DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training

Xiaodan Xing, Qingfeng Li, Mengya Yuan, Hao Wei, Zhong Xue, Tao Wang, Feng Shi, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to extract efficient disease-related features from FC matrices. Considering the time-dependent nature of brain activity, we computed dynamic FC matrices with sliding windows and implemented a graph convolution-based LSTM (long short-term memory) layer to process dynamic graphs. Moreover, the demographics of patients were also used as additional outputs to guide the classification. In this paper, we proposed to utilize the demographic information as extra outputs and to share parameters among three networks predicting subject status, gender, and age, which serve as assistant tasks. We tested the performance of the proposed architecture in ADNI II dataset to classify Alzheimer's disease patients from normal controls. The classification accuracy, sensitivity, and specificity reach 90.0%, 91.7%, and 88.6%, respectively, on ADNI II dataset.

Original languageEnglish
Pages (from-to)1259-1269
Number of pages11
JournalCerebral Cortex
Issue number2
Publication statusPublished - 2021 Feb 1


  • Connectome
  • GCN
  • fMRI

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience


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