In this study, we present a new pipeline designed for the analysis and comparison of non-conventional animal brain models, such as sheep, without relying on neuroanatomical priors. This innovative approach combines an automatic MRI segmentation with graph neural networks (GNNs) to overcome the limitations of traditional methods. Conventional tools often depend on predefined anatomical atlases and are typically limited in their ability to adapt to the unique characteristics of developing brains or non-conventional animal models. By generating regions of interest directly from MR images and constructing a graph representation of the brain, our method eliminates biases associated with predefined templates. Our results show that the GNN-based pipeline is more efficient in terms of accuracy for an age prediction task (63.22%) compared to a classical CNN architecture (59.77%). GNNs offer notable advantages, including improved interpretability and the ability to model complex relational structures within brain data. Overall, our approach provides a promising solution for unbiased, adaptable, and interpretable analysis of brain MRIs, particularly for developing brains and non-conventional animal models.