Association Between Fractional Amplitude of Low-Frequency Spontaneous Fluctuation and Degree Centrality in Children and Adolescents.

The fractional amplitude of low-frequency fluctuations (fALFFs) of the BOLD signal have been successfully applied as exploratory tools in neuroimaging. This metric has been useful in mapping brain functional changes in many clinical populations. However, little is known about the neurophysiological correlates of fALFF.

Regional Dynamics of the Resting Brain in Amyotrophic Lateral Sclerosis Using Fractional Amplitude of Low-Frequency Fluctuations and Regional Homogeneity Analyses.

Resting-state functional magnetic resonance imaging has been playing an important role in the study of amyotrophic lateral sclerosis (ALS). Although functional connectivity is widely studied, the patterns of spontaneous neural activity of the resting brain are important mechanisms that have been used recently to study a variety of conditions but remain less explored in ALS. Here we have used fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) to study the regional dynamics of the resting brain of nondemented ALS patients compared with healthy controls.

Commute Time as a Method to Explore Brain Functional Connectomes.

Graph theory has been extensively applied to investigate complex brain networks in current neuroscience research. Many metrics derived from graph theory, such as local and global efficiencies, are based on the path length between nodes. These approaches are commonly used in analyses of brain networks assessed by resting-state functional magnetic resonance imaging, although relying on the strong assumption that information flow throughout the network is restricted to the shortest paths.

Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks.

In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a complex associative koniocortex network. In the initial network, intrinsic and synaptic plasticity govern the shifting of the activation function, and the modification of synaptic weights, respectively.