Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals

Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

International audienceOscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations , and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals

Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP

Bridging structure and function with brain network modeling

High-throughput neuroimaging technology enables rapid acquisition of vast amounts of structural and functional data on multiple spatial and temporal scales. While novel methods to extract information from these data are continuously developed, there is no principled approach for the systematic integration of distinct experimental results into a common theoretical framework, yet. The central result of this dissertation is a biophysically-based framework for brain network modeling that links structural and functional data across scales and modalities and integrates them with dynamical systems theory. Specifically, the publications in this thesis i. introduce an automated pipeline that extracts structural and functional information from multimodal imaging data to construct and constrain brain models, ii. link whole-brain models with empirical EEG-fMRI (simultaneous electroencephalography and functional magnetic resonance imaging) data to integrate neural signals with simulated activity, iii. propose a framework for reverse-engineering neurophysiological dynamics and mechanisms underlying commonly observed features of neural activity, iv. document a software module that makes users acquainted with theory and practice of brain modeling, v. associate aging with structural and functional connectivity and vi. examine how parcellation size and short-range connectivity affect model dynamics. Taken together, these results form a novel approach that enables reverse-engineering of neurophysiological processes and mechanisms on the basis of biophysically-based brain models.Zusammenfassung Hochdurchsatzverfahren zur neuronalen Bildgebung ermöglichen die schnelle Erfassung großer Mengen an strukturellen und funktionellen Daten über verschiedenen räumlichen und zeitlichen Skalen. Obwohl ständig neue Methoden zur Verarbeitung der in diesen Daten enthaltenen Informationen entwickelt werden gibt es bisher kein systematisches Verfahren um experimentelle Ergebnisse in einem gemeinsamen theoretischen Rahmenwerk zu integrieren und zu verknüpfen. Das Hauptergebnis dieser Dissertation ist ein biophysikalisch basiertes Gehirn- Netzwerkmodell das strukturelle und funktionelle Daten über verschiedene Skalen und Modalitäten hinweg verknüpft und mit dynamischer Systemtheorie vereint. Die hier zusammengefassten Publikationen i. stellen eine automatische Software-Pipeline vor die strukturelle und funktionelle Informationen aus multimodalen Bilddaten extrahiert um Gehirnmodelle zu konstruieren und zu parametrisieren, ii. verknüpfen Ganzhi rnmodel le mi t empi r i schen EEG- fMRT ( s imul tane Elektroenzephalographie und funktionelle Magnetresonanztomographie) Daten um neuronale Signale mit simulierter Aktivität zu integrieren, iii. schlagen ein Rahmenwerk vor um neurophysiologische Dynamiken und Mechanismen die häufig beobachteten Eigenschaften neuronaler Aktivität zu Grunde liegen zu rekonstruieren, iv. dokumentieren ein Software-Modul das Benutzer mit Theorie und Praxis der Gehirnmodellierung vertraut macht, v. assoziieren Alterungsprozesse mit struktureller und funktioneller Konnektivität und vi. untersuchen wie Gehirn-Parzellierung und lokale Konnektivität die Modelldynamik beeinflussen. Zusammengenommen ergibt sich ein neuartiges Verfahren das die Rekonstruktion neurophysiologischer Prozesse und Mechanismen ermöglicht und mit dessen Hilfe neuronale Aktivität auf verschiedenen räumlichen und zeitlichen Skalen anhand biophysikalisch basierter Modelle vorhersagt werden kann

