Limits...
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.

Show MeSH

Related in: MedlinePlus

Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for control and MCI classification.The values indicated are weighted averages for the two classes under consideration; i.e. control and MCI. Results are shown for 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3285682&req=5

pone-0032441-g004: Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for control and MCI classification.The values indicated are weighted averages for the two classes under consideration; i.e. control and MCI. Results are shown for 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.

Mentions: For the classification of control and MCI individuals, the highest sensitivity (93.0%) and specificity (92.8%) were achieved using the FA index with 500 voxel dataset (Fig. 4).


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)

Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for control and MCI classification.The values indicated are weighted averages for the two classes under consideration; i.e. control and MCI. Results are shown for 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0032441-g004: Sensitivity, specificity, accuracy and the area under the curve for a receiver operating characteristic curve (ROC AUC) for control and MCI classification.The values indicated are weighted averages for the two classes under consideration; i.e. control and MCI. Results are shown for 7 datasets – 100 voxels, 250 voxels, 500 voxels, 750 voxels, 1000 voxels, 2000 voxels and 3000 voxels. The voxels comprising these reduced datasets were selected by the ReliefF algorithm.
Mentions: For the classification of control and MCI individuals, the highest sensitivity (93.0%) and specificity (92.8%) were achieved using the FA index with 500 voxel dataset (Fig. 4).

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