If you would like to do research (e.g. as MSc, PhD student or Post-Doc) in the context of one of the 3 projects streams below, and have a degree in biomedical engineering, statistics, computational modeling/bioinformatics, medicine, biology, psychology, or related fields, email firstname.lastname@example.org.
Project Stream 1. The Neurobiology of Social Cognition.
Keywords. Oxytocin, genetics, neuropharmacology, neuroimaging, cognitive empathy, theory-of-mind, trust, cooperation, reward, reinforcement learning, mirror neurons, emotion recognition, anorexia nervosa, schizophrenia.
Context. Understanding the neurochemistry and circuitry mediating social cognition is key to treat a large range of neuropsychiatric disorders – as social deficits are often present at their origin and often do not subside with treatment. Working out what others think, intend and feel (i.e. cognitive empathy or theory-of-mind) is essential for optimal communication and cooperation and is dysfunctional in schizophrenia, borderline personality disorder, drug addiction, anorexia nervosa, and autism, among others. We are characterizing what molecules and brain pathways are involved in social cognition, for example: how does oxytocin promote cognitive empathy? Where does it act? What effect does it have in brain and sympathetic function?
Tools. We study healthy humans and patients with structural and functional neuroimaging (MRI, DTI and MRS), double blind placebo-controlled pharmacological and TMS administration, psychological testing, social cognition tasks, eye-tracking, pupilometry, skin conductance, EEG, DNA testing and computational modelling. We use mainly MATLAB, SPSS, and other more specific quantitative data analysis and task presentation software.
Collaborations. King’s College London (UK), Emory University (USA), IST (Portugal), ISCTE (Portugal), FPUL (Portugal), Champalimaud Neuroscience (Portugal), ISPA (Portugal), and The Netherlands Institute for Neuroscience (The Netherlands).
Project Stream 2. Multimodal biomarkers to predict the onset and prognosis of neuropsychiatric illnesses.
Keywords. Genetics, neuroimaging, environment, clinical biomarkers, schizophrenia, autism, Alzheimer’s, Parkinson’s.
Context. Psychiatry and, to a lesser extent, neurology are still fields of medicine that take very little advantage of quantitative, biological and objective measurements – with a lot of trial-and-error and one-size-fits-all therapeutics. This may be why diagnosis, prediction of prognosis and response to treatment are relatively inaccurate, late and expensive. For example, about a third of Alzheimer’s cases go on mis- or under-diagnosed; it is still undetected which one third of people with at-risk symptoms for schizophrenia go on to develop this chronic illness, and about one quarter of schizophrenia patients do not respond to their first line of treatment. Can we capitalize on the existing information in brain scans and other quantitative measurements to assist clinicians in deciding on patients’ diagnosis or prognosis, earlier and more accurately than currently – so that the correct treatment can start as soon as possible?
Tools. We are developing pattern recognition algorithms that can statistically predict the level of personalized risk of each new patient. To train these algorithms, we use pre-existing samples (free online or our own) containing neuroimaging and also genetic, psychological, environmental and clinical data. We use mainly MATLAB and machine learning tools.
Collaborations. King’s College London (UK), IBEB-FCUL (Portugal), Radboud University Nijmegen (The Netherlands), University College London (UK).
Project Stream 3. Genetic underpinnings of human brain function and structure.
Keywords. Genetics, neuroimaging, environment, verbal fluency, white matter, grey matter, brain connectivity, dopamine synthesis, fMRI, sMRI, DTI, PET, schizophrenia, bipolar disorder, polygenic risk scores, GWAs.
Context. Several aspects of brain function and structure are known to be highly heritable but little is known about what specific genes contribute to them. For example, while specific genetic variations have been associated with cognitive abilities and susceptibility to many psychiatric illnesses, we still do not know how they operate or increase risk. How do genetic variations modulate executive function such as verbal fluency and risk of bipolar disorder and schizophrenia? We will investigate their impact directly on brain activation, anatomy and dopamine synthesis.
Tools. We use an existing database of controls and bipolar disorder and schizophrenia patients, in whom MRI data (functional and structural MRI, Positron Emission Tomography (PET) and Diffusion Tensor Imaging –DTI) and genotyping (including genome-wide GWA) human data has been collected, to correlate genetic with neuroimaging measurements in healthy humans, and patients with schizophrenia and bipolar disorder. We mainly use MATLAB, SPM, FSL, free surfer and MIBCA software.
Collaborations. King’s College London (UK) and Oxford University Hospitals (UK).