Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability

Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and subclinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes, herein called dimensional neuroimaging endophenotypes (DNEs), which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2), autism spectrum disorder (ASD1,2,3), late-life depression (LLD1,2), and schizophrenia (SCZ1,2), in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5xE-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72x10-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We gratefully acknowledge the support of the iSTAGING consortium, funded by the National Institute on Aging through grant RF1 AG054409 at the University of Pennsylvania (CD). We also acknowledge the funding provided by the National Institute of Biomedical Imaging and Bioengineering at the University of Southern California through grant 5P41EB015922-25 (AT). Additionally, we acknowledge the funding program from the Rebecca L. Cooper Foundation at the University of Melbourne (AZ). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The current study used only the UK Biobank data under the application numbers: 35148 and 60698. No specific additional IRBs are required. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All GWAS summary statistics are publicly available at: http://labs.loni.usc.edu/medicine/