Prediction and classification of Alzheimer disease based on quantification of MRI deformation
Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate...
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| Veröffentlicht in: | PloS one 2017-03, Vol.12 (3), p.e0173372 |
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| description | Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination. |
| doi_str_mv | 10.1371/journal.pone.0173372 |
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High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0173372</identifier><identifier>PMID: 28264071</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Aging ; Algorithms ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; Alzheimers disease ; Amygdala ; Artificial intelligence ; Biology and Life Sciences ; Biomarkers ; Brain ; Brain - pathology ; Brain research ; Care and treatment ; Change detection ; Classification ; Cognitive ability ; Deformation ; Dementia ; Diagnosis ; Discriminant analysis ; Embedding ; Euclidean space ; Female ; Geriatrics ; Humans ; Image processing ; Image Processing, Computer-Assisted ; Image resolution ; Learning algorithms ; Machine Learning ; Magnetic resonance ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Medicine and Health Sciences ; Middle Aged ; Models, Statistical ; Neurodegenerative diseases ; Neuroimaging ; Neurology ; Neurosciences ; NMR ; Nuclear magnetic resonance ; Older people ; Pathological physiology ; Patients ; People and Places ; Regional analysis ; Reproducibility of Results ; Sensitivity and Specificity ; Studies ; Substantia grisea ; Teaching methods ; Temporal lobe ; Tomography</subject><ispartof>PloS one, 2017-03, Vol.12 (3), p.e0173372</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Long et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Long et al 2017 Long et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c791t-3d6b641d7f7ff5af1a2207fc3dd9b55091035771a8ca6209372ee99a4fa20fcf3</citedby><cites>FETCH-LOGICAL-c791t-3d6b641d7f7ff5af1a2207fc3dd9b55091035771a8ca6209372ee99a4fa20fcf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338815/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338815/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28264071$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Chen, Kewei</contributor><creatorcontrib>Long, Xiaojing</creatorcontrib><creatorcontrib>Chen, Lifang</creatorcontrib><creatorcontrib>Jiang, Chunxiang</creatorcontrib><creatorcontrib>Zhang, Lijuan</creatorcontrib><creatorcontrib>Alzheimer’s Disease Neuroimaging Initiative</creatorcontrib><title>Prediction and classification of Alzheimer disease based on quantification of MRI deformation</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Detecting early morphological changes in the brain and making early diagnosis are important for Alzheimer's disease (AD). High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. This deformation-based method has made full use of the pair-wise macroscopic shape difference between groups and consequently increased the power for discrimination.</description><subject>Adult</subject><subject>Aged</subject><subject>Aging</subject><subject>Algorithms</subject><subject>Alzheimer Disease - diagnostic imaging</subject><subject>Alzheimer Disease - pathology</subject><subject>Alzheimer's disease</subject><subject>Alzheimers disease</subject><subject>Amygdala</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Brain</subject><subject>Brain - pathology</subject><subject>Brain research</subject><subject>Care and treatment</subject><subject>Change detection</subject><subject>Classification</subject><subject>Cognitive ability</subject><subject>Deformation</subject><subject>Dementia</subject><subject>Diagnosis</subject><subject>Discriminant analysis</subject><subject>Embedding</subject><subject>Euclidean space</subject><subject>Female</subject><subject>Geriatrics</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image resolution</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Neurology</subject><subject>Neurosciences</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Older people</subject><subject>Pathological physiology</subject><subject>Patients</subject><subject>People and Places</subject><subject>Regional analysis</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Studies</subject><subject>Substantia grisea</subject><subject>Teaching methods</subject><subject>Temporal 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and classification of Alzheimer disease based on quantification of MRI deformation</title><author>Long, Xiaojing ; Chen, Lifang ; Jiang, Chunxiang ; Zhang, Lijuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c791t-3d6b641d7f7ff5af1a2207fc3dd9b55091035771a8ca6209372ee99a4fa20fcf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aging</topic><topic>Algorithms</topic><topic>Alzheimer Disease - diagnostic imaging</topic><topic>Alzheimer Disease - pathology</topic><topic>Alzheimer's disease</topic><topic>Alzheimers disease</topic><topic>Amygdala</topic><topic>Artificial intelligence</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Brain</topic><topic>Brain - pathology</topic><topic>Brain research</topic><topic>Care and treatment</topic><topic>Change 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High resolution magnetic resonance imaging can be used to help diagnosis and prediction of the disease. In this paper, we proposed a machine learning method to discriminate patients with AD or mild cognitive impairment (MCI) from healthy elderly and to predict the AD conversion in MCI patients by computing and analyzing the regional morphological differences of brain between groups. Distance between each pair of subjects was quantified from a symmetric diffeomorphic registration, followed by an embedding algorithm and a learning approach for classification. The proposed method obtained accuracy of 96.5% in differentiating mild AD from healthy elderly with the whole-brain gray matter or temporal lobe as region of interest (ROI), 91.74% in differentiating progressive MCI from healthy elderly and 88.99% in classifying progressive MCI versus stable MCI with amygdala or hippocampus as ROI. 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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Aged Aging Algorithms Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Alzheimers disease Amygdala Artificial intelligence Biology and Life Sciences Biomarkers Brain Brain - pathology Brain research Care and treatment Change detection Classification Cognitive ability Deformation Dementia Diagnosis Discriminant analysis Embedding Euclidean space Female Geriatrics Humans Image processing Image Processing, Computer-Assisted Image resolution Learning algorithms Machine Learning Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medicine and Health Sciences Middle Aged Models, Statistical Neurodegenerative diseases Neuroimaging Neurology Neurosciences NMR Nuclear magnetic resonance Older people Pathological physiology Patients People and Places Regional analysis Reproducibility of Results Sensitivity and Specificity Studies Substantia grisea Teaching methods Temporal lobe Tomography |
| title | Prediction and classification of Alzheimer disease based on quantification of MRI deformation |
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