A simultaneous EEG-fMRI protocol for exploring the development of hierarchical sensory processing
Poster No:
1317
Submission Type:
Abstract Submission
Authors:
Parvaneh Adibpour1, Vyacheslav Karolis1, Ines Tomazinho2, Beya Bonse2, Kamilah St Clair2, Wendy Norman2, Kathleen Colford2, Massimo Marenzana2, Fraser Aitken3, Claire Kabdebon4, Jonathan O'Muircheartaigh5, Tomoki Arichi1,6,7
Institutions:
1Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London, London, United Kingdom, 2Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom, 3School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 4Laboratoire de Sciences Cognitives et Psycholinguistique, Département d’études cognitives, ENS, CNRS, Paris, France, 5Department of Forensic and Neurodevelopmental Sciences, King’s College London, London, United Kingdom, 6Department of Bioengineering, Imperial College London, London, United Kingdom, 7Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
First Author:
Parvaneh Adibpour
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London
London, United Kingdom
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London
London, United Kingdom
Co-Author(s):
Vyacheslav Karolis
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London
London, United Kingdom
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London
London, United Kingdom
Fraser Aitken
School of Biomedical Engineering and Imaging Sciences, King’s College London
London, United Kingdom
School of Biomedical Engineering and Imaging Sciences, King’s College London
London, United Kingdom
Claire Kabdebon
Laboratoire de Sciences Cognitives et Psycholinguistique, Département d’études cognitives, ENS, CNRS
Paris, France
Laboratoire de Sciences Cognitives et Psycholinguistique, Département d’études cognitives, ENS, CNRS
Paris, France
Jonathan O'Muircheartaigh
Department of Forensic and Neurodevelopmental Sciences, King’s College London
London, United Kingdom
Department of Forensic and Neurodevelopmental Sciences, King’s College London
London, United Kingdom
Tomoki Arichi
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London|Department of Bioengineering, Imperial College London|Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom|London, United Kingdom
Centre for the Developing Brain, Department of Perinatal Imaging and Health, King’s College London|Department of Bioengineering, Imperial College London|Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust
London, United Kingdom|London, United Kingdom|London, United Kingdom
Introduction:
According to the predictive coding framework, sensory information is processed along a hierarchy, where higher-level processing areas generate predictions that are compared against incoming sensory evidence, and when not matched (i.e., deviance), a prediction error is generated, and the brain revises its model of the world [1]. Little is known about how the neural substrates of predictive processing emerge in the developing brain, which is of particular relevance for neurodevelopmental conditions such as Autism Spectrum Condition where atypical sensory network profiles [2] and predictive abilities [3] are reported. We aimed to establish a protocol for investigating the hierarchy of auditory predictive processing across development that can disentangle the different components of predictive processing in time and space by combining EEG and fMRI.
Methods:
The paradigm consists of blocks of sounds with 6 repetitions of a sequence composed of four speech syllables. Blocks consisted of either repetition of the same sound sequence (standard blocks) or occasional violation of regularities with sequences involving a deviant or a missing sound (deviant or omission blocks-Figure.1).
To test the paradigm, simultaneous EEG-fMRI data were acquired from 8 adults and a pilot dataset of 4 neonates (birth age =39.8 [37.6-40.9] gestational weeks, scan age = 41.3 [40-44.1] weeks). BOLD fMRI data were acquired on a Philips Achieva 3T MRI scanner with a 32-channel head coil, using an echo-planar imaging sequence with TR (infant 2001/adult 2500) ms, resolution (infant 2.08x2.08x2.9; adult 3.5x3.5x4.5 mm3). Preprocessing and GLM analysis were performed in FSL [4] to define subject-level activation maps. For adult group analysis, z-statistical maps from each subject were aligned to the subject's T1-weighted image and warped to the MNI152 space using ANTs [6], and group average effects were identified using nonparametric permutation testing.
EEG data was acquired using a 32-channel MR-compatible Brain Products system. MR gradient and pulse artefacts were removed from recorded data using Analyzer II software. Recordings were filtered (0.5-30 Hz), cleaned for motion artifacts using APICE preprocessing pipeline [6], interpolated for noisy channels, re-referenced with respect to average-reference, and then segmented into epochs relative to the onset of the last sound in the sequence. Auditory evoked responses were obtained by averaging the trials corresponding to each experimental condition. Average activity over fronto-central channels were compared between conditions using a paired t-test.
