scholarly journals Direction of information flow in large-scale resting-state networks is frequency-dependent

2016 ◽  
Vol 113 (14) ◽  
pp. 3867-3872 ◽  
Author(s):  
Arjan Hillebrand ◽  
Prejaas Tewarie ◽  
Edwin van Dellen ◽  
Meichen Yu ◽  
Ellen W. S. Carbo ◽  
...  
Keyword(s):  
Information Flow ◽  
Resting State ◽  
Large Scale ◽  
Phase Transfer ◽  
Occipital Cortex ◽  
Normal Brain ◽  
Theta Band ◽  
Beta Band ◽  
Frequency Dependent ◽  
Frequency Bands

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

scholarly journals Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

2022 ◽  
Vol 15 ◽  
Author(s):  
Jiakai Lian ◽  
Yuxi Luo ◽  
Minglong Zheng ◽  
Jiaxi Zhang ◽  
Jiuxing Liang ◽  
...  
Keyword(s):  
Mental Illness ◽  
Information Flow ◽  
Phase Transfer ◽  
Sleep Stages ◽  
Control Group ◽  
High Morbidity ◽  
Beta Band ◽  
Frequency Bands ◽  
Depressed Patients ◽  
Cortical Regions

Depression is a prevalent mental illness with high morbidity and is considered the main cause of disability worldwide. Brain activity while sleeping is reported to be affected by such mental illness. To explore the change of cortical information flow during sleep in depressed patients, a delay symbolic phase transfer entropy of scalp electroencephalography signals was used to measure effective connectivity between cortical regions in various frequency bands and sleep stages. The patient group and the control group shared similar patterns of information flow between channels during sleep. Obvious information flows to the left hemisphere and to the anterior cortex were found. Moreover, the occiput tended to be the information driver, whereas the frontal regions played the role of the receiver, and the right hemispheric regions showed a stronger information drive than the left ones. Compared with healthy controls, such directional tendencies in information flow and the definiteness of role division in cortical regions were both weakened in patients in most frequency bands and sleep stages, but the beta band during the N1 stage was an exception. The computable sleep-dependent cortical interaction may provide clues to characterize cortical abnormalities in depressed patients and should be helpful for the diagnosis of depression.

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

SLEEP ◽  
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.

scholarly journals Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency-bands and temporal scales

2020 ◽  
Author(s):  
Megan Godfrey ◽  
Krish D. Singh
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Fine Scale ◽  
Beta Band ◽  
Temporal Scales ◽  
Frequency Bands ◽  
Functional Connections ◽  
Connectivity Patterns ◽  
Oscillatory Coupling ◽  
Beta Frequency

AbstractRecent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity’) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We then compared these MRVE connectivity patterns to those derived using more standard amplitude-amplitude coupling measures, using methods designed to quantify the consistency of these patterns across participants.Using oscillatory amplitude envelope correlation (AEC), the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies’, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections and these showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band.It was also found that average mid-to fine scale variability within each region (fS ∼ 10-150Hz) negatively correlated with the region’s overall connectivity strength with other brain areas, as measured by fine scale MRVE correlation (fS ∼ 30-150Hz) and by alpha and beta band AEC. These findings suggest that local activity at frequencies fS ≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity.

scholarly journals Between-sex variability of resting state functional brain networks in amyotrophic lateral sclerosis (ALS)

2021 ◽  
Author(s):  
Francesca Trojsi ◽  
Federica Di Nardo ◽  
Giuseppina Caiazzo ◽  
Mattia Siciliano ◽  
Giulia D’Alvano ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Resting State ◽  
Large Scale ◽  
Structural Changes ◽  
Occipital Cortex ◽  
Structural Heterogeneity ◽  
Anterior Cingulate ◽  
Sensorimotor Network ◽  
The Right ◽  
Lateral Sclerosis

AbstractThe organization of brain functional connectivity (FC) has been shown to differ between sexes. Amyotrophic lateral sclerosis (ALS) is characterized by sexual dimorphism, showing sex-specific trends in site of onset, phenotypes, and prognosis. Here, we explored resting state (RS) FC differences within major large-scale functional networks between women and men in a sample of ALS patients, in comparison to healthy controls (HCs). A group-level independent component analysis (ICA) was performed on RS-fMRI time-series enabling spatial and spectral analyses of large-scale RS FC networks in 45 patients with ALS (20 F; 25 M) and 31 HCs (15 F; 16 M) with a focus on sex-related differences. A whole-brain voxel-based morphometry (VBM) was also performed to highlight atrophy differences. Between-sex comparisons showed: decreased FC in the right middle frontal gyrus and in the precuneus within the default mode network (DMN), in affected men compared to affected women; decreased FC in the right post-central gyrus (sensorimotor network), in the right inferior parietal gyrus (right fronto-parietal network) and increased FC in the anterior cingulate cortex and right insula (salience network), in both affected and non-affected men compared to women. When comparing affected men to affected women, VBM analysis revealed atrophy in men in the right lateral occipital cortex. Our results suggest that in ALS sex-related trends of brain functional and structural changes are more heavily represented in DMN and in the occipital cortex, suggesting that sex is an additional dimension of functional and structural heterogeneity in ALS.

