276 research outputs found
Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth
We focus on electromagnetoencephalography imaging of the neural activity and,
in particular, finding a robust estimate for the primary current distribution
via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably
fast maximum a posteriori (MAP) estimation technique which would be applicable
for both superficial and deep areas without specific a priori knowledge of the
number or location of the activity. To enable source distinguishability for any
depth, we introduce a randomized multiresolution scanning (RAMUS) approach in
which the MAP estimate of the brain activity is varied during the
reconstruction process. RAMUS aims to provide a robust and accurate imaging
outcome for the whole brain, while maintaining the computational cost on an
appropriate level. The inverse gamma (IG) distribution is applied as the
primary hyperprior in order to achieve an optimal performance for the deep part
of the brain. In this proof-of-the-concept study, we consider the detection of
simultaneous thalamic and somatosensory activity via numerically simulated data
modeling the 14-20 ms post-stimulus somatosensory evoked potential and field
response to electrical wrist stimulation. Both a spherical and realistic model
are utilized to analyze the source reconstruction discrepancies. In the
numerically examined case, RAMUS was observed to enhance the visibility of deep
components and also marginalizing the random effects of the discretization and
optimization without a remarkable computation cost. A robust and accurate MAP
estimate for the primary current density was obtained in both superficial and
deep parts of the brain.Comment: Brain Topogr (2020
Zeffiro user interface for electromagnetic brain imaging: a GPU accelerated FEM tool for forward and inverse computations in Matlab
This article introduces the Zeffiro interface (ZI) version 2.2 for brain
imaging. ZI aims to provide a simple, accessible and multimodal open source
platform for finite element method (FEM) based and graphics processing unit
(GPU) accelerated forward and inverse computations in the Matlab environment.
It allows one to (1) generate a given multi-compartment head model, (2) to
evaluate a lead field matrix as well as (3) to invert and analyze a given set
of measurements. GPU acceleration is applied in each of the processing stages
(1)-(3). In its current configuration, ZI includes forward solvers for
electro-/magnetoencephalography (EEG) and linearized electrical impedance
tomography (EIT) as well as a set of inverse solvers based on the hierarchical
Bayesian model (HBM). We report the results of EEG and EIT inversion tests
performed with real and synthetic data, respectively, and demonstrate
numerically how the inversion parameters affect the EEG inversion outcome in
HBM. The GPU acceleration was found to be essential in the generation of the FE
mesh and the LF matrix in order to achieve a reasonable computing time. The
code package can be extended in the future based on the directions given in
this article
Flexible Multi-layer Sparse Approximations of Matrices and Applications
The computational cost of many signal processing and machine learning
techniques is often dominated by the cost of applying certain linear operators
to high-dimensional vectors. This paper introduces an algorithm aimed at
reducing the complexity of applying linear operators in high dimension by
approximately factorizing the corresponding matrix into few sparse factors. The
approach relies on recent advances in non-convex optimization. It is first
explained and analyzed in details and then demonstrated experimentally on
various problems including dictionary learning for image denoising, and the
approximation of large matrices arising in inverse problems
Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)
In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced randomized multiresolution scanning (RAMUS) source space decomposition approach to reduce modelling errors, respectively. In particular, we aim to generalize the previously extensively used conditionally Gaussian prior (CGP) formalism to achieve distributional reconstructions with higher focality. For this purpose, we introduce as a hierarchical prior, a general exponential distribution, which we refer to as conditionally exponential prior (CEP). The first-degree CEP corresponds to focality enforcing Laplace prior, but it also suffers from strong depth bias, when applied in numerical modelling, making the deep activity unrecoverable. We sample over multiple resolution levels via RAMUS to reduce this bias as it is known to depend on the resolution of the source space. Moreover, we introduce a procedure based on the physiological a priori knowledge of the brain activity to obtain the shape and scale parameters of the gamma hyperprior that steer the CEP. The posterior estimates are calculated using iterative statistical methods, expectation maximization and iterative alternating sequential algorithm, which we show to be algorithmically similar and to have a close resemblance to the iterative â„“1 and â„“2 reweighting methods. The performance of CEP is compared with the recent sampling-based dipole localization method Sequential semi-analytic Monte Carlo estimation (SESAME) in numerical experiments of simulated somatosensory evoked potentials related to the human median nerve stimulation. Our results obtained using synthetic sources suggest that a hybrid of the first-degree CEP and RAMUS can achieve an accuracy comparable to the second-degree case (CGP) while being more focal. Further, the proposed hybrid is shown to be robust to noise effects and compares well with the dipole reconstructions obtained with SESAME.publishedVersionPeer reviewe
Sparse wavelet-based solutions for the M/EEG inverse problem
This paper is concerned with variational and Bayesian approaches to
neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy
of these problems is tackled by introducing sparsity inducing
regularization/priors in a transformed domain, namely a spatial wavelet domain.
Sparsity in the wavelet domain allows to reach ''data compression'' in the
cortical sources domain. Spatial wavelets defined on the mesh graph of the
triangulated cortical surface are used, in combination with sparse regression
techniques, namely LASSO regression or sparse Bayesian learning, to provide
localized and compressed estimates for brain activity from sensor data.
Numerical results on simulated and real MEG data are provided, which outline
the performances of the proposed approach in terms of localization
Multi-modal and multi-model interrogation of large-scale functional brain networks
Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome. To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data. We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears). Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours
MEM-diffusion MRI framework to solve MEEG inverse problem
International audienceIn this paper, we present a framework to fuse information coming from diffusion magnetic resonance imaging (dMRI) with Magnetoencephalography (MEG)/ Electroencephalography (EEG) measurements to reconstruct the activation on the cortical surface. The MEG/EEG inverse-problem is solved by the Maximum Entropy on the Mean (MEM) principle and by assuming that the sources inside each cortical region follow Normal distribution. These regions are obtained using dMRI and assumed to be functionally independent. The source reconstruction framework presented in this work is tested using synthetic and real data. The activated regions for the real data is consistent with the literature about the face recognition and processing network
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