Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD

Abstract

This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using support vector machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from diffusion tensor imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with ASD (mean age 10.5 ± 2.5 yr) as compared to 30 typically developing (TD) controls (mean age 10.3 ± 2.5 yr). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p<0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.

Figure 1

An example depicting the basic idea behind non-linear SVM. The samples are mapped into a high dimensional feature space where a separating hyperplane is constructed. Here the x-axis can be considered as FA and y-axis as MD to be specific to diffusion.

Figure 2

Figure showing sample (a) FA and (b) MD maps computed from DTI image. (c) ROI map of the EVE template showing 176 structures. These regions are implemented as the basis for the features used to construct the pattern classifier. Details about the Eve template can be found at (Mori, Oishi et al. 2008).

Figure 2

Figure showing sample (a) FA and (b) MD maps computed from DTI image. (c) ROI map of the EVE template showing 176 structures. These regions are implemented as the basis for the features used to construct the pattern classifier. Details about the Eve template can be found at (Mori, Oishi et al. 2008).

Figure 2

Figure showing sample (a) FA and (b) MD maps computed from DTI image. (c) ROI map of the EVE template showing 176 structures. These regions are implemented as the basis for the features used to construct the pattern classifier. Details about the Eve template can be found at (Mori, Oishi et al. 2008).

Figure 3

Flow chart summarizing the entire DTI based classification procedure.

Figure 3

Flow chart summarizing the entire DTI based classification procedure.

Figure 4

Fig. displaying the LOO classification performance (80% average accuracy). (a) Abnormality score plotted against number of subjects when LOO cross validation is performed. The line shows the separability between populations (b) PDF of the LOO score plotted. This delineates the separation between two groups. (c) When the abnormality scores are divided into 3 groups (TD, ASD LI− and ASD LI+) the non-language impaired lie between the TD and LI+. The EMD for LI+ and TD was 0.86, while for LI+ and LI− it was 0.18 (d) ROC curve for the LOO classification with AUC of 0.81.

Figure 4

Fig. displaying the LOO classification performance (80% average accuracy). (a) Abnormality score plotted against number of subjects when LOO cross validation is performed. The line shows the separability between populations (b) PDF of the LOO score plotted. This delineates the separation between two groups. (c) When the abnormality scores are divided into 3 groups (TD, ASD LI− and ASD LI+) the non-language impaired lie between the TD and LI+. The EMD for LI+ and TD was 0.86, while for LI+ and LI− it was 0.18 (d) ROC curve for the LOO classification with AUC of 0.81.

Figure 4

Fig. displaying the LOO classification performance (80% average accuracy). (a) Abnormality score plotted against number of subjects when LOO cross validation is performed. The line shows the separability between populations (b) PDF of the LOO score plotted. This delineates the separation between two groups. (c) When the abnormality scores are divided into 3 groups (TD, ASD LI− and ASD LI+) the non-language impaired lie between the TD and LI+. The EMD for LI+ and TD was 0.86, while for LI+ and LI− it was 0.18 (d) ROC curve for the LOO classification with AUC of 0.81.

Figure 4

Fig. displaying the LOO classification performance (80% average accuracy). (a) Abnormality score plotted against number of subjects when LOO cross validation is performed. The line shows the separability between populations (b) PDF of the LOO score plotted. This delineates the separation between two groups. (c) When the abnormality scores are divided into 3 groups (TD, ASD LI− and ASD LI+) the non-language impaired lie between the TD and LI+. The EMD for LI+ and TD was 0.86, while for LI+ and LI− it was 0.18 (d) ROC curve for the LOO classification with AUC of 0.81.

Figure 5

Output of feature selection and ranking: (a) Top ranked ROI’s mapped on template image. These ROI’s contributed largely towards classification based on s2n ranking (see table 2). The means and standard deviations of some of these ROI’s (shown by yellow arrows) are plotted in (b). The scatter plot for top features (Middle occipital gyrus MD vs Inferior occipital WM FA) is shown in (c) It can be noted that using only 2 top features cannot discriminate the populations. Plot (d) for other 2 ranked features (superior occipital gyrus (MD) and Insular right (MD) suggest the same. Therefore in (e) we have plotted the top 2 PCA components (after performing PCA on top 18 features) which show the discriminative power of all the 18 features together.

