Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

<strong>Abstract</strong> Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks. <strong>Data Description</strong> This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those. Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets. <strong>Paper &amp; Code</strong> The original paper for this article can be accessed here: https://ieeexplore.ieee.org/abstract/document/10196736 To access the codes relevant for this project, please access the project GitHub Repos: https://github.com/AndreiRoibu/AgeMapper If using this work, please cite it based on the above paper, or using the following BibTex: <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <strong>Data Access</strong> The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi <strong>Funding</strong> ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR ([e-mail]), SA ([e-mail]) and AILN ([e-mail]).摘要 大脑衰老是一个高度可变、在空间与时间上均呈异质性的过程，伴随大量结构与功能层面的改变。这会导致个体的实际年龄与通过神经影像数据推断出的大脑表观年龄之间出现差异。机器学习模型，尤其是卷积神经网络（Convolutional Neural Networks, CNNs），已被证实能够有效捕捉大脑衰老诱导变化的相关模式。预测年龄与实际年龄之间的差值被称为大脑年龄差（brain age deltas），其已成为探索促进大脑加速衰老或维持衰老韧性的相关因素（如病理状态或生活方式因素）的有效生物标志物。然而，过往研究仅依靠结构神经影像进行年龄预测，忽略了潜在具有信息价值的功能与微观结构变化。 本研究证实，源自不同磁共振成像（Magnetic Resonance Imaging, MRI）模态的多种对比参数可用于预测大脑年龄，每种参数均编码独特的大脑衰老信息。本研究借助三维卷积神经网络（3D CNNs）与英国生物样本库（UK Biobank）数据，发现源自结构磁共振成像、磁敏感加权成像、弥散磁共振成像与功能磁共振成像的57种对比参数均可有效预测大脑年龄。针对每种对比参数，本研究均发现其与非影像表型存在不同的关联模式，最终共得到191个独特且具有统计学显著性的关联结果。此外，本研究发现整合多种对比参数的数据，不仅可提升预测准确率，还能增强与非影像测量指标的相关性。 本研究结果表明，此前未被用于大脑年龄预测任务的其他三维对比参数与成像模态，同样编码了关于衰老大脑的独特信息。本研究可为未来探索大脑年龄差与非影像测量指标关联背后的因果机制提供研究起点。例如，鉴于部分药物与大脑加速衰老存在关联，本研究可用于监测药物的相关影响。此外，大脑年龄模型的持续优化，可推动其在临床试验的受试者招募与监测环节，以及医院的诊断与筛查任务中落地应用。 数据集说明 本数据集包含英国生物样本库（UK Biobank）中所有nIDPs的完整关联结果，并按受试者性别分为女性数据集与男性数据集。为便于数据处理，本研究还提供了两个精简数据集，仅包含通过错误发现率（False Discovery Rate, FDR）阈值的关联结果。鉴于本研究还开展了多对比参数整合的相关实验，因此也同步提供了对应的类似数据集。此外，本研究还提供了全局数据集，即男性与女性数据集关联结果的合并集合。 论文与代码 本文的原始论文可通过以下链接获取：https://ieeexplore.ieee.org/abstract/document/10196736。本项目的相关代码可访问项目GitHub仓库：https://github.com/AndreiRoibu/AgeMapper。若使用本研究成果，请依据上述论文进行引用，或采用以下BibTex格式： <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> 数据获取 本项目的数据可通过向英国生物样本库（UK Biobank）申请免费获取。若需了解单个nIDPs的详细信息，请访问英国生物样本库展示网站：https://biobank.ctsu.ox.ac.uk/showcase/search.cgi 资助信息 ACR受工程与物理科学研究委员会（EPSRC）项目EP/S024093/1、罗氏集团（F. Hoffmann-La Roche AG）以及1851年世博会皇家委员会颁发的2021年工业奖学金资助。SMS受惠康信托基金协作奖215573/Z/19/Z资助。AILN感谢医学科学院跳板奖计划（SBF005/1136）以及比尔及梅琳达·盖茨基金会的支持。FJL受惠康信托基金协作奖（215573/Z/19/Z）资助。WIN受惠康信托基金核心资助（203139/Z/16/Z）支持。本研究的计算工作受惠康信托基金（203141/Z/16/Z）与牛津大学国民保健服务国立健康研究院生物医学研究中心（NIHR Oxford BRC）资助。通讯作者：ACR（[e-mail]）、SA（[e-mail]）与AILN（[e-mail]）。