Lebo Wang1, Kaiming Li2, and Xiaoping Hu1,2
1Department of Electrical and Computer Engineering, University of California, Riverside, Riverside, CA, United States, 2Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
Synopsis
Traditional deep learning
architectures have met with limited performance improvement on fMRI data
analysis. Our connectivity-based graph convolutional network modeled fMRI data
as graphs and performed convolutions within connectivity-based neighborhood. We
demonstrate that our approach is substantially more robust in classifying Autism
Spectrum Disorder (ASD) patients from normal subjects compared with those in published
work. Extracting spatial features and averaging across frames are beneficial in
reducing variance and improving classification accuracy.
INTRODUCTION
There has been increasing interest in
applying deep learning techniques on resting-state functional magnetic resonance
imaging (rs-fMRI) data for large-scale analyses. It is promising to derive
neuroimaging biomarkers for the identification of subjects with psychiatric
disorders. However, traditional deep learning architectures struggle with
limited performance improvement on fMRI data analysis1-3. Previous work applied 3D convolutional
neural networks on voxel-wise fMRI data1. However, the
distant functional connectivity (FC) cannot be readily captured by traditional convolutional
neural networks (CNNs) on grid images, in which localized image features (such
as edges, corners, etc.) were considered as basic components in the Euclidean space.
In addition, the fMRI time course from single voxels were usually noisy and
unreliable. ROI-based fMRI data were
preferred to decrease the computational complexity and reduce the noise in the
time courses. The multilayer
perceptron (MLP) model was applied on ROI-based fMRI data2, and the RNN model
was further deployed to explore the temporal information between frames3. However, it is
very difficult to avoid overfitting for MLPs during training. In this work, we introduce
the connectivity-based graph convolutional network (cGCN) architecture on the graph
representation of ROI-based fMRI data to classify Autism Spectrum Disorder
(ASD) patients from normal subjects.METHODS
The FC matrix reflects the
connectivity between all pairs of regions, which is equivalent to an undirected
graph showing long-range interactions within the connectomic neighborhood. We
extended traditional CNNs on graph data to extract spatial features based on FC.
The architecture of cGCN is
shown in Fig.1. Starting from the group FC matrix, we obtained the k-nearest
neighbor (k-NN) graph to keep k top-correlative neighbors for each node, where k
was a hyperparameter related to the graph structure. After spatial features
were extracted from each frame, the averaging layer was applied to obtain
latent representations for the whole fMRI data, leading to a robust estimation
of spatial patterns that were classified by the softmax layer.
We evaluated the performance of cGCN
on the ABIDE (Autism Brain Imaging Data Exchange) dataset4 to classify ASD
patients from healthy controls. The ABIDE dataset consists of 1035 subjects in
total (505 ASD subjects and 530 normal controls) from 17 sites. The fMRI data
were preprocessed with bandpass filtering (0.01–0.1 Hz) and without global
signal regression. The Craddock 200 atlas5 was utilized to
extract ROI signals. Considering the heterogeneity between different imaging
sites, we chose the leave-one-site-out cross-validation to test the classification
accuracy of our cGCN architecture.RESULTS
The classification accuracy of cGCN
was obtained for data from each site with a range of k values (3, 5, 10 and 20).
As shown in Fig.2(a), the highest classification accuracy on average across
sites was 97.0% with k=3, in which 14 out of 17 sites achieved the best
performance among all k values. For all models with different k values, the
classification accuracy (except the SBL site) was consistently above 86%. Though
the lowest accuracy was obtained with k=10 on average across sites, the accuracy
obtained is still much higher than the chance level. The relationship between
the classification accuracy and the length of fMRI data is presented in
Fig.2(b). For clarity, only sites whose classification accuracy less than 100% is
shown along with their site names. Before saturation (less than 200 frames), the
classification accuracy was proportional to the length of fMRI data. DISCUSSION
Group FC can reflect the underlying
FC pattern with great robustness. The k-NN graph established the connectivity-based
neighborhood for each node to define convolutional neighbors for different
convolutional layers. Increasing the number of convolutional neighbors did not always
increase the performance, as is typically the case with traditional CNNs. One
possible reason is that convolutions on a great amount of neighbors may fail to
generate features related to local FC with good generalization. The observed
proportional relationship between classification accuracy and the length of
fMRI data indicates it is useful to reduce variation by averaging spatial
features from different frames. Compared with traditional deep learning
approaches on the ASD classification, cGCN substantially outperformed the 3D
CNN model (70.5%)1, the RNN model (70.1%)3 and the MLP model (70%)2.CONCLUSION
We introduced a connectivity-based
graph convolution neural network for classifying ASD patients from controls
with rs-fMRI data. Rather than performing convolution on rectilinear image
grids, our architecture modeled the fMRI data as graphs and performed convolutions
within connectivity-based neighborhood. Our results indicated that cGCN was
effective in achieving accurate classification of ASD patients. It was also
found that extracting spatial features from each frame and averaging across
frames were beneficial in reducing variance and improving classification
accuracy.Acknowledgements
Thanks to Alexandra Reardon for proofreading.References
- Zhao Y, Ge
F, Zhang S, et al. 3d Deep Convolutional Neural Network Revealed the Value of
Brain Network Overlap in Differentiating Autism Spectrum Disorder from Healthy
Controls. International Conference on
Medical Image Computing and Computer-Assisted Intervention. 2018. 172-180.
- Heinsfeld A S, Franco A R, Craddock R C, et al.
Identification of Autism Spectrum Disorder Using Deep Learning and the Abide
Dataset. NeuroImage: Clinical. 2018.
17: 16-23.
- Dvornek N
C, Ventola P, Pelphrey K A, et al. Identifying Autism from Resting-State Fmri
Using Long Short-Term Memory Networks. International
Workshop on Machine Learning in Medical Imaging. 2017. 362-370.
- Di Martino A, Yan C-G, Li Q, et al. The Autism Brain
Imaging Data Exchange: Towards a Large-Scale Evaluation of the Intrinsic Brain
Architecture in Autism. Molecular
psychiatry. 2014. 19: 659.
- Craddock R
C, James G A, Holtzheimer Iii P E, et al. A Whole Brain Fmri Atlas Generated
Via Spatially Constrained Spectral Clustering. Human Brain Mapping. 2012. 33: 1914-1928.