Introduction: Alzheimer's disease (AD) in Down syndrome (DS) is associated with changes in brain structure. It is unknown if thickness and volumetric changes can identify AD stages and if they are similar to other genetic forms of AD.
Methods: Magnetic resonance imaging scans were collected for 178 DS adults (106 nonclinical, 45 preclinical, and 27 symptomatic). Cortical thickness and subcortical volumes were compared between DS groups and evaluated as a staging metric using receiver operating characteristic analyses. Thickness patterns were compared to those previously reported in autosomal-dominant AD (ADAD).
Results: Decreased parietal and temporal lobe thickness differentiated amyloid positivity (area under the curve [AUC] = 0.83) and impairment (AUC = 0.81), and slightly outperformed subcortical volumes (AUC = 0.8/0.74). Thickness differences in DS were more widespread, severe, and had better discriminative ability than ADAD.
Discussion: Cortical thickness can stage AD pathology in DS. Identification of brain regions affected by AD may aid in tracking disease course and evaluating treatment effects.
Highlights: DSAD is associated with reduced temporal and parietal cortical thickness. DSAD is associated with smaller hippocampal and striatal volumes. Thickness differences can stage DSAD better than other forms of AD. DSAD thickness differences are more extensive and severe than ADAD.
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http://dx.doi.org/10.1002/alz.14519 | 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).
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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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