scholarly journals Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

2020 ◽  
Vol 4 (3) ◽  
pp. 658-677 ◽  
Author(s):  
Jonathan Wirsich ◽  
Enrico Amico ◽  
Anne-Lise Giraud ◽  
Joaquín Goñi ◽  
Sepideh Sadaghiani
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Joint Analysis ◽  
Spatially Correlated ◽  
Brain Connectome ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
Millisecond Range ◽  
Brain Functional Connectivity ◽  
Resting State Connectivity

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

scholarly journals Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

2019 ◽  
Author(s):  
Jonathan Wirsich ◽  
Enrico Amico ◽  
Anne-Lise Giraud ◽  
Joaquín Goñi ◽  
Sepideh Sadaghiani
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Joint Analysis ◽  
Spatially Correlated ◽  
Brain Connectome ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
Millisecond Range ◽  
Brain Functional Connectivity ◽  
Resting State Connectivity

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored.Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG-frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG-frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG.The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond-range in all canonical frequencies of FCEEG to second-range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

scholarly journals Behavioral Interpretations of Intrinsic Connectivity Networks

2011 ◽  
Vol 23 (12) ◽  
pp. 4022-4037 ◽  
Author(s):  
Angela R. Laird ◽  
P. Mickle Fox ◽  
Simon B. Eickhoff ◽  
Jessica A. Turner ◽  
Kimberly L. Ray ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Resting State ◽  
Brain Networks ◽  
Data Driven ◽  
Resting State Networks ◽  
Independent State ◽  
Intrinsic Connectivity ◽  
Mental Operations ◽  
Intrinsic Connectivity Networks ◽  
Insight Into

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

scholarly journals Resting-state connectivity studies as a marker of the acute and delayed effects of subanaesthetic ketamine administration in healthy and depressed individuals: A systematic review

2021 ◽  
Vol 5 ◽  
pp. 239821282110554
Author(s):  
Vasileia Kotoula ◽  
Toby Webster ◽  
James Stone ◽  
Mitul A Mehta
Keyword(s):  
Resting State ◽  
Emotional Regulation ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Reward Processing ◽  
Brain Areas ◽  
Delayed Effects ◽  
Treatment Naïve ◽  
Resting State Connectivity ◽  
The Brain

Acute ketamine administration has been widely used in neuroimaging research to mimic psychosis-like symptoms. Within the last two decades, ketamine has also emerged as a potent, fast-acting antidepressant. The delayed effects of the drug, observed 2–48 h after a single infusion, are associated with marked improvements in depressive symptoms. At the systems’ level, several studies have investigated the acute ketamine effects on brain activity and connectivity; however, several questions remain unanswered around the brain changes that accompany the drug’s antidepressant effects and how these changes relate to the brain areas that appear with altered function and connectivity in depression. This review aims to address some of these questions by focusing on resting-state brain connectivity. We summarise the studies that have examined connectivity changes in treatment-naïve, depressed individuals and those studies that have looked at the acute and delayed effects of ketamine in healthy and depressed volunteers. We conclude that brain areas that are important for emotional regulation and reward processing appear with altered connectivity in depression whereas the default mode network presents with increased connectivity in depressed individuals compared to healthy controls. This finding, however, is not as prominent as the literature often assumes. Acute ketamine administration causes an increase in brain connectivity in healthy volunteers. The delayed effects of ketamine on brain connectivity vary in direction and appear to be consistent with the drug normalising the changes observed in depression. The limited number of studies however, as well as the different approaches for resting-state connectivity analysis make it very difficult to draw firm conclusions and highlight the importance of data sharing and larger future studies.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

scholarly journals A resting-state network comparison of combat-related PTSD with combat-exposed and civilian controls

2019 ◽  
Vol 14 (9) ◽  
pp. 933-945
Author(s):  
Thomas J Vanasse ◽  
Crystal Franklin ◽  
Felipe S Salinas ◽  
Amy E Ramage ◽  
Vince D Calhoun ◽  
...  
Keyword(s):  
Resting State ◽  
Anterior Cingulate ◽  
Post Traumatic Stress ◽  
Resting State Functional Connectivity ◽  
Intrinsic Connectivity ◽  
Post Traumatic ◽  
Ptsd Symptom Severity ◽  
Intrinsic Connectivity Networks ◽  
Group Independent Component Analysis ◽  
Left Insula

Abstract Resting-state functional connectivity (rsFC) is an emerging means of understanding the neurobiology of combat-related post-traumatic stress disorder (PTSD). However, most rsFC studies to date have limited focus to cognitively related intrinsic connectivity networks (ICNs), have not applied data-driven methodologies or have disregarded the effect of combat exposure. In this study, we predicted that group independent component analysis (GICA) would reveal group-wise differences in rsFC across 50 active duty service members with PTSD, 28 combat-exposed controls (CEC), and 25 civilian controls without trauma exposure (CC). Intranetwork connectivity differences were identified across 11 ICNs, yet combat-exposed groups were indistinguishable in PTSD vs CEC contrasts. Both PTSD and CEC demonstrated anatomically diffuse differences in the Auditory Vigilance and Sensorimotor networks compared to CC. However, intranetwork connectivity in a subset of three regions was associated with PTSD symptom severity among executive (left insula; ventral anterior cingulate) and right Fronto-Parietal (perigenual cingulate) networks. Furthermore, we found that increased temporal synchronization among visuospatial and sensorimotor networks was associated with worse avoidance symptoms in PTSD. Longitudinal neuroimaging studies in combat-exposed cohorts can further parse PTSD-related, combat stress-related or adaptive rsFC changes ensuing from combat.

