A weighted small world network measure for assessing functional connectivity

Bolaños, Marcos E.; Bernat, Edward M.; He, Bin; Aviyente, Selin

doi:10.1016/j.jneumeth.2012.10.004

Cited by 54 publications

(30 citation statements)

References 54 publications

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“…Our results (i) replicate previous findings demonstrating that inter-channel phase synchrony between electrodes over ACC/mPFC and electrodes over lPFC increase following the commission of an error (Bolanos et al, 2013;Cavanagh et al, 2009), (ii) demonstrate that worry predicts both an increased ERN and decreased theta band synchrony, and (iii) demonstrate that worry 1 is indirectly related to poorer post-error behavior via its association with decreased theta synchrony. These findings highlight novel mechanisms by which worry influences action monitoring.…”

Section: Discussionsupporting

confidence: 89%

“…Replicating previous work, both time-domain ERPs (Gehring et al, 2012) and ICPS differed as a function of accuracy (Bolanos et al, 2013;Cavanagh et al, 2009 …”

Section: Neural Differentiation Between Errors and Correct Trialssupporting

confidence: 77%

“…We recently demonstrated how increases in these observed bivariate phase synchrony pairings during errors can also be understood in terms of increased small-world network organization involving these regions. Specifically, we utilized a node-based measure of small-world network organization to index contributions from each site to the overall network organization across the sites (Bolanos et al, 2013). Critically, sites consistent with the mPFC and lPFC were the central nodes in the small-world organization.…”

Section: The Action-monitoring Systemmentioning

confidence: 99%

“…Using the same approach previously employed by our group (Bolanos et al, 2013), time-varying complex energy time-frequency distribution (TFD) of all the EEG signals at each channel were obtained using the reduced interference distribution (RID) Rihaczek distribution (Aviyente et al, 2011). The RID-Rihaczek distribution computes a complex TFD with uniform timefrequency resolution, avoiding the trade-off between time and frequency resolution inherent to wavelet analysis.…”

Section: Interchannel Phase Synchrony (Icps)mentioning

confidence: 99%

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Sending mixed signals: worry is associated with enhanced initial error processing but reduced call for subsequent cognitive control

Moran

Bernat

Aviyente

et al. 2015

Social Cognitive and Affective Neuroscience

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Worry is reliably associated with overactive action-monitoring processes as measured by the error-related negativity (ERN). However, worry is not associated with error-related behavioral adjustments which are typically used to infer increased cognitive control following errors. We hypothesized that this disconnect between overactive action monitoring and unimproved post-error adjustments in worriers is the result of reduced functional integration between medial and lateral prefrontal regions during generation of the ERN, understood to have an important role in mediating controlled processing. To test this, we examined ERN amplitude and interchannel phase synchrony extracted from scalp-recorded electroencephalographic data during error processing in 77 undergraduates who performed a Flankers task. Correlational and path analytic results demonstrated that worry was related to both an enlarged ERN and reduced phase synchrony. Although not directly related to post-error behavioral adjustments, results also revealed that worry was indirectly related to poor post-error adjustments through its association with reduced phase synchrony. Therefore, worry seems to affect multiple components of the action-monitoring system. It is related not just with the initial response to the error, but also with the transmission of information between networks involved in cognitive control processes.

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Section: Discussionsupporting

confidence: 89%

“…Replicating previous work, both time-domain ERPs (Gehring et al, 2012) and ICPS differed as a function of accuracy (Bolanos et al, 2013;Cavanagh et al, 2009 …”

Section: Neural Differentiation Between Errors and Correct Trialssupporting

confidence: 77%

Section: The Action-monitoring Systemmentioning

confidence: 99%

Section: Interchannel Phase Synchrony (Icps)mentioning

confidence: 99%

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Sending mixed signals: worry is associated with enhanced initial error processing but reduced call for subsequent cognitive control

Moran

Bernat

Aviyente

et al. 2015

Social Cognitive and Affective Neuroscience

Self Cite

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“…where T N is True Negative and F P is False Positive. indicative of increased randomness and is in line with previous studies that reported 363 increased small-worldness for ERN compared to CRN [21]. Comparing Tables 1 and 2, it can be seen that three measures, small world 376 parameter, small world propensity and graph spectral entropy, were the most effective 377 features to differentiate between ERN and CRN networks.…”

supporting

confidence: 81%

Graph-to-Signal Transformation Based Classification of Functional Connectivity Brain Networks

Munia

Aviyente

2019

Preprint

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Complex network theory has been successful at unveiling the topology of the brain and showing alterations to the network structure due to brain disease, cognitive function and behavior. Functional connectivity networks (FCNs) represent different brain regions as the nodes and the connectivity between them as the edges of a graph. Graph theoretic measures provide a way to extract features from these networks enabling subsequent characterization and discrimination of networks across conditions. However, these measures are constrained mostly to binary networks and highly dependent on the network size. In this paper, we propose a novel graph-to-signal transform that overcomes these shortcomings to extract features from functional connectivity networks. The proposed transformation is based on classical multidimensional scaling (CMDS) theory and transforms a graph into signals such that the Euclidean distance between the nodes of the network is preserved. In this paper, we propose to use the resistance distance matrix for transforming weighted functional connectivity networks into signals. Our results illustrate how well-known network structures transform into distinct signals using the proposed graph-to-signal transformation. We then compute well-known signal features on the extracted graph signals to discriminate between FCNs constructed across different experimental conditions. Based on our results, the signals obtained from the graph-to-signal transformation allow for the characterization of functional connectivity networks, and the corresponding features are more discriminative compared to graph theoretic measures. Introduction 1The human brain is a highly interconnected network. While early studies of 2 neurophysiological and neuroimaging data focused on the analysis of isolated regions, i.e. 3 univariate analysis, most of the recent work indicates that the network organization of 4 the brain fundamentally shapes its function [1]. Thus, generating comprehensive maps 5 of brain connectivity, also known as connectomes, and characterizing these networks has 6 become a major goal of neuroscience [2,3]. Complex network theory has contributed 7 significantly to the characterization of the topology of FCNs, in particular in the 8 assessment of functional integration and segregation [4,5]. Specifically, graph theoretic 9 measures such as the path length and clustering coefficient have helped to characterize 10 small-world brain networks [6][7][8], and the degree distribution has been utilized to 11 characterize scale-free networks [9]. Over the last decade, the study of FCNs through 12 complex network theory has provided new means for discriminating between different 13 neural dysfunctions such as epilepsy [10,11], depression [12, 13], Alzheimer's 14 Disease [14, 15], and Parkinson's Disease [16]. 15 Although graph theoretical approaches provide an elegant way to describe the 16 topology of functional brain networks, these measures suffer from several major 17 shortcomings. First, most network measures are optimally suit...

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