Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics

The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100–900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.

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Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽

10.1371/journal.pone.0082715 ◽

2013 ◽

Vol 8 (12) ◽

pp. e82715 ◽

Cited By ~ 29

Author(s):

Guihua Jiang ◽

Xue Wen ◽

Yingwei Qiu ◽

Ruibin Zhang ◽

Junjing Wang ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Functional Networks ◽

Whole Brain ◽

Brain Functional Networks ◽

Fmri Study ◽

Topological Organization

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Noninvasive Ultrasonic Drug Uncaging Maps Whole-Brain Functional Networks

Neuron ◽

10.1016/j.neuron.2018.10.042 ◽

2018 ◽

Vol 100 (3) ◽

pp. 728-738.e7 ◽

Cited By ~ 23

Author(s):

Jeffrey B. Wang ◽

Muna Aryal ◽

Qian Zhong ◽

Daivik B. Vyas ◽

Raag D. Airan

Keyword(s):

Functional Networks ◽

Whole Brain ◽

Brain Functional Networks

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Determination of Dynamic Brain Connectivity via Spectral Analysis

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.655576 ◽

2021 ◽

Vol 15 ◽

Author(s):

Peter A. Robinson ◽

James A. Henderson ◽

Natasha C. Gabay ◽

Kevin M. Aquino ◽

Tara Babaie-Janvier ◽

...

Keyword(s):

Spectral Analysis ◽

Brain Structure ◽

Brain Connectivity ◽

Spatial Scales ◽

Effective Connectivity ◽

Physical Nature ◽

Building Blocks ◽

Brain Dynamics ◽

Compact Representation ◽

The Brain

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

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Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PLoS Computational Biology ◽

10.1371/journal.pcbi.1004445 ◽

2015 ◽

Vol 11 (8) ◽

pp. e1004445 ◽

Cited By ~ 42

Author(s):

Adrián Ponce-Alvarez ◽

Biyu J. He ◽

Patric Hagmann ◽

Gustavo Deco

Keyword(s):

Cortical Activity ◽

Activity Space ◽

Whole Brain ◽

Brain Modeling ◽

The Brain

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Awakening: Predicting external stimulation to force transitions between different brain states

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1905534116 ◽

2019 ◽

Vol 116 (36) ◽

pp. 18088-18097 ◽

Cited By ~ 28

Author(s):

Gustavo Deco ◽

Josephine Cruzat ◽

Joana Cabral ◽

Enzo Tagliazucchi ◽

Helmut Laufs ◽

...

Keyword(s):

Fundamental Problem ◽

Effective Connectivity ◽

Brain State ◽

Whole Brain ◽

External Stimulation ◽

Neuropsychiatric Diseases ◽

Brain Modeling ◽

Brain States ◽

Definition Of ◽

The Brain

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.

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

Cerebral Cortex ◽

10.1093/cercor/bhaa367 ◽

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.

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