Computational prediction of vulnerable points and interventions for dysfunctional synaptic plasticity in neuropsychiatric disorders

神经精神疾病中脆弱点的计算预测和突触可塑性功能障碍的干预措施

• 批准号: MR/S026630/1
• 负责人: Cian O'Donnell
• 金额: $ 56.06万
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS026630%2F1
• 关键词: Computational; prediction; vulnerable; points; interventions

Neuropsychiatric disorders such as Autism Spectrum Disorder (ASD) and Schizophrenia are widespread, with around 1.7% of children in the United States diagnosed with ASD, and around 0.7% of people being diagnosed with Schizophrenia at some point in life. Mental health problems in general account for more than 20% of disabilities in the UK and are estimated to cost the economy between £70-100 billion per year. Current behavioural and pharmaceutical treatments for neuropsychiatric disorders are effective for only a subset of patients, often carry unwanted side-effects, and treatment success is difficult to predict from patient to patient. These shortcomings reflect the fact that almost all existing drug treatments were discovered by chance, rather than being designed based on an understanding of disorder mechanisms.However, a recent wave of discoveries of 70-100 genetic mutations linked to each of ASD and Schizophrenia has given promising clues to the origins of these disorders. Many of the genes code implicated are important for synapses - the connections between neurons that mediate learning and memory in the brain. This implies that many neuropsychiatric disorders may in fact be disorders of synaptic plasticity.Academic and corporate laboratory researchers worldwide are now trying to figure out what brain changes the discovered genetic mutations cause, typically by studying genetically altered mice that should ideally mimic the human patients. However, most neuroscience research methods are painstakingly difficult and low-throughput, so progress is slow. In this NIRG we will instead use data-driven computational simulations of neurons and synapses, because they work much faster, and let us perform detailed virtual experiments that researchers might like to do in the lab, but can't. The computer simulations will be based on data from the lab of our experimental collaborators within the University of Bristol. This project will shortlist a number of highly vulnerable components of synapses, that can be used to direct future wet lab experiments. Finally, I will use the results to develop a new theory of dysfunctional information transmission at synapses in neuropsychiatric disorders, that could guide broader research in the field.

自闭症谱系障碍（ASD）和精神分裂症等神经精神疾病很普遍，美国约有1.7%的儿童被诊断患有自闭症谱系障碍，约0.7%的人在生命的某个阶段被诊断患有精神分裂症。总体而言，心理健康问题占英国残疾人数的20%以上，据估计，每年造成的经济损失在700亿至1000亿英镑之间。目前针对神经精神疾病的行为和药物治疗仅对一小部分患者有效，而且往往会产生不必要的副作用，而且很难预测每个患者的治疗效果。这些缺点反映了这样一个事实，即几乎所有现有的药物治疗都是偶然发现的，而不是基于对疾病机制的理解而设计的。然而，最近发现的70-100个与自闭症谱系障碍和精神分裂症相关的基因突变为这些疾病的起源提供了有希望的线索。许多涉及的基因编码对突触很重要，突触是神经元之间的连接，在大脑中调节学习和记忆。这意味着许多神经精神疾病实际上可能是突触可塑性的障碍。目前，世界各地的学术和企业实验室研究人员正试图弄清楚，发现的基因突变导致的大脑变化是什么，通常是通过研究经过基因改造的老鼠来模拟人类患者。然而，大多数神经科学研究方法难度大、通量低，因此进展缓慢。在这个NIRG中，我们将使用数据驱动的神经元和突触的计算模拟，因为它们的工作速度要快得多，并让我们进行详细的虚拟实验，研究人员可能想在实验室里做，但不能。计算机模拟将基于我们在布里斯托尔大学的实验合作者实验室的数据。这个项目将列出一些突触的高度脆弱的成分，这些成分可以用来指导未来的湿实验室实验。最后，我将利用这些结果来发展神经精神疾病中突触功能障碍信息传递的新理论，这可以指导该领域更广泛的研究。

项目成果

Fast-local and slow-global neural ensembles in the mouse brain.

• DOI: 10.1162/netn_a_00309
• 发表时间: 2023
• 期刊: NETWORK NEUROSCIENCE
• 影响因子: 4.7
• 作者: Delaney, Thomas J.;O'Donnell, Cian
• 通讯作者: O'Donnell, Cian

Bayesian analysis of phase data in EEG and MEG.

• DOI: 10.7554/elife.84602
• 发表时间: 2023-09-12
• 期刊: eLife
• 影响因子: 7.7
• 作者: Dimmock S;O'Donnell C;Houghton C
• 通讯作者: Houghton C

Acetylcholine boosts dendritic NMDA spikes in a CA3 pyramidal neuron model

乙酰胆碱增强 CA3 锥体神经元模型中的树突状 NMDA 峰值

• DOI: 10.1101/2021.03.01.433406
• 发表时间: 2021
• 作者: Humphries R

Fast-local and slow-global neural ensembles in the mouse brain

小鼠大脑中的快速局部和慢速全局神经元集群

• DOI: 10.1101/2022.07.14.500088
• 发表时间: 2022
• 作者: Delaney T

Bayesian analysis of phase data in EEG and MEG

EEG 和 MEG 相位数据的贝叶斯分析

• DOI: 10.31234/osf.io/2vcsy
• 发表时间: 2022
• 作者: Dimmock S