Code used for analysis done in Corbit, et al., 2024
Optogenetic behavior synchronization and analysis code ChR2StimProbGroomStereotype20PulsesExclSeiz.m: takes behavioral scoring event log excel sheet form Noldus Observer (one sheet per video/mouse) and calculates probability of behavior aligned to each trial of laser delivery. Designed to analyze scoring data that also includes marked pre-seizure activity (to exclude) and the stereotypies seen in the ALM-CS optogenetic stimulation experiments. ChR2StimProbRearGroom20Pulses.m: takes behavioral scoring event log excel sheet form Noldus Observer (one sheet per video/mouse) and calculates probability of behavior aligned to each trial of laser delivery. Designed to analyze scoring data that also includes rearing. ChR2StimProbAnyGroomGroomTrials021919.m: takes behavioral scoring event log excel sheet form Noldus Observer (one sheet per video/mouse) and calculates probability of behavior aligned to each trial of laser delivery. Designed to analyze scoring data that also includes “pseudogrooming” seen during laser trials in CS optogenetic stimulation experiments.
Photometry analysis code photometryStep1AlignTraces_allbehav.m: takes in photometry calcium data and manually scored behavior data (Observer) and aligns them relative to the experimental session time photometryStep1AlignTraces_locomotion.m: takes in photometry calcium data and position tracking data (Ethovision) and aligns them relative to the experimental session time photometryStep2GetTraces.m: pre-processes calcium data traces for later use photometryProcessTrials.m: align processed calcium data with behavior trials and plot downsampleforStats.m: downsamples behavior-aligned photometry traces in order to run stats on 0.5s bins photometryGetQuartilesAligned2Stop.m: aligns photometry data to behavior trials but groups trials based on bout length quartiles photometryStatsFromQuartiles.m: runs on quartiles data to quantify peaks in the traces and run group statistics plotBoutLengthHistFromQuartiles.m: runs on quartile data to create histogram showing all bout lengths used in analysis
Posture tracking (SLEAP) analysis and classifer code getBehavTS_CSstim.py/getBehavTS_ALM-CSstim.py: takes in SLEAP posture tracking and associated manual behavior scoring timestamps for preprocessing and alignment getFeatures_CSstim.py/getFeatures_ALM-CSstim.py: calculates features for classifier model based on posture tracking data classifiermodel.py: trains and tests a SVC multi-class model to quantify distinctness of evoked grooming behavior to natural grooming