guest ::
| Home > Online First > Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights |
| Home > Online First > Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights |
| Typ | Amount | VAT | Currency | Share | Status | Cost centre |
| Hybrid-OA | 0.00 | 0.00 | EUR | (DEAL) | ZB | |
| Sum | 0.00 | 0.00 | EUR | |||
| Total | 0.00 |
| Journal Article | FZJ-2025-05411 |
; ; ; ;
2025
Elsevier Science
Amsterdam [u.a.]
This record in other databases:
Please use a persistent id in citations: doi:10.1016/j.biopsych.2025.09.003
Abstract: Predictive analytics based on machine learning (ML) and artificial intelligence is a powerful tool enabling precision psychiatry and providing insights into brain-behavior relationships. However, given the mixed results observed in the field so far, making meaningful progress requires careful consideration of several key challenges to ensure the validity of models and findings, including overfitting, confounding biases, site effect harmonization, and interpretability, among others. First, we highlight limitations of cross-validation, a ubiquitous ML strategy used to prevent overfitting and obtain generalization estimates, emphasizing the risk of performance inflation and the need for independent validation. Next, we introduce different types of so-called third variables that can influence the examination of a brain-behavioral relationship of interest in different ways, using causal inference principles. We emphasize the biasing impact of confounding variables on ML models and summarize common mitigation strategies. We then discuss site-specific effects in multisite datasets, reviewing different harmonization strategies to reduce unwanted variability and site-specific noise. Finally, we explore post hoc model interpretation methods to enhance model transparency while cautioning against misinterpretation. By integrating rigorous result validation, confounder control, and interpretability techniques, researchers can ensure that ML models produce more reliable and generalizable findings and avoid spurious associations.KeywordsBrain-behavior associationsConfoundsCross-validationHarmonizationMachine learningModel interpretability
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; Nationallizenz
; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
|
The record appears in these collections: |