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Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies h...

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Autores principales: Sato, João Ricardo, Hoexter, Marcelo Queiroz, Fujita, André, Rohde, Luis Augusto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3449288/
https://www.ncbi.nlm.nih.gov/pubmed/23015782
http://dx.doi.org/10.3389/fnsys.2012.00068
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author Sato, João Ricardo
Hoexter, Marcelo Queiroz
Fujita, André
Rohde, Luis Augusto
author_facet Sato, João Ricardo
Hoexter, Marcelo Queiroz
Fujita, André
Rohde, Luis Augusto
author_sort Sato, João Ricardo
collection PubMed
description Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies have highlighted the relevance of neuroimaging not only to provide a more solid understanding about the disorder but also for possible clinical support. The ADHD-200 Consortium organized the ADHD-200 global competition making publicly available, hundreds of structural magnetic resonance imaging (MRI) and functional MRI (fMRI) datasets of both ADHD patients and typically developing (TD) controls for research use. In the current study, we evaluate the predictive power of a set of three different feature extraction methods and 10 different pattern recognition methods. The features tested were regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN). Our findings suggest that the combination ALFF+ReHo maps contain relevant information to discriminate ADHD patients from TD controls, but with limited accuracy. All classifiers provided almost the same performance in this case. In addition, the combination ALFF+ReHo+RSN was relevant in combined vs. inattentive ADHD classification, achieving a score accuracy of 67%. In this latter case, the performances of the classifiers were not equivalent and L2-regularized logistic regression (both in primal and dual space) provided the most accurate predictions. The analysis of brain regions containing most discriminative information suggested that in both classifications (ADHD vs. TD controls and combined vs. inattentive), the relevant information is not confined only to a small set of regions but it is spatially distributed across the whole brain.
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spelling pubmed-34492882012-09-26 Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction Sato, João Ricardo Hoexter, Marcelo Queiroz Fujita, André Rohde, Luis Augusto Front Syst Neurosci Neuroscience Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies have highlighted the relevance of neuroimaging not only to provide a more solid understanding about the disorder but also for possible clinical support. The ADHD-200 Consortium organized the ADHD-200 global competition making publicly available, hundreds of structural magnetic resonance imaging (MRI) and functional MRI (fMRI) datasets of both ADHD patients and typically developing (TD) controls for research use. In the current study, we evaluate the predictive power of a set of three different feature extraction methods and 10 different pattern recognition methods. The features tested were regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN). Our findings suggest that the combination ALFF+ReHo maps contain relevant information to discriminate ADHD patients from TD controls, but with limited accuracy. All classifiers provided almost the same performance in this case. In addition, the combination ALFF+ReHo+RSN was relevant in combined vs. inattentive ADHD classification, achieving a score accuracy of 67%. In this latter case, the performances of the classifiers were not equivalent and L2-regularized logistic regression (both in primal and dual space) provided the most accurate predictions. The analysis of brain regions containing most discriminative information suggested that in both classifications (ADHD vs. TD controls and combined vs. inattentive), the relevant information is not confined only to a small set of regions but it is spatially distributed across the whole brain. Frontiers Research Foundation 2012-09-24 /pmc/articles/PMC3449288/ /pubmed/23015782 http://dx.doi.org/10.3389/fnsys.2012.00068 Text en Copyright © 2012 Sato, Hoexter, Fujita and Rohde. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Neuroscience
Sato, João Ricardo
Hoexter, Marcelo Queiroz
Fujita, André
Rohde, Luis Augusto
Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title_full Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title_fullStr Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title_full_unstemmed Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title_short Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
title_sort evaluation of pattern recognition and feature extraction methods in adhd prediction
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3449288/
https://www.ncbi.nlm.nih.gov/pubmed/23015782
http://dx.doi.org/10.3389/fnsys.2012.00068
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