, M. R. Keezer, Surgical treatment for epilepsy: The potential gap between evidence and practice, Lancet Neurol., 15 (2016), 982-994.
    [2] J. S. Ebersole, S. M. Ebersole, Combining MEG and EEG source modeling in epilepsy evaluations, J. Clin. Neurophysiol., 27 (2010), 360-371.
    [3] S. Baxendale, The role of functional MRI in the presurgical investigation of temporal lobe epilepsy patients: a clinical perspective and review, J. Clin. Exp. Neuropsychol., 24 (2002), 664-676.
    [4] K. Lee, S. Tak, J. C. Ye, A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion, IEEE Trans. Med. Imaging, 30 (2010), 1076-1089.
    [5] F. Grouiller, L. Vercueil, A. Krainik, C. Segebarth, P. Kahane, O. David, Characterization of the hemodynamic modes associated with interictal epileptic activity using a deformable model‐based analysis of combined EEG and functional MRI recordings, Hum. Brain Mapp., 31 (2010), 1157-1173.
    [6] V. L. Morgan, R. R. Price, A. Arain, P. Modur, B. Abou-Khalil, Resting functional MRI with temporal clustering analysis for localization of epileptic activity without EEG, Neuroimage, 21 (2004), 473-481.
    [7] P. P. Mitra, S. Ogawa, X. Hu, K Uǧurbil, The nature of spatiotemporal changes in cerebral hemodynamics as manifested in functional magnetic resonance imaging, Magn. Reson. Med., 37 (1997), 511-518.
    [8] M. J. McKeown, S. Makeig, G. G. Brown, T. Jung, S. S. Kindermann, A. J. Bell, et al., Analysis of fMRI data by blind separation into independent spatial components, Hum. Brain Mapp., 6 (1998), 160-188.
    [9] J. H. Gao, S. H. Yee, Iterative temporal clustering analysis for the detection of multiple response peaks in fMRI, Magn. Reson. Imaging, 21 (2003), 51-53.
    [10] S. H. Yee, J. H. Gao, Improved detection of time windows of brain responses in fMRI using modified temporal clustering analysis, Magn. Reson. Imaging, 20 (2002), 17-26.
    [11] K. Hamandi, A. S. Haddadi, A. Liston, H. Laufs, D. R. Fish, L. Lemieux, fMRI temporal clustering analysis in patients with frequent interictal epileptiform discharges: comparison with EEG-driven analysis, Neuroimage, 26 (2005), 309-316.
    [12] V. L. Morgan, J. C. Gore, B. Abou-Khalil, Cluster analysis detection of functional MRI activity in temporal lobe epilepsy, Epilepsy Res., 76 (2007), 22-33.
    [13] Q. Song, H. Chen, D. Yao, G Lu, Z Zhang, Detecting Epileptic Activities from resting fMRI Time-course by using PCA algorithm, 2005 Int. Conf. Neural Networks Brain, (2005), 1552-1555.
    [14] W. Backfrieder, R. Baumgartner, M. Samal, E. Moser, H. Bergmann, Quantification of intensity variations in functional MR images using rotated principal components, Phys. Med. Biol., 41 (1996), 1425.
    [15] R. Rodionov, F. De Martino, H. Laufs, D. W. Carmichael, E. Formisano, M. Walker, et al., Independent component analysis of interictal fMRI in focal epilepsy: Comparison with general linear model-based EEG-correlated fMRI, Neuroimage, 38 (2007), 488-500.
    [16] C. H. Zhang, Y. Lu, B. Brinkmann, K Welker, G. Worrell, B. He, Using functional MRI alone for localization in focal epilepsy, 2014 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., (2014), 730-733.
    [17] V. D. Calhoun, T. Eichele, T. Adalı, E. A. Allen, Decomposing the brain: Components and modes, networks and nodes, Trends Cogn. Sci.., 16 (2012), 255-256.
    [18] B. Hunyadi, S. Tousseyn, P. Dupont, S. V. Huffel, M. D. Vos, W. V. Paesschen, A prospective fMRI-based technique for localising the epileptogenic zone in presurgical evaluation of epilepsy, Neuroimage, 113 (2015), 329-339.
    [19] M. J. McKeown, T. P. Jung, S. Makeig, G. Brown, S. S. Kindermann, T. Lee, et al., Spatially independent activity patterns in functional MRI data during the Stroop color-naming task, Proc. Natl. Acad. Sci., 95 (1998), 803-810.
