Behnam Kazemivash

The paper introduces a model to classify brain dynamics based on chronnectome complexity, showing high accuracy in distinguishing cognitive states and age-related changes.

This paper introduces st-DenseViT, a weakly supervised spatiotemporal vision transformer that generates 4D dynamic brain networks from fMRI data, showing significant differences between schizophrenia patients and healthy controls.

This paper introduces a deep residual CNN model to analyze 5D spatiotemporal brain dynamics in schizophrenia, revealing widespread group-level differences in spatiotemporal features.

This paper introduces a 5D brain parcellation method using deep residual learning to capture spatio-temporal dynamics in resting fMRI data, showing sensitivity to individual and group-level variations like age.