Exploring brain regions underlying predictive processes and precision encoding using 7-Tesla fMRI
Poster No:
716
Submission Type:
Abstract Submission
Authors:
Linzhi Tao1, Trevor Steward1,2, Joshua Corbett1, Marta Garrido1,3
Institutions:
1Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia, 2Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton, Victoria, Australia, 3Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
First Author:
Linzhi Tao
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, Australia
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, Australia
Co-Author(s):
Trevor Steward
Melbourne School of Psychological Sciences, The University of Melbourne|Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne
Parkville, Victoria, Australia|Carlton, Victoria, Australia
Melbourne School of Psychological Sciences, The University of Melbourne|Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne
Parkville, Victoria, Australia|Carlton, Victoria, Australia
Joshua Corbett
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, Australia
Melbourne School of Psychological Sciences, The University of Melbourne
Parkville, Victoria, Australia
Marta Garrido
Melbourne School of Psychological Sciences, The University of Melbourne|Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
Parkville, Victoria, Australia|Parkville, Victoria, Australia
Melbourne School of Psychological Sciences, The University of Melbourne|Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
Parkville, Victoria, Australia|Parkville, Victoria, Australia
Introduction:
The brain is constantly making predictions about the environment. It has been proposed that these predictions are weighted based on their precision, so that the internal model of the brain can optimally capture the statistical regularities in the environment and inform behaviour (Bastos et al., 2012; Friston, 2012). Disrupted precision modulation has been suggested in disorders including autism and psychosis (Ermakova et al., 2018; Lawson et al., 2014), highlighting the importance of precision modulation in predictive processes. However, where and how precision is encoded in the brain remains unclear. This study explored brain regions involved in predictive processes and precision encoding using ultrahigh-field fMRI.
Methods:
We designed a visual probabilistic cueing paradigm using gratings (Figure 1) to elicit predictions and to manipulate their precision. Participants were presented with a cue followed by two consecutive gratings and were asked to indicate whether the orientations of the gratings were identical or different. Precision was manipulated by cues with varied levels of precision that predicted whether the gratings were identical or different. A behavioural covariate was calculated as the difference in the number of correct responses between high- and low-precision conditions.
Thirty-one healthy individuals (16 female, 1 prefer not to say, age 27.8±6.9 years) participated. The data were acquired using a Siemens 7T MRI Scanner equipped with a 32-channel head coil. The functional sequence (T2*-weighted) consisted of a multiband Generalised Autocalibrating Partially Parallel Acquisitions (Griswold et al., 2002) accelerated Gradient-echo Echo-planar imaging sequence in the steady state (84 interleaved slices parallel to the anterior-posterior commissure line, slice thickness = 1.6mm, repetition time = 0.8s, echo time = 22.2ms, flip angle = 45°, field of view = 20.8cm, matrix size = 130×130, volume = 1084). High-resolution structural images (T1-weighted) was acquired using Magnetization-Prepared Rapid Gradient Echo sequence (Marques et al., 2010) to assist with functional image co-registration and normalisation. The data were preprocessed and analysed in Statistical Parametric Mapping 12. The preprocessing steps included realignment, motion correction, coregistration, segmentation, normalisation, physiological noise correction.
First- and second-level general linear models were used to analyse the overall task effect and changes in the estimated blood-oxygen-level-dependent (BOLD) responses between high and low precision conditions. The behavioural covariate was included in the second-level model to investigate the regions related to the differences in the participants' behavioural performance.
Thirty-one healthy individuals (16 female, 1 prefer not to say, age 27.8±6.9 years) participated. The data were acquired using a Siemens 7T MRI Scanner equipped with a 32-channel head coil. The functional sequence (T2*-weighted) consisted of a multiband Generalised Autocalibrating Partially Parallel Acquisitions (Griswold et al., 2002) accelerated Gradient-echo Echo-planar imaging sequence in the steady state (84 interleaved slices parallel to the anterior-posterior commissure line, slice thickness = 1.6mm, repetition time = 0.8s, echo time = 22.2ms, flip angle = 45°, field of view = 20.8cm, matrix size = 130×130, volume = 1084). High-resolution structural images (T1-weighted) was acquired using Magnetization-Prepared Rapid Gradient Echo sequence (Marques et al., 2010) to assist with functional image co-registration and normalisation. The data were preprocessed and analysed in Statistical Parametric Mapping 12. The preprocessing steps included realignment, motion correction, coregistration, segmentation, normalisation, physiological noise correction.
First- and second-level general linear models were used to analyse the overall task effect and changes in the estimated blood-oxygen-level-dependent (BOLD) responses between high and low precision conditions. The behavioural covariate was included in the second-level model to investigate the regions related to the differences in the participants' behavioural performance.
Results:
As expected, participants' response accuracy was significantly higher in the high precision condition than the low precision condition, t(53.2) = 5.61, p < .001 (Figure 1C). The overall task effect elicited activation in the anterior cingulate cortex, medial frontal gyrus, orbitofrontal cortex, temporal pole, putamen, and fusiform gyrus (Figure 2). However, there was no significant difference in the BOLD responses between the high and low precision conditions (with a significance thresholding for the whole brain using False Discovery Rate < .05). Similarly, no significant difference was found when including the behavioural covariate in the second-level model.
Conclusions:
Consistent with previous prediction studies (Ficco et al., 2021; Siman-Tov et al., 2019), we found prefrontal activations for the prediction task. The novelty of having varied levels of precision in the cueing paradigm successfully induced behavioural differences at different precision levels. However, we did not identify brain regions encoding prediction precision. This work adds to the discussion of whether precision is encoded by specific brain regions or at a different scale of neural processes such as at the laminar level and/or at the level of cellular processes.
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Keywords:
Cognition
FUNCTIONAL MRI
HIGH FIELD MR
1|2Indicates the priority used for review
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Provide references using APA citation style.
Ermakova, A. O., Knolle, F., Justicia, A., Bullmore, E. T., Jones, P. B., Robbins, T. W., Fletcher, P. C., & Murray, G. K. (2018). Abnormal reward prediction-error signalling in antipsychotic naive individuals with first-episode psychosis or clinical risk for psychosis. Neuropsychopharmacology, 43(8). https://doi.org/10.1038/s41386-018-0056-2
Ficco, L., Mancuso, L., Manuello, J., Teneggi, A., Liloia, D., Duca, S., Costa, T., Kovacs, G. Z., & Cauda, F. (2021). Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-95603-5
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Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., & Haase, A. (2002). Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA). Magnetic Resonance in Medicine, 47(6). https://doi.org/10.1002/mrm.10171
Lawson, R. P., Rees, G., & Friston, K. J. (2014). An aberrant precision account of autism. Frontiers in Human Neuroscience, 8(MAY). https://doi.org/10.3389/fnhum.2014.00302
Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P. F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage, 49(2). https://doi.org/10.1016/j.neuroimage.2009.10.002
Siman-Tov, T., Granot, R. Y., Shany, O., Singer, N., Hendler, T., & Gordon, C. R. (2019). Is there a prediction network? Meta-analytic evidence for a cortical-subcortical network likely subserving prediction. Neuroscience and Biobehavioral Reviews, 105. https://doi.org/10.1016/j.neubiorev.2019.08.012