Multiscale Granger causality analysis by \`a trous wavelet transform

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the \`a trous wavelet transform with cubic B-spline filter. We measure GC, at a given scale, by including the wavelet coefficients of the driver times series, at that scale, in the regression model of the target. To validate our method, we apply it to publicly available scalp EEG signals, and we find that the condition of closed eyes, at rest, is characterized by an enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the standard Granger causality is not significantly different in the two conditions.Comment: 4 pages, 3 figure

Biomedical Signal Analysis of the Brain and Systemic Physiology

Near-infrared spectroscopy (NIRS) is a non-invasive and easy-to-use diagnostic technique that enables real-time tissue oxygenation measurements applied in various contexts and for different purposes. Continuous monitoring with NIRS of brain oxygenation, for example, in neonatal intensive care units (NICUs), is essential to prevent lifelong disabilities in newborns. Moreover, NIRS can be applied to observe brain activity associated with hemodynamic changes in blood flow due to neurovascular coupling. In the latter case, NIRS contributes to studying cognitive processes allowing to conduct experiments in natural and socially interactive contexts of everyday life. However, it is essential to measure systemic physiology and NIRS signals concurrently. The combination of brain and body signals enables to build sophisticated systems that, for example, reduce the false alarms that occur in NICUs. Furthermore, since fNIRS signals are influenced by systemic physiology, it is essential to understand how the latter impacts brain signals in functional studies. There is an interesting brain body coupling that has rarely been investigated yet. To take full advantage of these brain and body data, the aim of this thesis was to develop novel approaches to analyze these biosignals to extract the information and identify new patterns, to solve different research or clinical questions. For this the development of new methodological approaches and sophisticated data analysis is necessary, because often the identification of these patterns is challenging or not possible with traditional methods. In such cases, automatic machine learning (ML) techniques are beneficial. The first contribution of this work was to assess the known systemic physiology augmented (f)NIRS approach for clinical use and in everyday life. Based on physiological and NIRS signals of preterm infants, an ML-based classification system has been realized, able to reduce the false alarms in NICUs by providing a high sensitivity rate. In addition, the SPA-fNIRS approach was further applied in adults during a breathing task. The second contribution of this work was the advancement of the classical fNIRS hyperscanning method by adding systemic physiology measures. For this, new biosignal analyses in the time-frequency domain have been developed and tested in a simple nonverbal synchrony task between pairs of subjects. Furthermore, based on SPA-fNIRS hyperscanning data, another ML-based system was created, which is able distinguish familiar and unfamiliar pairs with high accuracy. This approach enables to determine the strength of social bonds in a wide range of social interaction contexts. In conclusion, we were the first group to perform a SPA-fNIRS hyperscanning study capturing changes in cerebral oxygenation and hemodynamics as well as systemic physiology in two subjects simultaneously. We applied new biosignals analysis methods enabling new insights into the study of social interactions. This work opens the door to many future inter-subjects fNIRS studies with the benefit of assessing the brain-to-brain, the brain-to-body, and body-to-body coupling between pairs of subjects

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Impaired evoked and resting-state brain oscillations in patients with liver cirrhosis as revealed by magnetoencephalography

AbstractA number of studies suggest that the clinical manifestation of neurological deficits in hepatic encephalopathy results from pathologically synchronized neuronal oscillations and altered oscillatory coupling. In the present study spontaneous and evoked oscillatory brain activities were analyzed jointly with established behavioral measures of altered visual oscillatory processing. Critical flicker and fusion frequencies (CFF, FUF) were measured in 25 patients diagnosed with liver cirrhosis and 30 healthy controls. Magnetoencephalography (MEG) data were collected at rest and during a visual task employing repetitive stimulation. Resting MEG and evoked fields were analyzed. CFF and FUF were found to be reduced in patients, providing behavioral evidence for deficits in visual oscillatory processing. These alterations were found to be related to resting brain activity in patients, namely that the lower the dominant MEG frequency at rest, the lower the CFF and FUF. An analysis of evoked fields at sensor level indicated that in comparison to normal controls, patients were not able to dynamically adapt to flickering visual stimulation. Evoked activity was also analyzed based on independent components (ICs) derived by independent component analysis. The similarity between the shape of each IC and an artificial sine function representing the stimulation frequency was tested via magnitude squared coherence. In controls, we observed a small number of components that correlated strongly with the sine function and a high number of ICs that did not correlate with the sine function. Interestingly, patient data were characterized by a high number of moderately correlating components. Taken together, these results indicate a fundamental divergence of the cerebral resonance activity in cirrhotic patients

