Adaptive techniques for signal enhancement in the human electroencephalogram

This thesis describes an investigation of adaptive noise cancelling applied to human brain evoked potentials (EPs), with particular emphasis on visually evoked responses. The chief morphological features and signal properties of EPs are described. Consideration is given to the amplitude and spectral properties of the underlying spontaneous electroencephalogram and the importance of noise reduction techniques in EP studies is empnasised. A number of methods of enhancing EP waveforms are reviewed in the light of the known limitations of coherent signal averaging. These are shown to oe generally inadequate for enhancing individual EP responses. The theory of adaptive filters is reviewed with particular reference to adaptive transversal filters usiny the Widrow-Hoff algorithm. The theory of adaptive noise cancelling using correlated reference sources is presented, and new work is described which relates canceller performance to the magnitude-squared coherence function of the input signals. A novel filter structure, the gated adaptive filter, is presented and shown to yield improved cancellation without signal distortion when applied to repetitive transient signals in stationary noise under the condition of fast adaption. The signal processing software available is shown to be inadequate, and a comprehensive Fortran program developed for use on a PDP-11 computer is described. The properties of human visual evoked potentials and the EEO are investigated in two normal adults using a montage of 7 occipital electrodes. Signal enhancement of EPs is shown to be possible oy adaptive noise cancelling, and improvements in signal to noise in the range 2-10 dB are predicted. A discussion of filter strategies is presented, and a detailed investiyation of adaptive noise cancel liny performed usiny a ranye of typical EP data. Assessment of the results confirms the proposal that substantial improvement in sinyle EP response recoynition is achieved by this technique

Brain Responses Track Patterns in Sound

This thesis uses specifically structured sound sequences, with electroencephalography (EEG) recording and behavioural tasks, to understand how the brain forms and updates a model of the auditory world. Experimental chapters 3-7 address different effects arising from statistical predictability, stimulus repetition and surprise. Stimuli comprised tone sequences, with frequencies varying in regular or random patterns. In Chapter 3, EEG data demonstrate fast recognition of predictable patterns, shown by an increase in responses to regular relative to random sequences. Behavioural experiments investigate attentional capture by stimulus structure, suggesting that regular sequences are easier to ignore. Responses to repetitive stimulation generally exhibit suppression, thought to form a building block of regularity learning. However, the patterns used in this thesis show the opposite effect, where predictable patterns show a strongly enhanced brain response, compared to frequency-matched random sequences. Chapter 4 presents a study which reconciles auditory sequence predictability and repetition in a single paradigm. Results indicate a system for automatic predictability monitoring which is distinct from, but concurrent with, repetition suppression. The brain’s internal model can be investigated via the response to rule violations. Chapters 5 and 6 present behavioural and EEG experiments where violations are inserted in the sequences. Outlier tones within regular sequences evoked a larger response than matched outliers in random sequences. However, this effect was not present when the violation comprised a silent gap. Chapter 7 concerns the ability of the brain to update an existing model. Regular patterns transitioned to a different rule, keeping the frequency content constant. Responses show a period of adjustment to the rule change, followed by a return to tracking the predictability of the sequence. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis

Measurement of the reaction to stress and meditation using brain wave coherence and heart rate variability

Measurement of physiological parameters associated with the stress response and the relaxation response caused by various forms of meditation can provide valuable information about the reaction of the body to the mind and to the external environment. This study used two different techniques to evaluate physiological parameters. The first part examined the meditation response by recording the EEG and calculating the coherence between brain waves originating from different parts of the brain. It was found that high levels of coherence in the alpha portion of the EEG frequency band coincided with a restful state associated with the relaxation response. In an effort to measure the autonomic nervous system reaction to relaxation using heart rate variability analysis, it was found necessary to separate sympathetic from parasympathetic influences. This led to measuring the stress reaction in order to find the sympathetic contribution. The stress reaction was measured by acquiring skin temperature data and heart rate data, and comparing the changes in skin temperature to changes in heart rate variability calculated using time frequency analysis. Skin temperature was found to react gradually to sympathetic changes. A strong mental component was found to influence the stress reaction that was being measured

Decoding subjective emotional arousal from eeg during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Probing brain function with pharmacological MRI