Modeling the hemodynamic response function using EEG-fMRI data during eyes-open resting-state conditions and motor task execution

2020 ◽  
Author(s):  
Prokopis C. Prokopiou ◽  
Alba Xifra-Porxas ◽  
Michalis Kassinopoulos ◽  
Marie-Hélène Boudrias ◽  
Georgios D. Mitsis
Keyword(s):  
Response Function ◽  
Resting State ◽  
Motor Task ◽  
Field Potential ◽  
Hemodynamic Response ◽  
Bold Signal ◽  
Hemodynamic Response Function ◽  
Beta Band ◽  
Experimental Conditions ◽  
Frequency Bands

AbstractIn this work, we investigated the regional characteristics of the dynamic interactions between oscillatory sources of ongoing neural activity obtained using electrophysiological recordings and the corresponding changes in the BOLD signal using simultaneous EEG-fMRI measurements acquired during a motor task, as well as under resting conditions. We casted this problem within a system-theoretic framework, where we initially performed distributed EEG source space reconstruction and subsequently employed block-structured linear and non-linear models to predict the BOLD signal from the instantaneous power in narrow frequency bands of the source local field potential (LFP) spectrum (<100 Hz). Our results suggest that the dynamics of the BOLD signal can be sufficiently described as the convolution between a linear combination of the power profile within individual frequency bands with a hemodynamic response function (HRF). During the motor task, BOLD signal variance was mainly explained by the EEG oscillations in the beta band. On the other hand, during resting-state all frequency bands of EEG exhibited significant contributions to BOLD signal variance. Moreover, the contribution of each band was found to be region specific. Our results also revealed considerable variability of the HRF across different brain regions. Specifically, sensory-motor cortices exhibited positive HRF shapes, whereas parietal and occipital cortices exhibited negative HRF shapes under both experimental conditions.

scholarly journals Discovering frequency sensitive thalamic nuclei from EEG microstate informed resting state fMRI

2020 ◽  
Author(s):  
Simon Schwab
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Fmri ◽  
Blood Oxygenation ◽  
Strongly Correlated ◽  
Thalamic Nuclei ◽  
Frequency Bands ◽  
Beta Frequency Band ◽  
Oxygenation Level ◽  
Beta Frequency

Microstates (MS), the fingerprints of the momentarily and time-varying states of the brain derived from electroencephalography (EEG), are associated with the resting state networks (RSNs). However, using MS fluctuations along different EEG frequency bands to model the functional MRI (fMRI) signal has not been investigated so far, or elucidated the role of the thalamus as a fundamental gateway and a putative key structure in cortical functional networks. Therefore, in the current study, we used MS predictors in standard frequency bands to predict blood oxygenation level dependent (BOLD) signal fluctuations. We discovered that multivariate modeling of BOLD-fMRI using six EEG-MS classes in eight frequency bands strongly correlated with thalamic areas and large-scale cortical networks. Thalamic nuclei exhibited distinct patterns of correlations for individual MS that were associated with specific EEG frequency bands. Anterior and ventral thalamic nuclei were sensitive to the beta frequency band, medial nuclei were sensitive to both alpha and beta frequency bands, and posterior nuclei such as the pulvinar were sensitive to delta and theta frequency bands. These results demonstrate that EEG-MS informed fMRI can elucidate thalamic activity not directly observable by EEG, which may be highly relevant to understand the rapid formation of thalamocortical networks.

scholarly journals Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study

2021 ◽  
Author(s):  
Hang Yang ◽  
Hong Zhang ◽  
Xin Di ◽  
Shuai Wang ◽  
Chun Meng ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Frequency Range ◽  
Frequency Bands ◽  
Temporal Characteristics ◽  
Subcortical Regions ◽  
Cap Analysis ◽  
Spatial And Temporal Characteristics

