Identifying Autism Spectrum Disorder with Multi-Site fMRI via Low-Rank Domain Adaptation

Mingliang Wang, Daoqiang Zhang, Jiashuang Huang, Pew Thian Yap, Dinggang Shen, Mingxia Liu

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by a wide range of symptoms. Identifying biomarkers for accurate diagnosis is crucial for early intervention of ASD. While multi-site data increase sample size and statistical power, they suffer from inter-site heterogeneity. To address this issue, we propose a multi-site adaption framework via low-rank representation decomposition (maLRR) for ASD identification based on functional MRI (fMRI). The main idea is to determine a common low-rank representation for data from the multiple sites, aiming to reduce differences in data distributions. Treating one site as a target domain and the remaining sites as source domains, data from these domains are transformed (i.e., adapted) to a common space using low-rank representation. To reduce data heterogeneity between the target and source domains, data from the source domains are linearly represented in the common space by those from the target domain. We evaluated the proposed method on both synthetic and real multi-site fMRI data for ASD identification. The results suggest that our method yields superior performance over several state-of-the-art domain adaptation methods.

Original languageEnglish
Article number8787575
Pages (from-to)644-655
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number3
DOIs
Publication statusPublished - 2020 Mar

Keywords

  • Domain adaptation
  • autism spectrum disorder
  • fMRI
  • low-rank representation
  • multi-site data

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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