scholarly journals DREAM

2021 ◽  
Author(s):  
Zhu-Qing Gong ◽  
Peng Gao ◽  
Chao Jiang ◽  
Xiu-Xia Xing ◽  
Hao-Ming Dong ◽  
...  
Keyword(s):  
Brain Activity ◽  
Worked Examples ◽  
Individual Variability ◽  
Head Movements ◽  
Neural Oscillations ◽  
Head Motion ◽  
Multiple Frequency ◽  
Frequency Bands ◽  
Human Connectome Project ◽  
The Brain

AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation’s amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

scholarly journals DREAM: A Toolbox to Decode Rhythms of the Brain System

2020 ◽  
Author(s):  
Zhu-Qing Gong ◽  
Peng Gao ◽  
Chao Jiang ◽  
Xiu-Xia Xing ◽  
Hao-Ming Dong ◽  
...  
Keyword(s):  
Human Brain ◽  
Worked Examples ◽  
Head Movements ◽  
Neural Oscillations ◽  
Head Motion ◽  
Multiple Frequency ◽  
Frequency Bands ◽  
Brain System ◽  
Human Connectome Project ◽  
The Brain

AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies, which can be observed with multiple modalities. Following the natural log linear law of frequency distribution, these oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. This perspective on neural oscillations has been increasingly applied to study human brain function and related behaviors. In practice, relevant signals are commonly measured as a discrete time series, and thus the sampling period and number of samples determine the number and ranges of decodable frequency intervals. However, these limits have been often ignored by researchers who instead decode measured oscillations into multiple frequency intervals using a fixed sample period and numbers of samples. One reason for such misuse is the lack of an easy-to-use toolbox to implement automatic decomposition of frequency intervals. We report on a toolbox with a graphical user interface for achieving local and remote decoding rhythms of the brain system (DREAM) which is accessible to the public via GitHub. We provide worked examples of DREAM used to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM analyzed the head motion oscillations and found that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age interval from 7 to 9 years. It is interesting that the higher frequency bands contains more head movements, and showed stronger age-motion associations but the weaker sex-motion interactions. Using the fast functional magnetic resonance imaging data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. A novel result indicated that the higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, these findings demonstrated the applicability of DREAM for frequency-specific human brain mapping as well as the assessments on their measurement reliability and validity.

scholarly journals Single or Multi-Frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data

2016 ◽  
Author(s):  
Gustavo Deco ◽  
Joana Cabral ◽  
Mark W. Woolrich ◽  
Angus B.A Stevner ◽  
Tim J. van Hartevelt ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Area ◽  
Temporal Structure ◽  
Single Frequency ◽  
Multiple Frequency ◽  
Whole Brain ◽  
Frequency Bands ◽  
Band Limited ◽  
The Brain

AbstractDuring rest, envelopes of band-limited on-going MEG signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of the simulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies.Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.

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

2020 ◽  
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.

scholarly journals Quantification of EEG Brain Activity in the Self-Paced Regime

2018 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Joana Silva ◽  
A. Martins Da Silva ◽  
Luís Coelho
Keyword(s):  
Brain Activity ◽  
Rhythmic Activity ◽  
The Self ◽  
Automatic Quantification ◽  
Frequency Bands ◽  
Cerebral Activity ◽  
Cognitive Information ◽  
Specific Frequency ◽  
Electroencephalogram Eeg ◽  
The Brain

The processing of motor, sensory and cognitive information by the brain can result in changes of the electroencephalogram (EEG) by Event Related Desynchronization (ERD) or Event Related Synchronization (ERS). The first one concerns a decrease in the amplitude of a rhythmic activity while the second corresponds to its increase. The analysis of these two phenomena in specific frequency bands - alpha (8-13 Hz) and beta (14-30 Hz) - allows the understanding of the cerebral activity. This study focuses on the quantification of cerebral activity by determining the ERD and ERS on the referred band, induced by self-paced movements, by using EEGLAB and MATLAB tools. This was achieved by the creation of a new and automatic quantification algorithm. The results indicate that a greater desynchronization of the signal is accompanied by a decrease in the amplitude of the same. As a conclusion, the cerebral activity varies in terms of synchronization and desynchronization among certain frequency bands in several zones, according to the tasks performed.

