Related Experiment Video
Updated: May 18, 2026
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter
S Haller1, P Missonnier, F R Herrmann
1Service neuro-diagnostique et neuro-interventionnel DISIM, Hôpitaux Universitaires de Genève, Rue GabriellePerret-Gentil 4, 1211 Genève 14, Switzerland. sven.haller@hcuge.ch
Background And Purpose:
MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI.
Materials And Methods:
Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs.
Results:
The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI.
Conclusions:
Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.
Insights
Mild cognitive impairment (MCI) subtypes, including amnestic (aMCI) and non-amnestic (naMCI) forms, can be distinguished using diffusion tensor imaging (DTI). DTI analysis of white matter integrity accurately classifies MCI subtypes.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- Mild cognitive impairment (MCI) is increasingly recognized with distinct subtypes, including single-domain amnestic MCI (sd-aMCI), single-domain fluid MCI (sd-fMCI), and multiple-domain amnestic MCI (md-aMCI).
- Understanding the underlying white matter integrity differences is crucial for accurate diagnosis and prognosis.
Purpose of the Study:
- To discriminate between the newly defined subtypes of MCI using diffusion tensor imaging (DTI).
- To investigate white matter integrity alterations in different MCI subtypes.
Main Methods:
- Diffusion tensor imaging (DTI) was performed on 66 participants across three MCI subtypes: sd-aMCI (n=18), sd-fMCI (n=13), and md-aMCI (n=35).
- Group-level analysis utilized tract-based spatial statistics (TBSS), while individual classification employed support vector machines (SVMs).
Main Results:
- Group analysis revealed decreased fractional anisotropy (FA) in md-aMCI compared to sd-aMCI within a bilateral network, and more pronounced FA reduction in md-aMCI versus sd-fMCI in specific white matter tracts.
- SVM analysis achieved high classification accuracy (around 97%) for distinguishing MCI subtypes based on white matter FA.
- No significant group differences were found between sd-fMCI and sd-aMCI, or for other diffusion parameters.
Conclusions:
- The md-aMCI subgroup exhibits the most significant white matter integrity damage at the group level.
- Individual classification using SVM analysis of white matter FA demonstrates high accuracy in differentiating MCI subtypes, suggesting DTI's potential as a diagnostic tool.
