Aging and Environmental Associated Changes of Facial Soft Tissues are Detectable on Clinical High Resolution MRI Scans

PURPOSE: Facial aging is a multifactorial process involving both soft tissues and bony structures1. Factors including volume loss, gravity, muscle laxity, and cellular damage contribute to decreased skin and soft tissue elasticity, resulting in increased mobility of facial soft tissues1-2. Variations in soft tissue integrity have been attributed to age, sun exposure, and smoking. While facial aging has been extensively examined histologically, the present study sought to leverage clinical MRI to quantify facial soft tissue movement (STM) and correlate with environmental and demographic factors. MATERIALS & METHODS: Sixty-eight patients underwent high resolution MRI scans, which included two identical scans at the beginning and end of imaging separated by approximately 45 minutes. MRIcron was used to label 49 reproducible bony and soft tissue facial landmarks on all scans. An avatar scan was used to co-register and scale all scans. For each patient, early and late scans were coregistered, and mathematical voxel-wise absolute differences were used to render composite maps to highlight the change in soft tissue configuration over the 45 minute gap. Lines between close neighbor landmarks offered corresponding paths by which movement could be compared between patients. Linear regression was used to correlate average absolute differences with age, sex, smoking status, and sun exposure. RESULTS: Age was positively correlated with the greatest STM compared to sun exposure, sex, and smoking status. In the upper face, age was correlated with STM in the forehead (glabella to superior orbits, p=0.026), bony orbits (p-range=0.001-0.023), and orbital soft tissue (orbits to medial/lateral canthi, p-range=0.001-0.014). Age was associated with bilateral midface STM in the infraorbital region (between malar eminence, bilateral canthi and inferior orbit, p-range=0.001-0.019) and zygomatic region (malar eminence to auditory canal, p-range=0.001-0.032). In the lower face, age correlated with STM around the mouth and philtrum (lips, oral commissures, columella and nares, p-range=0.028-0.001), and between the mandible and mentum (p-range=0.001-0.031). Sun exposure was only associated with STM in the oral region (lips, columella, and nares, p-range=0.001-0.049) and infraorbital/nasal region (nares to medial canthus, p=0.001). Sex was only associated with STM around the mandibular angle (p=0.004). Smoking was not found to be associated with significant bilateral STM. CONCLUSIONS: To our knowledge this is the first study to examine facial STM using clinical in vivo MRI. This methodology identified facial regions most susceptible to changes from aging and various environmental factors. These results provide further understanding of the natural facial aging process and may be helpful in identifying rejuvenative targets in the future. References: 1. Ilankovan V. Anatomy of ageing face. Br J Oral Maxillofac Surg. 2014 Mar;52(3):195-202. doi: 10.1016/j.bjoms.2013.11.013. Epub 2013 Dec 23. PMID: 24370442. 2. Freytag L, Alfertshofer MG, Frank K, et al. Understanding Facial Aging Through Facial Biomechanics: A Clinically Applicable Guide for Improved Outcomes. Facial Plast Surg Clin North Am. 2022 May;30(2):125-133. doi: 10.1016/j.fsc.2022.01.001. PMID: 35501049.