Skip to main content
SpringerLink
Log in

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention

  • Original Article
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

Abstract

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand. Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level, but this remains a poorly understood process. To tackle this issue, we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains, and recorded scalp electroencephalographic signals from eight healthy participants. The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality (GC), a metric of frequency-specific effective connectivity. Using data-driven methods of pattern-classification and feature-analysis, we found that a change in the α band (12 Hz–15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift. Specifically, auditory attention induces more pronounced information flow in the α band, especially from the parietal–occipital areas to the temporal–parietal areas, compared to the case of visual attention, reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing. Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and, thereby, mediating cross-modal selective attention.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Driver J, Spence C. Cross-modal links in spatial attention. Philos Trans R Soc Lond B Biol Sci 1998, 353: 1319–1331.

    Google Scholar 

  2. Lavie N. Distracted and confused?: Selective attention under load. Trends Cogn Sci 2005, 9: 75–82.

    PubMed  Google Scholar 

  3. Laurienti PJ, Burdette JH, Wallace MT, Yen YF, Field AS, Stein BE. Deactivation of sensory-specific cortex by cross-modal stimuli. J Cogn Neurosci 2002, 14: 420–429.

    PubMed  Google Scholar 

  4. Rahne T, Bochmann M, von Specht H, Sussman ES. Visual cues can modulate integration and segregation of objects in auditory scene analysis. Brain Res 2007, 1144: 127–135.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Johnson JA, Zatorre RJ. Neural substrates for dividing and focusing attention between simultaneous auditory and visual events. Neuroimage 2006, 31: 1673–1681.

    PubMed  Google Scholar 

  6. Shomstein S, Yantis S. Control of attention shifts between vision and audition in human cortex. J Neurosci 2004, 24: 10702–10706.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Calderone DJ, Lakatos P, Butler PD, Castellanos FX. Entrainment of neural oscillations as a modifiable substrate of attention. Trends Cogn Sci 2014, 18: 300–309.

    PubMed  PubMed Central  Google Scholar 

  8. Gomez-Ramirez M, Kelly SP, Molholm S, Sehatpour P, Schwartz TH, Foxe JJ. Oscillatory sensory selection mechanisms during intersensory attention to rhythmic auditory and visual inputs: a human electrocorticographic investigation. J Neurosci 2011, 31: 18556–18567.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Lakatos P, Karmos G, Mehta AD, Ulbert I, Schroeder CE. Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 2008, 320: 110–113.

    CAS  PubMed  Google Scholar 

  10. Fiebelkorn IC, Kastner S. A rhythmic theory of attention. Trends Cogn Sci 2018.

  11. Fiebelkorn IC, Foxe JJ, Butler JS, Mercier MR, Snyder AC, Molholm S. Ready, set, reset: stimulus-locked periodicity in behavioral performance demonstrates the consequences of cross-sensory phase reset. J Neurosci 2011, 31: 9971–9981.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Romei V, Gross J, Thut G. Sounds reset rhythms of visual cortex and corresponding human visual perception. Curr Biol 2012, 22: 807–813.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Schroeder CE, Lakatos P. Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 2009, 32: 9–18.

    CAS  PubMed  Google Scholar 

  14. Canolty RT, Knight RT. The functional role of cross-frequency coupling. Trends Cogn Sci 2010, 14: 506–515.

    PubMed  PubMed Central  Google Scholar 

  15. Buzsaki G, Watson BO. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin Neurosci 2012, 14: 345–367.

    PubMed  PubMed Central  Google Scholar 

  16. Allport A, Wylie G. Task switching, stimulus-response bindings, and negative priming. Cambridge: MIT Press, 2000.

    Google Scholar 

  17. Koechlin E, Ody C, Kouneiher F. The architecture of cognitive control in the human prefrontal cortex. Science 2003, 302: 1181–1185.

    CAS  PubMed  Google Scholar 

  18. Ding MZ, Bressler SL, Yang WM, Liang HL. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 2000, 83: 35–45.

    CAS  PubMed  Google Scholar 

  19. Ding M, Chen Y, Bressler SL. Granger causality: basic theory and application to neuroscience. Handbook of time series analysis: recent theoretical developments and applications 2006: 437–460.

  20. Seth AK. A MATLAB toolbox for Granger causal connectivity analysis. J Neurosci Methods 2010, 186: 262–273.

    PubMed  Google Scholar 

  21. Richter CG, Coppola R, Bressler SL. Top-down beta oscillatory signaling conveys behavioral context in early visual cortex. Sci Rep 2018, 8: 12.

