Zhao Qingyu, Nooner Kate B, Tapert Susan F, Adeli Ehsan, Pohl Kilian M, Kuceyeski Amy, Sabuncu Mert R
Department of Radiology, Weill Cornell Medicine, New York, New York.
Department of Psychology, University of North Carolina Wilmington, Wilmington, North Carolina.
Biol Psychiatry Glob Open Sci. 2024 Sep 26;5(1):100397. doi: 10.1016/j.bpsgos.2024.100397. eCollection 2025 Jan.
Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology. We review the current landscape of computational research targeting population heterogeneity and argue that there is a need to expand from brain subtyping and behavioral phenotyping to analyses that focus on heterogeneity at the relational level. To this end, we review and suggest several existing ML models with the capacity to discern how external environmental and sociodemographic factors moderate the brain-behavior mapping function in a data-driven fashion. These heterogeneous ML models hold promise for enhancing the discovery of individualized brain-behavior associations and advancing precision psychiatry.
尽管基于神经影像学的机器学习（ML）模型作为神经精神研究中探究脑-行为关系的关键工具具有优势，但这些数据驱动的预测方法尚未为精神卫生保健带来实质性的、临床上可操作的见解。一个显著的障碍在于大多数ML研究对大样本中自然存在的异质性考虑不足。尽管许多ML算法通常被认为是个体水平分析，但它们是单峰且同质的，因此无法捕捉生物学与精神病理学之间潜在的异质关系。我们回顾了针对人群异质性的计算研究现状，并认为有必要从脑分型和行为表型分析扩展到关注关系层面异质性的分析。为此，我们回顾并推荐了几种现有的ML模型，这些模型有能力以数据驱动的方式辨别外部环境和社会人口统计学因素如何调节脑-行为映射功能。这些异质性ML模型有望促进个性化脑-行为关联的发现，并推动精准精神病学的发展。
