Neuroimaging Biomarkers in Alzheimer's Disease Progression

Neuroimaging biomarkers have transformed our understanding of Alzheimer’s disease by revealing the sequence and regional specificity of pathological changes long before clinical symptoms emerge. Structural MRI enables quantification of cortical thinning and hippocampal atrophy, while PET imaging of amyloid-β and tau aggregates provides molecular specificity. Combined with advanced computational models, these modalities delineate subtypes of disease progression, forecast individual trajectories and underpin precision medicine approaches. Imaging biomarkers inform early diagnosis, stage-wise stratification of patients in clinical trials and evaluation of therapeutic efficacy, thereby reducing trial durations and sample sizes. By integrating multimodal data, researchers now capture the heterogeneity of Alzheimer’s disease across populations, identify compensatory neural mechanisms and generate virtual cohorts to explore genetic determinants and environmental modifiers of progression.

Research from Nature Portfolio

Forecasting individual progression trajectories in Alzheimer’s disease introduced a statistical framework that predicts cognitive decline and imaging biomarker evolution from baseline data in over 96 000 cases worldwide. The model enriches clinical trials by identifying likely progressors, reducing required sample sizes by up to half, without bias across sex or geography and demonstrating greatest accuracy at the earliest disease stages.

AD Course Map charts Alzheimer’s disease progression developed a spatiotemporal atlas capturing variability in neuropsychological scores, patterns of hypometabolism, cortical thinning and hippocampal deformation. This atlas reveals strong genetic influences on progression speed and potential compensatory processes, outperforms competing benchmarks in prognostic accuracy and facilitates simulation of virtual patient cohorts for trial planning and personalised care.

Neuroimaging Biomarkers in Alzheimer's Disease Progression publication trend

The graph below shows the total number of articles in neuroimaging biomarkers in alzheimer's disease progression across all publications each year (not limited to Nature Index journals).

Technical terms

Amyloid-β accumulation: deposition of extracellular Aβ peptides detectable by PET tracers, representing an early hallmark of Alzheimer’s pathology.

Tau pathology: intracellular aggregation of hyperphosphorylated tau proteins forming neurofibrillary tangles visualised through selective PET ligands.

Spatiotemporal atlas: comprehensive map of regional brain changes over time constructed from longitudinal imaging datasets.

Event-based model: computational framework that orders biomarker abnormalities into a probabilistic sequence reflecting disease staging.

References

1. Forecasting individual progression trajectories in Alzheimer’s disease. Nature Communications (2023).
2. AD Course Map charts Alzheimer’s disease progression. Scientific Reports (2021).
3. Deep learning to predict rapid progression of Alzheimer’s disease from pooled clinical trials: A retrospective study. PLOS Digital Health (2024).
4. The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease. Alzheimer's Research & Therapy (2020).
5. DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders. NeuroImage (2019).