Neural representation of social information in prefrontal and somatosensory cortical areas of normal versus socially amnesic mice
ABG-129155 | Thesis topic | |
2025-03-05 | Public funding alone (i.e. government, region, European, international organization research grant) |

- Psychology, neurosciences
- Biology
- Health, human and veterinary medicine
Topic description
The NeuroSchool PhD Program of Aix-Marseille University (France) has launched its annual calls for PhD contracts for students with a master's degree in a non-French university and for international co-supervised PhDs.
This project is one of the proposed projects. Not all proposed projects will be funded, check our website for details.
State of the Art
Social interactions are fundamental for various behaviors such as cooperation, protection, and mating, relying on the ability to form social memories. In rodents, well-established behavioral paradigms assess social interactions and memory. The medial prefrontal cortex (mPFC) is known to play a role in these processes, but its precise neural encoding remains unclear. Additionally, recent studies suggest that the somatosensory cortex may also contribute to social information processing. However, the mechanisms by which cortical excitatory neurons encode social memory and interactions, particularly in the context of social amnesia, remain poorly understood.
Objectives
This study aims to:
- Investigate how excitatory neurons in the mPFC encode social interactions and memory at the single-cell and population levels.
- Determine the extent to which excitatory neurons in the somatosensory cortex contribute to social information encoding.
- Examine alterations in social coding in these cortical areas in Neurod2 knockout (KO) mice, a model of social amnesia.
Methods
The study utilizes INSCOPIX microendoscopic calcium imaging to record neuronal activity in behaving mice. Social memory tasks involve controlled interactions with familiar and novel conspecifics. Neural recordings will be analyzed using machine learning techniques, such as support vector machines and Bayesian decoders, to identify behaviorally relevant neuronal ensembles. Data from wild-type (WT) mice have already been collected, and the same procedures will be applied to Neurod2 KO mice to compare cortical encoding mechanisms.
Expected Results - We anticipate that:
- mPFC and somatosensory cortex exhibit distinct but complementary roles in encoding social interactions.
- Neurod2 KO mice show significant alterations in neuronal ensemble dynamics related to social memory.
- Machine learning models will accurately predict social partner identity based on neural activity, highlighting the specific contribution of cortical regions to social behavior.
Feasibility
This project is supported by an existing dataset from WT mice, ensuring methodological feasibility. Ethical approval for experiments involving animals has been obtained under the APAFIS agreement, complying with ethical guidelines for neuroscience research. The experimental and computational frameworks are well-established, enabling efficient data collection and analysis in Neurod2 KO mice.
Starting date
Funding category
Funding further details
Presentation of host institution and host laboratory
Within Aix Marseille Université, NeuroMarseille brings together 8 research laboratories and NeuroSchool, a graduate school in neuroscience, to increase the attractiveness of the university, international collaborations, interdisciplinarity, links with the clinical and industrial worlds and the integration of students into professional life.
Launched in July 2018, NeuroSchool unifies and harmonizes the training of the third year of the Bachelor of Life Sciences (Neuroscience track), the Master's and the PhD in Neuroscience.
PhD title
Country where you obtained your PhD
Institution awarding doctoral degree
Graduate school
Candidate's profile
- Master's degree from a non-French university in neuroscience or related field
- Fluent in English
We aim to recruit a highly motivated and talented individual with a strong interest in machine learning, quantitative analyses, and the neural bases of cognition and neurodevelopmental disorders. While prior experience in Python programming is not mandatory, it would be a valuable asset. The fellow’s workload will be distributed approximately as follows: 30% wet lab work (data acquisition, in collaboration with full-time engineer) and 70% dry lab tasks (data analysis).
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