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Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease.

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI anal... Full description

Journal Title: PloS one 2011, Vol.6(10), p.e25446
Main Author: Wolz, Robin
Other Authors: Julkunen, Valtteri , Koikkalainen, Juha , Niskanen, Eini , Zhang, Dong Ping , Rueckert, Daniel , Soininen, Hilkka , Lötjönen, Jyrki , Wolz, Robin
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0025446
Link: http://search.proquest.com/docview/907027818/?pq-origsite=primo
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title: Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease.
format: Article
creator:
  • Wolz, Robin
  • Julkunen, Valtteri
  • Koikkalainen, Juha
  • Niskanen, Eini
  • Zhang, Dong Ping
  • Rueckert, Daniel
  • Soininen, Hilkka
  • Lötjönen, Jyrki
  • Wolz, Robin
subjects:
  • Aged–Diagnosis
  • Alzheimer Disease–Pathology
  • Case-Control Studies–Diagnosis
  • Cerebral Cortex–Methods
  • Cognitive Dysfunction–Methods
  • Female–Methods
  • Humans–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
ispartof: PloS one, 2011, Vol.6(10), p.e25446
description: The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0025446
fulltext: fulltext
issn:
  • 19326203
  • 1932-6203
url: Link


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titleMulti-method analysis of MRI images in early diagnostics of Alzheimer's disease.
creatorWolz, Robin ; Julkunen, Valtteri ; Koikkalainen, Juha ; Niskanen, Eini ; Zhang, Dong Ping ; Rueckert, Daniel ; Soininen, Hilkka ; Lötjönen, Jyrki ; Wolz, Robin
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ispartofPloS one, 2011, Vol.6(10), p.e25446
identifierE-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0025446
subjectAged–Diagnosis ; Alzheimer Disease–Pathology ; Case-Control Studies–Diagnosis ; Cerebral Cortex–Methods ; Cognitive Dysfunction–Methods ; Female–Methods ; Humans–Methods ; Magnetic Resonance Imaging–Methods ; Male–Methods
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descriptionThe role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.
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17Gessert, Devon
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68Morris, John C
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95Karlawish, Jason H
96Wolk, David
97Smith, Charles D
98Jicha, Greg
99Hardy, Peter
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titleMulti-method analysis of MRI images in early diagnostics of Alzheimer's disease.
authorWolz, Robin ; Julkunen, Valtteri ; Koikkalainen, Juha ; Niskanen, Eini ; Zhang, Dong Ping ; Rueckert, Daniel ; Soininen, Hilkka ; Lötjönen, Jyrki ; Wolz, Robin
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7Lötjönen, Jyrki
8Weiner, Michael
9Aisen, Paul
10Petersen, Ronald
11Jack, Clifford R
12Jagust, William
13Trojanowki, John Q
14Toga, Arthur W
15Beckett, Laurel
16Green, Robert C
17Saykin, Andrew J
18Morris, John
19Liu, Enchi
20Montine, Tom
21Gamst, Anthony
22Thomas, Ronald G
23Donohue, Michael
24Walter, Sarah
25Gessert, Devon
26Sather, Tamie
27Harvey, Danielle
28Kornak, John
29Dale, Anders
30Bernstein, Matthew
31Felmlee, Joel
32Fox, Nick
33Thompson, Paul
34Schuff, Norbert
35Alexander, Gene
36Decarli, Charles
37Bandy, Dan
38Koeppe, Robert A
39Foster, Norm
40Reiman, Eric M
41Chen, Kewei
42Mathis, Chet
43Cairns, Nigel J
44Taylor-Reinwald, Lisa
45Trojanowki, J Q
46Shaw, Les
47Lee, Virginia M Y
48Korecka, Magdalena
49Crawford, Karen
50Neu, Scott
51Foroud, Tatiana M
52Potkin, Steven
53Shen, Li
54Kachaturian, Zaven
55Frank, Richard
56Snyder, Peter J
57Molchan, Susan
58Kaye, Jeffrey
59Quinn, Joseph
60Lind, Betty
61Dolen, Sara
62Schneider, Lon S
63Pawluczyk, Sonia
64Spann, Bryan M
65Brewer, James
66Vanderswag, Helen
67Heidebrink, Judith L
68Lord, Joanne L
69Johnson, Kris
70Doody, Rachelle S
71Villanueva-Meyer, Javier
72Chowdhury, Munir
73Stern, Yaakov
74Honig, Lawrence S
75Bell, Karen L
76Morris, John C
77Ances, Beau
78Carroll, Maria
79Leon, Sue
80Mintun, Mark A
81Schneider, Stacy
82Marson, Daniel
83Griffith, Randall
84Clark, David
85Grossman, Hillel
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88Detoledo-Morrell, Leyla
89Shah, Raj C
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94Onyike, Chiadi
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14Thomas, Ronald G
15Donohue, Michael
16Walter, Sarah
17Gessert, Devon
18Sather, Tamie
19Harvey, Danielle
20Kornak, John
21Dale, Anders
22Bernstein, Matthew
23Felmlee, Joel
24Fox, Nick
25Thompson, Paul
26Schuff, Norbert
27Alexander, Gene
28DeCarli, Charles
29Bandy, Dan
30Koeppe, Robert A
31Foster, Norm
32Reiman, Eric M
33Chen, Kewei
34Mathis, Chet
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36Taylor-Reinwald, Lisa
37Trojanowki, J Q
38Shaw, Les
39Lee, Virginia M Y
40Korecka, Magdalena
41Crawford, Karen
42Neu, Scott
43Foroud, Tatiana M
44Potkin, Steven
45Shen, Li
46Kachaturian, Zaven
47Frank, Richard
48Snyder, Peter J
49Molchan, Susan
50Kaye, Jeffrey
51Quinn, Joseph
52Lind, Betty
53Dolen, Sara
54Schneider, Lon S
55Pawluczyk, Sonia
56Spann, Bryan M
57Brewer, James
58Vanderswag, Helen
59Heidebrink, Judith L
60Lord, Joanne L
61Johnson, Kris
62Doody, Rachelle S
63Villanueva-Meyer, Javier
64Chowdhury, Munir
65Stern, Yaakov
66Honig, Lawrence S
67Bell, Karen L
68Morris, John C
69Ances, Beau
70Carroll, Maria
71Leon, Sue
72Mintun, Mark A
73Schneider, Stacy
74Marson, Daniel
75Griffith, Randall
76Clark, David
77Grossman, Hillel
78Mitsis, Effie
79Romirowsky, Aliza
80deToledo-Morrell, Leyla
81Shah, Raj C
82Duara, Ranjan
83Varon, Daniel
84Roberts, Peggy
85Albert, Marilyn
86Onyike, Chiadi
87Kielb, Stephanie
88Rusinek, Henry
89de Leon, Mony J
90Glodzik, Lidia
91Doraiswamy, P Murali
92Petrella, Jeffrey R
93Coleman, R Edward
94Arnold, Steven E
95Karlawish, Jason H
96Wolk, David
97Smith, Charles D
98Jicha, Greg
99Hardy, Peter
100...
atitleMulti-method analysis of MRI images in early diagnostics of Alzheimer's disease.
jtitlePloS one
risdate20110101
volume6
issue10
spagee25446
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eissn1932-6203
formatjournal
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ristypeJOUR
doi10.1371/journal.pone.0025446
urlhttp://search.proquest.com/docview/907027818/
date2011-01-01