Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline
Ioanna Skampardoni, Ilya M. Nasrallah, Junhao Wen, Yuhan Cui, Ahmed Abdulkadir, Zhijian Yang, Guray Erus, Elizabeth Mamourian, Ashish Singh, Haochang Shou, Li Shen, Konstantina Nikita, Christos Davatzikos,- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data‐driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium.
Method
Structural brain morphologic patterns of CU individuals were independently examined in four decade‐long intervals spanning ages 45 to 85. Within each interval, Smile‐GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile‐GAN probability scores were used as phenotypes in genome‐wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019).
Result
Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previously associated with white matter microstructure and regional brain volumes, showed opposite effects for A1 (protection) and A2 (risk), consistent with the neuroimaging patterns. WDR41 in A3 group was previously associated with the age of Alzheimer’s disease onset (Herold et al., 2016).
Conclusion
Reproducible neuroimaging patterns defined by regional atrophy and WMH burden were identified across CU individuals and demonstrated unique genetics. Further research is needed to elucidate the neuropathological pathways that mediate these relationships.