Toothy: an interactive platform for dentate spike curation.

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• Dentate spikes (DSs) are hippocampal population events occurring during low-arousal states.
• DSs are classified into two types (DS1 and DS2), with DS2 associated with memory encoding.
• A standardized method for DS detection and classification is currently lacking.

Purpose of the Study:

• To introduce Toothy, an open-source software tool for the detection and classification of DSs in large-scale electrophysiological recordings.
• To provide a standardized and reproducible pipeline for analyzing hippocampal population events.

Main Methods:

• Toothy utilizes a three-step workflow: LFP recording ingestion/preprocessing, hippocampal event detection, and DS type classification via CSD profiles.
• The tool supports multiple recording formats and offers customizable parameters and interactive review.
• Toothy can also detect sharp wave-ripples (SPW-Rs) for comparative analysis.

Main Results:

• Toothy provides a robust and flexible platform for DS detection and classification.
• The tool enables reproducible analysis of hippocampal dynamics.
• Facilitates comparative studies between DSs and SPW-Rs.

Conclusions:

• Toothy offers a standardized, reproducible pipeline for DS detection and classification.
• This tool advances research into hippocampal dynamics and memory encoding.
• Enables broader investigation of neural population events across diverse research settings.