Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes
This work addresses classification of ASD using brain shape data, which is an incremental advancement in medical imaging analysis.
The paper tackled the problem of jointly analyzing multiple functionally related brain structures to classify autism spectrum disorder (ASD) by developing a method combining non-Euclidean statistics and non-parametric integrative analysis, and verified it on structural shape data with effective and robust results.
This paper considers joint analysis of multiple functionally related structures in classification tasks. In particular, our method developed is driven by how functionally correlated brain structures vary together between autism and control groups. To do so, we devised a method based on a novel combination of (1) non-Euclidean statistics that can faithfully represent non-Euclidean data in Euclidean spaces and (2) a non-parametric integrative analysis method that can decompose multi-block Euclidean data into joint, individual, and residual structures. We find that the resulting joint structure is effective, robust, and interpretable in recognizing the underlying patterns of the joint variation of multi-block non-Euclidean data. We verified the method in classifying the structural shape data collected from cases that developed and did not develop into Autistic Spectrum Disorder (ASD).