Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling

Alzheimer’s disease (AD) has been traditionally associated with episodic memory impairment and medial temporal lobe atrophy. However, recent literature has highlighted the existence of atypical forms of AD, presenting with different cognitive and radiological profiles. Failure to appreciate the heterogeneity of AD in the past has led to misdiagnoses, diagnostic delays, clinical trial failures and risks limiting our understanding of the disease. AD research requires the incorporation of new analytic methods that are as free as possible from the intragroup homogeneity assumption underlying case-control approaches according to which patients belonging to the same group are comparable to each other. Neuroanatomical normative modelling is a promising technique allowing for modelling the variation in neuroimaging profiles and then assessing individual deviations from the respective distribution. Here, neuroanatomical normative modelling was applied for the first time to a real-world clinical cohort of Alzheimer’s disease patients (n=86) who had a positive amyloid PET scan and a T1-weighted MR performed as part of their diagnostic workup. The model indexed normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. Transfer learning was used to recalibrate the normative model on a validation cohort (n=20) of scanner-matched cognitively normal individuals. Brain heterogeneity was quantified as z-scores at each of the 148 ROIs generated within each AD patient. Z-scores < -1.96 defined as outliers. Clinical features including disease severity, presenting phenotypes and comorbidities were collected from health records to explore their association with outlier profiles. Amyloid quantification was performed using an automated PET-only driven method to examine the association between amyloid burden and outliers. The total number of individual outliers ( total outlier count ) in biomarker-confirmed AD clinical patients ranged between 1 and 120 out of 148 (median 21.5). The superior temporal sulcus was the region with the highest count of outliers (60%) in AD patients. The mean proportion of outliers was higher in the temporal (31.5%) than in the extratemporal (19.1%) regions and up to 20% of patients had no temporal outliers. We found higher mean outlier count in patients with non-amnestic phenotypes, at more advanced disease stages and without depressive symptoms. Amyloid burden was negatively associated with outlier count. This study corroborates the heterogeneity of brain atrophy in AD and provides evidence that this approach can be used to explore anatomo-clinical correlations at an individual level. ### Competing Interest Statement JL is employed by Hermes Medical Solutions and obtains a salary from them, he is Vice President of Research and Development at Hermes Medical Solutions. ZW previously participated in the Eli Lilly PET advisory board and was an amyloid-PET read trainer. RP previously sat on an advisory board for Eli Lilly and received support from GE for research imaging from 2014 to 2018. PM has given an educational talk at a meeting organised by GE. None of the authors currently have funding or support from any commercial organisation involved in amyloid PET imaging. ### Funding Statement The work was funded by Alzheimers Society (grant number P75464) and supported by the NIHR Biomedical Research Centre at Imperial College London; the EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) (EP/S021930/1); the Department of Healths National Institute for Health Research funded University College London Hospitals Biomedical Research Centre. In addition, A.F.M. gratefully acknowledges funding from the Dutch Organization for Scientific Research via a VIDI fellowship (grant number 016.156.415). None of the funders were involved in the conduct of the study or preparation of the article. ### 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: Ethical approval for this study was obtained from the Camden and Kings Cross UK Research Ethics Committee (REC number 20/LO/0442) and the Cambridge East Research Ethics Committee (REC number 10/H0304/70). 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors