Encoding models that predict brain response patterns to stimuli are one way
to capture this relationship between variability in bottom-up neural systems
and individual's behavior or pathological state. However, they generally need a
large amount of training data to achieve optimal accuracy. Here, we propose and
test an alternative personalized ensemble encoding model approach to utilize
existing encoding models, to create encoding models for novel individuals with
relatively little stimuli-response data. We show that these personalized
ensemble encoding models trained with small amounts of data for a specific
individual, i.e. ~300 image-response pairs, achieve accuracy not different from
models trained on ~20,000 image-response pairs for the same individual.
Importantly, the personalized ensemble encoding models preserve patterns of
inter-individual variability in the image-response relationship. Additionally,
we show the proposed approach is robust against domain shift by validating on a
prospectively collected set of image-response data in novel individuals with a
different scanner and experimental setup. Finally, we use our personalized
ensemble encoding model within the recently developed NeuroGen framework to
generate optimal stimuli designed to maximize specific regions' activations for
a specific individual. We show that the inter-individual differences in face
areas responses to images of animal vs human faces observed previously is
replicated using NeuroGen with the ensemble encoding model. Our approach shows
the potential to use previously collected, deeply sampled data to efficiently
create accurate, personalized encoding models and, subsequently, personalized
optimal synthetic images for new individuals scanned under different
experimental conditions