A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks

This article proposes a model to diagnose autism patients using graphical neural networks. A graphical neural network relates the subjects (nodes) using the features (edges). In our model, radiomic features obtained from sMRI are used as edges, and spatial-temporal data obtained through rs-fMRI are used as nodes. The similarity between first-order and texture features from the sMRI data of subjects are derived using radiomics to construct the edges of a graph. The features from brain summaries are assembled and learned using 3DCNN to represent the features of each node of the graph. Using the structural similarities of the brain rather than phenotypic data or graph kernel functions provides better accuracy. The proposed model was applied to a standard dataset, ABIDE, and it was shown that the classification results improved with the use of both spatial (sMRI) and statistical measures (brain summaries of rs-fMRI) instead of using only medical images.

Automated summary

This article proposes a model using graphical neural networks to diagnose autism patients. In the model, radiomic features from sMRI are used as edges, and spatial-temporal data from rs-fMRI are used as nodes. The graph's edges are constructed using the similarity of first-order and texture features derived from the sMRI data. The features of each node of the graph are represented by assembling and learning the features from brain summaries using 3DCNN. The model improves classification results by utilizing the structural similarities of the brain.