Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.
This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2113 - 2125 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950876&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950876 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950876 164568654 171950876 171950876 10.1007/s10278-023-00872-3 171950876 ppf: 2113 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification. aug: au: Tang, Qiling Lu, Yuhong Cai, Bilian Wang, Yan affil: School of Biomedical Engineering, South Central Minzu University, 430074, Wuhan, China sug: subj: Functional Connectivity Evaluation Mild Cognitive Impairment Diagnosis Human Learning Funding Source Magnetic Resonance Imaging Methods Descriptive Statistics Time Series Models, Statistical Conceptual Framework Task Performance and Analysis ab: This work presents a novel approach to estimate brain functional connectivity networks via generative learning. Due to the complexity and variability of rs-fMRI signal, we consider it as a random variable, and utilize variational autoencoder networks to encode it as a confidence distribution in the latent space rather than as a fixed vector, so as to establish the relationship between them. First, the mean time series of each brain region of interest is mapped into a multivariate Gaussian distribution. The correlation between two brain regions is measured by the Jensen-Shannon divergence that describes the statistical similarity between two probability distributions, and then the adjacency matrix is created to indicate the functional connectivity strength of pairwise brain regions. Meanwhile, our findings show that the adjacency matrices obtained at VAE latent spaces of different dimensionalities have good complementarity for MCI identification in precision and recall, and the classification performance can be further boosted by an efficient cascade of classifiers. This proposal constructs brain functional networks from a statistical modeling standpoint, improving the statistical ability of population data and the generalization ability of observation data variability. We evaluate the proposed framework over the task of identifying subjects with MCI from normal controls, and the experimental results on the public dataset show that our method significantly outperforms both the baseline and current state-of-the-art methods. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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