Abstract
Background: In patients with Alzheimer’s disease (AD), cerebral blood flow (CBF) is decreased from the early stages. CBF in AD is currently estimated from Z-scores using statistical analysis. However, the Z-score is not considered the impaired area ratio.
Methods: In the present study, a novel indicator, ΣzS, associated with brain surface area and Zscores, is defined and the association with regional CBF has been estimated using Mini-Mental State Examination (MMSE) scores, which indicate the severity of cognitive impairment in patients with AD.
Results: A negative correlation was detected between ΣzS in the posterior cingulate gyrus and the subset numbers 1, 2, and 5 of the total MMSE scores. Furthermore, a negative correlation was detected between the total MMSE score and ΣzS in Brodmann area 30, which is a subdivided area of the brain.
Conclusion: These results suggest that ΣzS may be a useful indicator of CBF metabolism, and thus may improve the current understanding of cognitive function in patients with AD.
Keywords: Cerebral blood flow, Alzheimer's disease, mini-mental state examination, brodmann area, retrosplenial cortex, statistical analysis.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
27
1