Personalizing functional Magnetic Resonance Protocols for Studying Neural Substrates of Motor Deficits in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative movement disorder characterized by a large number of motor and non-motor deficits, which significantly contribute to reduced quality of life. Despite the definition of the broad spectrum of clinical characteristics, mechanisms triggering illness, the nature of its progression and a character of therapeutic effects still remain unknown. The enormous advances in magnetic resonance imaging (MRI) in the last decades have significantly affected the research attempts to uncover the functional and structural abnormalities in PD and have helped to develop and monitor various treatment strategies, of which dopamine replacement strategies, mainly in form of levodopa, has been the gold standard since the late seventies and eighties. Motor, task-related functional MRI (fMRI) has been extensively used to assess the pathological state of the motor circuitry in PD. Several studies employed motor paradigms and fMRI to review the functional brain responses of participants to levodopa treatment. Interestingly, they provided conflicting results. Wide spectrum of symptoms, variability and asymmetry of the disease presentation, several treatment approaches and their divergent outcomes make PD enormously heterogeneous. In this work we hypothesized that not considering the disease heterogeneity might have been an adequate cause for the discrepant results in aforementioned studies. We show that not accounting for the disease variability might indeed compromise the results and invalidate the consequent interpretations. Accordingly, we propose and formalize a statistical approach to account for the intra and inter subject variability. This might help to minimize this bias in future motor fMRI studies revealing the functional brain dysfunction and contribute to the understanding of still unknown pathophysiological mechanisms underlying PD

Coupling and stochasticity in mesoscopic brain dynamics

The brain is known to operate under the constant influence of noise arising from a variety of sources. It also organises its activity into rhythms spanning multiple frequency bands. These rhythms originate from neuronal oscillations which can be detected via measurements such as electroen-cephalography (EEG) and functional magnetic resonance (fMRI). Experimental evidence suggests that interactions between rhythms from distinct frequency bands play a key role in brain processing, but the dynamical mechanisms underlying this cross-frequency interactions are still under investigation. Some rhythms are pathological and harmful to brain function. Such is the case of epileptiform rhythms characterising epileptic seizures. Much has been learnt about the dynamics of the brain from computational modelling. Particularly relevant is mesoscopic scale modelling, which is concerned with spatial scales exceeding those of individual neurons and corresponding to processes and structures underlying the generation of signals registered in the EEG and fMRI recordings. Such modelling usually involves assumptions regarding the characteristics of the background noise, which represents afferents from remote, non-modelled brain areas. To this end, Gaussian white noise, characterised by a flat power spectrum, is often used. In contrast, macroscopic fluctuations in the brain typically follow a `1/f b ¿ spectrum, which is a characteristic feature of temporally correlated, coloured noise. In Chapters 3-5 of this Thesis we address by means of a stochastically driven mesoscopic neuronal model, the three following questions. First, in Chapter 3 we ask about the significance of deviations from the assumption of white noise in the context of brain dynamics, and in particular we study the role that temporally correlated noise plays in eliciting aberrant rhythms in the model of an epileptic brain. We find that the generation of epileptiform dynamics in the model depends non-monotonically on the noise correlation time. We show that this is due to the maximisation of the spectral content of epileptogenic rhythms in the noise. These rhythms fall into frequency bands that indeed were experimentally shown to increase in power prior to epileptic seizures. We explain these effects in terms of the interplay between specific driving frequencies and bifurcation structure of the model. Second, in Chapter 4 we show how coupling between cortical modules leads to complex activity patterns and to the emergence of a phenomenon that we term collective excitability. Temporal patterns generated by this model bear resemblance to clinically observed characteristics of epileptic seizures. In that chapter we also introduce a fast method of tracking a loss of stability caused by excessive inter-modular coupling in a neuronal network. Third, in Chapter 5 we focus on cross-frequency interactions occurring in a network of cortical modules, in the presence of coloured noise. We suggest a mechanism that underlies the increase of power in a fast rhythm due to