Asynchronous Functional Brain Network Construction with Spatiotemporal
Transformer for MCI Classification
Abstract
Construction and analysis of functional brain networks (FBNs) with
rs-fMRI is a promising method to diagnose functional brain diseases.
Nevertheless, the existing methods suffer from several limitations.
First, the functional connectivities (FCs) of the FBN are usually
measured by the temporal co-activation level between rs-fMRI time series
from regions of interest (ROIs). While enjoying simplicity, the existing
approach implicitly assumes simultaneous co-activation of all the ROIs,
and models only their synchronous dependencies. However, the functional
coactivation is not necessarily always synchronous due to the time lag
of information flow and cross-time interactions between ROIs. Therefore,
it is desirable to model the asynchronous functional interactions.
Second, the traditional methods usually construct FBNs at individual
level for feature extraction and classification, leading to large
variability and degraded diagnosis accuracy when modeling asynchronous
FBN. Third, the FBN construction and analysis are conducted in two
independent steps without joint alignment for the target diagnosis task.
To address the first limitation, this paper proposes an effective
sliding-window-based method to model spatiotemporal FCs in Transformer.
Regarding the second limitation, we propose to learn common and
individual FBNs adaptively with the common FBN as prior knowledge, thus
alleviating the variability and enabling the network to focus on the
individual disease-specific asynchronous FCs. To address the third
limitation, the common and individual asynchronous FBNs are built and
analyzed by an integrated network, enabling end-to-end training and
improving the flexibility and discriminativity. The effectiveness of the
proposed method is consistently demonstrated on three data sets for MCI
diagnosis.