Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

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Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

Network Neuroscience ◽

10.1162/netn_a_00177 ◽

2020 ◽

pp. 1-21

Author(s):

Alexandra Anagnostopoulou ◽

Charis Styliadis ◽

Panagiotis Kartsidis ◽

Evangelia Romanopoulou ◽

Vasiliki Zilidou ◽

...

Keyword(s):

Down Syndrome ◽

Functional Connectivity ◽

Cognitive Training ◽

Resting State ◽

Network Architecture ◽

Right Hemisphere ◽

Random Network ◽

Brain Activity ◽

Hierarchical Organization ◽

The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

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Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

10.1101/2021.02.08.430334 ◽

2021 ◽

Author(s):

Takashi Nakano ◽

Masahiro Takamura ◽

Haruki Nishimura ◽

Maro Machizawa ◽

Naho Ichikawa ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Posterior Cingulate Cortex ◽

Fmri Data ◽

Independent Test ◽

The Individual ◽

The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

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Altered Intra- and Interregional Synchronization in Resting-State Cerebral Networks Associated with Chronic Tinnitus

Neural Plasticity ◽

10.1155/2015/475382 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 20

Author(s):

Yu-Chen Chen ◽

Jian Zhang ◽

Xiao-Wei Li ◽

Wenqing Xia ◽

Xu Feng ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Inferior Frontal Gyrus ◽

Resting State Fmri ◽

Brain Regions ◽

Chronic Tinnitus ◽

Healthy Controls ◽

Tinnitus Distress ◽

The Right

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.

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Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.649074 ◽

2021 ◽

Vol 15 ◽

Author(s):

Na Xu ◽

Wei Shan ◽

Jing Qi ◽

Jianping Wu ◽

Qun Wang

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Preoperative Evaluation ◽

Brain Regions ◽

Electrophysiological Recording ◽

Epileptogenic Zone ◽

Resting State Functional Connectivity ◽

Presurgical Evaluation ◽

Resting State Connectivity ◽

The Brain

Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source–space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.

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Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/452503 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 21

Author(s):

Andreas A. Ioannides ◽

Stavros I. Dimitriadis ◽

George A. Saridis ◽

Marotesa Voultsidou ◽

Vahe Poghosyan ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Activity ◽

Clinical Applications ◽

Brain Areas ◽

Connectivity Patterns ◽

The Brain ◽

Typical Subject ◽

Different Parts ◽

Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

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Brain activity fluctuations propagate as waves traversing the cortical hierarchy

10.1101/2020.08.18.256610 ◽

2020 ◽

Author(s):

Yameng Gu ◽

Lucas E. Sainburg ◽

Sizhe Kuang ◽

Feng Han ◽

Jack W. Williams ◽

...

Keyword(s):

Functional Connectivity ◽

Spontaneous Activity ◽

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Hierarchical Organization ◽

Subcortical Structures ◽

Cortical Hierarchy ◽

Neural Origin ◽

The Brain

AbstractThe brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

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Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

10.1101/186098 ◽

2017 ◽

Cited By ~ 1

Author(s):

Giri P. Krishnan ◽

Oscar C. González ◽

Maxim Bazhenov

Keyword(s):

Computational Model ◽

Resting State ◽

Large Scale ◽

Brain Activity ◽

Brain Regions ◽

Brain Network ◽

Ion Concentration ◽

Brain State ◽

The Brain

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

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Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

10.1101/2020.12.11.422071 ◽

2020 ◽

Author(s):

Behnaz Yousefi ◽

Shella Keilholz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Spatial Organization ◽

Resting State Fmri ◽

Brain Regions ◽

Intrinsic Activity ◽

Resting State Networks ◽

Whole Brain ◽

Functional Connections ◽

Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

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Learning differentially reorganizes brain activity and connectivity

10.1101/2020.02.23.961623 ◽

2020 ◽

Author(s):

Maxwell A. Bertolero ◽

Azeez Adebimpe ◽

Ankit N. Khambhati ◽

Marcelo G. Mattar ◽

Daniel Romer ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Learning Performance ◽

Functional Architecture ◽

Time Space ◽

Novel Objects ◽

Value Learning ◽

The Brain

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

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Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy

Cerebral Cortex ◽

10.1093/cercor/bhab064 ◽

2021 ◽

Author(s):

Yameng Gu ◽

Lucas E Sainburg ◽

Sizhe Kuang ◽

Feng Han ◽

Jack W Williams ◽

...

Keyword(s):

Functional Connectivity ◽

Spontaneous Activity ◽

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Hierarchical Organization ◽

Subcortical Structures ◽

Cortical Hierarchy ◽

Neural Origin ◽

The Brain

Abstract The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

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