Rotation 1: Biological plausible models of visual perceptual learning

该博士学位的目标是在连接组学领域实施规范建模，目的是开发一种可以帮助诊断神经精神疾病的人工智能模型。传统的MRI分析涉及扫描的配准和分割，然而使用的图谱和参考来自健康的大脑模型。当扫描记录到这些地图集时，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型来克服这个问题。规范建模是一个框架，用于研究个体和对照组之间的差异（Marquand, 2016）。它已经在结构MRI数据上实现，以研究大脑发育随年龄的异质性（Bethlehem et al ., 2022）和不同的神经发育条件（Bedford et al ., 2023）。我们提出了一个大规模的规范模型，该模型来源于临床研究可以访问的数千个人，克服了这些研究中经常遇到的小样本量问题。我们将把这种方法纳入功能性磁共振成像（fMRI）领域，以研究连通性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，因此了解个体连通性的差异可以告诉我们潜在的疾病进展。到目前为止，使用图论方法来研究连通性，该方法假设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分析非固定拓扑结构的新工具。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制的几何深度学习形式。它们已经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。我们希望在个体层面上捕捉到大脑连接模式的偏差，从而提供诊断信息。这将使我们在理解和诊断神经精神疾病方面更进一步。

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.