Simultaneous meg/eeg recordings for the study of source domain brain connectivity in neurodegenerative diseases

  1. Bruña Fernández, Ricardo
Supervised by:
  1. Ernesto Pereda de Pablo Director

Defence university: Universidad Politécnica de Madrid

Fecha de defensa: 12 March 2019

  1. Luis Enrique Gómez Aguilera Chair
  2. Andres Santos Lleo Secretary
  3. Adria Tauste Campo Committee member
  4. James Becker Committee member
  5. Jesús Poza Crespo Committee member

Type: Thesis


Recently, the understanding of the brain function has shifted from the classic localizationism, sometimes called phrenological model, to the modern model of the connectome. In this new framework the different brain region, although highly specialized, are highly communicated in such a way that the brain, as a whole, carries out the cognitive functions. With this in mind, some neurological diseases have started to be studied from the connectomics. Classic neurological diseases, as Alzheimer’s disease or epilepsy, nowadays, are considered disconnection of overconnection syndromes, respectively. Unraveling the way in which different brain regions communicate is not an easy task. Actual approaches are based on the study of Functional Connectivity (FC), this is, the statistical dependency between the activity of separated brain regions. In the special case of electrophysiology, as for electroencephalography (EEG) or magnetoencephalography (MEG), a popular FC model is the model of Phase Synchronization (PS). According to this model, when two regions communicate with each other the instantaneous phase of their derived time series locks, instead of locking their amplitudes. This model is highly plausible and presents the advantage that a phase lock requires much smaller energy than an amplitude lock. A popular method for the study of PS is Phase Locking Value (PLV). This method estimates the FC from the PS between two time series. When the signals under study comes from an EEG or MEG acquisition in sensor space, the calculation of the PLV between each pair of sensors is straightforward. However, when the objective is to study brain connectivity the required methodology becomes more complicated. First, the information, acquired in sensor space, must be mapped to the generators, defined as source of activity in the cortex. This mapping, termed source reconstruction, is generally an ill-posed problem, and the resulting source-space time series are highly inter-dependent. In the second place, and due to this inter-dependency, the source-space time series must be grouped in brain regions, and the FC must be estimated between pairs of regions, instead of pairs of time series. Last, these calculations are computationally expensive, and it is paramount to find efficient formulations that allow for the estimation of whole-brain FC in reasonable times. In this Thesis we address these three problems. The current Doctoral Thesis is organized as follows. The first three chapters act as introduction, describing the state of the art on neuroimaging techniques (Chapter 1), acquisition and processing of electrophysiological signals (Chapter 2) and reconstruction of the sources of activity of these signals (Chapter 3). These three chapters serve to establish the reader in the context where this Thesis is built. Then, the main objective of the Thesis is formulated, along with some secondary objectives based on the confirmation of a series of hypotheses (Chapter 4). The next three chapters propose and evaluate methodologies aimed to solve the three problems presented above. The first one evaluates different conduction models for EEG and MEG, in order to identify the model that provides the higher reconstruction accuracy with a reasonable computational cost (Chapter 5). The second propose a computationally efficient reformulation on the classical algorithm for PLV that allows for a speed-up of a factor of 100 in the calculation of whole brain synchronization (Chapter 6). The third of these chapters proposed and evaluates a new methodology to calculate PS between brain regions without information lost (Chapter 7). The next two chapters evaluate the ability of FC, based on the calculation of PS between brain regions, to identify altered connectivity patterns in preclinical population in a study on pathological aging (Chapter 8) and in alcohol-addicted patients (Chapter 9) when compared to healthy controls. The Thesis finishes with some general conclusions, where posed hypotheses are evaluated, along with the degree of completion of the proposed objectives (Chapter 10).