Investigation of neural activity in Schizophrenia during resting-state MEG : using non-linear dynamics and machine-learning to shed light on information disruption in the brain

Environ 25% de la population mondiale est atteinte de troubles psychiatriques qui sont typiquement associés à des problèmes comportementaux, fonctionnels et/ou cognitifs et dont les corrélats neurophysiologiques sont encore très mal compris. Non seulement ces dysfonctionnements réduisent la qualité de vie des individus touchés, mais ils peuvent aussi devenir un fardeau pour les proches et peser lourd dans l’économie d’une société. Cibler les mécanismes responsables du fonctionnement atypique du cerveau en identifiant des biomarqueurs plus robustes permettrait le développement de traitements plus efficaces. Ainsi, le premier objectif de cette thèse est de contribuer à une meilleure caractérisation des changements dynamiques cérébraux impliqués dans les troubles mentaux, plus précisément dans la schizophrénie et les troubles d’humeur. Pour ce faire, les premiers chapitres de cette thèse présentent, en intégral, deux revues de littératures systématiques que nous avons menées sur les altérations de connectivité cérébrale, au repos, chez les patients schizophrènes, dépressifs et bipolaires. Ces revues révèlent que, malgré des avancées scientifiques considérables dans l’étude de l’altération de la connectivité cérébrale fonctionnelle, la dimension temporelle des mécanismes cérébraux à l’origine de l’atteinte de l’intégration de l’information dans ces maladies, particulièrement de la schizophrénie, est encore mal comprise. Par conséquent, le deuxième objectif de cette thèse est de caractériser les changements cérébraux associés à la schizophrénie dans le domaine temporel. Nous présentons deux études dans lesquelles nous testons l’hypothèse que la « disconnectivité temporelle » serait un biomarqueur important en schizophrénie. Ces études explorent les déficits d’intégration temporelle en schizophrénie, en quantifiant les changements de la dynamique neuronale dite invariante d’échelle à partir des données magnétoencéphalographiques (MEG) enregistrés au repos chez des patients et des sujets contrôles. En particulier, nous utilisons (1) la LRTCs (long-range temporal correlation, ou corrélation temporelle à longue-distance) calculée à partir des oscillations neuronales et (2) des analyses multifractales pour caractériser des modifications de l’activité cérébrale arythmique. Par ailleurs, nous développons des modèles de classification (en apprentissage-machine supervisé) pour mieux cerner les attributs corticaux et sous-corticaux permettant une distinction robuste entre les patients et les sujets sains. Vu que ces études se basent sur des données MEG spontanées enregistrées au repos soit avec les yeux ouvert, ou les yeux fermées, nous nous sommes par la suite intéressés à la possibilité de trouver un marqueur qui combinerait ces enregistrements. La troisième étude originale explore donc l’utilité des modulations de l’amplitude spectrale entre yeux ouverts et fermées comme prédicteur de schizophrénie. Les résultats de ces études démontrent des changements cérébraux importants chez les patients schizophrènes au niveau de la dynamique d’invariance d’échelle. Elles suggèrent une dégradation du traitement temporel de l’information chez les patients, qui pourrait être liée à leurs symptômes cognitifs et comportementaux. L’approche multimodale de cette thèse, combinant la magétoencéphalographie, analyses non-linéaires et apprentissage machine, permet de mieux caractériser l’organisation spatio-temporelle du signal cérébrale au repos chez les patients atteints de schizophrénie et chez des individus sains. Les résultats fournissent de nouvelles preuves supportant l’hypothèse d’une « disconnectivité temporelle » en schizophrénie, et étendent les recherches antérieures, en explorant la contribution des structures cérébrales profondes et en employant des mesures non-linéaires avancées encore sous-exploitées dans ce domaine. L’ensemble des résultats de cette thèse apporte une contribution significative à la quête de nouveaux biomarqueurs de la schizophrénie et démontre l’importance d’élucider les altérations des propriétés temporelles de l’activité cérébrales intrinsèque en psychiatrie. Les études présentées offrent également un cadre méthodologique pouvant être étendu à d’autres psychopathologie, telles que la dépression.Psychiatric disorders affect nearly a quarter of the world’s population. These typically bring about debilitating behavioural, functional and/or cognitive problems, for which the underlying neural mechanisms are poorly understood. These symptoms can significantly reduce the quality of life of affected individuals, impact those close to them, and bring on an economic burden on society. Hence, targeting the baseline neurophysiology associated with psychopathologies, by identifying more robust biomarkers, would improve the development of effective treatments. The first goal of this thesis is thus to contribute to a better characterization of neural dynamic alterations in mental health illnesses, specifically in schizophrenia and mood disorders. Accordingly, the first chapter of this thesis presents two systematic literature reviews, which investigate the resting-state changes in brain connectivity in schizophrenia, depression and bipolar disorder patients. Great strides have been made in neuroimaging research in identifying alterations in functional connectivity. However, these two reviews reveal a gap in the knowledge about the temporal basis of the neural mechanisms involved in the disruption of information integration in these pathologies, particularly in schizophrenia. Therefore, the second goal of this thesis is to characterize the baseline temporal neural alterations of schizophrenia. We present two studies for which we hypothesize that the resting temporal dysconnectivity could serve as a key biomarker in schizophrenia. These studies explore temporal integration deficits in schizophrenia by quantifying neural alterations of scale-free dynamics using resting-state magnetoencephalography (MEG) data. Specifically, we use (1) long-range temporal correlation (LRTC) analysis on oscillatory activity and (2) multifractal analysis on arrhythmic brain activity. In addition, we develop classification models (based on supervised machine-learning) to detect the cortical and sub-cortical features that allow for a robust division of patients and healthy controls. Given that these studies are based on MEG spontaneous brain activity, recorded at rest with either eyes-open or eyes-closed, we then explored the possibility of finding a distinctive feature that would combine both types of resting-state recordings. Thus, the third study investigates whether alterations in spectral amplitude between eyes-open and eyes-closed conditions can be used as a possible marker for schizophrenia. Overall, the three studies show changes in the scale-free dynamics of schizophrenia patients at rest that suggest a deterioration of the temporal processing of information in patients, which might relate to their cognitive and behavioural symptoms. The multimodal approach of this thesis, combining MEG, non-linear analyses and machine-learning, improves the characterization of the resting spatiotemporal neural organization of schizophrenia patients and healthy controls. Our findings provide new evidence for the temporal dysconnectivity hypothesis in schizophrenia. The results extend on previous studies by characterizing scale-free properties of deep brain structures and applying advanced non-linear metrics that are underused in the field of psychiatry. The results of this thesis contribute significantly to the identification of novel biomarkers in schizophrenia and show the importance of clarifying the temporal properties of altered intrinsic neural dynamics. Moreover, the presented studies offer a methodological framework that can be extended to other psychopathologies, such as depression