scholarly journals A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

2021 ◽  
Author(s):  
SUBBA REDDY OOTA ◽  
Archi Yadav ◽  
Arpita Dash ◽  
Surampudi Bapi Raju ◽  
Avinash Sharma
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
Complex Dynamics ◽  
Brain Activity ◽  
Structural Connectivity ◽  
Reaction Diffusion ◽  
Complex Nature ◽  
Relational Structures ◽  
Human Connectome Project ◽  
The Brain

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of human brain function.

scholarly journals The HCP 7T Retinotopy Dataset: Description and pRF Analysis

2018 ◽  
Author(s):  
Noah C. Benson ◽  
Keith W. Jamison ◽  
Michael J. Arcaro ◽  
An Vu ◽  
Matthew F. Glasser ◽  
...  
Keyword(s):  
Visual Processing ◽  
Large Scale ◽  
Individual Variability ◽  
7 Tesla ◽  
Individual Subject ◽  
Retinotopic Mapping ◽  
Human Connectome Project ◽  
The Brain ◽  
Model Based Analysis

AbstractAbout a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as ‘retinotopic mapping’. One of the datasets collected by the Human Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other HCP measures acquired in these same participants.

scholarly journals Brain hubs defined in the group do not overlap with regions of high inter-individual variability

2021 ◽  
Author(s):  
Derek Martin Smith ◽  
Brian T Kraus ◽  
Ally Dworetsky ◽  
Evan M Gordon ◽  
Caterina Gratton
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Critical Role ◽  
Brain Regions ◽  
Individual Variability ◽  
Spatial Proximity ◽  
Functional Magnetic Resonance ◽  
Multiple Networks ◽  
Human Connectome Project ◽  
The Brain

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability, to better understand if hubs are (a) relatively conserved across people, (b) locations with malleable connectivity, leading individuals to show variable hub profiles, or (c) artifacts arising from cross-person variation. To answer this question, we compared the locations of hubs and regions of strong idiosyncratic functional connectivity ("variants") in both the Midnight Scan Club and Human Connectome Project datasets. Group hubs defined based on the participation coefficient did not overlap strongly with variants. These hubs have relatively strong similarity across participants and consistent cross-network profiles. Consistency across participants was further improved when participation coefficient hubs were allowed to shift slightly in local position. Thus, our results demonstrate that group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density, which are based on spatial proximity and show higher correspondence to locations of individual variability.

Imagined Temporal Groupings Tune Oscillatory Neural Activity for Processing Rhythmic Sounds

2015 ◽  
Vol 3 (1-2) ◽  
pp. 172-188
Author(s):  
Brandon T. Paul ◽  
Per B. Sederberg ◽  
Lawrence L. Feth
Keyword(s):  
Multivariate Analysis ◽  
Neural Activity ◽  
Neural Oscillations ◽  
Neural Activation ◽  
Focus Attention ◽  
Complex Sound ◽  
Frequency Bands ◽  
Hierarchical Timing ◽  
Electroencephalogram Eeg ◽  
The Brain

Temporal patterns within complex sound signals, such as music, are not merely processed after they are heard. We also focus attention to upcoming points in time to aid perception, contingent upon regularities we perceive in the sounds’ inherent rhythms. Such organized predictions are endogenously maintained as meter — the patterning of sounds into hierarchical timing levels that manifest as strong and weak events. Models of neural oscillations provide potential means for how meter could arise in the brain, but little evidence of dynamic neural activity has been offered. To this end, we conducted a study instructing participants to imagine two-based or three-based metric patterns over identical, equally-spaced sounds while we recorded the electroencephalogram (EEG). In the three-based metric pattern, multivariate analysis of the EEG showed contrasting patterns of neural oscillations between strong and weak events in the delta (2–4 Hz) and alpha (9–14 Hz), frequency bands, while theta (4–9 Hz) and beta (16–24 Hz) bands contrasted two hierarchically weaker events. In two-based metric patterns, neural activity did not drastically differ between strong and weak events. We suggest the findings reflect patterns of neural activation and suppression responsible for shaping perception through time.

scholarly journals Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

2020 ◽  
Author(s):  
Parham Mostame ◽  
Sepideh Sadaghiani
Keyword(s):  
Information Exchange ◽  
Spatial Organization ◽  
Brain Activity ◽  
The State ◽  
Neural Oscillations ◽  
Cognitive State ◽  
State Dependency ◽  
Frequency Bands ◽  
Cognitive States ◽  
The Impact

AbstractFunctional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

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