Autism Spectrum Disorder and Deep Attractors in Neurodynamics
Behavior may be analyzed at many levels, from genes to psychological constructs characterizing mental events. Neurodynamics is at the middle level. It can be related to biophysical properties of neurons that depend on lower-level molecular properties and genetics and used to characterize high-level processes correlated with behavior and mental states. A good strategy that should help to find causal relations between different levels of analysis, showing how psychological constructs used in neuropsychiatry emerge from biology, is to identify biophysical parameters of neurons required for normal neural network activity and explore all changes that may lead to abnormal functions, behavioral symptoms, cognitive phenotypes, and psychiatric syndromes. Neural network computational simulations, as well as analysis of real brain signals, show importance of attractor states, providing language that can be used to explain many features of mental disorders. Computational simulations of neurodynamics may generate hypothesis for experimental verification and help to create mechanistic explanation of observed behavior. Autism spectrum disorder is used as an example of the usefulness of such approach, showing how deep attractors resulting from ion channel dysfunctions slow down attention shifts, influence connectivity, and lead to diverse developmental problems.
KeywordsNeurodynamics RDoC Mental disorders Autism spectrum disorder (ASD) Brain fingerprints Computational modeling
This research was supported by the National Science Center, Poland, UMO-2016/20/W/NZ4/00354. Visualizations of trajectories have been made using VISER Toolbox developed by Krzysztof Dobosz in our laboratory.
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