driving with a slow rhythm, and we find the noise parameters that best recapitulate experimental power spectra. Finally, in Chapter 6, we examine models of haemodynamic and metabolic brain processes, we test them on experimental data, and we consider the consequences of coupling them with mesoscopic neuronal models. Taken together, our results show the combined influence of noise and coupling in computational models of neuronal activity. Moreover, they demonstrate the relevance of dynamical properties of neuronal systems to specific physiological phenomena, in particular related to cross-frequency interactions and epilepsy. Insights from this Thesis could in the future empower studies of epilepsy as a dynamic disease, and could contribute to the development of treatment methods applicable to drug-resistant epileptic patients.El cervell opera sota la influència de sorolls amb diversos orígens. També organitza la seva activitat en una sèrie de ritmes que s'expandeixen en diverses bandes de freqüència. Aquests ritmes tenen el seu origen en les osci∙lacions neuronals i poden detectar-se via mesures com les electroencefalogràfiques (EEG) o la ressonància magnètica funcional (fMRI). Les evidències experimentals suggereixen que les interaccions entre ritmes operant en bandes de freqüència diferents juguen un paper central en el processat cerebral però els mecanismes dinàmics subjacents a les interaccions inter-freqüència encara estan investigant-se. Alguns ritmes són patològics i fan malbé el funcionament cerebral. És el cas dels ritmes epileptiformes que caracteritzen les convulsions epilèptiques. Fent servir el modelatge computacional s'ha après molt sobre la dinàmica del cervell. Especialment rellevant és el modelatge a l’escala mesoscòpica, que té a veure amb les escales espacials superiors a les de les neurones individuals i que correspon als processos que generen EEG i fMRI. Tal modelatge, en general, implica supòsits relatius a les característiques del soroll de fons que representa zones remotes del cervell no modelades. Amb aquesta finalitat s'utilitza sovint el soroll blanc gaussià, que es caracteritza per un espectre de potència pla. Les fluctuacions macroscòpiques en el cervell, però, normalment segueixen un espectre '1/fb', que és un tret característic de les correlacions temporals i el soroll de color. Als Capítols 3-5 d'aquesta tesi abordem mitjançant un model neuronal mesoscòpic forçat estocàsticament, les tres preguntes següents. En primer lloc, en el Capítol 3 ens preguntem sobre la importància de les desviacions de l'assumpció de soroll blanc en el context de la dinàmica del cervell i, en particular, estudiem el paper que juga el soroll amb correlació temporal en l'obtenció de ritmes aberrants d'un cervell epilèptic. Trobem que la generació de les dinàmiques epileptiformes depèn de forma monòtona del temps de correlació del soroll. Aquests ritmes es divideixen en bandes de freqüència que, segons, s'ha mostrat experimentalment, augmenten la seva potència espectral abans de les crisis epilèptiques. Expliquem aquests efectes en termes de la interacció entre les freqüències específiques del forçament i l'estructura de bifurcació del model. En segon lloc, en el Capítol 4 es mostra com l'acoblament entre mòduls corticals condueix a patrons d'activitat complexes i a l'aparició d'un fenomen que anomenem excitabilitat col∙lectiva. Els patrons temporals generats per aquest model s'assemblen a les observacions clíniques de les convulsions epilèptiques. En aquest capítol també introduïm un mètode d'anàlisi de la pèrdua d'estabilitat causada per l'acoblament inter-modular excessiu en les xarxes neuronals. En tercer lloc, en el Capítol 5 ens centrem en les interaccions inter-freqüència que es produeixen en una xarxa de mòduls corticals en presència de soroll de color. Suggerim un mecanisme subjacent a l'augment de la potència spectral de ritmes ràpids a causa del forçament amb un ritme lent, i veiem quins paràmetres del soroll descriuen millor els espectres de potència experimental. Finalment, en el Capítol 6, estudiem models dels processos hemodinàmics i metabòlics del cervell, els comparem amb dades experimentals i considerem les conseqüències del seu acoblament amb models neuronals mesoscopics. En conjunt, els nostres resultats mostren la influència combinada del soroll i l'acoblament en models computacionals de l'activitat neuronal. D'altra banda, també demostren la importància de les propietats dinàmiques dels sistemes neuronals en fenòmens fisiològics específics com les interaccions inter-frequència i l'epilèpsia. Els resultats d'aquesta Tesi contribueixen a potenciar l’estudi de l'epilèpsia com una malaltia dinàmica, i el desenvolupament de mètodes de tractament aplicables a pacients epilèptics resistents als fàrmacs.Postprint (published version

