2 research outputs found
Neural Topographic Factor Analysis for fMRI Data
Neuroimaging studies produce gigabytes of spatio-temporal data for a small
number of participants and stimuli. Rarely do researchers attempt to model and
examine how individual participants vary from each other -- a question that
should be addressable even in small samples given the right statistical tools.
We propose Neural Topographic Factor Analysis (NTFA), a probabilistic factor
analysis model that infers embeddings for participants and stimuli. These
embeddings allow us to reason about differences between participants and
stimuli as signal rather than noise. We evaluate NTFA on data from an in-house
pilot experiment, as well as two publicly available datasets. We demonstrate
that inferring representations for participants and stimuli improves predictive
generalization to unseen data when compared to previous topographic methods. We
also demonstrate that the inferred latent factor representations are useful for
downstream tasks such as multivoxel pattern analysis and functional
connectivity.Comment: 15 pages, 9 figures, associated source code available at
https://github.com/neu-spiral/HTFATorc
Deep Markov Spatio-Temporal Factorization
We introduce deep Markov spatio-temporal factorization (DMSTF), a generative
model for dynamical analysis of spatio-temporal data. Like other factor
analysis methods, DMSTF approximates high dimensional data by a product between
time dependent weights and spatially dependent factors. These weights and
factors are in turn represented in terms of lower dimensional latents inferred
using stochastic variational inference. The innovation in DMSTF is that we
parameterize weights in terms of a deep Markovian prior extendable with a
discrete latent, which is able to characterize nonlinear multimodal temporal
dynamics, and perform multidimensional time series forecasting. DMSTF learns a
low dimensional spatial latent to generatively parameterize spatial factors or
their functional forms in order to accommodate high spatial dimensionality. We
parameterize the corresponding variational distribution using a bidirectional
recurrent network in the low-level latent representations. This results in a
flexible family of hierarchical deep generative factor analysis models that can
be extended to perform time series clustering or perform factor analysis in the
presence of a control signal. Our experiments, which include simulated and
real-world data, demonstrate that DMSTF outperforms related methodologies in
terms of predictive performance for unseen data, reveals meaningful clusters in
the data, and performs forecasting in a variety of domains with potentially
nonlinear temporal transitions