{"title":"The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research","authors":"","doi":"10.1016/j.bpsgos.2024.100397","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":72373,"journal":{"name":"Biological psychiatry global open science","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biological psychiatry global open science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667174324001101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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.
尽管基于神经成像的机器学习(ML)模型作为神经精神研究中调查大脑与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健康护理带来实质性的、临床上可操作的见解。一个显著的障碍在于,大多数 ML 研究无法充分适应大型样本中的自然异质性。尽管通常被认为是个体层面的分析,但许多 ML 算法都是单模态和同质的,因此无法捕捉生物学与精神病理学之间潜在的异质性关系。我们回顾了当前针对群体异质性的计算研究,认为有必要从大脑亚型和行为表型扩展到关注关系层面异质性的分析。为此,我们回顾并提出了几种现有的 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因素是如何调节大脑-行为映射功能的。这些异质性 ML 模型有望促进个性化大脑行为关联的发现,并推动精准精神病学的发展。