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Advanced magnetic resonance imaging of neurodegenerative diseases

Overview of attention for article published in Neurological Sciences, November 2016
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Title
Advanced magnetic resonance imaging of neurodegenerative diseases
Published in
Neurological Sciences, November 2016
DOI 10.1007/s10072-016-2764-x
Pubmed ID
Authors

Federica Agosta, Sebastiano Galantucci, Massimo Filippi

Abstract

Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.

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Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Brazil 1 <1%
Unknown 120 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 17%
Student > Ph. D. Student 18 15%
Researcher 15 12%
Student > Bachelor 10 8%
Student > Doctoral Student 7 6%
Other 23 19%
Unknown 28 23%
Readers by discipline Count As %
Neuroscience 23 19%
Medicine and Dentistry 18 15%
Psychology 12 10%
Engineering 7 6%
Agricultural and Biological Sciences 5 4%
Other 19 16%
Unknown 38 31%