Biology subjects

Randeniya, R.

Publications and source records attributed to Randeniya, R..

4 recordsLinked to original sources

bioRxiv2018

Bayesian Model Selection Maps for group studies using M/EEG data

Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called Bayesian brains continuously create and update generative models of the world, inferring (hidden) causes from (sensory) consequences. Neuroimaging datasets enable the detailed investigation of such modelling and updating processes, and these datasets can themselves be analysed with Bayesian approaches. These offer methodological advantages over classical statistics. Specifically, any number of models can be compared, the models need not be nested, and the null model can be accepted (rather than only failing to be rejected as in frequentist inference). This methodological paper explains how to construct posterior probability maps (PPMs) for Bayesian Model Selection (BMS) at the group level using electroencephalography (EEG) or magnetoencephalography (MEG) data. The method has only recently been used for EEG data, after originally being developed and applied in the context of functional magnetic resonance imaging (fMRI) analysis. Here, we describe how this method can be adapted for EEG using the Statistical Parametric Mapping (SPM) software package for MATLAB. The method enables the comparison of an arbitrary number of hypotheses (or explanations for observed responses), at each and every voxel in the brain (source level) and/or in the scalp-time volume (scalp level), both within participants and at the group level. The method is illustrated here using mismatch negativity (MMN) data from a group of participants performing an audio-spatial oddball attention task. All data and code are provided in keeping with the Open Science movement. In so doing, we hope to enable others in the field of M/EEG to implement our methods so as to address their own questions of interest.

neuroscience↗

bioRxiv2018

White matter connectivity disruptions in the pre-clinical continuum of psychosis: A connectome study

BackgroundWidespread white matter disruptions in schizophrenia have been commonly reported, but it remains unanswered whether these abnormalities are associated with schizophrenia specifically or whether they range along a psychotic continuum into the healthy population. Investigating the extent of white matter connectivity disruptions specific to psychotic-like experiences in healthy individuals is insofar important as it is a necessary first step towards the development of prodromal psychosis biomarkers.\n\nMethodsHigh resolution, multi-shell diffusion-weighted magnetic resonance images were acquired from 89 healthy individuals. Whole-brain white matter fiber tracking was performed to quantify the strength of white matter connections. Network-based statistics were applied to white matter connections in a regression model in order to test for a linear relationship between streamline count and psychotic-like experiences.\n\nResultsA significant subnetwork was identified whereby streamline count declined with increasing quantity of psychotic-like experiences. This network of significant connectivity reductions affected all cortical lobes, subcortical structures and the cerebellum.\n\nConclusionA widespread network of linearly declining connectivity strength with increasing number of psychotic-like experiences was identified in healthy individuals. This finding is in line with white matter connectivity reductions reported from early to chronic stages of schizophrenia. We suggest that these white matter changes might be a potential biomarker for the identification of individuals at high risk for transitioning to psychosis.

neuroscience↗

bioRxiv2018

Structural connectivity facilitates functional connectivity of auditory prediction error generation within a fronto-temporal network

Auditory prediction errors, i.e. the mismatch between predicted and actual auditory input, are generated by a hierarchical functional network of cortical sources. This network is also interconnected by auditory white matter pathways. Hence it would be reasonable to assume that these structural and functional networks are quantitatively related, which is what the present study set out to investigate. Specifically, whether structural connectivity of auditory white matter pathways enables effective connectivity of auditory prediction error generation. Eighty-nine participants underwent diffusion weighted magnetic resonance imaging. Anatomically-constrained tractography was used to extract auditory white matter pathways, namely the bilateral arcuate fasciculus, the inferior occipito-frontal fasciculi (IOFF), and the auditory interhemispheric pathway, from which Apparent Fibre Density (AFD) was calculated. The same participants also underwent a stochastic oddball paradigm, which was used to elicit prediction error responses, while undergoing electroencephalographic recordings. Dynamic causal modelling (DCM) was used to investigate the effective connectivity of auditory prediction error generation in brain regions interconnected by the above mentioned auditory white matter pathways. Brain areas interconnected by all auditory white matter pathways best explained the dynamics of auditory prediction error responses. Furthermore, AFD in the right IOFF and right arcuate fasciculus significantly predicted the effective connectivity parameters underlying auditory prediction error generation. In conclusion, the generation of auditory prediction errors within an effectively connected, fronto-temporal network was found to be facilitated by the structural connectivity of auditory white matter pathways. These findings build upon the notion that structural connectivity facilitates dynamic interactions within brain regions that are effectively connected.\n\nSignificance statementThe brain continuously generates and updates hypotheses that predict forthcoming sensory input. Within the auditory domain, it has repeatedly been reported that these predictions about the auditory environment are facilitated by specific functional cortical connections. These functionally connected brain regions are also structurally connected via auditory white matter pathways. For the first time, this study provides quantitative evidence for a structural basis along which this functional network of auditory prediction error generation operates. This finding provides evidence for the notion that the functional connectivity of dynamically interacting brain areas is facilitated by structural connectivity amongst these brain areas.

neuroscience↗

bioRxiv2018

Reduced top-down connectivity as an underlying mechanism for psychotic experiences in healthy people

Our perceptions result from the brains ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying sensory learning and inference in stable and volatile environments, in a population of healthy individuals (N=75) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviourally recording sensory regularity learning errors, and used dynamic causal modelling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (N=31 and N=44 for the discovery and the validation datasets, respectively). First, we found that during stable conditions, participants demonstrated a stronger predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased regularity learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in sensory learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behaviour during sensory learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, non-clinical population. Significance StatementWhilst perceiving the world, we make inferences by learning the regularities present in the sensory environment. It has been argued that psychosis may emerge due to a failure to learn sensory regularities, resulting in an impaired representation of the world. Recently it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the non-clinical end of the psychosis continuum. We found that sensory learning is associated with brain prediction errors, and critically, it is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support that the continuum of psychosis extends into the non-clinical population.

neuroscience↗