Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth

Abstract

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (p_corr = .012) and lower functioning on the Children's Global Assessment Scale (p_corr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (p_corr = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Keywords: biomarkers; brain age; connectome; diffusion tensor imaging; machine learning.

FIGURE 1

Participant selection workflow. In brief, of 498 participants with CBCL‐Parent report data available, 263 with a CBCL score <60 were used for modeling with H2O's AutoML function using an 80%/20% train/test split. For statistical analysis, test set participants and participants with CBCL ≥60 with race/ethnicity data available were combined for a total of 249 participants

FIGURE 2

Scatterplot of predicted age versus chronological age. Scatterplot depicting the relationship between predicted and chronological age for the participants in the held‐out test sets for the best age models built using the unharmonized (N = 48), age‐harmonized (N = 48), and age‐outcome‐harmonized (N = 23) brain data

FIGURE 3

Scatterplot of BrainPAD versus outcome measures. Scatterplots depicting the relationship between BrainPAD and (a) CBCL, (b) CGAS, (c) Flanker, and (d) SDQ scores among participants in the evaluation dataset (N = 249). BrainPAD scores are adjusted for age, sex, race, ethnicity, and site (scanner)