Dynamic modulation of brain states using brain stimulation and neuroadaptive Bayesian optimization

使用大脑刺激和神经适应性贝叶斯优化动态调节大脑状态

• 批准号: BB/S008314/1
• 负责人: Ines Violante
• 金额: $ 59.94万
• 依托单位: University of Surrey
• 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=BB%2FS008314%2F1
• 关键词: Dynamic; modulation; brain; states; using

Like an orchestra that relies on the coordinated efforts of its members, the brain depends on its many regions working together to perform the multitude of cognitive functions that makes us human. These functions allow us to solve problems, retrieve relevant information from memory and select the responses necessary to perform a particular task. In order to do this, the brain must coordinate the interactions between regions located far apart. One of the greatest challenges of modern neuroscience is to understand how these interactions occur, and how their occurrence gives rise to efficient behaviour. A tool capable of influencing the interactions between brain regions could help scientists understand better how a particular pattern of brain activity is associated to efficient behaviour, such as being able to retain information in memory or solve a problem. Such a tool could then be applied to neurological and psychiatric conditions, where the interactions between brain regions might be malfunctioning.The objective of this project is to develop this tool. In order to do this, we will combine functional magnetic resonance imaging (fMRI), non-invasive electrical brain stimulation and machine learning. Each of these techniques brings a critical element to this tool.FMRI is a technique widely used by neuroscientists to provide images with information about brain function. Non-invasive electrical brain stimulation is a technique that applies low-voltage current through the scalp and can change the activity of neurons without requiring surgery to implant electrodes. This technique has been shown to influence brain function and the interactions between brain regions. Electrical brain stimulation, however, can be applied in many different ways, thereby making it difficult to know what would work for to influence a particular interaction between a set of brain regions. In addition, the results of brain stimulation can vary depending on factors such as a person's age, sex, brain anatomy and genetics. This makes creating a tool capable of identifying the stimulation parameters for each individual like 'finding a needle in a haystack'. This is why machine learning is necessary, where a computer program "learns" to identify which brain stimulation parameters optimally engage brain regions involved in cognitive functions in a time frame that would not be possible using conventional methodologies.In essence, our tool will use brain stimulation to influence how brain regions interact, fMRI data analysed while the participant is receiving a certain type of stimulation to inform on how the brain reacts to it, and machine learning to select the next stimulation that should be investigated. By the end of the experiment we will obtain a map with the brain's responses to different stimulation conditions, and a prediction of what the optimal stimulation condition to elicit a brain response is.This tool could then be used in many clinical conditions where inefficient communication between brain regions has been observed, such as psychiatric conditions and during rehabilitation after brain injury.

就像一个依赖于成员协调努力的管弦乐队一样，大脑依赖于它的许多区域一起工作来执行使我们成为人类的众多认知功能。这些功能使我们能够解决问题，从记忆中检索相关信息，并选择执行特定任务所需的反应。为了做到这一点，大脑必须协调相距甚远的区域之间的相互作用。现代神经科学的最大挑战之一是了解这些相互作用是如何发生的，以及它们的发生如何产生有效的行为。一种能够影响大脑区域之间相互作用的工具可以帮助科学家更好地了解特定的大脑活动模式如何与有效行为相关联，例如能够在记忆中保留信息或解决问题。这样的工具可以应用于神经和精神疾病，其中大脑区域之间的相互作用可能会出现故障。为了做到这一点，我们将结合联合收割机功能磁共振成像（fMRI），非侵入性脑电刺激和机器学习。这些技术中的每一项都为这项工具带来了关键元素。功能磁共振成像是神经科学家广泛使用的一种技术，它提供了有关大脑功能的图像信息。非侵入性脑电刺激是一种通过头皮施加低压电流的技术，可以改变神经元的活动，而无需手术植入电极。这项技术已被证明会影响大脑功能和大脑区域之间的相互作用。然而，脑电刺激可以以许多不同的方式应用，因此很难知道什么会影响一组大脑区域之间的特定相互作用。此外，脑刺激的结果可能会因人的年龄、性别、大脑解剖结构和遗传学等因素而异。这使得创建能够识别每个个体的刺激参数的工具，就像“大海捞针”一样。这就是为什么机器学习是必要的，其中计算机程序“学习”以识别哪些脑刺激参数在使用传统方法不可能的时间范围内最佳地参与参与认知功能的大脑区域。本质上，我们的工具将使用脑刺激来影响大脑区域如何相互作用，当参与者接受某种类型的刺激时，分析fMRI数据，以告知大脑如何对其做出反应，并通过机器学习选择下一个应该研究的刺激。在实验结束时，我们将获得大脑对不同刺激条件的反应图，并预测引发大脑反应的最佳刺激条件。然后，该工具可以用于许多观察到大脑区域之间通信效率低下的临床情况，例如精神病状况和脑损伤后的康复期间。

