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

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.

In order to support cognitive functions, the brain must coordinate the interactions among large-scale networks that cooperate and compete to allow for efficient transitions between brain states. Understanding how these operate, giving rise to different behaviours is one of the greatest challenges facing modern neuroscience.
The overarching aim of this project is to develop a framework capable of shaping the interactions between brain networks. In order to do this, we will combine transcranial alternating current stimulation (tACS) with a novel approach, neuroadaptive Bayesian optimization.
TACS is a promising tool to modulate brain function. The oscillatory electrical activity imposed by tACS has been shown to result in neural modulations that spread along brain networks. However, there are two main limitations to the application of tACS to modulate brain function: 1) the brain networks targeted by stimulation cannot be verified in the absence of brain imaging; 2) the stimulation parameters vary across individuals, due to a multitude of variables, such as age, sex and genetic factors. Thus, identifying the optimal stimulation protocol that drives a particular brain state in a given individual is like 'finding a needle in a haystack'. To address these fundamental challenges, we propose to use neuroadaptive Bayesian optimization, which uses a close-loop search combining real-time fMRI with machine learning. This approach conducts an automatic and intelligent search across the multitude of tACS parameters in order to identify those that optimally elicit a particular target state.
This framework has translational potential, as several psychiatric and neurological conditions are associated with impaired function of large-scale brain networks. The results of this project can lead to the development of therapeutic interventions that harness the potential of brain stimulation.

The research proposed in this application has the potential to generate results with substantial scope to have a wider societal and economic impact.

Societal Impact:
The health and wellbeing of the general public will benefit from this research. Several of the most common psychiatric and neurological conditions, including depression, obsessive-compulsive disorder, schizophrenia, stroke and traumatic brain injury, are associated with impaired function of large-scale brain networks. The treatment of such conditions depends on understanding the network organization of the brain. The application of brain stimulation in patient populations is currently largely agnostic to its impact in the function of brain networks and this can result in inefficient and costly treatment programmes. This research will provide important insights into the integration of non-invasive brain stimulation and modulation of the function of brain networks, allowing for more precise and cost-effective definition of treatment programmes. The PI has directly worked with clinical partners and has links to clinicians, hospitals and health providers (Imperial College Healthcare NHS Trust, King's College Hospital NHS Foundation Trust and Royal Surrey County Hospital) that can support the translation of findings into diagnostic and therapeutic interventions.

Economic Impact:
Direct and indirect costs associated with mental health and cognitive impairment after brain injury have a significant impact on the UK economy. These costs constitute an enormous problem for patients and extend to their families, the NHS and society, as they contribute to job loss, impact caregivers' productivity, can lead to family breakdown and recidivism. Unfortunately, there are limited treatment options for these conditions. The results of this project can lead to the development of therapeutic interventions that can harness the potential of brain stimulation. This project will contribute to enhance UK scientific excellence and competitiveness in brain research, which would lead to welfare benefits for patients and cost-benefits for the health-care system.

Commercial Impact:
The neurotechnology developed in this proposal could create strong links between academia and industry due to the potential to develop products associated with medical imaging and non-invasive brain stimulation interventions. Neurotechnologies for neuroenhancement using non-invasive brain stimulation devices constitute a rapidly growing industry, with more than a dozen companies selling non-invasive brain stimulation products (e.g. Foc.us, Halo Neuroscience, Caputron, etc.). According to SharpBrain, the brain fitness market is expected to grow to $6 billion by 2020 (Fortune, November 2015). However, there is currently a large gap between the understanding of the effects of these products in brain dynamics and behavior. Our research could contribute to the development of more evidence-informed neuroenhancement technologies which can tap into this market.

Public Engagement:
The PI has an excellent track record in initiating and organizing public engagement events with patients, in schools and participating in science festivals. We will disseminate the results of our research through public engagement events. These will aim at increasing the knowledge about brain research in humans, the use of neurotechnology and to improve awareness about the use of non-invasive brain stimulation devices for neuroenhancement, which interests a growing, often misinformed, DIY community (Scientific American, 2017).