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Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment.

O'Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O'Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H - PLoS ONE (2012)

Bottom Line: Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject.DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects.Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany. larodwyer@gmail.com

ABSTRACT
Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical.

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ROC curve for control and MCI classification.True positives refer to MCI volumes that are correctly classified as MCI, and false positives refer to volumes that are incorrectly labelled as MCI.
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pone-0032441-g005: ROC curve for control and MCI classification.True positives refer to MCI volumes that are correctly classified as MCI, and false positives refer to volumes that are incorrectly labelled as MCI.

Mentions: For the DA, DR and MD indices of diffusion, classification performance had a sensitivity and specificity in the range of ∼74–86% (Fig. 4). As peak performance of the SVM classifier occurs with the 500 voxel dataset, the receiver operating characteristic (ROC) curve is shown for this dataset for all 4 indices of diffusion (Fig. 5).


Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment.

O'Dwyer L, Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B, O'Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H - PLoS ONE (2012)

ROC curve for control and MCI classification.True positives refer to MCI volumes that are correctly classified as MCI, and false positives refer to volumes that are incorrectly labelled as MCI.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3285682&req=5

pone-0032441-g005: ROC curve for control and MCI classification.True positives refer to MCI volumes that are correctly classified as MCI, and false positives refer to volumes that are incorrectly labelled as MCI.
Mentions: For the DA, DR and MD indices of diffusion, classification performance had a sensitivity and specificity in the range of ∼74–86% (Fig. 4). As peak performance of the SVM classifier occurs with the 500 voxel dataset, the receiver operating characteristic (ROC) curve is shown for this dataset for all 4 indices of diffusion (Fig. 5).

Bottom Line: Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject.DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects.Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany. larodwyer@gmail.com

ABSTRACT
Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical.

Show MeSH
Related in: MedlinePlus