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Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Nature Biomedical Engineering (2025)Cite this article

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Abstract

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine DNEs derived from independent yet harmonized studies on Alzheimer’s disease, autism spectrum disorder, late-life depression and schizophrenia in the UK Biobank study. Phenome-wide associations align with genome-wide associations, revealing 31 genomic loci (P < 5 × 10−8/9) associated with the nine DNEs. The nine DNEs, along with their polygenic risk scores, significantly enhanced the predictive accuracy for 14 systemic disease categories, particularly for conditions related to mental health and the central nervous system, as well as mortality outcomes. These findings underscore the potential of the nine DNEs to capture the expression of disease-related brain phenotypes in individuals of the general population and to relate such measures with genetics, lifestyle factors and chronic diseases.

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Fig. 1: Study workflow.
Fig. 2: Phenome-wide associations of the nine DNEs.
Fig. 3: Genome-wide associations of the nine DNEs.
Fig. 4: The genetic correlation, colocalization, and causal networks of the nine DNEs.
Fig. 5: Additional prediction power of the nine DNEs and PRSs for 14 systemic diseases, cognition, and mortality outcomes.

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Data availability

The GWAS summary statistics and pre-trained AI models from this study are publicly accessible via the MEDICINE Knowledge Portal (https://labs-laboratory.com/medicine/) and Synapse (https://www.synapse.org/Synapse:syn64923248/wiki/630992)101. Genomic loci annotation used data from FUMA (https://fuma.ctglab.nl/). UKBB data can be requested at https://www.ukbiobank.ac.uk/. GWAS summary data from the PGC can be accessed at https://pgc.unc.edu/. MUSE atlas is generated via the pipeline at https://github.com/CBICA/MUSE. The raw data for the main figures and Supplementary Files 1–23 are publicly available via Zenodo at https://zenodo.org/records/15238099 (ref. 102).

Code availability

The software and resources used in this study are all publicly available via MLNI103 (HYDRA) at https://github.com/anbai106/mlni (DNEs for ASD1–3, LLD1–2, SCZ1–2) and Surreal-GAN104 at https://github.com/zhijian-yang/SurrealGAN (DNEs for AD1–2).

References

  1. Hwang, G. et al. Assessment of neuroanatomical endophenotypes of autism spectrum disorder and association with characteristics of individuals with schizophrenia and the general population. JAMA Psychiatry 80, 498–507 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Wen, J. et al. Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression. JAMA Psychiatry 79, 464–474 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat. Commun. 9, 4273 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. 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 

  6. Vogel, J. W. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat. Med. 27, 871–881 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wen, J. et al. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Med. Image Anal. 75, 102304 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ferreira, D., Nordberg, A. & Westman, E. Biological subtypes of Alzheimer disease: a systematic review and meta-analysis. Neurology 94, 436–448 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hodson, R. Precision medicine. Nature 537, S49–S49 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Davatzikos, C. et al. Precision diagnostics based on machine learning-derived imaging signatures. Magn. Reson. Imaging 64, 49–61 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Leonenko, G. et al. Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores. Nat. Commun. 12, 4506 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wen, J. et al. Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum. Transl. Psychiatry 14, 1–14 (2024).

    Article  Google Scholar 

  13. Chand, G. B. et al. Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning. Brain 143, 1027–1038 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 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 (2021).

  15. Wingo, T. S. et al. Shared mechanisms across the major psychiatric and neurodegenerative diseases. Nat. Commun. 13, 4314 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Anttila, V. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  PubMed  Google Scholar 

  17. Wen, J. et al. The genetic architecture of biological age in nine human organ systems. Nat. Aging 4, 1290–1307 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wen, J. Multiorgan biological age shows that no organ system is an island. Nat. Aging 4, 1182–1183 (2024).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Boquet-Pujadas, A. et al. MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics. Brief. Bioinform. 26, bbaf125 (2025).

    PubMed  PubMed Central  Google Scholar 

  21. Petersen, R. C. et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74, 201–209 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Di Martino, A. et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci. Data 4, 170010 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wen, J. et al. Subtyping brain diseases from imaging data. In Machine Learning for Brain Disorders (ed. Colliot, O.) 491–510 (Springer, 2023); https://doi.org/10.1007/978-1-0716-3195-9_16

  24. Kendler, K. & Neale, M. Endophenotype: a conceptual analysis. Mol. Psychiatry 15, 789–797 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cannon, T. D. & Keller, M. C. Endophenotypes in the genetic analyses of mental disorders. Annu. Rev. Clin. Psychol. 2, 267–290 (2006).

