AI-driven brain modelling for personalised cognitive enhancement

人工智能驱动的大脑建模，用于个性化认知增强

• 批准号: MR/X006107/1
• 负责人: Rui Ponte Costa
• 金额: $ 11.24万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX006107%2F1
• 关键词: AI; driven; brain; modelling; personalised

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.

认知衰退是痴呆症和轻度认知障碍(MCI)的核心特征，这两种疾病加在一起影响了超过130万人，给英国造成了超过350亿英镑的损失。有授权的认知增强药物来缓解痴呆症和MCI的认知衰退。然而，它们只为大约一半的服用者提供了适度的收益，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显著的能力来对患者进行分层，并预测痴呆症等大脑疾病的易感性。然而，这些算法的解决方案往往很难解释，也很难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。与标准方法不同，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，特定的神经调节剂能够使生物神经网络中的记忆发展，对扰动具有更高的弹性。在这个项目中，我们将使用我们的人工智能驱动的建模来研究神经调节器故障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，这对于减少痴呆症和MCI期间的认知衰退是最佳的。接下来，我们将模拟常用的认知增强药物，以研究为什么目前的药物只提供适度的改善，以及如何最好地将它们与认知训练结合起来。为了执行这项工作，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。总的来说，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。这一系列人工智能知情研究将导致个性化治疗，从而提高痴呆症和MCI的生活质量。

项目成果

Cerebro-cerebellar networks facilitate learning through feedback decoupling.

• DOI: 10.1038/s41467-022-35658-8
• 发表时间: 2023-01-04
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Boven, Ellen;Pemberton, Joseph;Chadderton, Paul;Apps, Richard;Costa, Rui Ponte
• 通讯作者: Costa, Rui Ponte