Global brain asymmetry and its variations in aging and related diseases

Functional lateralization is a cardinal feature of human brain, and deviations from typical lateralization are observed in various brain disorders. Although this phenomenon has been widely acknowledged in the field of human neuroscience, decades of research have shown that it is a challenge to bridge the gap between (a)typically lateralized functions and hemispheric differences in structure (termed structural asymmetry). To address this important question, the present study employed the state-of-the-art machine learning techniques to investigate the brain structural asymmetry and its associations with cognitive functions, aging, and aging-related diseases, by integrating large-scale datasets. Our proposed multivariate approach revealed previously unknown and substantial structural differences between the left and right hemispheres, and established the associations between the global brain asymmetry and lateralized functions including hand motor and emotion processing. Furthermore, at the population level we mapped the aging trajectories of the global brain asymmetry, and unveiled significant diagnosis-specific variations in patients with Alzheimer’s disease and Parkinson’s disease, and individuals carrying a relevant genetic risk for atypical brain aging (i.e., APOE4 carriers). These results demonstrated left-hemisphere-linked changes in aging, which has challenged the traditional “right hemi-aging” model, and offered a promising approach for assessing brain aging and related diseases. Overall, our study with a novel approach presents one of the largest-scale investigation of global brain asymmetry, and takes an important step forward in understanding the intricate interplay between structural asymmetry, lateralized functions, and brain aging in health and disease. Significance statement Functional lateralization is fundamental to the human brain, with deviations linked to various brain disorders. Bridging the gap between functional lateralization and structural asymmetry has been a long-standing challenge. Using advanced machine learning and large-scale datasets, this study introduced a multivariate global brain asymmetry approach and revealed previously unidentified structural differences between the brain hemispheres, correlating these with cognitive functions, aging, and diseases like Alzheimer’s and Parkinson’s. Contrary to the traditional “right hemi-aging” model, we found left-hemisphere-linked aging changes. This work provides new insights into brain asymmetry, lateralized functions, and aging, offering a promising approach for assessing brain health and disease. Classifications: Biological Sciences (Psychological and Cognitive Sciences);