<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2326</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>35</pageLast>
    <pageNumber/>
    <edition/>
    <issue>19(10)</issue>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-10-17</completedDate>
    <publishedDate>2024-10-17</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multiclass classification of Autism Spectrum Disorder, attention deficit hyperactivity disorder, and typically developed individuals using fMRI functional connectivity analysis</title>
    <abstract language="eng">Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML c</abstract>
    <parentTitle language="eng">plos one</parentTitle>
    <identifier type="url">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0305630</identifier>
    <identifier type="doi">https://doi.org/10.1371/journal.pone.0305630</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="HAB_Review">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Francisco A. Rodrigues</author>
    <author>Loriz Francisco Sallum</author>
    <author>Thaise Toutain</author>
    <author>Joel Porto</author>
    <author>Patricia Aguiar</author>
    <author>Christiane Thielemann</author>
    <author>Michael Möckel</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Aufmerksamkeitsdefizit-Syndrom</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autismus</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Funktionelle Kernspintomografie</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
</export-example>
