Capturing dynamic patterns of task-based functional connectivity with EEG

Karamzadeh, Nader; Medvedev, Andrei V.; Azari, Afrouz A; Gandjbakhche, Amir; Najafizadeh, Laleh

doi:10.1016/j.neuroimage.2012.10.032

Cited by 72 publications

(58 citation statements)

References 57 publications

(71 reference statements)

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“…Co-occurrence of test to retest changes in all investigated electrophysiological measures (ERP, EEG powers and PLV) may suggest their interdependence. Contribution of different brain sources to the waves of elicited ERP components [44][45][46] and theoretical investigations on interdependence between strength of connectivity and EEG spectral power 47,48 seem to confirm this notion. For example, direct investigations of the functional connectivity and ERP components elicited by oddball paradigm revealed functional connection between central and frontal regions during P300 waves 44 .…”

Section: Discussionmentioning

confidence: 75%

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Rogala

Kublik

Krauz

et al. 2020

Sci Rep

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Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2-45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.One of the main goals of neuroscience is to understand how neuronal activity organizes behavior. To elucidate this relationship, many studies have focused on the putative links between task-related neuronal electrical activity and behavioral performance 1-5 . In parallel, BOLD (blood-oxygen-level-dependent) imaging investigations have revealed that (i) resting-state (spontaneous) connectivity correlates with functionally activated networks 6-14 , and (ii) specifically with the performance of various cognitive functions, such as attention 15 , working memory 16,17 and fluid intelligence 18 . It has been further suggested that such correlations could be explained by the individual characteristics of brain networks 19,20 . However, the uncertain physiological origins of hemodynamic signals 21 provide limited insight into the mechanisms governing the relationship between resting-state and task-related activations and therefore behavioral outcomes.Recording electrophysiological activity is one way that the relationship between resting-state and behavioral performance can be directly assessed. Indeed, several investigations have already revealed correlations between specific EEG bands and different aspects of cognitive performance. For example, in the attentional domain, alpha oscillations have been proposed to clear sensory information from distractors 22 , the beta to gamma band ratio can assure critical-state dynamics for optimal information processing 23 and alpha and beta band activity can reduce attentional investment during rest 24 . Yet, none of the proposed mechanisms have characterized the intrinsic properties of resting-state networks such as power of EEG bands and connectivity, and how they could relate to functional connectivity underlying behavioral performance.We hypothesized that specific electrophysiological signatures of spontaneous, individual EEG activity in large-scale networks would predic...

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Section: Discussionmentioning

confidence: 75%

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Rogala

Kublik

Krauz

et al. 2020

Sci Rep

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“…However, they used same-time correlations to estimate the functional connections, which assume that brain regions get activated together and at the same time. Instead, the delayed interactions in a network have been incorporated in a variety of methods, for example multivariate autoregressive models [58,59], Granger causality [60,61], dynamic time-warping [62,63] and maximal delay correlation [64], which were able to provide a better characterization of the functional connectivity. Building on the assumption that quasi-simultaneous brain activity can only occur between nodes connected by direct paths, our results add to this body of literature by suggesting that the temporal delays can be used to discriminate direct from indirect connections.…”

Section: Discussionmentioning

confidence: 99%

Delayed correlations improve the reconstruction of the brain connectome

2020

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The brain works as a large-scale complex network, known as the connectome. The strength of the connections between two brain regions in the connectome is commonly estimated by calculating the correlations between their patterns of activation. This approach relies on the assumption that the activation of connected regions occurs together and at the same time. However, there are delays between the activation of connected regions due to excitatory and inhibitory connections. Here, we propose a method to harvest this additional information and reconstruct the structural brain connectome using delayed correlations. This delayedcorrelation method correctly identifies 70% to 80% of connections of simulated brain networks, compared to only 5% to 25% of connections detected by the standard methods; this result is robust against changes in the network parameters (small-worldness, excitatory vs. inhibitory connection ratio, weight distribution) and network activation dynamics. The delayed-correlation method predicts more accurately both the global network properties (characteristic path length, global efficiency, clustering coefficient, transitivity) and the nodal network properties (nodal degree, nodal clustering, nodal global efficiency), particularly at lower network densities. We obtain similar results in networks derived from animal and human data. These results suggest that the use of delayed correlations improves the reconstruction of the structural brain connectome and open new possibilities for the analysis of the brain connectome, as well as for other types of networks.

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“…The inflation was quite small (a 1.99% false positive rate with a p<0.01 threshold), especially relative to the no-regression fMRI results (42.58% false positive rate), but it was nonetheless higher than expected by chance (1%, given the p<0.01 threshold). This likely reflects the small amount of coincident timing induced by the simultaneous stimulation across neural units, suggesting regression-based removal of task-evoked non-fMRI data (Headley and Weinberger, 2013;Karamzadeh et al, 2010;Mill et al, 2017) could also be useful for reducing false positives. Supporting this possibility, investigations of task-state FC with multi-unit recording in animal models (i.e., not involving the BOLD signal) have tended to remove cross-event mean evoked responses prior to estimating correlations among neural time series (termed "noise correlations") in the interest of reducing false positives (Cafaro and Rieke, 2010;M.…”

Section: Limitations and Opportunities For Further Researchmentioning

confidence: 99%

Task activations produce spurious but systematic inflation of task functional connectivity estimates

Cole

Ito

Schultz

et al. 2018

Preprint

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Highlights:• Computational model shows task inflation of functional connectivity estimates • Hemodynamic response shape causes task activations to further inflate estimates • Standard approach to remove task activations leaves many false positives • Methods that flexibly fit hemodynamic response shape effectively correct inflation • Correction of functional connectivity inflation verified with empirical fMRI data . CC-BY-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/292045 doi: bioRxiv preprint first posted online Mar. 30, 2018; 2 Abstract Task-state functional connections -such as those measured using functional MRI (fMRI) -are thought to coordinate distributed cognitive processes throughout the brain. Utilizing a neural mass computational model we found that the conversion of neural signals into fMRI hemodynamic responses substantially and inappropriately inflates task-state functional connectivity (FC) estimates (temporal correlations). Such activation-induced inflation of task FC estimates was postulated previously, but this phenomenon has not been conclusively established either theoretically or empirically, leading many task FC studies to simply ignore the issue. We found that activationbased task FC inflation was primarily driven by task-evoked fMRI activations introducing a similar hemodynamic response shape to underlying neural time series. This demonstrates that isolating task-state network changes from task-evoked activations is essential for ensuring discovery of unique functional network effects, independent of mechanistically-distinct activation effects. Standard approaches for fitting and removing task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit hemodynamic response shapes (especially finite impulse response-based regression) effectively corrected the inflated correlations. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task FC inflation for both Pearson correlation and psychophysiological interaction approaches. These results demonstrate that removal of task activations using an approach that flexibly models hemodynamic response shape is an essential preprocessing step for valid estimation of task-state FC with fMRI.

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Capturing dynamic patterns of task-based functional connectivity with EEG

Cited by 72 publications

References 57 publications

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Delayed correlations improve the reconstruction of the brain connectome

Task activations produce spurious but systematic inflation of task functional connectivity estimates

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