Prior work has shown that internal representations of artificial neural networks can significantly predict brain responses elicited by unimodal stimuli (i.e. reading a book chapter or viewing static images). However, the computational modeling of brain representations of naturalistic video stimuli, such as movies or TV shows, still remains underexplored. In this work, we present a promising approach for modeling vision-language brain representations of video stimuli by a transformer-based model that represents videos jointly through audio, text, and vision. We show that the joint representations of vision and text information are better aligned with brain representations of subjects watching a popular TV show. We further show that the incorporation of visual information improves brain alignment across several regions that support language processing.