# Generalizable MRI normative modelling to detect age-inappropriate neurodegeneration

## TL;DR

This study creates brain charts to detect abnormal brain atrophy in neurodegenerative diseases by comparing individual MRI scans to age- and sex-adjusted norms.

## Contribution

The study introduces a generalizable method for individual-level detection of age-inappropriate neurodegeneration using large-scale normative MRI data.

## Key findings

- Regional centile scores predicted cognitive performance and tau PET uptake in Alzheimer’s disease.
- Distinct atrophy patterns were identified in different frontotemporal lobar degeneration phenotypes.
- The method effectively discriminated Alzheimer’s disease from healthy controls in independent datasets.

## Abstract

Determining whether MRI brain scans demonstrate atrophy that is beyond “normal for age” is challenging. Automated measurements of structural metrics in individual brain regions have shown promise as biomarkers of neurodegeneration, yet widely available reference standards that aid interpretation at the individual level are lacking. Normative modelling, enabling standardized “brain charts”, represents a significant step in addressing this challenge by generating individualized age- and sex- adjusted centile scores derived from large, aggregated datasets for MRI-derived quantitative metrics.

Using normative data from 56,173 participants across the life course, we have developed regional cortical thickness and amygdala/hippocampal volume brain charts (adjusted for total intracranial volume) that can be applied at the individual level. At the group level, we investigate whether regional centile scores relate to cognitive performance (mini-mental state examination) and discriminate individuals with neuropathological evidence of Alzheimer’s disease (n = 351) from propensity-matched controls from the National Alzheimer's Coordinating Center (NACC) dataset. In addition, we explored the relationships between disease stage, cognition, regional tau deposition and regional centile scores in amyloid-β-PET-positive individuals with Alzheimer’s disease dementia (n = 39) and mild cognitive impairment (n = 71) from the Alzheimer’s Disease Neuroimaging Initiative-3 (ADNI-3). We then extended this approach to phenotypes of frontotemporal lobar degeneration using the Neuroimaging in Frontotemporal Dementia dataset (n = 113).

We demonstrate BrainChart’s application to illustrative individual cases. At the group level, we show that in Alzheimer’s disease, regional centile scores from brain charting predicted cognitive performance, temporal lobe tau PET tracer uptake and discriminated disease groups from propensity matched cognitively normal controls in independent cohorts. Distinct patterns of age-inappropriate cortical atrophy were also evident in different clinical phenotypes of frontotemporal lobar degeneration from the Neuroimaging in Frontotemporal Dementia dataset.

Regional centile scores derived from an extensive normative dataset represent a generalizable method for objectively identifying atrophy in neurodegenerative diseases and can be applied to determine neurodegenerative atrophy at the individual level.

The online version contains supplementary material available at 10.1186/s13195-025-01872-x.