  Structural magnetic resonance imaging (sMRI) has shown great clinical value
and has been widely used in deep learning (DL) based computer-aided brain
disease diagnosis. Previous approaches focused on local shapes and textures in
sMRI that may be significant only within a particular domain. The learned
representations are likely to contain spurious information and have a poor
generalization ability in other diseases and datasets. To facilitate capturing
meaningful and robust features, it is necessary to first comprehensively
understand the intrinsic pattern of the brain that is not restricted within a
single data/task domain. Considering that the brain is a complex connectome of
interlinked neurons, the connectional properties in the brain have strong
biological significance, which is shared across multiple domains and covers
most pathological information. In this work, we propose a connectional style
contextual representation learning model (CS-CRL) to capture the intrinsic
pattern of the brain, used for multiple brain disease diagnosis. Specifically,
it has a vision transformer (ViT) encoder and leverages mask reconstruction as
the proxy task and Gram matrices to guide the representation of connectional
information. It facilitates the capture of global context and the aggregation
of features with biological plausibility. The results indicate that CS-CRL
achieves superior accuracy in multiple brain disease diagnosis tasks across six
datasets and three diseases and outperforms state-of-the-art models.
Furthermore, we demonstrate that CS-CRL captures more brain-network-like
properties, better aggregates features, is easier to optimize and is more
robust to noise, which explains its superiority in theory. Our source code will
be released soon.
