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

Harris, Clare D; Rowe, Elise G; Randeniya, Roshini; Garrido, Marta I.

doi:10.1101/365056

Cited by 4 publications

(9 citation statements)

References 68 publications

(83 reference statements)

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“…The experimental effect being analysed is known as Mismatch Negativity (MMN; Näätänen, 1990), which can be simply described as sensory prediction error, the difference between responses to predictable and unpredictable stimuli, or the brain's response to surprise. The dataset used was provided by Harris et al (2018)…”

Section: Resultsmentioning

confidence: 99%

“…This implies that, across the four conditions, activity evoked by unattended standard stimuli has the lowest amplitude, attended deviant is greatest, with attended standard and unattended deviant approximately equivalent . The probability map supporting this model, shown in Figure 3B (also described in Harris et al, 2018) was thresholded at 90% posterior probability.…”

Section: Posterior Probability Mapsmentioning

confidence: 94%

“…The evidence for the model can then be converted to a posterior probability map at the group level. For more detail on this methodology behind these computations, refer to Harris et al (2018) and Rosa et al (2010).…”

Section: Posterior Probability Mapsmentioning

confidence: 99%

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

Taylor

Garrido

2019

Preprint

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Queensland Fellowship (2016000071) and a Foundation Research Excellence Award (2016001844) to MIG. We would like to thank Tyler Hobson for discussions on computer graphics methods, Clare Harris for providing data, as well as Veronika Halász, Kit Melissa Larsen, Ilvana Dzafic, Jessica McFadyen and Chase Sherwell for providing feedback on the functionality of earlier versions of the toolbox. AbstractElectro-and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques currently used to illustrate these effects are currently limited to single-or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: 1) Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction;2) Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with 'cloud-like' appearance and augmented by crosssectional scalp maps indicating local maxima.These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statistical maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers.In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

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Section: Resultsmentioning

confidence: 99%

Section: Posterior Probability Mapsmentioning

confidence: 94%

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

Taylor

Garrido

2019

Preprint

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“…To compare the adaptation, prediction, and adaptation&prediction models for ERPs, we used posterior probability maps [34][35][36] . Epoched data were converted into scalp-map images of dimension 64x64 obtained using interpolation.…”

Section: Posterior Probability Mapsmentioning

confidence: 99%

“…We formalized three theoretical models to explain RS: the adaptation model, the prediction model and finally the combined model, in the following referred to adaptation&prediction. These three models were tested both at the scalp level using Bayesian mapping for M/EEG [34][35][36] and at the connectivity level using dynamical causal modelling (DCM) 37 . Firstly, we hypothesized that responses to repeated stimuli would show a parametric modulation with an overall decrease in connectivity within the tested network for the first repetitions, followed by an increase reflecting the prediction of new stimuli, in agreement with the combined adaptation&prediction model.…”

Section: Introductionmentioning

confidence: 99%

Individuals with 22q11.2 deletion syndrome show intact prediction but reduced adaptation in responses to repeated sounds: evidence from Bayesian mapping

Larsen

Moerup

Birknow

et al. 2018

Preprint

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Word counts: Abstract: 242, Abstract, main text, figure legends and acknowledgement: 4009, Number of figures: 4, tables: 0 supplemental information 1.Abstract Background: One of the most common copy number variants, the 22q11.2 microdeletion, confers an increased risk for schizophrenia. Since schizophrenia has been associated with an aberrant neural response to repeated stimuli through both reduced adaptation and prediction, we here hypothesized that this may also be the case in nonpsychotic individuals with a 22q11.2 deletion.Methods: We recorded high-density EEG from 19 individuals with 22q11.2 deletion syndrome (12-25 years), as well as 27 healthy volunteers with comparable age and sex distribution, while they listened to a sequence of sounds arranged in a roving oddball paradigm. Using posterior probability maps and dynamic causal modelling we tested three different models accounting for repetition dependent changes in cortical responses as well as in effective connectivity; namely an adaptation model, a prediction model, and a model including both adaptation and prediction.Results: Repetition-dependent changes were parametrically modulated by a combination of adaptation and prediction and were apparent in both cortical responses and in the underlying effective connectivity. This effect was reduced in individuals with a 22q11.2 deletion and was negatively correlated with negative symptom severity. Follow-up analysis showed that the reduced effect of the combined adaptation and prediction model seen in individuals with 22q11.2 deletion was driven by reduced adaptation rather than prediction failure.Conclusions: Our findings suggest that adaptation is reduced in individuals with a 22q11.2 deletion, which can be interpreted in light of the framework of predictive coding as a failure to suppress prediction errors. Abbreviations: DCM = Dynamic Causal Modelling, MMN = Mismatch Negativity, 22q11.2DS = 22q11.2 Deletion Syndrome

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Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics

Taylor

Garrido

2020

Neuroinform

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Electro-and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques currently used to illustrate these effects are currently limited to single-or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: 1) Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction;2) Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with 'cloud-like' appearance and augmented by crosssectional scalp maps indicating local maxima. These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statistical maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers.In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

show abstract

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

Abstract: 15

Cited by 4 publications

References 68 publications

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

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

Individuals with 22q11.2 deletion syndrome show intact prediction but reduced adaptation in responses to repeated sounds: evidence from Bayesian mapping

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics

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