McPartland Lab at Yale investigates how brain circuits support learning, memory, and decision making using in vivo recordings and genetic tools. The lab focuses on how ensembles of neurons encode experiences and how these codes are altered in disorders such as depression and addiction.
Researchers combine behavioral tasks, electrophysiology, and imaging to test hypotheses about flexible circuit computation. This perspective positions McPartland Lab as a key group for translational circuit neuroscience at Yale.
| Researcher | Role | Expertise | Key Focus |
|---|---|---|---|
| Heather A. Sullivan | Postdoctoral Researcher | In vivo electrophysiology | Prefrontal coding during memory tasks |
| David J. McLean | Graduate Student | Optogenetics & behavior | Circuit control of reinforcement learning |
| Emily R. Morris | Research Scientist | Electrophysiology & analysis | Network dynamics in depression models |
| McPartland Lab | Principal Investigator | Circuit neuroscience | Prefrontal function & pathology |
Prefrontal Circuit Function in Learning and Memory
The lab examines how prefrontal neurons represent ongoing tasks and expected outcomes. By recording ensembles during decision making, they identify transient cell assemblies that support flexible behavior.
Findings suggest that coordinated activity across layers underlies accurate prediction and rapid updating after feedback. These mechanisms are studied using maze tasks, reversal learning paradigms, and cue-controlled rewards.
Circuit Dysfunction in Depression Models
McPartland Lab models anhedonia and cognitive bias by perturbing circuit activity in rodents. They measure how prefrontal-basal ganglia loops bias action selection toward negative outcomes.
Electrophysiological and chemogenetic interventions reveal timing-locked changes that precede behavioral symptoms. This work provides a biophysical framework for how circuit miscomputation contributes to mood disorder traits.
Optogenetic and Chemogenetic Circuit Control
Using Cre-dependent opsins and designer receptors, the group manipulates defined pathways with millisecond precision. Targeted activation or silencing of prefrontal or striatal projections claries causal links to behavior.
Combined with quantitative behavior, these tools allow real-time testing of network models. The integration of control and recording supports closed-loop experiments that refine circuit theories.
Behavioral Electrophysiology and Data Analysis
Rigorous spike sorting, cross-validation, and drift correction ensure high-quality unit isolation. Head-fixed and freely moving preparations are analyzed with state-space and population decoding methods.
Open-source analysis pipelines promote transparency and reproducibility. Collaborative efforts with data science groups strengthen statistical power and multimodal data integration.
Future Directions for Circuit Neuroscience at Yale
McPartland Lab aims to extend long-term recordings during naturalistic behavior and develop closed-loop interventions that rescue maladaptive circuit patterns.
Efforts to expand multimodal datasets and theoretical partnerships will accelerate discovery. The lab is positioned as a hub for training the next generation of circuit neuroscientists focused on mental health.
- Target prefrontal-basal ganglia circuits in learning and depression models
- Combine electrophysiology, optogenetics, and behavior for causal insights
- Use open pipelines and collaborative analysis to ensure reproducibility
- Translate rodent circuit findings to human biomarkers and treatment strategies
- Develop closed-loop neuromodulation tools for circuit-based therapies
FAQ
Reader questions
What model systems and species does the McPartland Lab typically use for in vivo recording studies?
The lab primarily uses mice and rats, with standard operant and maze tasks designed to probe prefrontal network dynamics during learning and decision making.
Which techniques are combined to link circuit activity to behavior at the systems level?
The lab integrates in vivo electrophysiology, optogenetics, chemogenetics, imaging, and quantitative behavioral analysis to build causal models of circuit computation.
How do findings from rodent work translate to understanding human depression and addiction?
Observed circuit patterns and behavioral biases in rodents guide translational hypotheses, informing clinical studies of network biomarkers and treatment targets in patient populations.
What open science practices does the McPartland Lab follow to ensure reproducibility?
The group shares validated analysis code, standardized protocols, and curated datasets to support independent verification and collaborative model development across the field.