Heuristic model in joint EEG-fMRI analysis

This work deals with the joint EEG-fMRI analysis based on the heuristic model. There is described principle of the heuristic model which assumes that the BOLD (blood oxygen level depend) signal measured by fMRI (functional magnetic resonance imaging) is directly proportional to the spectral shift in the EEG signal. The paper describes algorithm of calculations which was implemented and tested on real data from 22 subjects. The study also monitors effect of different EEG information selection from electrodes of interest (averaging or principal component analysis)

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship

Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

EEG To FMRI Synthesis: Is Deep Learning a Candidate?

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source

Development and application of model selection methods for investigating brain function

The goal of any scientific discipline is to learn about nature, usually through the process of evaluating competing hypotheses for explaining observations. Brain research is no exception. Investigating brain function usually entails comparing models, expressed as mathematical equations, of how the brain works. The aim of this thesis is to provide and evaluate new model comparison techniques that facilitate this research. In addition, it applies existing comparison methods to disambiguate between hypotheses of how neuronal activity relates to blood ow, a topic known as neurovascular coupling. In neuroimaging, techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) allow to routinely image the brain, whilst statistical frameworks, such as statistical parametric mapping (SPM), allow to identify regionally specific responses, or brain activations. In this thesis, SPM is first used to address the problem of neurovascular coupling, and compare different putative coupling functions, which relate fMRI signals to different features of the EEG power spectrum. These inferences are made using linear models and a model selection approach based on F-tests. Although valid, this approach is restricted to nested models. This thesis then focuses on the development of a Bayesian technique, to construct posterior model probability maps (PPMs) for group studies. PPMs are analogous to F-tests but not limited to nested hypotheses. The work presented here then focuses again on neurovascular coupling, this time from a mechanistic perspective, not afforded by linear models. For this purpose, a detailed biophysical framework is used to explore the contribution of synaptic and spiking activity in the generation of hemodynamic signals in visual cortex, using simultaneous EEG-fMRI. This approach is a special case of brain connectivity models. Finally, using fMRI data, this thesis validates a recently proposed Bayesian approach for quickly comparing large numbers of connectivity models based on inverting a single model

Dynamic imaging of coherent sources reveals different network connectivity underlying the generation and perpetuation of epileptic seizures

The concept of focal epilepsies includes a seizure origin in brain regions with hyper synchronous activity (epileptogenic zone and seizure onset zone) and a complex epileptic network of different brain areas involved in the generation, propagation, and modulation of seizures. The purpose of this work was to study functional and effective connectivity between regions involved in networks of epileptic seizures. The beginning and middle part of focal seizures from ictal surface EEG data were analyzed using dynamic imaging of coherent sources (DICS), an inverse solution in the frequency domain which describes neuronal networks and coherences of oscillatory brain activities. The information flow (effective connectivity) between coherent sources was investigated using the renormalized partial directed coherence (RPDC) method. In 8/11 patients, the first and second source of epileptic activity as found by DICS were concordant with the operative resection site; these patients became seizure free after epilepsy surgery. In the remaining 3 patients, the results of DICS / RPDC calculations and the resection site were discordant; these patients had a poorer post-operative outcome. The first sources as found by DICS were located predominantly in cortical structures; subsequent sources included some subcortical structures: thalamus, Nucl. Subthalamicus and cerebellum. DICS seems to be a powerful tool to define the seizure onset zone and the epileptic networks involved. Seizure generation seems to be related to the propagation of epileptic activity from the primary source in the seizure onset zone, and maintenance of seizures is attributed to the perpetuation of epileptic activity between nodes in the epileptic network. Despite of these promising results, this proof of principle study needs further confirmation prior to the use of the described methods in the clinical praxis

Bayesian Comparison of Neurovascular Coupling Models Using EEG-fMRI

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal