TABLE 2.

Brain age prediction accuracy across all models

Model	MAE (years)	MRD	Optimal algorithm
Morphometry + WM Connectomes	1.1801	1.962	SEML—Family
WM alone	1.3494	2.634	SEML—Family
Morphometry alone	1.578	4.301	Deep learning (MLP)
Age‐harmonized—Morphometry + WM	0.261	0.128	SEML—Family
Age‐harmonized—WM alone	0.332	0.185	SEML—Family
Age‐harmonized—Morphometry alone	1.438	3.964	SEML—Family
Outcome‐harmonized—Morphometry + WM	0.880	1.270	SEML—Family
Outcome‐harmonized—WM alone	0.776	1.246	SEML—Family
Outcome‐harmonized—Morphometry alone	1.801	4.152	XGBoost
Global FA + eTIV	2.582	—	—
Global FA alone	2.858	—	—
eTIV alone	2.591	—	—

Note: SEML Models using both morphometry and white matter connectomes performed best when using the unharmonized brain data (top) and age‐harmonized brain data (middle). A SEML model using only the white matter connectomes performed best when using the outcome‐harmonized brain data (middle). All ML models outperformed linear models using FA and/or eTIV (bottom).

Abbreviations: FA, fractional anisotropy; MAE, mean absolute error; MLP, multi‐layer perceptron; MRD, mean residual deviance; SEML, stacked ensemble machine learning, WM, white matter.