    [20] C. H. Zhang, Y. Lu, B. Brinkmann, K. Welker, G. Worrell, B. He, Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI, Clin. Neurophysiol., 126 (2015), 27-38.
    [21] F. D. Martino, F. Gentile, F. Esposito, M. Balsi, F. D. Salle, R. Goebel, et al., Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers, Neuroimage, 34 (2007), 177-194.
    [22] D. Cordes, V. M. Haughton, K. Arfanakis, J. D. Carew, P. A. Turski, C. H. Moritz, et al., Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data, Am. J. Neuroradiol., 22 (2001), 1326-1333.
    [23] M. Negishi, R. Martuzzi, E. J. Novotny, D. D. Spencer, R. T. Constable, Functional MRI connectivity as a predictor of the surgical outcome of epilepsy, Epilepsia, 52 (2011), 1733-1740.
    [24] H. Chen, D. Yao, G. Lu, Z. Zhang, Q. Hu, Localization of latent epileptic activities using spatio-temporal independent component analysis of fMRI data, Brain Topogr., 19 (2006), 21-28.
    [25] X. J. Chai, A. N. Castañón, D. Öngür, S. Whitfield-Gabrieli, Anticorrelations in resting state networks without global signal regression, Neuroimage, 59 (2012), 1420-1428.
    [26] J. S. Archer, D. F. Abbott, A. B. Waites, G. D. Jackson, fMRI "deactivation" of the posterior cingulate during generalized spike and wave, Neuroimage, 20 (2003), 1915-1922.
    [27] E. Kobayashi, A. P. Bagshaw, C. Grova, F. Dubeau, J. Gotman, Negative BOLD responses to epileptic spikes, Hum. Brain Mapp., 27 (2010), 488-497.
    [28] S. Wang, Z. Zhang, G. Lu, L. Luo, Localization of brain activity by temporal anti-correlation with the posterior cingulate cortex, 2007 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., (2007), 5227-5230.
    [29] J. A. Maldjian, P. J. Laurienti, J. H. Burdette, Precentral gyrus discrepancy in electronic versions of the Talairach atlas, Neuroimage, 21 (2004), 450-455.
    [30] J. A. Maldjian, P. J. Laurienti, R. A. Kraft, J. H. Burdetteet, An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets, Neuroimage, 19 (2003), 1233-1239.
    [31] D. M. Cole, S. M. Smith, C. F. Beckmann, Advances and pitfalls in the analysis and interpretation of resting-state fMRI data, Front. Syst. Neurosci., 4 (2010), 8.
    [32] F. I. Karahanoglu, C. Caballero-Gaudes, F. Lazeyras, D. V. D. Ville, Total activation: fMRI deconvolution through spatio-temporal regularization, Neuroimage, 73 (2013), 121-134.
    [33] F. I. Karahanoğlu, F. Grouiller, C. C. Gaudes, M. Seeck, S. Vulliemoz, D. V. D. Ville, Spatial mapping of interictal epileptic discharges in fMRI with total activation, 2013 IEEE 10th Int. Symp. Biomed. Imaging, (2013), 1500-1503.
    [34] C. G. Bénar, D. W. Gross, Y. Wang, V. Petre, B. Pike, F. Dubeau, et al., The BOLD response to interictal epileptiform discharges, Neuroimage, 17 (2002), 1182-1192.
    [35] R. Lopes, J. M. Lina, F. Fahoum, J. Cotman, Detection of epileptic activity in fMRI without recording the EEG, Neuroimage, 60 (2012), 1867-1879.
    [36] I. Khalidov, J. Fadili, F. Lazeyras, D. V. D. Ville, M. Unser, Activelets: Wavelets for sparse representation of hemodynamic responses, Signal Process., 91 (2011), 2810-2821.
    [37] G. Bettus, E. Guedj, F. Joyeux, S. Confort-Gouny, E. Soulier, V. Laguitton, et al., Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms, Hum. Brain Mapp., 30 (2009), 1580-1591.
    [38] G. Bettus, F. Bartolomei, S. Confort-Gouny, E. Guedj, P. Chauvel, P. J. Cozzone, et al., Role of resting state functional connectivity MRI in presurgical investigation of mesial temporal lobe epilepsy, J. Neurol., Neurosurg., 81 (2010), 1147-1154.
