Spectral Graph Convolutions for Population-based Disease Prediction

By Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, M. J. Lee, Ricardo Guerrero, Ben Glocker, Daniel Rueckert. 2017

250 citations recorded by the source corpus.

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects’ individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Neurology

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Citations in the corpus

Dual-Primal Graph Convolutional Networks (2018, background): “Geometric deep learning has been successfully employed in a broad spectrum of applications, ranging from computer graphics, vision [31, 6, 43], and medicine [37, 48] to chemistry [12, 14] and high energy physics [21].”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, methodology): “…2019a; Mirakhorli and Mirakhorli, 2019; Grigis et al., 2020; Li et al., 2020; Zhang and Pierre, 2020; Zhang et al., 2021) and disease prediction (Parisot et al., 2017; Kazi et al., 2018, 2019a,b,c; Anirudh and Thiagarajan, 2019; Yang et al., 2019; Stankeviˇci¯ut˙e et al., 2020; Zhang and…”
Graph Neural Networks in Network Neuroscience (2021, background): “Blind-GCN [80] GCN L =‖ Ŷ − Y ‖F +α1 ‖W ‖2 +α2 ‖ Ĥ −H ‖ MLPe 3 7 3 s-GCN [85] GCN L = max(0, σ2+ − a) + max(0,m− (μ+ − μ−)) k-NNf 3 7 3 GC-LSTM& DS-GCN [86], [88] GCN L = α1Ldis + α2Lgen + α3La FC 3 7 3 Population-method1g [90] GCN L = − ∑ s p(y s) log q(ys) FC 3 7 3 Population-method2g, InceptionGC”
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications (2018, methodology): “…graph neural networks have been used in a variety of tasks, such as identifying diseases from brain connectivity networks [61] or population graphs [62], designing drugs using molecular graphs [63], segmenting 3D point clouds [64], optimizing shapes to be aerodynamic [65], and many more.”
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018, methodology): “The default structures for experiments using the phenotypic graph are the ones used in Parisot et al. (2017).”
Deep Learning on Graphs: A Survey (2018, unknown): “…[28], [66], [99], [134], chemistry and biology [46], [52], [55], [116], [117], physics [135], [136], disease and drug prediction [137], [138], [139], gene expression [140], natural language processing (NLP) [141], [142], computer vision [143], [144], [145], [146], [147], trafﬁc forecasting…”