    Google Scholar 

  22. Geweke J. Measurement of linear-dependence and feedback between multiple time-series. J Am Stat Assoc 1982, 77: 304–313.

    Google Scholar 

  23. Bressler SL, Seth AK. Wiener-Granger Causality: A well established methodology. Neuroimage 2011, 58: 323–329.

    PubMed  Google Scholar 

  24. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002, 113: 767–791.

    PubMed  Google Scholar 

  25. Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017, 38: 5391–5420.

    PubMed  PubMed Central  Google Scholar 

  26. Muller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B. Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring. J Neurosci Methods 2008, 167: 82–90.

    PubMed  Google Scholar 

  27. Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 2000, 8: 441–446.

    CAS  PubMed  Google Scholar 

  28. Chang C-C, Lin C-J. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2011, 2: 27.

    Google Scholar 

  29. Breiman L. Arcing classifiers. Ann Stat 1998, 26: 801–824.

    Google Scholar 

  30. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002, 46: 389–422.

    Google Scholar 

  31. Siegel M, Donner TH, Oostenveld R, Fries P, Engel AK. Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron 2008, 60: 709–719.

    CAS  PubMed  Google Scholar 

  32. van den Broek SP, Reinders F, Donderwinkel M, Peters M. Volume conduction effects in EEG and MEG. Electroencephalogr Clin Neurophysiol 1998, 106: 522–534.

    PubMed  Google Scholar 

  33. Nunez PL, Srinivasan R, Westdorp AF, Wijesinghe RS, Tucker DM, Silberstein RB, et al. EEG coherency. 1. Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephalogr Clin Neurophysiol 1997, 103: 499–515.

    CAS  PubMed  Google Scholar 

  34. Kaminski M, Blinowska KJ. Directed transfer function is not influenced by volume conduction-inexpedient pre-processing should be avoided. Front Comput Neurosci 2014, 8: 3.

    Google Scholar 

  35. Brunner C, Billinger M, Seeber M, Mullen TR, Makeig S. Volume conduction influences scalp-based connectivity estimates. Front Comput Neurosci 2016, 10: 4.

    Google Scholar 

  36. Kayser C, Petkov CI, Augath M, Logothetis NK. Functional imaging reveals visual modulation of specific fields in auditory cortex. J Neurosci 2007, 27: 1824–1835.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Sohrabpour A, Ye S, Worrell GA, Zhang WB, He B. Noninvasive electromagnetic source imaging and granger causality analysis: an electrophysiological connectome (econnectome) approach. IEEE Trans Biomed Eng 2016, 63: 2474–2487.

    PubMed  PubMed Central  Google Scholar 

  38. Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, de Peralta RG. EEG source imaging. Clin Neurophysiol 2004, 115: 2195–2222.

    PubMed  Google Scholar 

  39. Tonin L, Leeb R, Millan JD. Time-dependent approach for single trial classification of covert visuospatial attention. J Neural Eng 2012, 9: 9.

    Google Scholar 

  40. Trachel RE, Clerc M, Brochier TG. Decoding covert shifts of attention induced by ambiguous visuospatial cues. Front Hum Neurosci 2015, 9: 9.

    Google Scholar 

  41. Treder MS, Bahramisharif A, Schmidt NM, van Gerven MAJ, Blankertz B. Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention. J NeuroEng Rehabil 2011, 8: 9.

    Google Scholar 

  42. Frey JN, Mainy N, Lachaux JP, Muller N, Bertrand O, Weisz N. Selective modulation of auditory cortical alpha activity in an audiovisual spatial attention task. J Neurosci. 2014, 34: 6634–6639.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Bagherzadeh Y, Baldauf D, Pantazis D, Desimone RJN. Alpha synchrony and the neurofeedback control of spatial attention. Neuron 2020, 105: 577–587.e5.

    Google Scholar 

  44. Foxe JJ, Snyder AC. The role of alpha-band brain oscillations as a sensory suppression mechanism during selective attention. Front Psychol 2011, 2: 154.

    PubMed  PubMed Central  Google Scholar 

  45. Fu KMG, Foxe JJ, Murray MM, Higgins BA, Javitt DC, Schroeder CE. Attention-dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parieto-occipital alpha-band oscillations. Cognit Brain Res 2001, 12: 145–152.

    CAS  Google Scholar 

  46. Gould IC, Rushworth MF, Nobre AC. Indexing the graded allocation of visuospatial attention using anticipatory alpha oscillations. J Neurophysiol 2011, 105: 1318–1326.

