Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection

• Published in: IEEE transactions on medical imaging 2020-02, Vol.39 (2), p.478-487
• Main Authors: Kam, Tae-Eui, Zhang, Han, Jiao, Zhicheng, Shen, Dinggang
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Artificial neural networks; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; brain network; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - physiopathology; Convolutional neural networks; Deep Learning; Diagnosis; Diagnostic systems; Feature extraction; Functional magnetic resonance imaging; functional MRI; Humans; Image classification; Image Interpretation, Computer-Assisted - methods; Independent component analysis; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Middle Aged; mild cognitive impairment; Neural networks; Neurodegenerative diseases; Neurological diseases; Neurophysiology; resting state
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2019.2928790

While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, the voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, the dynamic BFN features can complement static BFN features and, at the meantime, different BFNs can help each other toward a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in early-stage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for a better-individualized diagnosis of various neurological diseases.