Advancing Model Validation and Interpretability in Brain Connectomics

Recent advances in connectomics have opened new frontiers in understanding the structural and functional organization of the brain. With the emergence of high-resolution brain atlases, computational modeling techniques, and multimodal imaging datasets, researchers now have unprecedented opportunities to unravel neural pathways, characterize complex connectivity patterns, and decode the underlying processing mechanisms in the brain. However, these developments also raise fundamental questions about model validity and interpretability. In particular, there remains a critical need to rigorously validate if in silico computational models of brain connectivity actually provide a reliable surrogate for in vivo brain anatomy and dynamics. Addressing this challenge relies on moving from correlation to causation, integrating diverse data modalities, and ensuring that complex models remain biologically meaningful – actually deepen understanding of brain function and disease rather than just being performant black boxes. This Research Topic aims to advance the reliability, interpretability, and translational value of connectomic models as tools for explaining and predicting brain organization and function. Achieving this objective requires integrative approaches that enhance model accuracy, interpretability, and validation, both in silico and through empirical or clinical datasets. Emphasis is placed on cross-modal integration of imaging, electrophysiological, and behavioral measures, as well as on model-interpretability techniques that elucidate how structural and functional connectivity features contribute to modeled outcomes and account for individual variability. By promoting rigorous analytical frameworks, reproducible methodologies, and validated cross-scale models, this Research Topic seeks to establish and advance mechanistic understanding and translational potential of connectomic analyses.We welcome original research, reviews, methodological articles, or open datasets addressing (but not limited to): - Application and validation of statistical, computational, or theoretical models on connectomic datasets, irrespective of species, scale, or modality. - Neural coding and circuit-level analysis grounded in anatomical connectivity. - Disease-specific connectomic modeling of altered network organization advancing mechanistic insights and biomarker development. - Clinical validation studies integrating computational and experimental insights to strengthen translational potential of connectomic models. - Construction and refinement of high-resolution brain atlases, open databases, and analytical pipelines supporting integrative analysis and reproducible connectomic research. - Collaborative, multidisciplinary efforts are especially encouraged. Topic editors: Rahul Biswas, Ketan Mehta Source: https://www.frontiersin.org/research-topics/78912/advancing-model-validation-and-interpretability-in-brain-connectomics