Brain songs framework used for discovering the relevant timescale of the human brain

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Brain songs framework used for discovering the relevant timescale of the human brain. / Deco, Gustavo; Cruzat, Josephine; Kringelbach, Morten L.

In: Nature Communications, Vol. 10, 583, 04.02.2019.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

APA

CBE

MLA

Vancouver

Author

Deco, Gustavo ; Cruzat, Josephine ; Kringelbach, Morten L. / Brain songs framework used for discovering the relevant timescale of the human brain. In: Nature Communications. 2019 ; Vol. 10.

Bibtex

@article{b95c4060021b4df7accdda1ef0ac67e8,
title = "Brain songs framework used for discovering the relevant timescale of the human brain",
abstract = "A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data.",
keywords = "CELL ASSEMBLIES, DYNAMICS, FUNCTIONAL CONNECTIVITY, INDEPENDENT COMPONENT ANALYSIS, MEG, MODEL, NETWORK, NEURONAL AVALANCHES, RESTING-STATE FMRI, SINGLE",
author = "Gustavo Deco and Josephine Cruzat and Kringelbach, {Morten L}",
year = "2019",
month = "2",
day = "4",
doi = "10.1038/s41467-018-08186-7",
language = "English",
volume = "10",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - Brain songs framework used for discovering the relevant timescale of the human brain

AU - Deco, Gustavo

AU - Cruzat, Josephine

AU - Kringelbach, Morten L

PY - 2019/2/4

Y1 - 2019/2/4

N2 - A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data.

AB - A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below 0.1 Hz), other neuroimaging modalities such as MEG and EEG suggest that much faster timescales may be equally or more relevant for discovering spatiotemporal structure. Here, we introduce a novel way to generate whole-brain neural dynamical activity at the millisecond scale from fMRI signals. This method allows us to study the different timescales through binning the output of the model. These timescales can then be investigated using a method (poetically named brain songs) to extract the spacetime motifs at a given timescale. Using independent measures of entropy and hierarchy to characterize the richness of the dynamical repertoire, we show that both methods find a similar optimum at a timescale of around 200 ms in resting state and in task data.

KW - CELL ASSEMBLIES

KW - DYNAMICS

KW - FUNCTIONAL CONNECTIVITY

KW - INDEPENDENT COMPONENT ANALYSIS

KW - MEG

KW - MODEL

KW - NETWORK

KW - NEURONAL AVALANCHES

KW - RESTING-STATE FMRI

KW - SINGLE

UR - http://www.scopus.com/inward/record.url?scp=85061029378&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-08186-7

DO - 10.1038/s41467-018-08186-7

M3 - Journal article

VL - 10

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 583

ER -