Advances in EEG-based functional connectivity approaches to the study of the central nervous system in health and disease

Functional brain connectivity is closely linked to the complex interactions between brain networks. In the last two decades, measures of functional connectivity based on electroencephalogram (EEG) data have proved to be an important tool for neurologists and clinical and non-clinical neuroscientists. Indeed, EEG-based functional connectivity may reveal the neurophysiological processes and networks underlying human cognition and the pathophysiology of neuropsychiatric disorders. This editorial discusses recent advances and future prospects in the study of EEG-based functional connectivity, with a focus on the main methodological approaches to studying brain networks in health and disease

Typical and Atypical Development of Functional Human Brain Networks: Insights from Resting-State fMRI

Over the past several decades, structural MRI studies have provided remarkable insights into human brain development by revealing the trajectory of gray and white matter maturation from childhood to adolescence and adulthood. In parallel, functional MRI studies have demonstrated changes in brain activation patterns accompanying cognitive development. Despite these advances, studying the maturation of functional brain networks underlying brain development continues to present unique scientific and methodological challenges. Resting-state fMRI (rsfMRI) has emerged as a novel method for investigating the development of large-scale functional brain networks in infants and young children. We review existing rsfMRI developmental studies and discuss how this method has begun to make significant contributions to our understanding of maturing brain organization. In particular, rsfMRI has been used to complement studies in other modalities investigating the emergence of functional segregation and integration across short and long-range connections spanning the entire brain. We show that rsfMRI studies help to clarify and reveal important principles of functional brain development, including a shift from diffuse to focal activation patterns, and simultaneous pruning of local connectivity and strengthening of long-range connectivity with age. The insights gained from these studies also shed light on potentially disrupted functional networks underlying atypical cognitive development associated with neurodevelopmental disorders. We conclude by identifying critical gaps in the current literature, discussing methodological issues, and suggesting avenues for future research

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

Magnetoencephalography as a tool in psychiatric research: current status and perspective

The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis

Novel topological and temporal network analyses for EEG functional connectivity with applications to Alzheimer’s disease

This doctoral thesis outlines several methodological advances in network science aimed towards uncovering rapid, complex interdependencies of electromagnetic brain activity recorded from the Electroencephalogram (EEG). This entails both new analyses and modelling of EEG brain network topologies and a novel approach to analyse rapid dynamics of connectivity. Importantly, we implement these advances to provide novel insights into pathological brain function in Alzheimer’s disease. We introduce the concept of hierarchical complexity of network topology, providing both an index to measure it and a model to simulate it. We then show that the topology of functional connectivity estimated from EEG recordings is hierarchically complex, existing in a scale between random and star-like topologies, this is a paradigm shift from the established understanding that complexity arises between random and regular topologies. We go on to consider the density appropriate for binarisation of EEG functional connectivity, a methodological step recommended to produce compact and unbiased networks, in light of its new-found hierarchical complexity. Through simulations and real EEG data, we show the benefit of going beyond often recommended sparse representations to account for a broader range of hierarchy level interactions. After this, we turn our attention to assessing dynamic changes in connectivity. By constructing a unified framework for multivariate signals and graphs, inspired by network science and graph signal processing, we introduce graph-variate signal analysis which allows us to capture rapid fluctuations in connectivity robust to spurious short-term correlations. We define this for three pertinent brain connectivity estimates- Pearson’s correlation coefficient, coherence and phase-lag index- and show its benefit over standard dynamic connectivity measures in a range of simulations and real data. Applying these novel methods to EEG datasets of the performance of visual short-term memory binding tasks by familial and sporadic Alzheimer’s disease patients, we uncover disorganisation of the topological hierarchy of EEG brain function and abnormalities of transient phase-based activity which paves the way for new interpretations of the disease’s affect on brain function. Hierarchical complexity and graph-variate dynamic connectivity are entirely new methods for analysing EEG brain networks. The former provides new interpretations of complexity in static connectivity patterns while the latter enables robust analysis of transient temporal connectivity patterns, both at the frontiers of analysis. Although designed with EEG functional connectivity in mind, we hope these techniques will be picked up in the broader field, having consequences for research into complex networks in general

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures

Strengths and weakness of neuroscientific investigations of childhood poverty: Future directions

The neuroscientific study of child poverty is a topic that has only recently emerged. In comparison with previous reviews (e.g., Hackman and Farah, 2009; Lipina and Colombo, 2009; Hackman et al., 2010; Raizada and Kishiyama, 2010; Lipina and Posner, 2012), our perspective synthesizes findings, and summarizes both conceptual and methodological contributions, as well as challenges that face current neuroscientific approaches to the study of childhood poverty. The aim of this effort is to identify target areas of study that could potentially help build a basic and applied research agenda for the coming years.Fil: Lipina, Sebastián Javier. Centro de Educación Médica e Investigaciones Clínicas “Norberto Quirno”; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Segretin, María Soledad. Centro de Educación Médica e Investigaciones Clínicas “Norberto Quirno”; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin