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Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.
Neuroimage 2015; 104:398-412N

Abstract

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.

Authors+Show Affiliations

Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland.Aix Marseille Université, CNRS, ENSAM, Université de Toulon, LSIS UMR 7296,13397, Marseille, France.Department of Psychiatry, University of Jena, Jahnstr 3, D-07743, Jena, Germany.Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland.Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101, Tampere, Finland. Electronic address: jussi.tohka@tut.fi.No affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

25312773

Citation

Moradi, Elaheh, et al. "Machine Learning Framework for Early MRI-based Alzheimer's Conversion Prediction in MCI Subjects." NeuroImage, vol. 104, 2015, pp. 398-412.
Moradi E, Pepe A, Gaser C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage. 2015;104:398-412.
Moradi, E., Pepe, A., Gaser, C., Huttunen, H., & Tohka, J. (2015). Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. NeuroImage, 104, pp. 398-412. doi:10.1016/j.neuroimage.2014.10.002.
Moradi E, et al. Machine Learning Framework for Early MRI-based Alzheimer's Conversion Prediction in MCI Subjects. Neuroimage. 2015 Jan 1;104:398-412. PubMed PMID: 25312773.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. AU - Moradi,Elaheh, AU - Pepe,Antonietta, AU - Gaser,Christian, AU - Huttunen,Heikki, AU - Tohka,Jussi, AU - ,, Y1 - 2014/10/12/ PY - 2014/07/08/received PY - 2014/09/16/revised PY - 2014/10/01/accepted PY - 2014/10/15/entrez PY - 2014/10/15/pubmed PY - 2015/12/24/medline KW - ADNI KW - Alzheimer's disease KW - Classification KW - Early diagnosis KW - Feature selection KW - Low density separation KW - Magnetic resonance imaging KW - Mild cognitive impairment KW - Semi-supervised learning KW - Support vector machine SP - 398 EP - 412 JF - NeuroImage JO - Neuroimage VL - 104 N2 - 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. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/25312773/Machine_learning_framework_for_early_MRI_based_Alzheimer's_conversion_prediction_in_MCI_subjects_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(14)00813-1 DB - PRIME DP - Unbound Medicine ER -