The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis.
Authors
Affiliations (11)
Affiliations (11)
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
- Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.
- Faculty of Medical and Health Sciences, Eisdell Moore Centre for Hearing and Balance Research, University of Auckland, Auckland, New Zealand.
- Department of Physiology and Centre for Brain Research, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
- Traumatic Brain Injury Network, Auckland University of Technology, Northcote, Auckland, New Zealand.
- Department of Pharmacology and Toxicology, Faculty of Biomedical and Molecular Sciences, University of Otago, Dunedin, New Zealand.
- Department of Engineering Science and Biomedical Engineering, University of Auckland, Auckland, New Zealand.
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand. [email protected].
- Medical Imaging Research Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand. [email protected].
- Centre for Co-Created Ageing Research, The University of Auckland, Auckland, New Zealand. [email protected].
- Centre for Brain Research, The University of Auckland, Auckland Bioengineering House, 70 Symonds Street, Auckland, 1010, New Zealand. [email protected].
Abstract
Physical exercise is known to delay the cognitive decline in the elderly. However, the effect of low-impact balance exercises such as yoga or Tai chi has not been explored in detail. This cross-sectional observational study used brain magnetic resonance imaging data to quantify and compare various brain structures between neurologically healthy adults aged between 55 and 65, divided into Control Group and Balance Exercise (BE) Group based on the self-reported balance exercise status. Various brain attributes such as brain age, cortical and subcortical volume, thickness, surface area, and mean curvature were extracted and computed using machine learning algorithm software like brainageR and FreeSurfer. Clinical functional assessments (balance, vestibular and cognitive measures) were also conducted for the participants. Statistical analyses were performed to determine any differences between the groups at a significance level of 5%. The BE group showed statistically significantly higher values for the right caudal anterior cingulate thickness, left and right superior temporal volume, left entorhinal volume and mean curvature, left frontal pole thickness, left superior temporal area and left inferior temporal thickness. A statistically significant cluster after correction for multiple comparisons was found in the left rostral middle frontal gyrus with a higher volume for BE group. Clinical functional assessments (balance, vestibular and cognitive) and brain age differences were nonsignificant. The significant brain regions in the BE group are involved in memory, cognition, focus, planning, language and auditory processing, decision making, emotional regulation and mental health and could be responsible for protecting and delaying the cognitive declines in the elderly.