The Effect of Common Signals on Power, Coherence and Granger Causality: Theoretical Review, Simulations, and Empirical Analysis of Fruit Fly LFPs Data

When analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. As a result, our understanding of how common signals affect neural connectivity estimation is incomplete. For example, little is known about recording preparations involving high spatial-resolution electrodes, used in linear array recordings. We address this gap through a combination of theoretical review, simulations, and empirical analysis of local field potentials recorded from the brains of fruit flies. We demonstrate how a framework that jointly analyzes power, coherence, and quantities based on Granger causality reveals the presence of common signals. We further show that subtracting spatially adjacent signals (bipolar derivations) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. We also show that Granger causality is adversely affected by common signals and that a quantity referred to as “instantaneous interaction” is increased in the presence of common signals. The theoretical review, simulation, and empirical analysis we present can readily be adapted by others to investigate the nature of the common signals in their data. Our contributions improve our understanding of how common signals affect power, coherence, and Granger causality and will help reduce the misinterpretation of functional and effective connectivity analysis

Establishing Mutual Links among Brain Structures

Lidský mozek je tvořen vzájemně propojenými populacemi nervových buněk, které formují anatomicky i funkčně oddělené struktury. Pro studium fyziologie a patologie lidského mozku je zcela zásadní znát, jak jsou tyto struktury propojeny a jak se mezi nimi šíří informace. Publikované metody na detekci vzájemných vazeb se velmi často omezují pouze na analýzu povrchového EEG, pracují s vymezeným počtem kontaktů a nezachycují dynamický vývoj konektivity při kognitivních procesech nebo při různých stavech vědomí. Současně nepopisují konektivitu patologických částí mozku, jejíž analýza by mohla zásadně přispět k výzkumu a léčbě dané patologie. Cílem této práce je návrh metodiky a následná analýza časového průběhu vzájemných vazeb mezi mozkovými strukturami z intrakraniálního EEG. Analyzovány jsou fyziologické procesy v průběhu kognitivní stimulace, a lokální konektivita patologických částí epileptického mozku při klidu a spánku. Výsledky přinášejí nové poznatky v oblasti základního výzkumu fyziologie lidského mozku, kterých bylo dosaženo pomocí inovativního postupu, jenž kombinuje metody konektivity a výpočty výkonů EEG signálů. V druhé části práce je analyzována lokální konektivita epileptického ložiska (SOZ). Výsledky popisují funkční oddělení SOZ od okolní tkáně a mohou přispět do klinické praxe léčby epilepsie.The Human brain consists of mutually connected neuronal populations that build anatomically and functionally separated structures. To understand human brain activity and connectivity, it is crucial to describe how these structures are connected and how information is spread. Commonly used methods often work with data from scalp EEG, with a limited number of contacts, and are incapable of observing dynamic changes during cognitive processes or different behavioural states. In addition, connectivity studies almost never analyse pathological parts of the brain, which can have a crucial impact on pathology research and treatment. The aim of this work is connectivity analysis and its evolution in time during cognitive tasks using data from intracranial EEG. Physiological processes in cognitive stimulation and the local connectivity of pathology in the epileptic brain during wake and sleep were analysed. The results provide new insight into human brain physiology research. This was achieved by an innovative approach which combines connectivity methods with EEG spectral power calculation. The second part of this work focuses on seizure onset zone (SOZ) connectivity in the epileptic brain. The results describe the functional isolation of the SOZ from the surrounding tissue, which may contribute to clinical research and epilepsy treatment.

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia BiomédicaThe present study aims at examining the correlation among different brain areas while the subjects performed an arithmetical task, and how these differ from the mental relations in the same subjects during a resting state. In order to this, both linear and nonlinear methods were used, i.e., both algorithms capable of detecting linear relations and algorithms capable of detecting correlations without assuming any type of parametric relationship between the signals were implemented. The first algorithm that was implemented was the cross-correlation function, which gives an estimate of how much two signals are linearly correlated, and estimates the delay between them, thus permitting to make inferences on causality. Furthermore, this algorithm was validated using the statistic method called surrogation, in order to test for the applicability of the algorithm on the signals that were to be processed. The next part of the study consisted on implementing two analogous algorithms, the coefficient of determination and the nonlinear regression coefficient. These coefficients both measure the fraction of reduction of variance that can be obtained by estimating the relationship between two signals according to a fitted line, the difference being that the former assumes a linear relation between both sets of samples and the latter doesn‟t previously assume any type of relationship between the signals. The main differences in correlation that were observed between the state of mental rest and between the arithmetic task performance were that in the former more brain sites were correlated, whereas during the task this synchrony was mainly verified between frontal and parietal areas, showing a decrease in the other locations. Furthermore, the estimates provided by the linear and nonlinear algorithms were very similar, suggesting that in this case the relationships among different neural networks were mainly linear, and thus validating the application of linear methods in this type of analysis in particular cases. Regarding the estimation of delays between signals and inferences on causality, no conclusive results were attained

