Search arXivSearch

arXiv · 1812.01291

Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals

Abstract

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral Pairwise Interacting Source Analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA allows to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e. volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the Pairwise Interacting Source Analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs, and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Federico Chella, Vittorio Pizzella, Filippo Zappasodi, Guido Nolte, Laura Marzetti. 2019-01-24. Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals. https://doi.org/10.1103/physreve.93.052420

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Voxel-Matching NORDIC: Non-local patch formation by time-series similarity increases tSNR in high-resolution BOLD fMRI

Submillimeter functional magnetic resonance imaging (fMRI) based on blood-oxygenation-level-dependent (BOLD) signal enables the study of brain function at the submillimeter level, uncovering insights into fine-scale organisations like cortical layers and columns. However, its inherently low contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) often limit its reliability and applicability. Noise Reduction with Distribution Corrected Principal Components Analysis (NORDIC PCA) is a locally low-rank denoising algorithm that reduces thermal noise levels in BOLD fMRI in a local patch manner. However, local patches often contain a mixture of signals from multiple tissues that negatively affects the low-rank structure of the patches, which limits the denoising capabilities of the algorithm. We propose an alternative approach for patch formation by gathering similar non-local voxels, dubbed voxel-matching (VM) NORDIC. The results on submillimeter-resolution BOLD fMRI data indicate that VM-NORDIC effectively promotes the low rank of the patches by boosting signal redundancy, allowing for more efficient noise attenuation. Moreover, the method barely affects spatial smoothness due to the non-local voxel selection based on time-series similarity. In particular, VM-NORDIC outperforms standard NORDIC with default local patching (Standard-NORDIC) in terms of temporal SNR (tSNR) (~9-90% larger than Standard-NORDIC; ~23-250% larger than the original) and spatial smoothness estimates (~20% of the smoothness induced by Standard-NORDIC). These improvements are fundamental to improving the validity and precision of fMRI studies at submillimeter resolutions.

physics.med-ph

Computed Tomography Reconstruction Algorithm Using Markov Random Field Model

X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.

physics.med-ph

Charge Collection Efficiency in Air-Vented Plane-Parallel Ionisation Chambers at Ultra-High Dose Rates: A Self-Consistent Garfield++ Monte Carlo Model Including Space-Charge Effects and Ion Recombination

Ultra-high dose rate (UHDR) irradiation used in FLASH radiotherapy can lead to significant reductions in charge collection efficiency (CCE) in plane-parallel ionisation chambers (PPICs) due to enhanced ion recombination, while the high charge densities involved can also induce strong space-charge-induced electric-field distortions. To investigate these coupled effects, we extended the Garfield++ Monte Carlo particle-transport framework by implementing ion--ion recombination and self-consistent space-charge electric field calculations. The developed Monte Carlo model couples particle transport, electron attachment, recombination processes, and dynamic electric-field distortions. The implementation was validated against analytical and numerical models from the literature. Excellent agreement was obtained for the free electron fraction (FEF), CCE, induced current, and electric field evolution under UHDR conditions. The simulations show that space charge can locally increase the electric field by more than a factor of four or reduce it to nearly zero in some regions of the chamber. The results further suggest that the reduction of the CCE under UHDR conditions is mainly driven by the decrease of the FEF caused by electric-field-dependent electron attachment. This finding indicates that the complex problem of recombination under UHDR conditions may be largely governed by the evolution of the FEF, opening promising perspectives for the development of improved analytical models and real-time correction methods for ionisation chamber dosimetry under UHDR irradiation conditions. This work provides a flexible and self-consistent Monte Carlo framework for investigating recombination phenomena and improving dosimetry models for FLASH radiotherapy.

physics.med-ph