Keywords
machine learning
neuroimaging
behavioral data
age specificity
model interpretability
neuroimaging
behavioral data
age specificity
model interpretability
Abstract
This study gives a comparative analysis on the performance of machine learning models, their interpretability, and age specificity on neuroimaging and behavioral data in a population in Nigeria. Using a quantitative framework, standardized cross-validation protocols were used to benchmark linear (ridge and LASSO) models, nonlinear (support vector regression) models and ensemble (random forest and gradient boosting) models. To assess how development affects predictive performance and how much the feature can be understood, an age stratification was introduced. Findings showed that the ensemble models were always of better predictive quality, especially when it comes to adult cohorts, whereas linear models are the best in terms of feature stability and interpretability. The behavioral data tended to have better predictive performance than did neuroimaging data, with lower noise-to-signal ratios and greater correspondence of features to outcomes. The age factor was also a major moderator with younger cohorts showing lower model generalization and interpretability. It was also found that there was an inverse correlation between predictive efficacy and interpretability, and this was due to the trade-offs of complex model architectures. These results highlight the relevance of context-dependent model appraisal models that combine various performance measures with interpretability and stability measures. The implications of the results to the responsible use of machine learning in neuroscience and behavioral studies in Nigeria are that it can be used in age-dependent applications.
References
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Chen, T. et al. (2021). "Learning to Optimize: A Primer and A Benchmark," J. Mach. Learn. Res., 23, 189. Available at: https://consensus.app/papers/learning-to-optimize-a-primer-and-a-benchmark-chen-chen/6353520647e4508aba86bcae7407ac88/
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Fenske, S. et al. (2025). "Sex differences in brain-behavior relationships in the first 2 years of life.," Cerebral cortex, 35 (6). Available at: https://doi.org/10.1093/cercor/bhaf133.
Geng, S. (2024). "Analysis of the Different Statistical Metrics in Machine Learning," Highlights in Science, Engineering and Technology. Available at: https://doi.org/10.54097/jhq3tv19.
Genon, S., Eickhoff, S. and Kharabian, S. (2022). "Linking interindividual variability in brain structure to behaviour," Nature Reviews Neuroscience, 23, 307-318. Available at: https://doi.org/10.1038/s41583-022-00584-7.
Hicks, S. et al. (2021). "On evaluation metrics for medical applications of artificial intelligence," Scientific Reports, 12. Available at: https://doi.org/10.1101/2021.04.07.21254975.
Kim, S. (2025). "Combined Multiple Machine Learning Performance Measure," IEEE Transactions on Instrumentation and Measurement, 74, 1-6. Available at: https://doi.org/10.1109/tim.2025.3562989.
Liu, Z. (2020). "Latent variable modelling of population neuroimaging and behavioural data," p. Available at: https://consensus.app/papers/latent-variable-modelling-of-population-neuroimaging-and-liu/3de3b0d9ca5250a7bf935d162c2c833a/.
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Marano, G. et al. (2025). "Neuroimaging and Emotional Development in the Pediatric Population: Understanding the Link between the Brain, Emotions, and Behavior," Pediatric Reports, 17, p. Available at: https://doi.org/10.3390/pediatric17030065
Mattson, P. et al. (2020). "MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance," IEEE Micro, 40, 8-16. Available at: https://doi.org/10.1109/mm.2020.2974843.
Michon, K. et al. (2022). "Person-specific and precision neuroimaging: Current methods and future directions," NeuroImage, 263, p. Available at: https://doi.org/10.1016/j.neuroimage.2022.119589.
Murtha, K. et al. (2025). "Comparing Brain-Behavior Relationships across Dimensional, Tail-Sampled, and Propensity-Matched Models," bioRxiv, p. Available at: https://doi.org/10.1101/2025.05.23.655740.
Naser, M. et al. (2025). "A Review of Benchmark and Test Functions for Global Optimization Algorithms and Metaheuristics," Wiley Interdisciplinary Reviews: Computational Statistics, 17, p. Available at: https://doi.org/10.1002/wics.70028.
Omidvarnia, A. et al. (2024). "Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes," Communications Biology, 7. Available at: https://doi.org/10.1038/s42003-024-06438-5.
Ooi, L.Q.R. et al. (2022). "Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI," NeuroImage, 263, p. Available at: https://doi.org/10.1016/j.neuroimage.2022.119636.
Plevris, V. et al. (2022). "Investigation of performance metrics in regression analysis and machine learning-based prediction models," 8th European Congress on Computational Methods in Applied Sciences and Engineering. Available at: https://doi.org/10.23967/eccomas.2022.155.
Rainio, O., Teuho, J. and Klen, R. (2024). "Evaluation metrics and statistical tests for machine learning," Scientific Reports, 14. Available at: https://doi.org/10.1038/s41598-024-56706-x.
Raja, V. et al. (2024). "Machine Learning Revolutionizing Performance Evaluation: Recent Developments and Breakthroughs," 2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), 780-785. Available at: https://doi.org/10.1109/icscss60660.2024.10625103.
Scheinost, D. et al. (2019). "Ten simple rules for predictive modeling of individual differences in neuroimaging," NeuroImage, 193, 35-45. Available at: https://doi.org/10.1016/j.neuroimage.2019.02.057.
Sui, J. et al. (2020). "Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises," Biological Psychiatry, 88, 818-828. Available at: https://doi.org/10.1101/2020.02.22.961136.
Terven, J. et al. (2023). "A comprehensive survey of loss functions and metrics in deep learning," Artificial Intelligence Review, 58, Available at: https://doi.org/10.1007/s10462-025-11198-7.
Thieu, N. (2024). "PerMetrics: A Framework of Performance Metrics for Machine Learning Models," J. Open Source Softw., 9, 6143. Available at: https://doi.org/10.21105/joss.06143.
Zhu, H., Li, T. and Zhao, B. (2022). "Statistical Learning Methods for Neuroimaging Data Analysis with Applications," Annual review of biomedical data science, 6, 73-104. Available at: https://doi.org/10.1146/annurev-biodatasci-020722-100353.