The cross-modal synthesis between structural magnetic resonance imaging
...
The neural dynamics underlying brain activity are critical to understand...
Federated learning (FL) enables the training of a model leveraging
decen...
Vision Transformer (ViT) is a pioneering deep learning framework that ca...
Communication within or between complex systems is commonplace in the na...
Deep learning algorithms for predicting neuroimaging data have shown
con...
Artificial intelligence has become pervasive across disciplines and fiel...
Functional magnetic resonance imaging (fMRI) data contain complex
spatio...
Recently, methods that represent data as a graph, such as graph neural
n...
Functional connectivity (FC) studies have demonstrated the overarching v...
Discovering distinct features and their relations from data can help us
...
Multivariate dynamical processes can often be intuitively described by a...
We propose to apply non-linear representation learning to voxelwise rs-f...
Neuroimaging studies often involve the collection of multiple data
modal...
Blind source separation algorithms such as independent component analysi...
Characterizing time-evolving networks is a challenging task, but it is
c...
The wide variety of brain imaging technologies allows us to exploit
info...
Large scale studies of group differences in healthy controls and patient...
In this study, we tested the interaction effect of multimodal datasets u...
Reducing the number of false positive discoveries is presently one of th...
Segmenting a structural magnetic resonance imaging (MRI) scan is an impo...
Functional magnetic resonance imaging (fMRI) of temporally-coherent bloo...
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are empl...
Variational methods that rely on a recognition network to approximate th...