Making the Invisible Visible: a Multi-Scale Imaging Approach to Detect and Characterise Cortical Pathology

大脑的许多疾病，包括癫痫、痴呆、多发性硬化症和精神健康障碍，都涉及大脑的最外层，即皮层。使用常规MRI作为其诊断或研究其病理生理学的一部分的关键挑战是灵敏度，即，皮质异常在其形态上可能很小和/或细微，因此被忽略。即使检测到异常信号，也不可能说出是什么驱动了这种信号变化，例如，细胞大小/形状/密度的差异。目前，这些信息只能通过切割组织并在显微镜下检查来获得。然而，MRI物理学的最新进展有望直接检测和表征大脑皮层中迄今为止不可见的组织异常。超强磁场提供更高分辨率的图像，而超强“梯度”提供对组织“微观结构”特性的敏感性，例如常规MRI无法看到的细胞密度，大小和形状。这些技术已经被应用于白色物质，但它们在大脑皮层的应用仍在很大程度上未被探索。在这里，我们将提供一个原理证明，通过结合最新的MRI硬件，物理学，显微镜，数学建模和人工智能（AI），我们不仅能够看到比以往更多患者的皮质异常，而且还可以获得有关细胞组成的相同信息，否则需要侵入性活检。为此，从现有的显微镜数据集开始，我们将建立皮质组织的超现实3D计算模型，并改变它们的属性以模仿我们在疾病中看到的东西，并了解这将如何改变不同设置下MRI扫描仪的信号。这将使我们能够选择扫描仪设置，最大限度地提高对疾病的敏感性，并了解我们的数学模型的哪些部分对病理学信息最丰富，例如，利用人工智能，我们将联合收割机结合超强磁体的空间分辨率和超强梯度的微观结构灵敏度，以创建具有前所未有的细节的新“混合”MRI图像。通过完全优化的MRI协议和模型，捕获在传统MRI上“不可见”的关键皮质特征，我们将扫描健康个体，以了解每个特征中有多少典型变化。我们假设皮质病理会导致一些模型参数超出这个标准范围，使我们能够自动检测到它们。为了验证我们的假设和验证我们的方法，我们将在手术切除致痫组织之前，在患有与皮质结构高度局部化异常相关的癫痫患者中试验我们的技术，称为“局灶性皮质发育不良”（FCD）。该组织将在实验扫描仪中经历长时间的成像，该实验扫描仪对组织微观结构的差异具有比人类MRI扫描仪更高的灵敏度。使用AI，我们将使用这些更详细的图像来增强在活体人脑中收集的图像的细节。使用样品的显微镜，我们将产生组织学的“地面真相”，并再次使用AI，更新我们的模型和采集协议，以最大限度地提高我们的管道的灵敏度和准确性。最后，我们将在常规MRI上没有可见疾病的患者（但症状与皮质异常一致）中测试我们的方法。如果预测到异常组织，我们将尝试使用任何切除组织的电子记录和显微镜进行验证。最终，对标准临床MRI不可见的病理学的检测可以指导更准确的网络询问，从而改善手术结果，扩大适合手术的患者人群，并深入了解患有影响皮质的疾病的人的相关认知和行为共病。

Many diseases of the brain, including epilepsy, dementia, multiple sclerosis & mental health disorders, involve its outermost layer, the cortex. A key challenge in using conventional MRI as part of their diagnosis, or to study their pathophysiology, is sensitivity, i.e., cortical abnormalities may be small and/or subtle in their morphology & therefore missed. Even if abnormal signal is detected, it is impossible to say what drives such signal changes, e.g., differences in cell size/shape/density. Currently such information can only be obtained by cutting out the tissue & examining it under a microscope. Recent advances in MRI physics, however, hold the promise of detecting & characterising heretofore invisible tissue abnormalities directly in the cortex. Ultra-strong magnetic fields give much higher resolution images, while ultra-strong 'gradients' provide sensitivity to tissue 'microstructure' properties such as cell density, size & shape that cannot be seen on conventional MRI. Such technologies have been applied to white matter, but their use in cortex remains largely unexplored. Here, we will provide a proof-of-principle that, by combining the latest in MRI hardware, physics, microscopy, mathematical modelling & artificial intelligence (AI), we will not only be able to see cortical abnormalities in more patients than ever before, but also obtain the same kind of information about cellular make-up that would otherwise require invasive biopsy. To this end, beginning with existing microscopy datasets, we will build ultra-realistic 3D computational models of cortical tissue & change their properties to mimic what we see in disease, & learn how this would change the signals from the MRI scanners under different settings. This will allow us to select the scanner settings that maximise sensitivity to disease & to learn which parts of our mathematical models are most informative about the pathology, e.g., accounting for cell size/shape/density.Using AI, we will combine the spatial resolution from ultra-strong magnets & the microstructural sensitivity from ultra-strong gradients, to create new 'hybrid' MRI images with unprecedented detail. With a fully optimised MRI protocol & models that capture the key cortical features that are otherwise 'invisible' on conventional MRI, we will scan healthy individuals to learn how much typical variation there is in each feature. We hypothesise that cortical pathology will lead to some model parameters falling outside of this normative range, allowing us to detect them automatically.To test our hypothesis & validate our approach, we will trial our technique in patients with a form of epilepsy that is associated with highly localised abnormalities in the structure of the cortex, called 'focal cortical dysplasia' (FCD), prior to surgery to remove epileptogenic tissue. This tissue will undergo prolonged imaging in an experimental scanner with even greater sensitivity to differences in tissue microstructure than human MRI scanners. Using AI, we will use these more detailed images to enhance the detail of the images collected in the living human brain. Using microscopy of the sample, we will then produce a histological 'ground truth' and, again using AI, update our models & acquisition protocol to maximise sensitivity & accuracy of our pipeline. Finally, we will test our approach on patients with no visible disease on conventional MRI (but where symptoms are consistent with cortical abnormality). Where abnormal tissue is predicted, we will attempt validation with electrical recordings & microscopy of any resected tissue. Ultimately, the detection of pathology invisible to standard clinical MRI may direct more accurate network interrogation thereby improving surgical outcomes, expand the population of patients suitable for surgery, & yield insight into associated cognitive and behavioural co-morbidities in people with diseases affecting the cortex.