Detecting abnormalities in resting-state dynamics: An unsupervised learning approach

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both approaches can learn useful representations of rs-fMRI data and demonstrate their novel application for abnormality detection in the context of discriminating autism patients from healthy controls.

[1]  Yong He,et al.  Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns , 2018, Human brain mapping.

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Jonghyun Choi,et al.  Learning Temporal Regularity in Video Sequences , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[5]  Dimitris Samaras,et al.  Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example , 2016, NeuroImage.

[6]  Shenghua Gao,et al.  Future Frame Prediction for Anomaly Detection - A New Baseline , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[7]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[8]  Eric Courchesne,et al.  A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. , 2012, Brain : a journal of neurology.

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[11]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[12]  Dewen Hu,et al.  Unsupervised classification of major depression using functional connectivity MRI , 2014, Human brain mapping.

[13]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

[14]  Jian Yu,et al.  Changes in dynamic functional connections with aging , 2018, NeuroImage.

[15]  Mert R. Sabuncu,et al.  Machine learning in resting-state fMRI analysis , 2018, Magnetic resonance imaging.

[16]  Dinggang Shen,et al.  A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI , 2015, MICCAI.