scholarly journals A METHOD FOR INTEGRATIVE ANALYSIS OF LOCAL AND GLOBAL BRAIN DYNAMICS

2021 ◽  
Author(s):  
Robyn L. Miller ◽  
Victor M Vergara ◽  
Vince Calhoun
Keyword(s):  
Resting State ◽  
Network Connectivity ◽  
High Dimensional ◽  
Group Differences ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Time Resolved ◽  
Localized Patterns ◽  
Global Brain ◽  
The Brain

The most common pipelines for studying time-varying network connectivity in resting state functional magnetic resonance imaging (rs-fMRI) operate at the whole brain level, capturing a small discrete set of 'states' that best represent time-resolved joint measures of connectivity over all network pairs in the brain. This whole-brain hidden Markov model (HMM) approach 'uniformizes' the dynamics over what is typically more than 1000 pairs of networks, forcing each time-resolved high-dimensional observation into its best-matched high-dimensional state. While straightforward and convenient, this HMM simplification obscures functional and temporal nonstationarities that could reveal systematic, informative features of resting state brain dynamics at a more granular scale. We introduce a framework for studying functionally localized dynamics that intrinsically embeds them within a whole-brain HMM frame of reference. The approach is validated in a large rs-fMRI schizophrenia study where it identifies group differences in localized patterns of entropy and dynamics that help explain consistently observed differences between schizophrenia patients and controls in occupancy of whole-brain dFNC states more mechanistically.

scholarly journals Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Network ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

Abstract Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50–64 years versus older group (n = 310); age range, 65–91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local–global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

scholarly journals Whole-brain dynamics in aging: disruptions in functional connectivity and the role of the rich club

2020 ◽  
Author(s):  
Anira Escrichs ◽  
Carles Biarnes ◽  
Josep Garre-Olmo ◽  
José Manuel Fernández-Real ◽  
Rafel Ramos ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Rich Club ◽  
Brain Functional Network ◽  
Age Related ◽  
Age Range ◽  
The Brain

AbstractNormal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state fMRI studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent (BOLD) signals to analyze resting-state fMRI data from 620 subjects divided into two groups (‘middle-age group’ (n=310); age range, 50-65 years vs. ‘older group’ (n=310); age range, 66-91 years). Applying the Intrinsic-Ignition Framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.

scholarly journals Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Robyn L. Miller ◽  
Zening Fu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Early Stage ◽  
Network Connectivity ◽  
Functional Network ◽  
Normal Brain ◽  
Whole Brain ◽  
Mild Dementia ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Brain

BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

scholarly journals Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

2020 ◽  
Author(s):  
Kyesam Jung ◽  
Simon B. Eickhoff ◽  
Oleksandr V. Popovych
Keyword(s):  
Data Processing ◽  
Resting State ◽  
Optimal Parameter ◽  
Structural Connectivity ◽  
Model Performance ◽  
Brain Dynamics ◽  
Dynamical Modeling ◽  
Whole Brain ◽  
Brain Connectome ◽  
Network Properties

AbstractDynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functional connectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT) used for extraction of SC, cortical parcellations based on functional and anatomical brain properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

scholarly journals Stuck in a State of Inattention? Functional Hyperconnectivity as an Indicator of Disturbed Intrinsic Brain Dynamics in Adolescents With Concussion: A Pilot Study

ASN NEURO ◽  
2018 ◽  
Vol 10 ◽  
pp. 175909141775380 ◽  
Author(s):  
Angela M. Muller ◽  
Naznin Virji-Babul
Keyword(s):  
Diffusion Tensor Imaging ◽  
Pilot Study ◽  
Resting State ◽  
Diffusion Tensor ◽  
Brain Dynamics ◽  
Public Health Issue ◽  
Brain Correlates ◽  
Brain States ◽  
The Brain ◽  
Structural Brain

Sports-related concussion in youth is a major public health issue. Evaluating the diffuse and often subtle changes in structure and function that occur in the brain, particularly in this population, remains a significant challenge. The goal of this pilot study was to evaluate the relationship between the intrinsic dynamics of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) and relate these findings to structural brain correlates from diffusion tensor imaging in a group of adolescents with sports-related concussions ( n = 6) and a group of healthy adolescent athletes ( n = 6). We analyzed rs-fMRI data using a sliding windows approach and related the functional findings to structural brain correlates by applying graph theory analysis to the diffusion tensor imaging data. Within the resting-state condition, we extracted three separate brain states in both groups. Our analysis revealed that the brain dynamics in healthy adolescents was characterized by a dynamic pattern, shifting equally between three brain states; however, in adolescents with concussion, the pattern was more static with a longer time spent in one brain state. Importantly, this lack of dynamic flexibility in the concussed group was associated with increased nodal strength in the left middle frontal gyrus, suggesting reorganization in a region related to attention. This preliminary report shows that both the intrinsic brain dynamics and structural organization are altered in networks related to attention in adolescents with concussion. This first report in adolescents will be used to inform future studies in a larger cohort.

