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A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort
While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise me...
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Published in: | NeuroImage (Orlando, Fla.) Fla.), 2012-11, Vol.63 (3), p.1478-1486 |
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description | While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.
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► A computational neurodegenerative disease progression score (DPS) is proposed ► The DPS combines measurements from multiple biomarkers ► Validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ► An Alzheimer's DPS (ADPS) is computed for each subject and time-point in ADNI ► Evidence for a common Alzheimer's disease progression within ADNI subjects |
doi_str_mv | 10.1016/j.neuroimage.2012.07.059 |
format | article |
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[Display omitted]
► A computational neurodegenerative disease progression score (DPS) is proposed ► The DPS combines measurements from multiple biomarkers ► Validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ► An Alzheimer's DPS (ADPS) is computed for each subject and time-point in ADNI ► Evidence for a common Alzheimer's disease progression within ADNI subjects</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2012.07.059</identifier><identifier>PMID: 22885136</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Algorithms ; Alzheimer Disease - metabolism ; Alzheimer Disease - psychology ; Alzheimer's disease ; Biomarkers ; Biomarkers - analysis ; Biomarkers - metabolism ; Brain research ; Cohort Studies ; Disease Progression ; Disease progression score ; Humans ; Medical imaging ; Neurodegenerative diseases ; Neurodegenerative Diseases - metabolism ; Neurodegenerative Diseases - psychology ; Proteins ; Severity of Illness Index</subject><ispartof>NeuroImage (Orlando, Fla.), 2012-11, Vol.63 (3), p.1478-1486</ispartof><rights>2012 Elsevier Inc.</rights><rights>Copyright © 2012 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited Nov 15, 2012</rights><rights>2012 Elsevier Inc. All rights reserved. 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-2a848ef04cc7c08bb5569e10a12aa2ab88bd292ecc2b49774522d425f45a27ad3</citedby><cites>FETCH-LOGICAL-c540t-2a848ef04cc7c08bb5569e10a12aa2ab88bd292ecc2b49774522d425f45a27ad3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22885136$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jedynak, Bruno M.</creatorcontrib><creatorcontrib>Lang, Andrew</creatorcontrib><creatorcontrib>Liu, Bo</creatorcontrib><creatorcontrib>Katz, Elyse</creatorcontrib><creatorcontrib>Zhang, Yanwei</creatorcontrib><creatorcontrib>Wyman, Bradley T.</creatorcontrib><creatorcontrib>Raunig, David</creatorcontrib><creatorcontrib>Jedynak, C. Pierre</creatorcontrib><creatorcontrib>Caffo, Brian</creatorcontrib><creatorcontrib>Prince, Jerry L.</creatorcontrib><creatorcontrib>for the Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>Alzheimer's Disease Neuroimaging Initiative</creatorcontrib><title>A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.
[Display omitted]
► A computational neurodegenerative disease progression score (DPS) is proposed ► The DPS combines measurements from multiple biomarkers ► Validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ► An Alzheimer's DPS (ADPS) is computed for each subject and time-point in ADNI ► Evidence for a common Alzheimer's disease progression within ADNI subjects</description><subject>Algorithms</subject><subject>Alzheimer Disease - metabolism</subject><subject>Alzheimer Disease - psychology</subject><subject>Alzheimer's disease</subject><subject>Biomarkers</subject><subject>Biomarkers - analysis</subject><subject>Biomarkers - metabolism</subject><subject>Brain research</subject><subject>Cohort Studies</subject><subject>Disease Progression</subject><subject>Disease progression score</subject><subject>Humans</subject><subject>Medical imaging</subject><subject>Neurodegenerative diseases</subject><subject>Neurodegenerative Diseases - metabolism</subject><subject>Neurodegenerative Diseases - psychology</subject><subject>Proteins</subject><subject>Severity