EFFECTS OF NEUROMODULATION ON NEUROVASCULAR COUPLING

The communication between neurons within neural circuits relies on neurotransmitters (glutamate, γ-aminobutyric acid (GABA)) and neuromodulators (acetylcholine, dopamine, serotonin, etc.). However, despite sharing similar molecular elements, neurotransmitters and neuromodulators are distinct classes of molecules and mediate different aspects of neural activity and metabolism. Neurotransmitters on one hand are responsible for synaptic signal transmission (classical transmission) while neuromodulators exert their functions by mediating different postsynaptic events that result in changes to the balance between excitation and inhibition. Neuromodulation, while essential to nervous system function, has been significantly more difficult to study than neurotransmission. This is principally due to the fact that effects elicited by neuromodulators are usually of slow onset, long lasting, and are not simply excitation or inhibition. In contrast to the effects of neurotransmitters, neuromodulators enable neurons to be more flexible in their ability to encode different sorts of information (e.g. sensory information) on a variety of time scales. However, it is important to appreciate that one of the challenges in the study of neuromodulation is to understand the extent to which neuromodulators’ actions are coordinated at all levels of brain function. That is, from the cellular and metabolic level to network and cognitive control. Therefore, understanding the molecules that mediate brain networks interactions is essential to understanding the brain dynamic, and also helps to put the cellular and molecular processes in perspective. Functional magnetic resonance imaging (fMRI) is a technique that allows access to various cellular and metabolic aspects of network communication that are difficult to access when studying one neuron at the time. Its non-invasiveness nature allows the comparison of data and hypotheses of the primate brain to that of the human brain. Hence, understanding the effects of neuromodulation on local microcircuits is needed. Furthermore, given the massive projections of the neuromodulatory diffuse ascending systems, fMRI combined with pharmacological and neurophysiological methods may provide true insight into their organization and dynamics. However, little is known about how to interpret the effects of neuromodulation in fMRI and neurophysiological data, for instance, how to disentangle blood oxygenation level dependent (BOLD) signal changes relating to cognitive changes (presumably neuromodulatory influences) from stimulus-driven or perceptual effects. The purpose of this dissertation is to understand the causal relationship between neural activity and hemodynamic responses under the influence of neuromodulation. To this end we present the results of six studies. In the first study, we aimed to establish a mass-spectrometry-based technique to uncover the distribution of different metabolites, neurotransmitters and neuromodulators in the macaque brain. We simultaneously measured the concentrations of these biomolecules in brain and in blood. In a second study, we developed a multimodal approach consisting of fMRI (BOLD and cerebral blood flow or CBF), electrophysiological recording with a laminar probe and pharmacology to assess the effects of neuromodulation on neurovascular coupling. We developed a pharmacological injection delivery system using pressure-operated pumps to reliably apply drugs either systemically or intracortically in the NMR scanner. In our third study, we systemically injected lactate and pyruvate to explore whether the plasma concentration of either of these metabolites affects the BOLD responses. This is important given that both metabolites are in a metabolic equilibrium; if this equilibrium is disrupted, changes in the NAD and NADH concentrations would elicit changes in the CBF. In a fourth study, we explored the influence of dopaminergic (DAergic) neuromodulation in the BOLD, CBF and neurophysiological activity. Here we found that DAergic neuromodulation dissociated the BOLD responses from the underlying