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Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine

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Abstract

Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.

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Abbreviations

RVM:

Relevance vector machine

fdRVM:

Frequency-domain RVM

MEG:

Magnetoencephalography

EEG:

Electroencephalography

DICS:

Dynamic imaging of coherent sources

ECD:

Equivalent current dipole

CSD:

Cross-spectral density

MNE:

Minimum-norm estimate

MCE:

Minimum-current estimate

MVAB:

Minimum variance adaptive beamformer

FWHM:

Full width at half maximum

SNR:

Signal-to-noise ratio

BEM:

Boundary element method

EM:

Expectation-maximization

References

  • Auranen T, Nummenmaa A, Hämäläinen MS, Jääskeläinen IP, Lampinen J, Vehtari A, Sams M (2005) Bayesian analysis of the neuromagnetic inverse problem with lp-norm priors. Neuroimage 26:870–884

    Article  PubMed  Google Scholar 

  • Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Process Mag 18:14–30

    Article  Google Scholar 

  • Belardinelli P, Ciancetta L, Staudt M, Pizzella V, Londei A, Birbaumer N, Romani GL, Braun C (2007) Cerebro-muscular and cerebro-cerebral coherence in patients with pre- and perinatally acquired unilateral brain lesions. Neuroimage 37:1301–1314

    Article  PubMed  CAS  Google Scholar 

  • Belardinelli P, Ortiz E, Barnes G, Noppeney U, Preissl H (2012) Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches. PLoS ONE 7:e51985

    Article  PubMed  CAS  Google Scholar 

  • Brookes MJ, Gibson AM, Hall SD, Furlong PL, Barnes GR, Hillebrand A, Singh KD, Holliday IE, Francis ST, Morris PG (2004) A general linear model for MEG beamformer imaging. Neuroimage 23:936–946

    Article  PubMed  Google Scholar 

  • Dalal SS, Sekihara K, Nagarajan SS (2006) Modified beamformers for coherent source region suppression. IEEE Transact Biomed Eng 53:1357–1363

    Article  Google Scholar 

  • Dalal SS, Guggisberg AG, Edwards E, Sekihara K, Findlay AM, Canolty RT, Knight RT, Barbaro NM, Kirsch HE, Nagarajan SS (2007) Spatial localization of cortical time-frequency dynamics. Conf Proc IEEE Eng Med Biol Soc 2007:4941–4944

    PubMed  Google Scholar 

  • Daskalakis ZJ, Christensen BK, Fitzgerald PB, Roshan L, Chen R (2002) The mechanisms of interhemispheric inhibition in the human motor cortex. J Physiol 543:317–326

    Article  PubMed  CAS  Google Scholar 

  • Friston KJ, Glaser DE, Henson RNA, Kiebel S, Phillips C, Ashburner J (2002) Classical and Bayesian inference in neuroimaging: applications. Neuroimage 16:484–512

    Article  PubMed  CAS  Google Scholar 

  • Friston K, Chu C, Mourão-Miranda J, Hulme O, Rees G, Penny W, Ashburner J (2008a) Bayesian decoding of brain images. Neuroimage 39:181–205

    Article  PubMed  Google Scholar 

  • Friston K, Harrison L, Daunizeau J, Kiebel S, Phillips C, Trujillo-Barreto N, Henson R, Flandin G, Mattout J (2008b) Multiple sparse priors for the M/EEG inverse problem. Neuroimage 39:1104–1120

    Article  PubMed  Google Scholar 

  • Ghosh A, Rho Y, McIntosh A, Kötter R, Jirsa V (2008) Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Comput Biol 4:e1000196

    Article  PubMed  Google Scholar 

  • Gross J, Ioannides A (1999) Linear transformations of data space in MEG. Phys Med Biol 44:2081–2097

    Article  PubMed  CAS  Google Scholar 

  • Gross J, Kujala J, Hamalainen M, Timmermann L, Schnitzler A, Salmelin R (2001) Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proc Natl Acad Sci 98:694–699

    Article  PubMed  CAS  Google Scholar 

  • Gross J, Timmermann L, Kujala J, Dirks M, Schmitz F, Salmelin R, Schnitzler A (2002) The neural basis of intermittent motor control in humans. Proc Natl Acad Sci 99:2299–2302

    Article  PubMed  CAS  Google Scholar 

  • Hari R, Salmelin R (1997) Human cortical oscillations: a neuromagnetic view through the skull. Trends Neurosci 20:44–48

    Article  PubMed  CAS  Google Scholar 

  • Hirschmann J, Özkurt T, Butz M, Homburger M, Elben S, Hartmann C, Vesper J, Wojtecki L, Schnitzler A (2011) Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson’s disease. Neuroimage 55:1159–1168

    Article  PubMed  CAS  Google Scholar 

  • Jensen O, Vanni S (2002) A new method to identify multiple sources of oscillatory activity from magnetoencephalographic data. Neuroimage 15:568–574

    Article  PubMed  Google Scholar 

  • Jerbi K, Lachaux JP, N’Diaye K, Pantazis D, Leahy RM, Garnero L, Baillet S (2007) Coherent neural representation of hand speed in humans revealed by MEG imaging. Proc Natl Acad Sci 104:7676–7681

