Rotation 1: Biological plausible models of visual perceptual learning

BBSRC战略主题：变革性技术这一博士学位的目标是在连接学领域实施标准化建模，目的是开发一种有助于神经精神疾病诊断的人工智能模型。传统的MRI分析涉及到扫描的配准和分割，然而使用的图谱和参考文献来自健康的大脑模型。当扫描被记录在这些图谱上时，某些区域的临床相关差异可以被掩盖。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控制组之间差异的框架(Marquand，2016)。它已经在结构磁共振数据上实施，以研究大脑发育随年龄(伯利恒等人，2022)和不同神经发育条件(Bedford等人，2023)的异质性。我们提出了一个大规模的标准化模型，该模型来源于临床研究可以接触到的数千个个体，克服了这些研究中经常遇到的小样本问题。我们将把这种方法结合到功能磁共振成像(FMRI)领域，以研究连接的异质性与疾病之间的联系。许多研究表明，精神障碍是通过大脑结构和功能的变化而发生的，因此，了解个人连接性的差异可以告知我们潜在的障碍进展。到目前为止，已经使用图论方法研究了连通性，这些方法假设相同的大脑位置在不同的人身上使用，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分析非固定拓扑结构的工具。图形神经网络(GNN)代表了一种为具有图形结构的数据量身定做的几何深度学习形式。它们已经被用于分析捕捉重要特征的静息状态功能磁共振数据(Azevedo等人)。2022年)。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结构进行编码。我们希望在提供诊断信息的个体水平上捕捉大脑连接模式的偏差。这将使我们在理解和诊断神经精神障碍方面更进一步。

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.