    PubMed  PubMed Central  Google Scholar 

  47. Rohenkohl G, Nobre AC. Alpha oscillations related to anticipatory attention follow temporal expectations. J Neurosci 2011, 31: 14076–14084.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Foxe JJ, Simpson GV, Ahlfors SP. Parieto-occipital similar to 10 Hz activity reflects anticipatory state of visual attention mechanisms. Neuroreport 1998, 9: 3929–3933.

    CAS  PubMed  Google Scholar 

  49. Hong XF, Sun JF, Tong SB. Functional brain networks for sensory maintenance in top-down selective attention to audiovisual inputs. IEEE Trans. Neural Syst Rehabil Eng 2013, 21: 734–743.

    PubMed  Google Scholar 

  50. Busse L, Roberts KC, Crist RE, Weissman DH, Woldorff MG. The spread of attention across modalities and space in a multisensory object. Proc Natl Acad Sci U S A 2005, 102: 18751–18756.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Degerman A, Rinne T, Pekkola J, Autti T, Jaaskelainen IP, Sams M, et al. Human brain activity associated with audiovisual perception and attention. Neuroimage 2007, 34: 1683–1691.

    PubMed  Google Scholar 

  52. Johnson JA, Zatorre RJ. Attention to simultaneous unrelated auditory and visual events: Behavioral and neural correlates. Cereb Cortex 2005, 15: 1609–1620.

    PubMed  Google Scholar 

  53. Sauseng P, Klimesch W, Stadler W, Schabus M, Doppelmayr M, Hanslmayr S, et al. A shift of visual spatial attention is selectively associated with human EEG alpha activity. Eur J Neurosci 2005, 22: 2917–2926.

    CAS  PubMed  Google Scholar 

  54. Johnson JA, Strafella AP, Zatorre RJ. The role of the dorsolateral prefrontal cortex in bimodal divided attention: Two transcranial magnetic stimulation studies. J Cogn Neurosci 2007, 19: 907–920.

    PubMed  Google Scholar 

  55. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci 2009, 32: 209–224.

    CAS  PubMed  Google Scholar 

  56. Gregoriou GG, Gotts SJ, Zhou H, Desimone R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 2009, 324: 1207–1210.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Roberts MJ, Lowet E, Brunet NM, Ter Wal M, Tiesinga P, Fries P, et al. Robust gamma coherence between macaque V1 and V2 by dynamic frequency matching. Neuron 2013, 78: 523–536.

    CAS  PubMed  Google Scholar 

  58. Vinck M, Lima B, Womelsdorf T, Oostenveld R, Singer W, Neuenschwander S, et al. Gamma-phase shifting in awake monkey visual cortex. J Neurosci 2010, 30: 1250–1257.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Laureys S, Owen AM, Schiff ND. Brain function in coma, vegetative state, and related disorders. Lancet Neurol 2004, 3: 537–546.

    PubMed  Google Scholar 

  60. Zhu YC, Jiang XX, Ji WD. The mechanism of cortico-striato-thalamo-cortical neurocircuitry in response inhibition and emotional responding in attention deficit hyperactivity disorder with comorbid disruptive behavior disorder. Neurosci Bull 2018, 34: 566–572.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Key Research and Development Program of China (2017YFA0105203), the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDB32040200 and XDB32030200), the Key Research Program of Frontier Sciences, CAS (QYZDJ-SSW-SMC019), and the National Natural Science Foundation of China (81871398, U1636121, and 31571003).

Author information

Author notes

  1. Weikun Niu and Yuying Jiang contributed equally to this work.

Authors and Affiliations

  1. Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Weikun Niu, Yuying Jiang, Xin Zhang, Tianzi Jiang, Yujin Zhang & Shan Yu

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Weikun Niu, Yuying Jiang, Xin Zhang, Tianzi Jiang, Yujin Zhang & Shan Yu

  3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Tianzi Jiang & Shan Yu

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Weikun Niu, Yuying Jiang, Tianzi Jiang & Shan Yu

Authors
  1. Weikun Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuying Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianzi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yujin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yujin Zhang or Shan Yu.

Ethics declarations

Conflict of interest

None.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niu, W., Jiang, Y., Zhang, X. et al. Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention. Neurosci. Bull. 36, 1009–1022 (2020). https://doi.org/10.1007/s12264-020-00550-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12264-020-00550-2

Keywords

Search

Navigation