List of available PhD projects

Group research interests - The NeuroInformatics group is a computational group with a strong interest in studying lifespan brain health. A multitude of factors can affect our brain health from as early as pre-conception. These factors can pose great threats to the brain, leading to immense missed developmental potential, global disease burden and disability. We aim to tackle the diversity of challenges to studying brain health across the lifespan my using big data (both in terms of number of individuals, as well as in terms of numbers of features) and by developing novel approaches to integrating data across modalities and scales. In line with this goal our research program has three broad pillars: 1. Normative models of lifespan brain health: in this pillar we generate normative references for different brain features that can be measured with non-invasive brain imaging techniques such as MRI or CT. We also use these normative reference models to study deviations in clinical cohorts ranging from developmental and rare genetics disorders to conditions of accelerated ageing such as Alzheimer's and related dementias. See for an example: Bethlehem, Seidlitz, White et al. 2022, Nature. 2. Multi-scale integration: in this pillar we aim to integrate other data modalities with neuroimaging to gain a more complete understand of what it is that we are actually measuring with in-vivo neuroimaging techniques. This often included integration with genomics and transcriptomics. See for examples of this: Warrier et al. 2023, Nat. Gen. & Romero-Garcia, Warrier et al. 2019, Mol. Psychiatry. 3. Predictive applications: in this pillar we aim to develop novel machine learning approaches for risk prediction of ill brain health. See for an example: Azevedo et al. 2022 Medical Image Analysis

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s): 

1. Imaging-genetics - integrating different data types at different scales is vital for a comprehensive understanding of brain health. We currently collaborate closely with Prof. Varun Warrier to integrate our large-scale imaging with genetics databases to both create a resource for the wider community but also to better understand the integrated relationships across brain phenotype and the biology underlying potential deviations in brain related phenotypes.

2. Normative models for atypical development (i.e., autism and adhd) - as we continue to improve our normative reference models there is an increasing need and interest to test the clinical or translational potential of these. While the group mainly focuses on testing the efficacy of these models in neurodevelopmental conditions such as autism and adhd, we also closely collaborate with a much wider network of researchers and clinicians to understand the translational potential in other clinical conditions.

3. Developing open tools for brain network analyses - with a strong interest in standardising approaches to quantify biological data we are building open workflow and tools for studying biological networks with a specific emphasis on network data derived from human in-vivo brain imaging.

4. Image segmentation and analysis - as we are dealing with more and bigger data we have several project underway to try and optimise processing pipelines for human in-vivo imaging. In addition, we are increasingly interested in integrating clinically acquired (as opposed to research grade) data and this requires processing adaptation and optimisation.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

1. Neuro-cognitive foundations of combinatorial language function. Studying how the brain encodes this defining feature of human language; how this capacity might have emerged, and to what extent these processes differ across languages

2. The encoding of semantic and syntactic information in bilingualism. The demands of learning and using multiple languages modulate the way our neural system encodes perceptual signal. Does this extend to the processing of higher-level linguistic information?

3. Determinants of second language learning. How different variables and their interactions affect L2 learning and ‘mould’ an individual learner? Can this be used to personalise L2 teaching?

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

Corvid cognition. This is my top priority given that my corvid palace was saved from closure https://www.theguardian.com/environment/2022/jun/19/queen-of-corvids-the-scientist-fighting-to-save-the-worlds-brainiest-birds https://www.newscientist.com/article/2329991-cambridge-lab-for-clever-birds-saved-from-closure-by-public-donations/

I’m particularly interested in exploring aspects of mental time travel, self control and social cognition, and the use of magic effects to investigate constraints on cognition, i.e. blind spots in seeing and roadblocks in thinking. This work is conducted at the Sub-department of Animal Behaviour at Madingley, but there is also the opportunity to incorporate field work both in Madingley woods and further afield. Cephalopod cognition. I’m fascinated by cephalopods in general. Topics include idynamic camouflage and its relationship to both cognition and magic, as well as other aspects of cognition such as curiosity. This work is generally conducted at the MBL in Plymouth as we do not (yet) have a cephalopod facility in Cambridge. Comparative cognition. Questions about what are the critical comparisons in comparative cognition, from studies investigating similarities and differences in performance on tasks that tap into cognition in children and crows, to questions about convergent evolution in corvids, apes and cephalopods. The lab is genuinely interdisciplinary with additional studies on adult humans, as well as monkeys, elephants, parrots, dogs and dolphins, and transdisciplinary approaches and applications to cognition from philosophy to modelling and AI. Cultural cognition in humans. A relatively recent focus has been to compare the development in young children, and existence in adults, of cultural differences in cognition, from self control and future planning to Theory of Mind especially Level 2 perspective taking. So far these comparisons have focused on Individualistic and collectivist cultures, focusing on differences between UK and Chinese populations.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

[1]. Early social stress (e.g. social deprivation) is known to have dramatic effects on the developing nervous system and often manifests as anxiety and other mood related disorders later in life. The JD group is interested in understanding the biological origins of so-called 'stress memories', the mechanisms underlying their persistence, and how knowing something about their underlying cause can help inform new treatments for mood related disorders in humans. To address these questions we use sophisticated behavioural tasks and procedures in rodents, longitudinal brain imaging, single cell RNA sequencing, and computational modelling.

[2] A further ongoing area of research involves the investigation of inhibitory microcircuits in visual perceptual learning. This specific form of learning is known to depend on the inhibitory neurotransmitter GABA but the precise mechanism is poorly understood. Using a range of convergent approaches, including MR spectroscopy to measure in-vivo GABA levels, touchscreen behavioural tasks, psychopharmacology, GABA biosensors, and intra-cortical drug delivery systems, we aim to provide a detailed understanding of the molecular and cellular mechanisms underlying visual perceptual learning. Such knowledge is relevant to everyday perceptual judgements and disorders such as autism and schizophrenia that implicate impairments in the development of inhibitory microcircuits.

[3] The JD group also a longstanding interest in the brain monoamine systems (dopamine, noradrenaline, serotonin) and uses state-of-the-art genetically engineered approaches to measure the release of monoamine neurotransmitters on rapid timescales with sub-second resolution. This technology is currently being used to understand the conditions that specifically recruit each of the monoamine systems. We are especially interested in the concept of error prediction coding and how anxiety traits affect the recruitment of different monoamine systems during situations where learning rates vary depending on the probability of rewarded outcomes. This research is relevant to depression and anxiety related disorders such as GAD and OCD.

[4] A final general area of interest involves research on the brain and psychological mechanisms of individual vulnerability for drug addiction. We are especially interested in addiction relevant traits (impulsivity, anxiety) and employ longitudinal brain imaging, genomics, circuit level investigations, and computational neuroscience to reveal novel mechanisms underlying individual predisposition for addiction.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

Cognitive computational neuroimaging studies of learning and brain plasticity: Our studies combine ultra-high field brain imaging (7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics that support learning and brain plasticity. AI and Neuroinformatics: We develop and translate AI-guided tools for a) understanding brain processes underlying cognition (e.g. learning and brain plasticity), b) early detection of brain and mental health disorders to inform clinical practice (healthcare, clinical trials). Our research involves working on research cohort and clinical data, developing machine learning models to synthesise biological (brain imaging, genetic) cognitive and epidemiological data, validating AI-guided tools in clinical data, translating these tools for adoption in healthcare and industry settings.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

The ability to remember personally experienced events in vivid, multisensory detail makes an immensely important contribution to everyday life. Such recollection is considered to involve reactivating sensory and perceptual details of an event, and the thoughts and feelings we had at the time, and integrating them into a conscious representation during retrieval. It makes possible a number of critical decision making abilities, such as distinguishing real experiences from those we might have imagined or been told about. However, the cognitive and neural mechanisms that underlie the subjective experience of remembering remain unresolved. The aim of PhD projects in the lab will be to help develop a detailed neurocognitive characterization of subjective memory, to better understand the way it may be impaired in health and disease, and to develop rehabilitation strategies to help those who may have reduced ability to re-experience the past.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

1. Neural network models of transsaccadic integration in natural environments: this project would train artificial neural networks to solve a problem facing the brain in daily life, that of combining information about objects captured across different gaze fixations. Training data will be obtained from mobile eye-tracking in real-world environments, and the models will make predictions that can be tested by human psychophysics experiments in the lab.  

2. Using EEG frequency tagging to index allocation of visual attention and investigate its role in working memory encoding. Rapid Invisible Frequency Tagging (RIFT) is a non-invasive method of tracking the processing of individuated visual objects in the human neural system over time. This project would combine RIFT with psychophysical methods and computational modelling to investigate when and how an attended object becomes a remembered object in the visual environment.  

3. Studying adaptation in the human perceptual system with Extended Reality (XR) technology: this project would measure short-term changes in perceptual judgement induced by exposing participants to virtual environments with distorted visual statistics. Computational models will discriminate between changes in visual encoding versus decoding, and test competing accounts of how the visual system is optimized for natural environments.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

1. The role of uncertainty in social processing (e.g., ambiguous facial expressions) -  Are the difficulties with social processing reported across many psychiatric disorders related to ambiguity in how we represent facial expressions, and are these difficulties affected by common psychiatric medication such as SSRIs? Is social uncertainty processed in the same way as uncertainty in other cognitive domains?

2. Translation of uncertainty tasks across species (humans and rodents) - Deeper understanding pf the mechanisms of uncertainty processing requires forward and back-translation of research across humans and animal models that are commonly used in medical research (e.g. rodents). Projects in this area would be co-supervised by Professor Jeff Dalley.

3. Learning under uncertainty in infancy - Studying how infants learn and adapt to uncertainty required novel neuroimaging tools that are suitable for little people who struggle to keep still. The lab has acquired a cutting-edge Diffuse Optimal Tomography (DOT) system and are interested in PhD students keen to use this method in developmental studies.

