Multiview Feature Learning With Multiatlas-Based Functional Connectivity Networks for MCI Diagnosis

• Published in: IEEE transactions on cybernetics Vol. 52; no. 7; pp. 1 - 12
• Main Authors: Zhang, Yu, Zhang, Han, Adeli, Ehsan, Chen, Xiaobo, Liu, Mingxia, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer's disease; Brain; Brain disease diagnosis; Diagnosis; Disease control; Diseases; Feature extraction; functional connectivity (FC); Image classification; Machine learning; Magnetic heads; Magnetic resonance imaging; Medical diagnosis; Medical imaging; mild cognitive impairment (MCI); multinetwork classification; Networks; Pathology; resting-state functional magnetic resonance imaging (rs-fMRI); Support vector machines
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3016953

Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of brain regions, and these regions are traditionally parcellated with a particular brain atlas. Most existing studies have adopted a predefined brain atlas for all subjects. However, the constructed FC networks inevitably ignore the potentially important subject-specific information, particularly, the subject-specific brain parcellation. Similar to the drawback of the ``single view'' (versus the ``multiview'' learning) in medical image-based classification, FC networks constructed based on a single atlas may not be sufficient to reveal the underlying complicated differences between normal controls and disease-affected patients due to the potential bias from that particular atlas. In this study, we propose a multiview feature learning method with multiatlas-based FC networks to improve MCI diagnosis. Specifically, a three-step transformation is implemented to generate multiple individually specified atlases from the standard automated anatomical labeling template, from which a set of atlas exemplars is selected. Multiple FC networks are constructed based on these preselected atlas exemplars, providing multiple views of the FC network-based feature representations for each subject. We then devise a multitask learning algorithm for joint feature selection from the constructed multiple FC networks. The selected features are jointly fed into a support vector machine classifier for multiatlas-based MCI diagnosis. Extensive experimental comparisons are carried out between the proposed method and other competing approaches, including the traditional single-atlas-based method. The results indicate that our method significantly improves the MCI classification, demonstrating its promise in the brain connectome-based individualized diagnosis of brain diseases.