    [39] F. Pittau, C. Grova, F. Moeller, F. Dubeau, Jean Gotman, Patterns of altered functional connectivity in mesial temporal lobe epilepsy, Epilepsia, 53 (2012), 1013-1023.
    [40] F. R. Pereira, A. Alessio, M. S. Sercheli, T. Pedro, E. Bilevicius, J. M. Rondina, et al., Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI, BMC Neurosci., 11 (2010), 66.
    [41] W. Liao, Z. Zhang, Z. Pan, D. Mantini, J. Ding, X. Duan, et al., Altered functional connectivity and small-world in mesial temporal lobe epilepsy, PLoS One, 5 (2010), e8525.
    [42] V. L. Morgan, B. P. Rogers, H. H. Sonmezturk, J. C. Gore, B Abou-Khalil, Cross hippocampal influence in mesial temporal lobe epilepsy measured with high temporal resolution functional magnetic resonance imaging, Epilepsia, 52 (2011), 1741-1749.
    [43] L. Maccotta, B. J. He, A. Z. Snyder, L. N. Eisenman, T. L. Benzinger, B. M. Ances, et al., Impaired and facilitated functional networks in temporal lobe epilepsy, Neuro Image Clin., 2 (2013), 862-872.
    [44] S. M. Stufflebeam, H. Liu, J. Sepulcre, N. Tanaka, R. L. Buckner, J. R. Madsen, Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging, J. Neurosurg., 114 (2011), 1693-1697.
    [45] S. Nedic, S. M. Stufflebeam, C. Rondinoni, T. R. Velasco, A. C. Santos, J. P. Leite, et al., Using network dynamic fMRI for detection of epileptogenic foci, BMC Neurol., 15 (2015), 262.
    [46] D. Hartman, J. Hlinka, M. Paluš, D. Mantini, M. Corbetta, The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks, Chaos: Int. J. Nonlinear Sci., 21 (2011), 013119.
    [47] M. Ke, X. Duan, F. Zhang, X. Yang, The research of the generalized tonic-clonic seizure epilepsy by resting state fMRI, First Int. Conf. Inf. Sci., Mach., Mater. Energy, (2015).
    [48] R. L. Buckner, J. Sepulcre, T. Talukdar, F. M. Krienen, H. Liu, T. Hedden, et al., Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease, J. Neurosci., 29 (2009), 1860-1873. doi: 10.1523/JNEUROSCI.5062-08.2009
    [49] J. Sepulcre, H. S. Liu, T. Talukdar, I. Martincorena,B. T. T. Yeo, R. L. Buckner, The organization of local and distant functional connectivity in the human brain, PLoS Comput. Biol., 6 (2010), e1000808.
    [50] R. P. Carne, T. J. O'Brien, C. J. Kilpatrick, L. R. MacGregor, R. J. Hicks, M. A. Murphy, et al., MRI-negative PET-positive temporal lobe epilepsy: a distinct surgically remediable syndrome, Brain, 127 (2004), 2276-2285. doi: 10.1093/brain/awh257
    [51] K. S. Hong, S. K. Lee, J. Y. Kim, D. S. Lee, C. K. Chung, Pre-surgical evaluation and surgical outcome of 41 patients with non-lesional neocortical epilepsy, Seizure,11 (2002), 184-192.
    [52] R. L. Kutsy, Focal extratemporal epilepsy: clinical features, EEG patterns, and surgical approach, J. Neurol. Sci., 166 (1999), 1-15.
    [53] J. T. Kim, S. J. Bai, K. O. Choi, Y. J. Lee, H. J. Park, D, S. Kim, et al., Comparison of various imaging modalities in localization of epileptogenic lesion using epilepsy surgery outcome in pediatric patients, Seizure, 18 (2009), 504-510.
    [54] I. Y. Capraz, G. Kurt, O. Akdemir, T. Hirfanoglu, Y. Oner, T. Sengezer, et al., Surgical outcome in patients with MRI-negative, PET-positive temporal lobe epilepsy, Seizure, 29 (2015), 63-68.
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Mathematical Biosciences and Engineering
3.9

Metrics

Article views(4782) PDF downloads(685) Cited by(4)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Tables(1)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog