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

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  • Gustavo Deco, School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC, 3800, Australia. gustavo.deco@upf.edu.
  • ,
  • Josephine Cruzat, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
  • ,
  • Morten L Kringelbach

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.

Original languageEnglish
Article number583
JournalNature Communications
Volume10
Number of pages13
ISSN2041-1723
DOIs
Publication statusPublished - 4 Feb 2019

    Research areas

  • CELL ASSEMBLIES, DYNAMICS, FUNCTIONAL CONNECTIVITY, INDEPENDENT COMPONENT ANALYSIS, MEG, MODEL, NETWORK, NEURONAL AVALANCHES, RESTING-STATE FMRI, SINGLE

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