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Brain networks are not immutable, static constructs; rather those networks are highly variable based on multiple time scales. Data on time-varying brain graphs generally takes the form of time series (or stacks) of graphs that form an ordered series of snapshots, for example data recorded in the course of learning or across developmental stages.
A number of online neuroscience databases are available which provide information regarding gene expression, neurons, macroscopic brain structure, and neurological or psychiatric disorders. Some databases contain descriptive and numerical data, some to brain function, others offer access to 'raw' imaging data, such as postmortem brain sections ...
As a physical system with graph-like properties, [6] a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.
Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system.More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network.
Figure 2 shows the three types of connectivity. The analysis is done using the directed graphs (see Sporns, O. (2007) [6] and Hilgetag, C. C. (2002) [19]). In the structural brain connectivity type, the connectivity is a sparse and directed graph. The functional brain connectivity has bidirectional graphs.
The model includes an interaction term between a psychological variable (task design) and physiological variable (the time series of a brain region). If the interaction term can explain the brain activation of another brain region after taking into account the main effects of the psychological and physiological variables, then it implies a task ...
Examples of networks with a single scale include the Erdős–Rényi (ER) random graph, random regular graphs, regular lattices, and hypercubes. Some models of growing networks that produce scale-invariant degree distributions are the Barabási–Albert model and the fitness model. In a network with a scale-free degree distribution, some ...
In the field of computational neuroscience, brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. [1] Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases .