Tracking brain dynamics via time-dependent network analysis

Dimitriadis, Stavros I.; Laskaris, Nikolaos A.; Tsirka, Vasso; Vourkas, Michael; Micheloyannis, Sifis; Fotopoulos, S.

doi:10.1016/j.jneumeth.2010.08.027

Cited by 122 publications

(110 citation statements)

References 58 publications

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“…Page 25 of 31 dynamic functional connectivity approach (Dimitriadis et al, 2010a(Dimitriadis et al, , 2012a(Dimitriadis et al, ,b,c, 2013a(Dimitriadis et al, , 2015a through the definition of functional connectivity microstates (Dimitriadis et al, 2013a(Dimitriadis et al, ,c,2015a and/or network microstates (Dimitriadis et al, 2013a(Dimitriadis et al, ,c,2015a. Our future studies with mTBI subjects will focus on dynamic crossfrequency coupling, their related microstates, and their symbolic dynamical signature on MEG resting state.…”

Section: Discussionmentioning

confidence: 99%

“…Topological filtering relies on graph-based analysis (Bullmore and Sporns, 2009;Bassett et al, 2009;He and Evans, 2010;Stam, 2010;Dimitriadis et al, 2010aDimitriadis et al, ,b,2012aDimitriadis et al, ,b,c,2013a, which is used to capture the structure of the neural Global cost efficiency is defined as the global efficiency GE at a given cost C minus the cost (GE-C), which typically has a positive maximum value at some cost , for an economical small-world network. Importantly, this metric of network topology is independent of arbitrary, investigator-specified thresholds.…”

Section: Significant Linksmentioning

confidence: 99%

“…Instead, the cost efficiency curve is estimated over a wide range of thresholds, and the behavior of the curve is summarized by its maximum value, which occurs at a data driven connection density or cost C (Bassett et al, 2009, Dimitriadis et al, 2015b. The above steps are described in the Supplementary Material and an implementation of the current approach is also available 1 (Dimitriadis et al, 2010a).…”

Section: Significant Linksmentioning

confidence: 99%

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Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Antonakakis

Dimitriadis

Zervakis

et al. 2016

International Journal of Psychophysiology

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Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients.Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI.

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Section: Discussionmentioning

confidence: 99%

Section: Significant Linksmentioning

confidence: 99%

Section: Significant Linksmentioning

confidence: 99%

See 1 more Smart Citation

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Antonakakis

Dimitriadis

Zervakis

et al. 2016

International Journal of Psychophysiology

Self Cite

122

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“…Whereas this consideration might be realistic for anatomical connectivity, it implies strict assumptions when functional connectivity patterns are encountered and seems unreasonable in the particular case of fast-recording modalities (EEG/MEG). Previous studies have already strongly supported the need for timevarying functional connectivity analysis (Allen et al 2012;De Vico Fallani et al 2008;Dimitriadis et al 2010bDimitriadis et al , 2012dIoannides et al 2012;Betzel et al 2012;Dimitriadis et al 2013a, b;Jamal et al 2014). Network metrics computed over different instantiations of the functional connectivity graph (FCG) can provide a quantitative view of the reconfigurable brain.…”

Section: Introductionmentioning

confidence: 90%

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

2015

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We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

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“…This window is chosen empirically or based on a priori information about the analyzed task [2]. A few attempts have been recently reported to avoid this constraint [7,8].However, most of the proposed algorithms are not adequate to track changes over very short durations (as in the case of responses evoked by visual stimuli).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Brain Functional Connectivity

et al. 2015

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Abstract-Brain functions are based on interactions between neural assemblies distributed within and across distinct cerebral regions. During cognitive tasks, these interactions are dynamic and take place at the millisecond time scale. In this context, the excellent temporal resolution (<1 ms) of the Electroencephalographic -EEG-signals allows for detection of very short-duration events and therefore, offers the unique opportunity to follow, over time, the dynamic properties of cognitive processes.In this paper we propose a new algorithm to track the functional brain connectivity dynamics. During picture recognition and naming task, this algorithm aims at segmenting high resolution (hr) EEG functional connectivity microstates. The proposed algorithm is based on the K-means clustering of the connectivity graphs obtained from the Phase Locking Values (PLV). Results show that the algorithm is able to track the brain functional connectivity dynamics during picture naming task.

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Tracking brain dynamics via time-dependent network analysis

Cited by 122 publications

References 58 publications

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

Spatiotemporal Analysis of Brain Functional Connectivity

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