We do not have any open postdoc or PhD positions at the moment. Please contact Athina Tzovara for inquiries.
The human brain is characterized by rich neural dynamics, which are the result of coordinated electrical neural activity. Stimuli from our environment, like sounds, trigger strong reactions in distributed brain regions. In our recent work, we are studying how the human brain processes sounds from the environment via rich recordings of neural activity in patients with epilepsy.
Computational techniques are increasingly employed to analyze the large amounts of data that are generated from invasive recordings of neural activity of the human brain. In this project, we will employ machine learning and simulation techniques to analyse neural signals, with the goal of better understanding how our brains react to sounds. These signals come with incredible temporal and spatial resolution and are thus ideal to study brain processes at the micro- and mesoscopic levels.
The project is suitable for a Master thesis project. The student working on it should be motivated to program in Python for analyzing rich datasets. This project will give experience with machine learning, signal processing, neuroscience and working with human data. No prior experience with neural signals analysis is required.
For more information please contact:
Riccardo Cusinato: riccardo.cusinato@unibe.ch
Athina Tzovara: athina.tzovara@unibe.ch
Computational techniques are increasingly used in the field of neuroscience. Signal processing and machine learning have promising clinical applications in automating the detection and characterisation of pathological patterns of neural activiy.
One tool that is commonly used to measure neural functions is electroencephalography -EEG-. EEG is a non-invasive technique that measures time-series of electric activity of the brain, though electrodes placed on the scalp. EEG is used as a diagnostic tool in neurological disorders, like epilepsy or coma. EEG recordings in coma patients carry information about the integrity of neural functions in the absence of consciousness, and can be used to predict the patients’ outcome and chances of survival. Recent work from our group has characterised patterns of EEG resposnes to sounds during the first day of coma. We showed that neural synchrony of EEG activity is predictive of patients’ outcome 3 months later, and that neural complexity is indicative of consciousness levels. However, it is not known how the synchrony and complexity of neural responses evolve over time.
This master thesis will use signal processing and machine learning techniques to analyse a rich EEG dataset of coma patients recorded over the first two days of coma. The goal is to evaluate how neural signals of coma patients evolve over time, and establish clinical predictors of their outcome.
The student working on this project will gain experience in signal processing of time series data, machine learning algorithms for biomedical data, and in the development of clinical biomarkers.
For more infomration please contact:
Sigurd Alnes: sigurd.alnes@inf.unibe.ch
Athina Tzovara: athina.tzovara@inf.unibe.ch
Interested in doing a master thesis in computational neuroscience ? We are about to start a prospective trial of forecasting seizures for people with epilepsy and we need your help. Please convince us that you are the passionate student who can bring the required coding competences and motivation to crack an important clinical problem.
At the University hospital of Bern (Inselspital) and the University of Geneva we are starting a new seizure forecasting project in 2022, duration 6-12 months, to be agreed upon. Part-time home-based work is possible, weekly meetings in Bern or Geneva are expected. The team is young and dynamic and includes engineers, biologists, physicists, and neurologists. The candidate will be trained conjointly by a computational neuroscientist (T. Proix; https://ndlab.ch/) and a neurologist (M. Baud, www.neuro-elab.com) who both specialize in quantitative neuroscience research. The candidate will mostly work on computational problems, but, if language skills allow (German or French) will have opportunities to interact directly with chronic epilepsy patients in the neurology department for the acquisition of intracranial EEG recordings. The task at hand will combine software development, EEG signal processing and machine learning.
Please send us your complete application (CV, motivation letter, references, etc…) to:
Isolated REM-sleep behavior disorder (iRBD) is an early stage of alpha-synucleinopathy and may occur in isolation more than 10 years before the diagnosis of PD or as part of its symptomatology, then termed secondary. Clinically, it is characterized by vivid dreams and their acting out during sleep. Currently, there is no available biomarker indicating disease progression of iRBD or its conversion to Parkinson’s disease. In analogy to Alzheimer’s disease, we know that a disturbance of the sleep-wake rhythm and circadian regulation occurs before the onset of full-blown Parkinson’s disease.
A method that can detect subtle early signs is actigraphy. Actigraphy uses continuous measurement of movement (acceleration moments) per minute over several days. Advantages of this method are numerous: on one hand its clinical reliability as well as good acceptance by patients, and on the other hand, the objective reliability and subsequent statistical evaluation of the measured values.
The goal of this master thesis is to analyze a rich actigraphy dataset. The student will then use this method retrospectively in a mixed cohort of patients with isolated and secondary RBD in order to examine the analysis results of the clinical subgroups in comparison to the control group and, in the case of several measurements of individual patients, to analyze the data over the course of the disease follow up. For this project programming skills in Python, Matlab or R are needed and interest in analyzing actigraphy data, recorded from wearable devices. The student that will work in this project will gain experience analyzing rich actigraphy datasets, and will have the opportunity to apply advanced data analysis methods on a clinical application. For more information, please contact:
Dr. Carolin Schäfer, Oberärztin, SWEZ, Neurologie, Inselspital : Carolin.schaefer@insel.ch
Prof. Athina Tzovara: athina.tzovara@inf.unibe.ch