  Decision making can be a complex process requiring the integration of several
attributes of choice options. Understanding the neural processes underlying
(uncertain) investment decisions is an important topic in neuroeconomics. We
analyzed functional magnetic resonance imaging (fMRI) data from an investment
decision study for stimulus-related effects. We propose a new technique for
identifying activated brain regions: Cluster, Estimation, Activation and
Decision (CEAD) method. Our analysis is focused on clusters of voxels rather
than voxel units. Thus, we achieve a higher signal to noise ratio within the
unit tested and a smaller number of hypothesis tests compared with the often
used General Linear Model (GLM). We propose to first conduct the brain
parcellation by applying spatially constrained spectral clustering. The
information within each cluster can then be extracted by the flexible Dynamic
Semiparametric Factor Model (DSFM) dimension reduction technique and finally be
tested for differences in activation between conditions. This sequence of
Cluster, Estimation, Activation and Decision admits a model-free analysis of
the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in
a significant correlation between the risk of choice options and changes in
fMRI signal in the anterior insula and dorsomedial prefrontal cortex.
Additionally, individual differences in decision-related reactions within the
DSFM time series predicted individual differences in risk attitudes as modeled
with the framework of the mean-variance model.