neural activity. Interestingly, the changes in the neural activity were tightly coupled to the effects seen in the CBF responses. In a subsequent study, we explored whether the effects of dopamine (DA) on the electrophysiological responses are cortical layer dependent and whether specific patterns of neural activity can be used to infer the effects of neuromodulation on the neural activity. This is important, given that different types of neural activity provide independent information about the amplitude and dynamics from BOLD responses, and studies have shown that these bands originate from different cortical layers. What this study revealed, is that local field potentials (LFPs) in the midrange frequencies can indeed provide indications about the sustained effects of neuromodulation on cortical sensory processing. Given the results from the previous study, in our sixth study, we aimed at understanding how different cortical layers may process incoming and outgoing information in the different LFP bands. These findings provide evidence that neuromodulation has profound effects on neurovascular coupling. By changing the excitation-inhibition balance of neural circuits, neuromodulators not only mediate the neural activity, but also adjust the metabolic demands. Therefore, understanding how the different types of neuromodulators affect the BOLD response is essential for an effective interpretation of fMRI-data, not only in tasks involving attentional and reward-related processes, but also for future diagnostic use of fMRI, since many psychiatric disorders are the result of alterations in neuromodulatory systems.Die Kommunikation zwischen den Neuronen innerhalb neuronalen Schaltkreise beruht auf Neurotransmitter (Glutamat, γ-Aminobuttersäure (GABA)) und Neuromodulatoren (Acetylcholin, Dopamin, Serotonin, etc.). Neurotransmitter und Neuromodulatoren sind jedoch unterschiedliche Klassen von Molekülen und verschiedenen Aspekte der neuronalen Aktivität und den Stoffwechsel vermitteln. Neurotransmitters sind einerseits verantwortlich für die synaptische Signalübertragung (klassische Übertragung), während ihre Funktionen ausüben, Neuromodulatoren durch verschiedene postsynaptischen Ereignisse zu vermitteln, die in Änderungen an der Balance zwischen Erregung und Hemmung führen. Neuromodulation , während wesentlich Funktion des Nervensystems hat sich als Neurotransmission wesentlich schwieriger gewesen, zu studieren. Dies ist hauptsächlich auf die Tatsache zurückzuführen, die durch Neuromodulatoren sind in der Regel von langsamen Beginn, langlebig, und sind nicht einfach Anregung oder Hemmung ausgelöst beeinflusst. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Im Gegensatz zu den Wirkungen von Neurotransmittern, Neuromodulatoren ermöglichen Neuronen flexibler zu sein in ihrer Fähigkeit, verschiedene Arten von Informationen (beispielsweise sensorische Informationen) auf einer Vielzahl von Zeitskalen zu kodieren. Jedoch ist es wichtig, dass eine der Herausforderungen bei der Untersuchung von Neuromodulations zu schätzen ist, das Ausmaß, in dem Neuromodulatoren Aktionen koordiniert sind auf allen Ebenen der Gehirnfunktion zu verstehen. Das heißt, von der zellulären und metabolischen Ebene zu vernetzen und kognitive Kontrolle. Daher die Moleküle zu verstehen, die Gehirn Netzwerke Interaktionen vermitteln ist wesentlich für das Verständnis des Gehirns dynamisch, und hilft auch, die zellulären und molekularen Prozesse in Perspektive zu setzen. Funktionellen Kernspintomographie (fMRI) ist eine Technik, die Zugang zu verschiedenen zellulären und metabolischen Aspekte der Netzwerk-Kommunikation ermöglicht, die schwer zugänglich sind, wenn zu der Zeit eines Neurons zu studieren. Seine nicht-Invasivität Natur ermöglicht den Vergleich von Daten und Hypothesen des Primatengehirn zu der des menschlichen Gehirns. Somit wurde das Verständnis der Auswirkungen der Neuromodulation auf lokale Mikro benötigt. Darüber hinaus sind die massiven Projektionen der neuromodulatorischen diffuse Aufstiegsanlagen gegeben, kombiniert fMRI mit pharmakologischen und neurophysiologischen Methoden wahren Einblick in ihre Organisation und Dynamik liefern. Allerdings ist nur wenig darüber bekannt, wie die Auswirkungen der Neuromodulations in fMRI und neurophysiologische Daten zu interpretieren, zum Beispiel, wie Blutoxydation pegelabhängig (BOLD) Signaländerungen in Bezug auf kognitive Veränderungen (vermutlich neuromodulatorischen Einflüsse) von Stimulus-driven