The complementary role of automated brain volumetry to stratify ADNI participants within the ATN framework

BackgroundThe amyloid, tau, neurodegeneration (ATN) framework provides a biological staging model of Alzheimer's disease (AD) using magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), or positron emission tomography (PET) biomarkers. MRI, being non-invasive, accessible, and cost-effective, holds promise as a biomarker.ObjectiveTo evaluate the utility of MRI-based automated brain volumetry in classifying cognitive impairment severity-cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia-as well as ATN profiles, independently.MethodsWe analyzed 394 subjects from the Alzheimer's Disease Neuroimaging Initiative. First, we assessed how well MRI volumetry stratifies cognitive stages. Next, we tested its ability to distinguish A + T + N+ from A-T-N- individuals while classifying clinical stages. Finally, we evaluated its predictive power for cognitive severity in A + T+ and A-T- subgroups, irrespective of neurodegeneration (N), to examine the added value of volumetry across AT profiles.ResultsMRI volumetry showed comparable performance to established biomarkers in identifying CU, MCI, and dementia, and offered complementary value when combined with phosphorylated tau. Hippocampal and temporal gray matter volumes distinguished A + T + N+ from A-T-N- classes with accuracies of 0.81 and 0.78, respectively. In A + T+ versus A-T- comparisons, the highest classification performance for cognitive severity was observed in the A-T- group.ConclusionsMRI-based brain volumetry can effectively classify cognitive stages and distinguish biological subtypes in AD. It is a promising tool for clinical staging and predicting impairment severity, especially when used alongside phosphorylated tau.