of Illness Index</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqFks2OFCEUhStG44yjr2BIXOimSqCgoFyYtBP_kjFudE0ouF1FpxpaoHqir-BLS0-P7c9mVhDud889wKkqRHBDMOlebhoPSwxuq0doKCa0waLBvL9XnRPc87rngt4_7HlbS0L6s-pRShuMcU-YfFidUSolJ213Xv1cIRO2uyXr7ILXM7oRtjCCh1jO9oCsS6AToF0MY4SUCoeSCRFeoU-Qp2CR9haVyjLnhK5dnlCeAK3mHxO4LcTn6SRxcu38iJx32R1HmDCFmB9XD9Z6TvDkdr2ovr57--XyQ331-f3Hy9VVbTjDuaZaMglrzIwRBsth4LzrgWBNqNZUD1IOlvYUjKED64VgnFLLKF8zrqnQtr2oXh91d8uwBWvA56hntYvFWfyugnbq34p3kxrDXrVMUNKRIvDiViCGbwukrLYuGZhn7SEsSRGOseB9x9jdKG0F7lvMRUGf_YduwhLLn9wICoo5bQ-C8kiZGFKKsD75JlgdwqE26k841CEcCgtVwlFan_5971Pj7zQU4M0RgPL6ewdRJePAG7AugsnKBnf3lF-3t9SS</recordid><startdate>20121115</startdate><enddate>20121115</enddate><creator>Jedynak, Bruno M.</creator><creator>Lang, Andrew</creator><creator>Liu, Bo</creator><creator>Katz, Elyse</creator><creator>Zhang, Yanwei</creator><creator>Wyman, Bradley T.</creator><creator>Raunig, David</creator><creator>Jedynak, C. 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Pierre</au><au>Caffo, Brian</au><au>Prince, Jerry L.</au><aucorp>for the Alzheimer's Disease Neuroimaging Initiative</aucorp><aucorp>Alzheimer's Disease Neuroimaging Initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2012-11-15</date><risdate>2012</risdate><volume>63</volume><issue>3</issue><spage>1478</spage><epage>1486</epage><pages>1478-1486</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>While neurodegenerative diseases are characterized by steady degeneration over relatively long timelines, it is widely believed that the early stages are the most promising for therapeutic intervention, before irreversible neuronal loss occurs. Developing a therapeutic response requires a precise measure of disease progression. However, since the early stages are for the most part asymptomatic, obtaining accurate measures of disease progression is difficult. Longitudinal databases of hundreds of subjects observed during several years with tens of validated biomarkers are becoming available, allowing the use of computational methods. We propose a widely applicable statistical methodology for creating a disease progression score (DPS), using multiple biomarkers, for subjects with a neurodegenerative disease. The proposed methodology was evaluated for Alzheimer's disease (AD) using the publicly available AD Neuroimaging Initiative (ADNI) database, yielding an Alzheimer's DPS or ADPS score for each subject and each time-point in the database. In addition, a common description of biomarker changes was produced allowing for an ordering of the biomarkers. The Rey Auditory Verbal Learning Test delayed recall was found to be the earliest biomarker to become abnormal. The group of biomarkers comprising the volume of the hippocampus and the protein concentration amyloid beta and Tau were next in the timeline, and these were followed by three cognitive biomarkers. The proposed methodology thus has potential to stage individuals according to their state of disease progression relative to a population and to deduce common behaviors of biomarkers in the disease itself.
[Display omitted]
► A computational neurodegenerative disease progression score (DPS) is proposed ► The DPS combines measurements from multiple biomarkers ► Validation with the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort ► An Alzheimer's DPS (ADPS) is computed for each subject and time-point in ADNI ► Evidence for a common Alzheimer's disease progression within ADNI subjects</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>22885136</pmid><doi>10.1016/j.neuroimage.2012.07.059</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Alzheimer Disease - metabolism Alzheimer Disease - psychology Alzheimer's disease Biomarkers Biomarkers - analysis Biomarkers - metabolism Brain research Cohort Studies Disease Progression Disease progression score Humans Medical imaging Neurodegenerative diseases Neurodegenerative Diseases - metabolism Neurodegenerative Diseases - psychology Proteins Severity of Illness Index |
title | A computational neurodegenerative disease progression score: Method and results with the Alzheimer's disease neuroimaging initiative cohort |
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