Separation of Synchronous Sources

This thesis studies the Separation of Synchronous Sources (SSS) problem, which deals with the separation of signals resulting from a linear mixing of sources whose phases are synchronous. While this study is made in a form independent of the application, a motivation from a neuroscience perspective is presented. Traditional methods for Blind Source Separation, such as Independent Component Analysis (ICA), cannot address this problem because synchronous sources are highly dependent. We provide sufficient conditions for SSS to be an identifiable problem, and quantify the effect of prewhitening on the difficulty of SSS. We also present two algorithms to solve SSS. Extensive studies on simulated data illustrate that these algorithms yield substantially better results when compared with ICA methods. We conclude that these algorithms can successfully perform SSS in varying configurations (number of sources, number of sensors, level of additive noise, phase lag between sources, among others). Theoretical properties of one of these algorithms are also presented. Future work is discussed extensively, showing that this area of study is far from resolved and still presents interesting challenges

Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology

To explain gating of memory encoding, magnetoencephalography (MEG) was analyzed over multi-regional network of negative correlations between alpha band power during cue (cue-alpha) and gamma band power during item presentation (item-gamma) in Remember (R) and No-remember (NR) condition. Persistent homology with graph filtration on alpha-gamma correlation disclosed topological invariants to explain memory gating. Instruction compliance (R-hits minus NR-hits) was significantly related to negative coupling between the left superior occipital (cue-alpha) and the left dorsolateral superior frontal gyri (item-gamma) on permutation test, where the coupling was stronger in R than NR. In good memory performers (R-hits minus false alarm), the coupling was stronger in R than NR between the right posterior cingulate (cue-alpha) and the left fusiform gyri (item-gamma). Gating of memory encoding was dictated by inter-regional negative alpha-gamma coupling. Our graph filtration over MEG network revealed these inter-regional time-delayed cross-frequency connectivity serve gating of memory encoding

Dynamic Oscillatory Interactions Between Neural Attention and Sensorimotor Systems

The adaptive and flexible ability of the human brain to preference the processing of salient environmental features in the visual space is essential to normative cognitive function, and various neurologically afflicted patient groups report negative impacts on visual attention. While the brain-bases of human attentional processing have begun to be unraveled, very little is known regarding the interactions between attention systems and systems supporting sensory and motor processing. This is essential, as these interactions are dynamic; evolving rapidly in time and across a wide range of functionally defined rhythmic frequencies. Using magnetoencephalography (MEG) and a range of novel cognitive paradigms and analytical techniques, this work attempts to fill critical gaps in this knowledge. Specifically, we unravel the role of dynamic oscillatory interactions between attention and three sensorimotor systems. First, we establish the importance of sub-second occipital alpha (8 – 14 Hz) oscillatory responses in visual distractor suppression during selective attention (Chapter 1) and their essential role in fronto-parietal attention networks during visual orienting (Chapter 2). Next, we examine the divergent effects of directed attention on multi-frequency primary somatosensory neural oscillations in the theta (4 – 8 Hz), alpha, and beta (18 – 26 Hz) bands (Chapter 3). Finally, we extend these findings to the motor system (Chapter 4), and find that the frontal and parietal beta-frequency oscillations known to support motor planning and execution are modulated equivalently by differing subtypes of attentional interference, whereas frontal gamma (64 – 84 Hz) oscillations specifically index the superadditive effect of this interference. These findings provide new insight into the dynamic nature of attention-sensorimotor interactions in the human brain, and will be the foundation for groundbreaking new studies of attentional deficits in patients with common neurological disorders (e.g., Alzheimer’s disease, HIV-associated neurocognitive disorders, Parkinson’s disease). With an enhanced knowledge of the temporal and spectral definitions of these impairments, new therapeutic interventions utilizing frequency-targeted neural stimulation can be developed

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Time-varying functional connectivity and dynamic neurofeedback with MEG: methods and applications to visual perception

Cognitive function involves the interplay of functionally-separate regions of the human brain. Of critical importance to neuroscience research is to accurately measure the activity and communication between these regions. The MEG imaging modality is well-suited to capturing functional cortical communication due to its high temporal resolution, on the millisecond scale. However, localizing the sources of cortical activity from the sensor measurements is an ill-posed problem, where different solutions trade-off between spatial accuracy, correcting for linear mixing of cortical signals, and computation time. Linear mixing, in particular, affects the reliability of many connectivity measures. We present a MATLAB-based pipeline that we developed to correct for linear mixing and compute time-varying connectivity (phase synchrony, Granger Causality) between cortically-defined regions interfacing with established toolboxes for MEG data processing (Minimum Norm Estimation Toolbox, Brainstorm, Fieldtrip). In Chapter 1, we present a new method for localizing cortical activation while controlling cross-talk on the cortex. In Chapter 2, we apply a nonparametric statistical test for measuring phase locking in the presence of cross-talk. Chapters 3 and 4 describe the application of the pipeline to MEG data collected from subjects performing a visual object motion detection task. Chapter 5 focuses on real-time MEG (rt-MEG) neurofeedback which is the real-time measurement of brain activity and its self-regulation through feedback. Typically neurofeedback modulates directly brain activation for the purpose of training sensory, motor, emotional or cognitive functions. Direct measures, however, are not suited to training dynamic measures of brain activity, such as the speed of switching between tasks, for example. We developed a novel rt-MEG neurofeedback method called state-based neurofeedback, where brain activity states related to subject behavior are decoded in real-time from the MEG sensor measurements. The timing related to maintaining or transitioning between decoded states is then presented as feedback to the subject. In a group of healthy subjects we applied the state-based neurofeedback method for training the time required for switching spatial attention from one side of the visual field to the other (e.g. left side to right side) following a brief presentation of a visual cue. In Chapter 6, we used our pipeline to investigate training-related changes in cortical activation and network connectivity in each subject. Our results suggested that the rt-MEG neurofeedback training resulted in strengthened beta-band connectivity prior to the switch of spatial attention, and strengthened gamma-band connectivity during the switch. There were two goals of this dissertation: First was the development of the MATLAB-based pipeline for computing time-evolving functional connectivity analysis in MEG and its application to visual motion perception. The second goal was the development of a real-time MEG neurofeedback method to train the dynamics of brain states and its application to a group of healthy subjects.2019-11-02T00:00:00

