Rotation 1: Biological plausible models of visual perceptual learning

旋转 1：视觉感知学习的生物学合理模型

• 批准号: 2887737
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887737
• 关键词: Rotation; Biological; plausible; models; visual

BBSRC strategic theme: Transformative technologiesThe objective of this PhD is to implement normative modelling in the field of connectomics with the aim of developing an AI model that can help in the diagnosis of neuropsychiatric disorders. Traditional MRI analysis involves registration and parcellation of scans, however the atlases and references used come from healthy brain models. When the scans are registered to these atlases, clinically relevant differences in certain regions can be masked. We will overcome this by implementing normative models. Normative modelling is a framework used to study differences between an individual and a control group (Marquand, 2016). It has already been implemented on structural MRI data to study heterogeneity in brain development through age (Bethlehem et al, 2022) and different neurodevelopmental conditions (Bedford et al, 2023). We propose a large-scale normative model derived from thousands of individuals that clinical studies can have access to, overcoming the issue with small sample sizes often encountered in these studies. We will incorporate this method to the field of functional MRI (fMRI) to study the link between heterogeneity in connectivity and disease. Many studies have shown that mental disorders occur via changes in brain structure and function, therefore understanding differences in an individual's connectivity can inform us of potential disorder progressions. So far, connectivity has been studied using graph theory approaches that assume the same brain locations are used across individuals, posing a harsh constraint on any analysis. Geometric deep learning is a new tool that can be used to analyse non fixed topological structures. Graphical neural networks (GNNs) represent a form of geometric deep learning tailored for data with a graph-like structure. They have already been used to analyse resting state fMRI data capturing significant features (Azevedo et al. 2022). We will use similar geometric deep learning approaches like graph-based networks and point cloud models to construct our model with the aim of encoding the brains architecture without the need of registration or parcellation. We hope to capture deviations in patterns of brain connectivity at the individual level that provide diagnostic information. This would take us one step further in understanding and diagnosing neuropsychiatric disorders.

BBSRC战略主题：该博士学位的目标是在连接组学领域实施规范建模，旨在开发一种可以帮助诊断神经精神疾病的AI模型。传统的MRI分析涉及扫描的配准和分割，然而所使用的图谱和参考来自健康的大脑模型。当扫描与这些图谱配准时，某些区域中的临床相关差异可能会被掩盖。我们将通过实施规范模式来克服这一问题。规范建模是一种用于研究个体和对照组之间差异的框架（马尔坎，2016）。它已经在结构MRI数据上实施，以研究不同年龄（Bethlehem et al，2022）和不同神经发育条件（贝德福德et al，2023）下大脑发育的异质性。我们提出了一个大规模的规范性模型，该模型来自临床研究可以访问的数千个个体，克服了这些研究中经常遇到的小样本量问题。我们将把这种方法结合到功能磁共振成像（fMRI）领域，研究连接的异质性和疾病之间的联系。许多研究表明，精神障碍是通过大脑结构和功能的变化发生的，因此了解个体连接的差异可以告知我们潜在的疾病进展。到目前为止，连通性是使用图论方法研究的，该方法假设不同个体使用相同的大脑位置，这对任何分析都构成了严格的限制。几何深度学习是一种新的工具，可用于分析非固定拓扑结构。图形神经网络（GNN）代表了一种几何深度学习的形式，适用于具有类似图形结构的数据。它们已被用于分析捕获重要特征的静息状态fMRI数据（Azevedo et al. 2022）。我们将使用类似的几何深度学习方法，如基于图的网络和点云模型来构建我们的模型，目的是在不需要注册或分割的情况下对大脑架构进行编码。我们希望在个体水平上捕捉大脑连接模式的偏差，以提供诊断信息。这将使我们在理解和诊断神经精神疾病方面更进一步。