NIHRDataInsights/HRCSData huggingface dataset:
Title: Neuronal identity and numbers in the development of neocortical activity
Funder: Wellcome Trust

Abstract: The function of the cerebral cortex relies on the cellular balance between excitatory (pyramidal cells) and inhibitory neurons (interneurons), which is disrupted in disorders such as autism or schizophrenia. The emergence of cortical function relies on spontaneous synchronous activity among large groups of pyramidal cells and interneurons, which later desynchronize for efficient information processing. Prior to this shift in network activity, interneuron numbers are adjusted through activity-dependent programmed cell death mechanisms. The contribution of interneuron programmed cell death to the change in network dynamics and the identity of the cell types participating in these events remain unclear. I will address these questions by combining in vivo longitudinal functional imaging of the postnatal somatosensory cortex with cell type identification via multiplex in situ RNA localization. This strategy will allow correlating the identity of one neuron with its activity pattern and participation in network dynamics during early postnatal development. I will perform complementary experiments in mouse models engineered to contain abnormal numbers of cortical interneurons, thereby assessing the importance of neuronal numbers for­ cortical function. This project addresses the fundamental question of how neuronal diversity and numbers contribute to the emergence of functional circuits during physiological and pathological brain wiring.

Lay abstract: The cerebral cortex is a region of the brain that controls complex functions like sensory perception, motor behaviours and cognition via highly organized neuronal circuits. These circuits develop during embryonic and early postnatal life and defects in their formation have been associated with medical conditions such as autism and schizophrenia. Consequently, understanding how cortical circuits develop and how disruption of this process may lead to disease are key questions in neuroscience and mental health. Using several genetic mouse models and state of the art techniques for monitoring brain activity, I will investigate how the final number of different types of neurons in the cerebral cortex contribute to the progressive emergence of functional cortical circuits. This project will provide key information to build a conceptual framework for the progressive assembly of cortical circuits and the aetiology of neurodevelopmental disorders.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Principles behind neural representations in complex tasks
Funder: Wellcome Trust

Abstract: Generalisation is central to intelligent behaviour. Recent work has shown the classic dichotomy of hippocampal function, relational memory and spatial cognition, can be unified through the language of generalisation. Here the structure of the task (spatial navigation, transitive inference etc) is learned and abstracted in order to be reused when entering new environments. Animals (and hippocampus) do more than just space and memory. They display complex behaviours that are reused and arbitrarily combined in novel situations. This raises the exciting possibility that animals learn the structure of behaviours and generalise them to new situations (e.g. the steps to baking a cake are reusable for making cookies). The common role of generalisation and abstraction would unify representations of behavioural (reinforcement) learning with spatial map-like learning.My research will build detailed theories and precise computational models to find a formalism with a common language for representations of reinforcement learning and space. Though much of the reinforcement learning literature has focused on more intuitive notions of reward and value to find in neural data, my models will predict that neurons additionally represent the structure of the behavioural tasks. I will look for this neural signature in data from both old and ongoing experiments.

Lay abstract: A grand aim of neuroscience is understanding how the brain represents knowledge. Knowledge is not just a piece of information, but a web of information and their relationships - knowing a family means knowing the people and how they relate to each other. The brain learns/represents these underlying structures (e.g. family tree), profiting from this knowledge when encountering new situations (understanding new families). Learning and generalising these web structures is fundamental to intelligent behaviour.My research provides principles for learning these web structures, showing that models imbued with such principles account for known neural representations and predict novel neural phenomena. In particular I am interested in how hippocampus plays a role both in spatial cognition and higher level cognitive functions. Are the same principles at play for both cases? Can we build models of higher level cognitive tasks? I investigate these questions with theory, models, and data analysis.

Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Using genetics to stratify patients and improve prediction of clinically relevant outcomes in psychiatry and neurology
Funder: Wellcome Trust

Abstract: This project investigates the genetic basis of psychiatric disorders and neurological diseases with three key goals.First, to gain insight into the molecular mechanisms underlying genetic associations with these outcomes through the integration of genome-wide association study (GWAS) summary statistics with a range of newly released functional genomic annotations. These include variant-level annotations such as the effect of genetic variation on gene expression, methylation and chromosome structure within relevant tissues and cell types, as well as gene-level annotations such as expression profiles across tissues, cell types and developmental stages.Second, functionally informed polygenic scores, such as imputed and observed gene expression risk scores, will be stratified by enriched functional genomic annotations and used for clustering patients into aetiological subtypes, which will then be characterised using sociodemographic and clinical variables.Lastly, this study will evaluate the utility of machine learning models containing functionally informed polygenic scores and cluster membership information for prediction of diagnosis, prognosis and treatment response. I will use data from a range of ancestral populations to improve the accuracy of genetic predictors in all populations. Together this project can inform novel drug development, identify clinical subgroups, and promote personalised medicine through improved prediction of clinically relevant outcomes.

