Skip to main content
padlock icon - secure page this page is secure

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Buy Article:

$52.00 + tax (Refund Policy)

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 magnetic resonance imaging (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 [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers.

Results:

The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. 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 statistical independence. 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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: hippocampus; machine learning; magnetic resonance imaging; resting-state; semantics

Document Type: Research Article

Affiliations: 1: Departments of Neuroscience 2: Electronic and Electrical Engineering, Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Physics and Astronomy, School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK 3: Cardiovascular Science, University of Sheffield, Sheffield 4: Electronic and Electrical Engineering, Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) 5: Departments of Neuroscience, Electronic and Electrical Engineering, Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)

Publication date: October 1, 2017

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more