  Patients initially diagnosed with early mild cognitive impairment (eMCI) are
known to be a clinically heterogeneous group with very subtle patterns of brain
atrophy. To examine the boarders between normal controls (NC) and eMCI,
Magnetic Resonance Imaging (MRI) was extensively used as a non-invasive imaging
modality to pin-down subtle changes in brain images of MCI patients. However,
eMCI research remains limited by the number of available MRI acquisition
timepoints. Ideally, one would learn how to diagnose MCI patients in an early
stage from MRI data acquired at a single timepoint, while leveraging
'non-existing' follow-up observations. To this aim, we propose novel supervised
and unsupervised frameworks that learn how to jointly predict and label the
evolution trajectory of intensity patches, each seeded at a specific brain
landmark, from a baseline intensity patch. Specifically, both strategies aim to
identify the best training atlas patches at baseline timepoint to predict and
classify the evolution trajectory of a given testing baseline patch. The
supervised technique learns how to select the best atlas patches by training
bidirectional mappings from the space of pairwise patch similarities to their
corresponding prediction errors -when one patch was used to predict the other.
On the other hand, the unsupervised technique learns a manifold of baseline
atlas and testing patches using multiple kernels to well capture patch
distributions at multiple scales. Once the best baseline atlas patches are
selected, we retrieve their evolution trajectories and average them to predict
the evolution trajectory of the testing baseline patch. Next, we input the
predicted trajectories to an ensemble of linear classifiers, each trained at a
specific landmark. Our classification accuracy increased by up to 10% points in
comparison to single timepoint-based classification methods.
