Topic: small-worldness

Brain networks possess many non-random features that might present a key to the computational effectiveness and robustness of the nervous system. Information-transmission pathways in neuronal structure, especially in complex connectomes, seem to favor specific recurrent motifs. For intrinsically directional and weighted networks such as those in the brain, it is particularly important to use methods that are sensitive to the weight-encoded information. We use new weighted methods to assess statistics of motives in the connectomes of various animals: \textit{C. elegans}, tadpole, drosophila,

Since the brain operates as a scale-free network, the frequency-power plot of fMRI BOLD signals follows a power-law distribution. However, conventional deep neural network models that fail to account for this characteristic tend to underperform, lacking a proper model structure to capture the brain signal as a complex system adequately. In this paper, we propose DivfreqBERT, an end-to-end timeseries data model that leverages the features of scale-free networks to encode biological characteristics better. We employed Lorentzian and multi-fractal functions to partition the whole-brain dynamics i