scholarly journals Instantaneous Brain Dynamics Mapped to a Continuous State Space

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.

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

NeuroImage ◽  
2021 ◽  
pp. 118551
Author(s):  
J.A. Galadí ◽  
S. Silva Pereira ◽  
Y. Sanz Perl ◽  
M.L. Kringelbach ◽  
I. Gayte ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

scholarly journals Instantaneous brain dynamics mapped to a continuous state space

NeuroImage ◽  
2017 ◽  
Vol 162 ◽  
pp. 344-352 ◽  
Author(s):  
Jacob C.W. Billings ◽  
Alessio Medda ◽  
Sadia Shakil ◽  
Xiaohong Shen ◽  
Amrit Kashyap ◽  
...  
Keyword(s):  
State Space ◽  
Brain Dynamics ◽  
Continuous State Space ◽  
Continuous State

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

NeuroImage ◽  
2018 ◽  
Vol 169 ◽  
pp. 46-56 ◽  
Author(s):  
Gustavo Deco ◽  
Joana Cabral ◽  
Victor M. Saenger ◽  
Melanie Boly ◽  
Enzo Tagliazucchi ◽  
...  
Keyword(s):  
In Silico ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

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

2017 ◽  
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.

Evolutionary Dynamics and Long-Run Selection

2006 ◽  
Vol 6 (1) ◽  
Author(s):  
Yossi Feinberg
Keyword(s):  
State Space ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Deterministic Dynamics ◽  
Long Run ◽  
Continuous State Space ◽  
Continuous State ◽  
Space Dynamics ◽  
Random Shocks

We study the emergence and nature of long-run behavior of continuous state space dynamics that are subjected to random shocks. It is shown that the fine details of the underlying deterministic dynamics may be crucial in determining the evolution of the system. In particular, a risk dominated strategy can emerge in an evolutionary game subject to symmetric decaying perturbations.

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

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.

scholarly journals Latent functional connectivity underlying multiple brain states

2021 ◽  
Author(s):  
Ethan M McCormick ◽  
Katelyn L Arnemann ◽  
Takuya Ito ◽  
Stephen Jose Hanson ◽  
Michael W Cole
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Architecture ◽  
Brain Activity ◽  
Brain Network ◽  
Mathematical Framework ◽  
Human Connectome Project ◽  
Brain States ◽  
Task Conditions ◽  
Psychometric G

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

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

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.

scholarly journals Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

2018 ◽  
Vol 20 (1) ◽  
pp. 171-196 ◽  
Author(s):  
Bin He ◽  
Abbas Sohrabpour ◽  
Emery Brown ◽  
Zhongming Liu
Keyword(s):  
Temporal Resolution ◽  
Functional Neuroimaging ◽  
Dimensional Space ◽  
Brain Activity ◽  
Brain Dynamics ◽  
High Temporal Resolution ◽  
Spatiotemporal Resolution ◽  
Source Imaging ◽  
Brain Functions ◽  
Wide Range

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

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

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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