ADHD classification within and cross cohort using an ensembled feature selection framework

Dongren Yao, Hailun Sun, Xiaojie Guo, Vince D. Calhoun, Li Sun, Jing Sui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood. However, as lacking objective measures, several studies have questioned the stability in diagnosing of ADHD from childhood to adulthood. In this study, we propose a novel feature selection framework based on functional connectivity (FCs) pattern, the so-called 'FS-RIWEL,' which could classify ADHD from age-matched healthy controls (HCs) with \sim 80% accuracy (both for children and adults). More importantly, the feature space learned from child ADHD dataset can discriminate adult ADHD from HCs at \sim 70% accuracy. To the best of our knowledge, this is the first attempt to perform a cross-cohort prediction between the adult and child ADHD using FC features. In addition, the most frequently selected FCs indicate that ADHD exhibit widely-impaired FC patterns in frontoparietal, basal ganglia, cerebellum network and so on suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.

Original languageEnglish (US)
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1265-1269
Number of pages5
ISBN (Electronic)9781538636411
DOIs
StatePublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
CountryItaly
CityVenice
Period4/8/194/11/19

Keywords

  • ADHD
  • Classification
  • Ensemble
  • Feature selection
  • Functional connectivity

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Yao, D., Sun, H., Guo, X., Calhoun, V. D., Sun, L., & Sui, J. (2019). ADHD classification within and cross cohort using an ensembled feature selection framework. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 1265-1269). [8759533] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759533