Brain aging patterns in a large and diverse cohort of 49,482 individuals

Peters, R. Ageing and the brain. Postgrad. Med J. 82, 84–88 (2006).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davatzikos, C., Xu, F., An, Y., Fan, Y. & Resnick, S. M. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132, 2026–2035 (2009).

Article  PubMed  PubMed Central  Google Scholar 

Dubois, B. et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 12, 292–323 (2016).

Article  PubMed  Google Scholar 

Davatzikos, C. Machine learning in neuroimaging: progress and challenges. Neuroimage 197, 652–656 (2019).

Article  PubMed  Google Scholar 

Tian, Y. E. et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat. Med. 29, 1221–1231 (2023).

Article  CAS  PubMed  Google Scholar 

Habes, M. et al. Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns. Transl. Psychiatry 6, e775 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang, Z. et al. A deep learning framework identifies dimensional representations of Alzheimer’s disease from brain structure. Nat. Commun. 12, 7065 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang, X. et al. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 113, E6535–e6544 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wen, J. et al. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Med. Image Anal. 75, 102304 (2022).

Article  PubMed  Google Scholar 

Yang, Z., Wen, J. & Davatzikos, C. Surreal-GAN: Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patterns. International Conference on Learning Representations (ICLR, 2022).

Habes, M. et al. The brain chart of aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans. Alzheimers Dement. 17, 89–102 (2021).

Article  CAS  PubMed  Google Scholar 

Cox, S. R. et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat. Commun. 7, 13629 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hedden, T. & Gabrieli, J. D. E. Insights into the ageing mind: a view from cognitive neuroscience. Nat. Rev. Neurosci. 5, 87–96 (2004).

Article  CAS  PubMed  Google Scholar 

Wang, M. C., Shah, N. S., Carnethon, M. R., O’Brien, M. J. & Khan, S. S. Age at diagnosis of diabetes by race and ethnicity in the United States from 2011 to 2018. JAMA Intern. Med. 181, 1537–1539 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Huang, X., Lee, K., Wang, M. C., Shah, N. S. & Khan, S. S. Age at diagnosis of hypertension by race and ethnicity in the US from 2011 to 2020. JAMA Cardiol. 7, 986–987 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Abbott, A. Dementia: a problem for our age. Nature 475, S2–S4 (2011).

Article  CAS  PubMed  Google Scholar 

Dwyer, D. B. et al. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium. Mol. Psychiatry 28, 2008–2017 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Stark, K. & Massberg, S. Interplay between inflammation and thrombosis in cardiovascular pathology. Nat. Rev. Cardiol. 18, 666–682 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Rose-John, S., Winthrop, K. & Calabrese, L. The role of IL-6 in host defence against infections: immunobiology and clinical implications. Nat. Rev. Rheumatol. 13, 399–409 (2017).

Article  CAS  PubMed  Google Scholar 

Dutta, G., Barber, D. S., Zhang, P., Doperalski, N. J. & Liu, B. Involvement of dopaminergic neuronal cystatin C in neuronal injury-induced microglial activation and neurotoxicity. J. Neurochem. 122, 752–763 (2012).

Article  CAS  PubMed  Google Scholar 

Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–d1012 (2019).

Article  CAS  PubMed  Google Scholar 

Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao, B. et al. Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706). Mol. Psychiatry 26, 3943–3955 (2021).

Article  PubMed  Google Scholar 

Seshadri, S. et al. Genetic correlates of brain aging on MRI and cognitive test measures: a genome-wide association and linkage analysis in the Framingham study. BMC Med. Genet. 8, S15 (2007).

Article  PubMed  PubMed Central  Google Scholar 

Leonardsen, E. H. et al. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol. Psychiatry 28, 3111–3120 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Wen, J. et al. The genetic architecture of multimodal human brain age. Nat. Commun. 15, 2604 (2024).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chauhan, G. et al. Association of Alzheimer’s disease GWAS loci with MRI markers of brain aging. Neurobiol. Aging 36, 1765.e1767–1765.e1716 (2015).

Article  Google Scholar 

Binnewies, J. et al. Lifestyle-related risk factors and their cumulative associations with hippocampal and total grey matter volume across the adult lifespan: a pooled analysis in the European Lifebrain consortium. Brain Res. Bull. 200, 110692 (2023).

Article  PubMed  Google Scholar 

Fotuhi, M., Do, D. & Jack, C. Modifiable factors that alter the size of the hippocampus with ageing. Nat. Rev. Neurol. 8, 189–202 (2012).

Article  CAS  PubMed  Google Scholar 

Kapasi, A., DeCarli, C. & Schneider, J. A. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol. 134, 171–186 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Savva, G. M. et al. Age, neuropathology, and dementia. N. Engl. J. Med. 360, 2302–2309 (2009).

Article  CAS  PubMed  Google Scholar 

Dong, A. et al. Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers. Brain 140, 735–747 (2017).

PubMed  Google Scholar 

Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subt

Comments (0)

No login
gif