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Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment.
Psychiatry Res. 2013 May 30; 212(2):89-98.PR

Abstract

Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.

Authors+Show Affiliations

Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. carlos.aguilar@ki.seNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23541334

Citation

Aguilar, Carlos, et al. "Different Multivariate Techniques for Automated Classification of MRI Data in Alzheimer's Disease and Mild Cognitive Impairment." Psychiatry Research, vol. 212, no. 2, 2013, pp. 89-98.
Aguilar C, Westman E, Muehlboeck JS, et al. Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment. Psychiatry Res. 2013;212(2):89-98.
Aguilar, C., Westman, E., Muehlboeck, J. S., Mecocci, P., Vellas, B., Tsolaki, M., Kloszewska, I., Soininen, H., Lovestone, S., Spenger, C., Simmons, A., & Wahlund, L. O. (2013). Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment. Psychiatry Research, 212(2), 89-98. https://doi.org/10.1016/j.pscychresns.2012.11.005
Aguilar C, et al. Different Multivariate Techniques for Automated Classification of MRI Data in Alzheimer's Disease and Mild Cognitive Impairment. Psychiatry Res. 2013 May 30;212(2):89-98. PubMed PMID: 23541334.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment. AU - Aguilar,Carlos, AU - Westman,Eric, AU - Muehlboeck,J-Sebastian, AU - Mecocci,Patrizia, AU - Vellas,Bruno, AU - Tsolaki,Magda, AU - Kloszewska,Iwona, AU - Soininen,Hilkka, AU - Lovestone,Simon, AU - Spenger,Christian, AU - Simmons,Andrew, AU - Wahlund,Lars-Olof, Y1 - 2013/03/29/ PY - 2012/02/03/received PY - 2012/11/05/revised PY - 2012/11/15/accepted PY - 2013/4/2/entrez PY - 2013/4/2/pubmed PY - 2013/11/20/medline SP - 89 EP - 98 JF - Psychiatry research JO - Psychiatry Res VL - 212 IS - 2 N2 - Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters. SN - 1872-7123 UR - https://www.unboundmedicine.com/medline/citation/23541334/Different_multivariate_techniques_for_automated_classification_of_MRI_data_in_Alzheimer's_disease_and_mild_cognitive_impairment_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0925-4927(12)00297-1 DB - PRIME DP - Unbound Medicine ER -