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The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods. 

Abstract Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with “top‐down” inversion analyses that assimilate atmospheric CO₂observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System‐Flux system for North America (CMS‐Flux‐NA) that allows high resolution atmospheric transport (0.5° × 0.625°). Relative to a 2018 baseline, we find the 2019 US Midwest growing season net carbon uptake is reduced by 11–57 TgC (3%–16%, range across assimilated CO₂data sets). These estimates are found to be consistent with independent “bottom‐up” estimates of carbon uptake based on vegetation remote sensing (15–78 TgC). We then investigate current limitations in tracking regional carbon budgets using “top‐down” methods. In a set of observing system simulation experiments, we show that the ability of atmospheric CO₂inversions to capture regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space‐based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top‐down estimates of ecosystem responses to extreme climate events.