  Predictive models allow subject-specific inference when analyzing disease
related alterations in neuroimaging data. Given a subject's data, inference can
be made at two levels: global, i.e. identifiying condition presence for the
subject, and local, i.e. detecting condition effect on each individual
measurement extracted from the subject's data. While global inference is widely
used, local inference, which can be used to form subject-specific effect maps,
is rarely used because existing models often yield noisy detections composed of
dispersed isolated islands. In this article, we propose a reconstruction
method, named RSM, to improve subject-specific detections of predictive
modeling approaches and in particular, binary classifiers. RSM specifically
aims to reduce noise due to sampling error associated with using a finite
sample of examples to train classifiers. The proposed method is a wrapper-type
algorithm that can be used with different binary classifiers in a diagnostic
manner, i.e. without information on condition presence. Reconstruction is posed
as a Maximum-A-Posteriori problem with a prior model whose parameters are
estimated from training data in a classifier-specific fashion. Experimental
evaluation is performed on synthetically generated data and data from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on
synthetic data demonstrate that using RSM yields higher detection accuracy
compared to using models directly or with bootstrap averaging. Analyses on the
ADNI dataset show that RSM can also improve correlation between
subject-specific detections in cortical thickness data and non-imaging markers
of Alzheimer's Disease (AD), such as the Mini Mental State Examination Score
and Cerebrospinal Fluid amyloid-$\beta$ levels. Further reliability studies on
the longitudinal ADNI dataset show improvement on detection reliability when
RSM is used.