    Article  PubMed  Google Scholar 

  26. Gottesman, I. I. & Gould, T. D. The endophenotype concept in psychiatry: etymology and strategic intentions. Am. J. Psychiatry 160, 636–645 (2003).

    Article  PubMed  Google Scholar 

  27. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    Article  PubMed  Google Scholar 

  29. Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wen, J. et al. Genomic loci influence patterns of structural covariance in the human brain. Proc. Natl Acad. Sci. USA 120, e2300842120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Guan, H. & Liu, M. Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69, 1173–1185 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012).

    Article  PubMed  Google Scholar 

  33. Joseph, C., Wang, L., Wu, R., Manning, K. J. & Steffens, D. C. Structural brain changes and neuroticism in late-life depression: a neural basis for depression subtypes. Int. Psychogeriatr. 33, 515–520 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. 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 

  35. McCracken, C. et al. Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants. Nat. Commun. 13, 7839 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jiang, L., Zheng, Z., Fang, H. & Yang, J. A generalized linear mixed model association tool for biobank-scale data. Nat. Genet. 53, 1616–1621 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Wightman, D. P. et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat. Genet. 53, 1276–1282 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Demontis, D. et al. Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat. Genet. 55, 198–208 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet. 53, 817–829 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS) Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).

    Article  Google Scholar 

  46. O’Donovan, M. C. What have we learned from the Psychiatric Genomics Consortium. World Psychiatry 14, 291–293 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cheverud, J. M. A comparison of genetic and pehnotypic correlations. Evolution 42, 958–968 (1988).

    Article  PubMed  Google Scholar 

  48. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  49. McCutcheon, R. A., Krystal, J. H. & Howes, O. D. Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry 19, 15–33 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Hordyjewska-Kowalczyk, E. et al. Functional analysis of novel RUNX2 mutations identified in patients with cleidocranial dysplasia. Clin. Genet. 96, 429–438 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Wamsley, B. et al. Molecular cascades and cell type–specific signatures in ASD revealed by single-cell genomics. Science 384, eadh2602 (2024).

    Article  PubMed  Google Scholar 

  53. Ayalew, M. et al. Convergent functional genomics of schizophrenia: from comprehensive understanding to genetic risk prediction. Mol. Psychiatry 17, 887–905 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Siokas, V. et al. Myelin-associated oligodendrocyte basic protein rs616147 polymorphism as a risk factor for Parkinson’s disease. Acta Neurol. Scand. 145, 223–228 (2022).

    Article  CAS  PubMed  Google Scholar 

  55. Arnold, S. E. et al. Dysregulation of olfactory receptor neuron lineage in schizophrenia. Arch. Gen. Psychiatry 58, 829–835 (2001).

    Article  CAS  PubMed  Google Scholar 

  56. Turetsky, B. I., Moberg, P. J., Arnold, S. E., Doty, R. L. & Gur, R. E. Low olfactory bulb volume in first-degree relatives of patients with schizophrenia. Am. J. Psychiatry 160, 703–708 (2003).

    Article  PubMed  Google Scholar 

  57. Almandil, N. B. et al. Exome-wide analysis identify multiple variations in olfactory receptor genes (OR12D2 and OR5V1) associated with autism spectrum disorder in Saudi females. Front. Med. 10, 1051039 (2023).

    Article  Google Scholar 

  58. Kuo, P.-H. et al. Genome-wide association study for autism spectrum disorder in Taiwanese Han population. PLoS ONE 10, e0138695 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Warnica, W. et al. Copy number variable microRNAs in schizophrenia and their neurodevelopmental gene targets. Biol. Psychiatry 77, 158–166 (2015).

    Article  CAS  PubMed  Google Scholar 

  60. Christen-Zaech, S. et al. Early olfactory involvement in Alzheimer’s disease. Can. J. Neurol. Sci. 30, 20–25 (2003).

    Article  CAS  PubMed  Google Scholar 

  61. Murphy, C. Olfactory and other sensory impairments in Alzheimer disease. Nat. Rev. Neurol. 15, 11–24 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 1–21 (2022).

    Google Scholar 

  64. 23andMe Research Team. et al. Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways. Nat. Commun. 9, 1470 (2018).

