Relationships between multiple dimensions of executive functioning and resting-state networks in adults

Roye, Scott; Castagna, Peter J.; Calamia, Matthew; Vito, Alyssa N. De; Lee, Tae‐Ho; Greening, Steven G.

doi:10.1016/j.neuropsychologia.2020.107418

Cited by 12 publications

(11 citation statements)

References 44 publications

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“…The data were despiked (AFNI's 3dDespike “new” algorithm), realigned, normalized (directly to 3 mm MNI space using a SPM template), and smoothed (AFNI's 3dBlurToFWHM 6 mm) using the Data Processing Assistant for Resting‐State fMRI (DPARSF‐A). Slice‐timing correction was not performed as it has minimal impact on BOLD functional connectivity and variability (Wu et al, 2011), and is generally not employed in data acquired with multi‐band sequences (e.g., Zachary et al, 2021; Roye et al, 2020; Vieira, Rondinoni, & Garrido Salmon, 2020). FMRIB's independent component analysis (ICA) based Xnoiseifier (FIX) was then used to automatically classify noise and non‐noise components in each single subject ICA and subsequently regress out noise components.…”

Section: Methodsmentioning

confidence: 99%

Dynamic functional connectivity profile of the salience network across the life span

Snyder

Uddin

Nomi

2021

Human Brain Mapping

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The insular cortex and anterior cingulate cortex together comprise the salience or midcingulo‐insular network, involved in detecting salient events and initiating control signals to mediate brain network dynamics. The extent to which functional coupling between the salience network and the rest of the brain undergoes changes due to development and aging is at present largely unexplored. Here, we examine dynamic functional connectivity (dFC) of the salience network in a large life span sample (n = 601; 6–85 years old). A sliding‐window analysis and k‐means clustering revealed five states of dFC formed with the salience network, characterized by either widespread asynchrony or different patterns of synchrony between the salience network and other brain regions. We determined the frequency, dwell time, total transitions, and specific state‐to‐state transitions for each state and subject, regressing the metrics with subjects' age to identify life span trends. A dynamic state characterized by low connectivity between the salience network and the rest of the brain had a strong positive quadratic relationship between age and both frequency and dwell time. Additional frequency, dwell time, total transitions, and state‐to‐state transition trends were observed with other salience network states. Our results highlight the metastable dynamics of the salience network and its role in the maturation of brain regions critical for cognition.

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Section: Methodsmentioning

confidence: 99%

Dynamic functional connectivity profile of the salience network across the life span

Snyder

Uddin

Nomi

2021

Human Brain Mapping

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“…(2017) demonstrated that individuals with mild cognitive impairment (MCI) were slower, but similarly accurate when compared to those without MCI. Moreover, studies have suggested that accounting for processing speed statistically may improve the specificity of test performance interpretations ( Albinet et al., 2012 ; Karr, Hofer, Iverson, & Garcia-Barrera, 2019 ; Nigg et al., 2017 ; Roye et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The Influence of Processing Speed, Attention, and Inhibition on Texas Functional Living Scale Performance

Roye

Linck

Hoffmeister

et al. 2022

Archives of Clinical Neuropsychology

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Objective Attention, inhibition, and processing speed are related to functional decline among older adults. This study attempts to clarify the relationships between these cognitive factors and adaptive functioning. Method We examined relationships between attention, inhibition, and processing speed, with scores on the Texas Functional Living Scale (TFLS), a performance-based measure of daily functioning, in a mixed clinical sample of 530 older adults who were referred for an outpatient neuropsychological evaluation. Results The current study used a confirmatory factor analysis (CFA) to derive a three-factor cognitive model consisting of attention, inhibition, and processing speed. Results from a hierarchical regression, which included factor scores from the CFA, revealed that processing speed was the only significant predictor of TFLS performance when all three cognitive factors were included within a single model. Conclusion These results highlight the influence of processing speed as an important indicator of functional decline among a clinical population of older adults.

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“…These patterns of activity extend beyond the traditionally recognized frontal and parietal regions, involving subcortical and lower-level regions (Jurado and Rosselli 2007 ; Bettcher et al 2016 ), whereby individuals with higher cognitive performance appear to benefit from more distributed resting-state network activity (Reineberg et al 2015 ). An example of this diffuse processing is represented by the different network-to-network interactions that underlie individual differences in aspects of higher-level cognition, which have been reported both during resting-state conditions (Tian et al 2013 ; Roye et al 2020 ; Jolles et al 2020 ), as well as during online task execution (Kelly et al 2008 ; Sala-Llonch et al 2012 ; Zhang et al 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Topographical functional correlates of interindividual differences in executive functions in young healthy twins

et al. 2021

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Executive functions (EF) are a set of higher-order cognitive abilities that enable goal-directed behavior by controlling lower-level operations. In the brain, those functions have been traditionally associated with activity in the Frontoparietal Network, but recent neuroimaging studies have challenged this view in favor of more widespread cortical involvement. In the present study, we aimed to explore whether the network that serves as critical hubs at rest, which we term network reliance, differentiate individuals as a function of their level of EF. Furthermore, we investigated whether such differences are driven by genetic as compared to environmental factors. For this purpose, resting-state functional magnetic resonance imaging data and the behavioral testing of 453 twins from the Colorado Longitudinal Twins Study were analyzed. Separate indices of EF performance were obtained according to a bifactor unity/diversity model, distinguishing between three independent components representing: Common EF, Shifting-specific and Updating-specific abilities. Through an approach of step-wise in silico network lesioning of the individual functional connectome, we show that interindividual differences in EF are associated with different dependencies on neural networks at rest. Furthermore, these patterns show evidence of mild heritability. Such findings add knowledge to the understanding of brain states at rest and their connection with human behavior, and how they might be shaped by genetic influences.

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Relationships between multiple dimensions of executive functioning and resting-state networks in adults

Cited by 12 publications

References 44 publications

Dynamic functional connectivity profile of the salience network across the life span

Dynamic functional connectivity profile of the salience network across the life span

The Influence of Processing Speed, Attention, and Inhibition on Texas Functional Living Scale Performance

Topographical functional correlates of interindividual differences in executive functions in young healthy twins

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