The Essential of Applying Nonlinear-Analysis to Validate Experiments, Assessing Superior Brain Functions: Case-Study of a Bayesian-Model of Inhibitory Control in ADHD

Document Type: Research Paper

Authors

1 Institute for cognitive and brain sciences, Shahid Beheshti University, Tehran, Iran

2 Institute for cognitive and Brain Sciences, Shahid Beheshti University, Tehran Iran

3 Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran

Abstract

In the last decades, nonlinear methods have been applied, in a large number of studies from the computational neuroscience field, to describe neuronal implementations of superior brain functions. Superior brain functions, called cognitive functions, control our behavior. Therefore, they should be assessed through evaluating individual performances in the experiments, using standard tasks, which represent the condition that cognitive functions are required. The mathematical models of cognitive functions, in neuronal implementation level, are based on real condition of standard cognitive tasks. However, it is not validated whether applied task conditions are appropriate to represent neuronal implementation of a cognitive function. Hence, as a case-study, we used a developed Bayesian Model to assess whether GoNoGo task is valid to be applied for neural measurement and modeling neural implementation of Inhibitory Control (IC). As GoNoGo is the most common task used for neural measurement of impaired cognitive function (IC) in ADHD, we fit the model to behavioral data of two groups of children/adolescents with and without ADHD. The results demonstrated that the model could simulated the behavioral data; and also the model parameters could differentiate the groups significantly. However, neural implementation of IC may not be represented through the rewarded condition of GoNoGo task. We concluded that before modeling neural implementation of cognitive functions, it is essential to apply nonlinear methods to validate current behavioral experiments computationally; or to design new model-based experiments for using in neural measurements

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