Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 21; no. 4; pp. 582 - 595
• Main Authors: Ke, Hengjin, Chen, Dan, Yao, Quanming, Tang, Yunbo, Wu, Jia, Monaghan, Jessica, Sowman, Paul, McAlpine, David
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024
• Subjects: Algorithms; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention Deficit Disorder with Hyperactivity - physiopathology; Automatic feature construction; Brain - diagnostic imaging; Data models; deep factor learning; Deep Learning; Diseases; Feature extraction; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; MRI; Neural Networks, Computer; Neuroimaging; Neuroimaging - methods; neuroimaging data analysis; Parkinson Disease - diagnostic imaging; Stability analysis; tensor; Tensors
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2023.3252577

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.