With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as "scanner effects", can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.
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http://dx.doi.org/10.1016/j.neuroimage.2017.11.024 | DOI Listing |
J Bone Miner Res
January 2025
Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
The socioeconomic burden of hip fractures, the most severe osteoporotic fracture outcome, is increasing and the current clinical risk assessment lacks sensitivity. This study aimed to develop a method for improved prediction of hip fracture by incorporating measurements of bone microstructure and composition derived from high-resolution peripheral quantitative computed tomography (HR-pQCT). In a prospective cohort study of 3028 community-dwelling women aged 75 to 80, all participants answered questionnaires and underwent baseline examinations of anthropometrics and bone by dual x-ray absorptiometry (DXA) and HR-pQCT.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, 670 W Baltimore St, HSF III, R1173, Baltimore, MD, 21202, USA.
The brain entropy (BEN) reflects the randomness of brain activity and is inversely related to its temporal coherence. In recent years, BEN has been found to be associated with a number of neurocognitive, biological, and sociodemographic variables such as fluid intelligence, age, sex, and education. However, evidence regarding the potential relationship between BEN and brain structure is still lacking.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.
Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented.
View Article and Find Full Text PDFJ Ultrasound Med
January 2025
Department of Radiodiagnosis, Government Medical College and Hospital, Chandigarh, India.
Objectives: To determine the efficacy of quantitative shear wave elastography in differentiating benign and malignant axillary lymph nodes (ALN).
Methods: Exactly 127 lymph nodes from 127 patients with clinically palpable axillary swelling were examined by both B-mode sonography and elastography from November 2022 to March 2024. Gray-scale sonograms were evaluated based on: the short-axis diameter, shape, hilum, maximum cortical thickness, and border of the ALN.
Front Aging Neurosci
January 2025
Department of Neurology, West China Hospital of Sichuan University, Chengdu, China.
Purpose: Differentiating between Alzheimer's disease (AD) and frontotemporal dementia (FTD) can be challenging due to overlapping cognitive and behavioral manifestations. Evidence regarding non-invasive and early-stage biomarkers remains limited. Our aim was to identify retinal biomarkers for the risk of AD and FTD in populations without dementia and explore underlying brain structural mechanisms.
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