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Machine learning support to individual diagnosis of mild cognitive impairment using multimodal MRI and cognitive assessments

De Marco, M., Beltrachini, Leandro, Biancardi, A., Frangi, A. and Venneri, A. 2017. Machine learning support to individual diagnosis of mild cognitive impairment using multimodal MRI and cognitive assessments. Alzheimer Disease and Associated Disorders 31 (4) , pp. 278-286. 10.1097/WAD.0000000000000208

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Background: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting state functional MRI (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients. Methods: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric (sMRI) and BOLD-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers. Results: The accuracy of the RS-fMRI-based classifier was not significantly different from that of the cognitive classifier (≈85%). RS-fMRI features improved the accuracy of both volumetric and cognitive indices (mixed sMRI+RS-fMRI: ≈85%; mixed sMRI+RSfMRI+ cognitive: ≈90%). The volumetric classifier had a significantly worse accuracy (≈80%). The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions. Conclusion: Feature selection was profoundly driven by non-collinearity. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically-relevant brain alterations typical of MCI might be subtle and not inferable from group analysis.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Cardiff University Brain Research Imaging Centre (CUBRIC)
Physics and Astronomy
Uncontrolled Keywords: Machine Learning, Magnetic Resonance Imaging, Semantics, Hippocampus, Resting-state
Publisher: Lippincott, Williams & Wilkins
ISSN: 0893-0341
Date of First Compliant Deposit: 27 June 2017
Date of Acceptance: 3 July 2017
Last Modified: 08 May 2019 13:48

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