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Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI

Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild...

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Autores principales: Zhang, Tingting, Zhao, Zanzan, Zhang, Chao, Zhang, Junjun, Jin, Zhenlan, Li, Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727827/
https://www.ncbi.nlm.nih.gov/pubmed/31555157
http://dx.doi.org/10.3389/fpsyt.2019.00572
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author Zhang, Tingting
Zhao, Zanzan
Zhang, Chao
Zhang, Junjun
Jin, Zhenlan
Li, Ling
author_facet Zhang, Tingting
Zhao, Zanzan
Zhang, Chao
Zhang, Junjun
Jin, Zhenlan
Li, Ling
author_sort Zhang, Tingting
collection PubMed
description Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01–0.08 Hz; slow-4: 0.027–0.08 Hz; slow-5: 0.01–0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI–EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer’s disease at an early stage.
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spelling pubmed-67278272019-09-25 Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI Zhang, Tingting Zhao, Zanzan Zhang, Chao Zhang, Junjun Jin, Zhenlan Li, Ling Front Psychiatry Psychiatry Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01–0.08 Hz; slow-4: 0.027–0.08 Hz; slow-5: 0.01–0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI–EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer’s disease at an early stage. Frontiers Media S.A. 2019-08-27 /pmc/articles/PMC6727827/ /pubmed/31555157 http://dx.doi.org/10.3389/fpsyt.2019.00572 Text en Copyright © 2019 Zhang, Zhao, Zhang, Zhang, Jin and Li http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Zhang, Tingting
Zhao, Zanzan
Zhang, Chao
Zhang, Junjun
Jin, Zhenlan
Li, Ling
Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title_full Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title_fullStr Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title_full_unstemmed Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title_short Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI
title_sort classification of early and late mild cognitive impairment using functional brain network of resting-state fmri
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727827/
https://www.ncbi.nlm.nih.gov/pubmed/31555157
http://dx.doi.org/10.3389/fpsyt.2019.00572
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