Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics

Taylor, Jeremy A; Garrido, Marta I.

doi:10.1101/534784

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The significant areas (over electrodes) for a given contrast are visualised within the topographic plot. The colour indicates a correspondence with the T-statistic above significance, with yellow being the strongest, see also Taylor et al, 2019 34 for further description on this visualisation method.…”

Section: Resultsmentioning

confidence: 99%

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

Larsen

Dzafic

Darke

et al. 2019

Preprint

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BackgroundThe ability to generate a precise internal model of statistical regularities is impaired in schizophrenia. Predictive coding accounts of schizophrenia suggest that psychotic symptoms may be explained by a failure to build precise beliefs or a model of the world. The precision of this model may vary with context. For example, in a noisy environment the model will be more imprecise compared to a model built in an environment with lower noise. However compelling, this idea has not yet been empirically studied in schizophrenia. Methods: In this study, 62 participants engaged in a stochastic mismatch negativity paradigm with high and low precision. We included inpatients with a schizophrenia spectrum disorder (N=20), inpatients with a psychiatric disorder but without psychosis (N=20), and healthy controls (N=22), with comparable sex ratio and age distribution. Bayesian mapping and dynamic causal modelling were employed to investigate the underlying microcircuitry of precision encoding of auditory stimuli. Results: We found strong evidence (exceedance p > 0.99) for differences in the underlying connectivity associated with precision encoding between the three groups as well as on the continuum of psychotic-like experiences assessed across all participants. Critically, we show changes in interhemispheric connectivity between the two inpatient groups, with some connections further aligning on the continuum of psychotic-like experiences. Conclusions: While our results suggest continuity in backward connectivity alterations with psychotic-like experiences regardless of diagnosis, they also point to specificity for the schizophrenia spectrum disorder group in interhemispheric connectivity alterations.

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Section: Resultsmentioning

confidence: 99%

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

Larsen

Dzafic

Darke

et al. 2019

Preprint

View full text Add to dashboard Cite

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Predicting subclinical psychotic-like experiences on a continuum using machine learning

Taylor

Larsen

Dzafic

et al. 2018

Preprint

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= 174 words Full Text = 3962 words Abstract Previous studies of psychosis using machine learning methods have primarily been concerned with binary classification of patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict individual psychotic experiences on a continuum between these two extremes in otherwise healthy people. From responses evoked by an auditory oddball paradigm, behavioural measures, neural activity, and effective connectivity were extracted as potential contributing features. Optimal performance was achieved using spatiotemporal maps of neural activity in response to frequent sounds, with late-P50 and early-N100 time windows contributing most to higher schizotypy scores. Effective connectivity estimates, in particular top-down frontotemporal connections, were also predictive of psychotic symptoms.As a proof-of-concept, these findings demonstrate that individual psychotic experiences in healthy people can be predicted from EEG data alone, whilst also supporting the idea of altered sensory responses and the dysconnection hypothesis in schizophrenia, as well as the notion that psychosis may exist on a continuum.

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Auditory prediction errors and auditory white matter microstructure as predictors of psychotic experiences in healthy individuals

Randeniya

Oestreich

Garrido

2019

Preprint

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Our sensory systems actively predict sensory information based on previously learnt patterns. An inability to accurately predict forthcoming information results in prediction errors. Individuals with schizophrenia consistently show reduced auditory prediction errors as well as reduced microstructure in auditory white matter pathways. However, it is not clear if also healthy individuals with psychotic experiences demonstrate such deficits. Participants underwent electroencephalography (EEG) recordings while listening to a simple two-tone duration deviant oddball paradigm (N=103) and a stochastic oddball paradigm (N=89). A subset of participants (N=89) also underwent diffusion-weighted magnetic resonance imaging (MRI), from which fractional anisotropy (FA), a measure of overall white matter microstructure, was obtained for auditory pathways namely the auditory interhemispheric pathway, as well as the left and right arcuate fasciculi. We investigated both structural and functional predictors of positive psychotic experiences in healthy participants as measured by the Community Assessment for Psychic Experiences positive dimension (CAPE+) scores. Prediction errors evokedby the classical oddball paradigm failed to reveal significant effects, whereas the stochastic oddball paradigm revealed significant clusters at typical mismatch negativity periods predictive of CAPE+ scores. Furthermore, we show that white matter microstructure from auditory pathways in addition to mismatches significantly predict CAPE+ scores. We suggest that structural and functional prediction error measures together may have potential in predicting psychotic experiences in the healthy population.3

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Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics

Cited by 3 publications

References 28 publications

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

Aberrant connectivity in auditory precision encoding in schizophrenia spectrum disorder and across the continuum of psychotic-like experiences

Predicting subclinical psychotic-like experiences on a continuum using machine learning

Auditory prediction errors and auditory white matter microstructure as predictors of psychotic experiences in healthy individuals

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