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

2019 ◽  
Author(s):  
Felix Siebenhühner ◽  
Sheng H Wang ◽  
Gabriele Arnulfo ◽  
Anna Lampinen ◽  
Lino Nobili ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Resting State ◽  
Large Scale ◽  
Phase Synchronization ◽  
Functional Organization ◽  
Brain Regions ◽  
Neuronal Processing ◽  
Frequency Coupling ◽  
Cross Frequency Coupling ◽  
Frequency Phase

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.

scholarly journals Time scale separation of information processing between sensory and associative regions

2020 ◽  
Author(s):  
P Sorrentino ◽  
G Rabuffo ◽  
R Rucco ◽  
F Baselice ◽  
E Troisi Lopez ◽  
...  
Keyword(s):  
Information Processing ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
High Temporal Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Speed Of Information Processing ◽  
The Brain

AbstractStimulus perception is assumed to involve the (fast) detection of sensory inputs and their (slower) integration. The capacity of the brain to quickly adapt, at all times, to unexpected stimuli suggests that the interplay between the slow and fast processes happens at short timescales. We hypothesised that, even during resting-state, the flow of information across the brain regions should evolve quickly, but not homogeneously in time. Here we used high temporal-resolution Magnetoencephalography (MEG) signals to estimate the persistence of the information in functional links across the brain. We show that short- and long-lasting retention of the information, entailing different speeds in the update rate, naturally split the brain into two anatomically distinct subnetworks. The “fast updating network” (FUN) is localized in the regions that typically belong to the dorsal and ventral streams during perceptive tasks, while the “slow updating network” (SUN) hinges classically associative areas. Finally, we show that only a subset of the brain regions, which we name the multi-storage core (MSC), belongs to both subnetworks. The MSC is hypothesized to play a role in the communication between the (otherwise) segregated subnetworks.Significance statementThe human brain constantly scans the environment in search of relevant incoming stimuli, and appropriately reconfigures its large-scale activation according to environmental requests. The functional organization substanding these bottom-up and top-down processes, however, is not understood. Studying the speed of information processing between brain regions during resting state, we show the existence of two spatially segregated subnetworks processing information at fast- and slow-rates. Notably, these networks involve the regions that typically belong to the perception stream and the associative regions, respectively. Therefore, we provide evidence that, regardless of the presence of a stimulus, the bottom-up and top-down perceptive pathways are inherent to the resting state dynamics.

scholarly journals Electrophysiological foundations of the human default-mode network revealed by brain-wide intracranial-EEG recordings during resting-state and cognition

2020 ◽  
Author(s):  
Anup Das ◽  
Carlo de los Angeles ◽  
Vinod Menon
Keyword(s):  
Slow Wave ◽  
Default Mode Network ◽  
Verbal Memory ◽  
Resting State ◽  
Large Scale ◽  
Phase Synchronization ◽  
Functional Organization ◽  
Memory Encoding ◽  
Intracranial Eeg ◽  
Default Mode

AbstractInvestigations using noninvasive functional magnetic resonance imaging (fMRI) have provided significant insights into the unique functional organization and profound importance of the human default mode network (DMN), yet these methods are limited in their ability to resolve network dynamics across multiple timescales. Electrophysiological techniques are critical to address these challenges, yet few studies have explored the neurophysiological underpinnings of the DMN. Here we investigate the brain-wide electrophysiological organization of the DMN in a common large-scale network framework consistent with prior fMRI studies. We used brain-wide intracranial EEG (iEEG) recordings, and evaluated intra- and cross-network interactions during the resting-state and cognition. Our analysis revealed significantly greater intra-DMN phase iEEG synchronization in the slow-wave (< 4 Hz) while DMN interactions with other brain networks was higher in all higher frequencies. Crucially, slow-wave intra-DMN synchronization was observed in the task-free resting-state and during verbal memory encoding and recall. Compared to resting-state, intra-DMN phase synchronization was significantly higher during both memory encoding and recall. Slow-wave intra-DMN phase synchronization increased during successful memory retrieval, highlighting its behavioral relevance. Finally, analysis of nonlinear dynamic causal interactions revealed that the DMN is a causal outflow network during both memory encoding and recall. Our findings identify dynamic spectro-temporal network features that allow the DMN to maintain a balance between stability and flexibility, intrinsically and during task-based cognition, provide novel insights into the neurophysiological foundations of the human DMN, and elucidate network mechanisms by which it supports cognition.

