Tracking the Time-Varying Cortical Connectivity Patterns by Adaptive Multivariate Estimators

2008 ◽  
Vol 55 (3) ◽  
pp. 902-913 ◽  
Author(s):  
L. Astolfi ◽  
F. Cincotti ◽  
D. Mattia ◽  
F. De Vico Fallani ◽  
A. Tocci ◽  
...  
Keyword(s):  
Time Varying ◽  
Cortical Connectivity ◽  
Connectivity Patterns

P31.4 Detection of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies

2006 ◽  
Vol 117 ◽  
pp. 149-150
Author(s):  
L. Astolfi ◽  
F. Cincotti ◽  
D. Mattia ◽  
F. De Vico Fallani ◽  
A. Colosimo ◽  
...  
Keyword(s):  
High Resolution ◽  
Time Varying ◽  
Cortical Connectivity ◽  
Connectivity Patterns ◽  
High Resolution Eeg

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 Cortical Connectivity during General Anesthesia in Surgical Patients

2019 ◽  
Vol 130 (6) ◽  
pp. 885-897 ◽  
Author(s):  
Phillip E. Vlisides ◽  
Duan Li ◽  
Mackenzie Zierau ◽  
Andrew P. Lapointe ◽  
Ka I. Ip ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
General Anesthesia ◽  
Temporal Dynamics ◽  
Transition Probabilities ◽  
Surgical Patients ◽  
Phase Lag ◽  
Cortical Connectivity ◽  
Single Measure ◽  
Connectivity Patterns ◽  
Weighted Phase Lag Index

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional connectivity across the cortex has been posited to be important for consciousness and anesthesia, but functional connectivity patterns during the course of surgery and general anesthesia are unknown. The authors tested the hypothesis that disrupted cortical connectivity patterns would correlate with surgical anesthesia. Methods Surgical patients (n = 53) were recruited for study participation. Whole-scalp (16-channel) wireless electroencephalographic data were prospectively collected throughout the perioperative period. Functional connectivity was assessed using weighted phase lag index. During anesthetic maintenance, the temporal dynamics of connectivity states were characterized via Markov chain analysis, and state transition probabilities were quantified. Results Compared to baseline (weighted phase lag index, 0.163, ± 0.091), alpha frontal–parietal connectivity was not significantly different across the remaining anesthetic and perioperative epochs, ranging from 0.100 (± 0.041) to 0.218 (± 0.136) (P > 0.05 for all time periods). In contrast, there were significant increases in alpha prefrontal–frontal connectivity (peak = 0.201 [0.154, 0.248]; P < 0.001), theta prefrontal–frontal connectivity (peak = 0.137 [0.091, 0.182]; P < 0.001), and theta frontal–parietal connectivity (peak = 0.128 [0.084, 0.173]; P < 0.001) during anesthetic maintenance. Additionally, shifts occurred between states of high prefrontal–frontal connectivity (alpha, beta) with suppressed frontal–parietal connectivity, and high frontal–parietal connectivity (alpha, theta) with reduced prefrontal–frontal connectivity. These shifts occurred in a nonrandom manner (P < 0.05 compared to random transitions), suggesting structured transitions of connectivity during general anesthesia. Conclusions Functional connectivity patterns dynamically shift during surgery and general anesthesia but do so in a structured way. Thus, a single measure of functional connectivity will likely not be a reliable correlate of surgical anesthesia.

Time-Varying Cortical Connectivity Estimation from Noninvasive, High-Resolution EEG Recordings

2010 ◽  
Vol 24 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Laura Astolfi ◽  
Febo Cincotti ◽  
Donatella Mattia ◽  
Fabrizio De Vico Fallani ◽  
Giovanni Vecchiato ◽  
...  
Keyword(s):  
High Resolution ◽  
Normal Population ◽  
Control Group ◽  
Time Varying ◽  
Cortical Areas ◽  
Eeg Data ◽  
Connectivity Patterns ◽  
Eeg Recordings ◽  
Parietal Cortices ◽  
High Resolution Eeg

Objective: In this paper, we propose a body of techniques for the estimation of rapidly changing connectivity relationships between EEG signals estimated in cortical areas, based on the use of adaptive multivariate autoregressive modeling (AMVAR) for the estimation of a time-varying partial directed coherence (PDC). This approach allows the observation of rapidly changing influences between the cortical areas during the execution of a task, and does not require the stationarity of the signals. Methods: High resolution EEG data were recorded from a group of spinal cord injured (SCI) patients during the attempt to move a paralyzed limb. These data were compared with the time-varying connectivity patterns estimated in a control group during the real execution of the movement. Connectivity was estimated with the use of realistic head modeling and the linear inverse estimation of the cortical activity in a series of regions of interest by using time-varying PDC. Results: The SCI population involved a different cortical network than those generated by the healthy subjects during the task performance. Such a network differs for the involvement of the parietal cortices, which increases in strength near to the movement imagination onset for the SCI when compared to the normal population. Conclusions: The application of time-varying PDC allows tracking the evolution of the connectivity between cortical areas in the analyzed populations during the proposed tasks. Such details about the temporal evolution of the connectivity patterns estimated cannot be obtained with the application of the standard estimators of connectivity.

scholarly journals A Signal-Processing-Based Approach to Time-Varying Graph Analysis for Dynamic Brain Network Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ali Yener Mutlu ◽  
Edward Bernat ◽  
Selin Aviyente
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Dynamic Network ◽  
Principal Component ◽  
Brain Network ◽  
Event Related Potential ◽  
Time Varying ◽  
Error Related Negativity ◽  
Connectivity Patterns

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity. In this paper, we propose a dynamic network summarization approach to describe the time-varying evolution of connectivity patterns in functional brain activity. The proposed approach is based on first identifying key event intervals by quantifying the change in the connectivity patterns across time and then summarizing the activity in each event interval by extracting the most informative network using principal component decomposition. The proposed method is evaluated for characterizing time-varying network dynamics from event-related potential (ERP) data indexing the error-related negativity (ERN) component related to cognitive control. The statistically significant connectivity patterns for each interval are presented to illustrate the dynamic nature of functional connectivity.

Extracting Information from Cortical Connectivity Patterns Estimated from High Resolution EEG Recordings: A Theoretical Graph Approach

2007 ◽  
Vol 19 (3) ◽  
pp. 125-136 ◽  
Author(s):  
Fabrizio De Vico Fallani ◽  
Laura Astolfi ◽  
Febo Cincotti ◽  
Donatella Mattia ◽  
Andrea Tocci ◽  
...  
Keyword(s):  
High Resolution ◽  
Cortical Connectivity ◽  
Connectivity Patterns ◽  
Eeg Recordings ◽  
High Resolution Eeg
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