Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Study of cortical rhythmic activity and connectivity with magnetoencephalography

Intracranial recordings in animals and neuroimaging studies on humans have indicated that oscillatory activity and its modulations may play a fundamental role in large-scale neural information processing. Furthermore, rhythmic interactions between cortical areas have been detected across a variety of tasks with electroencephalography (EEG) and magnetoencephalography (MEG). This kind of coupling has been proposed to be a key mechanism through which information is integrated across segregated areas. So far, rhythmic interactions have been analyzed primarily at the EEG/MEG sensor level, without explicit knowledge of cortical areas involved. In this thesis work we developed new methods that can be used to image oscillatory activity and coherence at the cortical level with MEG. Dynamic Imaging of Coherent Sources (DICS) enables localization of interacting areas both using external reference signals and directly from the MEG data. When the interacting areas have been determined it is possible to use additional measures beyond coherence to further quantify interactions within the networks. DICS was originally designed for study of continuous data; its further development into event-related DICS (erDICS) adds the possibility to image modulations of rhythmic activity that are locked to stimulus or movement timing. Furthermore, permutation testing incorporated into erDICS allows the evaluation of the statistical significance of the results. Analysis of simulated and real data showed that DICS and erDICS yield accurate localization and quantification of oscillatory activity and coherence. Comparison of DICS to other methods of localizing oscillatory activity revealed that it is equally accurate and that it can better separate the activity originating from two nearby areas. We applied DICS to two datasets, recorded from groups of subjects while they performed slow finger movements and when they were reading continuously. In both cases, we were able to systematically identify interacting cortico-cortical networks and, using phase coupling and causality measures, to quantify the manner in which the nodes within these networks influenced each other. Furthermore, we compared the identified reading network to results reported in neurophysiological and hemodynamic activation studies. In addition to areas typically detected in activation studies of reading the network included areas that are normally found in language production rather than perception tasks, indicating more extensive networking of neural systems than usually observed in activation studies

Speech-brain synchronization: a possible cause for developmental dyslexia

152 p.Dyslexia is a neurological learning disability characterized by the difficulty in an individual¿s ability to read despite adequate intelligence and normal opportunities. The majority of dyslexic readers present phonological difficulties. The phonological difficulty most often associated with dyslexia is a deficit in phonological awareness, that is, the ability to hear and manipulate the sound structure of language. Some appealing theories of dyslexia attribute a causal role to auditory atypical oscillatory neural activity, suggesting it generates some of the phonological problems in dyslexia. These theories propose that auditory cortical oscillations of dyslexic individuals entrain less accurately to the spectral properties of auditory stimuli at distinct frequency bands (delta, theta and gamma) that are important for speech processing. Nevertheless, there are diverging hypotheses concerning the specific bands that would be disrupted in dyslexia, and which are the consequences of such difficulties on speech processing. The goal of the present PhD thesis was to portray the neural oscillatory basis underlying phonological difficulties in developmental dyslexia. We evaluated whether phonological deficits in developmental dyslexia are associated with impaired auditory entrainment to a specific frequency band. In that aim, we measured auditory neural synchronization to linguistic and non-linguistic auditory signals at different frequencies corresponding to key phonological units of speech (prosodic, syllabic and phonemic information). We found that dyslexic readers presented atypical neural entrainment to delta, theta and gamma frequency bands. Importantly, we showed that atypical entrainment to theta and gamma modulations in dyslexia could compromise perceptual computations during speech processing, while reduced delta entrainment in dyslexia could affect perceptual and attentional operations during speech processing. In addition, we characterized the links between the anatomy of the auditory cortex and its oscillatory responses, taking into account previous studies which have observed structural alterations in dyslexia. We observed that the cortical pruning in auditory regions was linked to a stronger sensitivity to gamma oscillation in skilled readers, but to stronger theta band sensitivity in dyslexic readers. Thus, we concluded that the left auditory regions might be specialized for processing phonological information at different time scales (phoneme vs. syllable) in skilled and dyslexic readers. Lastly, by assessing both children and adults on similar tasks, we provided the first evaluation of developmental modulations of typical and atypical auditory sampling (and their structural underpinnings). We found that atypical neural entrainment to delta, theta and gamma are present in dyslexia throughout the lifespan and is not modulated by reading experience