Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis
Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effect...
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| Veröffentlicht in: | Human brain mapping 2015-12, Vol.36 (12), p.4758-4770 |
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| creator | Coupé, Pierrick Fonov, Vladimir S. Bernard, Charlotte Zandifar, Azar Eskildsen, Simon F. Helmer, Catherine Manjón, José V. Amieva, Hélène Dartigues, Jean-François Allard, Michèle Catheline, Gwenaelle Collins, D. Louis |
| description | Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR‐based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three‐City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P |
| doi_str_mv | 10.1002/hbm.22926 |
| format | Article |
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Louis</creator><creatorcontrib>Coupé, Pierrick ; Fonov, Vladimir S. ; Bernard, Charlotte ; Zandifar, Azar ; Eskildsen, Simon F. ; Helmer, Catherine ; Manjón, José V. ; Amieva, Hélène ; Dartigues, Jean-François ; Allard, Michèle ; Catheline, Gwenaelle ; Collins, D. Louis ; Alzheimer's Disease Neuroimaging Initiative ; The Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><description>Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR‐based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three‐City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three‐City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer‐based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD. Hum Brain Mapp 36:4758–4770, 2015. © 2015 Wiley Periodicals, Inc.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.22926</identifier><identifier>PMID: 26454259</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Aged ; Aged, 80 and over ; Alzheimer ; Alzheimer Disease - pathology ; Area Under Curve ; Cohort Studies ; Computer Science ; Databases, Factual - statistics & numerical data ; Dementia - pathology ; Disease Progression ; Electronic Data Processing ; Female ; hippocampus ; Hippocampus - pathology ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging - methods ; Male ; Medical Imaging ; MRI biomarker ; Psychiatric Status Rating Scales ; Reproducibility of Results ; ROC Curve</subject><ispartof>Human brain mapping, 2015-12, Vol.36 (12), p.4758-4770</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><rights>Attribution - NonCommercial - NoDerivatives</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6186-2cc6cd8f8bb178caee10cba4e18b7730cca9bbf836bd7eaeef538fed208564b83</citedby><cites>FETCH-LOGICAL-c6186-2cc6cd8f8bb178caee10cba4e18b7730cca9bbf836bd7eaeef538fed208564b83</cites><orcidid>0000-0003-2709-3350</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6869408/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6869408/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,725,778,782,883,1414,27907,27908,45557,45558,53774,53776</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26454259$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-01219781$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Coupé, Pierrick</creatorcontrib><creatorcontrib>Fonov, Vladimir S.</creatorcontrib><creatorcontrib>Bernard, Charlotte</creatorcontrib><creatorcontrib>Zandifar, Azar</creatorcontrib><creatorcontrib>Eskildsen, Simon F.</creatorcontrib><creatorcontrib>Helmer, Catherine</creatorcontrib><creatorcontrib>Manjón, José V.</creatorcontrib><creatorcontrib>Amieva, Hélène</creatorcontrib><creatorcontrib>Dartigues, Jean-François</creatorcontrib><creatorcontrib>Allard, Michèle</creatorcontrib><creatorcontrib>Catheline, Gwenaelle</creatorcontrib><creatorcontrib>Collins, D. Louis</creatorcontrib><creatorcontrib>Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>The Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><title>Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis</title><title>Human brain mapping</title><addtitle>Hum. Brain Mapp</addtitle><description>Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR‐based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three‐City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three‐City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer‐based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD. 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Louis</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><general>Wiley</general><general>John Wiley and Sons Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>1XC</scope><scope>VOOES</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2709-3350</orcidid></search><sort><creationdate>201512</creationdate><title>Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis</title><author>Coupé, Pierrick ; Fonov, Vladimir S. ; Bernard, Charlotte ; Zandifar, Azar ; Eskildsen, Simon F. ; Helmer, Catherine ; Manjón, José V. ; Amieva, Hélène ; Dartigues, Jean-François ; Allard, Michèle ; Catheline, Gwenaelle ; Collins, D. 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Louis</creatorcontrib><creatorcontrib>Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>The Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Coupé, Pierrick</au><au>Fonov, Vladimir S.</au><au>Bernard, Charlotte</au><au>Zandifar, Azar</au><au>Eskildsen, Simon F.</au><au>Helmer, Catherine</au><au>Manjón, José V.</au><au>Amieva, Hélène</au><au>Dartigues, Jean-François</au><au>Allard, Michèle</au><au>Catheline, Gwenaelle</au><au>Collins, D. Louis</au><aucorp>Alzheimer's Disease Neuroimaging Initiative</aucorp><aucorp>The Alzheimer's Disease Neuroimaging Initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum. Brain Mapp</addtitle><date>2015-12</date><risdate>2015</risdate><volume>36</volume><issue>12</issue><spage>4758</spage><epage>4770</epage><pages>4758-4770</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR‐based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three‐City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three‐City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer‐based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD. Hum Brain Mapp 36:4758–4770, 2015. © 2015 Wiley Periodicals, Inc.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>26454259</pmid><doi>10.1002/hbm.22926</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-2709-3350</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Human brain mapping, 2015-12, Vol.36 (12), p.4758-4770 |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Aged Aged, 80 and over Alzheimer Alzheimer Disease - pathology Area Under Curve Cohort Studies Computer Science Databases, Factual - statistics & numerical data Dementia - pathology Disease Progression Electronic Data Processing Female hippocampus Hippocampus - pathology Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging - methods Male Medical Imaging MRI biomarker Psychiatric Status Rating Scales Reproducibility of Results ROC Curve |
| title | Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis |
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