1 / 19

Probabilistic ICA to dissect modality-specific and amodal constituents of sensory ERPs

Probabilistic ICA to dissect modality-specific and amodal constituents of sensory ERPs. A. Mouraux 1,3 , G.D. Iannetti 2 1 FMRIB Centre, 2 Department of Physiology, Anatomy and Genetics, University of Oxford (UK). 3 Unité READ, Université Catholique de Louvain (BE). Tactile

vondra
Download Presentation

Probabilistic ICA to dissect modality-specific and amodal constituents of sensory ERPs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Probabilistic ICA to dissect modality-specific and amodal constituents of sensory ERPs A. Mouraux1,3, G.D. Iannetti2 1FMRIB Centre, 2Department of Physiology, Anatomy and Genetics, University of Oxford (UK).3Unité READ, UniversitéCatholique de Louvain (BE)

  2. Tactile Somatosensory ERP Nociceptive Somatosensory LEP Auditory ERP Visual ERP

  3. Although very similar in shape and scalp topography, vertex potentials are believed to reflect a combination of modality-specific and multimodal brain activities. Vertex potential Modality-specific Amodal unique for a given sensory modality common to all sensory modalities

  4. Tactile Somatosensory ERP Nociceptive Somatosensory LEP Auditory ERP Visual ERP What is the respective contribution of modality-specific and amodal brain activity to these recorded ERPs? Do we have methods to address this question?

  5. Can this method be addressed using a blind source separation algorithm? The signals recorded at sensors are modelled as a linear mixture of the source signals by an unknown mixing matrix.

  6. Can this method be addressed using a blind source separation algorithm? Blind source separation aims at finding an unmixing matrix that would recover the original source signals note that u ≠ s because of scaling and permutations

  7. Probabilistic Independent Component Analysis (PICA) Independent Component Analysis (ICA) Under-fitting Non-square matrix Over-fitting Probabilistic ICA: non-square unmixing matrix square unmixing matrix overfitting leads to the appearance of spurious ICs! underfitting discards valuable information and leads to suboptimal signal extraction Probabilistic ICA avoids the problem of overfitting by constraining ICA to an objective estimate of the dimensionality of the data, obtained through Bayesian analysis. The number of estimated ICs is equal to the number of sensors Beckmann & Smith (2004) IEEE Trans Med Imaging

  8. Here, we applied Probabilistic ICA to somatosensory, auditory and visual ERPs ... to solve the cocktail party occurring inside our brain ...

  9. Visual ERP Nociceptive somatosensory ERP Tactile somatosensory ERP Auditory ERP 4 blocks 38-42 stimuli / block ISI = 5-10 s

  10. ICs contributing to the time courses of all four ERPs were categorized as amodal. In this waveform, amodal and modality-specific responses will have distinct time courses. Provided that amodal and modality-specific responses project differently onto the scalp sensors, PICA should separate amodal and modality-specific responses in distinct ICs For each subject, auditory, somatosensory and visual ERPs were concatenated into a single waveform ICs contributing to the time course of a specific ERP were categorized as modality-specific. ICs contributing to the time course of a nociceptive and tactile somatosensory ERPs were categorized as somatosensory-specific.

  11. Amodal activity • On average, amodal activity explained : • 61% of the auditory ERP waveform • 66% of the non-nociceptive somatosensory ERP waveform • 76% of the nociceptive somatosensory ERP waveform • 55% of the visual ERP waveform

  12. Somatosensory-specific activity explained • 34% of the non-nociceptive somatosensory ERP waveform • 25% of the nociceptive somatosensory ERP waveform Auditory-specific activity explained 32% of the auditory ERP waveform Visual-specific activity explained 36% of the visual ERP waveform

  13. Conclusion Probabilistic ICA can be used to separate sensory ERPs into its amodal and modality-specific constituents Amodal brain responses represent the bulk of auditory, somatosensory and visual vertex potentials. Modality-specific brain responses represent only a fraction of the early part of auditory, somatosensory and visual vertex potentials.

  14. Subtracting the contribution of amodal activity (activity contributing to all four ERP waveforms)

  15. Subtracting the contribution of visual-specificactivity (activity contributing uniquely to the visual ERP)

  16. Subtracting the contribution of auditory-specificactivity (activity contributing uniquely to the auditory ERP)

  17. Subtracting the contribution of somatosensory-specificactivity (activity contributing to both the non-nociceptive and nociceptive somatosensory ERP)

  18. Subtracting the contribution of somatosensory-specificactivity (activity contributing to uniquely to the non-nociceptiveor the nociceptivesomatosensory ERP)

  19. Thank You! Acknowledgements. We thank Drs Christian Beckmann, Léon Plaghki and Meng Liang for their insightful comments. André Mouraux is a Marie-Curie post-doctoral Research Fellow, and a “Chargé de recherches” of the Belgian National Fund for Scientific Research (FNRS). Giandomenico Iannetti is a University Research Fellow for The Royal Society.

More Related