NIHRDataInsights/HRCSData huggingface dataset:
Title: New approaches to understanding Social-Cognition in Anorexia Nervosa
Funder: Wellcome Trust

Abstract: Anorexia Nervosa (AN) is a complex disorder with substantial treatment resistance and high mortality rate. It has been suggested that social-emotional difficulties maintain the illness by increasing social isolation and anxiety. Although a few studies have examined the brain processes that underlie social-emotional difficulties in AN, the small sample sizes, outdated paradigms and statistical methods limit the conclusions that can be drawn from these studies. Thus, there is need to improve current methodology to better understand these challenges in AN and their role in illness maintenance to reduce the devastating impact of this disorder. My aim is to develop new, ecologically valid paradigms to assess social-cognition in and outside of the MRI environment as well as novel statistical methods to model neuroimaging data from the naturalistic tasks and identify biomarkers of illness maintenance in the multimodal data. These new methodologies will be applied to a large study examining the brain and behavioural correlates of social-emotional processing in 140 emerging adults with and without AN. This project will test the hypothesis that people with AN show atypical emotional reactivity and negative interpretation bias, and that these difficulties contribute negatively to poor prognosis.

Lay abstract: Anorexia nervosa (AN) is a complex disorder with high mortality rate and poor treatment response. Social-emotional difficulties have been suggested to contribute to treatment resistance in AN by heightening social anxiety and increasing reliance on eating disorder behaviours as coping mechanisms. Therefore, there is pressing need for better understanding of the processes that underlie these difficulties in AN. I aim to develop new, naturalistic paradigms to assess behavioural and brain responses to key aspects of social-cognition, namely interpretation biases and atypical emotional reactivity. I will also build new statistical models to improve the understanding of brain processes that underlie these social-emotional difficulties in AN and explore biomarkers of illness outcome. These new methods will be applied to a large case-control study of 140 emerging adults aged 18-25 with and without AN providing valuable insights into the key difficulties in acute AN and their impact on prognosis.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Mapping dynamic trajectories of development in autism spectrum disorder: gaining new insights into aetiology and heterogeneity
Funder: Wellcome Trust

Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterised by substantial behavioural heterogeneity. Although differences in neurobiological functioning are found in individuals with ASD, much of the extant work has used cross-sectional designs and is therefore ill-equipped to answer questions of directionality. I will use advanced statistical techniques to model directional pathways between brain functioning, cognition and behavioural outcomes. First, I will obtain a detailed characterisation of brain functioning across the early developmental period in infants at high and low risk for developing ASD (Objective 1). I will model different trajectories of early brain development that may precede a diagnosis of ASD, and test whether these differ from those found in individuals without a diagnosis, and whether they predict later profiles of cognitive functioning and behavioural symptoms. Next, I will integrate one sample from Objective 1 with longitudinal data from a large sample of individuals with an established diagnosis of ASD to comprehensively test uni- vs. bi-directional pathways between neurobiological functioning, cognition and behavioural symptoms (Objective 2). Better understanding of these pathways may allow for interventions to be targeted towards mechanisms underpinning specific symptoms, rather than global diagnosis. Given the heterogeneity within ASD, this may lead to more effective interventions.

Lay abstract: Autism spectrum disorder (ASD) is characterised by difficulties in social communication, repetitive behaviours and restrictive interests. The presentation of ASD can vary enormously from person to person, with variation in the types of symptoms they may display, their intellectual ability, and whether or not they experience additional psychiatric symptoms. I will combine advanced statistical models with sensitive measurements of brain functioning, to better map the links between brain functioning, cognition and behaviour in ASD across the lifespan. First, I will model pathways of early brain development in infants at risk of ASD, and map these trajectories of brain development onto later cognitive functioning and ASD symptoms in toddlerhood. Second, I will delineate the directional pathways between brain functioning, cognition and behavioural symptoms in individuals with an established diagnosis of ASD. Increased understanding of the neurobiological mechanisms that underpin the symptoms of ASD may help to identify new targets for intervention.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Material classification by man and machine: Understanding human perception using psychophysics and convolutional neural networks
Funder: Wellcome Trust

