Mapping the neural circuit of credit assignment for a new targeted intervention in addiction

Imagine that you cannot wear your lucky socks for an upcoming test. In the event of failure, will you blame your absent clothing or your lack of preparation? The ability to identify which actions cause a particular event to occur is called "credit assignment". This ability allows individuals to properly make decisions and learn from their mistakes.

Problems with credit assignment are linked to various mental health conditions, like addiction and obsessive-compulsive-disorders where individuals continue to believe that their drug-taking or rituals will lead to positive outcomes [1]. However, clinicians tend to define and diagnose mental illnesses in terms of their clinical symptoms, not by their underlying psychological traits or biological abnormalities [2]. No-one has yet studied how changes in the brain lead to the problems of credit assignment that are seen in psychiatric disorders. Solving this riddle will help us understand how humans can work out cause and effect, as well as what happens when they lose this ability.

My plan with this fellowship is to i) extract clinically-relevant traits that describe a person's ability - or lack thereof - for credit assignment from a large database, ii) map them onto brain mechanisms, and iii) restore the identified circuit dysfunction and therefore reduce the related maladaptive behaviours in patients suffering from addiction. To do so, I will, in a first stage, collect a large-scale dataset ("big-data") from an online study where participants will assign credit to distinct stimuli that predict a variety of events. Computational learning models will be used to explain this large dataset by teasing apart the hidden attentional and learning features of credit assignment [3-5] and relate them to various psychiatric dimensions. These will then be contrasted against neural data (acquired with fMRI while participants carry out the same credit assignment task). This will help map out the full neural circuitry involved in credit assignment and relate it to the phenotype of mental health issues.

In the second stage of the fellowship, I plan to use a cutting-edge technique called ultrasound neurostimulation to target the different parts of the brain that cause pathological credit assignment and over-reliance on habits. Ultrasound neurostimulation is an early-stage, non-invasive therapeutic technology that has the potential to improve the lives of millions of patients with mental health conditions by stimulating brain tissues with millimetre accuracy [6]. My previous research has recently shown that ultrasound can safely modulate activity in deep brain areas in macaques to elicit precise behavioural changes [7]. Importantly, its safe use in humans has also been established [8-9]. In sum, ultrasound neurostimulation will be used to restore the brain regions involved in credit assignment and alleviate the corresponding negative symptoms in patients.

This approach has the potential to help the nearly two million patients suffering from maladaptive addictive behavioural patterns by designing new stimulation paradigms that effectively restore brain function. Moreover, besides addictive disorders, ultrasound brain therapy could also be used to restore normal functioning of brain circuits involved in anxiety, mood disorders, and obsessive-compulsive disorders for which effective therapies are desperately needed.

[1] Everitt &al. NatNeuro. 8,1481-1489(2005). [2] Hyman &al. NatRevNeuro. 8,725-732(2007). [3] Fouragnan &al. NatComm. 6,8107(2015). [4] Fouragnan &al. SciRep. 7,4762(2017). [5] Queirazza, Fouragnan &al. forthcoming at Science Advances (2019). [6] Aubry JoAcoustSocAm. 143,1731-1731 (2018). [7] Fouragnan &al. NatNeuro. 22,797-808(2019). [8] Fomenko &al. BrainStim. 11,1209-1217(2018). [9] Tsai &al. MedHypo. 84,381-383 (2015).

The goal of my fellowship is to identify the biomarkers of credit assignment in the human brain to develop a new circuit-based intervention for substance abuse disorders. Addiction is the 3rd most costly mental health condition in the UK (~£10 billion per annum) with very few effective treatments. My interdisciplinary work will use computational psychiatry [1,2] and a groundbreaking low-intensity ultrasound neurostimulation technique (TUS) [3,4] to improve our understanding of the relationship between the brain's neurobiology, its environment and mental symptoms. The research/innovation originating from this Fellowship is strongly aligned with UK Government priorities. Social, economic and political impacts described below will arise through the development of 1) cost-effective markers of psychiatric disorders translatable into clinical practice (WP1 - deliverables B-C) and 2) the proof of concept that TUS can be used as a treatment to reduce maladaptive behaviours in addiction (WP3-4: deliverables D-F).

Social impact: [1] Vulnerable groups and mental health patients will directly benefit from the FLF. By improving psychiatric classification, diagnosis of mental conditions and treatment for addiction using a new technology, the FLF is directly relevant for the BBSRC and MRC TTL initiative. Moreover, the application for TUS neurostimulation can be extended to many mental illnesses. Its benefits over current pharmacological treatment include: more efficient treatments, elimination of negative side effects and improved patient compliance. [2] Clinicians will benefit from more advanced and effective diagnostic tools and treatments. [3] Education & skills development: the project knowledge will be disseminated in workshop, tutorials and project to students, academics and clinicians throughout the project. [4] Third sector impact: I will work with addiction/rehabilitation and mental health services community to provide educational workshops (ex: Broadreach house charity, Plymouth).

Academic impact: [1] The FLF will foster collaboration with researchers involved in neuroscience, physics, engineering, resulting in 4 high impact articles over 4 years. Results will be disseminated at 2 EU conferences and workshops p.a., and internationally every 2 years. [2] Algorithms, code and data created during the FLF will impact other disciplines such as AI and neurosurgery, with potential growth in neurological, oncological, and musculoskeletal applications.

Economic and political impact: [1] Widescale adoption of TUS will result in the demand for equipment manufacturers, increasing jobs and CAPEX, thus boosting economy. The validation of TUS and publication of its effectiveness together with open source access to new diagnostic algorithms, support Innovate UK's "emerging and enabling technologies" and "health and life science". [2] Cost savings are envisaged due to improved diagnosis and stratification of patients thus enabling accurate treatment from first encounter with health services. TUS has the potential to improve health of addicts and thus reduces relapse and associated cost to society. [3] Alignment with government policy and MRC, BBSRC and UKRI strategic priorities including increased effectiveness of public services (NHS). [4] By proposing causal manipulation in humans as precise as studies in animals [3], we will approach the human as the ultimate experimental participants for improving human health. This meets the BBSRC's priority: "The replacement, refinement and reduction (3Rs) in research using animals". [5] In the long term the TUS technique has potential to reduce drug production and waste, resulting in less deforestation, packaging and transportation.

[1] Fouragnan &al. NatComm. 6,8107(2015). [2] Queirazza, Fouragnan &al. forthcoming Science Advances (bioRxiv, 224410). [3] Fouragnan &al. NatNeuro. 22,797-808(2019). [4] Folloni &al. Neuron 101,1109-1116(2019).