To test the paradigm, simultaneous EEG-fMRI data were acquired from 8 adults and a pilot dataset of 4 neonates (birth age =39.8 [37.6-40.9] gestational weeks, scan age = 41.3 [40-44.1] weeks). BOLD fMRI data were acquired on a Philips Achieva 3T MRI scanner with a 32-channel head coil, using an echo-planar imaging sequence with TR (infant 2001/adult 2500) ms, resolution (infant 2.08x2.08x2.9; adult 3.5x3.5x4.5 mm3). Preprocessing and GLM analysis were performed in FSL [4] to define subject-level activation maps. For adult group analysis, z-statistical maps from each subject were aligned to the subject's T1-weighted image and warped to the MNI152 space using ANTs [6], and group average effects were identified using nonparametric permutation testing.
EEG data was acquired using a 32-channel MR-compatible Brain Products system. MR gradient and pulse artefacts were removed from recorded data using Analyzer II software. Recordings were filtered (0.5-30 Hz), cleaned for motion artifacts using APICE preprocessing pipeline [6], interpolated for noisy channels, re-referenced with respect to average-reference, and then segmented into epochs relative to the onset of the last sound in the sequence. Auditory evoked responses were obtained by averaging the trials corresponding to each experimental condition. Average activity over fronto-central channels were compared between conditions using a paired t-test.
Results:
Preliminary analyses indicated EEG auditory evoked responses to the sequence of 4 sounds with a decrease in amplitude from the 1st to the 3rd sound consistent with habituation (Figure 2.a). For the last (4th) sound, a mismatch response to deviance (deviant/omission vs standard) was elicited in both adults and neonates (Figure 2.b). Mismatch responses had a frontocentral scalp distribution, with a negative polarity in adults and a positive polarity in neonates; and were slower in neonates. FMRI analyses identified significant clusters of functional activity in response to sound blocks within the primary auditory cortices, planum temporal as well as superior temporal and frontal gyri (Figure 2.c).
Conclusions:
We have established an experimental procedure for simultaneous EEG-fMRI studies to explore the neural correlates of detecting violations of regularity during sound sequence processing. In-scanner EEG mismatch responses were consistent with previous reports [7], confirming that although less mature in their responses than adults, neonates are sensitive to the statistics of auditory sequences. This will enable detailed investigation of the link between EEG and fMRI activations such as the spatial correlates of individual components of the EEG responses involved in predictive processing.
Learning and Memory:
Learning and Memory Other
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Novel Imaging Acquisition Methods:
BOLD fMRI
EEG
Multi-Modal Imaging 2
Keywords:
Cognition
Development
Electroencephaolography (EEG)
FUNCTIONAL MRI
1|2Indicates the priority used for review
Provide references using author date format
[2] Guiraud, J. A., Kushnerenko, E., Tomalski, P., Davies, K., Ribeiro, H., Johnson, M. H., & BASIS team. (2011). Differential habituation to repeated sounds in infants at high risk for autism. Neuroreport, 22(16), 845-849.
[3] Sinha, P., Kjelgaard, M. M., Gandhi, T. K., Tsourides, K., Cardinaux, A. L., Pantazis, D., ... & Held, R. M. (2014). Autism as a disorder of prediction. Proceedings of the national academy of sciences
[4] Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data. NeuroImage, 14(6), 1370–1386 www.fmrib.ox.ac.uk/fsl
[5] Avants, B. B., Tustison, N., & Song, G. (2009). Advanced normalization tools (ANTS). Insight j, 2(365), 1-35.
[6] Fló, A., Gennari, G., Benjamin, L., & Dehaene-Lambertz, G. (2022). Automated Pipeline for Infants Continuous EEG (APICE): A flexible pipeline for developmental cognitive studies. Developmental Cognitive Neuroscience, 54, 101077.
[7] Dehaene-Lambertz, G., & Gliga, T. (2004). Common neural basis for phoneme processing in infants and adults. Journal of cognitive neuroscience, 16(8), 1375-1387.