Dr Lena Oestreich

Research Fellow/Senior Research off

UQ Centre for Clinical Research
Faculty of Medicine

Overview

Qualifications

  • Doctorate Degree, University of New South Wales
  • Bachelor of Science (Psychology), University of Groningen

Publications

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Grants

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Supervision

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Available Projects

  • Post-stroke depression is a common neuropsychiatric complication, which develops approximately 3 months after stroke and adversely affects recovery. To date, it is impossible to determine whether a patient is likely to develop post-stroke depression based on the location and size of their infarct alone.

    This PhD program provides a unique cross-disciplinary research opportunity between Neuroscience, Engineering and Medicine. The primary goal of this project is to use machine learning algorithms to identify predictive markers of post-stroke depression. The outcome of this project will facilitate early detection of stroke survivors vulnerable to develop post-stroke depression.

    The successful candidate will have access to large behavioral, cognitive and neuroimaging datasets from healthy controls and diverse samples of stroke survivors. There is potential for student-led ideas for projects investigating post-stroke complications using computational and neuroimaging methods. A working knowledge of computer programming (MATLAB/Python), Linux shell scripting, clinical applications of machine learning algorithms and/or analysis of clinical neuroimaging data ( (particularly MRI) would be of benefit to someone working on this project. Highly motivated students with strengths in computer programming, neuroimaging, engineering and/or computational sciences are encouraged to apply.

  • Post-stroke depression is a common neuropsychiatric complication after stroke, which adversely affects recovery. To date, there is no evidence for a link between lesion location and depressive symptom development. Brain-based mechanisms and risk-factors leading to this debilitating condition are therefore poorly understood.

    This PhD program provides a unique cross-disciplinary research opportunity between Neuroscience, Engineering and Medicine. The primary focus of this project is to develop and test neuroimaging network analytical methods to investigate structure-function brain dynamics in healthy individuals. These methods will then be applied to neuroimaging data collected from post-stroke depression patients to investigate structural and functional changes caused by lesions originating in various regions of the brain. The aim of this project is to elucidate mechanisms by which lesions induce changes in remote brain regions that lead to the development of post-stroke depression.

    The successful candidate will have access to large behavioral, cognitive and neuroimaging datasets from healthy controls and diverse samples of stroke survivors. There is potential for student-led ideas for projects investigating post-stroke complications using neuroimaging methods. A working knowledge of basic computer programming (MATLAB/Python) ), Linux shell scripting and analysis of neuroimaging (MRI) data would be of benefit to someone working on this project. Highly motivated students with strengths in neuroimaging, biological psychology, neuropsychiatry and/or computational sciences are encouraged to apply.

View all Available Projects

Publications

Journal Article

Conference Publication

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

Completed Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Post-stroke depression is a common neuropsychiatric complication, which develops approximately 3 months after stroke and adversely affects recovery. To date, it is impossible to determine whether a patient is likely to develop post-stroke depression based on the location and size of their infarct alone.

    This PhD program provides a unique cross-disciplinary research opportunity between Neuroscience, Engineering and Medicine. The primary goal of this project is to use machine learning algorithms to identify predictive markers of post-stroke depression. The outcome of this project will facilitate early detection of stroke survivors vulnerable to develop post-stroke depression.

    The successful candidate will have access to large behavioral, cognitive and neuroimaging datasets from healthy controls and diverse samples of stroke survivors. There is potential for student-led ideas for projects investigating post-stroke complications using computational and neuroimaging methods. A working knowledge of computer programming (MATLAB/Python), Linux shell scripting, clinical applications of machine learning algorithms and/or analysis of clinical neuroimaging data ( (particularly MRI) would be of benefit to someone working on this project. Highly motivated students with strengths in computer programming, neuroimaging, engineering and/or computational sciences are encouraged to apply.

  • Post-stroke depression is a common neuropsychiatric complication after stroke, which adversely affects recovery. To date, there is no evidence for a link between lesion location and depressive symptom development. Brain-based mechanisms and risk-factors leading to this debilitating condition are therefore poorly understood.

    This PhD program provides a unique cross-disciplinary research opportunity between Neuroscience, Engineering and Medicine. The primary focus of this project is to develop and test neuroimaging network analytical methods to investigate structure-function brain dynamics in healthy individuals. These methods will then be applied to neuroimaging data collected from post-stroke depression patients to investigate structural and functional changes caused by lesions originating in various regions of the brain. The aim of this project is to elucidate mechanisms by which lesions induce changes in remote brain regions that lead to the development of post-stroke depression.

    The successful candidate will have access to large behavioral, cognitive and neuroimaging datasets from healthy controls and diverse samples of stroke survivors. There is potential for student-led ideas for projects investigating post-stroke complications using neuroimaging methods. A working knowledge of basic computer programming (MATLAB/Python) ), Linux shell scripting and analysis of neuroimaging (MRI) data would be of benefit to someone working on this project. Highly motivated students with strengths in neuroimaging, biological psychology, neuropsychiatry and/or computational sciences are encouraged to apply.