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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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Related in: MedlinePlus

Top 500 voxels selected for classification by the Relieff algorithm.(a) Classification of control and MCI groups. The highest accuracy for this classification was achieved by the FA index. Here we show a cluster of voxels selected by the algorithm which is located in the forceps major. (b) Classification of control, MCIna and MCIa groups. For this classification of three groups, the highest accuracy was again achieved with the FA index. Here we show two significant clusters of voxels selected by Relieff. Similar to the two group classification, the forceps major was also implicated in three group classification. An additional significant cluster is located in the fronto-occipital fasciculus. Both (a) and (b) show the same sagittal slice in the right hemisphere (x = 29).
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pone-0032441-g009: Top 500 voxels selected for classification by the Relieff algorithm.(a) Classification of control and MCI groups. The highest accuracy for this classification was achieved by the FA index. Here we show a cluster of voxels selected by the algorithm which is located in the forceps major. (b) Classification of control, MCIna and MCIa groups. For this classification of three groups, the highest accuracy was again achieved with the FA index. Here we show two significant clusters of voxels selected by Relieff. Similar to the two group classification, the forceps major was also implicated in three group classification. An additional significant cluster is located in the fronto-occipital fasciculus. Both (a) and (b) show the same sagittal slice in the right hemisphere (x = 29).

Mentions: Following classification, we subsequently created images depicting the location of some of clusters of voxels selected the ReliefF algorithm. For the control versus MCI classification, a significant cluster of voxels contained within the FA dataset that produced sensitivity and specificity of 93.25 and 92.8% respectively using the top 500 voxels was visualised (Fig. 9a). In this instance, we present the largest cluster of voxels selected by ReliefF which was located in the forceps major in the right hemisphere (Fig. 9a).


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)

Top 500 voxels selected for classification by the Relieff algorithm.(a) Classification of control and MCI groups. The highest accuracy for this classification was achieved by the FA index. Here we show a cluster of voxels selected by the algorithm which is located in the forceps major. (b) Classification of control, MCIna and MCIa groups. For this classification of three groups, the highest accuracy was again achieved with the FA index. Here we show two significant clusters of voxels selected by Relieff. Similar to the two group classification, the forceps major was also implicated in three group classification. An additional significant cluster is located in the fronto-occipital fasciculus. Both (a) and (b) show the same sagittal slice in the right hemisphere (x = 29).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0032441-g009: Top 500 voxels selected for classification by the Relieff algorithm.(a) Classification of control and MCI groups. The highest accuracy for this classification was achieved by the FA index. Here we show a cluster of voxels selected by the algorithm which is located in the forceps major. (b) Classification of control, MCIna and MCIa groups. For this classification of three groups, the highest accuracy was again achieved with the FA index. Here we show two significant clusters of voxels selected by Relieff. Similar to the two group classification, the forceps major was also implicated in three group classification. An additional significant cluster is located in the fronto-occipital fasciculus. Both (a) and (b) show the same sagittal slice in the right hemisphere (x = 29).
Mentions: Following classification, we subsequently created images depicting the location of some of clusters of voxels selected the ReliefF algorithm. For the control versus MCI classification, a significant cluster of voxels contained within the FA dataset that produced sensitivity and specificity of 93.25 and 92.8% respectively using the top 500 voxels was visualised (Fig. 9a). In this instance, we present the largest cluster of voxels selected by ReliefF which was located in the forceps major in the right hemisphere (Fig. 9a).

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