Fast transient networks in spontaneous human brain activity

To provide an effective substrate for cognitive processes, functional brain networks should be able to reorganize and coordinate on a sub-second temporal scale. We used magnetoencephalography recordings of spontaneous activity to characterize whole-brain functional connectivity dynamics at high temporal resolution. Using a novel approach that identifies the points in time at which unique patterns of activity recur, we reveal transient (100–200 ms) brain states with spatial topographies similar to those of well-known resting state networks. By assessing temporal changes in the occurrence of these states, we demonstrate that within-network functional connectivity is underpinned by coordinated neuronal dynamics that fluctuate much more rapidly than has previously been shown. We further evaluate cross-network interactions, and show that anticorrelation between the default mode network and parietal regions of the dorsal attention network is consistent with an inability of the system to transition directly between two transient brain states.

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Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

10.1101/2020.02.04.934554 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Head Motion ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

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Temporal circuit of macroscale dynamic brain activity supports human consciousness

Science Advances ◽

10.1126/sciadv.aaz0087 ◽

2020 ◽

Vol 6 (11) ◽

pp. eaaz0087 ◽

Cited By ~ 11

Author(s):

Zirui Huang ◽

Jun Zhang ◽

Jinsong Wu ◽

George A. Mashour ◽

Anthony G. Hudetz

Keyword(s):

Default Mode Network ◽

Functional Role ◽

Brain Activity ◽

Human Consciousness ◽

Attention Network ◽

Attention Networks ◽

Default Mode ◽

Dorsal Attention Network ◽

Brain States

The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

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Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

10.1101/2020.09.04.283002 ◽

2020 ◽

Author(s):

Z. Zavecz ◽

K. Janacsek ◽

P. Simor ◽

M.X. Cohen ◽

D. Nemeth

Keyword(s):

Functional Connectivity ◽

Memory Consolidation ◽

Brain Activity ◽

Serial Order ◽

Order Information ◽

Novel Approach ◽

Continuous Eeg ◽

Induced Changes ◽

Probability Information ◽

The Brain

AbstractLong-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation.Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

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Modulation of the spontaneous brain activity and functional connectivity in the triple resting-state networks following the visual oddball paradigm

10.1101/2021.01.26.428223 ◽

2021 ◽

Author(s):

Hasan Sbaihat ◽

Ravichandran Rajkumar ◽

Shukti Ramkiran ◽

Abed Al-Nasser Assi ◽

N. Jon Shah ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Cognitive Activity ◽

Large Body ◽

Oddball Paradigm ◽

Resting State Networks ◽

Wide Range ◽

Visual Oddball Paradigm ◽

Visual Oddball

AbstractThe default mode network (DMN), the salience network (SN), and the central executive network (CEN) could be considered as the core resting-state brain networks (RSN) due to their involvement in a wide range of cognitive tasks. Despite the large body of knowledge relating to their regional spontaneous activity (RSA) and functional connectivity (FC) of these networks, less is known about the influence of task-associated activity on these parameters and on the interaction between these three networks. We have investigated the effects of the visual-oddball paradigm on three fMRI measures (amplitude of low-frequency fluctuations for RSA, regional homogeneity for local FC, and degree centrality for global FC) in these three core RSN networks. A rest-task-rest paradigm was used and the RSNs were identified using independent component analysis (ICA) on the resting-state data. We found that the task-related brain activity induced different patterns of significant changes within the three RS networks. Most changes were strongly associated with the task performance. Furthermore, the task-activity significantly increased the inter-network correlations between the SN and CEN as well as between the DMN and CEN, but not between the DMN and SN. A significant dynamical change in RSA, alongside local and global FC within the three core resting-state networks following a simple cognitive activity may be an expression of the distinct involvement of these networks in the performance of the task and their various outcomes.

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073 Whole-brain network analysis of neural oscillations during light sleep

SLEEP ◽

10.1093/sleep/zsab072.072 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A30-A31

Author(s):

Joline Fan ◽

Kiwamu Kudo ◽

Kamalini Ranasinghe ◽

Hirofumi Morise ◽

Anne Findlay ◽

...

Keyword(s):

