Brain disorders represent a high global burden, with an estimate of one-third of the western population being affected by some form of mental health conditions or neurological pathologies in the course of their life1. This has an impact not only to patients and their caregivers, but it comes with huge economic and social costs for the society, estimated in hundred billion pounds per year in UK only1.

One of the emerging methods to treat an increasing variety of medical conditions in an efficient way is precision medicine. Precision medicine can simply be defined as “delivery of the right treatment at the right time, every time, to the right person”.

Precision medicine is an approach to treat a disease accounting for individual variability in genetic and environmental factors and to obtain the maximal efficacy from the therapy or prevention2. In precision medicine for brain disorders, neuroimaging plays an important role in developing non-invasive biomarkers for an early diagnosis and follow up of the treatment, avoiding the patient to undergo procedures such as biopsies or other more traditional invasive approaches3. Neuroimaging biomarkers can be functional (e.g. fMRI) or metabolic (e.g. FDG PET) markers and are currently used to assess if a patient belongs to a subpopulation of subjects that responds to a particular drug, among other applications4.

Thanks to the exponential increase of neuroimaging data availability, a wide range of MRI and PET-based atlases has recently been made publicly available to the wide scientific community. Normative brain atlases are not new in the field of neuroscience, but are becoming more and more popular for detecting structural and functional abnormalities in brain disorders at the individual level, as well as for characterising pharmacological response in imaging studies, or simply for reporting the findings of a study in a common space5. Open access tools include a variety of brain templates for structural MRI data (e.g. the MNI305 anatomical atlas6), functional data (e.g. individual whole-brain atlases with resting state fMRI7) or molecular systems (e.g. Neurobiology Research Unit serotonin atlases8).

Despite the big volume of data and the possibility to get free access to it, neuroimaging normative atlases have not reached their full potential because of some technical challenges and the biological variability of the data. As a result, these tools have struggled to make an impact beyond the original application for which they have been created.

Project aim

This project aims to develop and validate the use of normative PET neuroimaging for the delivery of individualised patient diagnosis and treatment guidance.

Up to this date, PET neuroimaging has only been used in the context of experimental medicine to gain mechanistic understanding on the pathology of brain disorders using group level statistics.

The basic idea is to generate normative atlases not just by averaging the whole sample across subjects, but in such a way as to obtain a neuroimaging equivalent of the growth charts for babies. Having a complete and robust statistical representation of the normal population will allow to assess patient-specific alterations and predict the potential efficacy of a treatment.

The characterization of normal variability could be used to develop new methods for clinical trials and imaging studies that could potentially reduce the patient exposure in these studies.


  1. Olesen J, Gustavsson A, Svensson M, Wittchen HU, Jönsson B. The economic cost of brain disorders in Europe. Eur J Neurol. 2012;19(1):155-162. doi:10.1111/j.1468-1331.2011.03590.
  2. Giardino A, Gupta S, Olson E, et al. Role of Imaging in the Era of Precision Medicine. Acad Radiol. 2017;24(5):639-649. doi:10.1016/j.acra.2016.11.021
  3. Phillip Kuo, MD P. Personalized Medicine Advances Toward Radiology. 2013.
  4. Neuroimaging in Precision Medicine – Imagilys. Accessed June 4, 2020.
  5. Beaulieu A. Voxels in the Brain. Soc Stud Sci. 2001;31(5):635-680. doi:10.1177/030631201031005001
  6. Evans AC, Collins DL, Mills SR, Brown ED, Kelly RL, Peters TM. 3D statistical neuroanatomical models from 305 MRI volumes. In: IEEE Nuclear Science Symposium & Medical Imaging Conference. Publ by IEEE; 1994:1813-1817. doi:10.1109/nssmic.1993.373602
  7. Doucet GE, Lee WH, Frangou S. Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases. Hum Brain Mapp. 2019;40(15):4577-4587. doi:10.1002/hbm.24722
  8. Beliveau V, Ganz M, Feng L, et al. A high-resolution in vivo atlas of the human brain’s serotonin system. J Neurosci. 2017;37(1):120-128. doi:10.1523/JNEUROSCI.2830-16.2016