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Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691406/ https://www.ncbi.nlm.nih.gov/pubmed/33242116 http://dx.doi.org/10.1186/s40708-020-00120-2 |
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author | Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna |
author_facet | Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna |
author_sort | Rangaprakash, D. |
collection | PubMed |
description | Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity. |
format | Online Article Text |
id | pubmed-7691406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-76914062020-11-30 Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna Brain Inform Research Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity. Springer Berlin Heidelberg 2020-11-26 /pmc/articles/PMC7691406/ /pubmed/33242116 http://dx.doi.org/10.1186/s40708-020-00120-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Research Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment |
title | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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title_full | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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title_fullStr | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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title_full_unstemmed | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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title_short | Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment
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title_sort | density-based clustering of static and dynamic functional mri connectivity features obtained from subjects with cognitive impairment |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691406/ https://www.ncbi.nlm.nih.gov/pubmed/33242116 http://dx.doi.org/10.1186/s40708-020-00120-2 |
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