scholarly journals In-phase and in-antiphase connectivity in EEG

2021 ◽  
Author(s):  
Christian O'Reilly ◽  
John D Lewis ◽  
Rebecca J Theilmann ◽  
Mayada Elsabbagh ◽  
Jeanne Townsend
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Significant Proportion ◽  
Brain Regions ◽  
Field Potentials ◽  
Volume Conduction ◽  
Confounding Effect ◽  
Neuronal Communication ◽  
Neural Communication ◽  
Eeg Connectivity

Zero-lag synchrony is generally discarded from functional connectivity studies to eliminate the confounding effect of volume conduction. Demonstrating genuine and significant unlagged synchronization between distant brain regions would indicate that most electroencephalography (EEG) connectivity studies neglect an important mechanism for neuronal communication. We previously demonstrated that local field potentials recorded intracranially tend to synchronize with no lag between homotopic brain regions. This synchrony occurs most frequently in antiphase, potentially supporting corpus callosal inhibition and interhemispheric rivalry. We are now extending our investigation to EEG. By comparing the coherency in a recorded and a surrogate dataset, we confirm the presence of a significant proportion of genuine zero-lag synchrony unlikely to be due to volume conduction or to recording reference artifacts. These results stress the necessity for integrating zero-lag synchrony in our understanding of neural communication and for disentangling volume conduction and zero-lag synchrony when estimating EEG sources and their functional connectivity.

scholarly journals Using structural connectivity to augment community structure in EEG functional connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 761-787 ◽  
Author(s):  
Katharina Glomb ◽  
Emeline Mullier ◽  
Margherita Carboni ◽  
Maria Rubega ◽  
Giannarita Iannotti ◽  
...  
Keyword(s):  
Community Structure ◽  
White Matter ◽  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Source Analysis ◽  
Volume Conduction ◽  
Functional Relationships ◽  
The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

scholarly journals On the Specificity and Permanence of Electroencephalography Functional Connectivity

2021 ◽  
Vol 11 (10) ◽  
pp. 1266
Author(s):  
Yibo Zhang ◽  
Ming Li ◽  
Hui Shen ◽  
Dewen Hu
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Clinical Medicine ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Identification Accuracy ◽  
Eeg Connectivity ◽  
Statistical Coupling ◽  
The Individual ◽  
Individual Specificity

Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications.

scholarly journals Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

2020 ◽  
Author(s):  
Daril E. Brown ◽  
Jairo I. Chavez ◽  
Derek H. Nguyen ◽  
Adam Kadwory ◽  
Bradley Voytek ◽  
...  
Keyword(s):  
Motor Control ◽  
Local Field ◽  
Local Field Potentials ◽  
Brain Regions ◽  
Network Activity ◽  
Field Potentials ◽  
Population Activity ◽  
Time Frequency ◽  
Useful Signal ◽  
Complex Motor

AbstractNeuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors.Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

Transient loss of interhemispheric functional connectivity following unilateral cortical spreading depression in awake rats

Cephalalgia ◽  
2020 ◽  
pp. 033310242097017
Author(s):  
Lyudmila V Vinogradova ◽  
Elena M Suleymanova ◽  
Tatiana M Medvedeva
Keyword(s):  
Functional Connectivity ◽  
Local Field ◽  
Cortical Spreading Depression ◽  
Spreading Depression ◽  
Critical Role ◽  
Local Field Potentials ◽  
Abnormal Behavior ◽  
Field Potentials ◽  
Freely Behaving

Objective Growing evidence shows a critical role of network disturbances in the pathogenesis of migraine. Unilateral pattern of neurological symptoms of aura suggests disruption of interhemispheric interactions during the early phase of a migraine attack. Using local field potentials data from the visual and motor cortices, this study explored effects of unilateral cortical spreading depression, the likely pathophysiological mechanism of migraine aura, on interhemispheric functional connectivity in freely behaving rats. Methods Temporal evolution of the functional connectivity was evaluated using mutual information and phase synchronization measures applied to local field potentials recordings obtained in homotopic points of the motor and visual cortices of the two hemispheres in freely behaving rats after induction of a single unilateral cortical spreading depression in the somatosensory S1 cortex and sham cortical stimulation. Results Cortical spreading depression was followed by a dramatic broadband loss of interhemispheric functional connectivity in the visual and motor regions of the cortex. The hemispheric disconnection started after the end of the depolarization phase of cortical spreading depression, progressed gradually, and terminated by 5 min after initiation of cortical spreading depression. The network impairment had region- and frequency-specific characteristics and was more pronounced in the visual cortex than in the motor cortex. The period of impaired neural synchrony coincided with post-cortical spreading depression electrographic aberrant activation of the ipsilateral cortex and abnormal behavior. Conclusion The study provides the first evidence that unilateral cortical spreading depression induces a reversible loss of functional hemispheric connectivity in the cortex of awake animals. Given a critical role of long-distance cortical synchronization in sensory processing and sensorimotor integration, the post-cortical spreading depression breakdown of functional connectivity may contribute to neuropathological mechanisms of aura generation.

