A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration

Henson, Richard N.; Wakeman, Daniel G.; Litvak, Vladimir; Friston, Karl J.

doi:10.3389/fnhum.2011.00076

Cited by 108 publications

(131 citation statements)

References 44 publications

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“…This scheme is implemented within the parametric empirical Bayes framework of SPM8 (73)(74)(75). This approach allows the use of multiple priors, in the form of source covariance matrices, which constrain the resulting source solutions and are optimized by maximizing the negative free-energy approximation to the model evidence (76).…”

Section: Methodsmentioning

confidence: 99%

“…This method produces smooth solutions similar to those obtained by the LORETA method (77), which are appropriate for assessing activation overlap across participants. Multimodal fusions of the data were achieved by using a heuristic to convert all sensor data to a common scale and by weighting additional error covariance matrices for each sensor type to maximize the model evidence (72,75). Such an approach allows the noise levels associated with each sensor type to be estimated directly from the data; by maximizing the negative free energy, sensor types with high estimated levels of noise contribute less to the resulting source solutions.…”

Section: Methodsmentioning

confidence: 99%

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Perceptual learning of degraded speech by minimizing prediction error

Sohoglu

Davis

2016

Proc. Natl. Acad. Sci. U.S.A.

105

154

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Human perception is shaped by past experience on multiple timescales. Sudden and dramatic changes in perception occur when prior knowledge or expectations match stimulus content. These immediate effects contrast with the longer-term, more gradual improvements that are characteristic of perceptual learning. Despite extensive investigation of these two experience-dependent phenomena, there is considerable debate about whether they result from common or dissociable neural mechanisms. Here we test single-and dualmechanism accounts of experience-dependent changes in perception using concurrent magnetoencephalographic and EEG recordings of neural responses evoked by degraded speech. When speech clarity was enhanced by prior knowledge obtained from matching text, we observed reduced neural activity in a peri-auditory region of the superior temporal gyrus (STG). Critically, longer-term improvements in the accuracy of speech recognition following perceptual learning resulted in reduced activity in a nearly identical STG region. Moreover, short-term neural changes caused by prior knowledge and longer-term neural changes arising from perceptual learning were correlated across subjects with the magnitude of learninginduced changes in recognition accuracy. These experience-dependent effects on neural processing could be dissociated from the neural effect of hearing physically clearer speech, which similarly enhanced perception but increased rather than decreased STG responses. Hence, the observed neural effects of prior knowledge and perceptual learning cannot be attributed to epiphenomenal changes in listening effort that accompany enhanced perception. Instead, our results support a predictive coding account of speech perception; computational simulations show how a single mechanism, minimization of prediction error, can drive immediate perceptual effects of prior knowledge and longer-term perceptual learning of degraded speech.perceptual learning | predictive coding | speech perception | magnetoencephalography | vocoded speech S uccessful perception in a dynamic and noisy environment critically depends on the brain's capacity to change how sensory input is processed based on past experience. Consider the way in which perception is enhanced by accurate prior knowledge or expectations. Sudden and dramatic changes in subjective experience can occur when a distorted and otherwise unrecognizable perceptual object is seen or heard after the object's identity is revealed (1-4). Such effects occur almost immediately; striking changes in perceptual outcomes occur over a timescale of seconds or less. However, not all effects of past experience emerge as rapidly as these effects of prior knowledge. With perceptual learning, practice in perceiving certain types of stimuli results in gradual and incremental improvements in perception that develop over a timescale of minutes or longer (Fig. 1A) (5, 6, 7). Critically, perceptual learning can generalize beyond the stimuli experienced during training, e.g., to visual forms presented in dif...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Perceptual learning of degraded speech by minimizing prediction error

Sohoglu

Davis

2016

Proc. Natl. Acad. Sci. U.S.A.

105

154

View full text Add to dashboard Cite

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“…The source localization procedure is contingent on a single regularization parameter: the prior sparsity level. Sparsity is a common assumption, employed in estimating ill-posed inverse solutions, and widely applied in EEG imaging [12,39,28,42]. In EEG, the sparsity assumption is motivated by the apparently sparse focal nature of brain activation [16].…”

Section: Forward Model Representationmentioning

confidence: 99%

“…We apply the EEG recordings and structural MRI data from 16 healthy subjects (F=7, M=9, age=23-31 years) from the multimodal dataset acquired by R. Henson and D. Wakeman [39,40]. Functional MRI (fMRI) and MEG were also recorded but not applied in this study.…”

Section: Neuroimaging Datamentioning

confidence: 99%

Data-driven forward model inference for EEG brain imaging

2016

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Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.

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“…Note that these components may embody different types of informative priors, e.g., different smoothing functions, medical knowledge, fMRI priors (Henson et al, 2011).…”

Section: Multiple Sparse Priors Algorithmmentioning

confidence: 99%

Multiple sparse volumetric priors for distributed EEG source reconstruction

Strobbe¹,

Mierlo²,

Vos³

et al. 2014

NeuroImage

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We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source * Corresponding author -phone number: +32484777651 space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models.We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skullbrain head models and extended 4-layered head models including CSF. We compared with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies.

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A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration

Cited by 108 publications

References 44 publications

Perceptual learning of degraded speech by minimizing prediction error

Perceptual learning of degraded speech by minimizing prediction error

Data-driven forward model inference for EEG brain imaging

Multiple sparse volumetric priors for distributed EEG source reconstruction

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