Mindfulness and dynamic functional neural connectivity in children and adolescents

Marusak, Hilary A.; Thomason, Moriah E.; Elrahal, Farrah; Peters, Craig; Kundu, Prantik; Lombardo, Michael; Calhoun, Vince D.; Goldberg, Elimelech; Cohen, Carl M.; Taub, Jeffrey W.

doi:10.1101/106021

Cited by 13 publications

(15 citation statements)

References 48 publications

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“…Compared to low trait mindfulness individuals, high trait mindfulness individuals show a higher level of transitions between brain dFC states and spend more time in one dFC state associated with task-readiness (Lim et al, 2018). Marusak et al similarly found that dFC (but not sFC) is related to mindfulness in youths such that more mindful youths have a higher level of transitions between dFC states (Marusak et al, 2018). Additionally, Cabral et al (Cabral et al, 2017) report a relationship between cognitive performance in older adults and slow switching between dFC states.…”

Section: Dynamic Functional Connectivity (Time-varying Functional Patmentioning

confidence: 99%

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Iraji¹,

Faghiri²,

Lewis³

et al. 2020

Preprint

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Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

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Section: Dynamic Functional Connectivity (Time-varying Functional Patmentioning

confidence: 99%

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Iraji¹,

Faghiri²,

Lewis³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In exploratory analysis, we found that change in PVT performance across state was correlated with the change in the percentage of state transitions during resting-state scans. We have previously proposed a link between state transitions and the ability of the brain to refocus attention , which may represent a marker of cognitive flexibility (Li et al 2017, Marusak et al 2018; this theory is supported by data in macaques showing that sedation is associated with a loss of the rich repertoire of states seen in wakefulness (Barttfeld et al 2015). The negative effects of sleep deprivation on cognitive flexibility are well known (Harrison andHorne 1999, Durmer andDinges 2005), with increasing sleep pressure interfering with top-down executive function maintained by the prefrontal cortex (PFC).…”

Section: Decrease In State Transitions After Sleep Deprivation Are Almentioning

confidence: 99%

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Teng

Ong

Patanaik

et al. 2019

Preprint

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Running title: Vulnerability to sleep deprivation is associated with connectivity statesThe authors of this manuscript have no financial conflicts of interest to disclose. AbstractRobustly linking dynamic functional connectivity (DFC) states to behaviour is an important goal of the fledgling research using these methods. Here, we demonstrate in an independent dataset that two connectivity states previously linked to arousal and sustained attention are associated with individual differences in vulnerability to sleep deprivation. 32 healthy participants underwent two sets of resting-state functional magnetic resonance imaging (fMRI) scans, once in a well-rested state and once after a single night of total sleep deprivation. Individual differences in vulnerability to this manipulation were measured using changes in Psychomotor Vigilance Test (PVT) performance. DFC and clustering analysis on the resting state fMRI data revealed five centroids that were highly correlated with those found in previous work, including two states associated with high and low arousal. Changes in these arousal states from the well-rested to the sleep-deprived condition specifically were correlated with changes in PVT performance. Global signal was also correlated with this change in univariate analysis, but was not a significant incremental predictor in a multivariate model. Although head motion increased following sleep deprivation, this did not predict change in PVT performance. Our results provide good evidence of the reliability, validity, and reproducibility of DFC measures, particularly with regard to measuring arousal and attention.

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“…In our analyses, we formulated the temporal variability measure (equation 3) of each node in the dynamic FCN to capture the deviations of the node's role at each time from the static network. This measure is motivated by earlier studies that report measurable differences between static FCN and snapshots of the dynamic FCNAllen et al (2012); Marusak et al (2017Marusak et al ( , 2018. Moreover, any network level change can potentially influence how a node interacts with the rest of the network.…”

Section: Discussionmentioning

confidence: 99%

Linking flexibility of brain networks to cognitive development in preschool children

Chamakura

Daimi

Watanabe³

et al. 2020

Preprint

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Recent studies of functional connectivity networks (FCNs) suggest that the reconfiguration of brain network across time, both at rest and during task, is linked with cognition in human adults. In this study, we tested this prediction, i.e. cognitive ability is associated with a flexible brain network in preschool children of 3-4 years -a critical age, representing a 'blossoming period' for brain development. We recorded magnetoencephalogram (MEG) data from 88 preschoolers, and assessed their cognitive ability by a battery of cognitive tests. We estimated FCNs obtained from the source reconstructed MEG recordings, and characterized the temporal variability at each node using a novel path-based measure of temporal variability; the latter captures reconfiguration of the node's interactions to the rest of the network across time. Using connectome predictive modeling, we demonstrated that the temporal variability of fronto-temporal nodes in the dynamic FCN can reliably predict out-of-scanner performance of short-term memory and attention distractability in novel participants. Further, we observed that the network-level temporal variability increased with age, while individual nodes exhibited an inverse relationship between temporal variability and node centrality. These results demonstrate that functional brain networks, and especially their reconfiguration ability, are important to cognition at an early but a critical stage of human brain development.

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Mindfulness and dynamic functional neural connectivity in children and adolescents

Abstract: Background: Mindfulness is a non-judgmental, present-centered awareness and acceptance of

Cited by 13 publications

References 48 publications

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Cognitive vulnerability to sleep deprivation is robustly associated with two dynamic connectivity states

Linking flexibility of brain networks to cognitive development in preschool children

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