Toolbox-Romano-et-al

Toolbox-Romano-et-al analyzes neuronal population calcium dynamics from fluorescence imaging datasets to identify event-related responses and activity-correlated neuronal clusters.

Key Features:

• Comprehensive Computational Workflow: Performs image pre-processing, segmentation, and estimation of significant single-neuron single-trial fluorescence signals.
• Event-Related Neuronal Response Mapping: Maps event-related neuronal responses to link neuronal activity to experimental stimuli or events.
• Detection of Activity-Correlated Clusters: Implements clustering to detect activity-correlated neuronal clusters and dynamically activated neuronal assemblies.
• Exploration and Analysis of Population Dynamics: Analyzes population dynamics and compares detected cluster features against surrogate control datasets.
• Modular and Versatile Design: Provides modular computational modules that can be used independently or integrated into broader analysis workflows.

Scientific Applications:

• Large-scale calcium imaging analysis: Processes large-scale neuronal calcium fluorescence datasets, including data from optogenetics experiments.
• Neuronal circuit dynamics: Characterizes circuit-level dynamics and synaptic interactions through event-related response mapping and assembly detection.
• Distributed population coding and assemblies: Identifies dynamically activated neuronal assemblies to study distributed population coding mechanisms.
• Network-level and disease-relevant studies: Supports investigation of network-level brain functions and analyses relevant to cognitive processes and neurological disorders.
• Statistical validation: Enables assessment of cluster significance by comparison with surrogate control datasets.

Methodology:

Image pre-processing, segmentation, estimation of significant single-neuron single-trial fluorescence signals, mapping of event-related responses, clustering to detect activity-correlated neuronal clusters and dynamically activated assemblies, and comparison of cluster features to surrogate control datasets.