Multi-Task Learning for Efficient Diagnosis of ASD and ADHD using Resting-State fMRI Data

Zhi-An Huang, Rui Liu, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Increasing mental disorders have emerged as an urgent public health concern such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). Related mental disorders may share high overlap in clinical symptoms. Therefore, their diagnosis can be challenging to merely rely on the observation of cognitive phenotypes and behavioral manifestations. Unfortunately, there is no additional support of biochemical markers, laboratory tests, or neuroimaging analysis, which can be used as a diagnostic gold standard currently. Over the past decades, resting-state functional magnetic resonance imaging (rs-fMRI) has been considered as one of the most promising modality to capture the intrinsic neural activation patterns between regions in the brain. In this work, we focus on ASD and ADHD due to their high prevalence and relevance with the aim to exploit the multi-task learning (MTL) paradigm for their diagnosis. To the best of our knowledge, this is the first time to make use of the disease-specific heterogeneities for the MTL classification of ASD and ADHD via rs-fMRI signal. We propose a novel graph-based feature selection method to filter out irrelevant functional connectivity features. Then an efficient structure of multi-gate mixture-of-experts (MMoE) is applied to the MTL classification framework. Finally, the experiment results demonstrate that the proposed model can achieve a reliable classification performance in a short term, yielding the mean accuracies of 0.687±0.005 and 0.650±0.014 in ASD and ADHD datasets, respectively. The graph-based feature selection method and MMoE model are demonstrated to make great contribution to performance improvement.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728169262
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow


  • attention deficit hyperactivity disorder (ADHD)
  • Autism spectrum disorder (ASD)
  • functional connectivity (FC)
  • functional magnetic resonance imaging (fMRI)
  • multi-task learning (MTL)

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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