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, which 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 the 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.
Highlights
Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms
We used machine learning algorithms (Fig. 1a) to classify human amyloid precursor protein (hAPP)-overexpressing mice (J20) and control, wildtype mice into genotype classes at 4 months and 13 months based on kinematic indices of gait
The J20 animals exhibited lower maximum running speed and an increased number of dragged steps. These observations are consistent with the gait deficits reported in patients with AD
Summary
Motor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. Three-dimensional kinematic analysis of gait has emerged as a powerful tool for quantitative assessment in subjects with a wide range of neurological conditions[20,21,22,23,24,25,26]. It provides information about trajectories, velocities, accelerations, and angles of movement of different body parts. Quantitative measurements of gait have been developed[19,42,43,44] to assist in rehabilitation[34,45] and to evaluate the effectiveness of various therapies[25], quantitative gait assessment as a diagnostic tool is in its infancy because representative kinematic features of specific pathologies remain u ncharacterized[7,46,47,48]
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