Which multiband factor should you choose for your resting-state fMRI study?

Risk, Benjamin B.; Murden, Raphiel; Wu, Junjie; Nebel, Mary Beth; Venkataraman, Arun; Zhang, Zhengwu; Qiu, Deqiang

doi:10.1016/j.neuroimage.2021.117965

Cited by 55 publications

(55 citation statements)

References 77 publications

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“…In contrast, the use of MB-sequences allows to better resolve the physiological signals in their respective frequency bands. This, in turn, allows for a better control of physiological noise (Risk et al, 2021;Yang & Lewis, 2021). However, we cannot draw a firm conclusion about possible differential influences of motion and noise on the results of our study vs. those of Long et al (2019).…”

Section: Methodological Considerationsmentioning

confidence: 82%

“…Furthermore, we chose the window size in accordance with the rule that it should be equal to 1/fmin (Leonardi & Van De Ville, 2015). This suggestion is motivated by the fact that due to their higher temporal resolution, MB-sequences might be prone to lower signal-to-noise ratio SNR caused by noise amplification (Risk et al, 2021). However, we cannot rule out that such long window sizes might hamper the sensitivity to small changes, as calculating connectivity matrices over longer periods serves as a smoothing kernel minimizing the ability to resolve TVFC (Vergara et al, 2019).…”

Section: Potential Limitationsmentioning

confidence: 99%

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Probing the association between resting state brain network dynamics and psychological resilience

Kraft

Fiebach

2021

Preprint

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This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience. To this end, we acquired multiband resting-state BOLD fMRI (TR = .675 sec) from 60 participants and assessed resilience with three psychological questionnaires. Time resolved functional connectivity was calculated by performing a sliding window approach on averaged time series from the Schaefer 100 parcellation, resulting in 753 connectivity matrices. Node degree was calculated as a proxy for time-varying connectivity. Multilayer modularity detection was performed to track network reconfigurations over time and to calculate node flexibility (the number of times a node changes community assignment) and node promiscuity (the fraction of communities a node participates in). We found no significant correlations between resilience and any of the measures of dynamic functional connectivity, neither at nodal, functional network, or global levels. Results were also robust against down-sampling the data to resemble a conventional TR of ~2 sec (as in the original study) and against the application of different windowing schemes (overlapping vs. non-overlapping, different window lengths). We discuss several methodological reasons that may account for the difference in results between the present and the original study, to inform future network neuroscience studies of mental health.

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Section: Methodological Considerationsmentioning

confidence: 82%

Section: Potential Limitationsmentioning

confidence: 99%

Probing the association between resting state brain network dynamics and psychological resilience

Kraft

Fiebach

2021

Preprint

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“…Further, here fingers on the right hand are always used for button presses, so associated somatomotor activation should be most prominent in the left hemisphere. Task responses are often suitable targets for positive control analysis, because the occurrence of movement can be objectively verified (unlike e.g., psychological states, whose existence is necessarily inferred), and motor activity is generally strong, focal, and located in a low g-factor area (Risk et al 2021).…”

Section: Positive Control Analysesmentioning

confidence: 99%

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

Etzel

Freund

et al. 2021

Preprint

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Cognitive control is a critical higher mental function, which is subject to considerable individual variation and is impaired in a range of mental health disorders. We describe here the initial release of Dual Mechanisms of Cognitive Control (DMCC) project data, the DMCC55B dataset, with 55 healthy unrelated young adult participants. Each participant performed four well-established cognitive control tasks (Stroop, AX-CPT, Cued Task-Switching, and Sternberg Working Memory) while undergoing moderately high-resolution functional MRI scanning. The dataset includes a range of state and trait self-report questionnaires, as well as behavioural tasks assessing individual differences in cognitive ability. The DMCC project is on-going and features additional components (e.g., related participants, manipulations of cognitive control mode, resting state fMRI, longitudinal testing) that will be publicly released following study completion. This DMCC55B subset is released early with the aim of encouraging wider use and greater benefit to the scientific community. The DMCC55B dataset is suitable for benchmarking and methods exploration, as well as analyses of task performance and individual differences.

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“…Suffice to say that many of the advances in our understanding of brain connectivity in health and disease have come from research using MRI and PET imaging. MRI has been widely used to study resting-state functional connectivity, as it provides exquisite spatial resolution with moderate temporal resolution (sub-second resolution is now obtainable with fast imaging techniques like multiband acquisition; Feinberg et al, 2010Feinberg et al, , 2013; see Risk et al, 2021 for resting-state multiband fMRI). FDG-PET has also been used to identifying specific patterns of glucose metabolism and the "metabolic connectivity" of regions of the brain.…”

Section: Scope Of the Current Reviewmentioning

confidence: 99%

The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

Deery¹,

Paolo²,

Moran³

et al. 2021

Preprint

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The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2,938 records, 144 studies were included. Results on 11 network measures were summarised and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function. Higher-order networks reliably showed age differences, whereas basic processing and control networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for increased hemispheric lateralisation at rest, reduced BOLD signal variability within-subjects and altered patterns and speed of dynamic connectivity. Research on connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

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Which multiband factor should you choose for your resting-state fMRI study?

Cited by 55 publications

References 77 publications

Probing the association between resting state brain network dynamics and psychological resilience

Probing the association between resting state brain network dynamics and psychological resilience

The Dual Mechanisms of Cognitive Control dataset, a theoretically-guided within-subject task fMRI battery

The older adult brain is less modular, more integrated and less efficient at rest: A systematic review of large-scale resting-state functional brain networks across the adult lifespan.

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