Lo sviluppo di tecniche di risonanza magnetica funzionale (fMRI) ha rivoluzionato le ricerca neuroscientifica clinica, determinando la possibilit\ue0 di investigare le dinamiche spazio-temporali dell\u2019attivit\ue0 cerebrale in maniera non invasiva e con grande accuratezza. Sebbene la tecnica sia stata originariamente sviluppata in ambito clinico, essa ha il potenziale di poter essere utilizzata in ambito preclinico come efficace strumento investigativo e traslazionale. Tuttavia, l\u2019implementazione preclinica di questi metodi \ue8 complicata da una serie di costrizioni sperimentali, in primis l\u2019utilizzo di anestetici, che minano fortemente il potenziale traslazionale di queste tecniche. Il recente sviluppo di tecniche di "MRI farmacologico" (phMRI) offre la possibilit\ue0 di superare alcune delle limitazioni sperimentali correlate all\u2019implementazione di approcci fMRI classici in animali da laboratorio. La tecnica si basa sull'utilizzo di metodi fMRI per mappare alterazioni di attivit\ue0 cerebrale prodotte dalla somministrazione di sostanze psicoattive. Studi preliminari hanno evidenziato la capacit\ue0 di generare robusti e specifici segnali phMRI anche in condizioni di anestesia, ed ha dimostrato la possibilit\ue0 di stimolare selettivamente diversi sistemi di neurotrasmettitori. Sfruttando la conservazione di circuiti cerebrali tra specie, tecniche phMRI offrono quindi l\u2019opportunit\ue0 di ampliare in maniera significativa il repertorio di stimolazione neuronale a disposizione in ambito preclinico, consentendo di indagare selettivamente specifici aspetti della funzione cerebrale in diversi stati di precondizionamento neuronale. In tale contesto, le attivit\ue0 di ricerca di questa tesi sono state finalizzate ad ampliare il campo di applicazione di metodi phMRI preclinici in due diversi ambiti sperimentali: a) come modalit\ue0 di indagine traslazionale, qualora applicata a modelli di malattia clinicamente rilevanti, b) pi\uf9 in generale come piattaforma investigativa per l'indagine della funzione cerebrale e della sua topologia funzionale in contesti sperimentali diversi. In un primo gruppo di studi, tecniche phMRI sono state impiegate per mappare i circuiti neuronali attivati da antagonisti del recettore del glutammato NMDA nel cervello del ratto (Sezione 4.1). Tali composti, grazie alle loro propriet\ue0 psicotogeniche, sono ampiamente sfruttati come modelli sperimentali di schizofrenia in animali ed in volontari allo scopo di valutare e validare nuovi trattamenti per la malattia. I risultati di questa ricerca hanno evidenziato uno specifico circuito corticolimbo- talamico che risulta essere attivato da antagonisti NMDAR sia nell'uomo che in Riassunto XII specie precliniche, e che \ue8 risultato essere modulabile da meccanismi antipsicotici diversi (Sezione 4.2). Il potenziale traslazionale dei metodi phMRI \ue8 stato ulteriormente avvalorato da un secondo gruppo di studi, in cui un approccio multi-parametrico \u201cphMRI-based\u201d \ue8 stato impiegato per indagare molteplici aspetti della funzione cerebrale in un modello murino di dipendenza da cocaina. Questa linea di investigazione ha evidenziato multiple alterazioni della funzione cerebrale basale e reattiva nel cervello di roditori esposti alla cocaina strettamente connesse a quelle osservate in analoghi studi di imaging su pazienti cocaina-dipendenti (Sezione 4.2). In una terza linea d\u2019 investigazione, l'uso combinato di avanzate strategie di targeting neuro-genetico (pharmaco-genetic silencing) e phMRI si \ue8 dimostrato efficace nello stabilire correlazioni dirette tra cellule, circuito e comportamento in linee di topo geneticamente modificate. Questi studi hanno portato all\u2019identificazione di una nuova e circoscritta popolazione neuroni nell'amigdala, in grado di controllare qualitativamente la risposta comportamentale alla paura attraverso il reclutamento di circuiti colinergici corticali (Sezione 4.3) Infine, l'approccio phMRI si \ue8 dimostrato uno strumento potente e versatile per l\u2019implementazione di misure di connettivit\ue0 funzionale nel cervello di roditori. Questo aspetto ha permesso l\u2019esplorazione di nuovi approcci statistici per l\u2019analisi della topologia funzionale del cervello basati sulla rappresentazione di misure di connettivit\ue0 in termini di reti complesse (Sezione 4.4). Complessivamente, i risultati di questo lavoro avvalorano il potenziale traslazionale di metodi phMRI nell\u2019ambito di diverse aree delle neuroscienze e della psicofarmacologia. La combinazione di