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

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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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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Connectome Harmonic Decomposition of Human Brain Dynamics Reveals a Landscape of Consciousness

10.1101/2020.08.10.244459 ◽

2020 ◽

Cited By ~ 1

Author(s):

A.I. Luppi ◽

J. Vohryzek ◽

M.L. Kringelbach ◽

P.A.M. Mediano ◽

M.M. Craig ◽

...

Keyword(s):

Human Brain ◽

Wide Spectrum ◽

Brain Activity ◽

Conscious Experience ◽

Brain Dynamics ◽

Biological Phenomena ◽

Level Of Consciousness ◽

Harmonic Decomposition ◽

Meaningful Relationships ◽

Constituent Elements

ABSTRACTA central question in neuroscience is how cognition and consciousness arise from human brain activity. Here, we decompose cortical dynamics of resting-state functional MRI into their constituent elements: the harmonics of the human connectome. Mapping a wide spectrum of consciousness onto these elementary brain states reveals a generalisable connectome harmonic signature of loss of consciousness, whether due to anaesthesia or severe brain injury. Remarkably, its mirror-reversed image corresponds to the harmonic signature of the psychedelic state induced by ketamine or LSD, identifying meaningful relationships between neurobiology, brain function, and conscious experience. The repertoire of connectome harmonics further provides a fine-tuned indicator of level of consciousness, sensitive to differences in anaesthetic dose and clinically relevant sub-categories of patients with disorders of consciousness. Overall, we reveal that the emergence of consciousness from human brain dynamics follows the same universal principles shared by a multitude of physical and biological phenomena: the mathematics of harmonic modes.

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

NeuroImage ◽

10.1016/j.neuroimage.2021.118551 ◽

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

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EEG activity during balance platform test in humans

Cybernetics and Physics ◽

10.35470/2226-4116-2019-8-3-132-136 ◽

2019 ◽

pp. 132-136 ◽

Cited By ~ 1

Author(s):

Vladimir Khorev ◽

Artem Badarin ◽

Vladimir Antipov ◽

Vladimir Maksimenko ◽

Semen Kurkin

Keyword(s):

Cerebral Cortex ◽

Human Brain ◽

Brain Activity ◽

Body Posture ◽

Future Research ◽

Eeg Activity ◽

Common Features ◽

Computer Interfaces ◽

New Directions ◽

Brain States

In order to analyze different human brain states related to perception and maintaining of body posture, we implemented an experiment with a balance platform. It is known the cerebral cortex regulates subcortical postural centers to maintain upright balance and posture and balance demands. However, the cortical mechanisms that support standing balance remain elusive. In this work, we present an EEG-based analysis during execution of balance responses with distinct postural demands. The results suggest the existence of common features in the EEG structure associated with distinct activity during balance maintaining. This may give new directions for future research in the field of brain activity, and for the development of brain-computer interfaces.

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

Scientific Reports ◽

10.1038/s41598-017-17546-0 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 52

Author(s):

Selen Atasoy ◽

Leor Roseman ◽

Mendel Kaelen ◽

Morten L. Kringelbach ◽

Gustavo Deco ◽

...

Keyword(s):

Human Brain ◽

Brain Activity ◽

Harmonic Decomposition

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Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2018.09.010 ◽

2018 ◽

Vol 309 ◽

pp. 175-187 ◽

Cited By ~ 10

Author(s):

Daria La Rocca ◽

Nicolas Zilber ◽

Patrice Abry ◽

Virginie van Wassenhove ◽

Philippe Ciuciu

Keyword(s):

Human Brain ◽

Brain Activity ◽

Brain Dynamics ◽

Self Similarity ◽

Scale Free

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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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Oscillatory brain states govern spontaneous fMRI network dynamics

10.1101/393389 ◽

2018 ◽

Cited By ~ 7

Author(s):

Daniel Gutierrez-Barragan ◽

M. Albert Basson ◽

Stefano Panzeri ◽

Alessandro Gozzi

Keyword(s):

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Genetic Alterations ◽

Spontaneous Brain Activity ◽

Oscillatory Dynamics ◽

Co Activation ◽

Clustering Approach ◽

Brain States ◽

Functional States

AbstractSpontaneous brain activity as assessed with resting-state fMRI exhibits rich spatiotemporal structure. However, the principles by which brain-wide patterns of spontaneous fMRI activity reconfigure and interact with each other, remain unclear. We devised a frame-wise clustering approach to map spatiotemporal dynamics of spontaneous fMRI activity with voxel resolution in the resting mouse brain. We show that brain-wide patterns of fMRI co-activation can be reliably mapped at the group and subject level, defining a restricted set of recurring brain states characterized by rich network structure. We document that these functional states are characterized by contrasting patterns of spontaneous fMRI activity and exhibit coupled oscillatory dynamics, with each state occurring at specific phases of global fMRI signal fluctuations. Finally, we show that autism-associated genetic alterations result in the engagement of non-canonical brain states and altered coupled oscillatory dynamics. Our approach reveals a new set of fundamental principles guiding the spatiotemporal organization of resting state fMRI activity, and its disruption in brain disorders.

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On the edge of criticality: strength-dependent perturbation unveils delicate balance between fluctuation and oscillation in brain dynamics

10.1101/2021.09.23.461520 ◽

2021 ◽

Author(s):

Yontatan Sanz Perl ◽

Anira Escrichs ◽

Enzo Tagliazucchi ◽

Morten L Kringelbach ◽

Gustavo Deco

Keyword(s):

Human Brain ◽

Brain Function ◽

Large Scale ◽

Brain Activity ◽

Brain Dynamics ◽

Functional Networks ◽

Oscillatory Regime ◽

Delicate Balance ◽

Local Strength ◽

Global Strength

Going beyond previous research, we use strength-dependent perturbation to obtain a deeper understanding of the mechanisms underlying the emergence of large-scale brain activity. Despite decades of research, we still have a shallow understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used global strength-dependent perturbation to give a causal mechanistic description of human brain function providing a delicate balance between fluctuation and oscillation on the edge of criticality. After application of precise local strength-dependent perturbations and measuring the well-known perturbative complexity index, we demonstrated that the overall balance is shifted towards a fluctuating regime which is superior in terms of enhancing different functional networks compared to the oscillatory regime. This framework can generate specific, testable empirical predictions to be tested in human stimulation studies with strength-dependent rather than constant perturbation. Overall, our novel strength-dependent perturbation framework demonstrates that the human brain is poised on the edge of criticality, between fluctuations to oscillations, allowing for maximal flexibility.

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