The normative modelling framework for traumatic brain injury

Abstract

This review examines the principles, applications and methodological foundations of normative modelling, emphasizing its potential to assist in mitigating longstanding challenges in traumatic brain injury (TBI) research and management. TBI remains a major global health concern, with an incidence exceeding 50-60 million cases worldwide. Progress in research and clinical practice has been hindered by the complex and heterogeneous nature of TBI, arising from diverse aetiologies, injury mechanisms and pathophysiological processes that lead to variable clinical presentations. A significant obstacle, particularly present within neuroimaging, is the continued reliance on classification scales and analytical models that do not account for nuanced differences among patients. For example, the Glasgow Coma Scale and many prevalent models categorize injury severity levels by assuming homogeneity within groups, which inevitably results in heterogeneity and obscures individual variability. Similarly, traditional case-control research designs separate injury and control groups to conduct group difference testing, diluting valuable individual data by focusing on mean comparisons. We advocate for a paradigm shift towards normative modelling-a flexible framework that assesses individual differences by comparing patients to a reference cohort. This approach moves beyond traditional methods that emphasize group differences, addressing the limitations of conventional classification by avoiding the aggregation of TBI patients into heterogeneous categories based on simplistic measures. By capturing the full spectrum of variability, normative modelling has the potential to improve patient selection in clinical trials and foster more personalized treatment strategies. Adopting this innovative approach aims to enhance outcomes for TBI patients by emphasizing individual variability rather than relying on broad group classifications. Normative modelling promises to transform the translation of TBI research into clinical practice, ultimately driving progress towards more effective, tailored interventions.

Keywords: heterogeneity; neuroimaging; normative modelling; personalized medicine; traumatic brain injury.

Figure 1

The assumptions of the case-control approach versus the reality of TBI. This figure contrasts the theoretical assumptions of case-control studies with the heterogeneous nature of TBI. (A) A representation of the traditional case-control approach, where the groups (TBI cases and healthy controls) are considered homogeneous, with a distinct separation between them. (B) A more realistic depiction of TBI research, where the boundary between groups is blurred, with notable overlap in characteristics between the TBI and control groups. (C) The idealized assumption in case-control studies, portraying TBI as a homogeneous condition, where comparisons between TBI and control brains are expected to apply universally to all patients. (D) A more accurate depiction of TBI’s heterogeneity, showing that collapsing a broad spectrum of injuries into a single case group results in findings that are less applicable to any individual patient, thereby oversimplifying the complex nature of the condition. TBI = traumatic brain injury.

Figure 2

Illustrating normative models: understanding individual variability. This figure presents an example of a normative model, where each brain represents an individual. The quantile curves indicate z-scores, with a z-score of 0 representing the expected value (mean). In this case, researchers have determined that a z-score of ±2 indicates an extreme deviation from the norm. The brain outlined in red visually represents an individual with such an extreme deviation within the model, highlighting the capacity of normative models to identify and interpret significant variations in individual characteristics. TBI = traumatic brain injury.

Figure 3

Hypothetical symptom-trajectory curves derived from a normative model of post-TBI recovery. This schematic shows how individual deviation scores (z-scores) could track symptom burden over time since injury. Three exemplar trajectories are depicted: a mild deviation (z ≈ −1.0) that improves rapidly; a moderate deviation (z ≈ −2.0) that resolves more slowly; and a severe deviation (z < −2.0) that remains elevated for many months. Although symptom burden is used here for illustration, any TBI-relevant variable, neurocognitive performance, blood biomarkers, quantitative MRI metrics, can be substituted, allowing clinicians to visualize patient-specific recovery paths against an established reference distribution. TBI = traumatic brain injury.

Figure 4

Integrating multimodal variables within a normative-modelling framework for TBI. The diagram depicts a single TBI patient whose data span four domains: blood biomarkers; quantitative MRI (cortical thickness); computer-based neurocognitive testing; and self-reported symptoms. Deviation scores reveal markedly elevated biomarkers and pronounced cortical thinning, yet neurocognitive performance and symptom ratings fall within the reference range. By consolidating heterogeneous information into a common z-score metric, normative modelling highlights pathophysiological abnormalities that might otherwise be overlooked, thereby supporting a more comprehensive and individualized characterization of injury. Federated data platforms are critical for generating the large, diverse reference cohorts required to compute these scores reliably. TBI = traumatic brain injury.