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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.
Neuroplasticity is the process by which neurons adapt to a disturbance over time, and most often occurs in response to repeated exposure to stimuli. [27] Aerobic exercise increases the production of neurotrophic factors [note 1] (e.g., BDNF, IGF-1, VEGF) which mediate improvements in cognitive functions and various forms of memory by promoting blood vessel formation in the brain, adult ...
By analyzing different metrics from these connection matrices from the network, one can obtain a topological analysis of the desired graph; and this is referred to as the human brain network in the field of neuroscience. [12] One of the core architectures in brain network models is the "small-world" architecture.
Brain-derived neurotrophic factor (BDNF), or abrineurin, [5] is a protein [6] that, in humans, is encoded by the BDNF gene. [7] [8] BDNF is a member of the neurotrophin family of growth factors, which are related to the canonical nerve growth factor (NGF), a family which also includes NT-3 and NT-4/NT-5.
Interaction between dorsal and ventral attention networks enables dynamic control of attention in relation to top-down goals and bottom-up sensory stimulation. [1]The dorsal attention network (DAN), also known anatomically as the dorsal frontoparietal network (D-FPN), is a large-scale brain network of the human brain that is primarily composed of the intraparietal sulcus (IPS) and frontal eye ...
The global dynamics of a network of neurons depend on at least the first three of four attributes: individual neuron dynamics (primarily, their thresholds or excitability) information transfer between neurons (generally either synapses or gap junctions; network topology; external forces (such as thermodynamic gradients)