Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling

Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models. The proposed method achieves robust circuit inference by iteratively integrating parameter estimates from local microscale and global meso- or macroscale circuits, derived from signals across various scales and modalities. Parameters estimated from high-resolution data within specific regions inform global circuit estimation by constraining neural properties in unobserved regions, while large-scale circuit data help elucidate detailed local circuitry. Using a virtual ground truth system, we validated the method across diverse experimental settings, combining calcium imaging (CaI), voltage-sensitive dye imaging (VSDI), and blood-oxygen-level-dependent (BOLD) signals—each with distinct coverage and resolution. Our reciprocal and iterative parameter estimation approach markedly improves the accuracy of neural property and connectivity estimates compared to traditional one-step estimation methods. This iterative integration of local and global parameters presents a reliable approach to inferring extensive, complex neural circuits from partially observed, multimodal, and multiscale data, showcasing how information from different scales reciprocally enhances entire circuit parameter estimation.Author summary: Reliable estimation of a computational neural circuit model requires integrating data from various brain imaging techniques, each providing unique but limited insights into brain activity at different spatial and temporal scales. Combining these multiscale, multimodal data sources is challenging due to trade-offs between spatial coverage and resolution inherent in each modality. In this study, we introduce a novel dynamic causal modeling (DCM) framework that overcomes these challenges by allowing partially observed data across scales to jointly inform the estimation of neural circuit parameters. Our method is distinctive in its use of a shared neural state model, paired with modality-specific observation models, to iteratively integrate local, high-resolution data and global, lower-resolution data. This reciprocal approach leverages detailed local circuit information to constrain parameter estimation for unobserved regions within the broader network, while also using global circuit data to refine local circuit estimates. This iterative, multiscale integration enables more accurate circuit inference than traditional one-step methods, which typically struggle with sparse data and complex neural structures. By demonstrating how information from different scales and modalities can complement each other, our framework provides a powerful tool for reconstructing neural circuits from incomplete data. This approach has broad implications for advancing our understanding of complex neural systems, particularly in preclinical research, where comprehensive, multiscale neural data are often scarce.