# Data-driven normative values based on generative manifold learning for quantitative MRI

## TL;DR

This paper introduces a new AI method to create personalized brain MRI reference values, offering a more holistic approach compared to traditional average-based methods.

## Contribution

The novel contribution is using generative manifold learning to derive global, personalized normative values from quantitative MRI data.

## Key findings

- Generative manifold learning captures global patterns in MRI data better than traditional average-based methods.
- The method was tested on epilepsy and Alzheimer’s patients, showing potential for improved diagnostic accuracy.
- Personalized normative values consider inter-structure relationships, enhancing the interpretation of brain abnormalities.

## Abstract

In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approach to personalized normative values for each brain structure using an unsupervised Artificial Intelligence technique known as generative manifold learning. We test the potential benefit of these personalized normative values in comparison with the more traditional average normative values on a population of patients with drug-resistant epilepsy operated for focal cortical dysplasia, as well as on a supplementary healthy group and on patients with Alzheimer’s disease.