Assessing neural network dynamics under normal and altered states of consciousness with MEG : methodological challenges and proposed solutions for atypical power spectra

Cette dernière décennie a vu un certain nombre d'avancées significatives en mathématiques, en apprentissage computationnel et en traitement de signal, qui n'ont pas encore été pleinement exploitées en neurosciences. En particulier, l'évaluation de la connectivité dans les réseaux neuronaux peut grandement bénéficier de ces travaux. Nous proposons ici d'exploiter ces outils pour combler partiellement le fossé considérable qui existe encore entre la recherche connectomique à grande échelle (largement centrée sur des mesures indirectes de l'activité cérébrale comme l'Imagerie par résonance magnétique fonctionnelle (IRMf)) et les mesures physiologiques plus directes de l'activité cérébrale. Il est particulièrement important de combler ce fossé pour l'étude des propriétés physiologiques associées à divers états de conscience normaux et anormaux, notamment les troubles psychiatriques, le sommeil, l'anesthésie ou les états induits par les drogues. Les travaux récents sur l'induction d'états de conscience altérés par des agonistes non sélectifs de la sérotonine, tels que la psilocybine et le Diéthyllysergamide (LSD), en sont de bons exemples. Au cours des cinq dernières années, une résurgence rapide de la recherche sur la neurobiologie des tryptamines psychédéliques s'est produite, après une interruption d'un demi-siècle. Bien que ces substances présentent un grand potentiel pour éclairer des aspects jusqu'ici non interrogés du fonctionnement normal et anormal du cerveau, l'ampleur et le caractère inhabituel des changements qu'elles provoquent posent de sérieux défis aux chercheurs. La découverte de méthodes convaincantes et évolutives pour étudier ces données est d'une grande importance si nous voulons tirer parti de la fenêtre unique que ces substances atypiques offrent sur les aspects centraux de la conscience et des fonctions cérébrales anormales. Dans la présente thèse, nous résumons l'état actuel de la neuro-imagerie électrophysiologique en ce qui concerne l'étude des tryptamines psychédéliques, et nous démontrons un certain nombre de lacunes évidentes dans la recherche électrophysiologique actuelle sur les psychédéliques. Nous offrons également quelques modestes contributions méthodologiques au domaine. L'utilité de ces contributions est soutenue par quelques résultats empiriques intrigants, bien que préliminaires. Dans le premier chapitre, nous présentons l'histoire de la recherche neuroscientifique sur le LSD. Il a été rapporté que le LSD induit des déplacements de pics dans les spectres de puissance, en même temps que des diminutions de l'amplitude des pics. Le fait que ces effets soient liés entre eux et que la plupart des recherches menées jusqu'à présent n'aient pas cherché à les distinguer est uniformément négligé dans la littérature, ce qui, selon nous, peut conduire à de fausses interprétations. Le chapitre 2 examine certains des avantages plausibles ainsi que les obstacles sérieux à la recherche sur la connectivité du cerveau entier par magnétoencéphalographie (MEG), et propose plusieurs stratégies pour surmonter ces limites méthodologiques. Celles-ci comprennent des stratégies d'imagerie de source convaincantes, des développements nouveaux et récents dans la décomposition spectrale, des mesures de connectivité insensibles à la conduction volumique, et des implémentations évolutives de métriques de couplage interfréquence bien établies. Nous montrons que ces techniques peuvent être étendues à une grille corticale et sous-corticale de plus haute résolution que celle qui existe actuellement. Nous discutons également d'une mise en œuvre allégée de statistiques non paramétriques adaptées à ces données. Le troisième chapitre a pour but de démontrer l'efficacité de ces procédures, en montrant les résultats empiriques d'une étude de la connectivité du cerveau entier sous LSD par MEG. Le quatrième et dernier chapitre discute de ces résultats, ainsi que des précautions nécessaires et des orientations futures prometteuses pour ce type de recherche. Il propose des approches computationnelles supplémentaires qui pourraient étendre la portée de ces recherches et, plus