Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Maciej Plocharski, Lasse Riis Østergaard, The Alzheimer’s Disease Neuroimaging Initiative

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Prominent changes in sulcal morphology and cortical thickness characterize the neurodegeneration in Alzheimer’s disease (AD). A combination of these measures has a potential of predicting AD and distinguishing it from mild cognitive impairment (MCI) and cognitively normal control subjects (CN). The purpose of this study was to propose a machine learning and pattern recognition approach of combining sulcal morphology features and cortical thickness measures as biomarkers for AD. Sulcal features (depth, length, mean and Gaussian curvature, surface area) and cortical thickness measures were extracted from 241 T1 MRI scans from ADNI database (81 AD, 75 MCI, 85 CN). SVM classifiers provided the highest accuracy of 95.0%, 93.0% sensitivity, and 97.0% specificity (AUC of 0.95) when classifying CN and AD. The majority of the features were located in the left hemisphere, which in AD is reported to be more severely affected by atrophy, and to lose gray matter faster than the right. Results indicate that a combination of sulcal and cortical features provides high classification results, which are competitive with the state-of-the-art techniques.
OriginalsprogEngelsk
TitelImage Analysis : 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings
RedaktørerMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
Antal sider12
ForlagSpringer
Publikationsdato2019
Sider427-438
ISBN (Trykt)978-3-030-20204-0
ISBN (Elektronisk)978-3-030-20205-7
DOI
StatusUdgivet - 2019
Begivenhed21st Scandinavian Conference on Image Analysis, SCIA 2019 - Norrköping, Sverige
Varighed: 11 jun. 201913 jun. 2019

Konference

Konference21st Scandinavian Conference on Image Analysis, SCIA 2019
LandSverige
ByNorrköping
Periode11/06/201913/06/2019
NavnLecture Notes in Computer Science
Vol/bind11482
ISSN0302-9743

Citer dette

Plocharski, M., Østergaard, L. R., & The Alzheimer’s Disease Neuroimaging Initiative (2019). Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment. I M. Felsberg, P-E. Forssén, J. Unger, & I-M. Sintorn (red.), Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings (s. 427-438). Springer. Lecture Notes in Computer Science, Bind. 11482 https://doi.org/10.1007/978-3-030-20205-7_35
Plocharski, Maciej ; Østergaard, Lasse Riis ; The Alzheimer’s Disease Neuroimaging Initiative. / Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment. Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. red. / Michael Felsberg ; Per-Erik Forssén ; Jonas Unger ; Ida-Maria Sintorn. Springer, 2019. s. 427-438 (Lecture Notes in Computer Science, Bind 11482).
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abstract = "Prominent changes in sulcal morphology and cortical thickness characterize the neurodegeneration in Alzheimer’s disease (AD). A combination of these measures has a potential of predicting AD and distinguishing it from mild cognitive impairment (MCI) and cognitively normal control subjects (CN). The purpose of this study was to propose a machine learning and pattern recognition approach of combining sulcal morphology features and cortical thickness measures as biomarkers for AD. Sulcal features (depth, length, mean and Gaussian curvature, surface area) and cortical thickness measures were extracted from 241 T1 MRI scans from ADNI database (81 AD, 75 MCI, 85 CN). SVM classifiers provided the highest accuracy of 95.0{\%}, 93.0{\%} sensitivity, and 97.0{\%} specificity (AUC of 0.95) when classifying CN and AD. The majority of the features were located in the left hemisphere, which in AD is reported to be more severely affected by atrophy, and to lose gray matter faster than the right. Results indicate that a combination of sulcal and cortical features provides high classification results, which are competitive with the state-of-the-art techniques.",
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Plocharski, M, Østergaard, LR & The Alzheimer’s Disease Neuroimaging Initiative 2019, Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment. i M Felsberg, P-E Forssén, J Unger & I-M Sintorn (red), Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. Springer, Lecture Notes in Computer Science, bind 11482, s. 427-438, Norrköping, Sverige, 11/06/2019. https://doi.org/10.1007/978-3-030-20205-7_35

Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment. / Plocharski, Maciej; Østergaard, Lasse Riis; The Alzheimer’s Disease Neuroimaging Initiative.

Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. red. / Michael Felsberg; Per-Erik Forssén; Jonas Unger; Ida-Maria Sintorn. Springer, 2019. s. 427-438 (Lecture Notes in Computer Science, Bind 11482).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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AU - The Alzheimer’s Disease Neuroimaging Initiative

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N2 - Prominent changes in sulcal morphology and cortical thickness characterize the neurodegeneration in Alzheimer’s disease (AD). A combination of these measures has a potential of predicting AD and distinguishing it from mild cognitive impairment (MCI) and cognitively normal control subjects (CN). The purpose of this study was to propose a machine learning and pattern recognition approach of combining sulcal morphology features and cortical thickness measures as biomarkers for AD. Sulcal features (depth, length, mean and Gaussian curvature, surface area) and cortical thickness measures were extracted from 241 T1 MRI scans from ADNI database (81 AD, 75 MCI, 85 CN). SVM classifiers provided the highest accuracy of 95.0%, 93.0% sensitivity, and 97.0% specificity (AUC of 0.95) when classifying CN and AD. The majority of the features were located in the left hemisphere, which in AD is reported to be more severely affected by atrophy, and to lose gray matter faster than the right. Results indicate that a combination of sulcal and cortical features provides high classification results, which are competitive with the state-of-the-art techniques.

AB - Prominent changes in sulcal morphology and cortical thickness characterize the neurodegeneration in Alzheimer’s disease (AD). A combination of these measures has a potential of predicting AD and distinguishing it from mild cognitive impairment (MCI) and cognitively normal control subjects (CN). The purpose of this study was to propose a machine learning and pattern recognition approach of combining sulcal morphology features and cortical thickness measures as biomarkers for AD. Sulcal features (depth, length, mean and Gaussian curvature, surface area) and cortical thickness measures were extracted from 241 T1 MRI scans from ADNI database (81 AD, 75 MCI, 85 CN). SVM classifiers provided the highest accuracy of 95.0%, 93.0% sensitivity, and 97.0% specificity (AUC of 0.95) when classifying CN and AD. The majority of the features were located in the left hemisphere, which in AD is reported to be more severely affected by atrophy, and to lose gray matter faster than the right. Results indicate that a combination of sulcal and cortical features provides high classification results, which are competitive with the state-of-the-art techniques.

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Plocharski M, Østergaard LR, The Alzheimer’s Disease Neuroimaging Initiative. Sulcal and Cortical Features for Classification of Alzheimer’s Disease and Mild Cognitive Impairment. I Felsberg M, Forssén P-E, Unger J, Sintorn I-M, red., Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings. Springer. 2019. s. 427-438. (Lecture Notes in Computer Science, Bind 11482). https://doi.org/10.1007/978-3-030-20205-7_35