D J J Farnell1, S Richmond2, J Galloway2, A I Zhurov2, P Pirttiniemi3, T Heikkinen3, V Harila3, H Matthews4, P Claes5. 1. School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom. Electronic address: farnelld@cardiff.ac.uk. 2. School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom. 3. Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland. 4. Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium; Facial Sciences Research Group, Murdoch Children's Research Institute, Melbourne; Department of Paediatrics, University of Melbourne, Melbourne, Australia. 5. Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium; Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium; Department of Electrical Engineering, ESAT/PSI, KU Leuven, 3000 Leuven, Belgium.
Abstract
BACKGROUND AND OBJECTIVES: Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females. METHODS: 3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix. RESULTS: Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2). CONCLUSIONS: Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.
BACKGROUND AND OBJECTIVES: Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females. METHODS: 3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix. RESULTS: Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2). CONCLUSIONS: Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.
Authors: Stephen Leslie; Bruce Winney; Garrett Hellenthal; Dan Davison; Abdelhamid Boumertit; Tammy Day; Katarzyna Hutnik; Ellen C Royrvik; Barry Cunliffe; Daniel J Lawson; Daniel Falush; Colin Freeman; Matti Pirinen; Simon Myers; Mark Robinson; Peter Donnelly; Walter Bodmer Journal: Nature Date: 2015-03-19 Impact factor: 49.962
Authors: Mari Nelis; Tõnu Esko; Reedik Mägi; Fritz Zimprich; Alexander Zimprich; Draga Toncheva; Sena Karachanak; Tereza Piskácková; Ivan Balascák; Leena Peltonen; Eveliina Jakkula; Karola Rehnström; Mark Lathrop; Simon Heath; Pilar Galan; Stefan Schreiber; Thomas Meitinger; Arne Pfeufer; H-Erich Wichmann; Béla Melegh; Noémi Polgár; Daniela Toniolo; Paolo Gasparini; Pio D'Adamo; Janis Klovins; Liene Nikitina-Zake; Vaidutis Kucinskas; Jūrate Kasnauskiene; Jan Lubinski; Tadeusz Debniak; Svetlana Limborska; Andrey Khrunin; Xavier Estivill; Raquel Rabionet; Sara Marsal; Antonio Julià; Stylianos E Antonarakis; Samuel Deutsch; Christelle Borel; Homa Attar; Maryline Gagnebin; Milan Macek; Michael Krawczak; Maido Remm; Andres Metspalu Journal: PLoS One Date: 2009-05-08 Impact factor: 3.240