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Neuromodulatory-prefrontal interactions in primates

Reference	BB/W003392/1
Principal Investigator / Supervisor	Professor Matthew Rushworth
Co-Investigators / Co-Supervisors	Professor Laurence Hunt, Professor Steven Kennerley, Dr Jill O'Reilly, Dr Mark Stokes
Institution	University of Oxford
Department	Experimental Psychology
Funding type	Research
Value (£)	4,429,363
Status	Current
Type	Research Grant
Start date	01/06/2022
End date	02/10/2027
Duration	64 months

Abstract

Two brain systems - the ascending neuromodulatory systems (ANS) and prefrontal/anterior cingulate cortex (PFC/ACC) - cooperate and compete to guide behaviour in primates. We hypothesize that ANS, ubiquitous in vertebrates, holds estimates about fundamental features of the environment, such as its average value, and uncertainty about those estimates. We hypothesize that this information determines patterns of behavioural change and stability. We hypothesize, however, that the other key determinant of behavioural change is PFC/ACC, a brain structure unique to, or highly specialized in, primates. We hypothesize PFC/ACC holds similar information but in higher dimensional, multivariate formats in a manner reflecting association between environment features. For example, rather than representing just average value, we hypothesize PFC/ACC: 1) represents distribution of value across the environment and its pattern of change over time; 2) holds multiple choice representations simultaneously and encodes associations between such choices. We will establish how these systems interact by recording simultaneously from both using neuroimaging. We will exploit tools we have recently developed for doing this in macaques in order subsequently to: 1) examine consequences of disruption/manipulation of one system on the other system and on behaviour; 2) record patterns of distributed activity at the single neuron level. Circuit disruption/manipulation will be performed by using transcranial focal ultrasound stimulation (TUS) techniques making it possible to: 1) repeatedly and reversibly manipulate neural activity even deep in the brain in minimally invasive ways; 2) manipulate the influence of specific ANS neuromodulators on specific PFC/ACC regions. Large scale neuronal recordings will be performed by high density neurophysiological recording of multiple neurons. Analyses and descriptions of both behaviour and neural activity will employ state-of-the-art computational models.

Summary

We aim to understand how two brain systems cooperate and compete to guide behaviour. One system consists of the ascending neuromodulatory systems (ANS). These comprise groups, called nuclei, of brain cells (neurons) that send projections across the brain to exert broad influences over behaviour. The other brain system is prefrontal and anterior cingulate cortex (PFC/ACC). Whereas ANS are present in all mammals and many other animals, PFC/ACC is uniquely specialized in primates. We hypothesize that ANS represent key features of an organism's environment that guide fundamental aspects of behaviour. For example, one nucleus (the raphe nucleus) may encode how good the environment is on average: does it yield rewards at a high or low rate? If the organism is in a highly rewarding environment then all may be well, but if not, then it may be time to seek a better alternative. Other ANS nuclei may encode the organism's uncertainty about such estimates. If the organism's estimates are very uncertain then the environment may be changing, and the organism needs to seek more information to establish a better estimate of the situation. Like ANS, PFC/ACC also represents information about the environment such as reward richness and uncertainty to guide strategic behaviours. What then enables PFC/ACC to support the sophisticated behaviours we observe in primates? How does it interact with ANS? We will test several ideas. One central idea is that information in PFC/ACC is much richer, or 'high dimensional', than information in ANS. For example, PFC/ACC might hold much more specific information about the value of all choices available in the environment. It may encode relationships between component pieces of information. This could guide behaviour in more sophisticated ways and this would be apparent when we compare PFC/ACC and ANS activity. Several brain activity measurements are required. They are made in a primate called a macaque. One approach measures activity with a magnetic resonance imaging (MRI) scanner. Crucially, this simultaneously tells us about activity in PFC/ACC and ANS so that we can compare them and study their interactions. It also enables links to be drawn with human MRI studies. We will exploit our recently developed protocols for MRI recording of PFC/ACC and ANS while animals engage in a rich repertoire of behaviour. A second approach involves electrodes recording activity from the actual computing units of the brain - the neurons - on the millisecond time scale on which they operate. We will use the latest electrodes to record many tens or even hundreds of neurons simultaneously. This allows us to study rich, 'high dimensional' information encoding in PFC/ACC. Finally, we will use a technique, transcranial ultrasound stimulation (TUS), which transiently disrupts activity in a comparatively non-invasive way. Importantly, this approach establishes how activity in one brain region leads to activity elsewhere and ultimately causes behaviour. We are one of very few research teams in the world that can undertake these studies. We believe that developing such an understanding will become important for artificially intelligent (AI) agents. There are precise, mathematical ways to describe reward information and its optimal use in behavioural guidance. Descriptions resembling observations made in ANS already underpin state-of-the-art AI algorithms that learn behaviour via trial-and-error. These do not require the programmer to explicitly program how the agent should behave, but instead specify the agent's high-level goal: the agent then learns what actions achieve this goal. It may be possible to develop the next generation of AI algorithms to achieve their goals by learning more like primates do; PFC/ACC may allow primates to abstract information away from multiple examples, learn the structure of environments, and perform rapid inferences based on individual observations, in a way that AI agents currently cannot.

Committee	Research Committee A (Animal disease, health and welfare)
Research Topics	Neuroscience and Behaviour, Systems Biology
Research Priority	X – Research Priority information not available
Research Initiative	Longer and Larger Grants (LoLas) [2007-2015]
Funding Scheme	X – not Funded via a specific Funding Scheme

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