IcoConv : Explainable brain cortical surface analysis for ASD classification

Abstract

In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms. We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method. One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.

Keywords: ASD; Shape Analysis; brain.

Fig. 1.

Architecture for the ASD classification task. To initiate our analysis, we begin by capturing views of the unique characteristics of each cerebral hemisphere - the left and the right - as they are projected onto the spherical surface. The vantage point follow an icosahedron subdivision. We use a feature extraction network (resnet18, SpectFormer) on each individual view. We experiment with different IcoConv (IcoConv for icosahedron and convolution) operators that pool the information from all views. Finally, we concatenate the left/right outputs and normalized demographics. We perform a final linear layer for the classification.

Fig. 2.

Different IcoConv operators. IcoConv2D arranges the features extracted from adjacent views in 3×3 grid and performs an additional 2D Convolution. IcoConv1D aranges the features and performs a 1D Convolution followed by Average/Max pooloing. IcoLinear stacks the features and performs a Linear layer.

Fig. 3.

Left, posterior, right views for the left hemisphere above and right hemisphere below. The gradcam maps are generated using only the correctly classified HR+ subjects and using HR+ as the target class. It indicates that features from the right hemisphere are preferred over the left ones. The name of the area are based on this labeling map [9]

Fig. 4.

The top left figure shows a plot of importance for features concatenated with normalized demographic values. The bottom left is only demographics to highlight that gender is the most important feature for the Random Forest classifier. The right plot shows an experiment with the gender removed from the analysis and shows that the amygdala is the most important feature in the demographics for the classification task.