Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness

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Resumé

Mild cognitive impairment (MCI) is an intermediate condition between healthy ageing and dementia. The amnestic MCI is often a high risk factor for subsequent Alzheimer’s disease (AD) conversion. Some MCI patients never develop AD (MCI non-converters, or MCInc), but some do progress to AD (MCI converters, or MCIc). The purpose of this study was to predict future AD-conversion in patients with MCI using machine learning with sulcal morphology and cortical thickness measures as classification features. 32 sulci per subject were extracted from 1.5T T1-weighted ADNI database MRI scans of 90 MCIc and 104 MCInc subjects. We computed sulcal morphology features and cortical thickness measurements for support vector machine classification to identify structural patterns distinguishing future AD conversions. The linear kernel classifier trained with these features was able to predict 87.0% of MCI subjects as future converters, (89.7% sensitivity, 84.4% specificity, 0.94 AUC), using 10-fold cross-validation. These results using sulcal and cortical features are superior to the state-of-the-art methods. The most discriminating predictive features were observed in the temporal and frontal lobes in the left hemispheres, and in the entorhinal cortices, which is consistent with literature. However, we also observed structural changes in the cingulate and calcarine cortices, suggesting that the limbic and occipital lobe atrophy may be linked to AD conversion.
OriginalsprogEngelsk
TitelWorld Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic
RedaktørerLenka Lhotska, Lucie Sukupova, Igor Lacković, Geoffrey S. Ibbott
Antal sider6
Vol/bind68
ForlagSpringer
Publikationsdato1 jan. 2019
Udgave1
Sider69-74
ISBN (Trykt)978-981-10-9034-9
ISBN (Elektronisk)978-981-10-9035-6
DOI
StatusUdgivet - 1 jan. 2019
BegivenhedWorld Congress on Medical Physics and Biomedical Engineering - Prague, Tjekkiet
Varighed: 3 jun. 20188 jun. 2018

Konference

KonferenceWorld Congress on Medical Physics and Biomedical Engineering
LandTjekkiet
ByPrague
Periode03/06/201808/06/2018
NavnIFMBE Proceedings
Vol/bind68(1)
ISSN1680-0737

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Alzheimer Disease
Occipital Lobe
Entorhinal Cortex
Gyrus Cinguli
Frontal Lobe
Temporal Lobe
Cognitive Dysfunction
Area Under Curve
Atrophy
Dementia
Magnetic Resonance Imaging
Databases
Sensitivity and Specificity

Emneord

    Citer dette

    Plocharski, M., & Østergaard, L. R. (2019). Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness. I L. Lhotska, L. Sukupova, I. Lacković, & G. S. Ibbott (red.), World Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic (1 udg., Bind 68, s. 69-74). Springer. IFMBE Proceedings, Bind. 68(1) https://doi.org/10.1007/978-981-10-9035-6_13
    Plocharski, Maciej ; Østergaard, Lasse Riis. / Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness. World Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic. red. / Lenka Lhotska ; Lucie Sukupova ; Igor Lacković ; Geoffrey S. Ibbott. Bind 68 1. udg. Springer, 2019. s. 69-74 (IFMBE Proceedings, Bind 68(1)).
    @inproceedings{8482a5af0ea5454b80a52b8f1a387260,
    title = "Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness",
    abstract = "Mild cognitive impairment (MCI) is an intermediate condition between healthy ageing and dementia. The amnestic MCI is often a high risk factor for subsequent Alzheimer’s disease (AD) conversion. Some MCI patients never develop AD (MCI non-converters, or MCInc), but some do progress to AD (MCI converters, or MCIc). The purpose of this study was to predict future AD-conversion in patients with MCI using machine learning with sulcal morphology and cortical thickness measures as classification features. 32 sulci per subject were extracted from 1.5T T1-weighted ADNI database MRI scans of 90 MCIc and 104 MCInc subjects. We computed sulcal morphology features and cortical thickness measurements for support vector machine classification to identify structural patterns distinguishing future AD conversions. The linear kernel classifier trained with these features was able to predict 87.0{\%} of MCI subjects as future converters, (89.7{\%} sensitivity, 84.4{\%} specificity, 0.94 AUC), using 10-fold cross-validation. These results using sulcal and cortical features are superior to the state-of-the-art methods. The most discriminating predictive features were observed in the temporal and frontal lobes in the left hemispheres, and in the entorhinal cortices, which is consistent with literature. However, we also observed structural changes in the cingulate and calcarine cortices, suggesting that the limbic and occipital lobe atrophy may be linked to AD conversion.",
    keywords = "MRI, Alzheimer’s disease, Mild cognitive impairment, Sulcal morphology, Cortical thickness, SVM classification",
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    Plocharski, M & Østergaard, LR 2019, Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness. i L Lhotska, L Sukupova, I Lacković & GS Ibbott (red), World Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic. 1 udg, bind 68, Springer, IFMBE Proceedings, bind 68(1), s. 69-74, World Congress on Medical Physics and Biomedical Engineering, Prague, Tjekkiet, 03/06/2018. https://doi.org/10.1007/978-981-10-9035-6_13

    Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness. / Plocharski, Maciej; Østergaard, Lasse Riis.

    World Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic. red. / Lenka Lhotska; Lucie Sukupova; Igor Lacković; Geoffrey S. Ibbott. Bind 68 1. udg. Springer, 2019. s. 69-74.

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

    TY - GEN

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    AU - Plocharski, Maciej

    AU - Østergaard, Lasse Riis

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    N2 - Mild cognitive impairment (MCI) is an intermediate condition between healthy ageing and dementia. The amnestic MCI is often a high risk factor for subsequent Alzheimer’s disease (AD) conversion. Some MCI patients never develop AD (MCI non-converters, or MCInc), but some do progress to AD (MCI converters, or MCIc). The purpose of this study was to predict future AD-conversion in patients with MCI using machine learning with sulcal morphology and cortical thickness measures as classification features. 32 sulci per subject were extracted from 1.5T T1-weighted ADNI database MRI scans of 90 MCIc and 104 MCInc subjects. We computed sulcal morphology features and cortical thickness measurements for support vector machine classification to identify structural patterns distinguishing future AD conversions. The linear kernel classifier trained with these features was able to predict 87.0% of MCI subjects as future converters, (89.7% sensitivity, 84.4% specificity, 0.94 AUC), using 10-fold cross-validation. These results using sulcal and cortical features are superior to the state-of-the-art methods. The most discriminating predictive features were observed in the temporal and frontal lobes in the left hemispheres, and in the entorhinal cortices, which is consistent with literature. However, we also observed structural changes in the cingulate and calcarine cortices, suggesting that the limbic and occipital lobe atrophy may be linked to AD conversion.

    AB - Mild cognitive impairment (MCI) is an intermediate condition between healthy ageing and dementia. The amnestic MCI is often a high risk factor for subsequent Alzheimer’s disease (AD) conversion. Some MCI patients never develop AD (MCI non-converters, or MCInc), but some do progress to AD (MCI converters, or MCIc). The purpose of this study was to predict future AD-conversion in patients with MCI using machine learning with sulcal morphology and cortical thickness measures as classification features. 32 sulci per subject were extracted from 1.5T T1-weighted ADNI database MRI scans of 90 MCIc and 104 MCInc subjects. We computed sulcal morphology features and cortical thickness measurements for support vector machine classification to identify structural patterns distinguishing future AD conversions. The linear kernel classifier trained with these features was able to predict 87.0% of MCI subjects as future converters, (89.7% sensitivity, 84.4% specificity, 0.94 AUC), using 10-fold cross-validation. These results using sulcal and cortical features are superior to the state-of-the-art methods. The most discriminating predictive features were observed in the temporal and frontal lobes in the left hemispheres, and in the entorhinal cortices, which is consistent with literature. However, we also observed structural changes in the cingulate and calcarine cortices, suggesting that the limbic and occipital lobe atrophy may be linked to AD conversion.

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    Plocharski M, Østergaard LR. Prediction of Alzheimer’s disease in mild cognitive impairment using sulcal morphology and cortical thickness. I Lhotska L, Sukupova L, Lacković I, Ibbott GS, red., World Congress on Medical Physics and Biomedical Engineering, 3-8 June 2018, Prague, Czech Republic. 1 udg. Bind 68. Springer. 2019. s. 69-74. (IFMBE Proceedings, Bind 68(1)). https://doi.org/10.1007/978-981-10-9035-6_13