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

The project will form part of a wider cohort study investigating microstructural brain development and social cognitive development in typically developing children and adolescents aged 8-18 years. The PhD project will involve analysing data from ultra-high-resolution MRI scans to investigate how brain microstructure relates to behavioural measures from experimental tasks and to mental health outcomes, and how this changes across age during adolescence. 

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

Impact of inflammation on cognition, inflammation and memory development, AI models of infant cognition

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):

Computational Political Psychology: Using clustering and dimensionality reduction techniques to understand how citizens political preferences are structured, and how (or whether) citizens are genuinely organized into distinct ideological groups. Data driven dimensionality approaches are particularly useful for interrogating the structure of political attitudes, for example how well political attitudes fall along one axis (left right), or are better captured by multiple different axes (economic and social). Clustering approaches (K-Means, Latent Class Analysis) are particularly useful for understanding the extent to which citizens are clustered into coherently opposed groups. Across both of these approaches we are keen to extend the focus of political psychology outside of the US and Europe, and test the extent to which different dimensionality structures might be needed to account for the structure of political attitudes in different countries. Similarly with clustering techniques, we are interested in exploring whether different political systems (such as first past the post or proportional representation) result in different levels forms of clustering.  

Experimental Political Psychology: A lot of insights into political psychology rely on a correlational analysis of the beliefs, personality types, moral values or cognitive profiles that are associated with certain forms of political behaviour. As a political psychology lab, we are interested in testing the causal power of those associations through carefully designed experiments. This could take the form of a message framing experiment, where support for a given policy agenda (such as climate change) could be framed in different values (equality vs patriotism), to test whether people have fixed views on a particular policy or are more influenced by the way in which that policy is framed. 

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s):     

Visual Attention and Awareness. A primary focus of the lab is to develop new behavioural measures to profile visual awareness during complex tasks. Findings from this work have motivated novel security screening strategies in our applied work. In collaboration, we are currently expanding this work to include M/EEG correlates.  Social Cognition, with special emphasis on Gaze Perception  A second focus concerns rapid appraisal of social information by human observers, especially in relation to eye gaze. At one level, another person’s eyes are just like other visual stimuli, at another, they indicate the presence of another mind and are seeing as well as being seen. Our previous and current work investigates how high level knowledge that a person can see influences our perception of eyes and responses to them.       

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s): 

1.    Cross-species study of selection and representation of sensory information, for example in collaboration with Dr. Poort or Dr. Beltramo (Department of Physiology, Development and Neuroscience)

2.    Using non-invasive neuroimaging to track disruption and compensation to attentional processing after brain injury (e.g., Stroke)

3.    Novel (including EEG-based) approaches to assessing receptive language in non-speaking autistic individuals

Lab website - https://www.psychol.cam.ac.uk/research/research-centres/influence-and-technology-lab

Lab research interests - Why do we believe some things but not others? Why do we share information about ourselves and the groups we belong to, or groups we dislike? What are the effects of information and propaganda during wartime? How do communication technologies shape how we feel, think, and behave? What makes interventions to address policy challenges effective? And how do we measure these things, with what kind of data? These are some of the overarching questions that the Influence and Technology Lab seeks to answer. To do so, we rely on the most advanced methods in the social and computational sciences, by building on methodological and theoretical frameworks from computer science, psychology, and area studies. The lab has a strong cross-cultural focus, collecting data and conducting experiments in many countries around the world and combining methodological rigour with an in-depth understanding of the societies under study.   

Lab group methods - Computational modelling, Machine learning and AI, Agent-Based Modelling, Surveys, Field Studies, Psychometrics

In addition to PhD proposals on topics that the candidate has devised themselves, I am currently welcoming PhD proposals related to the following topic(s): 

1) The digital manipulation economy: inauthentic content is rife in online environments, from fake likes to sophisticated bot-driven propaganda campaigns. We are interested in understanding the marketplace that sells inauthentic activity, and how we can make its products and services more expensive.  

2) The effects of (information) warfare: modern conflict is kinetic as well as informational. We are interested in understanding how war and information intersect, both during and after (military) intergroup conflict.  

3) The social science of generative AI: As (generative) AI tools become part of our daily routines, understanding how they interact with humans is critical. We are interested in understanding how humans and AI interact, and the downstream effects of these interactions. We are especially interested in how humans interact with multiple AI agents, for example on social media.  

4) The political psychology of social media: Why we consume and share content in online environments, and how doing so impacts beliefs, attitudes, and behaviours, is an important scientific as well as policy question. We want to understand these phenomena in countries around the world, and to understand the causal (psychological) mechanisms that drive people’s behaviour in social networks.  

5) Intervention and implementation science: Addressing societal challenges is a complex task. We are interested in understanding what makes psychological and behavioural interventions (e.g., against polarisation, misinformation, or other societal phenomena) effective rather than merely efficacious (effectively asking how we can take these interventions from the petri dish to the pharmacy) and how to predict interventions’ impact in the real world.  

For more information, see: https://www.psychol.cam.ac.uk/influence-and-technology-lab/research