Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the first time, we develop Transformer for integrated FBN modeling, analysis and brain disorder classification with rs-fMRI data by proposing a Diffusion Kernel Attention Network to address the specific challenges. Specifically, directly applying Transformer does not necessarily admit optimal performance in this task due to its extensive parameters in the attention module against the limited training samples usually available. Looking into this issue, we propose to use kernel attention to replace the o
Credit: HBP
Epilepsy is one of the most common neurological disorders, affecting over 50 Million people worldwide. Patients suffer from seizures caused by sudden neuronal activity engaging at times large networks of the brain. In a third of all cases the disease is resistant to drugs. The most common treatment option for these patients is surgical removal of the epileptogenic zone , the areas of the brain, where the seizures emerge. Surgery success depends on locating these areas as precisely as possible. But in clinical practice this often proves very difficult, and the average surgery success rate remains at only around 60% , says Viktor Jirsa. Any improvement would have major impact for many patients .