[Comp-neuro] PhD FELLOWSHIP IN INTERDISCIPLINARY NEUROSCIENCE @ LISBON (pre-selection 20th Mar, application 28th Mar)

Prata, Diana diana.prata at kcl.ac.uk
Fri Mar 15 13:20:48 CET 2019

For divulgence - many thanks.

PhD FELLOWSHIP IN INTERDISCIPLINARY NEUROSCIENCE @ LISBON (pre-selection 20th Mar, application 28th Mar)

The annual FCT state-sponsored PhD scholarship application call is now open (deadline 28th of March - see conditions in https://www.fct.pt/apoios/bolsas/concursos/individuais2019.phtml.en) and is an excellent opportunity to be part of a PhD project at the Biomedical Neuroscience Lab (Diana Prata's lab; dpratalab.wordpress.com<http://dpratalab.wordpress.com>) at the University of Lisbon.

If you are interested in a project within one the research streams below, please contact the PI (diana.prata at kcl.ac.uk<mailto:diana.prata at kcl.ac.uk>) for a skype interview by 20th of March, to ascertain mutual interest. This being ascertained, your FCT PhD scholarship (deadline 28th Mar) application can be associated with one of the lab's projects.

Research stream 1: Neurobiology of Social Cognition

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 is essential for optimal communication and cooperation and is dysfunctional in schizophrenia and other illnesses. We are characterizing the physiology involved in social cognition, for example: how does oxytocin promote social reinforcement learning? What effect does it have in brain and behaviour? How does it interact with other neurotransmitter systems?

Tools. We will study healthy humans and schizophrenia patients with structural and functional neuroimaging (MRI, DTI and MRS), double blind placebo-controlled pharmacological administration, psychological testing, social cognition tasks, eye-tracking, pupilometry, skin conductance response, EEG, DNA/proteomics testing and computational modelling. We use mainly MATLAB, SPSS, and other more specific quantitative data analysis and task presentation software.

Sponsors. European Commission, Portuguese Science and Technology Foundation, and Bial Foundation.

Main collaborations. King’s College London (UK), Emory University (USA), 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, Python and machine learning tools.

Sponsors. Portuguese Science and Technology Foundation, NIHR (National Institute for Health Research, UK)

Main Collaborations. King’s College London (UK), Radboud University Nijmegen (The Netherlands), University of Munich (Germany)

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