Purpose: The aim of this study was to investigate the regional differences between the morphologic and functional changes in the same patients with frontotemporal dementia (FTD) using statistical parametric mapping and voxel-based morphometry (VBM).
Methods: Thirteen FTD patients (mean age, 64.9 years old; mean MMSE score, 17.7), 20 sex-matched Alzheimer's disease (AD) patients (mean age, 65.0 years old; mean MMSE score, 17.5), and 20 normal volunteers (mean age, 65.2 years old; mean MMSE score, 29.0) underwent both [(18)F]FDG positron emission tomography and three-dimensional spoiled gradient echo MRI. Statistical parametric mapping was used to conduct a VBM analysis of the morphologic data, which were compared voxel by voxel with the results of a similar analysis of glucose metabolic data.
Results: FTD patients showed decreased grey matter volume and decreased glucose metabolism in the frontal lobe and anterior temporal lobe. In addition, there was a clear asymmetry in grey matter volume in FTD patients by the VBM analysis while the glucose metabolic data showed little asymmetry. In AD patients, glucose metabolic reduction occurred in the bilateral posterior cingulate gyri and parietal lobules while grey matter density decreased the least in the same patients.
Conclusion: In FTD, metabolic and morphologic changes occur in the bilateral frontal lobe and temporal lobe with a limited asymmetry whereas there was considerable discordance in the AD group.
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http://dx.doi.org/10.1007/s00259-008-0871-5 | DOI Listing |
Dig Dis Sci
January 2025
Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Objectives: As one of the most common complications of laryngopharyngeal reflux or gastroesophageal reflux disease, dental erosion presents a significant association with laryngopharyngeal reflux. This study aimed to elucidate the role of laryngopharyngeal reflux and gastroesophageal reflux disease on the severity and occurrence of dental erosion in adult populations.
Methods: A comprehensive search was performed in the databases of PubMed/MEDLINE, Web of Science, Cochrane Library, and Scopus for English literature published from July 1999 to June 2024.
Brain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFMagn Reson Med
January 2025
Université Grenoble Alpes, INSERM, U1216, Grenoble Institute Neurosciences, GIN, Grenoble, France.
Purpose: This study proposes a novel, contrast-free Magnetic Resonance Fingerprinting (MRF) method using balanced Steady-State Free Precession (bSSFP) sequences for the quantification of cerebral blood volume (CBV), vessel radius (R), and relaxometry parameters (T , T , T *) in the brain.
Methods: The technique leverages the sensitivity of bSSFP sequences to intra-voxel frequency distributions in both transient and steady-state regimes. A dictionary-matching process is employed, using simulations of realistic mouse microvascular networks to generate the MRF dictionary.
Cogn Affect Behav Neurosci
January 2025
Departamento de Psicología ClínicaPsicobiología y MetodologíaFacultad de Psicología, Universidad de La Laguna, 38200, La Laguna, Tenerife, Spain.
Sci Rep
January 2025
Faculty of Computers and Information, Minia University, Minia, Egypt.
This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency's Downscaler Model (DS) to predict Particulate Matter ([Formula: see text]) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.
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