In this paper, we explored the potential of semi-supervised approaches to classify individuals with progressive disorders in the absence of long-term follow-up evaluations. We applied a strategy based on semi-supervised SVM in the ADNI study, and obtained an indicator of AD-like atrophy patterns that has good predictive power of conversion from MCI to AD. The principal difference between our semi-supervised approach and related fully-supervised techniques lies in the fact that the supervised approaches assume that the heterogeneous structure of the population is known. In the case of MCI, these supervised approaches assume that the population of MCI consists of MCI-converters and MCI-non-converters. In contrast, our semi-supervised approach does not make strong assumptions about the structure of MCI, but rather attempts to disentangle the heterogeneity of MCI via high-dimensional pattern analysis.
Analysis of volumetric differences of AD-like and normal-like MCI showed that AD-like MCI had reduced GM volumes in a number of brain regions, including superior, middle and inferior temporal gyri, anterior hippocampus and amygdala, orbitofrontal cortex, posterior cingulate and the adjacent precuneous, insula and fusiform gyrus (i.e., ). Moreover, pronounced was the larger size of the temporal horns of the ventricles (i.e., ). These results indicate that AD-like MCI may have already reached levels of widespread and significant brain atrophy at baseline.
Similar, albeit somewhat less severe, deferences were observed between normal-like MCI-NC and AD-like MCI-NC (i.e., and ). These results indicate that the pathological changes in the AD-like MCI-NC are significantly more progressed, and that this particular group of non-converters is likely to convert to AD in the future.
Additionally, we observed that there is no significant difference in the patterns of atrophy in AD-like MCI-NC and AD-like MCI-C. This suggests that the AD-like MCI share the same patterns of atrophy regardless of the most recent conversion status. A more prolonged evaluation of MCI-NC subjects from ADNI is required to validate the hypothesis that AD-like MCI-NC are more likely to convert to AD.
The classification function derived from the semi-supervised SVM was found to have relatively good sensitivity, in that almost all MCI-C patients where classified as AD-like. Not unexpectedly, specificity was limited. This is largely due to the short follow-up periods in this study. Since MCI patients convert to AD at a rate of approximately 15% annually, it is anticipated that many MCI-NC will convert to AD in the near future. Although future studies with longer follow-up times will refine our estimates of specificity, our results indicated that positive values of the classification function in MCI-NC were associated with lower MMSE scores, and with higher rates of decline in MMSE scores.
The results in indicate that if the labeled subsets are formed from the AD and CN subjects, then the semi-supervised classifier only slightly outperforms its supervised counterpart. At the same time, if AD and CN subjects are not available, and the labeled subsets are formed from the MCI subjects based on the extreme rates of changes in cognitive scores, then the difference in classification performance is quite significant. We found that AUC of the semi-supervised classifier was equal to 0.69, while the supervised classifier yielded AUC equal to 0.61. Overall, the MCI-C/MCI-NC classification problem is characterized by exceptionally low separability and difficulty, and predicting short-term cognitive decline from baseline scans is bound to be very limited, albeit it is largely improved by semi-supervised classification. The large difference in performance of the classifiers can be explained by the fact that the labeled data in the experiment in Section 3.3 was selected based on the extreme values of the MMSE slopes. MMSE scores are very noisy and are not sufficiently good indicators of conversion to AD. As the result, the labels of the subjects in the labeled subsets were uncertain, which may have hampered the performance of the fully-supervised classifier. At the same time, the availability of the unlabeled data allows the semi-supervised classifier to better learn the manifold of MCI subjects, and hence to build a more reliable separation function. On the other hand, if the labeled sets are formed from AD and CN subjects, the labels of the subjects in the labeled subsets are much more reliable, and given a sufficiently large number of labeled subjects it is possible to obtain a fully-supervised classifier that performs on a par with the semi-supervised approach.
As it can be seen from our experiments, the choice of the semi-supervised SVM parameters has significant effect on the classification performance. A possible strategy to selecting the parameters would be to use participants with longer follow-up evaluations, and hence more certain labels, in the parameters optimization stage. Unfortunately, due to the short follow-up period in the ADNI, the certainty in the labels of MCI subjects is questionable. At the same time, some studies such as the Baltimore Longitudinal Study of Aging (BLSA) (Resnick et al., 2003
) have the follow-up period of more than fifteen years, and therefore may allow to select a validation set with reliable ground truth data on which the optimal parameters can be found.
Finally, we would like to comment on the AD/controls classification performance of both traditional and semi-supervised SVM. Existing literature suggests that differences in processing protocols, classification algorithms and feature selection procedures may results in significantly different classification accuracy ranging from less than 80% in some cases (Cuingnet et al., 2010
), to up to 90% in others (Misra et al., 2009
). In this respect, our AD/controls classification results are moderate. This can be attributed in part to the fact that due to the complexity of the semi-supervised SVM learning problem in 2, we had to restrict our comparative analysis to linear versions of both semi-supervised and traditional SVM.
In summary, we investigated the ability of semi-supervised classification to address specific challenges that arise in studies of progressive disorders and that are due to uncertainty in diagnostic information. The main goal of our paper was to explore whether semi-supervised analysis of MRI data is more preferable to commonly used fully-supervised paradigm under similar conditions. Our analysis suggests that in some scenarios semi-supervised strategy may be more preferable. Specifically, if the number of labeled images is small, semi-supervised approach appears to yield higher classification accuracy. Application of our proposed approach to the problem of classifying MCI subjects within a short-follow-up study yielded encouraging results. The results indicate that pathological patterns can be accurately detected and quantified even if training information is limited. The fact that our results agree with findings obtained using fully supervised approaches may serve as additional computational justification for the assumptions made by these approaches. While in this paper we applied the semi-supervised classification approach to the problem of identifying patterns of AD-like pathology in subjects with MCI, our proposed framework can also be applied to analyzing other progressive disorders. In the future we plan to further explore the potential of semi-supervised classification in the studies of aging, as well as in studies of other diseases.