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Calibration experiments precede multicenter trials to identify potential sources of variance and bias. In support of future imaging studies of mental health disorders and their treatment, the Neuro/PsyGRID consortium commissioned a calibration experiment to acquire functional and structural MRI from twelve healthy volunteers attending five centers on two occasions. Measures were derived of task activation from a working memory paradigm, fractal scaling (Hurst exponent) from resting fMRI, and grey matter distributions from T(1) -weighted sequences. At each intracerebral voxel a fixed-effects analysis of variance estimated components of variance corresponding to factors of center, subject, occasion, and within-occasion order, and interactions of center-by-occasion, subject-by-occasion, and center-by-subject, the latter (since there is no intervention) a surrogate of the expected variance of the treatment effect standard error across centers. A rank order test of between-center differences was indicative of crossover or noncrossover subject-by-center interactions. In general, factors of center, subject and error variance constituted >90% of the total variance, whereas occasion, order, and all interactions were generally <5%. Subject was the primary source of variance (70%-80%) for grey-matter, with error variance the dominant component for fMRI-derived measures. Spatially, variance was broadly homogenous with the exception of fractal scaling measures which delineated white matter, related to the flip angle of the EPI sequence. Maps of P values for the associated F-tests were also derived. Rank tests were highly significant indicating the order of measures across centers was preserved. In summary, center effects should be modeled at the voxel-level using existing and long-standing statistical recommendations.
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http://dx.doi.org/10.1002/hbm.21210 | DOI Listing |
Curr Atheroscler Rep
September 2025
Department of Medicine, Division of Cardiovascular Disease, University of Alabama at Birmingham, 521 19th Street South-GSB 444, Birmingham, AL, 35233, USA.
Purpose Of Review: This review examines cardiovascular disease (CVD) risk prediction models relevant to older adults, a rapidly expanding population with elevated CVD risk. It discusses model characteristics, performance metrics, and clinical implications.
Recent Findings: Some models have been developed specifically for older adults, while several others consider a broader age range, including some older individuals.
Rev Sci Instrum
September 2025
Key Laboratory for Laser Plasmas (MoE) and School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China.
Neutron Time-of-Flight (nTOF) detectors are key diagnostics to detect thermonuclear neutrons in laser-fusion experiments. This diagnostic, however, is often plagued by strong gamma-ray noise prior to neutron signals, especially in harsh fast-ignition (FI) environments. To address this issue, a combination of low-afterglow liquid scintillators with time-gated photomultiplier tubes as necessary nTOF components would be a natural solution.
View Article and Find Full Text PDFJMIR Form Res
September 2025
Department of Critical Care Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangdong Provincial Geriatrics Institute, No. 106, Zhongshaner Rd, Guangzhou, 510080, China, 86 15920151904.
Background: Point-of-care ultrasonography has become a valuable tool for assessing diaphragmatic function in critically ill patients receiving invasive mechanical ventilation. However, conventional diaphragm ultrasound assessment remains highly operator-dependent and subjective. Previous research introduced automatic measurement of diaphragmatic excursion and velocity using 2D speckle-tracking technology.
View Article and Find Full Text PDFGlob Chang Biol
September 2025
Department of Agronomy, Purdue University, West Lafayette, Indiana, USA.
Understanding how interactive management practices and climatic behavior influence soybean [Glycine max (L.) Merr.] productivity is imperative to inform future production systems under changing climate.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
September 2025
Class incremental learning (CIL) offers a promising framework for continuous fault diagnosis (CFD), allowing networks to accumulate knowledge from streaming industrial data and recognize new fault classes. However, current CIL methods assume a balanced data stream, which does not align with the long-tail distribution of fault classes in real industrial scenarios. To fill this gap, this article investigates the impact of long-tail bias in the data stream on the CIL training process through the experimental analysis.
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