Figure 5

Output of feature selection and ranking: (a) Top ranked ROI’s mapped on template image. These ROI’s contributed largely towards classification based on s2n ranking (see table 2). The means and standard deviations of some of these ROI’s (shown by yellow arrows) are plotted in (b). The scatter plot for top features (Middle occipital gyrus MD vs Inferior occipital WM FA) is shown in (c) It can be noted that using only 2 top features cannot discriminate the populations. Plot (d) for other 2 ranked features (superior occipital gyrus (MD) and Insular right (MD) suggest the same. Therefore in (e) we have plotted the top 2 PCA components (after performing PCA on top 18 features) which show the discriminative power of all the 18 features together.

Figure 5

Output of feature selection and ranking: (a) Top ranked ROI’s mapped on template image. These ROI’s contributed largely towards classification based on s2n ranking (see table 2). The means and standard deviations of some of these ROI’s (shown by yellow arrows) are plotted in (b). The scatter plot for top features (Middle occipital gyrus MD vs Inferior occipital WM FA) is shown in (c) It can be noted that using only 2 top features cannot discriminate the populations. Plot (d) for other 2 ranked features (superior occipital gyrus (MD) and Insular right (MD) suggest the same. Therefore in (e) we have plotted the top 2 PCA components (after performing PCA on top 18 features) which show the discriminative power of all the 18 features together.

Figure 5

Output of feature selection and ranking: (a) Top ranked ROI’s mapped on template image. These ROI’s contributed largely towards classification based on s2n ranking (see table 2). The means and standard deviations of some of these ROI’s (shown by yellow arrows) are plotted in (b). The scatter plot for top features (Middle occipital gyrus MD vs Inferior occipital WM FA) is shown in (c) It can be noted that using only 2 top features cannot discriminate the populations. Plot (d) for other 2 ranked features (superior occipital gyrus (MD) and Insular right (MD) suggest the same. Therefore in (e) we have plotted the top 2 PCA components (after performing PCA on top 18 features) which show the discriminative power of all the 18 features together.

Figure 5

Output of feature selection and ranking: (a) Top ranked ROI’s mapped on template image. These ROI’s contributed largely towards classification based on s2n ranking (see table 2). The means and standard deviations of some of these ROI’s (shown by yellow arrows) are plotted in (b). The scatter plot for top features (Middle occipital gyrus MD vs Inferior occipital WM FA) is shown in (c) It can be noted that using only 2 top features cannot discriminate the populations. Plot (d) for other 2 ranked features (superior occipital gyrus (MD) and Insular right (MD) suggest the same. Therefore in (e) we have plotted the top 2 PCA components (after performing PCA on top 18 features) which show the discriminative power of all the 18 features together.

Figure 6

Linear regression between abnormality score of TD’s and ASD’s from the LOO validation against (a) SRS score and (b) SCQ score. Mis-classified subjects were not considered in this regression analysis. For the correctly classified subjects, the correlation coefficient ‘r’ for was −0.152 and −0.12 for SRS and SCQ respectively for the ASD patients. While for TD subjects it was −0.177 and −0.205 for SRS and SCQ respectively. The linear fit indicates that the abnormality score reduces (towards −1) as the clinical scores increase in patients.

Figure 6

Linear regression between abnormality score of TD’s and ASD’s from the LOO validation against (a) SRS score and (b) SCQ score. Mis-classified subjects were not considered in this regression analysis. For the correctly classified subjects, the correlation coefficient ‘r’ for was −0.152 and −0.12 for SRS and SCQ respectively for the ASD patients. While for TD subjects it was −0.177 and −0.205 for SRS and SCQ respectively. The linear fit indicates that the abnormality score reduces (towards −1) as the clinical scores increase in patients.