Recurrence quantification analysis of dynamic brain networks

Lopes, Marinho A.; Zhang, Jiaxiang; Krzemiński, Dominik; Hamandi, Khalid; Chen, Qi; Livi, Lorenzo; Masuda, Naoki

doi:10.1111/ejn.14960

Cited by 30 publications

(20 citation statements)

References 71 publications

(120 reference statements)

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“…The microstate connectivity approach presented here also has potential as an alternative approach for studying dynamic functional connectivity. A typical approach for dynamic functional connectivity in the current literature is the use of a sliding window (de Pasquale et al, 2010; Brookes et al, 2014; O’Neill et al, 2015; de Pasquale et al, 2016; Lopes et al, 2020), which is limited by the arbitrary choice of window size meaning development of novel methods beyond the sliding window are crucial (O’Neill et al, 2018). Our MVPA analysis indicated that windowing via microstate labelling and concatenation of samples within a state (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…A second limitation of this approach is that it relies on the assumption of a discrete number of functional connectivity states. While this assumption does not apply in general to sliding-window connectivity studies, common subsequent analyses such as clustering networks (Allen et al, 2014; O’Neill et al, 2015; Mheich et al, 2015; Hassan et al, 2015) or recurrence analysis (Lopes et al, 2020) make similar assumptions, and hence microstate-windowing can be viewed as an alternative to these approaches without the reliance on window length.…”

Section: Discussionmentioning

confidence: 99%

“…Other methods for EEG/MEG analyses are available to examine to rapid changes in brain-state. One is to apply sliding window analysis to the EEG/MEG time courses (de Pasquale et al, 2010; Brookes et al, 2014; O’Neill et al, 2015; de Pasquale et al, 2016; Lopes et al, 2020) and subsequently cluster functional networks across windows (Allen et al, 2014; O’Neill et al, 2015; Mheich et al, 2015; Hassan et al, 2015). This approach has the limitation of the need for an arbitrary a priori selected window size: too short windows lead to results susceptible to noise, while too large windows result in non-stationarity at fast time scales being missed (O’Neill et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Tait

Zhang

2021

Preprint

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EEG microstate analysis is a useful approach for studying brain states - nicknamed `atoms of thought' - and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight into the cortical mechanisms underpinning these states. In this study, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level MEG microstates were associated with distinct functional connectivity patterns. Using a passive auditory paradigm, we further demonstrated that the occurrence probability of MEG microstates were altered by evoked auditory responses, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of MEG source-level microstates as a data-driven method for investigating brain dynamic activity and connectivity at the millisecond scale.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Tait

Zhang

2021

Preprint

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“…In a temporal network of PPIs, nodes represent proteins, and interactions between them are represented by time-varying edges. Temporal network theory has been used successfully in areas ranging from social interactions (Miritello et al 2011; Saramäki and Moro 2015; Gelardi et al 2020) to neuroscience (Pedreschi et al 2020; Lopes et al 2021). However, despite calls to use it more in biology (Przytycka et al 2010), it is still underused to study, e.g., PPI networks.…”

Section: Introductionmentioning

confidence: 99%

Inferring cell cycle phases from a partially temporal network of protein interactions

Lucas

Morris

Townsend-Teague

et al. 2021

Preprint

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The temporal organisation of biological systems into phases and subphases is often crucial to their functioning. Identifying this multiscale organisation can yield insight into the underlying biological mechanisms at play. To date, however, this identification requires a priori biological knowledge of the system under study. Here, we recover the temporal organisation of the cell cycle of budding yeast into phases and subphases, in an automated way. To do so, we model the cell cycle as a partially temporal network of protein-protein interactions (PPIs) by combining a traditional static PPI network with protein concentration or RNA expression time series data. Then, we cluster the snapshots of this temporal network to infer phases, which show good agreement with our biological knowledge of the cell cycle. We systematically test the robustness of the approach and investigate the effect of having only partial temporal information. The generality of the method makes it suitable for application to other, less well-known biological systems for which the temporal organisation of processes plays an important role.

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“…Other methods for EEG/MEG analyses are available to examine to rapid changes in brain-state. One is to apply sliding window analysis to the EEG/MEG time courses [24][25][26][27][28] and subsequently cluster functional networks across windows 21;26;29;30 . This approach has the limitation of the need for an arbitrary a priori selected window size: too short windows lead to results susceptible to noise, while too large windows result in non-stationarity at fast time scales being missed 10 .…”

Section: Introductionmentioning

confidence: 99%

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Tait

Zhang

2021

Preprint

Self Cite

View full text Add to dashboard Cite

EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

show abstract

Recurrence quantification analysis of dynamic brain networks

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 30 publications

References 71 publications

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

Inferring cell cycle phases from a partially temporal network of protein interactions

MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

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