Connectomics of human electrophysiology

Sadaghiani, Sepideh; Brookes, Matthew J.; Baillet, Sylvain

doi:10.1016/j.neuroimage.2021.118788

Cited by 94 publications

(72 citation statements)

References 163 publications

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“…It is noteworthy that the greatest contributions to the link between the two modalities came from slower oscillations, such as beta band. Multiple authors have reported that – since it captures slow haemodynamic coactivation – fMRI network connectivity would be mainly driven by slower rhythms [17, 32, 38, 74, 92]. Here we empirically confirm that although all frequency bands contribute to the emergence of fMRI networks, the greatest contributions come from relatively slower frequencies and the lowest contributions come from relatively faster frequencies.…”

Section: Discussionsupporting

confidence: 82%

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Shafiei

Baillet

Mišić

2021

Preprint

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Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these two types of neural activity remains unknown. Here we map electromagnetic networks (measured using magnetoencephalography; MEG) to haemodynamic networks (measured using functional magnetic resonance imaging; fMRI). We find that the relationship between the two modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex, potentially reflecting patterns of laminar differentiation, recurrent subcortical input and neurovascular coupling. Correspondence between the two is largely driven by slower rhythms, particularly the delta (2-4 Hz) and beta (15-29 Hz) frequency band. Moreover, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical micro-architecture and multi-modal connectivity patterns.

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Section: Discussionsupporting

confidence: 82%

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Shafiei

Baillet

Mišić

2021

Preprint

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“…This is not to say that the temporal organization of nervous systems -and functional brain networks, in particular -has gone uncharacterized. In fact, the opposite is true; time-varying connectivity analyses have come to occupy an increasingly large share of contemporary network neuroscience and connectomics [30][31][32][33][34][35][36] (despite several papers that cast doubt on the very premise that network change can be measured with fMRI [37,38]).…”

Section: Co-fluctuation Patterns Are Hierarchically Organized In Timementioning

confidence: 99%

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Betzel

Cutts

Tanner

et al. 2022

Preprint

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Edge time series decompose FC into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames, including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations. Here, we address those questions directly, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multi-scale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge and provide some practical guidance for future studies.

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“…It is of interest to compare properties of oscillating, quasiperiodic subnetworks based on hemodynamic signal changes reported here to recent results obtained from intra-cortical electrical measurements (Mostame et al, 2020;Sadaghiani et al, 2022). Although methods to estimate connectivity are different and the spatial coverage is more restricted, ECoG measurement of both tasks and rest suggests that fast intrinsic oscillations are dominated by a spatial organization that are shared across frequency bands (Mostame et al, 2021).…”

Section: Multi-scale Behavior Of Spatiotemporal Networkmentioning

confidence: 72%

Brain network integration, flexibility and quasicyclicity during task and rest

Fransson

Strindberg

2022

Preprint

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Previous studies have shown that a re-organization of the brain’s functional connectome expressed in terms of integration and segregation may play a pivotal role. However, it has been proven difficult to capture both processes within a single network-based framework. In this study we apply a hierarchical, spatiotemporally flexible network perspective onto fMRI data to track changes in integration and segregation in time. Our results show that network integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise which are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain’s functional connectome is a presence of quasiperiodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. The proposed approach to study spatiotemporal changes in network reconfiguration during rest as well as task performance could be useful to identify aberrant network motifs in disease.

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Connectomics of human electrophysiology

Cited by 94 publications

References 163 publications

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Hierarchical organization of spontaneous co-fluctuations in densely-sampled individuals using fMRI

Brain network integration, flexibility and quasicyclicity during task and rest

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