Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI

Al-sharoa, Esraa; Al-khassaweneh, Mahmood; Aviyente, Selin

doi:10.1109/tbme.2018.2854676

Cited by 43 publications

(46 citation statements)

References 58 publications

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“…Task (Scan Duration) Edge estimation γ ω Al-Sharoa et al (2019) Rest (8.8 min) Pearson's correlation coefficient 1 Bassett et al (2011) Motor learning (3.45 hrs) Pearson's correlation coefficient, wavelet coherence 1 Bassett et al (2013b) Motor learning (3.45 hrs) wavelet coherence 1 Bassett et al (2015) Motor learning (3.45 hrs) wavelet coherence 1 Rest (10 min/session, 91 sessions) wavelet coherence 1 Braun et al (2015) Working memory (~5 min) wavelet coherence 1 Braun et al (2016) Working memory (~5 min) wavelet coherence Rest (40 min) multi-taper coherence 1 Lehmann et al (2017) Simulated rest (12 min) Pearson's correlation coefficient 1.25 1.5 2 1 Li et al (2019) Rest (~6.7 min) wavelet correlation 1 Telesford et al (2016) Recognition memory (20 min), Strategic attention task (20 min) wavelet coherence 1 1 Tian et al (2019) Rest (7 min) Pearson's correlation coefficient 1 0.25 Wei et al (2017) Rest (6.75 min) conditional Granger causality 1 1 Wymbs et al (2012) Motor learning (3.45 hrs) Inter-key interval (IKI) 0.9 0.03 Zheng et al (2018) Rest (8 min) Pearson's correlation coefficient 1 1…”

Section: Authorsmentioning

confidence: 99%

“…Multilayer networks have been used to assess network reconfiguration during resting state (Mattar et al 2015, Wei et al 2017, He et al 2018, Khambhati et al 2018, Pedersen et al 2018, Zheng et al 2018, Al-Sharoa et al 2019, Feng et al 2019, Shao et al 2019, Lydon-Staley et al 2019a, Lydon-Staley et al 2019b, as well as during controlled cognitive tasks (Bassett et al 2011, Braun et al 2015, Chai et al 2016, Schlesinger et al 2017a, Schlesinger et al 2017b, Gerraty et al 2018, Cooper et al 2019). The present work extended previous studies by including naturalistic viewing paradigms.…”

Section: Movie Fmri Identified As the Most Reliable Conditionmentioning

confidence: 99%

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Measurement Reliability for Individual Differences in Multilayer Network Dynamics: Cautions and Considerations

Yang

Telesford

Franco

et al. 2020

Preprint

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HighlightsCaution is needed when implementing the generalized Louvain community detection code.We recommend optimizing multilayer network parameters using test-retest reliability.Scan duration was a much stronger determinant of reliability than scan condition.Minimal data need for movie condition is 20 min and for other conditions 30 min.Combining different conditions improved test-retest reliability of dynamic estimates. AbstractMultilayer network models have been proposed as an effective means to capture the dynamic configuration of distributed neural circuits and quantitatively describe how communities vary over time. However, test-retest reliabilities for multilayer network measures are yet to be fully quantified. Here, we systematically evaluated the impact of code implementation, network parameter selections, scan duration, and task condition on test-retest reliability of key multilayer network measures (i.e., flexibility, integration, recruitment). We found that each of these factors impacted reliability, although to differing degrees. The choice of parameters is a longstanding difficulty of multilayer modularity-maximization algorithms. As suggested by prior work, we found that optimal parameter selection was a key determinant of reliability. However, due to changes in implementation of the multilayer community detection algorithm, our findings revealed a more complex story than previously appreciated, as the parameter landscape of reliability was found to be dependent on the implementation of the software.Consistent with findings from the static functional connectivity literature, scan duration was found to be a much stronger determinant of reliability than scan condition. We found that both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks can be highly reliable when the parameters are optimized and scan duration is sufficient, although reliability in the movie watching condition was significantly higher than in the other three tasks. Accordingly, the minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, which is less than the 30 min needed for the other three tasks. Collectively, our results quantified test-retest reliability for multilayer network measures and support the utility of movie fMRI as a reliable context in which to investigate time-invariant network dynamics. Our practice of using test-retest reliability to optimize free parameters of multilayer modularity-maximization algorithms has the potential to enhance our ability to use these measures for the study of individual differences in cognitive traits. characterize these dynamic changes (Lurie et al. 2019) Multilayer network models have been proposed as an effective means of capturing the temporal dependence between distributed neural circuits and of quantitatively describing how communities vary over time (Mucha et al. 2010, Kivela et al. 2014). Multilayer network modelscan be used to optimize the partitioning of nodes into modules by maximizing a multilayer ...

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Section: Authorsmentioning

confidence: 99%

Section: Movie Fmri Identified As the Most Reliable Conditionmentioning

confidence: 99%

Measurement Reliability for Individual Differences in Multilayer Network Dynamics: Cautions and Considerations

Yang

Telesford

Franco

et al. 2020

Preprint

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“…Learning task 2: Community detection (a.k.a. networktopology identification), which aims at identifying communities/subgroups of nodes within a single state/layer, with rich applications in network neuroscience, e.g., [16][17][18].…”

Section: A Problem Statementmentioning

confidence: 99%

“…Stochastic block modeling for evolutionary clustering and tracking was proposed in [50,51], while a clustering algorithm, based on support vector machines and with high computational complexity, was introduced in [52,53]. Tensorfactorization approaches for streaming data were introduced in [17,54,55]. Nevertheless, none of the aforementioned sequential clustering algorithms considers the multilayernetwork setting.…”

Section: B Prior Artmentioning

confidence: 99%

“…Study [57] cannot perform subnetwork-sequence clustering since it offers no mechanism to incorporate inter-layer dependencies. Work [58] builds on sparse subspace clustering and follows a similar path with [17,57]: first, communities are detected, and then, based on the "change points" of the communities, states are clustered. Methods [17,58] can accommodate interlayer information, and they can be modified to perform also subnetwork-sequence clustering by considering each state as a layer, under the constraint that all states occupy the same time duration, which is dictated by their feature-extraction mechanisms.…”

Section: B Prior Artmentioning

confidence: 99%

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Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Ye¹,

Slavakis²,

Nakuci³

et al. 2020

Preprint

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This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information on tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework is validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electro-encephalogram<br> (EEG) data.

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Community detection in complex networks: From statistical foundations to data science applications

Dey

Tian

Gel

2021

WIREs Computational Stats

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Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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Tensor Based Temporal and Multilayer Community Detection for Studying Brain Dynamics During Resting State fMRI

Abstract: The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.

Cited by 43 publications

References 58 publications

Measurement Reliability for Individual Differences in Multilayer Network Dynamics: Cautions and Considerations

Measurement Reliability for Individual Differences in Multilayer Network Dynamics: Cautions and Considerations

Fast Sequential Clustering in Riemannian Manifolds for Dynamic and Time-Series-Annotated Multilayer Networks

Community detection in complex networks: From statistical foundations to data science applications

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