Lay abstract: Background: Many genetic variants have been linked to psychiatric and neurological problems. However, the biological mechanisms by which these genetic variants cause these problems are unclear, limiting the extent to which these findings can be used to improve clinical care.My approach: Recently, our understanding of how genetic variation affects biological processes in the brain has progressed significantly. I will use this knowledge to uncover biological processes by which genetic variation cause psychiatric and neurological problems. I will then attempt to separate patients into subgroups based on the specific biological processes driving their condition. I will then test whether this information can be used to improve prediction of clinical outcomes.Impact: My work will provide insight into the biology of neurological and psychiatric problems, allowing us to separate patients into more specific groups to provide personalised healthcare and so improve prevention, diagnosis, and treatment.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Using Neurofind to make individualised inferences in first episode psychosis: feasibility, acceptability and clinical utility in a real-world setting
Funder: Wellcome Trust

Abstract: Brain-based disorders, including psychiatric and neurological illnesses, represent 10.4% of the global burden of disease. At present, there are no established imagingbased tests for detecting these disorders, monitoring their progression over time and optimising treatment. In a previous Wellcome Innovator Award, Prof Andrea Mechelli and team at KCL developed Neurofind - a user-friendly web-based tool, which usesdeep learning technology to quantify neuroanatomical abnormalities from structural Magnetic Resonance Imaging scans. This tool compares an individual scan against a reference database and generates an individualised report that could support diagnostic, prognostic and treatment decisions in individual patients. In this follow-on award, the feasibility, acceptability, safety and clinical utility of Neurofind will now be assessed in a real-world setting. Neurofind will be tested in patients with first episode psychosis recruited from the South London and Maudsley NHS Foundation Trust. The study aims to test if Neurofind is feasible, acceptable, safe and if the information in the individualised patient reports it produces is predictive of clinical outcomes at 6- months follow-up. If successful, the proposed trial will lead to the adoption of Neurofind in real-world clinical services, with tangible benefits for patients, clinicians and service providers

Lay abstract: Brain-based disorders, including psychiatric and neurological illnesses, represent 10.4% of the global burden of disease. At present, there are no established imaging-based tests for detecting these disorders, monitoring their progression over time and optimising treatment. As part of our previous Innovator Award, we have developed Neurofind - a user-friendly web-based tool which uses deep learning technology to quantify neuroanatomical abnormalities from structural Magnetic Resonance Imaging scans. The tool compares an individual scan against a reference database of 22,370 scans, and generates an individualised report that could support diagnostic, prognostic and treatment decisions in individual patients. Here we propose to assess the feasibility, acceptability, safety and clinical utility of the tool in a real-world setting. We will use Neurofind in 525 patients with first episode psychosis recruited from six clinical services within the South London and Maudsley NHS Foundation Trust. We hypothesise that the tool will be considered feasible, acceptable and safe by clinicians and patients, and that the information in the individualised report will be predictive of clinical outcomes at 6-months follow-up. If successful the proposed trial will lead to the adoption of Neurofind in real-world clinical services, with tangible benefits for patients, clinicians and service providers.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Assessing Awareness in Severe Dementia
Funder: Wellcome Trust

Abstract: This study is the first to objectively investigate the extent of subjective awareness in severe dementia, its underlying neural correlates and whether awareness changes with environmental stimuli. Transcranial Magnetic Stimulation (TMS), Electroencephalography (EEG), Event Related Potentials (ERPs) and functional Magnetic Resonance Imaging (fMRI) are robust techniques for assessing awareness in clinical groups who are unable to communicate. I will now apply these in dementia to investigate: 1) The variation in the level of awareness in people with severe dementia, using TMS-EEG markers. 2) In response to visual and auditory stimuli, is there ERP and fMRI evidence of brain activity beyond primary sensory cortices in a fronto-parietal network, reflecting preservation of higher level awareness in people with severe dementia? 3) The correlation between different objective biomarkers of subjective awareness, severity of dementia, and clinical and caregiver judgements of subjective awareness. The results will elucidate what people with severe dementia subjectively experience and to what extent they are aware of people and their environment. This novel understanding will support meaningful interactions and communication with family and caregivers, and enable the targeting of interventions and optimisation of environments to improve awareness, quality of life and care for people with severe dementia.

