Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

Abstract

Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, brain regions change in different ways and at different rates. Unitary brain-age indices used in previous studies represent developmental status averaged across the whole brain and therefore do not capture the divergent developmental trajectories of various brain structures. Importantly, this staggered developmental unfolding, determined by genetics and postnatal experience, is implicated in the progression of psychiatric and neurological disorders. Here we propose an analytic method for computing a multidimensional brain-age index (MBAI) that provides regional age predictions. Using a database of 556 subjects (ages 8-21) that includes psychiatric and neurological patients as well as healthy controls, we conducted robust regression and cluster analyses to identify clusters of imaging features with distinct developmental trajectories. We then built machine-learning models to obtain brain-age predictions from each of the identified clusters to form the MBAI. Our results show that the MBAI provides a flexible analysis of region-specific brain-age changes that are invisible to unidimensional brain-age prediction methods. Importantly, brain ages computed from region-specific feature clusters contain complementary information and demonstrate differential ability to classify disorder groups (e.g., specific phobia, depression, ADHD) from healthy controls. Compared to unidimensional brain-age indices, we show that the MBAI is sensitive to alterations in brain structures and captures distinct regional change patterns which may serve as biomarkers that may contribute to our understanding of healthy and pathological brain development and to the characterization, diagnosis, and, potentially, treatment of various disorders.

Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization

“…As noted in the brain-age prediction literature, brain age is typically underestimated for older subjects and overestimated for younger subjects (Cole et al, 2017;Liang et al, 2019;Smith et al, 2019), and the estimated brain-age gap therefore tends to be negatively correlated with chronological age. Several studies have investigated the possible underlying reasons for this systematic bias and found that it is likely due to regression toward the mean (Liang et al, 2019;Smith et al, 2019;Niu et al, 2020Niu et al, , 2022.…”

Relative Brain Age Is Associated with Socioeconomic Status and Anxiety/Depression Problems in Youth

“…can confound interstudy comparison (Lee et al, 2021). These methodological choices must be considered when comparing results across studies, and some researchers emphasize the importance of multidimensional approaches to capture the potentially divergent developmental trajectories of various brain structures (Niu et al, 2021;Rokicki et al, 2021). Alternatively, brain age prediction algorithms may be tailored to the research question (Keding et al, 2021).…”