Morphological imaging biomarkers in the hippocampus for the detection of mild cognitive impairment
- Estefanía Pérez Aguirre,
- Didier Torres Guzmán,
- Verónica Medina Bañuelos,
- Universidad Autónoma Metropolitana,
- Universidad Nacional Autónoma de México,
Publication Information
Output type
Original language
SpanishArticle number
e2250Journal (Volume, Issue Number)
Revista Cubana de Investigaciones Biomedicas (Volume 43)Publication milestones
- Published - 01/01/2024
Publication status
ISSN
0864-0300Publication IDs
- Scopus: 85203301437
Abstract
Introduction: The hippocampus is one of the most studied brain structures in the development of Alzheimer's disease because its morphology is affected from the stage of mild cognitive impairment. Quantitative metrics assess morphological changes of different brain structures by neuroimaging analysis. Discrete compactness and tortuosity have been reported as possible biomarkers of significant changes between populations of healthy control subjects and subjects with mild cognitive impairment. Purpose: To evaluate the performance of discrete compactness and tortuosity in the hippocampi (right and left) as possible magnetic resonance neuroimaging biomarkers to discriminate between populations of controls and subjects with mild cognitive impairment. Methods: 98 subjects were analyzed, including 49 healthy and 49 with mild cognitive impairment. Magnetic resonance images and other patient data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The structure of the hippocampi (right and left) was segmented using image processing techniques and volume, normalized volume, discrete compactness and tortuosity metrics were calculated. Statistical analysis of the data was performed in order to differentiate the classes. The metrics were incorporated into an automatic random forest classifier to discriminate between study populations. Results: The statistical analysis showed statistically significant differences for the calculated metrics with a p-value < 0.01 between the two study classes. The automatic random forest classification algorithm, based on the indicators of volume, normalized volume, discrete compactness and tortuosity, achieved an accuracy in the training stage of 89.83 % ± 0.052 %. For the test stage with reserved data, the final accuracy was 85 %. Conclusions: Discrete compactness and tortuosity are sensitive to morphological changes in the right and left hippocampus, and characterize the stage of mild cognitive impairment; therefore, they can be considered imaging biomarkers, useful in the detection of mild cognitive impairment when the first symptoms of Alzheimer's disease begin to appear.
