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Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imagin... Full description

Journal Title: NeuroImage January 1, 2015, Vol.104, pp.398-412
Main Author: Moradi, Elaheh
Other Authors: Pepe, Antonietta , Gaser, Christian , Huttunen, Heikki , Tohka, Jussi , Moradi, Elaheh
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.10.002
Link: http://search.proquest.com/docview/1634269732/?pq-origsite=primo
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title: Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
format: Article
creator:
  • Moradi, Elaheh
  • Pepe, Antonietta
  • Gaser, Christian
  • Huttunen, Heikki
  • Tohka, Jussi
  • Moradi, Elaheh
subjects:
  • Aged–Pathology
  • Aged, 80 and Over–Pathology
  • Alzheimer Disease–Pathology
  • Biomarkers–Methods
  • Brain–Methods
  • Cognitive Dysfunction–Methods
  • Databases, Factual–Methods
  • Female–Methods
  • Humans–Methods
  • Image Interpretation, Computer-Assisted–Methods
  • Machine Learning–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
  • Middle Aged–Methods
  • Risk Factors–Methods
  • Biomarkers
  • Adni
  • Alzheimer'S Disease
  • Classification
  • Early Diagnosis
  • Feature Selection
  • Low Density Separation
  • Magnetic Resonance Imaging
  • Mild Cognitive Impairment
  • Semi-Supervised Learning
  • Support Vector Machine
ispartof: NeuroImage, January 1, 2015, Vol.104, pp.398-412
description: Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction. •Multi-step procedure combining several ideas for early AD-to-MCI conversion prediction•MRI biomar
language: eng
source:
identifier: E-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.10.002
fulltext: fulltext
issn:
  • 10959572
  • 1095-9572
url: Link


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titleMachine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
creatorMoradi, Elaheh ; Pepe, Antonietta ; Gaser, Christian ; Huttunen, Heikki ; Tohka, Jussi ; Moradi, Elaheh
contributorWeiner, Michael W (correspondence author) ; Aisen, Paul (record owner) ; Petersen, Ronald ; Jack, Clifford R ; Jagust, William ; Trojanowki, John Q ; Beckett, Laurel ; Green, Robert C ; Saykin, Andrew J ; Morris, John ; Shaw, Leslie M ; Khachaturian, Zaven ; Sorensen, Greg ; Carrillo, Maria ; Kuller, Lew ; Raichle, Marc ; Paul, Steven ; Davies, Peter ; Fillit, Howard ; Hefti, Franz ; Holtzman, Davie ; Mesulam, M Marcel ; Potter, William ; Snyder, Peter ; Schwartz, Adam ; Green, Robert C ; Montine, Tom ; Petersen, Ronald ; Thomas, Ronald G ; Donohue, Michael ; Walter, Sarah ; Gessert, Devon ; Sather, Tamie ; Jiminez, Gus ; Beckett, Laurel ; Harvey, Danielle ; Donohue, Michael ; Jack, Clifford R ; Bernstein, Matthew ; Fox, Nick ; Thompson, Paul ; Schuff, Norbert ; Decarli, Charles ; Borowski, Bret ; Gunter, Jeff ; Senjem, Matt ; Vemuri, Prashanthi ; Jones, David ; Kantarci, Kejal ; Ward, Chad ; Jagust, William ; Koeppe, Robert A ; Foster, Norm ; Reiman, Eric M ; Chen, Kewei ; Mathis, Chet ; Landau, Susan ; Morris, John C ; Cairns, Nigel J ; Householder, Erin ; Taylor-Reinwald, Lisa ; Shaw, Leslie M ; Trojanowki, John Q ; Lee, Virginia ; Korecka, Magdalena ; Figurski, Michal ; Toga, Arthur W ; Crawford, Karen ; Neu, Scott ; Saykin, Andrew J ; Foroud, Tatiana M ; Potkin, Steven ; Shen, Li ; Faber, Kelley ; Kim, Sungeun ; Nho, Kwangsik ; Khachaturian, Zaven ; Thal, Leon ; Snyder, Peter J ; Potter, William ; Paul, Steven ; Albert, Marylyn ; Frank, Richard ; Khachaturian, Zaven ; Hsiao, John ; Kaye, Jeffrey ; Quinn, Joseph ; Lind, Betty ; Carter, Raina ; Dolen, Sara ; Schneider, Lon S ; Pawluczyk, Sonia ; Beccera, Mauricio ; Teodoro, Liberty ; Spann, Bryan M ; Brewer, James ; Vanderswag, Helen ; Fleisher, Adam ; Heidebrink, Judith L ; Lord, Joanne L ; Petersen, Ronald ; Mason, Sara S ; Albers, Colleen S ; Knopman, David ; Johnson, Kris ; Doody, Rachelle S ; Villanueva-Meyer, Javier ; Chowdhury, Munir ; Rountree, Susan ; Dang, Mimi ; Stern, Yaakov ; Honig, Lawrence S ; Bell, Karen L ; Ances, Beau ; Morris, John C ; Carroll, Maria ; Leon, Sue ; Householder, Erin ; Mintun, Mark A ; Schneider, Stacy ; Oliver, Angela ; Marson, Daniel ; Griffith, Randall ; Clark, David ; Geldmacher, David ; Brockington, John ; Roberson, Erik ; Grossman, Hillel ; Mitsis, Effie ; Detoledo-Morrell, Leyla ; Shah, Raj C ; Duara, Ranjan ; Varon, Daniel ; Greig, Maria T ; Roberts, Peggy ; Albert, Marilyn ; Onyike, Chiadi ; D'Agostino, Daniel ; Kielb, Stephanie ; Galvin, James E ; Pogorelec, Dana M ; Cerbone, Brittany ; Michel, Christina A ; Rusinek, Henry ; Glodzik, Lidia ; De Santi, Susan ; Doraiswamy, P Murali ; Petrella, Jeffrey R ; Wong, Terence Z ; Arnold, Steven E ; Karlawish, Jason H ; Wolk, David ; Smith, Charles D ; Jicha, Greg ; Hardy, Peter ; Sinha, Partha ; Oates, Elizabeth ; Conrad, Gary ; Lopez, Oscar L ; Oakley, Maryann ; Simpson, Donna M ; Porsteinsson, Anton P ; Goldstein, Bonnie S ; Martin, Kim ; Makino, Kelly M ; Ismail, M Saleem ; Mulnard, Ruth A ; Thai, Gaby ; Mc-Adams-Ortiz, Catherine ; Womack, Kyle ; Mathews, Dana ; Quiceno, Mary ; Diaz-Arrastia, Ramon ; King, Richard ; Weiner, Myron ; Martin-Cook, Kristen ; Devous, Michael ; Levey, Allan I ; Lah, James J ; Cellar, Janet S ; Burns, Jeffrey M ; Anderson, Heather S ; Swerdlow, Russell H ; Apostolova, Liana ; Tingus, Kathleen ; Woo, Ellen ; Silverman, Daniel H S ; Lu, Po H ; Bartzokis, George ; Graff-Radford, Neill R ; Parfitt, Francine ; Kendall, Tracy ; Johnson, Heather ; Farlow, Martin R ; Hake, Ann Marie ; Matthews, Brandy R ; Herring, Scott ; Hunt, Cynthia ; van Dyck, Christopher H ; Carson, Richard E ; Macavoy, Martha G ; Chertkow, Howard ; Bergman, Howard ; Hosein, Chris ; Black, Sandra ; Stefanovic, Bojana ; Caldwell, Curtis ; Hsiung, Ging Yuek Robin ; Feldman, Howard ; Mudge, Benita ; Assaly, Michele ; Kertesz, Andrew ; Rogers, John ; Trost, Dick ; Bernick, Charles ; Munic, Donna ; Kerwin, Diana ; Mesulam, Marek-Marsel ; Lipowski, Kristine ; Wu, Chuang-Kuo ; Johnson, Nancy ; Sadowsky, Carl ; Martinez, Walter ; Villena, Teresa ; Turner, Raymond Scott ; Johnson, Kathleen ; Reynolds, Brigid ; Sperling, Reisa A ; Johnson, Keith A ; Marshall, Gad ; Frey, Meghan ; Yesavage, Jerome ; Taylor, Joy L ; Lane, Barton ; Rosen, Allyson ; Tinklenberg, Jared ; Sabbagh, Marwan N ; Belden, Christine M ; Jacobson, Sandra A ; Sirrel, Sherye A ; Kowall, Neil ; Killiany, Ronald ; Budson, Andrew E ; Norbash, Alexander
ispartofNeuroImage, January 1, 2015, Vol.104, pp.398-412
identifierE-ISSN: 1095-9572 ; DOI: 10.1016/j.neuroimage.2014.10.002
subjectAged–Pathology ; Aged, 80 and Over–Pathology ; Alzheimer Disease–Pathology ; Biomarkers–Methods ; Brain–Methods ; Cognitive Dysfunction–Methods ; Databases, Factual–Methods ; Female–Methods ; Humans–Methods ; Image Interpretation, Computer-Assisted–Methods ; Machine Learning–Methods ; Magnetic Resonance Imaging–Methods ; Male–Methods ; Middle Aged–Methods ; Risk Factors–Methods ; Biomarkers ; Adni ; Alzheimer'S Disease ; Classification ; Early Diagnosis ; Feature Selection ; Low Density Separation ; Magnetic Resonance Imaging ; Mild Cognitive Impairment ; Semi-Supervised Learning ; Support Vector Machine
languageeng
source
descriptionMild cognitive impairment (MCI) is a transitional stage between age-related cognitive decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be important to identify MCI patients at high risk for conversion to AD. In this study, we present a novel magnetic resonance imaging (MRI)-based method for predicting the MCI-to-AD conversion from one to three years before the clinical diagnosis. First, we developed a novel MRI biomarker of MCI-to-AD conversion using semi-supervised learning and then integrated it with age and cognitive measures about the subjects using a supervised learning algorithm resulting in what we call the aggregate biomarker. The novel characteristics of the methods for learning the biomarkers are as follows: 1) We used a semi-supervised learning method (low density separation) for the construction of MRI biomarker as opposed to more typical supervised methods; 2) We performed a feature selection on MRI data from AD subjects and normal controls without using data from MCI subjects via regularized logistic regression; 3) We removed the aging effects from the MRI data before the classifier training to prevent possible confounding between AD and age related atrophies; and 4) We constructed the aggregate biomarker by first learning a separate MRI biomarker and then combining it with age and cognitive