Advertisement

Analysis of Electroencephalographic Dynamic Functional Connectivity in Alzheimer’s Disease

  • Pablo Núñez
  • Jesús Poza
  • Carlos Gómez
  • Saúl J. Ruiz-Gómez
  • Víctor Rodríguez-González
  • Miguel Ángel Tola-Arribas
  • Mónica Cano
  • Roberto Hornero
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/2)

Abstract

The aim of this study was to characterize the dynamic functional connectivity of resting-state electroencephalographic (EEG) activity in Alzheimer’s disease (AD). The magnitude squared coherence (MSCOH) of 50 patients with dementia due to AD and 28 cognitively healthy controls was computed. MSCOH was estimated in epochs of 60 s subdivided in overlapping windows of different lengths (1, 2, 3, 5 and 10 s; 50% overlap). The effect of epoch length was tested on MSCOH and it was found that MSCOH stabilized at a window length of 3 s. We tested whether the MSCOH fluctuations observed reflected actual changes in functional connectivity by means of surrogate data testing, with the standard deviation of MSCOH chosen as the test statistic. The results showed that the variability of the measure could be due to dynamic functional connectivity. Furthermore, a significant reduction in the dynamic MSCOH connectivity of AD patients compared to controls was found in the delta (0–4 Hz) and beta-1 (13–30 Hz) bands. This indicated that AD patients show lesser variation in neural connectivity during resting state. Finally, a correlation between relative power and standard deviation was found, suggesting that an increase/peak in power spectrum could be a pre-requisite for dynamic functional connectivity in a specific frequency band.

Keywords

Alzheimer’s disease Dynamic functional connectivity Electroencephalogram Neural dynamics Coherence Relative power 

Notes

Acknowledgements

This study has been partially funded by projects TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ and FEDER, the project ‘Análisis y correlación entre el genoma completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer’ (Inter-regional cooperation program VA Spain-Portugal POCTEP 2014–2020) of the European Commission and FEDER, and project VA037U16 of the ‘Junta de Castilla y León and FEDER. P. Núñez and S. J. Ruiz are in receipt of predoctoral grants co-financed by the ‘Junta de Castilla y León’ and ESF.

Conflict of Interest

There are no conflicts of interest that could influence this research work.

References

  1. 1.
    Babiloni C, et al.: Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms. Int J Psychophysiol 103, 88–102 (2015).Google Scholar
  2. 2.
    Hansen ECA, et al.: Functional connectivity dynamics: Modeling the switching behavior of the resting state. Neuroimage 105, 525–535 (2015).Google Scholar
  3. 3.
    Hutchison RM, et al.: Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).Google Scholar
  4. 4.
    McKhann G, et al.: The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging- Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7(3), 263–26 (2011).Google Scholar
  5. 5.
    Roach BJ, Mathalon DH.: Event-related EEG time-frequency analysis: An overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophr Bull 34(5), 907–926 (2008).Google Scholar
  6. 6.
    Hindriks R, et al.: Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage 127, 242–256 (2016).Google Scholar
  7. 7.
    Fraschini M, et al.: The effect of epoch length on estimated EEG functional connectivity and brain network organisation. J Neural Eng 13(3), 36015 (2016).Google Scholar
  8. 8.
    Prichard D, Theiler J.: Generating surrogate data for time series with several simultaneously measured variables. Phys Rev Lett 73(7), 951–954 (1994).Google Scholar
  9. 9.
    Poza J, et al.: Spatio-Temporal fluctuations of neural dynamics in mild cognitive impairment and Alzheimer’s disease. Curr Alzheimer Res 14(9), 924–936 (2017).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Biomedical Engineering GroupUniversity of ValladolidValladolidSpain
  2. 2.Neurology ServiceRío Hortega University HospitalValladolidSpain
  3. 3.Clinical Neurophysiology ServiceRío Hortega University HospitalValladolidSpain

Personalised recommendations