Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

Uddin, Lucina Q.; Yeo, B. T. Thomas; Spreng, R. Nathan

doi:10.1007/s10548-019-00744-6

Cited by 505 publications

(555 citation statements)

References 139 publications

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“…Extensive neuroimaging research has associated activation of a core salience network (SN), involved in the processing of affective, painful stimuli [19][20][21] . Comprised of two key regions, the anterior insula and anterior cingulate, activation of this network has been implicated across pre-existing compassion and empathy fMRI studies to date 22 .…”

mentioning

confidence: 99%

Neurophysiological and behavioural markers of compassion

Kim

Parker

Doty

et al. 2020

Sci Rep

102

115

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The scientific study of compassion is burgeoning, however the putative neurophysiological markers of programs which actively train distress tolerance, such as Compassionate Mind Training (CMT), are less well known. Herein we offer an integrative, multi-method approach which investigated CMT at neural, physiological, self-report, and behavioural levels. Specifically, this study first assessed participants' neural responses when confronted with disappointments (e.g., rejection, failure) using two fundamental self-regulatory styles, self-criticism and self-reassurance. Second, participant's heartrate variability (HRV) -a marker of parasympathetic nervous system response -was assessed during compassion training, pre-and post-a two-week self-directed engagement period. We identified neural networks associated with threat are reduced when practicing compassion, and heightened when being self-critical. In addition, cultivating compassion was associated with increased parasympathetic response as measured by an increase in HRV, versus the resting-state. Critically, cultivating compassion was able to shift a subset of clinically-at risk participants to one of increased parasympathetic response. Further, those who began the trial with lower resting HRV also engaged more in the intervention, possibly as they derived more benefits, both self-report and physiologically, from engagement in compassion.

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mentioning

confidence: 99%

Neurophysiological and behavioural markers of compassion

Kim

Parker

Doty

et al. 2020

Sci Rep

102

115

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“…robustly revealed the macroscale intrinsic network architecture of the human brain (e.g., Uddin, Yeo et al 2019). These networks of RSFC are built from symmetrical correlation matrices.…”

Section: Directed Information Transfer Within and Between Network Smentioning

confidence: 99%

“…Substantial progress has been made in delineating large-scale functional brain networks using resting-state functional connectivity (RSFC) (e.g., Biswal, Yetkin et al 1995, Power, Cohen et al 2011, Yeo, Krienen et al 2011, Uddin, Yeo et al 2019. The most commonly used method for measuring RSFC is to calculate a pairwise correlation coefficient between the low frequency BOLD signals of a set of priori determined brain regions of interest (ROIs).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal functional interactivity among large-scale brain networks

Doerschuk

Keilholz

et al. 2020

Preprint

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┼ These authors share senior authorship. AbstractThe macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting-state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N=200) we applied prediction correlation techniques to four resting state fMRI runs (total TRs = 4800). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

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“…Among its characteristic network features is community structure, the presence of reproducible modules consisting of regions that are internally densely coupled, reflecting their coherent and correlated activity over time. These intrinsic connectivity (Damoiseaux et al 2006), or resting-state networks (RSNs), have become enshrined in the cognitive neuroscience literature, providing a fundamental taxonomy and topographic reference frame for mapping brain/behavior relations (Ito et al 2017;Uddin et al 2019). Canonical sets of RSNs have been proposed (Power et al 2011;Yeo et al 2011), and their consistent spatial layout has been shown to reflect patterns of co-activation in task-driven fMRI activation studies (Laird et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

Sporns

Faskowitz

Teixera

et al. 2020

Preprint

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Functional connectivity (FC) describes the statistical dependence between brain regions in resting-state fMRI studies and is usually estimated as the Pearson correlation of time courses. Clustering reveals densely coupled sets of regions constituting a set of resting-state networks or functional systems. These systems manifest most clearly when FC is sampled over longer epochs lasting many minutes but appear to fluctuate on shorter time scales. Here, we propose a new approach to track these temporal fluctuations. Un-wrapping FC signal correlations yields pairwise co-fluctuation time series, one for each node pair/edge, and reveals fine-scale dynamics across the network. Co-fluctuations partition the network, at each time step, into exactly two communities. Sampled over time, the overlay of these bipartitions, a binary decomposition of the original time series, very closely approximates functional connectivity. Bipartitions exhibit characteristic spatiotemporal patterns that are reproducible across participants and imaging sessions and disclose fine-scale profiles of the time-varying levels of expression of functional systems. Our findings document that functional systems appear transiently and intermittently, and that FC results from the overlay of many variable instances of system expression. Potential applications of this decomposition of functional connectivity into a set of binary patterns are discussed.

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Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks

Cited by 505 publications

References 139 publications

Neurophysiological and behavioural markers of compassion

Neurophysiological and behavioural markers of compassion

Spatiotemporal functional interactivity among large-scale brain networks

Dynamic Expression of Brain Functional Systems Disclosed by Fine-Scale Analysis of Edge Time Series

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