The Laminar Patterns of Predicting Content in Learned Audio-Visual Associations
Poster No:
2034
Submission Type:
Abstract Submission
Authors:
Mahdi Enan1, Agustin Castellanos1, Ryszard Auksztulewicz2, Federico de Martino1
Institutions:
1Maastricht University, Maastricht, Limburg, 2Free University Berlin, Berlin, Berlin
First Author:
Co-Author(s):
Introduction:
To make sense of the external world, our brains use contextual information to predict upcoming stimuli. Previous studies have shown that content ("what") and temporal ("when") predictions are supported by different mechanisms (Auksztulewicz et al., 2018). In particular, "what" predictions are linked to stimulus-specific gain modulation. Here, we aimed to understand how these mechanisms are grounded within the laminar cortical architecture using laminar fMRI. Within the predictive coding framework, we hypothesized that the laminar responses for prediction errors increase towards the surface in lateral temporal areas and toward middle layers in inferior frontal areas, whereas for predictable content, we expected strong activation in deep layers of lateral temporal areas.
Methods:
Using an audio-visual associative learning paradigm, we examine the responses to predictable and unpredictable stimuli in 11 subjects. Within predictable stimuli, we distinguish responses to valid, invalid predictions, and to the unexpected omission of the audio content. Prior to scanning, subjects were presented with valid predictable and unpredictable stimuli in a behavioral session in which we recorded reaction times to the detection of the heard syllable (PA/GA). We acquired data on 7T SIEMENS Magnetom scanner (anatomical at 0.7mm isotropic and functional 0.8mm isotropic). Functional preprocessing: slice timing correction, motion correction, distortion correction, and co-registration. Anatomical preprocessing: denoising, skull-stripping, inhomogeneity correction, cortical depth sampling, and cortex-based alignment only for visualization. We manually drew 9 cortical regions of interest (parsOrbitalis, parsTriangularis, HG, PP, PT, aSTG, pSTG, pMTG, and TPOj) based on anatomical markers and extracted 5 hippocampus regions (SUB, CA1, CA2, CA3, and DG) using the hippocampal segmentation factory (Poiret et al., 2023). We fitted a GLM to estimate single trial responses using GLMSingle (Prince et al., 2022). We performed both univariate analysis and decoding on the beta coefficients. The statistical significance of the both analyses was assessed using permutation testing and corrected for multiple comparisons using FDR.
·Experimental Design
Results:
Consistent with previous research, we found that, predictable content significantly decreased reaction times compared to unpredictable content. At the group level, the difference between valid and invalid stimuli and also the difference between valid and predictable omitted stimuli modulated superficial cortical layers in inferior frontal regions. The difference between predictable and unpredictable stimuli modulated only early auditory cortex region planum polare with no significant interaction between layer and condition. When the stimuli were omitted, the difference between predictable omitted and unpredictable omitted stimuli modulated early and intermediate auditory regions mostly in superficial layers. Using univariate tests we could not distinguish the response to the heard syllable in the cortex or hippocampus. Using decoding, we found significant above chance level decoding accuracy of the specific syllable in hippocampus area CA3 using valid trials, with increased decoding including the invalid trials coded based on the expectation but not when coding them based on heard stimulus.
·Univariate Test Between Valid and Invalid Trials
Conclusions:
Content validity showed a consistent modulation in middle and superficial IFG regions in line with the canonical micro-circuit for predictive coding suggesting that prediction errors are encoded in supragranular layers (Bastos et al., 2012). Stimulus predictability can be seen as a signature of precision since the predictable stimuli have 75% chance and the unpredictable stimuli 50% chance and thus an increased precision of the predictable content recruits more intermediate areas when the predictable content is omitted. In addition, our multivariate analyses support the idea that area CA3 in the hippocampus encodes stimulus expectations rather than stimulus content.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Classification and Predictive Modeling
Multivariate Approaches
Univariate Modeling
Perception, Attention and Motor Behavior:
Perception: Multisensory and Crossmodal 1
Keywords:
Computational Neuroscience
Cortex
Cortical Layers
FUNCTIONAL MRI
Hearing
Machine Learning
Multivariate
Perception
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical microcircuits for predictive coding. Neuron, 76(4), 695-711.
Poiret, C., Bouyeure, A., Patil, S., Grigis, A., Duchesnay, E., Faillot, M., ... & Noulhiane, M. (2023). A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics. Frontiers in Neuroinformatics, 17, 1130845.
Prince, J. S., Charest, I., Kurzawski, J. W., Pyles, J. A., Tarr, M. J., & Kay, K. N. (2022). Improving the accuracy of single-trial fMRI response estimates using GLMsingle. Elife, 11, e77599.