Research ArticleCOGNITIVE NEUROSCIENCE

Cognitive chimera states in human brain networks

See allHide authors and affiliations

Science Advances  03 Apr 2019:
Vol. 5, no. 4, eaau8535
DOI: 10.1126/sciadv.aau8535
  • Fig. 1 Design of the in silico experiments.

    (A) We construct personalized BNMs by estimating white matter anatomical connectivity of the brain using DSI. This connectivity is combined with a brain parcellation scheme with 76 cortical and subcortical regions to obtain a large-scale connectivity map of the regional brain volume. These regions constitute the nodes of the structural brain network whose dynamics are simulated by nonlinear WCOs, coupled through the structural connectivity map of a given subject (see Materials and Methods). (B) In the resulting data-driven models of the spatiotemporal dynamics of the brain, each brain region is systematically stimulated across a cohort of 30 subjects. The spread of the stimulation is measured through synchronization within the brain network.

  • Fig. 2 Emergence of dynamical states within a cognitively informed framework.

    (A) Cognitive system–level synchronization matrices (whose entries denote the extent of synchronization between cognitive systems) for coherent, chimera, and metastable states. (B) Comparison of different frameworks. A dot represents a brain region in a subject, and its position indicates the global synchrony and chimera index (traditional measures) produced in the BNM upon stimulation of this region. The color of a dot identifies the classification of the emergent state into one of the cognitively defined states. A coherent state has a high global synchrony value and low chimera index (red). Both chimera (yellow) and metastable (blue) states show lower global synchrony. Since chimera states can comprise different patterns, they can have either a higher global synchrony or a higher chimera index than metastable states. (C to E) The origin of these states follows the connectivity of the stimulated brain region. (C) The global synchrony is positively correlated with the weighted degree of brain regions (r = 0.81; P < 10−308), indicating that the network hubs are more likely to produce a coherent state. (D) The chimera index is weakly and negatively correlated with the weighted degree (r = −0.61; P = 6.4 × 10−229), indicating that stimulation of lower degree nodes is more likely to produce an ideal chimera state with half of the population synchronized and the other half desynchronized. (E) Average shortest path length of brain regions to the network core (rich club) is negatively correlated with global synchrony (r = −0.74; P < 10−308), indicating that brain regions with a lower path length to the core are more likely to produce a coherent state.

  • Fig. 3 Distributed origin of dynamical states.

    Separate depictions of brain network regions (nodes) that produce each of the three dynamical states: (A) coherent, (B) chimera, and (C) metastable. Both axial and coronal views of the brain are presented to visualize the anatomical location of the node in the left (LH) and right (RH) hemispheres. The radius of a node represents the normalized occurrence or likelihood of a given state upon stimulation of that node across all the individuals. As illustrated by the variability in node size, different states can emerge upon the stimulation of a single node across subjects, with larger nodes indicating that the region is more likely to consistently produce patterns of a given state. (A) While regions distributed throughout the brain are capable of producing a coherent state, the coherent state is relatively more likely to originate from the stimulation of nodes close to the midline of the brain. (B) Chimera states are likely to be originated by the stimulation of nodes that are relatively equally distributed within the brain; however, these nodes may vary in the spatial patterns of synchronization that they produce. (C) A metastable state is more likely to originate from the stimulation of brain regions away from the midline of the brain.

  • Fig. 4 Contribution of cognitive systems to dynamical states.

    (A) Coherent states are likely to result when nodes within the medial default mode and subcortical systems are stimulated. (B) Chimera states emerge upon the stimulation of nodes that are equally distributed across all the cognitive systems. (C) Metastable states frequently occur after stimulation of nodes within auditory, cingulo-opercular, frontoparietal, and ventral temporal association systems. This distribution indicates the dominance of a particular type of cognitive role within the nodes of different cognitive systems.

  • Fig. 5 Patterns of synchronization and cognitive chimera states.

    (A to I) Prevalent patterns of synchrony that emerge as regions within different cognitive systems are stimulated across all subjects. When brain regions were stimulated both within a given cognitive system and across all subjects, different patterns of synchronization emerged. Some of the patterns were found to be repeated for different regions and/or subjects, while some occurred occasionally. If the same pattern occurred when each brain region within a cognitive system was stimulated and if this was true across all 30 subjects, then the pattern would have a frequency of 100%. Here, we show patterns that occurred at least 3% of the time for stimulation of brain regions within a cognitive system across all subjects. Each panel represents stimulation of regions within a particular cognitive system. Each row represents one pattern of synchronization, and each column represents the state of a cognitive system. Cognitive systems that belong to the synchronized population are colored orange, and cognitive systems that remain desynchronized are colored white. Thus, a fully orange or white row represents a coherent or metastable state, respectively. Chimera states show different patterns of coloring depending on the cognitive systems that are recruited to the synchronized group. Different rows of patterns are stacked on the basis of their relative occurrences (mentioned on the right side). To summarize the observed patterns, (J) The probability with which different cognitive systems can be synchronized when the regions within a given system are stimulated across subjects (shown along the vertical axis).

  • Fig. 6 Classification of cognitive systems based on pattern robustness.

    To estimate the consistency of emergent patterns of synchronization within cognitive systems, we constructed a measure called robustness that estimates the similarity between a set of patterns. Within a cognitive system, we calculate robustness across two dimensions: patterns that are produced after stimulating each region across subjects (subject robustness) and patterns that are produced after stimulating different regions of the system within each subject (region robustness). In the parameter space constructed along these dimensions, we can cluster cognitive systems into four groups that suggest a 2 by 3 partitioning of the robustness space. This partitioning allows us to dissociate subject-specific and region-specific variability in observed patterns.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/4/eaau8535/DC1

    Fig. S1. Optimizing personalized BNMs and applying targeted regional stimulation.

    Fig. S2. Distribution of brain volume within cognitive systems.

    Fig. S3. Metastable state and metastability index.

    Fig. S4. Relation between connectivity, position, and emergent cognitive state.

    Fig. S5. Effect of changing synchronization threshold on the distribution of states.

    Fig. S6. Likelihood of the emergence of dynamical states across cognitive systems.

    Fig. S7. Patterns of synchrony for randomly partitioned brain networks.

    Fig. S8. Normalized contribution of brain regions to the prevalent patterns of synchronization.

    Fig. S9. Clustering of cognitive systems using pattern robustness.

    Table S1. Assignment of brain regions to cognitive systems.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Optimizing personalized BNMs and applying targeted regional stimulation.
    • Fig. S2. Distribution of brain volume within cognitive systems.
    • Fig. S3. Metastable state and metastability index.
    • Fig. S4. Relation between connectivity, position, and emergent cognitive state.
    • Fig. S5. Effect of changing synchronization threshold on the distribution of states.
    • Fig. S6. Likelihood of the emergence of dynamical states across cognitive systems.
    • Fig. S7. Patterns of synchrony for randomly partitioned brain networks.
    • Fig. S8. Normalized contribution of brain regions to the prevalent patterns of synchronization.
    • Fig. S9. Clustering of cognitive systems using pattern robustness.
    • Table S1. Assignment of brain regions to cognitive systems.

    Download PDF

    Files in this Data Supplement:

Stay Connected to Science Advances

Navigate This Article