oder Wahrnehmungseffekte zu entwirren. Der Zweck dieser Arbeit ist es, die kausale Beziehung zwischen neuronaler Aktivität und hämodynamischen Reaktionen unter dem Einfluss der Neuromodulations zu verstehen. Zu diesem Zweck stellen wir die Ergebnisse von sechs Studien. In der ersten Studie wollten wir eine auf Massenspektrometrie basierende Technik einzurichten, um die Verteilung von verschiedenen Metaboliten, Neurotransmittern und Neuromodulatoren in Makakengehirn aufzudeckenWir maßen gleichzeitig die Konzentrationen dieser Biomoleküle im Gehirn und im Blut. In einer zweiten Studie entwickelten wir einen multimodalen Ansatz, bestehend aus fMRI (BOLD und zerebralen Blutflusses oder CBF), elektrophysiologische Aufzeichnung mit einer laminaren Sonde und Pharmakologie, die Auswirkungen der Neuromodulation auf neurovaskulären Kopplung zu beurteilen. Wir entwickelten eine pharmakologische Injektionsverabreichungssystem druckbetriebenen Pumpen mit zuverlässiger Medikamente gelten entweder systemisch oder intrakortikale im NMR-Scanner. In unserer dritten Studie injizierten wir systemisch Laktat und Pyruvat zu untersuchen, ob die Plasmakonzentration von entweder dieser Metaboliten die BOLD-Antworten beeinflusst. Dies ist wichtig, dass beide gegeben Metaboliten in einem Stoffwechselgleichgewicht sind; wenn dieses Gleichgewicht gestört ist, Veränderungen in den NAD und NADH-Konzentrationen würden Veränderungen in der CBF entlocken. In einer vierten Studie untersuchten wir den Einfluss von dopaminergen (DA-erge) -Neuromodulation im BOLD, CBF und neurophysiologische Aktivität. Hier fanden wir, dass DAerge -Neuromodulation die BOLD-Antworten von der zugrunde liegenden neuronalen Aktivität distanzierte. Interessanterweise waren verbunden, um die Veränderungen in der neuronalen Aktivität eng auf die in den CBF Reaktionen gesehen Wirkungen. In einer nachfolgenden Studie untersuchten wir, ob die Wirkungen von Dopamin (DA) auf die elektrophysiologischen Reaktionen sind Rindenschicht abhängig, und ob bestimmte Muster der neuronalen Aktivität verwendet werden kann, die Wirkungen von Neuromodulations auf die neurale Aktivität zu schließen. Dies ist wichtig, da verschiedene Arten von neuralen Aktivität liefern unabhängige Informationen über die Amplitude und die Dynamik von BOLD-Antworten, und Studien haben gezeigt, dass diese Bands aus verschiedenen kortikalen Schichten stammen. Was diese Studie ergab, dass lokale Feldpotentiale (LFP) in den mittleren Frequenzen in der Tat Hinweise über die nachhaltige Wirkung der Neuromodulation auf die kortikale sensorische Verarbeitung zur Verfügung stellen kann. In Anbetracht der Ergebnisse der früheren Studie, in unserer sechsten Studie wollten wir auf das Verständnis, wie die verschiedenen kortikalen Schichten verarbeiten kann ein- und ausgehenden Informationen in den verschiedenen LFP-Bands. Diese Ergebnisse belegen, dass -Neuromodulation profunde Auswirkungen auf die neurovaskulären Kopplung hat. Durch die Veränderung der Erregungs Hemmung Gleichgewicht neuronaler Schaltkreise vermitteln Neuromodulatoren nicht nur die neurale Aktivität, sondern auch die metabolischen Anforderungen anzupassen. Daher verstehen, wie die verschiedenen Arten von Neuromodulatoren beeinflussen die BOLD-Antwort für eine effektive Interpretation von fMRI-Daten notwendig ist, nicht nur in Aufgaben attentional und Belohnung bezogenen Prozessen mit, sondern auch für zukünftige diagnostische Verwendung von fMRI, da viele psychiatrische Störungen sind das Ergebnis von Veränderungen in neuromodulatorischen Systemen.La comunicación de las neuronas en los circuitos neuronales depende de los neurotransmisores (glutamato, acido γ-amino-butírico o GABA) y los neuromoduladores (acetilcolina, dopamina, serotonina, etc.). Sin embargo, tanto neurotransmisores como neuromoduladores son diferentes clases de moléculas y median diferentes aspectos de la actividad neuronal y del metabolismo, a pesar de compartir elementos moleculares muy similares. Los neurotransmisores, por una lado, son responsables de la transmisión sináptica de la información mientras que los neuromoduladores median diferentes eventos pos-sinápticos que resultan en cambios en el balance de la excitación e inhibición. La influencia de la neuromodulación es esencial para la función del sistema nerviosos, sin embargo es más difícil de estudiar que neurotransmisión. Esto se debe a que los efectos de los neuromoduladores suelen ser de un inicio lento, de larga duración, y no