Advertisement

Brain-Connectivity Analysis to Differentiate Phasmophobic and Non-phasmophobic: An EEG Study

Conference paper
  • 153 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

Brain-connectivity refers to a pattern of functional or effective connectivity of distinct modules of human brain due to interactions between them. In this paper, the authors have attempted to conduct a brain connectivity based analysis to study the brain circuitry in subjects from their electroencephalographic (EEG) data, while they are engaged in playing a horror video game. The main motive of our work is to understand the differences in the effective connectivity among phasmophobic and non-phasmophobic subjects. In the present analysis, we propose a modified version of the causality test, named as Convergent Cross Mapping (CCM) to perform the analysis. The proposed CCM improves the performance of the standard CCM with an added feature of finding the possible direction of causation in terms of conditional entropy or maximum information transfer among the brain signal-sources. Experimental results and statistical analysis show that the proposed method shows superior efficacy in estimating the directed brain-connectivity as compared to the very well-known classical Granger Causality, classical CCM and other off-the-shelf brain-connectivity algorithms.

Keywords

Brain connectivity Phasmophobia Convergent cross mapping EEG Entropy 

References

  1. 1.
    Åhs, F., et al.: Arousal modulation of memory and amygdala-parahippocampal connectivity: a pet-psychophysiology study in specific phobia. Psychophysiology 48(11), 1463–1469 (2011)CrossRefGoogle Scholar
  2. 2.
    Åhs, F., et al.: Disentangling the web of fear: amygdala reactivity and functional connectivity in spider and snake phobia. Psychiatry Res. Neuroimaging 172(2), 103–108 (2009)CrossRefGoogle Scholar
  3. 3.
    Britton, J.C., Gold, A.L., Deckersbach, T., Rauch, S.L.: Functional MRI study of specific animal phobia using an event-related emotional counting stroop paradigm. Depression Anxiety 26(9), 796–805 (2009)CrossRefGoogle Scholar
  4. 4.
    Chowdhury, A., Dewan, D., Ghosh, L., Konar, A., Nagar, A.K.: Brain connectivity analysis in color perception problem using convergent cross mapping technique. In: Nagar, A.K., Deep, K., Bansal, J.C., Das, K.N. (eds.) Soft Computing for Problem Solving 2019. AISC, vol. 1138, pp. 287–299. Springer, Singapore (2020).  https://doi.org/10.1007/978-981-15-3290-0_22CrossRefGoogle Scholar
  5. 5.
    Clark, A.T., et al.: Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96(5), 1174–1181 (2015)CrossRefGoogle Scholar
  6. 6.
    Danti, S., Ricciardi, E., Gentili, C., Gobbini, M.I., Pietrini, P., Guazzelli, M.: Is social phobia a “mis-communication’’ disorder? Brain functional connectivity during face perception differs between patients with social phobia and healthy control subjects. Front. Syst. Neurosci. 4, 152 (2010)CrossRefGoogle Scholar
  7. 7.
    Das, S., Halder, A., Bhowmik, P., Chakraborty, A., Konar, A., Nagar, A.: Voice and facial expression based classification of emotion using linear support vector machine. In: 2009 Second International Conference on Developments in eSystems Engineering, pp. 377–384. IEEE (2009)Google Scholar
  8. 8.
    De Vries, Y.A., et al.: Childhood generalized specific phobia as an early marker of internalizing psychopathology across the lifespan: results from the world mental health surveys. BMC Med. 17(1), 1–11 (2019)CrossRefGoogle Scholar
  9. 9.
    Del Casale, A.: Functional neuroimaging in specific phobia. Psychiatry Res. Neuroimaging 202(3), 181–197 (2012)CrossRefGoogle Scholar
  10. 10.
    Demenescu, L., et al.: Amygdala activation and its functional connectivity during perception of emotional faces in social phobia and panic disorder. J. Psychiatric Res. 47(8), 1024–1031 (2013)CrossRefGoogle Scholar
  11. 11.
    Deppermann, S., et al.: Functional co-activation within the prefrontal cortex supports the maintenance of behavioural performance in fear-relevant situations before an itbs modulated virtual reality challenge in participants with spider phobia. Behav. Brain Res. 307, 208–217 (2016)CrossRefGoogle Scholar
  12. 12.
    Eaton, W.W., Bienvenu, O.J., Miloyan, B.: Specific phobias. Lancet Psychiatry 5(8), 678–686 (2018)CrossRefGoogle Scholar
  13. 13.
    Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica J. Econometric Soc. 37, 424–438 (1969)CrossRefGoogle Scholar
  14. 14.
    Grös, D.F., Antony, M.M.: The assessment and treatment of specific phobias: a review. Curr. Psychiatry Rep. 8(4), 298–303 (2006)CrossRefGoogle Scholar
  15. 15.
    Hilbert, K., Evens, R., Maslowski, N.I., Wittchen, H.U., Lueken, U.: Neurostructural correlates of two subtypes of specific phobia: a voxel-based morphometry study. Psychiatry Res. Neuroimaging 231(2), 168–175 (2015)CrossRefGoogle Scholar
  16. 16.
    Indovina, I., Conti, A., Lacquaniti, F., Staab, J.P., Passamonti, L., Toschi, N.: Lower functional connectivity in vestibular-limbic networks in individuals with subclinical agoraphobia. Front. Neurol. 10, 874 (2019)CrossRefGoogle Scholar
