A SMARTTR workflow for multi-ensemble atlas mapping and brain-wide network analysis.

Area of Science:

• Neuroscience
• Computational Biology
• Systems Neuroscience

Background:

• Activity-dependent strategies have advanced memory research by labeling immediate early gene (IEG) ensembles.
• Existing methods lack brain-wide mapping capabilities for multiple ensembles and downstream network analysis.

Purpose of the Study:

• Introduce a scalable workflow and R package (SMARTTR) for analyzing and mapping multiple IEG ensembles.
• Apply this workflow to understand the neurobiological underpinnings of learned helplessness (LH).

Main Methods:

• Developed a scalable workflow for analyzing coronally-sectioned datasets from activity-dependent tagging systems.
• Introduced SMARTTR, an R package for ensemble mapping, statistical testing, and network visualization.
• Mapped IEG ensembles in a learned helplessness mouse model involving inescapable shock (IS) and context training (CT).

Main Results:

• Network analysis revealed decreased centrality in spatial/contextual regions and increased influence in somatosensory/affective regions after IS compared to CT.
• The substantia nigra became highly influential during LH expression, suggesting a role in motor activity regulation.
• Inescapable shock (IS) significantly decreased ensemble reactivation across cortical, hippocampal, and amygdalar regions, indicating a potential neurobiological signature of LH.

Conclusions:

• The SMARTTR workflow provides unprecedented brain-wide mapping of multiple IEG ensembles and network analysis.
• This approach offers novel insights into the neurobiology of stress and learned helplessness.
• Suppressed ensemble reactivation may serve as a key neurobiological marker for learned helplessness.