Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification

• Published in: IEEE transactions on medical imaging Vol. 40; no. 12; pp. 3843 - 3855
• Main Authors: Hu, Rongyao, Peng, Ziwen, Zhu, Xiaofeng, Gan, Jiangzhang, Zhu, Yonghua, Ma, Junbo, Wu, Guorong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Biomarkers; Biomedical imaging; Brain; Brain - diagnostic imaging; Connectivity analysis; Connectome; Correlation; Dementia disorders; Diagnosis; Diagnostic systems; Discrete Wavelet Transform; Frequencies; Frontotemporal dementia; Functional connectivity; Functional magnetic resonance imaging; High frequency; Humans; Image processing; Machine learning; Magnetic Resonance Imaging; Medical diagnosis; Medical imaging; Network analysis; Neural networks; neuro-disease diagnosis; Neurodegenerative diseases; Neuroimaging; Obsessive compulsive disorder; Oscillations; Oxygenation; resting state fMRI; semi-supervised learning; Source code; Support vector machines; Wavelet transforms
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2021.3099641

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the <inline-formula> <tex-math notation="LaTeX">\ell _{{1}} </tex-math></inline-formula>-SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna .