Auditory expectation violations elicit distinct laminar responses in temporal cortex
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
1062
Submission Type:
Abstract Submission
Authors:
Lonike Faes1, Isma Zulfiqar2, Luca Vizioli3, Yuan-Hao Wu4, Jiyun Shin4, Ryszard Auksztulewicz1, Lucia Melloni5, Kâmil Uludağ6, Essa Yacoub3, Federico de Martino1
Institutions:
1Maastricht University, Maastricht, The Netherlands, 2University College London, London, United Kingdom, 3Center for Magnetic Resonance Research, Minneapolis, MN, 4New York University, New York, NY, 5Ruhr-Universität Bochum, Bochum, Germany, 6Toronto University, Toronto, ON
First Author:
Co-Author(s):
Introduction:
The ability of the brain to anticipate incoming sensory information is crucial for navigating our dynamic environments, in which we are often confronted with incomplete and noisy inputs. In auditory environments, actively predicting what we will hear next facilitates auditory stream segregation and understanding speech in noise [6,8,9], illustrating the relevance of predictive processing in auditory perception. In predictive coding (PC), it is postulated that the brain actively infers underlying probable causes of sensory input. This process of inference occurs through hierarchical exchange of information across brain areas [2,5]. Within this framework, feedforward and feedback streams perform specialized roles, in which predictions are fed back and prediction-errors (the mismatch between predictions and sensory input) are fed forward to higher-order areas [4].
Here, we probe violations of expectations by presenting sound sequences that are either predictable, deviate from predictions or omit part of the sequence while measuring laminar gradient echo blood oxygenation level-dependent (GE-BOLD) responses using high-resolution functional magnetic resonance imaging (fMRI).
Here, we probe violations of expectations by presenting sound sequences that are either predictable, deviate from predictions or omit part of the sequence while measuring laminar gradient echo blood oxygenation level-dependent (GE-BOLD) responses using high-resolution functional magnetic resonance imaging (fMRI).
Methods:
Scanning was performed on a MAGNETOM 7T scanner (Siemens Healthineers). Ten healthy participants listened to sequences of syllables. A total of six types of sequences were presented, grouped in three conditions (Figure 1). Participants always expected to hear the same four syllables. The sequences either adhered to this expectation, or violated this expectation by presenting a deviant syllable or omitting the last syllable. Global predictability was manipulated by presenting the predictable condition about 60% of the time, whereas the other two conditions were presented only 20% each.
High-resolution anatomical data were collected using MP2RAGE (0.75 mm isotropic) which were manually segmented to delineate the gray matter curvature. Moreover, high-resolution functional data were acquired at 0.8 mm isotropic with GE-BOLD. Preprocessing included slice-scan time correction, motion correction, temporal filtering of 7 cycles (using BrainVoyager) and distortion correction using opposite phase encoding (using FSL). To mitigate the effect of vascular draining, we have analyzed the data using a previously proposed laminar BOLD model, to disentangle underlying neuronal activity from (unrelated) vascular changes within the dynamic causal modeling (DCM) framework [3,7]. This model includes a neuronal part (with excitatory and inhibitory neuronal populations recurrently connected at three cortical depths), a neurovascular coupling part and a part that allows the generation of laminar BOLD responses.
We investigated the modulatory effect that the deviant stimuli have, compared to predictable stimuli, on the laminar response. The target of this modulation was the self-excitation connection of the excitatory neuronal population (σ) that has been shown to modulate the changes in the overall amplitude of the BOLD response.
High-resolution anatomical data were collected using MP2RAGE (0.75 mm isotropic) which were manually segmented to delineate the gray matter curvature. Moreover, high-resolution functional data were acquired at 0.8 mm isotropic with GE-BOLD. Preprocessing included slice-scan time correction, motion correction, temporal filtering of 7 cycles (using BrainVoyager) and distortion correction using opposite phase encoding (using FSL). To mitigate the effect of vascular draining, we have analyzed the data using a previously proposed laminar BOLD model, to disentangle underlying neuronal activity from (unrelated) vascular changes within the dynamic causal modeling (DCM) framework [3,7]. This model includes a neuronal part (with excitatory and inhibitory neuronal populations recurrently connected at three cortical depths), a neurovascular coupling part and a part that allows the generation of laminar BOLD responses.
We investigated the modulatory effect that the deviant stimuli have, compared to predictable stimuli, on the laminar response. The target of this modulation was the self-excitation connection of the excitatory neuronal population (σ) that has been shown to modulate the changes in the overall amplitude of the BOLD response.
Results:
Here, we present the layer-dependent modulation results of five participants that best explains the difference in BOLD response between the deviant and predictable stimuli. Our first results point towards a modulation effect of our deviant stimuli in deep layers across all regions of the temporal lobe (Figure 2). Moreover, in some of the temporal cortical regions of interest, superficial layers are also modulated by the presentation of a stimulus that violated the expectation.
Conclusions:
In short, we can non-invasively measure auditory responses to predictable and deviant syllable sequences across multiple regions of the auditory cortex. In line with the canonical model for PC [1], we show that the violation of expectations results in both deep layer effects, which we provisionally interpret as global model update, and in superficial layer effects (i.e. prediction-error).
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Other Methods
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular 2
Keywords:
Cortex
Cortical Layers
FUNCTIONAL MRI
HIGH FIELD MR
Modeling
Perception
Other - Audition
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
[2] Friston, K. J. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836. https://doi.org/10.1098/rstb.2005.1622
[3] Havlicek, M., & Uludağ, K. (2020). A dynamical model of the laminar BOLD response. NeuroImage, 204, 116209. https://doi.org/10.1016/j.neuroimage.2019.116209
[4] Heilbron, M., & Chait, M. (2018). Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex? Neuroscience, 389, 54–73. https://doi.org/10.1016/j.neuroscience.2017.07.061
[5] Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. https://doi.org/10.1038/4580
[6] Sohoglu, E., & Davis, M. H. (2016). Perceptual learning of degraded speech by minimizing prediction error. Proceedings of the National Academy of Sciences, 113(12). https://doi.org/10.1073/pnas.1523266113
[7] Uludag, K., & Havlicek, M. (2021). Determining laminar neuronal activity from BOLD fMRI using a generative model. Progress in Neurobiology, 207, 102055. https://doi.org/10.1016/j.pneurobio.2021.102055
[8] Winkler, I., & Czigler, I. (2012). Evidence from auditory and visual event-related potential (ERP) studies of deviance detection (MMN and vMMN) linking predictive coding theories and perceptual object representations. International Journal of Psychophysiology, 83(2), 132–143. https://doi.org/10.1016/j.ijpsycho.2011.10.001
[9] Winkler, I., Denham, S. L., & Nelken, I. (2009). Modeling the auditory scene: Predictive regularity representations and perceptual objects. Trends in Cognitive Sciences, 13(12), 532–540. https://doi.org/10.1016/j.tics.2009.09.003