How is the brain age index calculated?

Brain Age Index Calculation

The brain age index is calculated by training machine learning models on structural MRI data from healthy individuals to predict chronological age, then applying this model to new subjects—the difference between predicted brain age and actual chronological age yields the "brain age gap" or "brain-predicted age difference" (brain-PAD), which serves as a biomarker of accelerated or decelerated brain aging. 1, 2

Core Methodology

Training Phase

• Machine learning models are trained on large datasets of healthy control subjects using volumetric brain features extracted from structural MRI scans, typically T1-weighted images 2
• The training dataset should include healthy individuals across the full adult age spectrum to establish normative aging patterns—for example, one study used 385 healthy controls from the IXI and OASIS datasets 1
• Deep learning frameworks or regression algorithms (such as LASSO regression) are applied to learn the relationship between brain structural features and chronological age 2, 3

Feature Extraction

• Volumetric measurements are the primary input features, including normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness, and hippocampal volume 1
• Multimodality neuroimaging can enhance prediction accuracy by incorporating T2-FLAIR, T2*, diffusion-MRI, task fMRI, and resting-state fMRI data alongside structural imaging 3
• Studies using multimodality approaches achieve higher prediction accuracy (r = 0.78, mean absolute error = 3.55 years) compared to single-modality approaches 3

Brain Age Prediction

• The trained model is applied to new individual MRI scans to generate a predicted brain age 1, 2
• Brain age gap (or brain-PAD) is calculated as: Predicted Brain Age minus Chronological Age 1, 3
• A positive brain-PAD indicates accelerated brain aging (brain appears older than chronological age), while a negative value suggests preserved or younger-appearing brain structure 3

Performance Benchmarks

• State-of-the-art models achieve mean absolute errors of 4.06-4.21 years with correlation coefficients (r) of 0.96-0.97 between predicted and chronological age 2
• Prediction accuracy is comparable across heterogeneous datasets, confirming the robustness of deep learning frameworks 2
• The BrainAGE (Brain Age Gap Estimation) method represents the first and most widely applied concept for this approach, established over the past decade 4

Advanced Approaches: Brain Age Vector

• Traditional single brain age gap metrics compromise regional variations in brain aging, losing spatial specificity valuable for disease differentiation 5
• The brain age vector approach combines brain age modeling with Shapley Additive Explanations (SHAP) to measure brain aging as a feature contribution vector, attributing regional contributions to age estimation 5
• This method demonstrates superior spatial specificity in distinguishing disorder-specific patterns—for example, medial temporal involvement in prodromal Alzheimer's disease versus striatal involvement in prodromal Parkinson's disease 5
• Brain age vector achieves area under the curve of 83.39% for detecting progressive MCI and 72.28% for prodromal Parkinson's disease, outperforming single brain age gap metrics 5

Clinical Validation

• Brain age indices correlate significantly with traditional neuropsychological screening tools including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ) 1
• Brain-PAD shows significant associations with modifiable risk factors including stroke history, diabetes, smoking, and alcohol intake 3
• The metric is sensitive to cognitive measures, though not all cognitive domains show equal associations 3

Critical Considerations

• Age distribution in training data affects model performance—uniform representation across the lifespan improves generalizability 2
• Healthy control definition is crucial—training on truly healthy populations without neurological or psychiatric conditions ensures the model captures normal aging rather than pathological processes 2
• Scanner variability and acquisition protocols must be standardized or accounted for, as these technical factors can introduce measurement error comparable to or exceeding biological effects 6
• Diurnal fluctuations and hydration status can affect brain volume measurements by up to 0.55-0.72%, which is comparable to yearly atrophy rates in some conditions 6

Common Pitfalls to Avoid

• Avoid applying models trained on narrow age ranges to individuals outside that range, as extrapolation reduces accuracy 2
• Do not ignore confounding factors such as age, sex, brain size, lifestyle factors (physical activity, alcohol, smoking), and systemic conditions (diabetes, hypertension, obesity) that independently affect brain volume 6
• Be cautious with single time-point assessments—longitudinal changes in brain-PAD may be more informative than cross-sectional values for tracking disease progression 6
• Recognize that "normal" brain age does not exclude pathology—some high-functioning individuals with early neurodegeneration may still fall within normal ranges 7