scholarly journals The statistical challenge of finding spontaneous changes in functional connectivity in high-dimensional fMRI data

2020 ◽  
Author(s):  
Diego Vidaurre
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Regions ◽  
Single Step ◽  
High Dimensional ◽  
Fast Time ◽  
Time Varying ◽  
Time Resolved ◽  
Neuronal Communication ◽  
Bayesian Mixture

An important question in neuroscience is whether or not we can interpret spontaneous variations in the pattern of correlation between brain areas, which we refer to as functional connectivity or FC , as an index of dynamic neuronal communication in fMRI . That is, can we measure time-varying FC reliably? And, if so, can FC reflect information transfer between brain regions at relatively fast-time scales? Answering these questions in practice requires dealing with the statistical challenge of having high-dimensional data and a comparatively lower number of time points or volumes. A common strategy is to use PCA to reduce the dimensionality of the data, and then apply some model, such as the hidden Markov model (HMM) or a Bayesian mixture of distributions, to find a set of distinct FC patterns or states. The distinct spatial properties of these FC states together with the time resolved switching between them offer a flexible description of time-varying FC . In this work, we show that in this context PCA can suffer from systematic biases and loss of sensitivity for the purposes of finding time-varying FC . To get around these issues, we propose a novel variety of the HMM, the HMM- PCA , where the states are themselves PCA decompositions. Since PCA is based on the data covariance, the state-specific PCA decompositions reflect distinct patterns of FC . We show, theoretically and empirically, that fusing dimensionality reduction and time-varying FC estimation in one single step can avoid these problems and outperform alternative approaches, eventually facilitating the quantification of transient communication in the brain.

scholarly journals A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation

2021 ◽  
Vol 17 (4) ◽  
pp. e1008580
Author(s):  
Diego Vidaurre
Keyword(s):  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Information Transfer ◽  
Brain Regions ◽  
Single Step ◽  
Fast Time ◽  
Time Varying ◽  
Time Resolved ◽  
Neuronal Communication ◽  
Common Strategy

An important question in neuroscience is whether or not we can interpret spontaneous variations in the pattern of correlation between brain areas, which we refer to as functional connectivity or FC, as an index of dynamic neuronal communication in fMRI. That is, can we measure time-varying FC reliably? And, if so, can FC reflect information transfer between brain regions at relatively fast-time scales? Answering these questions in practice requires dealing with the statistical challenge of having high-dimensional data and a comparatively lower number of time points or volumes. A common strategy is to use PCA to reduce the dimensionality of the data, and then apply some model, such as the hidden Markov model (HMM) or a mixture model of Gaussian distributions, to find a set of distinct FC patterns or states. The distinct spatial properties of these FC states together with the time-resolved switching between them offer a flexible description of time-varying FC. In this work, I show that in this context PCA can suffer from systematic biases and loss of sensitivity for the purposes of finding time-varying FC. To get around these issues, I propose a novel variety of the HMM, named HMM-PCA, where the states are themselves PCA decompositions. Since PCA is based on the data covariance, the state-specific PCA decompositions reflect distinct patterns of FC. I show, theoretically and empirically, that fusing dimensionality reduction and time-varying FC estimation in one single step can avoid these problems and outperform alternative approaches, facilitating the quantification of transient communication in the brain.

scholarly journals Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

2021 ◽  
Vol 12 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Tianzhi Tang ◽  
Leila Fakhraei ◽  
Xuanyu Wu ◽  
Sidharth Hulyalkar ◽  
...  
Keyword(s):  
Local Field ◽  
Single Unit ◽  
Large Scale ◽  
Brain Activity ◽  
Low Cost ◽  
Local Field Potentials ◽  
Field Potential ◽  
Brain Regions ◽  
Field Potentials ◽  
Brain Target

Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

scholarly journals Using structural connectivity to augment community structure in EEG functional connectivity

2019 ◽  
Author(s):  
Katharina Glomb ◽  
Emeline Mullier ◽  
Margherita Carboni ◽  
Maria Rubega ◽  
Giannarita Iannotti ◽  
...  
Keyword(s):  
Community Structure ◽  
White Matter ◽  
Functional Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Source Analysis ◽  
Eeg Signal ◽  
Volume Conduction ◽  
Structural Connectome ◽  
Eeg Functional Connectivity

AbstractRecently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity (SC) in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between non-connected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG- and volume-conduction-free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known - and confirmed in this study - that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting “filtered” signal correlation matrices to those obtained from fMRI and find that such “graph filtering” improves the agreement between EEG and fMRI functional connectivity structure. This suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

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