Biology subjects

Koechli, L.

Publications and source records attributed to Koechli, L..

3 recordsLinked to original sources

bioRxiv2024

Test-retest reliability of auditory MMN measured with OPM-MEG

In this paper, we report results from an investigation of auditory mismatch responses as measured by magnetoencephalography (MEG) based on optically pumped magnetometers (OPM). Specifically, as part of a quality control study, we examined the reliability and validity of auditory mismatch negativity (MMN) recordings, obtained with a newly installed OPM-MEG system. Based on OPM-MEG data from 30 healthy volunteers, measured twice with an established auditory MMN paradigm with frequency deviants, we examined the following questions: First, we focused on construct validity and examined whether OPM-MEG measurements of MMN responses (in terms of event-related fields, ERFs) were qualitatively comparable to previous MMN findings from studies using EEG or MEG based on superconducting quantum interference devices (SQUIDs). In particular, we examined whether significant MMN responses measured by OPM-MEG occurred in a comparable time window and showed a similar topography as in previous EEG/MEG studies of MMN. Second, we quantified test-retest reliability of MMN amplitude and latency over two separate measurement sessions. The results of our analyses show that MMN responses recorded with OPM-MEG are in good agreement with previously reported MMN results in terms of timing and topography. Furthermore, the comparison of group-level MMN topographies and timeseries shows excellent consistency across the two measurement sessions. Our quantitative test-retest reliability analyses at the sensor level indicate good reliability for MMN amplitude, but poor reliability for MMN latency. Overall, our findings suggest that OPM-MEG measurements of auditory MMN (i) are comparable to results from EEG and SQUID-based MEG and (ii) show good test-retest reliability for amplitude measures at the sensor level. Notably, these results were achieved in an "out of the box" state of the OPM-MEG system, shortly after installation and without further optimisation. The reason for the insufficient reliability for MMN latency we observed is currently under investigation and represents an important target for future improvements.

neuroscience↗

bioRxiv2024

Thermoceptive predictions and prediction errors in the anterior insula

Contemporary theories of interoception propose that the brain constructs a model of the body for predicting the states and allostatic needs of all organs, including the skin, and updates this model using prediction error signals. However, empirical tests of this proposal are scarce in humans. This computational neuroimaging study investigated the presence and location of thermoceptive predictions and prediction errors in the brain using probabilistic manipulations of skin temperature in a novel interoceptive learning paradigm. Using functional MRI in healthy volunteers, we found that a Bayesian model provided a better account of participants skin temperature predictions than a non-Bayesian model. Further, activity in a network including the anterior insula was associated with trial-wise predictions and precision-weighted prediction errors. Our findings provide further evidence that the anterior insula plays a key role in implementing the brains model of the body, and raise important questions about the structure of this model.

neuroscience↗

bioRxiv2024

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that harnessing information from two different data streams (binary responses and continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

neuroscience↗