Functional Connectivity ◽

Information Flow ◽

Phase Transfer ◽

Sleep Onset ◽

Adaptive Beamforming ◽

Neural Oscillations ◽

High Temporal Resolution ◽

Whole Brain ◽

Frequency Bands ◽

Brain Functional Connectivity

Abstract Introduction Sleep is a highly stereotyped phenomenon that is ubiquitous across species. Although behaviorally appearing as a homogeneous process, sleep has been recognized as cortically heterogenous and locally dynamic. PET/fMRI studies have provided key insights into regional activation and deactivation with sleep onset, but they lack the high temporal resolution and electrophysiology for understanding neural interactions. Using simultaneous electrocorticography (EEG) and magnetoencephalography (MEG) imaging, we systematically characterize whole-brain neural oscillations and identify frequency specific, cortically-based patterns associated with sleep onset. Methods In this study, 14 healthy subjects underwent simultaneous EEG and MEG imaging. Sleep states were determined by scalp EEG. Eight 15s artifact-free epochs, e.g. 120s sensor time series, were selected to represent each behavioral state: N1, N2 and wake. Atlas-based source reconstruction was performed using adaptive beamforming methods. Functional connectivity measures were computed using imaginary coherence and across regions of interests (ROIs, segmentation of 210 cortical regions with Brainnetome Atlas) in multiple frequency bands, including delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), sigma (12-15Hz), beta (15-30Hz), and gamma (30-50Hz). Directional phase transfer entropy (PTE) was also evaluated to determine the direction of information flow with transition to sleep. Results We show that the transition to sleep is encoded in a spatially and temporally specific dynamic pattern of whole-brain functional connectivity. With sleep onset, there is increased functional connectivity diffusely within the delta frequency, while spatially specific profiles in other frequency bands, e.g. increased fronto-temporal connectivity in the alpha frequency band and fronto-occipital connectivity in the theta band. In addition, rather than a decoupling of anterior-posterior regions with transition to sleep, there is a spectral shift to delta frequencies observed in the synchrony and information flow of neural activity. Conclusion Sleep onset is cortically heterogeneous, composed of spatially and temporally specific patterns of whole-brain functional connectivity, which may play an essential role in the transition to sleep. Support (if any) Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the NIH under Award Number (5TL1TR001871-05 to JMF). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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Meditation-State Functional Connectivity (msFC): Strengthening of the Dorsal Attention Network and Beyond

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2012/680407 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 34

Author(s):

Brett Froeliger ◽

Eric L. Garland ◽

Rachel V. Kozink ◽

Leslie A. Modlin ◽

Nan-Kuei Chen ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Control Group ◽

Meditation Practice ◽

Regression Analyses ◽

Resting State Networks ◽

Resting State Functional Connectivity ◽

Attention Network ◽

Dorsal Attention Network ◽

Meditation Experience

Meditation practice alters intrinsic resting-state functional connectivity (rsFC) in the default mode network (DMN). However, little is known regarding the effects of meditation on other resting-state networks. The aim of current study was to investigate the effects of meditation experience and meditation-state functional connectivity (msFC) on multiple resting-state networks (RSNs). Meditation practitioners (MPs) performed two 5-minute scans, one during rest, one while meditating. A meditation naïve control group (CG) underwent one resting-state scan. Exploratory regression analyses of the relations between years of meditation practice and rsFC and msFC were conducted. During resting-state, MP as compared to CG exhibited greater rsFC within the Dorsal Attention Network (DAN). Among MP, meditation, as compared to rest, strengthened FC between the DAN and DMN and Salience network whereas it decreased FC between the DAN, dorsal medial PFC, and insula. Regression analyses revealed positive correlations between the number of years of meditation experience and msFC between DAN, thalamus, and anterior parietal sulcus, whereas negative correlations between DAN, lateral and superior parietal, and insula. These findings suggest that the practice of meditation strengthens FC within the DAN as well as strengthens the coupling between distributed networks that are involved in attention, self-referential processes, and affective response.

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EPA-1478 – Resting state networks and brain functional connectivity under ECT

European Psychiatry ◽

10.1016/s0924-9338(14)78670-5 ◽

2014 ◽

Vol 29 ◽

pp. 1

Author(s):

O. Pogarell

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Networks ◽

Brain Functional Connectivity

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The Difference of Brain Functional Connectivity between Eyes-Closed and Eyes-Open Using Graph Theoretical Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/976365 ◽

2013 ◽

Vol 2013 ◽

pp. 1-15 ◽

Cited By ~ 38

Author(s):

Bo Tan ◽

Xianxian Kong ◽

Ping Yang ◽

Zhenlan Jin ◽

Ling Li

Keyword(s):

Functional Connectivity ◽

Alpha Band ◽

Topological Parameters ◽

Eyes Closed ◽

Synchronization Likelihood ◽

Functional Brain Networks ◽

Eyes Open ◽

The Difference ◽

Path Lengths ◽

Brain Functional Connectivity

To study the differences in functional brain networks between eyes-closed (EC) and eyes-open (EO) at resting state, electroencephalographic (EEG) activity was recorded in 21 normal adults during EC and EO states. The synchronization likelihood (SL) was applied to measure correlations between all pairwise EEG channels, and then the SL matrices were converted to graphs by thresholding. Graphs were measured by topological parameters in theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz) bands. By changing from EC to EO states, mean cluster coefficients decreased in both theta and alpha bands, but mean shortest path lengths became shorter only in the alpha band. In addition, local efficiencies decreased in both theta and alpha bands, while global efficiencies in the alpha band increased inversely. Opening the eyes decreased both nodes and connections in frontal area in the theta band, and also decreased those in bilateral posterior areas in the alpha band. These results suggested that a combination of the SL and graph theory methods may be a useful tool for distinguishing states of EC and EO. The differences in functional connectivity between EC and EO states may reflect the difference of information communication in human brain.

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