Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

Deco, Gustavo; Cabral, Joana; Woolrich, Mark W.; Stevner, Angus; Hartevelt, Tim J. van; Kringelbach, Morten L.

doi:10.1016/j.neuroimage.2017.03.023

Cited by 185 publications

(211 citation statements)

References 65 publications

(126 reference statements)

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“…Very recently, a mechanistic model for this process has been proposed [38]. The authors have compared the performance of two models: in model A, each brain region generates oscillations in a single frequency; in model B, each brain region can generate oscillations in multiple frequency bands.…”

Section: Frequency-based Decompositionmentioning

confidence: 99%

Multilayer modeling and analysis of human brain networks

Domenico¹

2017

GigaScience

170

122

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Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer’s or Parkinson’s, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain.

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Section: Frequency-based Decompositionmentioning

confidence: 99%

Multilayer modeling and analysis of human brain networks

Domenico¹

2017

GigaScience

170

122

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“…The best way to characterize dFC remains under debate (Hutchison et al, ). Although the sliding‐window analysis is most commonly used to calculate successive dFC matrices (Sakoglu et al, ), the window size affects the temporal resolution, challenging its validity (Deco et al, ; Hindriks et al, ; Hutchison et al, ; Lindquist, Xu, Nebel, & Caffo, ; Preti, Bolton, & Ville, ; Shine et al, ). In the current study, we instead use a recently developed method, the Leading Eigenvector Dynamics Analysis (LEiDA), which calculates dFC at the instantaneous level (for each recorded frame), and allows identifying patterns of blood oxygen level dependent (BOLD) phase coherence, or FC states, that reoccur over time both within and across scanning sessions (Cabral, Vidaurre, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder

Figueroa

Cabral

Mocking

et al. 2019

Human Brain Mapping

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141

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Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined “static” functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted‐MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re‐emerging FC states during rest in 51 antidepressant‐free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas—important for cognitive control—with default mode network, striatum, and salience areas, involved in emotional and self‐referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal–striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted‐MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.

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“…In humans, large-scale oscillatory networks in several frequency bands characterize magnetoencephalography (MEG), electroencephalography (EEG), and stereo-EEG (SEEG) data during resting state (RS) activity [3][4][5][6][7][8] and in many cognitive functions [9][10][11][12][13]. Inter-areal synchronization of alpha (, 7-14 Hz) and beta (, [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz) oscillations in humans and non-human primates, respectively, is thought to regulate top-down or feedback communication [14][15][16][17][18][19]. In contrast, both  and gamma-band (, 30-100 Hz) oscillations and synchronization have been associated with bottom-up sensory processing and representation of object-specific sensory information [15,[20][21][22], and  oscillations are also with sensorimotor processing [23,24].…”

Section: Introductionmentioning

confidence: 99%

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Siebenhuehner

Wang

Arnulfo

et al. 2019

Preprint

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AbstractPhase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that inter-areal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state brain activity.To assess the functional organization of CFC networks, we identified brain-wide CFC networks at meso-scale resolution from stereo-electroencephalography (SEEG) and at macro-scale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from non-sinusoidal signals ubiquitous in neuronal activity. We show that genuine inter-areal CFC is present in human resting-state activity in both MEG and SEEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for inter-areal CFS and PAC being two distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

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Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data

Cited by 185 publications

References 65 publications

Multilayer modeling and analysis of human brain networks

Multilayer modeling and analysis of human brain networks

Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

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