Deep Paper Pool really deep.

Spectral Graph Convolutions for Population based Disease Prediction

Paper: arxiv
Code: github (tensorflow)

Key idea:

Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data.

Network Outline:

Task: to assign to each acquisition, corresponding to a subject and time point, a label l ∈ L describing the corresponding subject’s disease state (e.g. control or diseased).
Vertex: We represent the population as a graph where each subject is associated with an imaging feature vector and corresponds to a graph vertex.
Edge: The graph edge weights are derived from phenotypic data, and encode the pairwise similarity between subjects and the local neighbourhood system.
population graph’s adjacency matrix W is defined as follows:

where is similarity between subjects based on image measures. is a measure of distance between phenotypic measures (non-imaging measures). Here is a set of H non-imaging measures (e.g. subject’s gender and age.

GCN: check this blog and this paper Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Training: This structure is 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.
Network detail:

  • ReLU activation after graph convolutional layer ()
  • Softmax activation in final layer
  • Loss: cross-entropy
  • Unlabelled nodes are then assigned the labels maximising the softmax output.
  • Dropout
  • l2 regularisation

Dataset Detail:

Autism Brain Imaging Data Exchange (ABIDE)

  • Task: classify subjects healthy or suffering from Autism Spectrum Disorders (ASD).
  • Objective: exploit the acquisition information which can strongly affect the comparability of subjects.
  • Dataset: ABIDE
  • Dataset Detail:
    1. 871 subjects, 403 ASD and 468 healthy controls.
    2. 20 different sites
    3. Preprocessing pipeline from C-PAC & ROI from Harvard Oxford (HO) atlas, same as Ruckert2016
    4. The individual connectivity matrices are estimated by computing the Fisher transformed Pearson’s correlation coefficient between the representative rs-fMRI timeseries of each ROI in the HO atlas.
  • Input feature (vertex): vectorised functional connectivity matrix. And a ridge classifier is employed to select the most discriminative features from the training set.
  • Adjacency matrix (edge and weight):
    • is the correlation distance between the subjects’ rs-fMRI connectivity networks after feature selection.
    • non-imaging measures: subject’s gender and acquisition site

Alzheimer’s Disease Neuroimaging Initiative (ADNI)

  • Task: predict whether an MCI patient will convert to AD.
  • Objective: demonstrate the importance of exploiting longitudinal information, which can be easily integrated into our graph structure, to increase performance.
  • Dataset: ADNI
  • Dataset Detail:
    1. 540 subjects (1675 samples) with early/late MCI and contained longitudinal T1 MR images, 289 subjects (843 samples) diagnosed as AD
    2. Acquisitions after conversion to AD were not included.
  • Input feature (vertex): volumes of all 138 segmented brain structures
  • Adjacency matrix (edge and weight):
    • :
    • non-imaging measures: subject’s gender and age information


  • 10-fold stratified cross validation strategy used.
  • K = 3 order Chebyshev polynomials.
  • In ADNI, longitudinal acquisitions of the same subject are in the same fold.

Autism Brain Imaging Data Exchange (ABIDE)

  • Result: We show how integrating acquisition information allows to outperform the current state of the art on the whole dataset with a global accuracy of 69.5%.

Alzheimer’s Disease Neuroimaging Initiative (ADNI)

  • Result: an average accuracy of 77% on par with state of the art results, corresponding to a 10% increase over a standard linear classifier.