Neuroimaging techniques in Alzheimer's disease

Details

Serval ID
serval:BIB_1F310A5FA05C
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
Title
Neuroimaging techniques in Alzheimer's disease
Title of the conference
14th Congress of European Federation of Neurological Societies
Author(s)
Frackowiak R.
Address
Geneva, Switzerland, Septmber, 2010
ISBN
1351-5101
Publication state
Published
Issued date
2010
Peer-reviewed
Oui
Volume
17
Series
European Journal Of Neurology
Pages
666
Language
english
Notes
Publication type : Meeting Abstract
Abstract
Neuroimaging techniques provide valuable tools for diagnosing Alzheimer's disease (AD), monitoring disease progression and evaluating responses to treatment. There is currently a wide array of techniques available including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and, for recording electrical brain activity, electroencephalography (EEG). The choice of technique depends on the contrast between tissues of interest, spatial resolution, temporal resolution, requirements for functional data and the probable number of scans required. For example, while PET, CT and MRI can be used to differentiate between AD and other dementias, MRI is safer and provides better contrast of soft tissues. Neuroimaging is a technique spanning many disciplines and requires effective communication between doctors requesting a scan of a patient or group of patients and those with technical expertise. Consideration and discussion of the most suitable type of scan and the necessary settings to achieve the best results will help ensure appropriate techniques are chosen and used effectively. Neuroimaging techniques are currently expanding understanding of the structural and functional changes that occur in dementia. Further research may allow identification of early neurological signs ofAD, before clinical symptoms are evident, providing the opportunity to test preventative therapies. CombiningMRI and machine learning techniques may be a powerful approach to improve diagnosis ofAD and to predict clinical outcomes.
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Create date
01/09/2011 9:15
Last modification date
20/08/2019 13:55
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