Ensuring the quality of the Synaptic Physiology Dataset is of critical importance. We applied quality control criteria at several stages of data processing and analysis. During the processing stages, in order for data to proceed into successive stages it had to pass quality control for the prior stage. During analysis of connection properties, each analysis (e.g., strength, kinetics, short-term plasticity) had independent quality control metrics. Below is a workflow of the quality control metrics required for inclusion at each stage of processing and analysis.
Data was first processed to ensure that it was of good enough quality to detect a connection
Criteria for inclusion
|Step 1: Evaluate the quality of a single stimulus trial. This is meant as a relatively inclusive criteria to ensure that the data is complete and cells are not dead|
|Data to be evaluated: Postsynaptic recording of one stimulus trial||
Step 2: Evaluate quality of individual pulse within a stimulus train. If data passed Step 1 we want to ensure that the response to each stimulus pulse within the train is of good quality
|Data to be evaluated: Pre- and postsynaptic recording in response to one stimulus pulse||
|Step 3: Evaluate if the postsynaptic response meets criteria for detecting a connection.|
|Data to be evaluated: postsynaptic recording in response to one presynaptic spike||
|Step 4: If a connection was detected it was confirmed and postsynaptic responses were fit|
During characterization of a synapse, different QC criteria were applied based on the metric that was being analyzed.
|Latency||Average response curve fit matches user-passed fit|
|Kinetics||Average response curve fit matches user-passed fit|
|Resting-state amplitdue||Average response passes Step 3 for appropriate connection class|
|Short-term plasticity||Average response passes Step 3 for appropriate connection class|