Fasano, F., Mitolo, M., Gardini, S. et al. (3 more authors) (2018) Combining Structural Magnetic Resonance Imaging and Visuospatial Tests to Classify Mild Cognitive Impairment. Current Alzheimer Research, 15 (3). pp. 237-246. ISSN 1567-2050
Abstract
Background: Recently, efforts have been made to combine complementary perspectives in the assessment of Alzheimer type dementia. Of particular interest is the definition of the fingerprints of an early stage of the disease known as Mild Cognitive Impairment or prodromal Alzheimer's Disease. Machine learning approaches have been shown to be extremely suitable for the implementation of such a combination.
Methods: In the present pilot study we combined the machine learning approach with structural magnetic resonance imaging and cognitive test assessments to classify a small cohort of 11 healthy participants and 11 patients experiencing Mild Cognitive Impairment. Cognitive assessment included a battery of standardised tests and a battery of experimental visuospatial memory tests. Correct classification was achieved in 100% of the participants, suggesting that the combination of neuroimaging with more complex cognitive tests is suitable for early detection of Alzheimer Disease.
Results: In particular, the results highlighted the importance of the experimental visuospatial memory test battery in the efficiency of classification, suggesting that the high-level brain computational framework underpinning the participant's performance in these ecological tests may represent a “natural filter” in the exploration of cognitive patterns of information able to identify early signs of the disease.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Bentham Science Publishers. This is an author produced version of a paper subsequently published in Current Alzheimer Research. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Visuospatial memory; spatial abilities; support vector machine; magnetic resonance imaging; mild cognitive impairment; classification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Neuroscience (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Mar 2018 15:19 |
Last Modified: | 23 Jan 2019 01:38 |
Published Version: | https://doi.org/10.2174/1567205014666171030112339 |
Status: | Published |
Publisher: | Bentham Science Publishers |
Refereed: | Yes |
Identification Number: | 10.2174/1567205014666171030112339 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128697 |