5 accepted papers
Modeling the temporal dynamics of the human brain remains a core challenge in computational neuroscience and artificial intelligence. Traditional methods often ignore the biological spike characteristics of brain activity and find it difficult to reveal the dynamic dependencies and causal interactio…
As partial samples are often absent in certain views, incomplete multi-view clustering has become a challenging task. To tackle data with missing views, current methods either utilize the data similarity relations to recover missing samples or primarily consider the available information of existing…
Structure-function coupling (SC-FC coupling) refers to the correlation between the physical layout of structural connectivity (SC) in the brain and the activity patterns of functional connectivity (FC). However, most existing SC-FC coupling analysis methods lack system-level integration and overlook…
Structural-functional coupling (SC-FC coupling) offers an effective approach for analyzing structural-functional relationships, capable of revealing the dependency of functional activity on the underlying white matter architecture. However, extant SC-FC coupling analysis methods primarily center on
The existing multi-atlas brain network analysis methods rely on some simple fusion methods (i.e., add and concatenation) and do not consider the information redundancy caused by increased brain regions. To improve upon these, we propose the Sparse and Uncertain Fusion Transformers (SUFT) for multi-a…