Projects available at the Laboratory of Behavioral Genetics
Highly motivated students interested in performing a project in the Laboratory of Behavioral Genetics (https://www.epfl.ch/labs/lgc/) have two possibilities:
- Either choosing from the list of specific projects proposed below.
- Or contacting Prof. Carmen Sandi ([email protected]) directly to discuss alternative projects based on ongoing work in the lab.
Combined to machine learning, recent studies have shown that human BOLD activity in specific brain regions are capable of predicting the level of aversion elicited by aversive stimuli, and the role of each region depending on the type of stimuli. Understanding the relationship between different components of motivated behavior, including aversive stimuli, and the role of the motivational brain circuit is crucial to better disentangle the role of each brain region. The project will enable students to get their hands-on fMRI datasets and investigate different ML algorithms to predict different motivational components. This project requires basic knowledge in programming (Matlab, Python), machine learning, data visualization and signal processing.
As acquiring a sufficiently big imaging dataset to apply ML on is incredibly challenging, this project is a unique opportunity as most of the data has already been acquired. We are looking for an outstanding and motivated student to help both with finishing data acquisition as we will continue acquiring data in the course of the project, as well as testing different machine learning approaches to predict motivated behavior using brain activity.
Suggested publication: https://www.nature.com/articles/s41593-022-01082-w (paper cited above)
Interested candidates should contact Arthur Barakat: [email protected]
Impairments in motivated behaviour are a key feature in many stress-related disorders. Here, we investigate the effects of stress on motivated behaviour in rats differing in anxiety. We use a combination of techniques including, but not limited to, behavioural analyses (EPM, operant conditioning), microdialysis, immunofluorescent detection and quantification of protein expression, characterization of mitochondrial function and antisense-mediated modulation of gene expression.
Interested candidates should contact Prof. Carmen Sandi: [email protected]
There is great interest in understanding factors and mechanisms defining inter-individual differences in the vulnerability to develop stress-related disorders. The physiological stress response requires good functioning of both autonomous nervous system (ANS) and hypothalamus-pituitary-adrenal (HPA) axis. Our lab has developed a genetic rodent model of increased vulnerability to develop post-traumatic stress disorder (PTSD) that presents abnormal (either too high or too low) corticosterone responses to stressor exposure, as well as impaired vagal tone.
In this project, we will investigate the neurobiological mechanisms involved in impaired fear extinction in rats that show abnormal glucocorticoid responses to stress. In particular, the candidate will use a combination of techniques including behavioural (fear conditioning), immunohistochemistry to analyse relevant protein markers of fear consolidation, and statistical data analyses.
Interested candidates should contact Prof. Carmen Sandi: [email protected]
The combination of Virtual Reality (VR), motion tracking, autonomic response recording and EEG provides a versatile and information enriched way to study human behavior and neurophysiology in a laboratory setting. The analysis of the resulting multivariate datasets is a challenging task requiring a combined knowledge from different areas like signal processing, machine learning, statistics and data visualization.
We are looking for an outstanding student to help with both pre-processing and analysis of the output multivariate datasets comprised of full body motion, eye tracking, pupil dilation, respiration, heart rate, skin conductance, EMG, and EEG.
The student is expected to contribute to the development of computational models that predict stress response using physiological signals and EEG.
Although we expect the student to have basic knowledge in the aforementioned techniques and programming skills (Matlab and/or R and/or Python), this work is a learning experience and will offer expertise in an important combination of techniques.
Interested candidates should contact Prof. Carmen Sandi: [email protected]
The student will contribute to the development and piloting of a biofeedback application that integrates physiological signals (heart rate, heart rate variability, skin conductance and breathing rate). The aim is to study the effectiveness of our protocol to reduce anxious behavior or stress responses. Feedback can be delivered on a computer screen or later, via an immersive environment through a head mounted display, depending on the student’s progress.
Although we expect the student to have basic knowledge in the aforementioned techniques and programming skills (Matlab and/or R and/or Python), this work is a learning experience and will offer expertise in an important combination of techniques.
Interested candidates should contact Prof. Carmen Sandi: [email protected]