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倡导规范性建模的证据。

Evidence for embracing normative modeling.

机构信息

Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands.

Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands.

出版信息

Elife. 2023 Mar 13;12:e85082. doi: 10.7554/eLife.85082.

Abstract

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.

摘要

在这项工作中,我们扩展了 Rutherford 等人在 2022a 年引入的规范模型库,以包括规范模型,这些模型描绘了使用两个独特的静息态网络图谱(Yeo-17 和 Smith-10)测量的结构表面积和大脑功能连接的寿命轨迹,以及一个更新的在线平台,用于将这些模型转移到新的数据源。我们通过在几个基准测试任务中对规范建模和原始数据特征输出的特征进行直接比较,展示了这些模型的价值:大规模单变量组差异测试(精神分裂症与对照组)、分类(精神分裂症与对照组)和回归(预测一般认知能力)。在所有基准测试中,我们都展示了使用规范建模特征的优势,在组差异测试和分类任务中表现出最强的统计学显著结果。我们希望这些易于访问的资源能够促进规范建模在神经影像学社区中的更广泛采用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f093/10036120/eef56cb4a77e/elife-85082-fig1.jpg

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