Review of resting-state fMRI features across rodents and humans, comparing acquisition, preprocessing, and analysis choices that influence functional connectivity. The paper surveys time-averaged and time-varying metrics (e.g., sliding windows, quasi-periodic and co-activation patterns), links them to structural connectivity, and discusses applications to neurological and psychiatric disorders. It closes with open problems to make rodent-to-human translation more reliable.
Using Human Connectome Project data, the authors test whether parcel-wise complexity metrics (correlation dimension, approximate entropy, Lyapunov exponent) mainly reflect neural dynamics or are driven by basic BOLD properties. They find these complexity measures are reliable across scans and strongly tied to each region’s frequency profile, indicating that frequency content can confound interpretations of “complexity” in rs-fMRI.
Presents a modular ecosystem of containerized tools and workflows for petascale neuroimaging, enabling reproducible end-to-end processing across modalities like electron microscopy and X-ray microtomography. The framework standardizes storage, execution, and optimization and is demonstrated on synapse-level connectome estimation and large-scale cell density mapping.
Introduces an automated pipeline for estimating cellular densities and identifying laminar transitions in neuroanatomical images. The method combines patch extraction, cell detection, and sparse approximation for count data using total-variation regularization to robustly recover layer boundaries and density profiles across cortical and retinal samples.
Proposes ArCaDe, an automated approach to estimate spatially varying cell densities and detect laminar structure in cortical and retinal tissue. By modeling counts with a sparse, TV-regularized estimator and coupling it with patch-wise cell detection, the method recovers cytoarchitectonic transitions without manual annotation.
Demonstrates an NLP pipeline that identifies software and methods referenced in arXiv articles and uses the extracted names to explore usage patterns across research areas. The study argues that automated extraction at scale complements traditional literature surveys for tracking technologies and methods in scientific work.