généralement, de l'électrophysiologie du cerveau entier. Dans l'ensemble, le cadre méthodologique proposé dans ce travail surmonte les limitations endémiques précédentes, non seulement dans la recherche sur les psychédéliques, mais aussi dans la recherche électrophysiologique en général, et jette une lumière nouvelle sur sur les mécanismes centraux qui sous-tendent ces états de conscience anormaux, ainsi que sur les importantes précautions à prendre dans la recherche électrophysiologique.The past decade has seen a number of significant advances in mathematics, computational learning, and signal processing, which have yet to be deployed in neuroscience. In particular the assessment of connectivity in neural networks has much to gain from this work. Here we propose these tools be leveraged to partially bridge the considerable gap that still exists between large-scale connectomics research (largely centered around indirect measures of brain activity such as fMRI), and more direct, physiological measures of brain activity. Bridging this gap is especially important to the study of physiological properties associated with various normal and abnormal states of consciousness including Psychiatric conditions, sleep, anaesthesia or drug-induced states. Exemplary of such research, is recent work surrounding the induction of altered states of consciousness by non-selective serotonin agonists such as Psilocybin and LSD. During the past five years, a rapid resurgence of research into the neurobiology of Psychedelic tryptamines has transpired, following a half-century hiatus. While these substances hold great potential to illuminate hitherto uninterrogated aspects of normal and abnormal brain function, the scope and unusual character of the changes they illicit pose serious challenges to researchers. Uncovering cogent and scalable methods for investigating such data is a matter of great importance if we are to leverage the unique window such atypical substances provide into central aspects of consciousness and abnormal brain function. In the present thesis, we summarize the current state of electrophysiological neuroimaging as it pertains to the study of Psychedelic tryptamines, and demonstrate a number of clear shortcomings in current electrophysiological research on Psychedelics. We also offer some modest methodological contributions to the field. The utility of these contributions is supported by some intriguing, albeit preliminary, empirical findings. In the first chapter, we present the history of neuroscientific research on LSD. LSD has been reported to induce peak shifts in power spectra, alongside decreases in peak amplitude. The fact that these effects are inter-related and most research so far has not sought to disambiguate them is uniformly overlooked in the literature, which we believe may lead to false interpretations. Chapter Two discusses some of the plausible advantages as well as serious barriers to whole-brain connectivity research in MEG, proposing several strategies to overcome these methodological limitations. These include cogent source imaging strategies, novel and recent developments in spectral decomposition, connectivity measures insensitive to volume conduction, and scalable implementations of well-established cross-frequency coupling metrics. We show that these techniques can be extended to a higher resolution cortical and subcortical grid than previously shown. We also discuss a lightweight implementation of non-parametric statistics suitable to such data. Chapter Three serves to demonstrate the efficacy of these procedures, showing empirical results from a whole-brain study of connectivity under LSD in MEG. The fourth and final chapter discusses these results, as well as necessary precautions and promising future directions for this kind of research. It proposes additional computational approaches that might extend the scope of such research and whole-brain electrophysiology more generally. Taken together, the methodological framework proposed in this work overcomes previous limitations endemic not only in Psychedelics research, but electrophysiological research broadly, and sheds new light on central mechanisms underlying these abnormal states of consciousness, as well as important precautions in electrophysiological research