    Article  Google Scholar 

  65. Navari, S. & Dazzan, P. Do antipsychotic drugs affect brain structure? A systematic and critical review of MRI findings. Psychol. Med. 39, 1763–1777 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Chopra, S. et al. Differentiating the effect of antipsychotic medication and illness on brain volume reductions in first-episode psychosis: a longitudinal, randomised, triple-blind, placebo-controlled MRI study. Neuropsychopharmacology 46, 1494–1501 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zeldovich, L. Cold parenting? Childhood schizophrenia? How the diagnosis of autism has evolved over time. Science https://doi.org/10.1126/science.aau1206 (2018).

    Article  Google Scholar 

  68. Moreau, C. A. et al. Dissecting autism and schizophrenia through neuroimaging genomics. Brain 144, 1943–1957 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Fu, Z. et al. Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder. Hum. Brain Mapp. 42, 80–94 (2021).

    Article  PubMed  Google Scholar 

  70. Bereczki, E. et al. Synaptic markers of cognitive decline in neurodegenerative diseases: a proteomic approach. Brain 141, 582–595 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Jiang, C.-C. et al. Signalling pathways in autism spectrum disorder: mechanisms and therapeutic implications. Signal Transduct. Target. Ther. 7, 1–36 (2022).

    CAS  Google Scholar 

  72. Chi, S., Yu, J.-T., Tan, M.-S. & Tan, L. Depression in Alzheimer’s disease: epidemiology, mechanisms, and management. J. Alzheimers Dis. 42, 739–755 (2014).

    Article  PubMed  Google Scholar 

  73. Dafsari, F. S. & Jessen, F. Depression—an underrecognized target for prevention of dementia in Alzheimer’s disease. Transl. Psychiatry 10, 1–13 (2020).

    Article  Google Scholar 

  74. Ly, M. et al. Late-life depression and increased risk of dementia: a longitudinal cohort study. Transl. Psychiatry 11, 1–10 (2021).

    Article  Google Scholar 

  75. Lalousis, P. A. et al. Neurobiologically based stratification of recent-onset depression and psychosis: identification of two distinct transdiagnostic phenotypes. Biol. Psychiatry 92, 552–562 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Woo, M. Eyes hint at hidden mental-health conditions. Nature https://www.nature.com/articles/d41586-019-01114-9 (2019).

  77. Tahsili-Fahadan, P. & Geocadin, R. G. Heart–brain axis. Circ. Res. 120, 559–572 (2017).

    Article  CAS  PubMed  Google Scholar 

  78. Leng, F. & Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat. Rev. Neurol. 17, 157–172 (2021).

    Article  PubMed  Google Scholar 

  79. Murphy, C. E., Walker, A. K. & Weickert, C. S. Neuroinflammation in schizophrenia: the role of nuclear factor kappa B. Transl. Psychiatry 11, 1–13 (2021).

    Article  Google Scholar 

  80. Miller, A. H. & Raison, C. L. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat. Rev. Immunol. 16, 22–34 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Tan, A. H., Lim, S. Y. & Lang, A. E. The microbiome–gut–brain axis in Parkinson disease — from basic research to the clinic. Nat. Rev. Neurol. 18, 476–495 (2022).

    Article  PubMed  Google Scholar 

  82. Morais, L. H., Schreiber, H. L. & Mazmanian, S. K. The gut microbiota–brain axis in behaviour and brain disorders. Nat. Rev. Microbiol. 19, 241–255 (2021).

    Article  CAS  PubMed  Google Scholar 

  83. Tost, H., Champagne, F. A. & Meyer-Lindenberg, A. Environmental influence in the brain, human welfare and mental health. Nat. Neurosci. 18, 1421–1431 (2015).

    Article  CAS  PubMed  Google Scholar 

  84. Ambitious Project Announced to Create the World’s Largest Longitudinal Imaging Dataset (UK Biobank, 2022); https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/ambitious-project-announced-to-create-the-world-s-largest-longitudinal-imaging-dataset

  85. Wang, R., Chaudhari, P. & Davatzikos, C. Embracing the disharmony in medical imaging: a simple and effective framework for domain adaptation. Med. Image Anal. 76, 102309 (2022).

    Article  PubMed  Google Scholar 

  86. Varol, E., Sotiras, A. & Davatzikos, C. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage 145, 346–364 (2017).

    Article  PubMed  Google Scholar 

  87. Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Doshi, J. et al. MUSE: MUlti-atlas region segmentation utilizing ensembles of registration algorithms and parameters, and locally optimal atlas selection. Neuroimage 127, 186–195 (2016).

