Human brain dynamics and spatiotemporal trajectories during threat processing
- Department of Electrical and Computer Engineering, University of Maryland, United States
- Department of Psychology, University of Maryland, United States
- Maryland Neuroimaging Center, University of Maryland, United States
Figures
Figure 1
Determining shared dynamics during threat processing.
Switching linear dynamical systems (SLDSs) were used to model functional MRI (fMRI) time series data () from a set of brain regions of interest (ROIs). The framework assumes that time series data can be segmented into a set of discrete states. The model represents brain signals in terms of a set of latent variables (). For each state , the temporal evolution of the system is specified via a linear dynamical system with both intrinsic and input-related components. In the diagram, the system starts in state (white star) and transitions to state (the colored patches in the middle represent the subspaces associated with the two states). In state , the system evolves according to the dynamics matrix and input contributions (). Overall, as states switch temporally, so does the corresponding linear dynamical system governing the system’s trajectory.
Figure 2 with 2 supplements
Brain states, state transitions, and input stimuli.
Stimuli were categorized into 20 bins based on circle distance and movement direction: for approach, for retreat (1 indicates circles are farthest and 10 indicates circle collision). (A) Table entries indicate the probability of being in a state given the input stimulus category. (B) Table entries indicate the probability of a state transition given the input stimulus category. In both tables, cells highlighted in red indicate states/transitions significantly associated with the corresponding stimulus category (, corrected for multiple comparisons). The color scale indicates probability.
Figure 2—figure supplement 1
Brain states, state transitions, and input stimuli: version with 10 stimulus categories.
Stimuli were categorized into bins based on circle distance and movement direction indicated with for approach and for retreat (1 indicates circles are farthest and 5 indicates circle collision). (A) Table entries indicate the probability of being in a state given the input stimulus category. (B) Table entries indicate the probability of a state transition given the input stimulus category. In both tables, cells highlighted in red indicate states/transitions significantly associated with the corresponding stimulus category (p<0.05, corrected for multiple comparisons). The color scale indicates probability.
Figure 2—figure supplement 2
Brain states, state transitions, and input stimuli: version with 30 stimulus categories.
Stimuli were categorized into bins based on circle distance and movement direction indicated with for approach and for retreat (1 indicates circles are farthest and 15 indicates circle collision). (A) Table entries indicate the probability of being in a state given the input stimulus category. (B) Table entries indicate the probability of a state transition given the input stimulus category. In both tables, cells highlighted in red indicate states/transitions significantly associated with the corresponding stimulus category (p<0.05, corrected for multiple comparisons). The color scale indicates probability.
Figure 3 with 1 supplement
Voxelwise state activity maps and state contrast maps.
State activity maps for STATE 4 and STATE 5 and the contrast of the two states. Maps were corrected for multiple comparisons by thresholding voxels at and at the cluster level at .
Figure 3—figure supplement 1
Voxelwise state activity maps for all states.
Activity maps indicate signal intensity at a voxel when the state was ‘on’. Maps were corrected for multiple comparisons by thresholding voxels at and cluster level at .
Figure 4
Region and network responses during state transitions.
Vertical lines indicate the time of state transition. State transitions are arranged in a chained manner: to facilitate continuity in reading responses across state transitions. Error bars correspond to the 95% confidence interval based on the standard error of the mean across participants.
Figure 5 with 1 supplement
Attractor maps.
State attractors projected onto the space of regions of interest (85 ROIs) visualized on a brain surface map. At each ROI, the color scale represents activity strength at the attractor’s fixed point. ROI boundaries are marked in black.
Figure 5—figure supplement 1
Stability of states.
Distribution of the norms of the largest eigenvalue of the dynamics matrix () across bootstrap samples. A state was considered an attractor if 95% of the norms were smaller than 1.
Figure 6
Evolution of state-specific trajectories.
Trajectories were determined in the latent space and projected onto a two-dimensional vector field for illustration (coordinate axes are specific to each state). The average trajectory across participants, starting at the white star, is initially shown in green and switches to red when the majority of trajectories (across participants) switch to another state. Whereas the trajectory includes endogenous and exogenous contributions to temporal evolution, the vector field represents the endogenous contribution only. The gray arrows indicate the effect of the state’s endogenous dynamics matrix, showing the direction and magnitude of evolution after a single time step. The blue cross indicates the state’s fixed point attractor; the star indicates the start of the trajectory. PC: principal component.
Figure 7 with 2 supplements
Effect of external inputs on steering state trajectories and driving state transitions.
(A) Left: Trajectory at in state . Inputs (colored arrows) could perturb the trajectory (translucent paths of the same color) by steering the evolution toward the state’s centroid (green) or away from it (pink). When the input directed it away from the state’s centroid, the input could push the system to switch into state (red). Right: The input effect was measured via the cosine of the angle between the input vector and the line joining and state centroids. (B and C) Input effects with rows and columns representing states/state-transitions and input categories, respectively. Only states with significant association with inputs and only significant state transitions are shown (see Figure 2). Cells with significant effects are highlighted in blue (, corrected for multiple comparisons).
Figure 7—figure supplement 1
Effect of external inputs on steering state trajectories and driving state transitions: version with 10 stimulus categories.
(A and B) Input effects with rows and columns representing states/state-transitions and input categories, respectively. Only states with significant associations with inputs and only significant state transitions are shown. Cells with significant effects are highlighted in blue (, corrected for multiple comparisons).
Figure 7—figure supplement 2
Effect of external inputs on steering state trajectories and driving state transitions: version with 30 stimulus categories.
(A and B) Input effects with rows and columns representing states/state-transitions and input categories, respectively. Only states with significant associations with inputs and only significant state transitions are shown. Cells with significant effects are highlighted in blue (, corrected for multiple comparisons).
Figure 8 with 1 supplement
Brain region importance.
(A) Importance measure and average BOLD signal for STATE 5 from state onset () until the state’s average lifetime. (B) Average importance measure time series (left) and average BOLD signal for all regions of interest (ROIs) during the average lifetime of STATE 5. (C) Spearman’s rank correlation coefficient between average BOLD signal (across participants) and average importance measure (across participants) from state onset () until the average lifetime of STATE 5. PFC: prefrontal cortex.
Figure 8—figure supplement 1
Importance measure of brain regions for all states.
Average importance time series (top row) and average BOLD signal for all regions of interest (ROIs) during the average lifetime of all states ( indicates onset time for each state). PFC: prefrontal cortex.
Figure 9
Testing generalizability of the switching linear dynamical system (SLDS) model.
Appendix 1—figure 1
Model fit evidence lower bound (ELBO) criterion as a function of the number of latent dimensions determined for multiple values of the number of states ().
Error bars indicate the 95% confidence interval based on standard error across leave-one-out samples.
Appendix 1—figure 2
Consistency of state sequences across left-out participants as a function of the number of states ().
Error bars indicate the 95% confidence interval based on standard error across leave-one-out samples.
Appendix 1—figure 3
Average state transition matrix across bootstrap samples.
State transitions highlighted in red have occurred more frequently than expected by chance (, corrected for multiple comparisons).
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Human brain dynamics and spatiotemporal trajectories during threat processing
eLife 14:RP102539.
https://doi.org/10.7554/eLife.102539.3
