  Mild cognitive impairment (MCI) is a high-risk dementia condition which
progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per
year. Characterization of group-level differences between two subtypes of MCI -
stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the
mechanisms of MCI progression and enable possible delay of transition from MCI
to AD. Functional connectivity (FC) is considered as a promising way to study
MCI progression since which may show alterations even in preclinical stages and
provide substrates for AD progression. However, the representative FC patterns
during AD development for different clinical groups, especially for sMCI and
pMCI, have been understudied. In this work, we integrated autoencoder and
multi-class classification into a single deep model and successfully learned a
set of clinical group related feature vectors. Specifically, we trained two
non-linear mappings which realized the mutual transformations between original
FC space and the feature space. By mapping the learned clinical group related
feature vectors to the original FC space, representative FCs were constructed
for each group. Moreover, based on these feature vectors, our model achieves a
high classification accuracy - 68% for multi-class classification (NC vs SMC vs
sMCI vs pMCI vs AD).
