International Journal of Nonlinear Analysis and Applications

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 Department of Mathematics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, Semnan, 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 by evaluating individual performances in the experiments, using standard tasks, which represent the condition that cognitive functions are required. The mathematical models of cognitive functions, at the neuronal implementation level, are based on the real condition of standard cognitive tasks. However, it is not validated whether applied task conditions are appropriate to represent the neuronal implementation of a cognitive function. Hence, as a case study, we used a developed Bayesian Model to assess whether the 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 simulate the behavioral data, and also the model parameters could differentiate the groups significantly. However, the neural implementation of IC may not be represented through the rewarded condition of the GoNoGo task. We concluded that before modeling the 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 use in neural measurements.

Keywords

[1] S. Bitzer, H. Park, F. Blankenburg, and S.J. Kiebel, Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model, Front. Hum. Neurosci. 8 (2014), 102.
[2] J. Cao, Q. Liu, S. Arik, J. Qiu, H. Jiang, and A. Elaiw, Computational neuroscience, Comput. Math. Meth. Med. 2014 (2014), 120280.
[3] S.B.N. Chopin, Expectation propagation for likelihood-free inference, J. Amer. Statist. Assoc. 109 (2014), no. 505, 315–333.
[4] T. Colibazzi, Journal Watch review of Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders, J. Am. Psychoanal. Assoc. 62 (2014), no. 4, 709–710.
[5] P. de Zeeuw, J. Weusten, S. van Dijk, J. van Belle, and S. Durston, Deficits in cognitive control, timing and reward sensitivity appear to be dissociable in ADHD, PLoS One 7 (2012), no. 12, 51416.
[6] P.R. Fard, H. Park, A. Warkentin, S.J. Kiebel, and S. Bitzer, A Bayesian reformulation of the extended drift-diffusion model in perceptual decision making, Front. Comput. Neurosci. 11 (2017), 29.
[7] T.U. Hauser, V.G. Fiore, M. Moutoussis, and R.J. Dolan, Computational psychiatry of ADHD: Neural gain impairments across Marrian levels of analysis, Trends Neurosci. 39 (2016), no. 2, 63–73.
[8] M. Hoogman, J. Bralten, D.P. Hibar, M. Mennes, M.P. Zwiers, L.S. Schweren, K.J. van Hulzen, S.E. Medland, E. Shumskaya, N. Jahanshad, and P. de Zeeuw, Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis, Lancet Psychiatry 4 (2017), no. 4, 310–319.
[9] K. Konrad and S.B. Eickhoff, Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder, Hum. Brain. Mapp. 31 (2010), no. 6, 904–916.
[10] N. Kriegeskorte and P.K. Douglas, Cognitive computational neuroscience, Nat. Neurosci. 21 (2018), no. 9, 1148–1160.
[11] D. Man and A. Vision, A Computational Investigation into the Human Representation and Processing of Visual Information, MIT Press, 1982.
[12] P.R. Montague, R.J. Dolan, K.J. Friston, and P. Dayan, Computational psychiatry, Trends Cogn. Sci. 16 (2012), no. 1, 72–80.
[13] G. Piccinini and O. Shagrir, Foundations of computational neuroscience, Curr. Opin. Neurobio. 25 (2014), 25–30.
[14] R. Ratcliff and G. McKoon, The diffusion decision model: theory and data for two-choice decision tasks, Neural Comput. 20 (2008), no. 4, 873–922.
[15] T.W. Robbins, Cognition: The ultimate brain function, Neuropsychopharmacology 36 (2011), no. 1, 1–2.
[16] E.T. Rolls, Computational neuroscience, Reference Module in Neuroscience and Biobehavioral Psychology, 2017.
[17] F. Samea, S. Soluki, V. Nejati, M. Zarei, S. Cortese, S.B. Eickhoff, M. Tahmasian, and C.R. Eickhoff, Brain alterations in children/adolescents with ADHD revisited: A neuroimaging meta-analysis of 96 structural and functional studies, Neurosci. Biobehav. Rev. 100 (2019), 1–8.
[18] T.V. Wiecki and M.J. Frank, Neurocomputational models of motor and cognitive deficits in Parkinson’s disease, Prog. Brain Res. 183 (2010), 275–297.
[19] T.V. Wiecki, Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry: Clustering and Classification, Clinical Psychological Science, 2015.
International Journal of Nonlinear Analysis and Applications
Volume 14, Issue 11
November 2023
Pages 233-239
  • Receive Date: 02 May 2019
  • Revise Date: 10 June 2019
  • Accept Date: 12 July 2019