scholarly journals Brain-wide neural co-activations in resting human

2021 ◽  
Author(s):  
Lei Ding ◽  
Guofa Shou ◽  
Yoon-Hee Cha ◽  
John A. Sweeney ◽  
Han Yuan
Keyword(s):  
Resting State ◽  
Activation Patterns ◽  
Spontaneous Neural Activity ◽  
Co Activation ◽  
Eeg Data ◽  
Functional States ◽  
Processing Techniques ◽  
Human Brains ◽  
Signal Processing Techniques ◽  
Organizing Principles

AbstractSpontaneous neural activity in human as assessed with resting-state functional magnetic resonance imaging (fMRI) exhibits brain-wide coordinated patterns in the frequency of <0.1Hz. However, fast brain-wide networks at the timescales of neuronal events (milliseconds to sub-seconds) and their spatial, spectral, and propagational characteristics remain unclear due to the temporal constraints of hemodynamic signals. With milli-second resolution and whole-head coverage, scalp-based electroencephalography (EEG) provides a unique window into brain-wide networks with neuronal-timescale dynamics, shedding light on the organizing principles of brain function. Using state-of-the-art signal processing techniques, we reconstructed cortical neural tomography from resting-state EEG and extracted component-based co-activation patterns (cCAPs). These cCAPs revealed brain-wide intrinsic networks and their dynamics, indicating the configuration/reconfiguration of resting human brains into recurring and propagating functional states, which are featured with the prominent spatial phenomena of global patterns and anti-state pairs of co-(de)activations. Rich oscillational structures across a wide frequency band (i.e., 0.6Hz, 5Hz, and 10Hz) were embedded in the dynamics of these functional states. We further identified a superstructure that regulated between-state propagations and governed a significant aspect of brain-wide network dynamics. These findings demonstrated how resting-state EEG data can be functionally decomposed using cCAPs to reveal rich structures of brain-wide human neural activations.

scholarly journals Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

2019 ◽  
Author(s):  
Jianfeng Zhang ◽  
Zirui Huang ◽  
Shankar Tumati ◽  
Georg Northoff
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Peak Period ◽  
Physiological Noise ◽  
Activation Patterns ◽  
Global Signal ◽  
Co Activation ◽  
Human Connectome Project ◽  
Partial Dissociation ◽  
Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

scholarly journals Resting-State Co-activation Patterns as Promising Candidates for Prediction of Alzheimer’s Disease in Aged Mice

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohit H. Adhikari ◽  
Michaël E. Belloy ◽  
Annemie Van der Linden ◽  
Georgios A. Keliris ◽  
Marleen Verhoye
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Occurrence Rate ◽  
Seed Region ◽  
Periodic Patterns ◽  
Activation Patterns ◽  
Co Activation

Alzheimer’s disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-β (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting-state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task-positive resting-state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (a mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed RSNs and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and eight wild type littermates at 18 months of age. Subsequently, we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly co-activated and co-deactivated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level-dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near-perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate in the development of a diagnostic, and potentially, prognostic RS-fMRI biomarker of AD.

scholarly journals Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State fMRI Study in Propofol-Induced Loss of Consciousness

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e100012 ◽  
Author(s):  
Enrico Amico ◽  
Francisco Gomez ◽  
Carol Di Perri ◽  
Audrey Vanhaudenhuyse ◽  
Damien Lesenfants ◽  
...  
Keyword(s):  
Resting State ◽  
Cingulate Cortex ◽  
Resting State Fmri ◽  
Posterior Cingulate Cortex ◽  
Loss Of Consciousness ◽  
Activation Patterns ◽  
Posterior Cingulate ◽  
Fmri Study ◽  
Co Activation

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

scholarly journals Resting-state Co-activation Patterns as Accurate Predictors of Alzheimer’s Disease in Aged Mice

2020 ◽  
Author(s):  
Mohit H. Adhikari ◽  
Michaël E. Belloy ◽  
Annemie Van der Linden ◽  
Georgios A. Keliris ◽  
Marleen Verhoye
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Neurodegenerative Disorder ◽  
Occurrence Rate ◽  
Seed Region ◽  
Resting State Networks ◽  
Periodic Patterns ◽  
Activation Patterns ◽  
Co Activation

AbstractAlzheimer’s disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-beta (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task positive resting state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed resting state networks (RSNs) and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and 8 wild type littermates at 18 months of age. Subsequently we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly activated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate for a diagnostic, and potentially, prognostic biomarker of Alzheimer’s disease.

scholarly journals Co-activation patterns in resting-state fMRI signals

NeuroImage ◽  
2018 ◽  
Vol 180 ◽  
pp. 485-494 ◽  
Author(s):  
Xiao Liu ◽  
Nanyin Zhang ◽  
Catie Chang ◽  
Jeff H. Duyn
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Activation Patterns ◽  
Co Activation

scholarly journals Oscillatory brain states govern spontaneous fMRI network dynamics

2018 ◽  
Author(s):  
Daniel Gutierrez-Barragan ◽  
M. Albert Basson ◽  
Stefano Panzeri ◽  
Alessandro Gozzi
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Genetic Alterations ◽  
Spontaneous Brain Activity ◽  
Oscillatory Dynamics ◽  
Co Activation ◽  
Clustering Approach ◽  
Brain States ◽  
Functional States

AbstractSpontaneous brain activity as assessed with resting-state fMRI exhibits rich spatiotemporal structure. However, the principles by which brain-wide patterns of spontaneous fMRI activity reconfigure and interact with each other, remain unclear. We devised a frame-wise clustering approach to map spatiotemporal dynamics of spontaneous fMRI activity with voxel resolution in the resting mouse brain. We show that brain-wide patterns of fMRI co-activation can be reliably mapped at the group and subject level, defining a restricted set of recurring brain states characterized by rich network structure. We document that these functional states are characterized by contrasting patterns of spontaneous fMRI activity and exhibit coupled oscillatory dynamics, with each state occurring at specific phases of global fMRI signal fluctuations. Finally, we show that autism-associated genetic alterations result in the engagement of non-canonical brain states and altered coupled oscillatory dynamics. Our approach reveals a new set of fundamental principles guiding the spatiotemporal organization of resting state fMRI activity, and its disruption in brain disorders.

scholarly journals Spectral Analysis of Electroencephalographic Data in Serious Games

2021 ◽  
Vol 11 (6) ◽  
pp. 2480
Author(s):  
Branko Babusiak ◽  
Marian Hostovecky ◽  
Maros Smondrk ◽  
Ladislav Huraj
Keyword(s):  
Serious Games ◽  
Brain Activity ◽  
Logic Gates ◽  
Logical Thinking ◽  
Mental Task ◽  
Basic Logic ◽  
Eeg Activity ◽  
Eeg Data ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.

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