Towards an Efficient Validation of Dynamical Whole-brain Models

2021 ◽  
Author(s):  
Kevin J. Wischnewski ◽  
Simon B. Eickhoff ◽  
Viktor K. Jirsa ◽  
Oleksandr V. Popovych
Keyword(s):  
Structural Connectivity ◽  
Three Dimensional ◽  
Bayesian Optimization ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Phase Oscillators ◽  
Dimensional Parameter ◽  
Whole Brain ◽  
Brain Models

Abstract Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

scholarly journals Effects of Cognitive Training in Mild Cognitive Impairmentmeasured by Resting State Functional Imaging

2020 ◽  
Vol 10 (11) ◽  
pp. 175
Author(s):  
Seungho Kim ◽  
Eunhee Park ◽  
Hyunsil Cha ◽  
Jae-Chang Jung ◽  
Tae-Du Jung ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Training Program ◽  
Cognitive Training ◽  
Resting State ◽  
Cognitive Abilities ◽  
Motor Coordination ◽  
Network Connectivity ◽  
Functional Magnetic Resonance ◽  
Function Test ◽  
The Brain

Mild cognitive impairment (MCI) is defined as an intermediate state of cognitive alteration between normal aging and dementia. In this study, we performed a functional network connectivity analysis using resting-state functional magnetic resonance imaging to investigate the association between changes in functional connectivity in the brain and the improvement in cognitive abilities after cognitive training. A computerized cognitive training program was used to improve the abilities of fifteen participants with MCI. The cognitive training program (Comcog), which consists of three weekly sessions totaling 90 min, was conducted with all participants over six weeks. The cognitive abilities before (pre-Comcog) and after (post-Comcog) the cognitive training process were measured using a neurocognitive function test. After the Comcog, the participants enhanced their visual and verbal memories, attention, and visuo-motor coordination. The functional connectivity between cingulo-opercular (CON) and default mode (DMN) showed significant improvements after Comcog training. Therefore, our study suggests that cognitive training may improve the cognitive abilities of participants. This improvement was associated with an increase in the functional connectivity between DMN and CON. The increase in functional connectivity after cognitive training was specifically associated with overall cognitive functions, including executive, memory, decision-making, and motivational functions.

scholarly journals Alterations of resting state functional network connectivity in the brain of nicotine and alcohol users

NeuroImage ◽  
2017 ◽  
Vol 151 ◽  
pp. 45-54 ◽  
Author(s):  
Victor M. Vergara ◽  
Jingyu Liu ◽  
Eric D. Claus ◽  
Kent Hutchison ◽  
Vince Calhoun
Keyword(s):  
Resting State ◽  
Network Connectivity ◽  
Functional Network ◽  
Functional Network Connectivity ◽  
Alcohol Users ◽  
The Brain

scholarly journals Intrinsic timescales as an organizational principle of neural processing across the whole rhesus macaque brain

2021 ◽  
Author(s):  
Ana M.G. Manea ◽  
Anna Zilverstand ◽  
Kamil Ugurbil ◽  
Sarah R. Heilbronner ◽  
Jan Zimmermann
Keyword(s):  
High Field ◽  
Temporal Dynamics ◽  
High Dimensional ◽  
Neural Processing ◽  
Ultra High Field ◽  
Whole Brain ◽  
Spatial Gradients ◽  
Organizational Principle ◽  
Computational Property ◽  
The Brain

Hierarchical temporal dynamics are a fundamental computational property of the brain; however, there are no whole-brain, noninvasive investigations into timescales of neural processing in animal models. To that end, we used the spatial resolution and sensitivity of ultra-high field fMRI to probe timescales across the whole macaque brain. We uncovered within-species consistency between timescales estimated from fMRI and electrophysiology. Crucially, we were not only able to demonstrate that we can replicate existing electrophysiological hierarchies, but we extended these to whole brain topographies. Our results validate the complementary use of hemodynamic and electrophysiological intrinsic timescales, establishing a basis for future translational work. Second, with those results in hand, we were able to show that one facet of the high-dimensional FC topography of any region in the brain is closely related to hierarchical temporal dynamics. We demonstrated that intrinsic timescales are organized along spatial gradients that closely match functional connectivity gradient topographies across the whole brain. We conclude that intrinsic timescales are an unifying organizational principle of neural processing across the whole brain.

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