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

Respostas eletroencefalográficas durante estimulação magnética transcraniana em adultos normais

Electroencephalogram (EEG) is a well-known painless and noninvasive method, measuring real time brain electrical activity. Transcranial Magnetic Stimulation (TMS) it is another noninvasive, painless and safe method, to modulate, by either activating or inhibiting nerve cells activity. TMS has been increasingly used as a tool in neurosciences. However, its basic mechanisms are not fully understood. Simultaneous TMS/EEG recordings are technically challenging, but could bring some missing information. To investigate the effect of TMS in EEG signals of the primary motor cortex area of normal subjects, describing its properties in the time and frequency domain eleven normal subjects were submitted to TMS single pulses during EEG. TMS was also applied to a non-human head model: muskmelon (Cucumis sp.). In the time domain the evoked potentials P60, N100, P190 and N280 were obtained. Frequency domain results showed initially oscillations between 0.5 to 70 Hz. After capacitor recharging time correction, set away from the epochs, oscillations were within 0.5 and 20Hz. Data from other publications were replicated in our population. This information can be useful for motor pathways future studies. TMS capacitors recharge time must be set-up to avoid undesired neuronal activation that could put results in doubt. It is also advisable to the researchers who record TMS/EEG that the recharge time must also be clearly informed in methods section.Eletroencefalograma (EEG) é um método conhecido para medir em tempo real a atividade elétrica cerebral. Estimulação Magnética Transcraniana (EMT) é um método não invasivo de ativação ou inibição da atividade elétrica neural, sendo uma ferramenta cada vez mais importante nas neurociências. No entanto, os mecanismos neuronais da EMT não são plenamente conhecidos. O registro simultâneo do EEG durante a EMT é tecnicamente desafiador. Mas, poderia acrescentar conhecimentos a esta lacuna. Para investigar o efeito da EMT no EEG da área primária motora de sujeitos normais, onze sujeitos normais foram submetidos a pulsos únicos de EMT. Estimulação foi aplicada também a um modelo não humano de crânio: melão (Cucumis sp.). No domínio do tempo, foram obtidos os componentes dos potenciais evocados P60, N100, P190 e N280. Os resultados no domínio da frequência mostraram incialmente oscilações entre 0,5 a 70 Hz. Após a correção do tempo de recarga dos capacitores, as oscilações foram de 0,5 a 20 Hz. Os dados replicaram os resultados de outros pesquisadores. As informações obtidas poderão ser úteis em futuros estudos sobre a fisiologia e enfermidades das vias motoras. O tempo de recarga dos capacitores, que necessita ser configurado apropriadamente, para evitar as épocas de interesse e, consequentemente ativação neural indesejada pela corrente elétrica durante a recarga. Também é recomendável para os pesquisadores que lidam com o registro simultâneo do EEG durante a EMT, que atentem para possíveis artefatos provocados pela recarga dos capacitores, devendo informá-la na seção Métodos

Deep Learning for Electrophysiological Investigation and Estimation of Anesthetic-Induced Unconsciousness

Neuroscience has made a number of advances in the search for the neural correlates of consciousness, but our understanding of the neurophysiological markers remains incomplete. In this work, we apply deep learning techniques to resting-state electroencephalographic (EEG) measures of healthy participants under general anesthesia, for the investigation and estimation of altered states of consciousness. Specifically, we focus on states characterized by different levels of unconsciousness and anesthetic depths, based on definitions and metrics from contemporary clinical practice. Our experiments begin by exploring the ability of deep learning to extract relevant electrophysiological features, under a cross-subject decoding task. As there is no state-of-theart model for EEG analysis, we compare two widely used deep learning architectures - convolutional neural networks (cNNs) and multilayer perceptrons (MLPs) - and show that cNNs perform effectively, using only one second of the raw EEG signals. Relying on cNNs, we derive a novel 3D architecture design and a standard preprocessing pipeline, which allows us to exploit the spatio-temporal structure of the EEG, as well as to integrate different acquisition systems and datasets under a common methodology. We then focus on the nature of different predictive tasks, by investigating classification and regression algorithms under a variety of clinical ground-truths, based on behavioral, pharmacological, and psychometrical evidence for consciousness. Our findings provide several insights regarding the interaction across the anesthetic states, the electrophysiological signatures, and the temporal dynamics of the models. We also reveal an optimal training strategy, based on which we can detect progressive changes in levels of unconsciousness, with higher granularity than current clinical methods. Finally, we test the generalizability of our deep learning-based EEG framework, across subjects, experimental designs, and anesthetic agents (propofol, ketamine and xenon). Our results highlight the capacity of our model to acquire appropriate, task-related, cross-study features, and the potential to discover common cross-drug features of unconsciousness. This work has broader significance for discovering generalized electrophysiological markers that index states of consciousness, using a data-driven analysis approach. It also provides a basis for the development of automated, machine-learning driven, non-invasive EEG systems for real-time monitoring of the depth of anesthesia, which can advance patients' comfort and safety