Lay abstract: When people have severe dementia, it can be very difficult to know how much of their environment or themselves they are aware of. This is distressing for families and caregivers; and makes it difficult to know how best to communicate and care for them or improve their quality of life. However, recent advances in neuroimaging can now shed light on the lived experience of people who are unable to communicate. Measuring brain activity whilst looking at faces, watching films or listening to music can reliably indicate the level and nature of a person's awareness, and this understanding has profoundly improved care for people with 'disorders of consciousness'. I will apply these techniques, for the first time to assess the awareness of people with severe dementia. This will deepen our understanding of the lived experience of severe dementia and form the basis for future improvements in communication and care.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Hippocampal subfield contributions to episodic memory loss in amnesia
Funder: Wellcome Trust

Abstract: Hippocampal damage impairs episodic memory, but it is unknown precisely why. Research in non-humans suggests that damage to a specific hippocampal subfield (cornu Ammonis 3, CA3) impairs computational processes called pattern separation (the ability to distinguish between similar but distinct memories; PS), and pattern completion (the ability to recall a full memory from partial cues; PC). I have shown that focal bilateral human CA3 pathology causes extensive episodic amnesia alongside reduced functional connectivity between the hippocampus and episodic memory network. Episodic amnesia could arise from disrupted PS/PC during learning and/or retrieval, or, from aberrant interactions between PS/PC and other cognitive processes supported by the hippocampus, critical to episodic re-experiencing (temporal sequencing, visuospatial re-experiencing). This Fellowship will use functional MRI (fMRI) in a well-characterized cohort of patients to investigate the effects of CA3 pathology on episodic learning and retrieval.Goals: 1. To understand how CA3 damage disrupts the normal hippocampal-cortical interactions required for episodic learning and retrieval. 2. To investigate whether episodic amnesia arises solely from a failure of PS/PC or from disrupted interactions between PS/PC and other processes such as temporal ordering or visuospatial re-experiencing. 3. To seek mechanistic understandings of how CA3 pathology disrupts episodic memory retrieval across the lifetime using fMRI.

Lay abstract: The hippocampus is a brain region that is important for helping us recall our past experiences, which are known as episodic memories. Research in non-humans has generated several computational theories about how hippocampal damage causes episodic memory loss. It is not completely clear, however, whether these theories are relevant to human memory. Damage involving just the human hippocampi is rare, making research into these theories challenging.I have assembled a group of rare human patients with focal hippocampal damage. I will translate what is known from animal research into these patients using cutting-edge behavioural tests and brain imaging techniques. This will enable me to understand how such lesions impact normal hippocampal function and its interaction with other brain regions during memory retrieval.This work will also contribute novel methods for detecting hippocampal dysfunction that may have possible applications in the future for the early detection of pathologies such as dementia.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: The neural dynamics of how expectation modulates visual object recognition
Funder: Wellcome Trust

Abstract: How do we understand what we see? Recognising objects depends on dynamic transformations of information from vision to semantics - but in the real world, our understanding of what we see is shaped by the environment. When we see an object, we are already in a complex and rich environment, which leads to expectations about the kind of things we may see. The over-arching vision for this fellowship is to understand how the environment shapes the neural dynamics and mechanisms of object recognition, most saliently in terms of the fundamental components of recognition - visual and semantic processing. This research will test how environmental context changes neural dynamics prior to seeing objects, and how context impacts ongoing visual and semantic processes. This will be addressed through lab-based studies and real-world neuroscience, which presents the ultimate test of how the environment changes our perceptions. I will deploy a range of cutting-edge methodologies; computational modelling, pattern similarity analysis, neural oscillations, connectivity, and augmented reality, within a convergent multimodal brain imaging framework (fMRI, MEG, EEG and mobile EEG). This research will drive forward models of object recognition, confronting the fundamental issue of how our perception of the world influences the dynamics of recognition.

