Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features
Kamarajan, C.; Ardekani, B. A.; Pandey, A. K.; Meyers, J. L.; Chorlian, D. B.; Kinreich, S.; Pandey, G.; Richard, C.; de Viteri, S. S.; Kuang, W.; Porjesz, B.
Show abstract
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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach 94%
- A correlational study between microstructural, macrostructural and functional age-related changes in the human visual cortex 94%
- Behavioural cue reactivity to alcohol and non-alcohol-related stimuli among individuals with severe alcohol use disorder: an fMRI study with a visual task 93%
Similar papers in this journal
- Reliability and Validity of Cognitive Workload in Older Adults 93%
- Abnormal Functional Network Topology and its Dynamics during Sustained Attention Processing Significantly Implicate Post-TBI Attention Deficits in Children 93%
- Lower nerve growth factor levels in major depression and suicidal behaviors: effects of adverse childhood experiences and recurrence of illness 91%
Similar papers in this journal
- Accurate sex prediction of cisgender and transgender individuals without brain size bias 93%
- Investigation of Genetic Variants and Causal Biomarkers Associated with Brain Aging 92%
- Effect of MIND Diet Intervention on Cognitive Performance and Brain Structure in Healthy Obese Women: A Randomized Controlled Trial 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.