项目成果

Dynamic causal modelling of phase-amplitude interactions

• DOI: 10.1016/j.neuroimage.2019.116452
• 发表时间: 2020-03-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Fagerholm, Erik D.;Moran, Rosalyn J.;Friston, Karl J.
• 通讯作者: Friston, Karl J.

Neural diffusivity and pre-emptive epileptic seizure intervention.

• DOI: 10.1371/journal.pcbi.1008448
• 发表时间: 2020-12
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Fagerholm ED;Tangwiriyasakul C;Friston KJ;Violante IR;Williams S;Carmichael DW;Perani S;Turkheimer FE;Moran RJ;Leech R;Richardson MP
• 通讯作者: Richardson MP

Brain Networks Underlying Strategy Execution and Feedback Processing in an Efficient Functional Magnetic Resonance Imaging Neurofeedback Training Performed in a Parallel or a Serial Paradigm.

• DOI: 10.3389/fnhum.2021.645048
• 发表时间: 2021
• 期刊: Frontiers in human neuroscience
• 影响因子: 2.9
• 作者: Dewiputri WI;Schweizer R;Auer T
• 通讯作者: Auer T

Real-time and Recursive Estimators for Functional MRI Quality Assessment.

用于功能 MRI 质量评估的实时和递归估计器。

• DOI: 10.1007/s12021-022-09582-7
• 发表时间: 2022
• 期刊: Neuroinformatics
• 影响因子: 3
• 作者: Davydov N

A checklist for assessing the methodological quality of concurrent tES-fMRI studies (ContES checklist): a consensus study and statement.

• DOI: 10.1038/s41596-021-00664-5
• 发表时间: 2022-03
• 期刊: Nature protocols
• 影响因子: 14.8
• 作者: Ekhtiari H;Ghobadi-Azbari P;Thielscher A;Antal A;Li LM;Shereen AD;Cabral-Calderin Y;Keeser D;Bergmann TO;Jamil A;Violante IR;Almeida J;Meinzer M;Siebner HR;Woods AJ;Stagg CJ;Abend R;Antonenko D;Auer T;Bächinger M;Baeken C;Barron HC;Chase HW;Crinion J;Datta A;Davis MH;Ebrahimi M;Esmaeilpour Z;Falcone B;Fiori V;Ghodratitoostani I;Gilam G;Grabner RH;Greenspan JD;Groen G;Hartwigsen G;Hauser TU;Herrmann CS;Juan CH;Krekelberg B;Lefebvre S;Liew SL;Madsen KH;Mahdavifar-Khayati R;Malmir N;Marangolo P;Martin AK;Meeker TJ;Ardabili HM;Moisa M;Momi D;Mulyana B;Opitz A;Orlov N;Ragert P;Ruff CC;Ruffini G;Ruttorf M;Sangchooli A;Schellhorn K;Schlaug G;Sehm B;Soleimani G;Tavakoli H;Thompson B;Timmann D;Tsuchiyagaito A;Ulrich M;Vosskuhl J;Weinrich CA;Zare-Bidoky M;Zhang X;Zoefel B;Nitsche MA;Bikson M
• 通讯作者: Bikson M