reflejan excitación o inhibición. En contraste a los efectos de los neurotransmisores, los neuromoduladores permiten que las neuronas sean más flexibles en su habilidad de codificar diferentes tipos de información (por ejemplo, información sensorial) en varias escalas temporales. Sin embargo, es importante darse cuenta que uno de objetivos primordiales en el estudio de neuromodulación es el de entender el grado en que la acción de los neuromoduladores está coordinada a todos los niveles de la función cerebral. Es decir, desde los aspectos celulares y metabólicos hasta los niveles de redes neuronales y control cognitivo. Por lo tanto, comprender los forma en la que diferentes moléculas median la interacción entre redes neuronal es esencial para el entendimiento de la dinámica cerebral, y también nos ayudara a comprender los procesos celulares y moleculares asociados a la percepción. La resonancia magnética funcional (fMRI, por sus siglas en inglés) es una técnica que permite acceder a varios aspectos celulares y metabólicos de la comunicación entre redes neuronales que suele ser de difícil acceso. Al mismo tiempo y debido que la fMRI es de naturaleza no invasiva, también permite comparar resultados e hipótesis entre humanos y primates. Por lo tanto, entender los efectos de la neuromodulación en la actividad de los circuitos neuronales es de alta relevancia. Dado que las proyecciones anatómicas de los sistemas de neuromoduladores, el uso de fMRI en combinación con farmacología y neurofisiología puede incrementar nuestro conocimiento sobre la estructura y dinámica de los sistemas de neuromoduladores. Sin embargo, poco se sabe sobre cómo interpretar los efectos de neuromodulation usando fMRI y neurofisiología, por ejemplo, como diferenciar los cambios en la señal BOLD que están relacionados a diferentes estados cognitivos (presumiblemente reflejando la influencia de neuromodulation). El propósito de esta disertación es la de comprender la relación causal que existe entre la actividad neural y la respuesta hemodinámica bajo la influencia de neuromodulación. Para tal fin presentamos los resultados de seis estudios que fueron producto de esta disertacion. En el primer estudio, desarrollamos una técnica basada en espectrometría de masa para detectar y medir la concentración de diferente metabolitos, neurotransmisores y neuromoduladores en el cerebro de primates. Dicha cuantificación se desarrollo simultáneamente tanto in sangre y cerebro. En un segundo estudio, utilizamos varias técnicas de fMRI (BOLD y flujo cerebral sanguíneo, CBF por sus siglas en ingles), registros electrofisiológicos con electrodos laminares y farmacología para acceder a los efectos de neuromodulation en el acople neurovascular. Para este fin, desarrollamos un sistema de inyecciones, basada en cambios de presión, para aplicar substancias sistémicamente o intracorticalmente dentro de un escáner de resonancia magnética. En nuestro tercer estudio, comparamos los efectos de lactato y piruvato para explorar como el desequilibrio metabólico de estas dos substancias afecta la respuesta BOLD. Esto es de gran importancia ya que ambas substancias metabólicas usualmente están en equilibrio. Sin embargo, cuando dicho equilibrio es interrumpido, los procesos metabólicos que acontecen en la mitocondria afectan las concentraciones de NAD y NADH causado cambios en el CBF. En un cuarto estudio, exploramos los efectos de las modulación dopaminergica (DAergic) en las señales BOLD, CBF y en la actividad neuronal. Encontramos que la modulación DAergic disocia las respuesta BOLD de la respuesta neuronal. Interesalmente, los cambios que observamos en la actividad de las neuronas estaba altamente acoplados a los efectos que observamos en la señal de CBF. En un estudio subsecuente, exploramos si los efectos de dopamina en la actividad neuronal es diferentes en las distintas capas de la corteza cerebral. Al mismo tiempo y ya que los neuromoduladores afectan la actividad de circuitos neuronales, exploramos si dichos efectos pueden usados como marcadores de la influencia de la neuromodulación . Esto es importante, ya que diferentes tipos de actividad neuronal brinda información sobre la amplitud y dinámica de la repuesta BOLD, y estudies han demostrado que estas bandas se originan de diferentes capas cortical. Este estudio revelo, que los potenciales de capo (LFPs, por sus siglas en ingles) en frecuencias intermedias puede ser indicativos sobre los efectos de neuromodulation en el