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Brain network dynamics are hierarchically organized in time

  1. Mark W. Woolricha,b
  1. aOxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom;
  2. bOxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
  1. Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved September 28, 2017 (received for review April 3, 2017)

  1. Fig. 2.

    Transitions between brain networks are not random, giving rise to two very distinct sets of states, which are referred to as metastates. (A) Transition probability matrix (Top Left) indicates the probability of transitioning from any state to another, showing that some transitions are much more likely than others. This is apparent when shown in graph format (Top Right and Bottom), where the nodes represent brain states and the thickness of the arrows represents the state transition probability (transitions were thresholded for readability). (B) FO matrix, which contains the total time spent in each state per subject, exhibits exceptionally strong correlations between states across subjects. Even more strongly than the transition probability matrix, these correlations indicate a clear hierarchical metastate structure. Hierarchical clustering (illustrated above the FO correlation matrix) confirms this result. (C) Transition probability matrix for the 100 subjects with the highest occupancy for metastate 1 and the 100 subjects with the highest occupancy for metastate 2.

  2. Fig. 3.

    Two metastates contain distinctive functional areas. Whereas the first metastate is associated with sensory (somatic, visual, and auditory) and motor regions, the second metastate involves areas related to higher order cognition (including regions of the DMN, language, and extensive prefrontal areas). This is apparent in both the activation level and connectivity. (A) In the first case, we look at the average absolute amplitude of each region within the metastate, which can be interpreted as a measure of the amount of deviation from average activity levels. (B) In the second case, we compute the connectedness, or degree, defined as the sum of functional connectivity of each region with the rest of the brain.

  3. Fig. 4.

    Brain network dynamics and the metastate profile are subject-specific, relate to behavior, and are heritable. (A) Using behavioral traits (well-being, intelligence, and personality) as regressors, we can significantly predict the states’ FO and (even more accurately) the metastate profile. H0, null hypothesis. (B) Metastate profile separates “positive” from “negative” traits, which correlate to the metastate profile with the opposite signs; the statistical significance of this correlation is highlighted (variables correlate when they have the same color and anticorrelate otherwise), and the font size corresponds to the magnitude of the correlation. TN, true negatives; TP, true positives. (C) FO of the metastates for a given session can be accurately predicted for each subject using information from the other sessions, suggesting that the metastate profile is a very specific subject fingerprint. (D) Metastate (Left) and state (Middle) distributions are strongly heritable; the state distribution is heritable (but to a lesser extent) if we regress out the metastate information (Right). Non-ident., nonidentical.

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