scholarly journals Stroke-related alterations in inter-areal communication revealed via Granger causality analysis

2021 ◽  
Author(s):  
Michele Allegra ◽  
Chiara Favaretto ◽  
Nicholas Metcalf ◽  
Maurizio Corbetta ◽  
Andrea Brovelli
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Information Transfer ◽  
Large Scale ◽  
Functional Organization ◽  
Information Flows ◽  
Causality Analysis ◽  
Stroke Patients ◽  
Granger Causality Analysis

ABSTRACTNeuroimaging and neurological studies suggest that stroke is a brain network syndrome. While causing local ischemia and cell damage at the site of injury, stroke strongly perturbs the functional organization of brain networks at large. Critically, functional connectivity abnormalities parallel both behavioral deficits and functional recovery across different cognitive domains. However, the reasons for such relations remain poorly understood. Here, we tested the hypothesis that alterations in inter-areal communication underlie stroke-related modulations in functional connectivity (FC). To this aim, we used resting-state fMRI and Granger causality analysis to quantify information transfer between brain areas and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was strongly decreased with respect to healthy controls. Second, information transfer within the affected hemisphere, and from the affected to the intact hemisphere was reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they were correlated with impaired performance in several behavioral domains. Overall, our results support the hypothesis that stroke perturbs inter-areal communication within and across hemispheres, and suggest novel therapeutic approaches aimed at restoring normal information flow.SIGNIFICANCE STATEMENTA thorough understanding of how stroke perturbs brain function is needed to improve recovery from the severe neurological syndromes affecting stroke patients. Previous resting-state neuroimaging studies suggested that interaction between hemispheres decreases after stroke, while interaction between areas of the same hemisphere increases. Here, we used Granger causality to reconstruct information flows in the brain at rest, and analyze how stroke perturbs them. We showed that stroke causes a global reduction of inter-hemispheric communication, and an imbalance between the intact and the affected hemisphere: information flows within and from the latter are impaired. Our results may inform the design of stimulation therapies to restore the functional balance lost after stroke.

scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cortical Excitability ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Organization ◽  
Individual Level ◽  
Slow Activity ◽  
Lag Structure

Abstract Spontaneous infra-slow (&lt;0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

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

2016 ◽  
Vol 102 ◽  
pp. 1-11 ◽  
Author(s):  
Marios Antonakakis ◽  
Stavros I. Dimitriadis ◽  
Michalis Zervakis ◽  
Sifis Micheloyannis ◽  
Roozbeh Rezaie ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Resting State ◽  
Frequency Coupling ◽  
Cross Frequency Coupling

scholarly journals Development of Network Synchronization Predicts Language Abilities

2016 ◽  
Vol 28 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Sam M. Doesburg ◽  
Keriann Tingling ◽  
Matt J. MacDonald ◽  
Elizabeth W. Pang
Keyword(s):  
Large Scale ◽  
Phase Synchronization ◽  
Age Groups ◽  
Brain Regions ◽  
Childhood And Adolescence ◽  
Network Synchronization ◽  
Language Abilities ◽  
Generation Task ◽  
Development Of Language ◽  
Verb Generation

Synchronization of oscillations among brain areas is understood to mediate network communication supporting cognition, perception, and language. How task-dependent synchronization during word production develops throughout childhood and adolescence, as well as how such network coherence is related to the development of language abilities, remains poorly understood. To address this, we recorded magnetoencephalography while 73 participants aged 4–18 years performed a verb generation task. Atlas-guided source reconstruction was performed, and phase synchronization among regions was calculated. Task-dependent increases in synchronization were observed in the theta, alpha, and beta frequency ranges, and network synchronization differences were observed between age groups. Task-dependent synchronization was strongest in the theta band, as were differences between age groups. Network topologies were calculated for brain regions associated with verb generation and were significantly associated with both age and language abilities. These findings establish the maturational trajectory of network synchronization underlying expressive language abilities throughout childhood and adolescence and provide the first evidence for an association between large-scale neurophysiological network synchronization and individual differences in the development of language abilities.

