ArticleIEEE transactions on medical imaging2021
Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 55 citations in OpenAlex.
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- Spatiotemporal alterations of thalamo-cortical effective connectivity in major depressive disorder patients.BMC psychiatry · 2025Article
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- ZipAEr: A compressive convolutional autoencoder for high-dimensional spatial omics data at subcellular resolution.Research square · 2025Article
- Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks.Frontiers in neuroscience · 2025Article
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks.Frontiers in neuroscience · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study.Frontiers in neuroscience · 2023Article
- Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network.IEEE transactions on medical imaging · 2022Article
- Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.Information processing & management · 2022Article
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
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 l
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.