Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity

Machine learning has been widely used to develop classification models for autism spectrum disorder (ASD) using neuroimaging data. Recently, studies have shifted towards using large multi-site neuroimaging datasets to boost the clinical applicability and statistical power of results. However, the cl...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2023-01, Vol.42 (1), p.1-1
Main Authors: Kunda, Mwiza, Zhou, Shuo, Gong, Gaolang, Lu, Haiping
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Autism
Autism Spectrum Disorder - diagnostic imaging
autism spectrum disorders
Autistic Disorder - diagnostic imaging
Brain - diagnostic imaging
Brain mapping
Classification
Data exchange
Data heterogeneity
Datasets
domain adaptation
Embedding
Feature extraction
fMRI
functional connectivity
Functional magnetic resonance imaging
Germanium
Humans
Machine learning
Magnetic Resonance Imaging - methods
Medical imaging
Neural networks
Neuroimaging
Statistical analysis
Statistics
Time series analysis
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Summary:Machine learning has been widely used to develop classification models for autism spectrum disorder (ASD) using neuroimaging data. Recently, studies have shifted towards using large multi-site neuroimaging datasets to boost the clinical applicability and statistical power of results. However, the classification performance is hindered by the heterogeneous nature of agglomerative datasets. In this paper, we propose new methods for multi-site autism classification using the Autism Brain Imaging Data Exchange (ABIDE) dataset. We firstly propose a new second-order measure of functional connectivity (FC) named as Tangent Pearson embedding to extract better features for classification. Then we assess the statistical dependence between acquisition sites and FC features, and take a domain adaptation approach to minimize the site dependence of FC features to improve classification. Our analysis shows that 1) statistical dependence between site and FC features is statistically significant at the 5% level, and 2) extracting second-order features from neuroimaging data and minimizing their site dependence can improve over state-of-the-art classification results, achieving a classification accuracy of 73%. The code is available at https: //github.com/kundaMwiza/fMRI-site-adaptation.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2022.3203899