Poster No:
2492
Submission Type:
Abstract Submission
Authors:
Nina Raduner1,2,3,4,5, Maya Schneebeli1, Carmen Providoli1,3,4,5, Sarah Di Pietro1,4,5, Saurabh Bedi6,5, Ella Casimiro6,5, Nora Raschle2,5,4, Michael Von Rhein3,5, Christian Ruff6,4,5,7, Silvia Brem1,4,5,7
Institutions:
1Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry, Zurich, Switzerland, 2Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland, 3University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland, 4Neuroscience Center Zurich ZNZ, Zurich, Switzerland, 5URPP Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland, 6Department of Neuroeconomics, University Zurich, Zurich, Switzerland, 7equal contribution,
First Author:
Nina Raduner
Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry|Jacobs Center for Productive Youth Development, University of Zurich|University Children's Hospital Zurich and University of Zurich|Neuroscience Center Zurich ZNZ|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland
Co-Author(s):
Maya Schneebeli
Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry
Zurich, Switzerland
Carmen Providoli
Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry|University Children's Hospital Zurich and University of Zurich|Neuroscience Center Zurich ZNZ|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland
Sarah Di Pietro
Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry|Neuroscience Center Zurich ZNZ|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland
Saurabh Bedi
Department of Neuroeconomics, University Zurich|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland
Ella Casimiro
Department of Neuroeconomics, University Zurich|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland
Nora Raschle
Jacobs Center for Productive Youth Development, University of Zurich|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich|Neuroscience Center Zurich ZNZ
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland
Michael Von Rhein
University Children's Hospital Zurich and University of Zurich|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich
Zurich, Switzerland|Zurich, Switzerland
Christian Ruff
Department of Neuroeconomics, University Zurich|Neuroscience Center Zurich ZNZ|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich|equal contribution
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland|
Silvia Brem
Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry|Neuroscience Center Zurich ZNZ|URPP Adaptive Brain Circuits in Development and Learning, University of Zurich|equal contribution
Zurich, Switzerland|Zurich, Switzerland|Zurich, Switzerland|
Introduction:
Living in a multisensory world requires learning to integrate information from different sensory modalities. Multisensory integration is particularly important for language function, as it optimises behaviour by improving processing speed and memory performance1–3. From a brain perspective, learning and processing of multisensory information depend on an extended cortico-striato-thalamic network4. This study aimed to characterise the dynamics of multisensory learning across sensory modalities in children's brains.
Methods:
We collected data from 18 healthy children (14 female, age=9.4±1.9 yrs) performing two audiovisual (AV) and two tactile-visual (TV) runs of a multisensory associative learning task during fMRI (3T). Each run involved learning associations between 4 visual (symbols) and 4 auditory (environmental sounds) or tactile (vibrations) stimuli. A sound or tactile stimulus was presented with two visual symbols during stimulus presentation. Participants then chose the symbol matching the auditory/tactile stimulus and received feedback on their choice.
We used 5 variations of the Rescorla-Wagner model5 to describe the learning process. Successful parameter recovery on simulated data was only possible for 3 out of the 5 models, which were then used for further analyses. These described learning based on 1) the chosen visual stimuli, 2) the chosen AV/TV pair, and 3) on both presented AV/TV pairs. AIC and BIC of the three models were then compared within each participant. We also compared the best fitting model for AV runs to the best fitting model for TV runs. Further, model outputs (e.g., prediction error (PE) and learning rate) were correlated with the BOLD signal.
Results:
Children achieved an overall accuracy of 73% in AV and 66% in TV runs. We observed faster reaction times (RT) and higher accuracies (ACC) towards the end of the runs (ps<.01). Generally, RTs were slower for TV runs compared to AV runs (ps<.001).
When comparing the models, model 2 showed the best fit for both AV (BIC=53.3) and TV (BIC=58.0) runs, fitting the data best in 38 runs. Model 3 fitted slightly worse (AV: BIC = 54.7; TV: BIC = 60.2), being the best fit for 28 runs.
As expected, activity in sensory areas was found in occipital and temporal regions during AV runs and in occipital and pre- and post-central regions during TV runs. PE modulated brain activity in the bilateral precuneus , parahippocampal gyri, nuclei accumbens , amygdalae and putamen. However, there was no difference in PE processing between AV and TV runs. In addition, the learning rate during stimulus presentation in AV runs covaried with brain activation in the right superior parietal lobule (SPL), right planum temporale (PT) and right central operculum.
Conclusions:
Our results of decreasing RTs and increasing ACCs indicate that children in general were able to learn the associations of audio- visual and tactile-visual stimuli despite notable individual differences. During PE processing, expected areas in striatal and hippocampal regions showed increased activity. This suggests that children not only adjusted their knowledge based on the differences between expected and actual outcomes but also that these differences are encoded in their brain . Activation in the SPL and the PT are both linked to attention in relation to visual or auditory processing, respectively (Alahmadi, 2021; Jäncke et al., 2003). Activity in these regions, influenced by the AV learning rate, may reflect how attention is allocated to visual and auditory stimuli. Thus, higher learning rates might correspond to increased attention to auditory and visual cues, leading to more effective learning. To summarise, our findings suggest that our task provides a framework for exploring developmental changes and impairments in multisensory learning and integration.
Learning and Memory:
Learning and Memory Other
Lifespan Development:
Normal Brain Development: Fetus to Adolescence
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Perception, Attention and Motor Behavior:
Perception: Multisensory and Crossmodal 1
Keywords:
Cognition
Computational Neuroscience
Development
FUNCTIONAL MRI
Learning
Other - Multisensory Integration
1|2Indicates the priority used for review
Provide references using author date format
Alahmadi, A. A. S. (2021). Investigating the sub-regions of the superior parietal cortex using functional magnetic resonance imaging connectivity. Insights into Imaging, 12(1). https://doi.org/10.1186/s13244-021-00993-9
Denervaud, S., Gentaz, E., Matusz, P. J., & Murray, M. M. (2020). Multisensory Gains in Simple Detection Predict Global Cognition in Schoolchildren. Scientific Reports, 10(1), 1394. https://doi.org/10.1038/s41598-020-58329-4
Jäncke, L., Specht, K., Shah, J. N., & Hugdahl, K. (2003). Focused attention in a simple dichotic listening task: An fMRI experiment. Cognitive Brain Research, 16(2). https://doi.org/10.1016/S0926-6410(02)00281-1
Rescorla, R. A., & Wagner, A. R. (1972). A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement. Clasical Conditioning II: Current Research and Theory.
Van Den Brink, R. L., Cohen, M. X., Van Der Burg, E., Talsma, D., Vissers, M. E., & Slagter, H. A. (2014). Subcortical, modality-specific pathways contribute to multisensory processing in humans. Cerebral Cortex, 24(8). https://doi.org/10.1093/cercor/bht069