  17. 17.
    Kar, R., Konar, A., Chakraborty, A., Nagar, A.K.: Detection of signaling pathways in human brain during arousal of specific emotion. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3950–3957. IEEE (2014)Google Scholar
  18. 18.
    Kunas, S.L., et al.: The impact of depressive comorbidity on neural plasticity following cognitive-behavioral therapy in panic disorder with agoraphobia. J. Affect. Disord. 245, 451–460 (2019)CrossRefGoogle Scholar
  19. 19.
    Lange, I., et al.: Functional neuroimaging of associative learning and generalization in specific phobia. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 89, 275–285 (2019)CrossRefGoogle Scholar
  20. 20.
    Lueken, U., Kruschwitz, J.D., Muehlhan, M., Siegert, J., Hoyer, J., Wittchen, H.U.: How specific is specific phobia? Different neural response patterns in two subtypes of specific phobia. NeuroImage 56(1), 363–372 (2011)CrossRefGoogle Scholar
  21. 21.
    Luo, C., Zheng, X., Zeng, D.: Causal inference in social media using convergent cross mapping. In: 2014 IEEE Joint Intelligence and Security Informatics Conference, pp. 260–263. IEEE (2014)Google Scholar
  22. 22.
    Linares, I.M., Chags, M.H.N., Machado-de Sousa, J.P., Crippa, J.A.S., Hallak, J.E.C.: Neuroimaging correlates of pharmacological and psychological treatments for specific phobia. CNS Neurol. Disord. Drug Targets (Formerly Curr. Drug Targets-CNS Neurol. Disord.) 13(6), 1021–1025 (2014)Google Scholar
  23. 23.
    Maulsby, R.L.: Some guidelines for assessment of spikes and sharp waves in EEG tracings. Am. J. EEG Technol. 11(1), 3–16 (1971)CrossRefGoogle Scholar
  24. 24.
    McCracken, J.M., Weigel, R.S.: Convergent cross-mapping and pairwise asymmetric inference. Phys. Rev. E 90(6), 062903 (2014)CrossRefGoogle Scholar
  25. 25.
    Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., Hallett, M.: Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol. 115(10), 2292–2307 (2004)CrossRefGoogle Scholar
  26. 26.
    de Oliveira-Souza, R.: Phobia of the supernatural: a distinct but poorly recognized specific phobia with an adverse impact on daily living. Front. Psychiatry 9, 590 (2018)CrossRefGoogle Scholar
  27. 27.
    Pachana, N.A., Woodward, R.M., Byrne, G.J.: Treatment of specific phobia in older adults. Clin. Interv. Aging 2(3), 469 (2007)Google Scholar
  28. 28.
    Pascual-Marqui, R.D., et al.: Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find. Exp. Clin. Pharmacol. 24(Suppl D), 5–12 (2002)Google Scholar
  29. 29.
    Pearson, K.: Vii. Note on regression and inheritance in the case of two parents. Proc. R. Soc. London 58(347–352), 240–242 (1895)Google Scholar
  30. 30.
    Pukenas, K.: An algorithm based on the convergent cross mapping method for the detection of causality in uni-directionally connected chaotic systems. Math. Models Eng. 4(3), 145–150 (2018)CrossRefGoogle Scholar
  31. 31.
    Rathee, D., Cecotti, H., Prasad, G.: Estimation of effective fronto-parietal connectivity during motor imagery using partial granger causality analysis. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2055–2062. IEEE (2016)Google Scholar
  32. 32.
    Rosenbaum, D., et al.: Neuronal correlates of spider phobia in a combined fNIRS-EEG study. Sci. Rep. 10(1), 1–14 (2020)CrossRefGoogle Scholar
  33. 33.
    Rosenbaum, D., et al.: Cortical oxygenation during exposure therapy-in situ fNIRS measurements in arachnophobia. NeuroImage Clin. 26, 102219 (2020)CrossRefGoogle Scholar
  34. 34.
    Shoker, L., Sanei, S., Latif, M.A.: Removal of eye blinking artifacts from EEG incorporating a new constrained BSS algorithm. In: Processing Workshop Proceedings, 2004 Sensor Array and Multichannel Signal, pp. 177–181. IEEE (2004)Google Scholar
  35. 35.
    Sugihara, G., et al.: Detecting causality in complex ecosystems. Science 338(6106), 496–500 (2012)CrossRefGoogle Scholar
  36. 36.
    Sugihara, G., May, R.M.: Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344(6268), 734–741 (1990)CrossRefGoogle Scholar
  37. 37.
    Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981).  https://doi.org/10.1007/BFb0091924CrossRefGoogle Scholar
  38. 38.
    Toyama, K., Kimura, M., Tanaka, K.: Cross-correlation analysis of interneuronal connectivity in cat visual cortex. J. Neurophysiol. 46(2), 191–201 (1981)CrossRefGoogle Scholar
  39. 39.
    Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy–a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30(1), 45–67 (2011)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav. Brain Functions 7(1), 1–15 (2011)CrossRefGoogle Scholar
  41. 41.
    Zilverstand, A., Sorger, B., Sarkheil, P., Goebel, R.: fMRI neurofeedback facilitates anxiety regulation in females with spider phobia. Front. Behav. Neurosci. 9, 148 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Artificial Intelligence Laboratory, Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Mathematics and Computer ScienceLiverpool Hope UniversityLiverpoolUK

Personalised recommendations