phMRI e tecniche di manipolazione genetica avanzate definisce una nuova, potente piattaforma tecnologica per lo studio delle basi circuitali del comportamento in animali da laboratorio.The development of functional Magnetic Resonance Imaging (fMRI) has heralded a revolution in neuroscience, providing clinicians with a method to non-invasively investigate the spatio-temporal patterns of neuro-functional activity. Although primarily developed for human investigations, there exists significant scope for the application of fMRI in pre-clinical species as a translational and investigational platform across different areas of neuroscience and psychiatry research. However, the realization of this potential is hampered by a number of experimental constraints which make the application of fMRI methods to animal models less than straightforward. As a result, most fMRI research in laboratory species has been reduced to the employment of basic somato-sensory stimulation paradigms, thus greatly limiting the translational potential of the technique. An interesting approach to overcome some of these limitations has been dubbed \u201cpharmacological MRI\u201d (phMRI) and relies on the use of fMRI to map patterns of brain activity induced by psychoactive drugs. The approach has demonstrated the ability to elicit reliable fMRI signals even under anaesthesia, and to enable selective stimulation of different neurotransmitter systems. Building upon the homology between brain circuits in humans and laboratory animals, phMRI techniques thus offer the opportunity of significantly expanding the stimulation repertoire available to preclinical fMRI research, by allowing to selectively probe specific aspects of brain function under different preconditioning states. Within this framework, the research presented herein was aimed to broaden the scope of application of preclinical phMRI both as a translational technique, when applied to clinically-relevant disease models, and more generally as a versatile platform for the pre-clinical investigation of brain activity and its functional topology as a function of behavioural, pharmacological or genetic preconditioning. In a first group of studies, we developed a phMRI assay to map the circuitry activated by NMDAR antagonists in the rat brain. These psychotogenic compounds are widely exploited to model schizophrenia symptoms and to provide experimental models that may prove useful in the development of novel treatments for the disorder. The results of this research highlighted a conserved cortico-limbo-thalamic circuit that is activated by NMDAR antagonists both in humans and preclinical species, which can be modulated by existing and novel antipsychotic drugs (Section 4.1). The translational potential of phMRI measurements was further corroborated by a second group of studies, where a multi-parametric phMRI-based approach was applied to investigate multiple facets of brain function in a rodent cocaine selfSummary X administration model, a behavioural paradigm of established construct-validity for research of drug addiction. This line of investigation revealed specific basal and reactive functional alterations in the brain of cocaine-exposed rodents closely related to those observed in analogous neuroimaging studies in humans (Section 4.2). In a third line of investigation, the combined use of advanced neuro-genetic targeting strategies (i.e. pharmacogenetic silencing) and phMRI has proven successful in establishing direct correlations between cells, circuit and complex behaviours in genetically engineered mouse lines. These studies (Section 4.3) have led to the identification of a novel cell population in the amygdala that controls the behavioural response to fear through the recruitment of cholinergic circuits. Finally, the phMRI approach has proven a powerful tool to explore functional connectivity in rodents, and to map a variety of different neurotransmitter pathways by performing measures of correlated responses in spatially remote brain areas. This has provided a useful playground to explore novel statistical methods of analysis of functional connectivity represented in terms of complex networks (Section 4.4). Collectively, the results of this work strongly corroborate the translational use of phMRI approaches, and pave the way to the integrated implementation of phMRI and advance genetic manipulation as a novel powerful platform for basic neurobiological research

The Electrophysiology of Resting State fMRI Networks

Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects