Artificial Intelligence to Analyze the Cortical Thickness Through Age.

Sergio Ledesma,Pascal Fallavollita,Jason Steffener,Dora-Luz Almanza-Ojeda,Mario-Alberto Ibarra-Manzano

doi:10.3389/frai.2021.549255

Sergio Ledesma, Pascal Fallavollita + Show 3 more

Open Access

https://doi.org/10.3389/frai.2021.549255

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Abstract

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

Highlights

In the last few years, machine learning techniques have been used in common applications (Alpaydin, 2016)
We propose the use of artificial neural networks to model the thickness of the cortical thickness through life for different regions in the brain
This work analyzes the progress of the cortical thickness with age using Artificial Intelligence

Summary

Introduction

In the last few years, machine learning techniques have been used in common applications (Alpaydin, 2016). We use one technique from Artificial Intelligence to analyze the progress of the cortical thickness with age. The authors in (Steffener et al, 2016) indicate that brain aging can be analyzed taking into consideration the inevitable and universal effects of advancing age and the effects resulting from a lifetime of exposures. These effects and a decreased cortical thickness in some regions of the brain may be related to some mental disorders or cognitive decline (Fouche et al, 2017; Razlighi et al, 2017). Some studies have indicated correlations between disease states and cortical thickness, see the references in (Scott et al, 2009)

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