Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.

ABSTRACT: Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace ( http://painface.net ) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.

Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.

ABSTRACT: Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace (http://painface.net) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.

Application of machine learning classifiers for microcomputed tomography data assessment of mouse bone microarchitecture.

The current standard approach for analyzing cortical bone structure and trabecular bone microarchitecture from micro-computed tomography (microCT) is through classic parametric (e.g., ANOVA, Student's T-test) and nonparametric (e.g., Mann-Whitney U test) statistical tests and the reporting of p-values to indicate significance. However, on their own, these univariate assessments of significance fall prey to a number of weaknesses, including an increased chance of Type 1 error from multiple comparisons. Machine learning classification methods (e.g., unsupervised, k-means cluster analysis and supervised Support Vector Machine classification, SVM) simultaneously utilize an entire dataset comprised of many cortical structure or trabecular microarchitecture measures, thus minimizing bias and Type 1 error that are generated through multiple testing. Through simultaneous evaluation of an entire dataset, k-means and SVM thus provide a complementary approach to classic statistical analysis and enable a more robust assessment of microCT measures.

Microgravity-induced alterations of mouse bones are compartment- and site-specific and vary with age.

The age at which astronauts experience microgravity is a critical consideration for skeletal health and similarly has clinical relevance for musculoskeletal disuse on Earth. While astronauts are extensively studied for bone and other physiological changes, rodent studies enable direct evaluation of skeletal changes with microgravity. Yet, mouse spaceflight studies have predominately evaluated tissues from young, growing mice. We evaluated bone microarchitecture in tibiae and femurs from Young (9-week-old) and Mature (32-weeks-old) female, C57BL/6N mice flown in microgravity for ~2 and ~3 weeks, respectively. Microgravity-induced changes were both compartment- and site-specific. Changes were greater in trabecular versus cortical bone in Mature mice exposed to microgravity (-40.0% Tb. BV/TV vs -4.4% Ct. BV/TV), and bone loss was greater in the proximal tibia as compared to the distal femur. Trabecular thickness in Young mice increased by +25.0% on Earth and no significant difference following microgravity. In Mature mice exposed to microgravity, trabecular thickness rapidly decreased (-24.5%) while no change was detected in age-matched mice that were maintained on Earth. Mature mice exposed to microgravity experienced greater bone loss than Young mice with net skeletal growth. Moreover, machine learning classification models confirmed that microgravity exposure-driven decrements in trabecular microarchitecture and cortical structure occurred disproportionately in Mature than in Young mice. Our results suggest that age of disuse onset may have clinical implications in osteoporotic or other at-risk populations on Earth and may contribute to understanding bone loss patterns in astronauts.