scholarly journals Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

2019 ◽  
Author(s):  
Narges Moradi ◽  
Mehdy Dousty ◽  
Roberto C. Sotero
Keyword(s):  
Functional Connectivity ◽  
Empirical Mode Decomposition ◽  
Resting State ◽  
Large Scale ◽  
Pearson Correlation ◽  
Brain Regions ◽  
Connectivity Matrix ◽  
Correlation Pattern ◽  
Mode Decomposition ◽  
Global Signal

AbstractResting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

scholarly journals Cross-frequency coupling explains the preference for simple ratios in rhythmic behaviour and the relative stability across non-synchronous patterns

2021 ◽  
Vol 376 (1835) ◽  
pp. 20200333 ◽  
Author(s):  
Dobromir Dotov ◽  
Laurel J. Trainor
Keyword(s):  
Phase Synchronization ◽  
Scaling Law ◽  
Small Integer ◽  
Circle Map ◽  
Repetitive Nature ◽  
Farey Tree ◽  
Frequency Coupling ◽  
Complex Forms ◽  
Musical Rhythms ◽  
Cross Frequency Coupling

Rhythms are important for understanding coordinated behaviours in ecological systems. The repetitive nature of rhythms affords prediction, planning of movements and coordination of processes within and between individuals. A major challenge is to understand complex forms of coordination when they differ from complete synchronization. By expressing phase as ratio of a cycle, we adapted levels of the Farey tree as a metric of complexity mapped to the range between in-phase and anti-phase synchronization. In a bimanual tapping task, this revealed an increase of variability with ratio complexity, a range of hidden and unstable yet measurable modes, and a rank-frequency scaling law across these modes. We use the phase-attractive circle map to propose an interpretation of these findings in terms of hierarchical cross-frequency coupling (CFC). We also consider the tendency for small-integer attractors in the single-hand repeated tapping of three-interval rhythms reported in the literature. The phase-attractive circle map has wider basins of attractions for such ratios. This work motivates the question whether CFC intrinsic to neural dynamics implements low-level priors for timing and coordination and thus becomes involved in phenomena as diverse as attractor states in bimanual coordination and the cross-cultural tendency for musical rhythms to have simple interval ratios. This article is part of the theme issue ‘Synchrony and rhythm interaction: from the brain to behavioural ecology’.

scholarly journals The role of anatomical connection strength for interareal communication in macaque cortex

2020 ◽  
Author(s):  
Julien Vezoli ◽  
Martin Vinck ◽  
Conrado A. Bosman ◽  
Andre M. Bastos ◽  
Christopher M Lewis ◽  
...  
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Brain Regions ◽  
Connection Strength ◽  
Non Invasive ◽  
Large Scale Networks ◽  
Invasive Techniques ◽  
The Relationship ◽  
Anatomical Connection

What is the relationship between anatomical connection strength and rhythmic synchronization? Simultaneous recordings of 15 cortical areas in two macaque monkeys show that interareal networks are functionally organized in spatially distinct modules with specific synchronization frequencies, i.e. frequency-specific functional connectomes. We relate the functional interactions between 91 area pairs to their anatomical connection strength defined in a separate cohort of twenty six subjects. This reveals that anatomical connection strength predicts rhythmic synchronization and vice-versa, in a manner that is specific for frequency bands and for the feedforward versus feedback direction, even if interareal distances are taken into account. These results further our understanding of structure-function relationships in large-scale networks covering different modality-specific brain regions and provide strong constraints on mechanistic models of brain function. Because this approach can be adapted to non-invasive techniques, it promises to open new perspectives on the functional organization of the human brain.

Sign in / Sign up

Export Citation Format

Share Document