Capturing the non-stationarity of whole-brain dynamics underlying human brain states

Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development

Journal of Artificial General Intelligence ◽

10.2478/jagi-2013-0009 ◽

2013 ◽

Vol 4 (3) ◽

pp. 153-163 ◽

Cited By ~ 1

Author(s):

Jeff Alstott

Keyword(s):

Human Brain ◽

Complex Network ◽

Human Behavior ◽

Brain Dynamics ◽

Human Society ◽

Whole Brain ◽

Complex Technology ◽

The Impact

Abstract Whole brain emulation (WBE) is the possible replication of human brain dynamics that reproduces human behavior. If created, WBE would have significant impact on human society, and forecasts frequently place WBE as arriving within a century. However, WBE would be a complex technology with a complex network of prerequisite technologies. Most forecasts only consider a fraction of this technology network. The unconsidered portions of the network may contain bottlenecks, which are slowly-developing technologies that would impede the development of WBE. Here I describe how bottlenecks in the network can be non-obvious, and the merits of identifying them early. I show that bottlenecks may be predicted even with noisy forecasts. Accurate forecasts of WBE development must incorporate potential bottlenecks, which can be found using detailed descriptions of the WBE technology network. Bottlenecks identification can also increase the impact of WBE researchers by directing effort to those technologies that will immediately affect the timeline of WBE development

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Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

NeuroImage ◽

10.1016/j.neuroimage.2017.12.009 ◽

2018 ◽

Vol 169 ◽

pp. 46-56 ◽

Cited By ~ 35

Author(s):

Gustavo Deco ◽

Joana Cabral ◽

Victor M. Saenger ◽

Melanie Boly ◽

Enzo Tagliazucchi ◽

...

Keyword(s):

In Silico ◽

Brain Dynamics ◽

Whole Brain ◽

Brain States

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Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD

10.1101/163667 ◽

2017 ◽

Cited By ~ 1

Author(s):

Selen Atasoy ◽

Leor Roseman ◽

Mendel Kaelen ◽

Morten L. Kringelbach ◽

Gustavo Deco ◽

...

Keyword(s):

Human Brain ◽

Brain Activity ◽

Power Laws ◽

Lysergic Acid ◽

Brain Dynamics ◽

Co Activation ◽

Harmonic Decomposition ◽

Health And Disease ◽

Novel Method ◽

Brain States

ABSTRACTRecent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used ‘connectome-harmonic decomposition’, a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

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Turbulent-like dynamics in the human brain

10.1101/865923 ◽

2019 ◽

Author(s):

Gustavo Deco ◽

Morten L. Kringelbach

Keyword(s):

Human Brain ◽

Brain Function ◽

Information Transfer ◽

Large Scale ◽

Brain Dynamics ◽

Whole Brain ◽

Large Scale Network ◽

Maximal Sensitivity ◽

Scale Network ◽

Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

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Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

Communications Biology ◽

10.1038/s42003-021-02369-7 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Joan Rué-Queralt ◽

Angus Stevner ◽

Enzo Tagliazucchi ◽

Helmut Laufs ◽

Morten L. Kringelbach ◽

...

Keyword(s):

Human Brain ◽

Brain Activity ◽

Group Analysis ◽

Brain Dynamics ◽

Sleep Stages ◽

Temporal Representation ◽

Current State ◽

Brain States ◽

Spatio Temporal ◽

Low Dimensional

AbstractCurrent state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

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Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep

10.1101/2021.03.23.436551 ◽

2021 ◽

Author(s):

J. Rué-Queralt ◽

A. Stevner ◽

E. Tagliazucchi ◽

H. Laufs ◽

M. L. Kringelbach ◽

...

Keyword(s):

Human Brain ◽

Brain Activity ◽

Group Analysis ◽

Brain Dynamics ◽

Sleep Stages ◽

Temporal Representation ◽

Average Accuracy ◽

Brain States ◽

Spatio Temporal ◽

Low Dimensional

AbstractCurrent state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a novel method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this novel intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with an average accuracy of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.

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The effect of external stimulation on functional networks in the aging healthy human brain

10.1101/2021.11.19.469206 ◽

2021 ◽

Author(s):

Anira Escrichs ◽

Yonatan Sanz Perl ◽

Noelia Martinez-Molina ◽

Carles Biarnes ◽

Josep Garre ◽

...

Keyword(s):

In Silico ◽

Brain Area ◽

Resting State Fmri ◽

Brain Dynamics ◽

Healthy Human ◽

Whole Brain ◽

External Stimulation ◽

Rich Club ◽

Brain States ◽

The Brain

Understanding the brain changes occurring during aging can provide new insights for developing treatments that alleviate or reverse cognitive decline. Neurostimulation techniques have emerged as potential treatments for brain disorders and to improve cognitive functions. Nevertheless, given the ethical restrictions of neurostimulation approaches, in silico perturbation protocols based on causal whole-brain models are fundamental to gaining a mechanistic understanding of brain dynamics. Furthermore, this strategy could serve as a more specific biomarker relating local activity with global brain dynamics. Here, we used a large resting-state fMRI dataset divided into middle-aged (N=310, aged < 65 years) and older adults (N=310, aged >= 65) to characterize brain states in each group as a probabilistic metastable substate (PMS) space, each with a probabilistic occurrence and frequency. Then, we fitted the PMS to a whole-brain model and applied in silico stimulations with different intensities in each node to force transitions from the brain states of the older group to the middle-age group. We found that the precuneus, a brain area belonging to the default mode network and the rich club, was the best stimulation target. These findings might have important implications for designing neurostimulation interventions to revert the effects of aging on whole-brain dynamics.

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Instantaneous Brain Dynamics Mapped to a Continuous State Space

10.1101/157115 ◽

2017 ◽

Author(s):

Jacob Billings ◽

Alessio Medda ◽

Sadia Shakil ◽

Xiaohong Shen ◽

Amrit Kashyap ◽

...

Keyword(s):

State Space ◽

Dimensional Space ◽

Brain Activity ◽

Dimensional Structure ◽

Brain Dynamics ◽

Whole Brain ◽

Human Connectome Project ◽

Continuous State ◽

Brain States ◽

Space Dynamics

AbstractMeasures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain’s dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brain’s surface reveals the patterns of brain activity that support each experimental state.

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