| First Author | Huang R | Year | 2021 |
| Journal | Sci Rep | Volume | 11 |
| Issue | 1 | Pages | 3950 |
| PubMed ID | 33597593 | Mgi Jnum | J:303744 |
| Mgi Id | MGI:6512751 | Doi | 10.1038/s41598-021-82694-3 |
| Citation | Huang R, et al. (2021) Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration. Sci Rep 11(1):3950 |
| abstractText | 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. |
