Brain aging patterns among nine neurological disorders: A case-control study
by Chuang Liang, Godfrey Pearlson, Juan Bustillo, Peter Kochunov, Jiayu Chen, Xiangrong Zhang, Rongtao Jiang, Kent E. Hutchison, Jing Sui, Zening Fu, Xiao Yang, Yuhui Du, Daoqiang Zhang, Shile Qi, Vince D. Calhoun Background The difference between neuroimaging-predicted brain age and chronological age, the predicted age difference (PAD), has been studied as a potential biomarker reflecting…
Title: Brain Aging Patterns in Various Neurological Disorders
Background:
The study examined brain aging patterns across nine neurological disorders, comparing neuroimaging-predicted brain age to chronological age. Researchers aimed to identify neuroimaging features associated with brain age deviations and explore related gene expression profiles.
Methods and Findings:
The study analyzed structural MRI data from 45,900 healthy controls and 2,698 patients with developmental disorders, addiction, dementia, or psychiatric disorders. Cohen's d effect sizes were calculated to determine brain age deviations, accounting for age, age squared, sex, and site. Distinct brain patterns were identified for each diagnostic group, with higher PAD values linked to specific spatial brain patterns.
Prefrontal cortex involvement was common across all disorders, with disorder-specific brain patterns associated genes enriched in various biological processes.
Results:
Dementia showed the highest brain age deviation effects, with Alzheimer's disease (d = 0.97) and mild cognitive impairment (d = 0.84) demonstrating significant differences. Psychiatric disorders exhibited moderate brain age deviations, with schizophrenia (d = 0.53) and bipolar disorder (d = 0.53) showing notable differences. Addiction showed the lowest brain age deviations among the diagnostic groups (AD: d = 0.06; ASD: d = 0.06; ADHD: d = 0.01).
The study identified specific brain patterns associated with each disorder, including the frontotemporal network in psychiatric disorders and the fronto-occipital network in dementia.
Limitations:
The study acknowledged that psychiatric disorders and addiction have high comorbidity, which may have introduced confounding factors.
Conclusion:
The research reveals distinctive brain aging patterns for various neurological disorders, potentially serving as neuroimaging biomarkers for understanding neural aging mechanisms. Future studies should investigate the utility of these disorder-specific brain aging patterns as biomarkers for clinical decision-making.
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