1. Imaging biomarkers in neurodegeneration: current and future practices
- Author
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Ashwin V. Venkataraman, Nick C. Fox, Ross W. Paterson, Joana B. Pereira, Emma M. Coomans, William J. Jagust, Michael Schöll, Mar Estarellas, Anne Maass, Helen Beaumont, Meera Srikrishna, Stephen F. Carter, Peter N. E. Young, Eimear McGlinchey, David Berron, Matthew J. Betts, Rikki Lissaman, Daniel Jiménez, Antoinette O'Connor, Apollo - University of Cambridge Repository, and Carter, Stephen [0000-0002-9080-519X]
- Subjects
0301 basic medicine ,medicine.medical_specialty ,Neurology ,Cognitive Neuroscience ,Neuroimaging ,Review ,lcsh:RC346-429 ,lcsh:RC321-571 ,03 medical and health sciences ,0302 clinical medicine ,Alzheimer Disease ,Multimodal analysis ,Machine learning ,medicine ,Dementia ,Humans ,ddc:610 ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,lcsh:Neurology. Diseases of the nervous system ,11 Medical and Health Sciences ,medicine.diagnostic_test ,business.industry ,Geriatrics gerontology ,Neurodegeneration ,Neurodegenerative diseases ,Magnetic resonance imaging ,medicine.disease ,Magnetic Resonance Imaging ,030104 developmental biology ,PET ,Positron emission tomography ,Positron-Emission Tomography ,Neurology (clinical) ,business ,Neuroscience ,Alzheimer’s disease ,030217 neurology & neurosurgery ,Biomarkers ,MRI ,dementia - Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course “Biomarkers in neurodegenerative diseases”. In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
- Published
- 2020
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