Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants

Abstract

Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the 'data-driven' brain age using functional MRI (fMRI) and diffusion MRI (dMRI). Particularly, predicting the 'brain age' from connectomic data might help identify relevant connectional biomarkers of neurological disorders that emerge early or late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional connectomic data, here we unprecedentedly propose to predict the morphological age of the brain by solely using morphological brain networks (derived from T1-weighted images) in both healthy and disordered populations. Besides, although T1-weighted MRI was widely used for brain age prediction, it was leveraged from an image-based analysis perspective not from a connectomic perspective. Our method includes the following steps: (i) building multi-view morphological brain networks (M-MBN), (ii) feature extraction and selection, (iii) training a machine-learning regression model to predict age from M-MBN data, and (iv) utilizing our model to identify connectional brain features related to age in both autistic and healthy populations. We demonstrate that our method significantly outperforms existing approaches and discovered brain connectional morphological features that fingerprint the age of brain cortical morphology in both autistic and healthy individuals. In particular, we discovered that the connectional cortical thickness best predicts the morphological age of the autistic brain.

Figure 1

Age prediction accuracy for each of the 4 datasets (ASD Left Hemisphere, ASD Right Hemisphere, NC Left Hemisphere, and NC Right Hemisphere) using 4 benchmark methods against our proposed method (far right). Each method was evaluated on both averaged views and concatenated views.

Figure 2

Identification of morphological connectional features fingerprinting brain age. Circular graphs showing the top ranked 5 (A,B), 10 (C,D), and 15 (E,F) morphological connectional features that correlate most with age when using concatenation to combine the morphological views. Thicker edges indicate higher correlation with brain age.

Figure 3

Cortical brain regions of interest used for morphological brain network reconstruction. The numbers with corresponding names can be linked to the circular graphs in Fig. 2.

Figure 4

Proposed framework for predicting morphological brain age in healthy and disordered brains. (A) Construction of the multi-view brain networks from cortical morphology for each subject and the construction of the initial feature vector. For each subject $k\in \{1,\dots ,N\}$, we derive a morphological network ${ℳ}_k^m$ from the cortical surface $S_k^m$ mapped using a specific morphological attribute $m\in \{1,\dots ,M\}$. (B) Next, we extract the lower triangular part of the matrix as a morphological connectional feature vector. (C) Reduce the dimensionality of the data and retain only the most relevant features using a feature selection method. Next, we train a Random Forest model and utilize it to predict the morphological age of a testing brain. (D) Connectional morphological features encoding chronological brain age.