A new methodology for phase-locking value: a measure of true dynamic functional connectivity

2012 ◽  
Author(s):  
Tianhu Lei ◽  
K. Ty Bae ◽  
Timothy P. L. Roberts
Keyword(s):  
Functional Connectivity ◽  
Phase Locking ◽  
Dynamic Functional Connectivity ◽  
Phase Locking Value

scholarly journals Preservation of electrophysiological functional connectivity after partial corpus callosotomy: case report

2018 ◽  
Vol 22 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Kaitlyn Casimo ◽  
Fabio Grassia ◽  
Sandra L. Poliachik ◽  
Edward Novotny ◽  
Andrew Poliakov ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Corpus Callosum ◽  
Seizure Activity ◽  
Diffusion Tensor ◽  
Phase Locking ◽  
Corpus Callosotomy ◽  
Left And Right ◽  
Phase Locking Value ◽  
Anatomical Mri ◽  
Electrophysiological Methods

Prior studies of functional connectivity following callosotomy have disagreed in the observed effects on interhemispheric functional connectivity. These connectivity studies, in multiple electrophysiological methods and functional MRI, have found conflicting reductions in connectivity or patterns resembling typical individuals. The authors examined a case of partial anterior corpus callosum connection, where pairs of bilateral electrocorticographic electrodes had been placed over homologous regions in the left and right hemispheres. They sorted electrode pairs by whether their direct corpus callosum connection had been disconnected or preserved using diffusion tensor imaging and native anatomical MRI, and they estimated functional connectivity between pairs of electrodes over homologous regions using phase-locking value. They found no significant differences in any frequency band between pairs of electrodes that had their corpus callosum connection disconnected and those that had an intact connection. The authors’ results may imply that the corpus callosum is not an obligatory mediator of connectivity between homologous sites in opposite hemispheres. This interhemispheric synchronization may also be linked to disruption of seizure activity.

Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity

2021 ◽  
Author(s):  
Naishi Feng ◽  
Fo Hu ◽  
Hong Wang ◽  
Bin Zhou
Keyword(s):  
Functional Connectivity ◽  
Degrees Of Freedom ◽  
Phase Locking ◽  
Phase Lag ◽  
Neural Signals ◽  
Convolutional Network ◽  
Synchronization Likelihood ◽  
Motor Intention ◽  
External Equipment ◽  
Phase Locking Value

Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.

scholarly journals Functional connectivity between prefrontal cortex and striatum estimated by phase locking value

2016 ◽  
Vol 10 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Yan Zhang ◽  
Xiaochuan Pan ◽  
Rubin Wang ◽  
Masamichi Sakagami
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Phase Locking ◽  
Phase Locking Value

scholarly journals T224. THE FUNCTIONAL CONNECTIVITY DERIVED WITH BIVARIATE ANALYSIS, COHERENCE AND PHASE LOCKING VALUE IN PATIENTS WITH SCHIZOPHRENIA UNDER CLOZAPINE

2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S203-S203
Author(s):  
Yong Sik Kim ◽  
In Won Chung ◽  
Hee Yeong Jung ◽  
Tak Youn ◽  
Se Hyun Kim ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Phase Locking ◽  
Bivariate Analysis ◽  
Phase Locking Value

scholarly journals Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury

2016 ◽  
Vol 116 (4) ◽  
pp. 1840-1847 ◽  
Author(s):  
Ahmad Alhourani ◽  
Thomas A. Wozny ◽  
Deepa Krishnaswamy ◽  
Sudhir Pathak ◽  
Shawn A. Walls ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Mild Traumatic Brain Injury ◽  
Phase Locking ◽  
Brain Regions ◽  
Memory And Attention ◽  
Control Subjects ◽  
Cortical Regions ◽  
Phase Locking Value

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects ( n = 9) compared with age-matched control subjects ( n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

scholarly journals Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-beamformed Connectivity Analysis

2018 ◽  
Author(s):  
S. I. Dimitriadis ◽  
B. Routley ◽  
D. Linden ◽  
K.D. Singh
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Phase Locking ◽  
Dynamic Network ◽  
Dynamic Functional Connectivity ◽  
Functional Brain Networks ◽  
Static Approach ◽  
Intrinsic Connectivity Networks ◽  
Network Metrics

AbstractThe resting activity of the brain can be described by so-called intrinsic connectivity networks (ICNs), which consist of spatially and temporally distributed, but functionally connected, nodes. The coordinated activity of the resting state can be explored via magnetoencephalography (MEG) by studying frequency-dependent functional brain networks at the source level. Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown. This is a particular problem for studies of associations between ICN metrics and personality variables or other traits, and for studies of differences between patient and control groups, which both depend critically on the reliability of the metrics used. A detailed investigation of the reliability of metrics derived from resting-state MEG repeat scans is therefore a prerequisite for the development of connectomic biomarkers.Here, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation of the amplitude envelope (CorEnv). Using our approach, functional network microstates (FCμstates) were derived from the DFC and chronnectomics were computed from the evolution of FCμstates across experimental time. In both temporal scales, the reliability of network metrics (SFC), the FCμstates and the related chronnectomics were evaluated for every frequency band.Chronnectomic parameters and FCμstates were generally more reliable than node-wise static network metrics. CorEnv-based network metrics were more reproducible at the static approach. This analysis encourages the analysis of MEG resting-state via DFC.

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