A generative approach to human brain mapping

人脑绘图的生成方法

• 批准号: RGPIN-2022-04692
• 负责人: Diedrichsen, Jörn
• 金额: $ 4.01万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749803
• 关键词: generative; approach; human; brain; mapping

Functional magnetic resonance imaging (fMRI) provides an unparalleled opportunity to observe the activity in the human brain during any mental activity. Scientists are working on to use this technique to understand the neuronal processes that give rise to intelligent behavior, as well as understanding and predicting neuro-psychiatric diseases such as Schizophrenia or Alzheimer's. Despite its promise as diagnostic tool, however, fMRI is currently not broadly used in clinical practice. One important road block is that even normal, healthy individual brains have quite different spatial layouts and that we need a lot of data to reliably characterize it. Just like trees in a forest show a wide array of spatial arrangements of branches and leaves, human brains show a wide variety of spatial arrangement of different functional regions. This variability makes it very difficult to draw conclusions about the brains of specific groups of subjects, as we lose a lot of information when averaging data across brains. The large variability also makes it difficult to detect unusual brain organization. The long-term goal of my research program is to build a model of human brain organisation that captures the variability observed in brain maps measured with fMRI. The model has two parts. The spatial arrangement model captures the average layout of different brain regions in the human brain, as well as the variability. The second part models how this arrangement influences the observed data, be it from resting-state or task-based paradigms. This has the big advantage that the model can integrate information over many openly available functional imaging studies, and thus can be trained on data from many thousands of participants with different genders, ages, and ethnicities. The first part of the proposal will address the algorithmic and computational challenges of building such a model. In the second part, we will validate the model by testing its ability to predict the location of functional regions in individuals. Once completed, the model allows us to obtain a high-quality map of a brain from an individual, even though we may have 10-30min of data. Currently, longer scan times are necessary to obtain a reliable functional characterization of an individual brain. This model will be useful for 3 important applications: First, it will allow us to draw better conclusions from group studies, as we will be able to account for the individual variability within those groups. Second, we will be able to obtain a highly detailed map of individual brains even if we have only restricted data available. This makes individual functional mapping feasible, which then can be used in pre-surgical planning for brain tissue removal or implantation of brain stimulators. Finally, the model will help us to spot abnormal brain organisation more easily, to become better at predicting neuropsychiatric diseases, and to understand the underlying fundamental brain processes.

功能性磁共振成像（fMRI）提供了一个无与伦比的机会来观察人类大脑在任何精神活动中的活动。科学家们正致力于使用这种技术来了解引起智能行为的神经元过程，以及了解和预测神经精神疾病，如精神分裂症或阿尔茨海默氏症。尽管它的承诺作为诊断工具，然而，功能磁共振成像目前并没有广泛应用于临床实践。 一个重要的障碍是，即使是正常、健康的个体大脑也有相当不同的空间布局，我们需要大量的数据来可靠地描述它，就像森林中的树木显示出各种各样的树枝和树叶的空间布局一样，人类大脑显示出各种各样的不同功能区域的空间布局。这种可变性使得很难对特定受试者群体的大脑得出结论，因为当我们对大脑中的数据进行平均时，我们会丢失很多信息。这种巨大的变异性也使得人们很难发现不寻常的大脑组织。 我的研究计划的长期目标是建立一个人类大脑组织的模型，该模型可以捕捉到用功能磁共振成像测量的大脑地图中观察到的变化。该模型有两个部分。空间排列模型捕捉了人脑中不同大脑区域的平均布局，以及变异性。第二部分模拟这种安排如何影响观察到的数据，无论是从休息状态或基于任务的范式。这有一个很大的优势，即该模型可以整合许多公开可用的功能成像研究的信息，因此可以根据数千名不同性别、年龄和种族的参与者的数据进行训练。该提案的第一部分将解决建立这样一个模型的算法和计算挑战。在第二部分中，我们将通过测试模型预测个体功能区域位置的能力来验证模型。一旦完成，该模型允许我们从个人获得高质量的大脑地图，即使我们可能有10- 30分钟的数据。目前，需要更长的扫描时间来获得个体大脑的可靠功能表征。 这个模型将有三个重要的应用：首先，它将使我们能够从群体研究中得出更好的结论，因为我们将能够解释这些群体中的个体差异。其次，即使我们只有有限的数据，我们也能够获得一个非常详细的个体大脑地图。这使得个体功能映射变得可行，然后可以用于脑组织切除或脑刺激器植入的手术前计划。最后，该模型将帮助我们更容易地发现异常的大脑组织，更好地预测神经精神疾病，并了解潜在的基本大脑过程。