Abstract: The primary focus is to uncover computational mechanisms underlying material perception, with particular emphasis on “perceptual constancy” – the stability of judgements about objects in the world, despite changes in the proximal stimulus. My approach to this problem is to capitalise on recently developed deep-learning paradigms by evaluating and interpreting activation maps of convolutional neural networks (CNNs) that have been trained to replicate human performance in classifying material properties, such as colour, gloss and translucency, and to use these to identify candidate models of human perception. The key stages are to (i) generate computer-rendered training images with diagnostic conditions of lighting, viewpoint and material parameters; (ii) obtain human perceptual labels for 15,000 images using online methods, validated with lab-based measurement; (iii) train CNNs with either ground-truth or human labels; (iv) interpret the CNNs using visualisation tools; and (v) run psychophysical tests of CNN-inspired models of human vision. The goal is to learn about mechanisms of human material perception by decoding the internal structures of these networks and comparing their behavioural responses to new images. Cue-perturbation methods will be used to test the importance of the recovered features for human perception. Perceptually labelled images will be additionally analysed with traditional methods.

Lay abstract: We are surrounded by various materials (e.g., metal, cloth, plastic or glass), which we easily identify from sight alone. Importantly, the particular material impression typically does not collapse when the material is viewed from a different direction or under different lighting. We aim to understand the mechanisms underlying this perceptual stability using recently developed deep-learning algorithms. Networks trained using human perceptual classifications of materials are expected to show similar behavioral performance to human observers, replicating the same successes and errors. We then interrogate the internal structure of the networks to understand how the classifications are made. Finally, we test whether human performance can be (i) disrupted by modifying or removing information that the network suggests is important and (ii) preserved when the edits are unimportant. Understanding the mechanisms that support robust material perception may be useful for online shopping and improving safety-critical decisions such as road wetness by autonomous cars.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Deep Learning of Cerebral Cortex Microstructure
Funder: Wellcome Trust

Abstract: My research aims to bridge the gap between in vivo neuroimaging and underlying brain microstructure. MRI can inform us of shape or signal intensity changes in neurological and psychiatric diseases but has limited interpretability with respect to underlying neurobiological disturbances. Currently such specific measurement of neurobiology is only accessible through ex vivo histological techniques. I wish to further our understanding of how in vivo MRI reflects microstructure through three interrelated projects. 1) I will map the distribution of microstructural properties, including cytoarchitecture, myeloarchitecture and receptor-architecture to whole-brain reference atlases, thus representing these features in the space of brain scans. 2) I will create deep learning frameworks for analysing cerebral cortical structure. 3) I will use computational tools to link in vivo MRI cortical structure to its underlying microstructure in health and disease. I will use high-resolution and multi-modal structural MRI data to link interregional and interlaminar MRI differences to their underlying microstructural source. I will apply deep learning to a large cohort of patients with a form of surgically remediable epilepsy, focal cortical dysplasia, which is characterised by abnormal microstructure. This work aims to solve the clinical challenge of identifying subtle epileptogenic structural abnormalities from a patient’s presurgical MRI scan.

Lay abstract: Brain scanning techniques offer powerful ways of examining the living brain non-invasively and diagnosing neurological and psychiatric illness. But brain changes associated with such conditions are often too complex for the human eye. Artificial intelligence can identify such hidden patterns, but we also need to understand their underlying, microscopic causes. To link brain imaging with microscopic changes, I will work on three related projects: Using newly-developed microscopic-level imaging technology, I will create maps from donor brains with detailed information on neurons and connections that make up the brain. I will adapt deep learning tools, the basic instruments behind the present-day advances in artificial intelligence, to analyse these microscopic-level brain maps. I will use brain maps and deep learning to analyse high resolution MRI scans. In doing so, I will push the boundaries of what we can understand from brain scans. This will be extended to identify abnormalities causing epilepsy.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Bridging the gap between biological plausibility and function in models of prefrontal cortex
Funder: Wellcome Trust

Abstract: Prefrontal cortex (PFC) plays a fundamental role when we make complex decisions. However, there are very few biologically plausible neural-network models that can explain PFC activity during decision making. Additionally, most studies focus on sensory information that does not change during trials and decisions must be made at specific times, unlike our more dynamic natural environment. We aim to combine experimental recordings and biologically plausible neural-network models to identify decision-making mechanisms in PFC for a large variety of tasks. Our models will be biologically plausible in the sense that they will consist of distinct excitatory and inhibitory populations, we will train networks using reward-based training, and model neural activity will not be unnecessarily complex and nonlinear, in line with experimental recordings. We will examine the role that nonlinear neural activity plays in typical decision-making tasks and we will contrast this with more naturalistic tasks where sensory stimuli can change and decision times may vary. We will also examine learning-induced changes in the models from reward-based training with new recordings of PFC while monkeys learn context-dependent decision-making tasks. Finally, we will study circuit mechanisms in both our models and the new experimental recordings that enable rapid learning of new similar tasks.

