SMARTTR

SMARTTR wraps mapping capabilities with functions for statistical analysis and network visualization of brain-wide dual-ensemble coronal datasets in R.

Key Features:

• Multi-ensemble atlas registration: Maps traditionally coronally sectioned datasets produced by activity-dependent tagging systems.
• Statistical testing: Provides high-throughput mapping with statistical testing for analysis of multiple ensemble datasets.
• Network visualization: Generates network visualizations for brain-wide network analysis of ensemble data.
• External dataset import: Supports import of external datasets for comparative analysis.
• Overlap analysis: Enables mapping of multiple ensembles including their overlapping populations.

Scientific Applications:

• Memory encoding and retrieval studies: Analyzes immediate early gene ensembles in mice to investigate mechanisms of memory encoding, storage, and retrieval.
• Learned helplessness research: Maps ensembles underlying acquisition and expression of learned helplessness, a robust stress model.
• Network centrality analysis: Identifies changes in regional centrality and influence, such as decreased centrality of spatial and contextual processing regions and higher influence of somatosensory and affective processing regions following inescapable shock.
• Reactivation suppression detection: Detects robust decreases in reactivation activity across cortical, hippocampal, and amygdalar regions as a neurobiological signature of learned helplessness.
• Hypothesis generation: Serves as a hypothesis-generating toolkit for emergent insights from multiple ensemble datasets.

Methodology:

SMARTTR provides a workflow for high-throughput mapping and statistical testing of brain-wide dual-ensemble coronal datasets in R.