Theta oscillations mediate preactivation of highly expected word initial phonemes

Published: 22 June 2018Prediction has been proposed to be a fundamental neurocognitive mechanism. However, its role in language comprehension is currently under debate. In this magnetoencephalography study we aimed to find evidence of word-form phonological pre-activation and to characterize the oscillatory mechanisms supporting this. Participants were presented firstly with a picture of an object, and then, after a delay (fixed or variable), they heard the corresponding word. Target words could contain a phoneme substitution, and participants’ task was to detect mispronunciations. Word-initial phonemes were either fricatives or plosives, generating two experimental conditions (expect-fricative and expect-plosive). In the pre-word interval, significant differences (α = 0.05) emerged between conditions both for fixed and variable delays. Source reconstruction of this effect showed a brain-wide network involving several frequency bands, including bilateral superior temporal areas commonly associated with phonological processing, in a theta range. These results show that phonological representations supported by the theta band may be active before word onset, even under temporal uncertainty. However, in the evoked response just prior to the word, differences between conditions were apparent under variable- but not fixed-delays. This suggests that additional top-down mechanisms sensitive to phonological form may be recruited when there is uncertainty in the signal.This work was partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO), the Agencia Estatal de Investigación (AEI), the Fondo Europeo de Desarrollo Regional FEDER) (grant PSI2016– 77175-P to Mathieu Bourguignon, grant PSI2015–65694-P to Nicola Molinaro, Severo Ochoa programme SEV-2015–490 for Centres of Excellence in R&D), and by the Basque government (grant PI_2016_1_0014 to Nicola Molinaro). Further support derived from the AThEME project funded by the European Commission 7th Framework Programme, the ERC- 2011-ADG-295362 from the European Research Council. Finally, Mathieu Bourguignon was supported by the program Attract of Innoviris (grant 2015-BB2B-10) and by the Marie Skłodowska-Curie Action of the European Commission (grant 743562)

TEMPORAL CODING OF SPEECH IN HUMAN AUDITORY CORTEX

Human listeners can reliably recognize speech in complex listening environments. The underlying neural mechanisms, however, remain unclear and cannot yet be emulated by any artificial system. In this dissertation, we study how speech is represented in the human auditory cortex and how the neural representation contributes to reliable speech recognition. Cortical activity from normal hearing human subjects is noninvasively recorded using magnetoencephalography, during natural speech listening. It is first demonstrated that neural activity from auditory cortex is precisely synchronized to the slow temporal modulations of speech, when the speech signal is presented in a quiet listening environment. How this neural representation is affected by acoustic interference is then investigated. Acoustic interference degrades speech perception via two mechanisms, informational masking and energetic masking, which are addressed respectively by using a competing speech stream and a stationary noise as the interfering sound. When two speech streams are presented simultaneously, cortical activity is predominantly synchronized to the speech stream the listener attends to, even if the unattended, competing speech stream is 8 dB more intense. When speech is presented together with spectrally matched stationary noise, cortical activity remains precisely synchronized to the temporal modulations of speech until the noise is 9 dB more intense. Critically, the accuracy of neural synchronization to speech predicts how well individual listeners can understand speech in noise. Further analysis reveals that two neural sources contribute to speech synchronized cortical activity, one with a shorter response latency of about 50 ms and the other with a longer response latency of about 100 ms. The longer-latency component, but not the shorter-latency component, shows selectivity to the attended speech and invariance to background noise, indicating a transition from encoding the acoustic scene to encoding the behaviorally important auditory object, in auditory cortex. Taken together, we have demonstrated that during natural speech comprehension, neural activity in the human auditory cortex is precisely synchronized to the slow temporal modulations of speech. This neural synchronization is robust to acoustic interference, whether speech or noise, and therefore provides a strong candidate for the neural basis of acoustic background invariant speech recognition

Moving magnetoencephalography towards real-world applications with a wearable system

Imaging human brain function with techniques such as magnetoencephalography1 (MEG) typically requires a subject to perform tasks whilst their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or in adult studies that require unconstrained head movement (e.g. spatial navigation). Here, we develop a new type of MEG system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible due to the integration of new quantum sensors2,3 that do not rely on superconducting technology, with a novel system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution whilst subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Results compare well to the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterisation of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment, and understanding the pathophysiology of movement disorders