Dynamic reconfiguration of functional brain networks during working memory training

Finc, Karolina; Bonna, Kamil; He, Xiaosong; Lydon‐Staley, David M.; Kühn, Simone; Duch, Włodzisław; Bassett, Danielle S.

doi:10.1038/s41467-020-15631-z

Cited by 188 publications

(177 citation statements)

References 59 publications

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“…On the other hand, socio-cognitive training, and improvement ToM, may rather relate to complex, cognitively demanding, processing, such as perspective-taking, and therefor relates to the integration of functionally relevant, ToM, brain networks. These observations are broadly in line with our findings at baseline in the functional manifold and underscore the dissociation between attentional, socio-emotional, and socio-cognitive processes, as well as the dissociable functional profiles underlying network segregation and integration (Finc et al, 2020). Indeed, in a previous ReSource study focusing on the analyses of autonomic measurements during engagement in the different types of mental practices of each training module, the Presence module was reported to be more relaxing and requiring less efforts, whereas the two social modules, Affect and Perspective, were associated with increased effort and arousal (Lumma et al, 2015).…”

Section: Discussionsupporting

confidence: 90%

Functional and microstructural plasticity following social and interoceptive mental training

Valk

Kanske

Park

et al. 2020

Preprint

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SUMMARYSocial skills such as our abilities to understand feelings and thoughts are at the core of what makes us human. Here, we combined a unique longitudinal intervention study with cutting-edge connectomics to study the organization and plasticity of brain networks associated with different social skills (socio-affective, socio-cognitive, and attention-mindfulness). Baseline analysis in our cohort (n=332) showed that social brain networks have (i) compact and dissociable signatures in a low-dimensional manifold governed by gradients of brain connectivity, (ii) specific neurobiological underpinnings, and (iii) reflect inter-individual variations in social behavior. Furthermore, longitudinal brain network analyses following a 9-month training intervention indicated (iv) domain-specific reorganization of these signatures that was furthermore predictive of behavioral change in social functions. Multiple sensitivity analyses supported robustness. Our findings, thus, provide novel evidence on macroscale network organization and plasticity underlying human social cognition and behavior, and suggest connectome-reconfigurations as a mechanism of adult skill learning.

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Section: Discussionsupporting

confidence: 90%

Functional and microstructural plasticity following social and interoceptive mental training

Valk

Kanske

Park

et al. 2020

Preprint

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“…In the reviewed literature, when [γ=1, ω=1] was used, the range of flexibility is typically <0.25 ( Table 1). This discrepancy is likely because previous studies used the MMM (Bassett et al 2011, Bassett et al 2013a, Bassett et al 2013b, Finc et al 2020 (Figure S7). However, when the number of ROIs increases from 200 to 600, values of dynamic reconfiguration measures increase while a higher γ value is required to achieve good ICC values ( Figure S8).…”

Section: Parameter Optimization Based On Test-retest Reliabilitymentioning

confidence: 95%

“…Recently, there has been increased enthusiasm to utilize these methods in the neuroimaging field (Table 1). 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

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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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“…The neural basis of transfer has been poorly understood. Human fMRI studies have produced conflicting results about the effects of cognitive training, suggesting overall increases [13][14][15][16][17][18] , or decreases in activity [19][20][21][22] , or more subtle differences such as changes in network modularity 23,24 . Increases are interpreted as reflecting a higher level of activation or recruitment of a larger cortical area, decreases as suggestive of improvements in efficiency 25,26 .…”

Section: Mainmentioning

confidence: 99%

Training-induced prefrontal neuronal changes transfer between tasks

Tang

Riley

Singh

et al. 2020

Preprint

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Training to improve working memory is associated with changes in prefrontal activation and confers lasting benefits, some of which generalize to untrained tasks, though the issue remains contentious and the neural substrate underlying such transfer are unknown. To assess how neural activity changes induced by training transfer across tasks, we recorded single units, multi-unit activity (MUA) and local field potentials (LFP) with chronic electrode arrays implanted in the prefrontal cortex of two monkeys, as they were trained to perform cognitive tasks. Mastering different tasks was associated with distinct changes in neural activity, which included redistribution of power across frequency bands in the LFP, recruitment of larger numbers of MUA sites, and increase or decrease of mean neural activity across single units. In every training phase, changes induced by the actively learned task transferred to an untrained control task, which remained the same across the training period. The results explicate the neural basis through which training can transfer across cognitive tasks.

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Dynamic reconfiguration of functional brain networks during working memory training

Cited by 188 publications

References 59 publications

Functional and microstructural plasticity following social and interoceptive mental training

Functional and microstructural plasticity following social and interoceptive mental training

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

Training-induced prefrontal neuronal changes transfer between tasks

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