scholarly journals On the Variability of Functional Connectivity and Network Measures in Source-Reconstructed EEG Time-Series

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 5
Author(s):  
Matteo Fraschini ◽  
Simone Maurizio La Cava ◽  
Luca Didaci ◽  
Luigi Barberini
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Brain Regions ◽  
Practical Application ◽  
Arbitrary Choice ◽  
Network Measures ◽  
Connectivity Patterns ◽  
Eeg Connectivity ◽  
Source Level

The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably contributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often arbitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when reporting findings based on any connectivity metric.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

2020 ◽  
Author(s):  
Olaf Sporns ◽  
Joshua Faskowitz ◽  
Andreia Sofia Teixera ◽  
Richard F. Betzel
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
Pearson Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Fine Scale ◽  
Time Step ◽  
Functional Systems

AbstractFunctional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

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 Distinctive BOLD Connectivity Patterns in the Schizophrenic Brain: Machine-learning based comparison between various connectivity measures

2016 ◽  
Author(s):  
Hio-Been Han
Keyword(s):  
Functional Connectivity ◽  
Memory Task ◽  
Similarity Measures ◽  
Low Frequency ◽  
Pattern Discrimination ◽  
Brain Regions ◽  
Connectivity Pattern ◽  
Time Frequency ◽  
Connectivity Patterns ◽  
Network Properties

AbstractRecent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenic brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered wirings in the schizophrenic brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenic brain relative to healthy brain with various similarity measures in the time-frequency domain, while participants are performing the working memory task in the MRI scanner. To assess the distinctiveness of the connectivity pattern, discrimination performances of the pattern classifier machine trained with each similarity measure were compared. Interestingly, the classifier machine trained by time-lagging patterns of low frequency fluctuation (LFF) produced higher classifying sensitivity than the machines trained by other measures. Also, the classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that there are unobserved but considerable features in the functional connectivity pattern of schizophrenic brain which traditional emphasis on correlation analysis does not capture.

scholarly journals Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series

2021 ◽  
pp. 1-34
Author(s):  
Olaf Sporns ◽  
Joshua Faskowitz ◽  
Andreia Sofia Teixeira ◽  
Sarah A Cutts ◽  
Richard F. Betzel
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
Pearson Correlation ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Fine Scale ◽  
Time Step ◽  
Functional Systems

Functional connectivity (FC) describes the statistical dependence between neuronal populations or brain regions in resting-state fMRI studies and is commonly estimated as the Pearson correlation of time courses. Clustering or community detection reveals densely coupled sets of regions constituting resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs but appear to fluctuate on shorter time scales. Here, we propose a new approach to reveal temporal fluctuations in neuronal time series. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair or edge, and allows tracking of fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging runs, capture individual differences, and disclose fine-scale temporal expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

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 Resting-State SEEG May Help Localize Epileptogenic Brain Regions

Neurosurgery ◽  
2019 ◽  
Vol 86 (6) ◽  
pp. 792-801 ◽  
Author(s):  
Sarah E Goodale ◽  
Hernán F J González ◽  
Graham W Johnson ◽  
Kanupriya Gupta ◽  
William J Rodriguez ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Focal Epilepsy ◽  
Area Under The Curve ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Alpha Band ◽  
Connectivity Model ◽  
Connectivity Patterns ◽  
Neurosurgical Treatment

Abstract BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

scholarly journals Altered Functional Connectivity of the Primary Visual Cortex in Subjects with Amblyopia

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kun Ding ◽  
Yong Liu ◽  
Xiaohe Yan ◽  
Xiaoming Lin ◽  
Tianzi Jiang
Keyword(s):  
Functional Connectivity ◽  
Activity Patterns ◽  
Brain Regions ◽  
Visual Area ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Blurred Vision ◽  
Visual Areas ◽  
Connectivity Patterns ◽  
The Brain

Amblyopia, which usually occurs during early childhood and results in poor or blurred vision, is a disorder of the visual system that is characterized by a deficiency in an otherwise physically normal eye or by a deficiency that is out of proportion with the structural or functional abnormalities of the eye. Our previous study demonstrated alterations in the spontaneous activity patterns of some brain regions in individuals with anisometropic amblyopia compared to subjects with normal vision. To date, it remains unknown whether patients with amblyopia show characteristic alterations in the functional connectivity patterns in the visual areas of the brain, particularly the primary visual area. In the present study, we investigated the differences in the functional connectivity of the primary visual area between individuals with amblyopia and normal-sighted subjects using resting functional magnetic resonance imaging. Our findings demonstrated that the cerebellum and the inferior parietal lobule showed altered functional connectivity with the primary visual area in individuals with amblyopia, and this finding provides further evidence for the disruption of the dorsal visual pathway in amblyopic subjects.

scholarly journals Effect of Zolpidem in the Aftermath of Traumatic Brain Injury: An MEG Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Praveen Sripad ◽  
Jessica Rosenberg ◽  
Frank Boers ◽  
Christian P. Filss ◽  
Norbert Galldiks ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Brain Regions ◽  
Phase Lag ◽  
Before And After ◽  
Source Level ◽  
Weighted Phase Lag Index

In the past two decades, many studies have shown the paradoxical efficacy of zolpidem, a hypnotic used to induce sleep, in transiently alleviating various disorders of consciousness such as traumatic brain injury (TBI), dystonia, and Parkinson’s disease. The mechanism of action of this effect of zolpidem is of great research interest. In this case study, we use magnetoencephalography (MEG) to investigate a fully conscious, ex-coma patient who suffered from neurological difficulties for a few years due to traumatic brain injury. For a few years after injury, the patient was under medication with zolpidem that drastically improved his symptoms. MEG recordings taken before and after zolpidem showed a reduction in power in the theta-alpha (4–12 Hz) and lower beta (15–20 Hz) frequency bands. An increase in power after zolpidem intake was found in the higher beta/lower gamma (20–43 Hz) frequency band. Source level functional connectivity measured using weighted-phase lag index showed changes after zolpidem intake. Stronger connectivity between left frontal and temporal brain regions was observed. We report that zolpidem induces a change in MEG resting power and functional connectivity in the patient. MEG is an informative and sensitive tool to detect changes in brain activity for TBI.

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