procesamiento cortical. Dado los resultados en el estudio previo, en un sexto estudio, nos enfocamos a entender que tan diferentes las capas de la corteza procesan información entrante y saliente en diferentes frecuencias de los LFPs. Estos descubrimientos demuestran que los efectos de los neuromoduladores tiene una fuerte influencia en el acople neurovascular. Los neuromoduladores cambian el balance de excitación e inhibición de los circuitos neuronal, pero también median las demandas metabólicas. De esta manera, entender cómo interpretar los efectos de los neuromoduladores en la respuesta BOLD es esencial para una interpretación veraz y efectiva de los datos generados con fMRI. Estos resultados, no solo nos permiten comprender los procesos que están relacionados a la atención o de varios procesos cognitivos, sino que a su vez, nos permite comprender la señal de fMRI para su futuro uso en la medicina diagnostica, ya que muchas enfermedades psiquiátricas están asociadas a trastornos en el sistemas neuromoduladores

Machine Learning and Statistical Analysis of Complex Mathematical Models: An Application to Epilepsy

The electroencephalogram (EEG) is a commonly used tool for studying the emergent electrical rhythms of the brain. It has wide utility in psychology, as well as bringing a useful diagnostic aid for neurological conditions such as epilepsy. It is of growing importance to better understand the emergence of these electrical rhythms and, in the case of diagnosis of neurological conditions, to find mechanistic differences between healthy individuals and those with a disease. Mathematical models are an important tool that offer the potential to reveal these otherwise hidden mechanisms. In particular Neural Mass Models (NMMs), which describe the macroscopic activity of large populations of neurons, are increasingly used to uncover large-scale mechanisms of brain rhythms in both health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understand how dynamics change when parameters are varied. Despite they are considered low-dimensional in comparison to micro-scale neural network models, with regards to understanding the relationship between parameters and dynamics NMMs are still prohibitively high dimensional for classical approaches such as numerical continuation. We need alternative methods to characterise the dynamics of NMMs in high dimensional parameter spaces. The primary aim of this thesis is to develop a method to explore and analyse the high dimensional parameter space of these mathematical models. We develop an approach based on statistics and machine learning methods called decision tree mapping (DTM). This method is used to analyse the parameter space of a mathematical model by studying all the parameters simultaneously. With this approach, the parameter space can efficiently be mapped in high dimension. We have used measures linked with this method to determine which parameters play a key role in the output of the model. This approach recursively splits the parameter space into smaller subspaces with an increasing homogeneity of dynamics. The concepts of decision tree learning, random forest, measures of importance, statistical tests and visual tools are introduced to explore and analyse the parameter space. We introduce formally the theoretical background and the methods with examples. The DTM approach is used in three distinct studies to: • Identify the role of parameters on the dynamic model. For example, which parameters have a role in the emergence of seizure dynamics? • Constrain the parameter space, such that regions of the parameter space which give implausible dynamic are removed. • Compare the parameter sets to fit different groups. How does the thalamocortical connectivity of people with and without epilepsy differ? We demonstrate that classical studies have not taken into account the complexity of the parameter space. DTM can easily be extended to other fields using mathematical models. We advocate the use of this method in the future to constrain high dimensional parameter spaces in order to enable more efficient, person-specific model calibration

The role that choice of model plays in predictions for epilepsy surgery

This is the final version. Available on open access from Nature Research via the DOI in this recordMathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.Engineering and Physical Sciences Research Council (EPSRC)Medical Research Council (MRC)Epilepsy Research UKWellcome Trus