Spectral and coherence estimates on electroencephalogram recordings during arithmetical tasks

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do Grau de Mestre em Engenharia Biomédic

Discerning nonlinear brain dynamics from EEG:an application to autistic spectrum disorder in young children

A challenging goal in neuroscience is that of identifying specific brain patterns characterising autistic spectrum disorder (ASD). Genetic studies, together with investigations based on magnetic resonance imaging (MRI) and functional MRI, support the idea that distinctive structural features could exist in the ASD brain. In the developing brains of babies and small children, structural differences could provide the basis for different brain connectivity, giving rise to macroscopic effects detectable by e.g. electroencephalography (EEG). A significant body of research has already been conducted in this direction, mainly computing spectral power and coherence. Perhaps due to methodological limitations, together with high variability within and between the cohorts investigated, results have not been in complete agreement, and it is therefore still the case that the diagnosis of ASD is based on behavioural tests and interviews. This thesis describes a step-by-step characterisation and comparison of brain dynamics from ASD and neurotypical subjects, based on the analysis of multi-probe EEG time-series from male children aged 3-5 years. The methods applied are all ones that take explicit account of the intrinsically non-linear, open, and time-variable nature of the system. Time-frequency representations were first computed from the time-series to evaluate the spectral power and to categorise the ranges encompassing different activities as low-frequency (LF, 0.8-3.5 Hz), mid-range-frequency (MF, 3.5-12 Hz) or high-frequency (HF, 12-48 Hz). The spatial pathways for the propagation of neuronal activity were then investigated by calculation of wavelet phase coherence. Finally, deeper insight into brain connectivity was achieved by computation of the dynamical cross-frequency coupling between triplets of spatially distributed phases. In doing so, dynamical Bayesian inference was used to find the coupling parameters between the oscillators in the spatially-distributed network. The sets of parameters extracted by this means allowed evaluation of the strength of particular coupling components of the triplet LF, MF→HF, and enabled reconstruction of the coupling functions. By investigation of the form of the coupling functions, the thesis goes beyond conventional measures like the directionality and strength of an interaction, and reveals subtler features of the underlying mechanism. The measured power distributions highlight differences between ASD and typically developing children in the preferential frequency range for local synchronisation of neuronal activity: the relative power is generally higher at LF and HF, and lower at MF, in the ASD case. The phase coherence maps from ASD subjects also exhibited differences, with lower connectivity at LF and MF in the frontal and fronto-occipital pairs, and higher coherence at high frequencies for central links. There was higher inter-subject variability in a comparison of the forms of coupling functions in the ASD group; and a weaker coupling in their theta-gamma range, which can be linked with the cognitive features of the disorder. In conclusion, the approach developed in this thesis gave promising preliminary results, suggesting that a biomarker for ASD could be defined in terms of the described patterns of functional and effective connectivity computed from EEG measurements