Biology subjects

Barkema, P.

Publications and source records attributed to Barkema, P..

3 recordsLinked to original sources

bioRxiv2026

The post-hoc montage of perception: deep layers of primary visual cortex encode postdictive percepts

One of the most puzzling aspects of perception is postdiction: later information can change how a previous stimulus is perceived [1,2]. This indicates that perception is not a livestream of the external world, but rather a post-hoc reconstruction. How does the brain revise perception? It has been proposed that recurrent processing in the sensory hierarchy may sustain earlier information long enough for later information to impact it [3]. We hypothesised that this allows feedback from higher-order multisensory regions to alter the representation of earlier visual stimuli in the primary visual cortex (V1) [4]. Here, we tested this hypothesis using two postdictive illusions [5], where the presence or absence of a tone determines whether an illusory flash is perceived, or an actually presented flash is perceptually suppressed, respectively. This allowed us to isolate neural signals reflecting illusory percepts from sensory inputs in retinotopic space. We collected 7T layer-specific fMRI data while participants (N=23) experienced the illusions. We found that postdictive illusions induced similar activity patterns as real percepts, specifically in the deep layers of V1, suggesting postdictive feedback. Informational connectivity analyses suggested that the source of this feedback was the Superior Temporal Cortex (STC). We conclude that the postdictive montage of perception is reflected in V1, implemented through a delaying feedback loop with higher-order sensory cortex. This extends the role V1 plays in perceptual inference: beyond receiving sensory inputs reflecting the present and predictions of the future, it is involved in the post-hoc revision of perception.

neuroscience↗

bioRxiv2022

Evidence for Embracing Normative Modeling

In this work, we expand the normative model repository introduced in Rutherford et al. (2022a) to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we confirm the advantage (i.e., stronger effect sizes, more accurate classification and prediction) of using normative modeling features. We intend for these accessible resources to facilitate wider adoption of normative modeling across the neuroimaging community.

neuroscience↗

bioRxiv2022

Non-Gaussian Normative Modelling With Hierarchical Bayesian Regression

Normative modelling is an emerging technique for parsing heterogeneity in clinical cohorts. This can be implemented in practice using hierarchical Bayesian regression, which provides an elegant probabilistic solution to handle site variation in a federated learning framework. However, applications of this method to date have employed a Gaussian assumption, which may be restrictive in some applications. We have extended the hierarchical Bayesian regression framework to flexibly model non-Gaussian data with heteroskdastic skewness and kurtosis. To this end, we employ a flexible distribution from the sinh-arcsinh (SHASH) family, and introduce a novel reparameterisation and a Markov chain Monte Carlo sampling approach to perform inference in this model. Using a large neuroimaging dataset collected at 82 different sites, we show that the results achieved with this extension are equivalent or better than a warped Bayesian linear regression baseline model on most datasets, whilst providing better control over the parameters governing the shape of distributions that approach is able to model. We also demonstrate that the attained flexibility is useful for accurately modelling highly nonlinear relationships between aging and imaging derived phenotypes, which shows that the extension is important for pushing the field of normative modelling forward. All methods described here are available in the open-source pcntoolkit. HighlightsO_LIWe extended the Hierarchical Bayesian Regression framework for normative modelling C_LIO_LIOur extension allows modelling data with heteroskedastic skewness and kurtosis C_LIO_LIWe developed a reparameterization of the SHASH distribution, suitable for sampling C_LIO_LIWe provide the first implementation of the SHASH distribution in a fully Bayesian framework C_LIO_LIResults show that the extension outperforms current methods on various measures C_LI

neuroscience↗