Differentiating Functional Connectivity Patterns in Adhd and Autism Among the Young People: a Machine Learning Solution

dc.authorscopusid57202689821
dc.authorscopusid24823826600
dc.authorscopusid6701645061
dc.authorscopusid35218547100
dc.authorscopusid59564699500
dc.authorscopusid57218099942
dc.authorscopusid16643227200
dc.contributor.authorSütçübaşı, B.
dc.contributor.authorBallı, T.
dc.contributor.authorRoeyers, H.
dc.contributor.authorWiersema, J.R.
dc.contributor.authorÇamkerten, S.
dc.contributor.authorÖztürk, O.C.
dc.contributor.authorSonuga-Barke, E.
dc.date.accessioned2025-03-15T20:06:56Z
dc.date.available2025-03-15T20:06:56Z
dc.date.issued2025
dc.departmentKadir Has Universityen_US
dc.department-tempSütçübaşı B., Acıbadem University, Istanbul, Türkiye; Ballı T., Kadir Has University, Istanbul, Türkiye; Roeyers H., Ghent University, Belgium; Wiersema J.R., Ghent University, Belgium; Çamkerten S., İstinye University, Istanbul, Türkiye; Öztürk O.C., Acıbadem University, Istanbul, Türkiye; Metin B., Üsküdar University, Istanbul, Türkiye; Sonuga-Barke E., King’s College London, United Kingdomen_US
dc.description.abstractObjective: ADHD and autism are complex and frequently co-occurring neurodevelopmental conditions with shared etiological and pathophysiological elements. In this paper, we attempt to differentiate these conditions among the young people in terms of intrinsic patterns of brain connectivity revealed during resting state using machine learning approaches. We had two key objectives: (a) to determine the extent to which ADHD and autism could be effectively distinguished via machine learning from one another on this basis and (b) to identify the brain networks differentially implicated in the two conditions. Method: Data from two publicly available resting-state functional magnetic resonance imaging (fMRI) resources—Autism Brain Imaging Data Exchange (ABIDE) and the ADHD-200 Consortium—were analyzed. A total of 330 participants (65 females and 265 males; mean age = 11.6 years), comprising equal subgroups of 110 participants each for ADHD, autism, and healthy controls (HC), were selected from the data sets ensuring data quality and the exclusion of comorbidities. We identified region-to-region connectivity values, which were subsequently employed as inputs to the linear discriminant analysis algorithm. Results: Machine learning models provided strong differentiation between connectivity patterns in participants with ADHD and autism—with the highest accuracy of 85%. Predominantly frontoparietal network alterations in connectivity discriminate ADHD individuals from autism and neurotypical group. Networks contributing to discrimination of autistic individuals from neurotypical group were more heterogeneous. These included language, salience, and frontoparietal networks. Conclusion: These results contribute to our understanding of the distinct neural signatures underlying ADHD and autism in terms of intrinsic patterns of brain connectivity. The high level of discriminability between ADHD and autism, highlights the potential role of brain based metrics in supporting differential diagnostics. © The Author(s) 2025.en_US
dc.identifier.doi10.1177/10870547251315230
dc.identifier.issn1087-0547
dc.identifier.scopus2-s2.0-85218260570
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1177/10870547251315230
dc.identifier.urihttps://hdl.handle.net/20.500.12469/7224
dc.identifier.wosqualityQ2
dc.language.isoenen_US
dc.publisherSAGE Publications Inc.en_US
dc.relation.ispartofJournal of Attention Disordersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdhden_US
dc.subjectAutismen_US
dc.subjectFunctional Connectivityen_US
dc.subjectMachine Learningen_US
dc.subjectResting State Fmrien_US
dc.titleDifferentiating Functional Connectivity Patterns in Adhd and Autism Among the Young People: a Machine Learning Solutionen_US
dc.typeArticleen_US
dspace.entity.typePublication

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