Biomedical signal and image data analysis is a rapidly evolving field, focusing on the integration and interpretation of complex datasets sourced across various organs and scales. This complexity presents unique challenges in accurately capturing and reconstructing the interrelated physiological processes. Recent advancements have highlighted the potential to analyze data across multiple spatial and temporal scales, using diverse modalities such as physiological signals like EEG, ECG, and EMG, along with imaging techniques including MRI, fMRI, CT, PET, and ultrasound. Although significant progress has been made, there remains a need to refine these methodologies to better encapsulate the multivariate nature of human physiology and its systemic interactions. This requires attention to the fusion of data from heterogeneous sources and addressing the multiorgan interactions to model comprehensive physiological and pathological processes.

This Research Topic aims to advance the methodologies needed for analyzing and integrating multiscale, multiorgan, and multivariate complexity in biomedical data. Emphasis will be placed on developing approaches that enhance the accuracy and reliability of data interpretation across different scales and organs. Crucially, this will include addressing questions surrounding the effective fusion of broad-ranging data modalities and assessing their implications for understanding complex biological systems. Hypotheses related to improving multiresolution analysis, even as new models of systemic processes are tested, are of particular interest.

We welcome articles addressing, but not limited to, the following themes:

o Multiresolution analysis techniques

o Spatiotemporal filtering innovations

o Multiscale signal processing advancements

o Time-frequency and spatial decomposition strategies

o Radiomics and its application in systemic modeling

o Functional imaging and connectivity analysis methodologies

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Advanced Methodologies in Multiscale and Multiorgan Biomedical Data Analysis