Title: CBP: Learning Shared Cognitive Basis Space and Connectivity Patterns for Cross-Task Brain Dynamics Modeling
Primary area: applications to neuroscience & cognitive science
Keywords: Cross-Task Brain Dynamics, Cognitive mechanisms, Neural dynamics

Abstract: Understanding the brain dynamics underlying human cognition requires models that jointly achieve modeling capability and interpretability across tasks. Existing approaches either rely on large-scale fMRI foundation models, which learn strong but implicitly encoded representations that are difficult to map to mechanistic components, or explicit state-space models, which are interpretable but limited to single-task settings. We propose a dictionary-learning-based model called CBP that jointly learns shared \underline{\textbf{C}}ognitive \underline{\textbf{B}}ases and their connectivity \underline{\textbf{P}}atterns, treating task-evoked heterogeneity as multiple trajectories of a unified cognitive system. This formulation explicitly integrates cross-task information, improving modeling accuracy and generalization. Experiments demonstrate that our method (i) accurately recovers ground-truth components in synthetic data, (ii) significantly outperforms strong baselines on a 103-task fMRI dataset, revealing stable cognitive bases and connectivity patterns, and (iii) generalizes to HCP and multi-stage learning datasets, where it accurately predicts task states and learning outcomes while revealing the key cognitive connectivity patterns that drive them. Overall, our approach improves cross-task modeling accuracy and generalization while offering mechanistic insights into human cognition.