measures about the MCI subjects at the baseline by applying a random forest classifier. We experimentally demonstrated the added value of these novel characteristics in predicting the MCI-to-AD conversion on data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. With the ADNI data, the MRI biomarker achieved a 10-fold cross-validated area under the receiver operating characteristic curve (AUC) of 0.7661 in discriminating progressive MCI patients (pMCI) from stable MCI patients (sMCI). Our aggregate biomarker based on MRI data together with baseline cognitive measurements and age achieved a 10-fold cross-validated AUC score of 0.9020 in discriminating pMCI from sMCI. The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction. However, it is evident based on our results that combining MRI data with cognitive test results improved the accuracy of the MCI-to-AD conversion prediction. •Multi-step procedure combining several ideas for early AD-to-MCI conversion prediction•MRI biomarker using low density separation and auxiliary data from AD and NC subjects•Aggregate biomarker for combining MRI and cognitive test data•Cross-validated AUC 0.9020 for conversion prediction up to 3years before diagnosis
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titleMachine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
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0Aged–Pathology
1Aged, 80 and Over–Pathology
2Alzheimer Disease–Pathology
3Biomarkers–Methods
4Brain–Methods
5Cognitive Dysfunction–Methods
6Databases, Factual–Methods
7Female–Methods
8Humans–Methods
9Image Interpretation, Computer-Assisted–Methods
10Machine Learning–Methods
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11Khachaturian, Zaven
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18Fillit, Howard
19Hefti, Franz
20Holtzman, Davie
21Mesulam, M Marcel
22Potter, William
23Snyder, Peter
24Schwartz, Adam
25Montine, Tom
26Thomas, Ronald G
27Donohue, Michael
28Walter, Sarah
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30Sather, Tamie
31Jiminez, Gus
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33Bernstein, Matthew
34Fox, Nick
35Thompson, Paul
36Schuff, Norbert
37Decarli, Charles
38Borowski, Bret
39Gunter, Jeff
40Senjem, Matt
41Vemuri, Prashanthi
42Jones, David
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44Ward, Chad
45Koeppe, Robert A
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96Stern, Yaakov
97Honig, Lawrence S
98Bell, Karen L
99Ances, Beau
100...
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titleMachine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
authorMoradi, Elaheh ; Pepe, Antonietta ; Gaser, Christian ; Huttunen, Heikki ; Tohka, Jussi ; Moradi, Elaheh
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3Biomarkers–Methods
4Brain–Methods
5Cognitive Dysfunction–Methods
6Databases, Factual–Methods
7Female–Methods
8Humans–Methods
9Image Interpretation, Computer-Assisted–Methods
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7Petersen, Ronald
8Jack, Clifford R
9Jagust, William
10Trojanowki, John Q
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12Green, Robert C
13Saykin, Andrew J
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16Khachaturian, Zaven
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36Jiminez, Gus
37Harvey, Danielle
38Bernstein, Matthew
39Fox, Nick
40Thompson, Paul
41Schuff, Norbert
42Decarli, Charles
43Borowski, Bret
44Gunter, Jeff
45Senjem, Matt
46Vemuri, Prashanthi
47Jones, David
48Kantarci, Kejal
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55Landau, Susan
56Morris, John C
57Cairns, Nigel J
58Householder, Erin
59Taylor-Reinwald, Lisa
60Lee, Virginia
61Korecka, Magdalena
62Figurski, Michal
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65Neu, Scott
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33Bernstein, Matthew
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76Dolen, Sara
77Schneider, Lon S
78Pawluczyk, Sonia
79Beccera, Mauricio
80Teodoro, Liberty
81Spann, Bryan M
82Brewer, James
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98Bell, Karen L
99Ances, Beau
100...
atitleMachine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
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date2015-01-01