The resting-state human brain is a dynamic system that shows frequency-specific characteristics. Coactivation pattern (CAP) analysis has been recently used to identify recurring brain states sharing similar coactivation configurations. However, whether and how CAPs differ across different sub-frequency bands are unknown. In the current study, in addition to the typical low-frequency range (0.01 - 0.08 Hz), the spatial and temporal characteristics of CAPs in four sub-frequency bands, slow-5 (0.01 - 0.027 Hz), slow-4 (0.027 - 0.073 Hz), slow-3 (0.073 - 0.198 Hz), and slow-2 (0.198 - 0.25 Hz), were studied. Six CAP states were obtained for each band., The CAPs from the typical frequency range were spatially largely overlapped with those in slow-5, slow-4 and slow-3 but not with those in slow-2. With the increase of frequency, the CAP state became more unstable and resulted in an overall shorter persistence. The spatial and temporal characteristics of slow-4 and slow-5 were further compared, because they constitute most power of the resting-state fMRI signals. In general, slow-4 showed stronger coactivations or co-deactivations in subcortical regions, while slow-5 showed stronger coactivations or co-deactivations in large-scale cortical networks such as the dorsal attention network. Lastly, frequency-dependent dynamic alterations were also observed in schizophrenia patients. Combining the information obtained from both slow-5 and slow-4 increased the classification accuracy of schizophrenia patients than only using the typical range. In conclusion, our results revealed that the spatial and temporal characteristics of CAP state varied at different frequency bands, which could be helpful for identifying brain alterations in schizophrenia.

scholarly journals Two frequency bands contain the most stimulus-related information in visual cortex

2016 ◽  
Author(s):  
Christopher M. Lewis ◽  
Conrado A. Bosman ◽  
Nicolas M. Brunet ◽  
Bruss Lima ◽  
Mark J. Roberts ◽  
...  
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Single Cells ◽  
Field Potential ◽  
Oscillatory Activity ◽  
Natural Scenes ◽  
Beta Band ◽  
Frequency Bands ◽  
Related Information ◽  
Cortical Oscillations

AbstractSensory cortices represent the world through the activity of diversely tuned cells. How the activity of single cells is coordinated within populations and across sensory hierarchies is largely unknown. Cortical oscillations may coordinate local and distributed neuronal groups. Using datasets from intracortical multi-electrode recordings and from large-scale electrocorticography (ECoG) grids, we investigated how visual features could be extracted from the local field potential (LFP) and how this compared with the information available from multi-unit activity (MUA). MUA recorded from macaque V1 contained comparable amounts of information as simultaneously recorded LFP power in two frequency bands, one in the alpha-beta band and the other in the gamma band. ECoG-LFP contained information in the same bands as microelectrode-LFP, even when identifying natural scenes. The fact that information was contained in the same bands in both intracortical and ECoG recordings suggests that oscillatory activity could play similar roles at both spatial scales.

scholarly journals Working Memory Training Integrates Visual Cortex into Beta-Band Networks in Congenitally Blind Individuals

2017 ◽  
Author(s):  
Johanna M. Rimmele ◽  
Helene Gudi-Mindermann ◽  
Guido Nolte ◽  
Brigitte Röder ◽  
Andreas K. Engel
Keyword(s):  
Working Memory ◽  
Visual Cortex ◽  
Large Scale ◽  
Spectral Characteristics ◽  
Memory Training ◽  
Theta Band ◽  
Beta Band ◽  
Brain Areas ◽  
Congenitally Blind ◽  
Blind Individuals

AbstractCongenitally blind individuals activate the visual cortex during non-visual tasks. Such crossmodal reorganization is likely associated with changes in large-scale functional connectivity, the spectral characteristics of which can be assessed by analysis of neural oscillations. To test visual cortical integration into working memory networks, we recorded magnetoencephalographic data from congenitally blind and sighted individuals during resting state as well as during a voice-based working memory task prior to and following working memory training with voices, or tactile stimuli or a training-control condition. Auditory training strengthened beta-band (17.5-22.5 Hz) connectivity (imaginary coherency) in the blind and theta-band (2.5-5 Hz) connectivity in the sighted during the task, suggesting different neural coupling mechanisms. In the sighted, theta-band connectivity increased between brain areas involved in auditory working memory (inferior frontal, superior temporal, insular cortex). In the blind, beta-band networks largely emerged during the training, and connectivity increased between brain areas involved in auditory working memory and the visual cortex. The prominent involvement of the right fusiform face area in this beta-band network suggests a task-specific integration of visual cortex. Our findings highlight large-scale interactions as a key mechanism of functional reorganization following congenital blindness, and provide new insights into the spectral characteristics of the mechanism.

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