Lay abstract: How do we understand what we see? Recognising objects depends on dynamic transformations of information from vision to semantics - but in the real world, our understanding of what we see is shaped by the environment. When we see an object, we are already in a complex and rich environment, which leads to expectations about the kind of things we may see. My proposed research will test how the environment changes the dynamics of visual and semantic activity in the brain. I will combine lab-based and real world studies using a multimodal brain imaging framework - fMRI, MEG, EEG and mobile EEG, with innovative and emerging methodologies including augmented reality, computational modelling, multivariate analyses, neural oscillations and brain connectivity. This research will critically advance models of object recognition, which currently have little to say on the dynamic neural mechanisms of how the world interacts with our perception of objects.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Seeing into the future: Proactive adjustments of visual coding by the circadian clock
Funder: Wellcome Trust

Abstract: Circadian clocks are a ubiquitous feature of biology, driving daily rhythms in almost all biological processes. The brain is among the body-systems in which circadian rhythms are most prominent. Outside the master circadian pacemaker in the hypothalamus, those rhythms have largely been considered as mechanisms to adjust downstream behaviour/physiology. An open question is whether circadian clocks also regulate the brain’s core function of processing/transmitting information. Neural circuits are designed to efficiently extract, process and transmit relevant information, but as the optimal solution to this process can be time-of-day dependent, one might predict circadian control. Excitingly, my unpublished data in the mouse visual-system has revealed that neurophysiological responses to stylised stimuli do indeed vary across circadian time. This leads me to hypothesise that circadian clocks adjust information processing in the brain to meet predictable variations in demand. I will apply a multidisciplinary, system-level approach to address this hypothesis. I will determine how the clock optimises information processing across the mouse visual-system; what advantage this provides over well-described adaptive processes; and how this defines visual capabilities across the day. This work has the potential to establish new general principles of network-plasticity, and promises to advance our understanding of circadian and visual neuroscience.

Lay abstract: Almost all organisms use an in-built circadian clock to confine key aspects of their physiology and behaviour to particular times of day. This maximises their ability to thrive and survive through the changing demands of day-night cycle. I have recently found that circadian control extends to adjusting how the brain encodes and processes information from the sensory environment. I aim to determine how biological clocks adjust neural networks to change how they function in anticipation of a change in the environment, and to reveal how this ensures the brain works at its best across the day. This work has wide-ranging implications for the understanding of brain function, and will reveal how disruptions to our biological clock (as often occurs in our 24-hr society) might impact the complexity and power of the brain.

Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING
Health categories: MENTAL HEALTH;NEUROLOGICAL
Title: Improving self-awareness: Manipulating the neural substrates of self-belief
Funder: Wellcome Trust

Abstract: Beliefs about our skills and abilities (known as “self-beliefs”) may become divorced from reality, particularly in psychiatric and neurological disorders. For instance, someone with depression may think they are unable to succeed in new pursuits, making them unlikely to try in the first place. My goal is to identify core brain processes supporting self-beliefs, and in turn leverage this knowledge to develop interventions for restoring aberrant self-belief. I will focus on a network of brain regions in medial and lateral prefrontal cortex that is theorized to support estimates of self-ability. I will address three interlocking questions: 1) How are self-beliefs constructed? 2) Which factors permit the modification of self-belief? 3) How are self-beliefs used in the control of future behaviour? I will conduct fMRI and MEG experiments using novel decision paradigms to dissect elements of self-belief computation such as prior experience and task difficulty, and reveal how mental health is linked to these computations through large-scale online data collection. This work will inform behavioural and neurofeedback interventions to modify self-belief. The overarching goal of my research agenda is to produce a comprehensive, mechanistic account of self-belief construction, thereby informing efforts to restore appropriate self-awareness in disorders of mental health.

Lay abstract: As humans we often engage in self-reflection – how skilled am I at my job, did I make a good decision? Beliefs about our skills and abilities (known as “self-beliefs”) may not always match reality, particularly in disorders of mental health. For instance, someone with depression may think they won't be able to succeed in new pursuits, making them unlikely to try in the first place. Understanding how the human brain constructs these self-beliefs may lead to ways of restoring self-awareness, and help treat mental health disorders. I will conduct experiments using brain-imaging technologies to pinpoint the characteristics and location of neural activity which creates self-beliefs in the human brain. I will give people tasks to distinguish different influences on self-belief, such as previous experience and task difficulty, and collect data over the web to test large numbers of people and ask how these components are linked to mental health. Using this knowledge I will devise ways of changing self-beliefs, for instance by changing brain activity in real time. The ultimate goal of my research is to understand the machinery supporting self-beliefs, allowing us to intervene in cases in which these mechanisms are broken.

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