Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

eLORETA Active Source Reconstruction Applied to HD-EEG in Alzheimer’s Disease

  • Chapter
  • First Online:

Abstract

Alzheimer’s disease (AD) is a brain pathology that leads to a progressive loss of cognitive functions. In its first stage, it is often mistaken for normal aging because the symptoms are not severe. This condition is referred as Mild Cognitive Impairment (MCI). Electroencephalography (EEG) has been widely used for investigating AD. In particular, the resolution of the so-called EEG inverse problem allows you to reconstruct the distribution of brain active sources. Standard EEG is characterized by a low spatial resolution that can be improved by increasing the number of the recording sensors (HD-EEG). The purpose of this paper is the computation of the brain electrical activity by the eLORETA method for three groups of subjects: CNT (healthy subjects), MCI patients and AD patients. The novelty of this work is that eLORETA was applied to HD-EEG. The hallmark of AD is the shift of the EEG power spectrum to lower frequencies. The analysis of the results suggests that EEG of MCI and, even more, of AD is characterized by an increasing power in delta and theta bands as compared with CNT. Moreover, it has been shown that the greater source activation involves Brodmann areas typically affected by this pathology and  is consistent with it. eLORETA shows the involved Brodmann areas automatically.

This is a preview of subscription content,log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 20591
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 25739
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info
Hardcover Book
JPY 25739
Price includes VAT (Japan)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Mammone, N., Inuso, G., La Foresta, F., Versaci, M., Morabito, F.C.: Clustering of entropy topography in epileptic electroencephalography. Neural Comput. Appl.20(6), 825–833 (2011).https://doi.org/10.1007/s00521-010-0505-2

    Article  Google Scholar 

  2. Mammone, N. et al.: Permutation Jaccard distance-based hierarchical clustering to estimate EEG network density modifications in MCI subjects. IEEE Trans. Neural Networks Learn. Syst. 99, 1–14 (2018)

    Google Scholar 

  3. Mammone, N. et al.: Brain network analysis of compressive sensed high-density EEG SIGNALS in AD and MCI Subjects. IEEE Trans. Ind. Inf. 15(1), 527–536 (2019)

    Google Scholar 

  4. La Foresta, F. et al.: PCA and ICA for the extraction of EEG components in cerebral death assessment. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, vol. 4. IEEE (2005). https://doi.org/10.1109/ijcnn.2005.1556301

  5. Wolters, C.H. et al.: Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: a simulation and visualization study using high-resolution finite element modeling. NeuroImage 30(3), 813–826 (2006)

    Google Scholar 

  6. Srinivasan, R., Tucker, D.M., Murias, M.: Estimating the spatial Nyquist of the human EEG. Behav. Res. Methods Instr. Comput.30(1), 8–19 (1998)

    Article  Google Scholar 

  7. Ryynanen, O.R.M., Jari, A.K.H., Jaakko, A.M.: Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull. IEEE Trans. Biomed. Eng. 53(9), 1851–1858 (2006)

    Google Scholar 

  8. Jatoi, M.A. et al.: A survey of methods used for source localization using EEG signals. Biomed. Signal Proces. Control 11, 42–52 (2014)

    Google Scholar 

  9. Pascual-Marqui, Roberto D., Michel, Christoph M., Lehmann, Dietrich: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol.18(1), 49–65 (1994)

    Article  Google Scholar 

  10. Hämäläinen, M.S., Ilmoniemi, Risto J.: Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput.32(1), 35–42 (1994)

    Article  Google Scholar 

  11. Pascual-Marqui, R.D.: Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. (2007).http://arxiv.org/abs/0710.3341

  12. Rossini, P.M., et al.: Conversion from mild cognitive impairment to Alzheimer’s disease is predicted by sources and coherence of brain electroencephalography rhythms. Neuroscience143(3), 793–803 (2006)

    Article MathSciNet  Google Scholar 

  13. Gianotti Lorena, R.R. et al.: Correlation between disease severity and brain electric LORETA tomography in Alzheimer’s disease. Clin. Neurophysiol. 118(1), 186–196 (2007)

    Google Scholar 

  14. Fuchs, M. et al.: A standardized boundary element method volume conductor model. Clin. Neurophysiol.113(5), 702–712 (2002)

    Google Scholar 

  15. Tong, S., Nitish, V.T.: Quantitative EEG analysis methods and clinical applications. Artech House (2009)

    Google Scholar 

  16. Electrical Geodesics, I.: Geodesic sensor net technical manual. Elect. Geodes. Inc (2007)

    Google Scholar 

  17. Nunez, P.L. et al.: EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr. Clin. Neurophysiol. 103(5), 499–515 (1997)

    Google Scholar 

  18. Jeong, Jaeseung: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol.115(7), 1490–1505 (2004)

    Article  Google Scholar 

  19. Labate, D., La Foresta, F., Palamara, I., Morabito, G., Bramanti, A., Zhang, Z., Morabito, F.C.: EEG complexity modifications and altered compressibility in mild cognitive impairment and Alzheimer’s disease. In: Recent Advances of Neural Network Models and Applications, pp. 163–173. Springer, Cham. (2014).https://doi.org/10.1007/978-3-319-04129-2_17

  20. Moretti, D.V. et al.: Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 115(2), 299–308 (2004)

    Google Scholar 

  21. Dauwels, Justin, Vialatte, Francois, Cichocki, Andrzej: Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res.7(6), 487–505 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DICEAM—Università degli Studi Mediterranea di Reggio Calabria Feo di Vito, 89100, Reggio Calabria, Italy

    Serena Dattola, Giuseppina Inuso, Nadia Mammone, Francesco Carlo Morabito & Fabio La Foresta

  2. IRCCS Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, Messina, Italy

    Lilla Bonanno & Simona De Salvo

Authors
  1. Serena Dattola

    You can also search for this author inPubMed Google Scholar

  2. Giuseppina Inuso

    You can also search for this author inPubMed Google Scholar

  3. Nadia Mammone

    You can also search for this author inPubMed Google Scholar

  4. Lilla Bonanno

    You can also search for this author inPubMed Google Scholar

  5. Simona De Salvo

    You can also search for this author inPubMed Google Scholar

  6. Francesco Carlo Morabito

    You can also search for this author inPubMed Google Scholar

  7. Fabio La Foresta

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toSerena Dattola.

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia and IIASS, Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataró, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

Rights and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dattola, S.et al. (2021). eLORETA Active Source Reconstruction Applied to HD-EEG in Alzheimer’s Disease. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_49

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 20591
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 25739
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info
Hardcover Book
JPY 25739
Price includes VAT (Japan)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp