Time-varying nodal measures with temporal community structure: a cautionary note to avoid misinterpretation

Thompson, William Hedley; Kastrati, Gránit; Finc, Karolina; Wright, Jessey; Shine, James M.; Poldrack, Russell A.

doi:10.1101/659508

Cited by 6 publications

(7 citation statements)

References 26 publications

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“…From simulated rs-fMRI signal, timeresolved functional connectivity was computed using tapered sliding windows 15 . For each instance of time-resolved functional connectivity, three global network measures, mean participation coefficient (mean PC), mean temporal participation coefficient (mean TPC), and modularity, were calculated to track dynamic fluctuations between segregated and integrated patterns of functional connectivity 14,23,24,42 . The time series of mean PC, mean TPC, and modularity served as proxies of dynamic fluctuations between segregation and integration in the brain.…”

Section: Resultsmentioning

confidence: 99%

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Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

Fukushima

Sporns

2020

Commun Biol

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While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.

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Section: Resultsmentioning

confidence: 99%

“…A recent study 42 proposed TPC by extending PC in Eq. (2) to improve its interpretability with time-varying community partitions.…”

Section: Methodsmentioning

confidence: 99%

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

Fukushima

Sporns

2020

Commun Biol

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“…Since neuromodulators have been reported to alter degree as well (see section changes of the global strength of intrinsic correlations ), future studies should carefully consider alterations in global degree when examining topological metrics. In addition, studies that have examined changes in the time-varying topology should take into account the influence of temporal fluctuations of the community structure on topological metrics (Thompson et al, 2019).…”

Section: Changes In Intrinsic Cortical Correlations Under Pharmacologmentioning

confidence: 99%

Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations

Brink

Pfeffer

Donner

2019

Front. Hum. Neurosci.

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Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of longrange anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson's disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distil a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically inspired biomarkers and individualized treatments of these disorders.

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“…Another relevant limitation might appear the calculation of the participation coefficient (P), as well as the within-module degree (z), which are related to the particular modular architecture used as a shaper 36 . Thus, our results are related to the modularity analysis found in our dataset.…”

Section: Comparison Of Positive and Negative Functional Brain Networkmentioning

confidence: 99%

Functional connections between and within brain subnetworks under resting-state

Parente

Colosimo

2020

Sci Rep

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functional connections between and within brain subnetworks under resting-state fabrizio parente * & Alfredo colosimo the focus of this paper is on the functional role of brain regions focusing on their modular architecture and individual variability. our main assumption is that the more variable anti-correlation patterns reflect random connections, while the more conserved ones play a functional role. Within this framework, we expanded on previous results using a different database and a different methodological approach. Aiming to identify the role of specific functional connections within a global network organization which includes subnetworks, we found that the fronto-parietal module acts as the main source of anti-correlations. In addition, the pre-frontal regions (namely: frontal middle, frontal middle orbital, frontal inferior triangular) and the parietal inferior region are highly conserved and, at the same time, act as highly connected nodes, thus confirming their importance in functional modulation. fMRI technology allows to explore brain regions' activities and, in the case of spontaneous activation, reveals an organized structure with peculiar segregation and integration properties 1-3. In addition to mutual activation among different regions (corresponding to positive correlations), another kind of interactions have been measured, namely anti-correlations (or anti-phase signal dependences) 4-8. A major role was assigned to these interactions in the reciprocal de-activation between the Default Mode Network (DMN) and the Task Positive Network (TPN) 9-11. Several papers associated anti-correlation to schizophrenia 8 , working memory 6 , aging 6 , NMDA receptor 4,12 as well as intracranial electrophysiological measurement 5,13 and magnetoencephalographic recordings 14. A basic methodological problem, however, was raised by Murphy 15 , underlining the role of the Global Signal Regression (GSR) method in the insertion of artifactual anti-correlations. Alternative methods were designed to face that problem 16-18 and, more recently, evidences were obtained indicating a reproducible anti-correlation pattern between the DMN and the Dorsal Attention Network (DAN), as well as the Salience Network (SN) 19,20. The list of unsolved problems, however, still includes:

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Time-varying nodal measures with temporal community structure: a cautionary note to avoid misinterpretation

Cited by 6 publications

References 26 publications

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

Structural determinants of dynamic fluctuations between segregation and integration on the human connectome

Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations

Functional connections between and within brain subnetworks under resting-state

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