Applying neural networks for improving the MEG inverse solution

Magnetoencephalography (MEG) and electroencephalography (EEG) are appealing non-invasive methods for recording brain activity with high temporal resolution. However, locating the brain source currents from recordings picked up by the sensors on the scalp introduces an ill-posed inverse problem. The MEG inverse problem one of the most difficult inverse problems in medical imaging. The current standard in approximating the MEG inverse problem is to use multiple distributed inverse solutions – namely dSPM, sLORETA and L2 MNE – to estimate the source current distribution in the brain. This thesis investigates if these inverse solutions can be "post-processed" by a neural network to provide improved accuracy on source locations. Recently, deep neural networks have been used to approximate other ill-posed inverse medical imaging problems with accuracy comparable to current state-of- the-art inverse reconstruction algorithms. Neural networks are powerful tools for approximating problems with limited prior knowledge or problems that require high levels of abstraction. In this thesis a special case of a deep convolutional network, the U-Net, is applied to approximate the MEG inverse problem using the standard inverse solutions (dSPM, sLORETA and L2 MNE) as inputs. The U-Net is capable of learning non-linear relationships between the inputs and producing predictions about the site of single-dipole activation with higher accuracy than the L2 minimum-norm based inverse solutions with the following resolution metrics: dipole localization error (DLE), spatial dispersion (SD) and overall amplitude (OA). The U-Net model is stable and performs better in aforesaid resolution metrics than the inverse solutions with multi-dipole data previously unseen by the U-Net

Neuromagnetic Decomposition of Social Interaction

Not Available

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Expanding the applicability of magnetoencephalography

Magnetoencephalography (MEG) offers a unique way to non-invasively monitor the neural activity in the human brain. MEG is based on measuring the very weak magnetic fields generated by the electric currents in the active neurons. Such measurements allow, with certain limitations, estimating the underlying current distribution and thus the locations and time courses of the neural generators with an excellent temporal resolution. The aim of this Thesis was to advance MEG to certain realms that have been considered difficult or even impossible for it. Specifically, the included studies contributed to the modelling of the neural generators, detection of activity in the deep brain areas, analysis of oscillatory activity, and characterisation of neural states related to bistable perception. Estimating the sources of MEG signals is non-trivial as multiple current constellations can give rise to the same observed magnetic fields. As a new solution to this problem, we introduced an automatic Bayesian tracking algorithm that recovers the locations and time courses of a set of focal neural current sources from MEG data. The majority of MEG experiments have concentrated on brain signals originating in the neocortex due to the rapid decrease of the MEG signals as a function increasing source depth. Here, we demonstrated that neural activity deep in the brainstem can be detected and accurately localised by MEG in favourable conditions. We also explored the utility of stochastic resonance in varying the salience of a cognitive stimulus, and showed that the detection accuracy of visually-presented words correlated better with the amplitudes of the late than early responses. The temporal resolution provided by MEG was exploited in novel ways. We showed that oscillatory 20-Hz signals from the primary and secondary somatosensory cortex were transiently phase-locked in response to a stimulus, possibly signifying functional connectivity. We also introduced a frequency-tagging method employing dynamical noise to separate brain activations elicited by different parts of a visual scene: monitoring these rhythmic signals with MEG enabled us to probe the neural engagement in the early visual brain areas during bistable perception and thus to link subjective perceptual states to brain states

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Neurophysiological Aspects of Time Perception

In magneto- und electroencephalographic (MEG/EEG) experiments the source structure and the amplitudes of the evoked fields and potentials recorded during the fulfillment of an attentional task on discriminating durations were investigated. This was done for tones, tone pauses, pictures and picture pauses to investigate the dependence upon sensory input and modality. The attention-specific differences found include primary sensory responses and a parietal activation in particular. The tone and tone pause tasks were also examined using functional magnetic resonance imaging (fMRI) to gather model-independent evidence for the location and extension of the parietal network. Further, the relation between electrophysiological and psychometric data was explored, where the study on tones played a crucial role. Here the strength of the parietal activity was largest, the individual discriminative abilities were highest and the two variables were found to be significantly correlated. Finally, the discriminative ability for all tasks covaried with the individual rhythmic musical aptitude. The frame of this work is given by a sketch of neurophysiological and -psychological findings on the perception of time and music; findings which get embedded into philosophical considerations on time and on psychophysical correlations (mind-body-problem)

Processing of weak magnetic multichannel signals : the signal space separation method

This work concentrates on processing of multichannel magnetoencephalographic (MEG) data. The aim of the work is to improve the quality of the measured signals in order to enable reliable data analysis. A special requirement for the developed mathematical methods is that they should be applicable to all MEG measurements regardless of the level of cooperation of the subject. This is essential, e.g., with small children and in clinical investigations. In addition to MEG, the methods presented here can be used in other magnetic multichannel measurements, too. MEG measurements are used in basic brain research and recently also in clinical examinations. The method has excellent time resolution and reasonably good spatial resolution, which makes it a very useful tool in analysis of various brain functions. During the last 20 years, the instrumentation of MEG has been developed from devices containing less than 40 channels and limited coverage to whole-head systems with more than 300 channels. Yet, many of the signal processing and analysis methods used today date back to the time of the old instrumentation with limited coverage of the magnetic field. Traditionally, MEG investigations have been performed primarily only with cooperative subjects in order to avoid the characteristic problems of MEG, including signal distortions due to head movements and artifacts caused by sources attached to the body. In clinical measurements, however, the patient may have involuntary movements and carry artifact sources such as therapeutic stimulators. This work introduces the signal space separation method (SSS), which is based on Maxwell's equations and the generous spatial sampling by modern multichannel MEG devices. The thesis describes the theoretical foundations of SSS and its temporal extension tSSS, and demonstrates the results in several applications. SSS and tSSS are shown to significantly improve the quality of MEG data under conditions previously considered too challenging for meaningful analysis. Notably, the methods have potential to expand the applicability of MEG to some new patient groups, e.g., patients with deep brain stimulators

Sparse machine learning methods with applications in multivariate signal processing

This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility. The methods presented can be seen as modular building blocks that can be applied to a variety of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set of multivariate signals. In addition to testing on benchmark datasets, a series of empirical evaluations on real world datasets were carried out. These included: the classification of musical genre from polyphonic audio files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed Sensing (CS); analysis of human perception of different modulations of musical key from Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate the efficacy of the framework and highlight interesting directions of future research