    Article  PubMed  CAS  Google Scholar 

  • Kujala J, Pammer K, Cornelissen P, Roebroeck A, Formisano E, Salmelin R (2007) Phase coupling in a cerebro-cerebellar network at 8–13 Hz during reading. Cereb Cortex 17:1476–1485

    Article  PubMed  Google Scholar 

  • Liljeström M, Kujala J, Jensen O, Salmelin R (2005) Neuromagnetic localization of rhythmic activity in the human brain: a comparison of three methods. Neuroimage 25:734–745

    Article  PubMed  Google Scholar 

  • MacKay DJC (1992) Bayesian interpolation. Neural Comput 4:415–447

    Article  Google Scholar 

  • Mazaheri A, Nieuwenhuis ILC, van Dijk H, Jensen O (2009) Prestimulus alpha and mu activity predicts failure to inhibit motor responses. Hum Brain Mapp 30:1791–1800

    Article  PubMed  Google Scholar 

  • Mosher JC, Baillet S, Leahy RM (2004) Equivalence of linear approaches in bioelectromagnetic inverse solutions. IEEE Workshop on Statistical Signal Processing, pp 294–297

  • Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115:2292–2307

    Article  PubMed  Google Scholar 

  • Nummenmaa A, Auranen T, Hämäläinen MS, Jääskeläinen IP, Lampinen J, Sams M, Vehtari A (2007) Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. Neuroimage 35:669–685

    Article  PubMed  Google Scholar 

  • Osipova D, Takashima A, Oostenveld R, Fernández G, Maris E, Jensen O (2006) Theta and gamma oscillations predict encoding and retrieval of declarative memory. J Neurosci 26:7523–7531

    Article  PubMed  CAS  Google Scholar 

  • Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857

    Article  PubMed  CAS  Google Scholar 

  • Phillips C, Mattout J, Rugg M, Maquet P, Friston K (2005) An empirical Bayesian solution to the source reconstruction problem in EEG. Neuroimage 24:997–1011

    Article  PubMed  Google Scholar 

  • Salmelin R, Hari R (1994) Characterization of spontaneous MEG rhythms in healthy adults. Electroencephalogr Clin Neurophysiol 91:237–248

    Article  PubMed  CAS  Google Scholar 

  • Sato M, Yoshioka T, Kajihara S, Toyama K, Goda N, Doya K, Kawato M (2004) Hierarchical Bayesian estimation for MEG inverse problem. Neuroimage 23:806–826

    Article  PubMed  Google Scholar 

  • Scholkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12:1207–1245

    Article  PubMed  Google Scholar 

  • Sekihara K, Nagarajan SS, Poeppel D, Marantz A (2002) Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates. IEEE Trans Biomed Eng 49:1534–1546

    Article  PubMed  Google Scholar 

  • Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28:1178–1193

    Article  PubMed  Google Scholar 

  • Timmermann L, Gross J, Dirks M, Volkmann J, Freund HJ, Schnitzler A (2002) The cerebral oscillatory network of parkinsonian resting tremor. Brain 126:199–212

    Article  Google Scholar 

  • Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    Google Scholar 

  • Tipping ME (2004) Bayesian inference: an introduction to principles and practice in machine learning. Lecture notes in computer science, pp 41–62

  • Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 44:867–880

    Article  PubMed  Google Scholar 

  • Vrba J, Robinson SE (2001) Signal processing in magnetoencephalography. Methods 25:249–271

    Article  PubMed  CAS  Google Scholar 

  • Wipf D, Nagarajan S (2007) Beamforming using the relevance vector machine. ICML '07 Proceedings of the 24th international conference on Machine learning 1023–1030

  • Wipf D, Nagarajan S (2009) A unified Bayesian framework for MEG/EEG source imaging. Neuroimage 44:947–966

    Article  PubMed  Google Scholar 

  • Wipf D, Owen J, Attias H, Sekihara K, Nagarajan S (2009) Estimating the location and orientation of complex, correlated neural activity using MEG. Adv Neural Inform Process Syst 21. http://www.goldenmetallic.com/research/nips08.pdf

  • Wipf D, Owen J, Attias H, Sekihara K, Nagarajan S (2010) Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. Neuroimage 49:641–655

    Article  PubMed  Google Scholar 

  • Zoltowski MD (1988) On the performance analysis of the MVDR beamformer in the presence of correlated interference. IEEE Trans Acoust Speech Signal Process 36:945–947

    Article  Google Scholar 

  • Zumer JM, Attias HT, Sekihara K, Nagarajan SS (2007) A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data. Neuroimage 37:102–115

    Article  PubMed  Google Scholar 

Download references

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Authors and Affiliations

  1. O.V. Lounasmaa Laboratory, Brain Research Unit, Aalto University, Espoo, Finland

    P. Belardinelli, A. Jalava, J. Kujala & R. Salmelin

  2. Department of Psychology, Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, UK

    J. Gross

  3. Neurosurgery Department, Tübingen University Hospital, Otfried-Müller Str. 45, 72076, Tübingen, Germany

    P. Belardinelli

Authors
  1. P. Belardinelli
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  2. A. Jalava
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  3. J. Gross
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  4. J. Kujala
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  5. R. Salmelin
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Correspondence to P. Belardinelli.

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Belardinelli, P., Jalava, A., Gross, J. et al. Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine. Cogn Process 14, 357–369 (2013). https://doi.org/10.1007/s10339-013-0568-y

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  • DOI: https://doi.org/10.1007/s10339-013-0568-y

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