  Wide-field calcium imaging (WFCI) that records neural calcium dynamics allows
for identification of functional brain networks (FBNs) in mice that express
genetically encoded calcium indicators. Estimating FBNs from WFCI data is
commonly achieved by use of seed-based correlation (SBC) analysis and
independent component analysis (ICA). These two methods are conceptually
distinct and each possesses limitations. Recent success of unsupervised
representation learning in neuroimage analysis motivates the investigation of
such methods to identify FBNs. In this work, a novel approach referred as
LSTM-AER, is proposed in which a long short-term memory (LSTM) autoencoder (AE)
is employed to learn spatial-temporal latent embeddings from WFCI data,
followed by an ordinary least square regression (R) to estimate FBNs. The goal
of this study is to elucidate and illustrate, qualitatively and quantitatively,
the FBNs identified by use of the LSTM-AER method and compare them to those
from traditional SBC and ICA. It was observed that spatial FBN maps produced
from LSTM-AER resembled those derived by SBC and ICA while better accounting
for intra-subject variation, data from a single hemisphere, shorter epoch
lengths and tunable number of latent components. The results demonstrate the
potential of unsupervised deep learning-based approaches to identifying and
mapping FBNs.
