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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.

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Presentation on theme: "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."— Presentation transcript:

1 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)

2 Visual ERP Nociceptive Somatosensory LEP Auditory ERP Tactile Somatosensory 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 unique for a given sensory modality Modality-specific common to all sensory modalities Amodal

4 Visual ERP Nociceptive Somatosensory LEP Auditory ERP Tactile Somatosensory 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 The signals recorded at sensors are modelled as a linear mixture of the source signals by an unknown mixing matrix. Can this method be addressed using a blind source separation algorithm?

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

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

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

9 4 blocks stimuli / block ISI = 5-10 s Auditory ERPTactile somatosensory ERPNociceptive somatosensory ERPVisual ERP

10 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 courses of all four ERPs were categorized as amodal. 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 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 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 Amodal activity

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

13 Conclusion 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. Probabilistic ICA can be used to separate sensory ERPs into its amodal and modality-specific constituents

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

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

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

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

18 Subtracting the contribution of somatosensory-specific activity (activity contributing to uniquely to the non-nociceptive or the nociceptive somatosensory 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.


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