Functional connectivity predicts changes in attention over minutes, days, and months

Rosenberg, Monica D.; Scheinost, Dustin; Greene, Abigail S.; Avery, Emily W.; Kwon, Young Hye; Finn, Emily S.; Ramani, R.; Qiu, Maolin; Constable, R. Todd; Chun, Marvin M.

doi:10.1101/700476

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…This idea has been extended to maximize or minimize subject-specific and/or task-specific information (Pallarés et al, 2018 ; Xie et al, 2018 ). These subject-specific fingerprints have been used to track fluctuations in attention at the individual level (Rosenberg et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Uncovering differential identifiability in network properties of human brain functional connectomes

Rajapandian

Amico

Abbas

et al. 2020

Network Neuroscience

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The identifiability framework (𝕀 f) has been shown to improve differential identifiability (reliability across-sessions and -sites, and differentiability across-subjects) of functional connectomes for a variety of fMRI tasks. But having a robust single session/subject functional connectome is just the starting point to subsequently assess network properties for characterizing properties of integration, segregation, and communicability, among others. Naturally, one wonders whether uncovering identifiability at the connectome level also uncovers identifiability on the derived network properties. This also raises the question of where to apply the 𝕀 f framework: on the connectivity data or directly on each network measurement? Our work answers these questions by exploring the differential identifiability profiles of network measures when 𝕀 f is applied (a) on the functional connectomes, and (b) directly on derived network measurements. Results show that improving across-session reliability of functional connectomes (FCs) also improves reliability of derived network measures. We also find that, for specific network properties, application of 𝕀 f directly on network properties is more effective. Finally, we discover that applying the framework, either way, increases task sensitivity of network properties. At a time when the neuroscientific community is focused on subject-level inferences, this framework is able to uncover FC fingerprints, which propagate to derived network properties.

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Section: Introductionmentioning

confidence: 99%

Uncovering differential identifiability in network properties of human brain functional connectomes

Rajapandian

Amico

Abbas

et al. 2020

Network Neuroscience

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“…This method-theory synergy approach has been outlined within the context of communication neuroscience by Weber and colleagues (2015;2018). Emerging network neuroscience methods, including edge-centric connectivity analyses (Faskowitz et al, 2020), individual-specific functional mapping (Gratton et al, 2019), and network-based predictive modeling (Rosenberg et al, 2020) will undeniably create new opportunities for understanding. Although network science is far from a new concept in communication research (Monge & Contractor, 2003), the recent debut of network neuroscience approaches in communication has created new opportunities for…”

Section: Discussionmentioning

confidence: 99%

A Practical Introduction to Network Neuroscience for Communication Researchers

Fisher

Hopp

Weber

2020

Communication Methods and Measures

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The increasing adoption of brain imaging methods has greatly augmented our understanding of the neural underpinnings of communication processes. Enabled by recent advancements in mathematics and computational infrastructure, researchers have begun to move beyond traditional univariate analytic techniques in favor of methods that consider the brain in terms of evolving networks of interactions between brain regions. This network neuroscience approach is a potential boon to communication and media psychology research but also requires a careful look at the complications inherent in adopting a novel (and complex) methodological tool. In this manuscript, we provide an overview of network neuroscience in view of the needs of communication neuroscientists, discussing considerations that must be considered when constructing networks from neuroimaging data and conducting statistical tests on these networks. Throughout the manuscript, we highlight research domains in which network neuroscience is likely to be particularly useful for increasing theoretical clarity in communication and media psychology research.

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Edge-based general linear models capture high-frequency fluctuations in attention

Jones

Yoo

Chun

et al. 2023

Preprint

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Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from one minute to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-by-moment network fluctuations. Recently, researchers have "unfurled" traditional FC matrices in "edge cofluctuation time series" which measure time point-by-time point cofluctuations between regions. Here we apply event-based and parametric fMRI analyses to edge time series to capture high-frequency fluctuations in networks related to attention. In two independent fMRI datasets in which participants performed a sustained attention task, we identified a reliable set of edges that rapidly deflects in response to rare task events. Another set of edges varies with continuous fluctuations in attention and overlaps with a previously defined set of edges associated with individual differences in sustained attention. Demonstrating that edge-based analyses are not simply redundant with traditional regions-of-interest based approaches, up to one-third of reliably deflected edges were not predicted from univariate activity patterns alone. These results reveal the large potential in combining traditional fMRI analyses with edge time series to identify rapid reconfigurations in networks across the brain.

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Functional connectivity predicts changes in attention over minutes, days, and months

Cited by 3 publications

References 68 publications

Uncovering differential identifiability in network properties of human brain functional connectomes

Uncovering differential identifiability in network properties of human brain functional connectomes

A Practical Introduction to Network Neuroscience for Communication Researchers

Edge-based general linear models capture high-frequency fluctuations in attention

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