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Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration.
- Source :
-
Scientific Reports . 2/17/2021, Vol. 11 Issue 1, p1-16. 16p. - Publication Year :
- 2021
-
Abstract
- Motor deficits are observed in Alzheimer's disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 11
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 148803563
- Full Text :
- https://doi.org/10.1038/s41598-021-82694-3