Neuroanatomical profile of BMI implicates impulsive delay discounting and general cognitive ability

Obesity, Journal Year: 2023, Volume and Issue: 31(11), P. 2799 - 2808

Published: Oct. 19, 2023

Obesity is a disorder of excessive adiposity, typically assessed via the anthropometric density measure BMI. Numerous studies have implicated BMI with differences in brain structure, but highly inconsistent findings.Machine learning elastic net regression models cross-validation were conducted to characterize neuroanatomical morphometry profile associated 1100 participants (22% > 30, n = 242) from Human Connectome Project Young Adult project.Using five-fold cross-validation, multiregion substantively predicted (R2 10.05%), and this was robust held-out test set 8.87%). In terms specific regions, enriched for nodes default mode, executive control, salience networks. The relationship between itself partially mediated by impulsive delay discounting general cognitive ability.Taken together, these findings reveal machine learning-derived BMI, one that comprises motivational networks suggests functional links obesity are self-regulatory capacity function.

Language: Английский

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Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 30, 2023

Abstract Introduction Statistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings all areas research. Due the large number variables, this is particularly problematic neuroimaging While cross-validation frequently used multivariate machine learning approaches assess model replicability, benefits for mass-univariate brain analysis yet unclear. We investigated impact on size estimation univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. Methods A total n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI gray matter structure tested a standard linear approach. First, traditional non-cross-validated was conducted identify sample (as estimate realistic reference size). The (bootstrapped samples ranging n=25 n=3401) estimates across selected voxels with differing underlying (including lowest Linear effects estimated within training sets then applied unseen test set data, 5-fold cross-validation. Resulting (explained variance) investigated. Results Analysis (n=3401) without yielded mainly negative correlations density maximum R 2 p =.036 (peak voxel cerebellum). Effects exponentially decreasing size, up =.535 largest =.429 smallest effect. When applying cross-validation, did not generalize set. For minimum n=100 required start generalizing variance >0 data), while n=400 needed smaller =.005 generalize. null effect, found even n=3401. Effect obtained approached convergence samples. Discussion Cross-validation useful method counteract overestimation effects. Train converge which likely reflects good models such data n>100 sizes, generalization requires larger (n>400). should be foster accurate improve findings. provide open-source python code purpose ( https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb ).

Language: Английский

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Genetic prediction of the causal relationship between schizophrenia and tumors: a Mendelian randomized study

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Feb. 16, 2024

Patients with schizophrenia are at a higher risk of developing cancer. However, the causal relationship between and different tumor types remains unclear. Using two-sample, two-way Mendelian randomization method, we used publicly available genome-wide association analysis (GWAS) aggregate data to study cancer factors. These tumors included lung adenocarcinoma, squamous cell carcinoma, small-cell cancer, gastric alcohol-related hepatocellular involving lungs, breast, thyroid gland, pancreas, prostate, ovaries cervix, endometrium, colon colorectum, bladder. We inverse variance weighting (IVW) method determine In addition, conducted sensitivity test evaluate effectiveness causality. After adjusting for heterogeneity, evidence was observed (odds ratio [OR]=1.001, 95% confidence interval [CI], 1.000-1.001; P=0.0155). analysis, effect on consistent in both direction degree. no causality or reverse other found. This elucidated genetic predictors thereby providing basis prevention, pathogenesis, treatment patients

Language: Английский

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Understanding the potential mechanisms of disease modifying effects of physical activity and exercise in people with schizophrenia

Schizophrenia Research, Journal Year: 2024, Volume and Issue: 274, P. 381 - 391

Published: Oct. 28, 2024

Language: Английский

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White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals

Molecular Psychiatry, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Language: Английский

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