( C) The maximum value across temporal profiles at each brain node is then overlaid onto a brain render to obtain a brain map of the time scales of human brain fingerprints. ( B) To assess the temporal profile of dynamic brain fingerprints, the nodal ICC (sum across rows of the ICC matrices) averaged within Yeo functional networks is plotted across a more detailed time frame (from 3.6 to 576 s, in 10.8 s steps) for the unthresholded (left) and thresholded (right) ICC matrices. Bottom: The ICC edgewise scores on top are averaged across Yeo functional networks to better visualize patterns within and between functional subsystems. For completeness, an eighth subcortical subnetwork (S) was added at the end (see the “Brain atlas” section in Materials and Methods for details). The ICC matrices are ordered according to the seven resting-state subnetwork organization proposed by Yeo and colleagues ( 59), specifically visual (VIS), somatomotor (SM), dorsal attention (DA), ventral attention (VA), limbic (L), frontoparietal (FP), and default mode network (DMN). The ICC matrices are thresholded at 0.4, which is usually a lower limit to define a good ICC score ( 55, 56). ( A) Top: Edgewise intraclass correlation (ICC) for the most identifiable frame as a function of temporal scale. We hope that this investigation will advance our understanding of what makes our brains unique. Last, different cognitive functions appear to be meta-analytically implicated in dynamic fingerprints across time scales. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales, i.e., more visual-somatomotor at short temporal windows and more frontoparietal-DMN driven at increasing temporal windows. Using dynamic identifiability, we show that the best identification emerges at longer time scales however, short transient “bursts of identifiability,” associated with neuronal activity, persist even when looking at shorter functional interactions. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens. However, the time scales of human brain identifiability are still largely unexplored. The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience.
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