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要旨:
Introduction:
For understanding proper functioning of the neural circuits, one has to consider dynamical changes in brain activation unfolding over time: distinct networks can be topographically similar, but differ from each other in terms of temporal dynamics (Pavlova, 2017). Time is a key to understanding the organization of functional brain networks, since brain topography alone does not allow us to understand neural communication in the brain. In this work, we analyzed temporal dynamics of the BOLD response to point-light biological motion. This analysis was motivated by a desire to characterize the functional role of the brain areas playing in unison at different time points and, thus, making up diverse neural circuits engaged in body motion processing. Our previous work (Pavlova et al. 2017) was limited to the analysis of the temporal dynamics of the BOLD response within the brain areas exhibiting significant activation during processing of upright and inverted body motion (BM). Here, we used several methods for uncovering ensembles of regions over the whole brain exhibiting similar temporal dynamics
Methods:
By using whole-brain coverage, we conducted fMRI recording at field strength of 9.4 tesla during processing of point-light BM. Participants were administered a 2-AFC task: they indicated whether an upright walker or control configurations (the same displays shown upside-down that participants were not informed about) were presented. We used several methods for uncovering ensembles of brain regions exhibiting similar temporal dynamics: (1) Temporal contrasts analysis (four 5s bins). Independent of display condition, temporal contrasts of each bin with respect to the weighted average of 3 other bins were evaluated, either with positive (for a relative increase) or negative (for a relative decrease) weights resulting in 8 temporal contrasts. Then t-values were calculated for each of these temporal contrasts at each peak of activation from the parameter estimates extracted for each individual participant (Pavlova et al. 2017). Significance (P < 0.01, unc.) was reached for 4 out of all 8 temporal contrasts in: Bin1inc [1, −1/3, −1/3, −1/3], Bin2dec [1/3, −1, 1/3, 1/3], Bin3dec [1/3, 1/3, −1, 1/3], and Bin4inc [−1/3, −1/3, −1/3, 1]. Here the whole brain search was performed for these bin-patterns. For each voxel t-values for all 4 possible temporal bin-patterns were calculated, and then the pattern with greatest t-value was assigned to this voxel. (2) Independent component analysis (ICA) based on temporal pattern (bin size, 1.3s = TR). This analysis ((Calhoun et al., 2001) was motivated by a desire to uncover the temporal brain networks with finer temporal characteristics across the whole event epoch. (3) Kmeans clustering based on temporal pattern analysis (bin size, 5s). The contrast images were used to search for clusters of typical time courses by the kmeans clustering method (Lloyd, 1982), which partitions data into k mutually exclusive clusters with squared Euclidean metric to determine distances. The number of clusters was set to K=6 cluster centers and the algorithm was repeated 10 times with different randomly chosen centroid seeds using the k-means++ algorithm. The solution with the lowest within-cluster sums of point-to-centroid distances was selected as the final outcome.
Results:
All three methods resulted in rather similar distributed large-scale networks playing in harmony. For example, the outcome of the temporal contrast analysis is represented in Figure 1. The network with an increase in Bin 1 is rather sparse, and comprises a few portions of the occipital and frontal cortices, whereas the large-scale network with an increase in Bin 4 is likely involved in decision making and executive functions.
Conclusions:
The benefits and disadvantages of each of 3 methods for uncovering of temporal dynamics of the large-scale networks are discussed. Future work should be focused on interactions between brain regions making up the social brain.