    Article  PubMed  Google Scholar 

  89. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Price, A. L., Zaitlen, N. A., Reich, D. & Patterson, N. New approaches to population stratification in genome-wide association studies. Nat. Rev. Genet. 11, 459–463 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Abraham, G., Qiu, Y. & Inouye, M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets. Bioinformatics 33, 2776–2778 (2017).

    Article  CAS  PubMed  Google Scholar 

  92. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  94. The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article  PubMed Central  Google Scholar 

  95. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Wen, J. et al. Convolutional neural networks for classification of Alzheimer’s disease: overview and reproducible evaluation. Med. Image Anal. 63, 101694 (2020).

    Article  PubMed  Google Scholar 

  100. Nadeau, C. & Bengio, Y. Inference for the generalization error. Mach. Learn. 52, 239–281 (2003).

    Article  Google Scholar 

  101. Wen, J. MEDICINE: AI-derived GWAS summary statistics sharing portal. Preprint at https://doi.org/10.7303/syn64923248 (2025).

  102. Wen, J. DNE_Figures_Dataset. Zenodo https://doi.org/10.5281/zenodo.15238098 (2025).

  103. Wen, J. MLNI (version 0.1.4). GitHub https://github.com/anbai106/mlni (2025).

  104. Yang, Z. Surreal-GAN (version 0.1.1). GitHub https://github.com/zhijian-yang/SurrealGAN (2025).

Download references

Acknowledgements

We express our sincere gratitude to the UK Biobank team (https://www.ukbiobank.ac.uk/) for their invaluable contribution to advancing clinical research in our field. We thank the PGC (https://pgc.unc.edu/) for generously sharing the GWAS summary statistics with the scientific community. This study used the UK Biobank resource under application numbers 35148 (C.D.) and 60698 (A.Z.). J.W. leads the MULTI consortium under UK Biobank Application number 647044; J.W. and the Laboratory of AI and Biomedical Science (LABS) are supported by the start-up funding from Columbia University. We also gratefully acknowledge the support of the imaging-based SysTem for AGing and NeurodeGenerative diseases (iSTAGING) consortium, funded by the National Institute on Aging through grant RF1 AG054409 at the University of Pennsylvania (C.D.). In addition, we acknowledge the funding program from the Rebecca L. Cooper Foundation at the University of Melbourne (A.Z.). Y.E.T. is supported by a National Health and Medical Research Council Investigator Grant (APP2026413).

Author information

Authors and Affiliations

  1. Laboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA

    Junhao Wen & Aleix Boquet-Pujadas

  2. Department of Radiology, Columbia University, New York, NY, USA

    Junhao Wen

  3. New York Genome Center (NYGC), New York, NY, USA

    Junhao Wen

  4. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Junhao Wen

  5. Data Science Institute (DSI), Columbia University, New York, NY, USA

    Junhao Wen

  6. Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Department of Radiology, Columbia University, New York, NY, USA

    Junhao Wen

  7. Zuckerman Institute, Columbia University, New York, NY, USA

    Junhao Wen

  8. Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ioanna Skampardoni, Zhijian Yang, Yuhan Cui, Guray Erus, Ilya M. Nasrallah, Haochang Shou & Christos Davatzikos

  9. Systems Lab, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

    Ye Ella Tian & Andrew Zalesky

  10. Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA

    Gyujoon Hwang

  11. Department of Computer Science and Engineering, New York University, New York, NY, USA

    Erdem Varol

  12. Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA

    Ganesh B. Chand

  13. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Theodore D. Satterthwaite

  14. Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Haochang Shou & Li Shen

  15. Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA

    Arthur W. Toga

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Contributions

J.W. has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: J.W. and C.D. Acquisition, analysis or interpretation of data: all authors. Drafting of the paper: J.W. Critical revision of the paper for important intellectual content: J.W., I.S., Y.E.T., Z.Y., Y.C., G.E., G.H., E.V., A.B.-P., G.B.C., I.M.N., T.D.S., H.S, L.S., A.W.T., A.Z. and C.D. Statistical analysis: J.W.

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Correspondence to Junhao Wen or Christos Davatzikos.

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Nature Biomedical Engineering thanks Alexandra Badea, Luigi Lorenzini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Wen, J., Skampardoni, I., Tian, Y.E. et al. Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01412-w

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