Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.
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http://dx.doi.org/10.1016/j.neuroimage.2014.08.022 | DOI Listing |
Sci Adv
January 2025
Center for Alzheimer's and Neurodegenerative Diseases, Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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Center for Cognitive Interaction Technology (CITEC), Technical Faculty, Bielefeld University, Bielefeld, Germany.
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January 2025
Foundation Professor of General Practice, Discipline of General Practice, University of Galway, Galway, Ireland.
Background: The Irish Out-of-Hospital Cardiac Arrest registry (OHCAR) collects data based on the internationally recognised Utstein template. The Utstein comparator group (bystander witnessed and initial shockable rhythm) has specific relevance in benchmarking out-of-hospital cardiac arrest (OHCA) health system performance.
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Adv Mater
January 2025
Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569, Stuttgart, Germany.
The rapid advancement of covalent organic frameworks (COFs) in recent years has firmly established them as a new class of molecularly precise and highly tuneable porous materials. However, compared to other porous materials, such as zeolites and metal-organic frameworks, the successful integration of hierarchical porosity into COFs remains largely unexplored. The challenge lies in identifying appropriate synthetic methods to introduce secondary pores without compromising the intrinsic structural porosity of COFs.
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January 2025
Zhengzhou University of Light Industry, Zhengzhou, 450001, China.
Visual-language models (VLMs) excel in cross-modal reasoning by synthesizing visual and linguistic features. Recent VLMs use prompt learning for fine-tuning, allowing adaptation to various downstream tasks. TCP applies class-aware prompt tuning to improve VLMs generalization, yet its reliance on fixed text templates as prior knowledge can limit adaptability to fine-grained category distinctions.
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