scholarly journals Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving

2020 ◽  
Author(s):  
Mengting Liu ◽  
Robert A Backer ◽  
Rachel C Amey ◽  
Eric E Splan ◽  
Adam Magerman ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Functional Network ◽  
Functional Reorganization ◽  
Brain States ◽  
And Performance ◽  
Brain Functional Connectivity ◽  
The Relationship

Abstract Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals’ performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.

scholarly journals Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

2018 ◽  
Author(s):  
Javier Rasero ◽  
Hannelore Aerts ◽  
Jesus M. Cortes ◽  
Sebastiano Stramaglia ◽  
Daniele Marinazzo
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
The Other ◽  
Intrinsic Connectivity ◽  
Supervised Classifiers ◽  
Oxygenation Level ◽  
Intrinsic Connectivity Networks ◽  
Hidden Layer ◽  
Simple Neural Network

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" Blood Oxygenation Level Dependent time series, are increasingly being associated to cognitive, clinical, and behavioral aspects, and compared with the pattern of activity elicited by specific tasks. We study the reconfiguration of the brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. We use a large cohort of publicly available data in both resting and task-based fMRI paradigms; by trying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90\% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

scholarly journals Controlling for the effect of arterial-CO2 fluctuations in resting-state fMRI: Comparing end-tidal CO2 clamping and retroactive CO2 correction

2020 ◽  
Author(s):  
Ali M. Golestani ◽  
J. Jean Chen
Keyword(s):  
Resting State ◽  
Grey Matter ◽  
Brain Connectivity ◽  
Free Breathing ◽  
Resting State Fmri ◽  
Heart Beat ◽  
Physiological Noise ◽  
Intrinsic Connectivity ◽  
End Tidal ◽  
Global Brain

AbstractThe BOLD signal, as the basis of functional MRI, arises from both neuronal and vascular factors, with their respective contributions to resting state-fMRI still unknown. Among the factors contributing to “physiological noise”, dynamic arterial CO2 fluctuations constitutes the strongest and the most widespread modulator of the grey-matter rs-fMRI signal. Some important questions are: (1) if we were able to clamp arterial CO2 such that fluctuations are removed, what would happen to rs-fMRI measures? (2) falling short of that, is it possible to retroactively correct for CO2 effects with equivalent outcome? In this study 13 healthy subjects underwent two rs-fMRI acquisition: During the “clamped” run, end-tidal CO2 (PETCO2) is clamped to the average PETCO2 level of each participant, while during the “free-breathing” run, the PETCO2 level is passively monitored but not controlled. PETCO2 correction was applied to the free-breathing data by convolving PETCO2 with its BOLD response function, and then regressing out the result. We computed the BOLD resting-state fluctuation amplitude (RSFA), as well as seed-independent mean functional connectivity (FC) as the weighted global brain connectivity (wGBC). Furthermore, connectivity between conditions were compared using coupled intrinsic-connectivity distribution (ICD) method. We ensured that PETCO2 clamping did not significantly alter heart-beat and respiratory variation. We found that neither PETCO2 clamping nor correction produced significant change in RSFA and wGBC. In terms of the ICD, PETCO2 clamping and correction both reduced FC strength in the majority of grey matter regions, although the effect of PETCO2 correction is considerably smaller than the effect of PETCO2 clamping. Furthermore, while PETCO2 clamping reduced inter-subject variability in FC, PETCO2 correction increased the variability. Overall PETCO2 correction is not the equivalent of PETCO2 clamping, although it shifts FC values towards the same direction as clamping does.

scholarly journals Pain-free resting-state functional brain connectivity predicts individual pain sensitivity

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Tamas Spisak ◽  
Balint Kincses ◽  
Frederik Schlitt ◽  
Matthias Zunhammer ◽  
Tobias Schmidt-Wilcke ◽  
...  
Keyword(s):  
Resting State ◽  
Pain Sensitivity ◽  
Pain Perception ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Treatment Strategies ◽  
Individual Sensitivity ◽  
Functional Brain ◽  
Brain Connectome ◽  
Sensitivity To Pain

AbstractIndividual differences in pain perception are of interest in basic and clinical research as altered pain sensitivity is both a characteristic and a risk factor for many pain conditions. It is, however, unclear how individual sensitivity to pain is reflected in the pain-free resting-state brain activity and functional connectivity. Here, we identify and validate a network pattern in the pain-free resting-state functional brain connectome that is predictive of interindividual differences in pain sensitivity. Our predictive network signature allows assessing the individual sensitivity to pain without applying any painful stimulation, as might be valuable in patients where reliable behavioural pain reports cannot be obtained. Additionally, as a direct, non-invasive readout of the supraspinal neural contribution to pain sensitivity, it may have implications for translational research and the development and assessment of analgesic treatment strategies.

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