Abo Bibliothek: Guest
Critical Reviews™ in Biomedical Engineering

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 0278-940X

ISSN Online: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Comprehensive Review on Magnetic Resonance Imaging in Alzheimer's Disease

Volumen 44, Ausgabe 3, 2016, pp. 213-225
DOI: 10.1615/CritRevBiomedEng.2016019544
Get accessGet access

ABSTRAKT

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. However, definitive diagnosis of AD is only achievable postmortem and currently relies on clinical neurological evaluation. Magnetic resonance imaging (MRI) can evaluate brain changes typical of AD, including brain atrophy, presence of amyloid β (Aβ) plaques, and functional and biochemical abnormalities. Structural MRI (sMRI) has historically been used to assess the inherent brain atrophy present in AD. However, new techniques have recently emerged that have refined sMRI into a more precise tool to quantify the thickness and volume of AD-sensitive cerebral structures. Aβ plaques, a defining pathology of AD, are widely believed to contribute to the progressive cognitive decline in AD, but accurate assessment is only possible on autopsy. In vivo MRI of plaques, although currently limited to mouse models of AD, is a very promising technique. Measuring changes in activation and connectivity in AD-specific regions of the brain can be performed with functional MRI (fMRI). To help distinguish AD from diseases with similar symptoms, magnetic resonance spectroscopy (MRS) can be used to look for differing metabolite concentrations in vivo. Together, these MR techniques, evaluating various brain changes typical of AD, may help to provide a more definitive diagnosis and ease the assessment of the disease over time, noninvasively.

REFERENZIERT VON
  1. Jin Ping, Pan Yongming, Pan Zhiyong, Xu Jianqin, Lin Min, Sun Zhichao, Chen Minli, Xu Maosheng, Alzheimer-like brain metabolic and structural features in cholesterol-fed rabbit detected by magnetic resonance imaging, Lipids in Health and Disease, 17, 1, 2018. Crossref

  2. Massimi Lorenzo, Bukreeva Inna, Santamaria Giulia, Fratini Michela, Corbelli Alessandro, Brun Francesco, Fumagalli Stefano, Maugeri Laura, Pacureanu Alexandra, Cloetens Peter, Pieroni Nicola, Fiordaliso Fabio, Forloni Gianluigi, Uccelli Antonio, Kerlero de Rosbo Nicole, Balducci Claudia, Cedola Alessia, Exploring Alzheimer's disease mouse brain through X-ray phase contrast tomography: From the cell to the organ, NeuroImage, 184, 2019. Crossref

  3. Liu Kai, Li Jiasong, Raghunathan Raksha, Zhao Hong, Li Xuping, Wong Stephen T. C., The Progress of Label-Free Optical Imaging in Alzheimer’s Disease Screening and Diagnosis, Frontiers in Aging Neuroscience, 13, 2021. Crossref

  4. Liu Ke, Li Qing, Yao Li, Guo Xiaojuan, The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification, Frontiers in Neuroscience, 16, 2022. Crossref

  5. Orzyłowska Anna, Oakden Wendy, Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer’s Disease, Brain Sciences, 12, 1, 2021. Crossref

  6. Ma Justin P., Robbins Cason B., Lee Jia Min, Soundararajan Srinath, Stinnett Sandra S., Agrawal Rupesh, Plassman Brenda L., Lad Eleonora M., Whitson Heather, Grewal Dilraj S., Fekrat Sharon, Longitudinal Analysis of the Retina and Choroid in Cognitively Normal Individuals at Higher Genetic Risk of Alzheimer Disease, Ophthalmology Retina, 6, 7, 2022. Crossref

Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen Preise und Aborichtlinien Begell House Kontakt Language English 中文 Русский Português German French Spain