Machine-learning on brain connectomics: Individual prediction of cognitive functioning in health and cerebral small vessel disease

脑连接组学的机器学习：健康和脑小血管疾病中认知功能的个体预测

• 批准号: 454012190
• 负责人: Professor Dr. Simon Eickhoff
• 金额: --
• 依托单位: Klinik und Poliklinik für Neurologie
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/454012190?language=en
• 关键词: Machine; learning; brain; connectomics; Individual

The overarching aim of this project is to develop, implement, and evaluate computational models for the prediction of individual cognitive performance from brain connectomics in healthy subjects and patients with cerebral small vessel disease. Hereby we address a substantial gap in research, connectome-based predictive modeling have to date focused either on young healthy adults, or on classification of dementia, mainly Alzheimer’s disease, while only little work has been done to study brain-behavior association in elderly patients at risk or with pre-symptomatic vascular brain changes. Focusing on this demographic group, we will establish an improved machine-learning framework for out-of-sample prediction of cognitive phenotypes from neuroimaging data. This work will be based on three large, population-based cohorts (Hamburg City Heath Study: N=3000, 1000Brains: N=1200 and UKbiobank: N=48000), which provide comprehensive assessments of cognitive function. Importantly, we will consider different imaging derived measures (grey matter volume, structural and functional connectivity, white-matter hyperintensities) alone and in combination to arrive at optimal models for each sample and behavioral phenotype. These will be benchmarked against models using only socio-demographic information and other background data to elucidate the added value of imaging for individual prediction. One of the key challenges for employing machine-learning approaches in clinical settings, i.e., translational application, is the fact that clinical samples are usually too small for model training and validation. Here we propose a novel strategy to address this problems, namely meta-learning. The key idea is to exploit the fact that brain-behavior associations live on a confined manifold, i.e., most behaviors are correlated and relate to a limited set of neurobiological modes of variation. A machine-learning model trained on a large cohort to predict a particular behavior from neuroimaging data should therefore also capture information about other, related behaviors in a smaller, clinically relevant sample. In the second part of this project, we will thus develop, apply and evaluate a novel strategy of meta-learning to transfer models trained in large population samples to new datasets that reflect critical outcome measures for clinical applications but are too small for robust training of predictive algorithms on these. This strategy will be tested in a clinical use-case involving three different samples of patients with cerebral small vessel disease and stroke, respectively. By thism the current proposal brings together a “big-data” strategy for connectcome-based prediction of individual traits with a focus on enabling a transfer to clinical use-cases with limited sample sizes. Taken together our goal is to foster the computational exploitation of brain connectomics for risk assessment, phenotype prediction and potential clinical diagnostics in individual patients.

该项目的总体目标是开发，实施和评估计算模型，用于预测健康受试者和脑小血管疾病患者的脑连接组学的个体认知表现。在此，我们解决了研究中的一个重大空白，基于连接体的预测建模迄今为止一直专注于年轻健康的成年人，或对痴呆症的分类，主要是阿尔茨海默病，而只有很少的工作已经做了研究，在老年患者的大脑行为关联风险或症状前血管脑变化。针对这一人口统计学群体，我们将建立一个改进的机器学习框架，用于从神经成像数据中预测认知表型的样本外预测。这项工作将基于三个大型的、以人群为基础的队列（汉堡市健康研究：N=3000，1000脑：N=1200和英国生物银行：N=48000），这些队列提供了对认知功能的全面评估。重要的是，我们将考虑不同的成像衍生措施（灰质体积，结构和功能连接，白质高信号）单独和组合，以达到每个样本和行为表型的最佳模型。这些将与仅使用社会人口统计信息和其他背景数据的模型进行基准测试，以阐明成像对个体预测的附加价值。在临床环境中采用机器学习方法的关键挑战之一，即，翻译应用中，临床样本通常太小，无法进行模型训练和验证。在这里，我们提出了一种新的策略来解决这个问题，即元学习。关键的想法是利用大脑行为关联存在于有限流形上的事实，即，大多数行为是相互关联的，并且与有限的一组神经生物学变化模式有关。因此，在大型队列中训练的机器学习模型，从神经成像数据中预测特定行为，也应该在较小的临床相关样本中捕获有关其他相关行为的信息。因此，在本项目的第二部分，我们将开发、应用和评估一种新的元学习策略，将在大样本人群中训练的模型转移到新的数据集，这些数据集反映了临床应用的关键结果指标，但对于预测算法的稳健训练来说太小了。该策略将在临床用例中进行测试，该用例分别涉及三种不同的脑小血管疾病和中风患者样本。通过这一点，目前的提案汇集了一个“大数据”策略，用于基于连接的个体特征预测，重点是能够转移到样本量有限的临床用例。总之，我们的目标是促进脑连接组学的计算开发，用于个体患者的风险评估，表型预测和潜在的临床诊断。