BrainAGE, brain health, and mental disorders: A systematic review

Multimodal brain age indicators of internalising problems in early adolescence: A longitudinal investigation

BACKGROUND

Adolescence is a time of increased risk for the onset of internalising problems, particularly in females. However, how individual differences in brain maturation relate to the increased vulnerability for internalising problems in adolescence remains poorly understood due to a scarcity of longitudinal studies.

METHODS

Using Adolescent Brain Cognitive Development (ABCD) Study data, we examined longitudinal associations between multimodal brain age and youth internalising problems. Brain age models were trained, validated, and tested independently on T1-weighted (T1; N=9523), diffusion tensor (DTI; N=8834), and resting-state functional (rs-fMRI; N=8233) MRI data at baseline (Mage= 9.9 years) and 2-year follow-up (Mage= 11.9 years). Self-reported internalising problems were measured at 3-year follow-up (Mage= 12.9 years) using the Brief Problem Monitor.

RESULTS

Latent change score models demonstrated that although brain age gap (BAG) at baseline was not related to later internalising problems, an increase in BAG between timepoints was positively associated with internalising problems at 3-year follow-up in females but not males. This association between an increasing BAG and higher internalising problems was observed in the T1 (β = 0.067, SE = 0.050, pFDR = 0.020) and rs-fMRI β = 0.090, SE = 0.025, pFDR = 0.007) models but not DTI (β=-0.002, SE=0.053, pFDR = 0.932), and remained significant when accounting for earlier internalising problems.

CONCLUSIONS

A greater increase in BAG in early adolescence may reflect the heightened vulnerability shown by female youth to internalising problems. Longitudinal research is necessary to understand if this increasing BAG signifies accelerated brain development and its relationship to the trajectory of internalising problems throughout adolescence.

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.