An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients

Functional Connectivity Analysis on Resting-State Electroencephalography Signals Following Chiropractic Spinal Manipulation in Stroke Patients

Stroke impairments often present as cognitive and motor deficits, leading to a decline in quality of life. Recovery strategy and mechanisms, such as neuroplasticity, are important factors, as these can help improve the effectiveness of rehabilitation. The present study investigated chiropractic spinal manipulation (SM) and its effects on resting-state functional connectivity in 24 subacute to chronic stroke patients monitored by electroencephalography (EEG). Functional connectivity of both linear and non-linear coupling was estimated by coherence and phase lag index (PLI), respectively. Non-parametric cluster-based permutation tests were used to assess the statistical significance of the changes in functional connectivity following SM. Results showed a significant increase in functional connectivity from the PLI metric in the alpha band within the default mode network (DMN). The functional connectivity between the posterior cingulate cortex and parahippocampal regions increased following SM, t (23) = 10.45, p = 0.005. No significant changes occurred following the sham control procedure. These findings suggest that SM may alter functional connectivity in the brain of stroke patients and highlights the potential of EEG for monitoring neuroplastic changes following SM. Furthermore, the altered connectivity was observed between areas which may be affected by factors such as decreased pain perception, episodic memory, navigation, and space representation in the brain. However, these factors were not directly monitored in this study. Therefore, further research is needed to elucidate the underlying mechanisms and clinical significance of the observed changes

Frequency-based brain networks: From a multiplex framework to a full multilayer description

We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region and not between them. We study the second smallest eigenvalue of the combinatorial supra-Laplacian matrix of the multilayer network in detail, and we thereby show that the heterogeneity of interlayer edges and, especially, the fraction of missing edges crucially modify the spectral properties of the multilayer network. We illustrate our results with both synthetic network models and real data sets obtained from resting state magnetoencephalography. Our work demonstrates an important issue in the construction of frequency-based multilayer brain networks.Comment: 13 pages, 8 figure

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses

Investigation of the development of neural and behavioural auditory rhythmic sensitivity and of its contribution to reading acquisition

241 p.The main goal of the current doctoral dissertation was to examine the contribution of brain and behavioural rhythmic sensitivity during pre-reading stages to the development of future reading. To achieve this goal, we conducted a longitudinal study in which typically developing children were tested three times: twice before they had received formal reading instruction (T1: 4-5 y.o.; T2: 5-6 y.o.) and once after reading instruction was introduced in the school curriculum (T3: 6-7 y.o.). Along these three testing times, we used EEG to measure the children¿s brain rhythmic (oscillatory) activity in response to natural speech and to auditory signals modulated at rates relevant for speech perception (at the stress, syllabic and phonemic rates). The children also ran a battery of behavioural tasks that included a measure of rhythmic skills (tapping to a beat in synchrony) and several classical reading predictors (e.g. phonological awareness, phonological short-term memory). The longitudinal nature of this work allowed us testing for the first time the trajectory of brain coherence to auditory signals during early childhood. Furthermore, this is the first study finding a long-hypothesized relation between brain oscillatory activity at low frequency bands (0.5 Hz) in pre-reading stages and later reading achievement, such that right-lateralized brain responses to speech at T2 correlated significantly with children¿s reading achievement at T3. Regarding behavioural rhythm sensitivity, whereas rhythmic skills were tightly related to other reading predictors before reading was acquired (T1 and T2), we found no evidence that it contributed significantly to final reading outcome. Differences among measures of brain vs. behavioural rhythmic sensitivity are discussed, especially in the context of early detection and intervention of children at risk of developing reading disorders.bcbl: basque center on cognition, brain and languag

Investigation of the development of neural and behavioural auditory rhythmic sensitivity and of its contribution to reading acquisition

241 p.The main goal of the current doctoral dissertation was to examine the contribution of brain and behavioural rhythmic sensitivity during pre-reading stages to the development of future reading. To achieve this goal, we conducted a longitudinal study in which typically developing children were tested three times: twice before they had received formal reading instruction (T1: 4-5 y.o.; T2: 5-6 y.o.) and once after reading instruction was introduced in the school curriculum (T3: 6-7 y.o.). Along these three testing times, we used EEG to measure the children¿s brain rhythmic (oscillatory) activity in response to natural speech and to auditory signals modulated at rates relevant for speech perception (at the stress, syllabic and phonemic rates). The children also ran a battery of behavioural tasks that included a measure of rhythmic skills (tapping to a beat in synchrony) and several classical reading predictors (e.g. phonological awareness, phonological short-term memory). The longitudinal nature of this work allowed us testing for the first time the trajectory of brain coherence to auditory signals during early childhood. Furthermore, this is the first study finding a long-hypothesized relation between brain oscillatory activity at low frequency bands (0.5 Hz) in pre-reading stages and later reading achievement, such that right-lateralized brain responses to speech at T2 correlated significantly with children¿s reading achievement at T3. Regarding behavioural rhythm sensitivity, whereas rhythmic skills were tightly related to other reading predictors before reading was acquired (T1 and T2), we found no evidence that it contributed significantly to final reading outcome. Differences among measures of brain vs. behavioural rhythmic sensitivity are discussed, especially in the context of early detection and intervention of children at risk of developing reading disorders.bcbl: basque center on cognition, brain and languag

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity

An exploration of SSVEPs across development and autism spectrum conditions

This thesis contains an experimental investigation of networks dynamics across development and autism spectrum disorders (ASD). The interplay between functional segregation and integration within functional cortical networks was investigated based on the hypothesis that it plays a key role in development and ASD. Functional segregation refers to the synchronization between adjacent brain areas and functional integration indicates the synchronization between distributed brain regions. Steady-state visual evoked potentials (SSVEPs) to high contrast (90%) luminance and isoluminant chromatic (red-green) vertical gratings with two spatial frequencies (2.8 and 6 cpd) at 7.5 Hz (luminance) and 3.3 Hz (chromatic) were recorded in individuals with and without ASD. SSVEPs were analysed in the frequency and time domains to carrying out a detailed analysis of the dynamic functional connectivity elicited by perception of simple and complex visual stimuli. The first research study explored aged-related changes in networks dynamics. Participants were 30 children aged 7 to 17 and 11 adults from the typical population. Our results suggest functional reorganization from local to distributed networks across development, and that networks underpinning medium spatial frequency change would be a useful biomarker of typical brain function. The second research study explored potential changes in networks dynamics between children with and without ASD. Participants were 20 children aged 7 to 17 (10 with ASD and 10 age-matched typically developing). The result of this study is a potential EEG biomarker to characterize atypical brain function in autism. Our results suggest a direct relationship between functional segregation and functional integration during visual perception; atypical functional connectivity in lower processing mechanisms might contribute to the disruption in long-range functional integration reported in ASD, because both abnormalities occur in concert in the autistic brain. Overall this exploratory research shows that SSVEPs can elicit different functional networks involving local and distributed cortical brain systems, and can also show segregated and overlapping functional networks underlying neural mechanisms at early stages of visual processing during development and ASD. Therefore, SSVEPs would be a potentially useful technique to identify differences in the brains of people with and without autism