Biology subjects

Hindriks, R.

Publications and source records attributed to Hindriks, R..

6 recordsLinked to original sources

bioRxiv2024

Construction of invariant features for time-domain EEG/MEG signals using Grassmann manifolds

A challenge in interpreting features derived from source-space electroencephalography (EEG) and magnetoencephalography (MEG) signals is residual mixing of the true source signals. A common approach is to use features that are invariant under linear and instantaneous mixing. In the context of this approach, it is of interest to know which invariant features can be constructed from a given set of source-projected EEG/MEG signals. We address this question by exploiting the fact that invariant features can be viewed as functions on the Grassmann manifold. By embedding the Grassmann manifold in a vector space, coordinates are obtained that serve as building blocks for invariant features, in the sense that all invariant features can be constructed from them. We illustrate this approach by constructing several new bivariate, higher-order, and multidimensional functional connectivity measures for static and time-resolved analysis of time-domain EEG/MEG signals. Lastly, we apply such an invariant feature derived from the Grassmann manifold to EEG data from comatose survivors of cardiac arrest and show its superior sensitivity to identify changes in functional connectivity. Author SummaryElectroencephalography (EEG) and magnetoencephalography (MEG) are techniques to non-invasively measure brain activity in human subjects. This works by measuring the electric potentials on the scalp (EEG) or the magnetic fluxes surrounding the head (MEG) that are induced by currents flowing in the brains grey matter (the "brain activity"). However, reconstruction of brain activity from EEG/MEG sensor signals is an ill-posed inverse problem and, consequently, the reconstructed brain signals are linear superpositions of the true brain signals. This fact complicates the interpretation of the reconstructed brain activity. A common approach is to only use features of the reconstructed activity that are invariant under linear superpositions. In this study we show that all invariant features of reconstructed brain signals can be obtained by taking combinations of a finite set of fundamental features. The fundamental features are parametrized by a high-dimensional space known as the Grass-mann manifold, which has a rich geometric structure that can be exploited to construct new invariant features. Our study advances the systematic study of invariant properties of EEG/MEG data and can be used as a framework to systematize and interrelate existing results. We use the theory to construct a new invariant connectivity measure and apply it to EEG data from comatose survivors of cardiac arrest. We find that this measure enables superior identification of affected brain regions.

neuroscience↗

bioRxiv2022

Characterization of second-order mixing effects in reconstructed cross-spectra of random neural fields

Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagranges identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds.

neuroscience↗

bioRxiv2020

Graph neural fields: a framework for spatiotemporal dynamical models on the human connectome

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI); as an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome, and show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of BOLD fMRI data. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships. Author summaryThe human brain can be seen as an interconnected network of many thousands neuronal "populations"; in turn, each population contains thousands of neurons, and each is connected both to its neighbors on the cortex, and crucially also to distant populations thanks to long-range white matter fibers. This extremely complex network, unique to each of us, is known as the "human connectome graph". In this work, we develop a novel approach to investigate how the neural activity that is necessary for our life and experience of the world arises from an individual human connectome graph. For the first time, we implement a mathematical model of neuronal activity directly on a high-resolution connectome graph, and show that it can reproduce the spatial patterns of activity observed in the real brain with magnetic resonance imaging. This new kind of model, made of equations implemented directly on connectome graphs, could help us better understand how brain function is shaped by computational principles and anatomy, but also how it is affected by pathology and lesions.

neuroscience↗

bioRxiv2017

Reconstruction of resting-state networks from macaqueelectrocorticographic data

The discovery of haemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electrophysiological cortical rhythms are organized into RSNs. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or electroencephalography (EEG), which limits the spatial scale on which RSNs can be investigated. Due to their close proximity to the cortical surface, electroencephalographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms. In this study we propose using source-space independent component analysis for identifying generators of resting-state cortical rhythms as recorded with ECoG and reconstructing their network structure. Their network structure is characterized by two kinds of connectivity: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. Using simulated data, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated as a consequence of first-order and higher-order volume-conduction effects, which troubles the interpretation of interaction measures based on imaginary phase-locking or coherence. The methodology is applied to resting-state beta (15-30 Hz) rhythms within the motor system of a macaque monkey and leads to the identification of a functional network of seven cortical generators that are distributed across the sensorimotor system. The spatial extent of the identified generators, together with consistent phase-lags, suggests that these rhythms can be viewed as being spatially continuous with complex dynamics including traveling waves. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

neuroscience↗

bioRxiv2017

Human Resting-State Electrophysiological Networks In The Alpha Frequency Band: Evidence From Magnetoencephalographic Source Imaging

In the resting-state, extended regions of the human cortex engage in electrical oscillations within the alpha-frequency band (7-14 Hz) that can be measured outside the head by magnetoencephalography (MEG). Given the accumulating evidence that alpha oscillations play a fundamental role in attentional processing and working memory, it becomes increasingly important to characterize their cortical organization. Event-related studies have demonstrated that attentional allocation can modulate alpha power selectively within the visual, auditory, and somatosensory cortices, as well as in higher-level regions. Such studies demonstrate the existence of multiple generators by exploiting experimental contrasts and trial-averaging. The identification of alpha generators from resting-state data alone has proven much harder and, consequently, relatively little is known about their organization: Apart from the classical visual, somatosensory, and auditory rhythms, it is unclear how many more generators can be observed with MEG and how they are organized into functional networks. Such knowledge, however, possibly enables to delineate separate cognitive, perceptual, and motor processes that co-occur in the resting-state and is therefore important. In this study we use the resting-state MEG data-set provided by the Human Connectome Project to identify cortical alpha generators and to characterize their organization into functional networks. The large number of subjects (N = 94), multiple scans per subject, and state-of-the-art surface-based cortical registration enable a detailed characterization of alpha in human cortex. By applying non-negative matrix factorization to source-projected power fluctuations, we identify 16 reliable cortical generators in each hemisphere. These include the classical sensory alpha rhythms as well as several additional ones in the lateral occipital and temporal lobes and in inferior parietal cortex. We show that the generators are coordinated across hemispheres and hence form resting-state networks (RSNs), two of which are the default mode network (DMN) and the ventral attention network (VAN). Our study hence provides a further subdivision of RSNs within the alpha frequency band and shows that these RSNs are supported by alpha generators. As such, it links the classical literature on human alpha with more recent research into electrophysiological RNSs.

neuroscience↗

bioRxiv2016

Resting-state fMRI correlations: from link-wiseunreliability to whole brain stability

The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

neuroscience↗