Lay abstract: Our prefrontal cortex (PFC) plays a fundamental role when we make complex decisions, such as choosing a mode of transport in a foreign city on a rainy day. Recently, several computer-based, neural-network models have been developed to aid our understanding of PFC. However, model neural activity is often drastically different from real neural recordings and most studies consider rather rigid, unrealistic decision-making scenarios. We will combine computer modelling with real neural recordings to understand how PFC supports complex, naturalistic decision making. We will study how networks in PFC emerge naturally and progressively through reward-based learning and we will use new experimental recordings to study how PFC may enable rapid learning of new similar tasks. For example, when navigating a subway in an unfamiliar city, we use past experiences from previously used subways. Our approach will provide new experimentally validated mechanisms underlying complex cognition for a large variety of decision-making tasks.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Perceiving people from voices
Funder: Wellcome Trust

Abstract: When hearing voices, we can perceive a wealth of information about the speakers, such as their identity, regional background, and age. This project will investigate how listeners achieve this, thus probing fundamental mechanisms of person perception from voices. To achieve this, I will take a broad perspective to integrate the usually distinct fields of identity perception and the perception of other speaker characteristics. Through a programme of behavioural research using acoustic signal processing and methods from the statistical or distributional learning literature, I will first explore how listeners form representations of voice identities through a programme of training studies: What is the acoustic content of mental representations of voices? What kind of information is encoded in such representations? I will then extend these findings from voice identity perception to the perception of other speaker characteristics: Are such population-level representations formed based on similar mechanisms to identity representations or are there differences? Finally, I will conduct a magnetoencephalography (MEG) study to describe the timecourse of person perception from voices: Which speaker characteristics are decoded by listeners at which point during perception? Does familiarity with a person change the processing of that voice? Can the low-level acoustic properties (partially) explain the timecourse?

Lay abstract: Listeners cannot only perceive what is being said when hearing a voice, they can also glean information about who is talking: Are they familiar? Are they female or male? Listeners can, however, not recognise another person without knowing what their voice sounds like. For that, listeners need to have a mental representation of this person's voice. For this project, I will investigate the nature of representations of identity and other speaker characteristics. Using behavioural studies, I will explore which information is encoded in representations. Using magnetoencephalography (MEG), I will examine how different speaker characteristics are recognised over time, and how associated may mental representations interact. This project will help to better understand how people perceive others from their voices. With voice-based tech gaining popularity and hearing loss being endemic, the proposed research can inform the development of applications designed to support the daily lives of both clinical and non-clinical populations.

Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Dopaminergic reorganisation of cross-region neural communication
Funder: Wellcome Trust

Abstract: Dopamine (DA) neurons in the ventral tegmental area (VTA) are thought to provide a reinforcement signal that is essential for learning. Despite recent advances, how VTA-DA neurons coordinate and modulate communication and synaptic plasticity across brain regions to drive behavior is still an open question. We hypothesise that 1) DA enhances communication of behaviorally relevant information across cortex and striatum and 2) the content of the information varies across corticostriatal regions. To test this model, we propose an ambitious research program in mice, using state-of-the-art in vivo electrophysiology, optogenetics, neuropharmacology and computational techniques. First, we will use high density in vivo electrophysiology (Neuropixels) to record neural activity across different corticostriatal regions, while optogenetically manipulating VTA-DA neurons in mice performing a visuospatial decision-making task. We will then use advanced computational methods to examine how and what information is communicated across corticostriatal regions, and how this process is modulated by DA. Finally, we will causally evaluate how DA mediated changes in cross-region communication depend on the receptor, synapses and the neuronal populations involved. In conclusion, we propose a comprehensive study of how DA modulates cross-region communication to drive behavior, providing a needed bridge between our systems and synaptic level understanding of VTA-DA.

