From static to temporal network theory: Applications to functional brain connectivity

Thompson, William Hedley; Brantefors, Per; Fransson, Peter

doi:10.1162/netn_a_00011

Cited by 91 publications

(75 citation statements)

References 120 publications

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“…Our results elucidate the fastevolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.resolution [24]. The Power parcellation of the brain was used and the following ten subnetworks were studied (following the Cole et al, 2013 network template [25]): DMNdefault mode, SM -sensorimotor, VIS -visual, FP -fronto-parietal attention, SAsaliency, CO -cingulo-opercular, AU -auditory, Sub -subcortical, DA -dorsal attention and VA -ventral attention subnetwork [26].Our aim with the current study was two-fold.…”

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confidence: 70%

Brain network segregation and integration during an epoch-related working memory fMRI experiment

Fransson

Schiffler

Thompson

2018

Preprint

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The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0-and 2-back epochs. During the interspersed epochs of baseline, many subnetworks, including the default mode, visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fastevolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

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confidence: 70%

Brain network segregation and integration during an epoch-related working memory fMRI experiment

Fransson

Schiffler

Thompson

2018

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“…Perturbed functionality of RSNs contributes to a range of brain diseases including epilepsy [9], Alzheimer's disease [10], autism [11], depression [12] and schizophrenia [13]. Although alterations of RSNs have been subject to numerous studies, characterization of their complex dynamics remains an open question in the brain sciences [1,[14][15][16][17][18][19][20][21]. This is a significant challenge in modern neuroscience because temporal brain complexity may provide a quantitative view of brain function at the phenomenological level which in turn, may leads to the development of more efficient diagnostic and prognostic markers of brain diseases.…”

Section: Introductionmentioning

confidence: 99%

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Omidvarnia

Zalesky

Mansour

et al. 2019

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In 2014, McDonough and Nashiro [1] derived multiscale entropy -a marker of signal complexity-from resting state functional MRI data (rsfMRI), and found that functional brain networks displayed unique multiscale entropy fingerprints. This is a finding with potential impact as an imaging-based marker of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of this study was that rsfMRI data from only 20 healthy individuals was used for analysis. To overcome this limitation, we aimed to replicate McDonough and Nashiro's finding in a large cohort of healthy subjects. We used rsfMRI from the Human Connectome Project (HCP) comprising 936 gender-matched healthy young adults aged 22-35, each with 4 × 14.4-minute rsfMRI data from 100 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We also performed a test-retest analysis on the data of four recording sessions in 10 previously reported resting state networks (RSNs). Given that the morphology of multiscale entropy patterns is affected by the choice of the tolerance parameter (r ), we performed the analyses at two r values: 0.5, similar to the original study and 0.15, a commonly used option in the literature. Our results were similar to previous findings by McDonough and Nashiro emphasising high temporal complexity in the default mode network and fronto-parietal networks, and low temporal complexity in the cerebellum. We also investigated the effect of temporal resolution (determined by fMRI repetition time) by downsampling rsfMRI time series. At a low temporal resolution, we observed increased entropy and variance across datasets likely due to fewer data points in the multiscale entropy analysis. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially for r = 0.5. We also showed a positive relationship between temporal complexity of RSNs and fluid intelligence (people's capacity to reason and think flexibly), suggesting that complex dynamics is an important attribute of optimized brain function.

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“…State-specific network frames could then be reallocated to specific times, depending on which "state" the system is visiting at different times, reconstructing thus an effective temporal network with the same time-resolution as the original recordings (see e.g. 30 for an analogous approach used at the macro-scale of fMRI signals). In this way it would become possible to link temporal network reconfiguration events to actual behavior, probing hence their direct functional relevance.…”

Section: Discussionmentioning

confidence: 99%

Dynamic core periphery structure of information sharing networks in entorhinal cortex and hippocampus

Pedreschi

Bernard

Clawson

et al. 2020

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ABSTRACTNeural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time and as a function of changing global brain state (theta vs slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity.AUTHOR SUMMARYIt is generally thought that computations performed by local brain circuits rely on complex neural processes, associated to the flexible waxing and waning of cell assemblies, i.e. ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness”. Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists”. Finally, by avoiding averages over pre-imposed states, we show that within each global oscillatory state a rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

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From static to temporal network theory: Applications to functional brain connectivity

Cited by 91 publications

References 120 publications

Brain network segregation and integration during an epoch-related working memory fMRI experiment

Brain network segregation and integration during an epoch-related working memory fMRI experiment

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Dynamic core periphery structure of information sharing networks in entorhinal cortex and hippocampus

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