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Learning a Phenotypic-Attribute Attentional Brain Connectivity Embedding for ADHD Classification using rs-fMRI
Abstract
Automated diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) from brain's functional imaging has gained more interest due to its high prevalence rates among children. While phenotypic information, such as age and gender, is known to be important in diagnosing ADHD and critically affects the representation derived from fMRI brain images, limited studies have integrated phenotypic information when learning discriminative embedding from brain imaging for such an automatic classification task. In this work, we propose to integrate age and gender attributes through attention mechanism that is jointly optimized when learning a brain connectivity embedding using convolutional variational autoencoder derived from resting state functional magnetic resonance imaging (rs-fMRI) data. Our proposed framework achieves a state-of-the-art average of 86.22% accuracy in ADHD vs. typical develop control (TDC) binary classification task evaluated across five public ADHD-200 competition datasets. Furthermore, our analysis points out that there are insufficient linked connections to the brain region of precuneus in the ADHD group.
Figures
Complete architecture for our proposed phenotypic-attribute attentional embedding that is jointly learned using age and gender within a convolutional variational autoencoder. We evaluate our classification framework in recognizing ADHD in the global ADHD-200 datasets.
Complete architecture for our proposed phenotypic-attribute attentional embedding that is jointly learned using age and gender within a convolutional variational autoencoder. We evaluate our classification framework in recognizing ADHD in the global ADHD-200 datasets.
Authors
Publication Date
2020/07/20
Conference
EMBC
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
DOI
10.1109/embc44109.2020.9175789
Publisher
IEEE