AI-driven brain modelling for personalised cognitive enhancement

认知能力下降是痴呆症和轻度认知障碍（MCI）的核心特征，这两种疾病加起来影响了130多万人，造成英国350多亿英镑的损失。有一些认知增强药物获准用于减轻痴呆症和轻度认知障碍患者的认知能力下降。然而，它们只给大约一半的服药者提供了适度的收益，而且我们不知道如何使这些药物更有效。人工智能（AI）神经网络已经显示出对患者进行分层和预测痴呆症等脑部疾病易感性的卓越能力。然而，这些算法的解决方案往往难以解释，并为新的研究和治疗提供信息。为了解决这个问题，我们最近开始将人工智能神经网络映射到生物神经网络上。与标准方法相比，我们的人工智能驱动的大脑建模方法使我们能够将精确的神经元机制映射到健康和疾病的认知过程中。我们的模型预测，特定的神经调节剂使生物神经网络的记忆发展具有更高的抗扰动能力。在这个项目中，我们将使用人工智能驱动的模型来研究神经调节剂功能障碍如何加剧神经元死亡导致的认知功能下降。因此，我们的模型将产生特定的认知训练治疗，是减少痴呆和MCI期间认知能力下降的最佳选择。接下来，我们将模拟常用的认知增强药物来研究为什么目前的药物只能提供适度的改善，以及如何最好地将它们与认知训练结合起来。为了完成这项工作，我们将召集一个由计算、基础和临床神经科学领域的领导者与人工智能专家组成的团队。总体而言，该项目将开发第一个能够促进认知增强研究的人工智能驱动的大脑建模工具。这一系列基于人工智能的研究将导致个性化治疗，从而提高痴呆症和轻度认知障碍患者的生活质量。

Cognitive decline is a core feature of dementia and mild cognitive impairment (MCI) which combined affect over 1.3 million people costing the UK over £35 billion. There are cognitive-enhancing drugs licensed to alleviate cognitive decline in dementia and MCI. However, they provide only a modest gain to around half the people who take them, and we do not understand how to make these drugs more efficient. Artificial intelligence (AI) neural networks have shown remarkable ability to stratify patients and predict susceptibility to brain disorders, such as dementia. However, the solutions of these algorithms are often hard to interpret and inform new research and treatments. To address this, we have recently started mapping AI neural networks onto biological neural networks. In contrast to standard approaches, our AI-driven brain modelling approach enables us to map precise neuronal mechanisms onto cognitive processes in health and disease. Our models predict that specific neuromodulators enable the development of memories in biological neural networks with heightened resilience to perturbations. In this project we will use our AI-driven modelling to study how neuromodulator malfunction exacerbates the decline in cognitive function that results from neuronal death. Consequently, our model will generate specific cognitive training treatments that are optimal for reducing cognitive decline during dementia and MCI. Next, we will simulate commonly used cognitive-enhancing drugs to study why current drugs only offer modest improvements and how to best combine them with cognitive training. To perform this work, we will bring together a team of leaders in computational, fundamental, and clinical neuroscience with AI experts.Overall, this project will develop the first AI-driven brain modelling tool capable of facilitating cognitive enhancement research. This line of AI-informed research will lead to personalised treatments that will improve quality of life in dementia and MCI.