Resting state fMRI based multilayer network configuration in patients with schizophrenia

Gifford, George; Crossley, Nicolás; Kempton, Matthew J.; Morgan, Sarah E.; Dazzan, Paola; Young, Jonathan; McGuire, Philip

doi:10.1016/j.nicl.2020.102169

Cited by 63 publications

(63 citation statements)

References 72 publications

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“…Specifically, these measures have been used to link network dynamics to inter-individual differences in a broad range of functional domains, including motor learning ( Bassett et al, 2011 , 2015 ; Wymbs et al, 2012 ; Telesford et al, 2016 ), working memory ( Braun et al, 2015 ; Finc et al, 2020 ), attention ( Shine et al, 2016 ), language ( Chai et al, 2016 ), mood ( Betzel et al, 2017 ), creativity ( Feng et al, 2019 ; He et al, 2019 ), and reinforcement learning ( Gerraty et al, 2018 ). Additionally, dynamic network reconfiguration has been suggested as a potential biomarker for diseases, such as schizophrenia ( Braun et al, 2016 ; Gifford et al, 2020 ), temporal lobe epilepsy ( He et al, 2018 ), and depression ( Wei et al, 2017 ; Zheng et al, 2018 ; Shao et al, 2019 ; Han et al, 2020 ), and has been used to predict antidepressant treatment outcome ( Tian et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations

et al. 2021

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Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individua differences. However, test–retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test–retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test–retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed – a process likely to be limited for most due to the lack of test–retest samples to enable parameter optimization.

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Section: Introductionmentioning

confidence: 99%

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations

et al. 2021

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“…Clustering states in brain networks has enhanced understanding of brain disorders such as the Alzheimer disease and autism [11], depression [12], anxiety, epilepsy and schizophrenia [13]. It is worth stressing here that the border between the concepts of "state" and "layer" is blurry; viewing a state, or, observations over a time-window as a layer, for example, often facilitates learning tasks, e.g., [9,14]. Nevertheless, a subtle distinction is that a layer is usually well defined and known to the user a-priori, e.g., subjects in a clinical study or frequency bands, whereas states may not be provided beforehand and may need to be learned from the brain-network time-series; an epileptic seizure state, for example, may need to be separated from non-seizure ones through EEG time-series observations [15].…”

Section: A Problem Statementmentioning

confidence: 99%

“…Batch approaches for multilayer networks include also [9,44,45], with [45] being able to address both state clustering and community detection, but not subnetworksequence clustering since inter-layer information cannot be accommodated. Correlation matrices and hierarchical clustering were proposed in [14] to detect communities in multilayer brain networks. Works [16,38,40] explore inter-layer dependencies, but none of them considers the subnetwork-sequence clustering task.…”

Section: B Prior Artmentioning

confidence: 99%

“…The inverse correlation matrix was used as a feature in identifying network topology in [68,69]. The majority of state-of-the-art methods, such as the 4D-tensor method [17], the graph-signal-processing approach of [43], as well as [14], use a "sliding-window" correlation matrix as the feature that reveals network communities. However, only the present study exploits the entailing Riemannian geometry of the (kernel) correlation matrix to tackle community detection and even the rarely addressed subnetwork-sequence tracking.…”

Section: ) Kernel Correlationsmentioning

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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“…Specifically, these measures have been used to link network dynamics to inter-individual differences in a broad range of functional domains, including motor learning (Bassett et al 2011, Wymbs et al 2012, working memory (Braun et al 2015, Finc et al 2020), attention (Shine et al 2016), language (Chai et al 2016), mood , creativity (Feng et al 2019, and reinforcement learning (Gerraty et al 2018). Additionally, dynamic network reconfiguration has been suggested as a potential biomarker for diseases, such as schizophrenia (Braun et al 2016, Gifford et al 2020, temporal lobe epilepsy (He et al 2018), and depression (Wei et al 2017, Zheng et al 2018, Shao et al 2019, Han et al 2020, and has been used to predict antidepressant treatment outcome (Tian et al 2020).…”

Section: Introductionmentioning

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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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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Resting state fMRI based multilayer network configuration in patients with schizophrenia

Cited by 63 publications

References 72 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

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

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