Lay abstract: Midbrain dopamine (DA) neurons are thought to provide a teaching signal indicating the difference between the reward obtained and the expected at any given moment. This signal allows us to adjust expectations and learn through trial and error. Despite its clear behavioral role, how this signal is broadcast to the rest of the brain to physically implement these behavioral effects is an open question. We hypothesise that DA behavioral effects are mediated by modulating the communication strength between neurons containing reward-related information. To test this, we will perform large scale recordings of neural activity and behavioral analyses combined with dopamine manipulations in mice performing a simple decision-making task. These experiments will help explain if and how DA modulates information transfer across brain regions to mediate DA behavioral roles, providing a needed link between our understanding of DA at the cellular, network and behavioral level.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Mapping sensory-motor cerebellar circuits for adaptive behaviour
Funder: Wellcome Trust

Abstract: The cerebellar cortex is thought to form predictive associations between sensory inputs and motor commands: animals leverage these associations to coordinate and adapt movements to contextual changes in the environment. This sensory-motor control depends on Purkinje neurons, each of which receives sensory, motor and cognitive signals from one climbing fibre as well as thousands of parallel fibres, the axons of granule cells. Despite years of anatomical work, cerebellar sensory-motor representations have not been mapped at the cellular scale; similarly, the functional circuit connectivity underlying sensory-motor integration remains unexplored. To reveal the functional architecture of the mouse cerebellar cortex, I propose to use rabies monosynaptic tracing to map and functionally characterise the presynaptic ensemble of individual Purkinje neurons. I will validate my functional connectomics strategy with electron microscopy and elucidate whether the anatomical organisation of presynaptic circuits converging on Purkinje neurons is modular or random (Aim1). Then, I will harness functional imaging and optogenetics to map the topography of cortico-cerebellar representations, and discover whether Purkinje neurons pool inputs from related sensory-motor presynaptic modules (Aim2). Finally I will train mice in a sensory-guided forelimb task, and ascertain how these circuits are engaged by sensory-motor events, and their timing, during adaptive behaviour (Aim3).

Lay abstract: Even the simple task of grasping a cup depends on the well-timed coordination of muscle contractions based on sensory input. Sensory-motor signals required for similar adaptive movements are processed by Purkinje neurons in the cerebellum, a brain region essential for motor control. Despite their ethological significance, the neural circuits governing this processing remain unknown. I will investigate how the circuits converging on Purkinje neurons operate during adaptive behaviour. I will validate and deploy innovative tracers to visualise Purkinje presynaptic connectivity, and combine them with in vivo imaging and optogenetics to determine which sensory and motor signals engage these inputs. Finally, I will train mice to respond to a sensory stimulus with a forelimb movement, and establish which inputs trigger Purkinje neurons with the appropriate timing. This project will elucidate the functional architecture of cerebellar sensory-motor connectivity, providing the foundation to understand how cerebellar motor control is disrupted in disease.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: The role of claustrum in value-based decision-making
Funder: Wellcome Trust

Abstract: Human and animals make decisions to harvest the most valuable rewards. This process requires a precise valuation of choice options and appropriate action selection. Despite the fact that many brain regions have been implicated in different aspects of value computation and decision making, the roles of the claustrum, a region with extensive brain-wide connectivity, in value-based decisions have remained unknown. This fellowship combines circuit, behavioural and computational tools to determine the role of the claustrum in value-based decisions. I will train mice on a value-based decision-making paradigm. First, I will assess the importance of claustral neural activity in choice behaviour by combining viral injection strategies and three-photon microscopy. Then, I will create a biologically realistic spiking neural network and simulate the effect of claustrum on multiple upstream regions. By using Neuropixels systems and optogenetics, I will delineate how long-range inputs from the claustrum contribute to the value signal in frontal regions by recording spiking activity while silencing claustral cells. These results will provide fundamental insights into the neural circuitry of decision-making.

Lay abstract: We learn from past experience by evaluating how our previous choices resulted in a good or bad outcome. This calculation of how much we 'value' obtaining rewards enables us to optimise our behaviour to obtain rewards in the future. How do we create these values to perform responsible decision-making? How is the value represented in the brain?A distributed circuitry involving many brain regions has been implicated in the calculation, representation, and use of values. However, one brain region, the claustrum, has extensive connectivity to many of these regions but has not been well studied yet. This project focus on understanding the claustrum's role in value calculation and representation. I will use state-of-the-art methods in mice, including behaviour, computational models, advanced optical imaging, and electrophysiological recording. The project is an essential step towards understanding the role of the claustrum in executive function and determining the neural mechanisms of decision-making.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL