Dynamic shift in Behavioral Adaptation: contribution of genetically distinctive nucleus accumbens’ neuronal sub-types for depression

Major depressive disorder (MDD) affects ~5% of the global population and is a leading cause of disability. It is characterized by symptoms such as reduced motivation, heightened responses to negative experiences, social withdrawal, and anhedonia. However, patients differ widely in symptom profiles and recovery trajectories, and fewer than half respond effectively to current treatments. This highlights a critical need to better understand the biological mechanisms underlying depression.
Although several brain regions are known to be affected in MDD, the contribution of specific neuronal populations remains unclear. This gap is particularly relevant given the high cellular diversity within these regions, which may help explain differences in individual vulnerability and treatment response.
The nucleus accumbens (NAc), a key region involved in motivation and reward processing, has been strongly implicated in depression. Traditionally, its neurons are classified into two main types based on dopamine receptor expression (D1 and D2). However, emerging evidence—including our own findings—shows that these neurons are highly heterogeneous, with distinct subpopulations present in both healthy and depressive states.
DynamicDep aims to identify and characterize these neuronal subpopulations and their roles in depression-related behaviors, including resilience and susceptibility, in both sexes. Using advanced techniques such as spatial transcriptomics, calcium imaging, and optogenetics in clinically relevant animal models, this project seeks to uncover the neural circuit mechanisms underlying depression and support the development of more targeted therapies.

Funding Agency

Fundação para a Ciência e Tecnologia; COMPETE2030

 

 

Funding Agency

Fundação para a Ciência e Tecnologia; COMPETE2030

 

 

Project Reference

COMPETE2030-FEDER-00708300; 2023.17631.ICDT (https://doi.org/10.54499/2023.17631.ICDT)

Project Members

Main Project Outcomes

S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.

“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.