Event-dependent Analysis
Event-dependent Analysis - Related Videos
11:19
Spatiotemporal Analysis of Cytokinetic Events in Fission Yeast
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Cited by 6 •
2017
The fission yeast, Schizosaccharomyces pombe is an excellent model system to study cytokinesis, the final stage in cell division. Here we describe a microscopy approach to analyze different cytokinetic events in live fission yeast cells.
14:58
Methods for the Modulation and Analysis of NF-κB-dependent Adult Neurogenesis
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Cited by 1 •
2014
Methods for the manipulation and analysis of NF-κB-dependent adult hippocampal neurogenesis are described. A detailed protocol is presented for a dentate gyrus-dependent behavioral test (termed the spatial pattern separation-Barnes maze) for the investigation of cognitive outcome in mice. This technique should also help enable investigations in other experimental settings.
14:33
Event-related Potentials and the Oddball Task
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Given the overwhelming amount of information captured by the sensory organs, it is crucial that the brain is able to prioritize the processing of certain stimuli, to spend less effort on what might not be currently important and to attend to what is. One heuristic the brain uses is to ignore stimuli that are frequent or constant in favor of stimuli that are unexpected or unique. Therefore, rare events...
10:05
In Vitro Analysis of PDZ-dependent CFTR Macromolecular Signaling Complexes
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Cited by 7 •
2012
Cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel, has been reported to interact with various proteins and regulate important cellular processes; among them the CFTR PDZ motif-mediated interactions have been well documented. This protocol describes methods we developed to assemble a PDZ-dependent CFTR macromolecular signaling complex in vitro.
09:12
DNAzyme-dependent Analysis of rRNA 2’-O-Methylation
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2019
Here we present a protocol for DNAzyme-dependent cleavage of RNA. This enables fast and site-dependent analysis of RNA 2’-O-methylation. This approach can be used for the preliminary or major assessment of snoRNA activity.
FAQs
Alignment places responses from separate occurrences on a common temporal reference. This allows analysts to compare what happens at equivalent times relative to an event, rather than relying only on overall activity across a session. Using onset or offset as the reference can reveal whether a behavioral change precedes, follows, or terminates with the defined event.
Pre-event windows provide a reference for behavior before the event, while post-event windows show changes that follow it. Comparing these periods helps separate event-linked responses from ongoing activity that would otherwise appear part of the same pattern. The selected windows therefore shape whether changes in probability, frequency, duration, or latency are detected.
Different measures answer different behavioral questions. Response probability indicates how often a defined response occurs, frequency captures repeated occurrence, duration describes how long it persists, and latency records the delay relative to the event. Selecting among them lets the analysis match the outcome to questions about stimulus-response relationships, decisions, learning, or environmental cues.
Event-dependent analysis supports comparisons across trials, experimental conditions, or groups by applying the same event reference and behavioral measures to each dataset. Consistent alignment makes temporal differences easier to evaluate, while changes in response probability, frequency, duration, or latency provide distinct evidence about how behavior varies. This is useful when overall activity alone cannot show context-specific effects.
Begin by identifying and recording the defined events and the behavioral responses to be examined. Align each behavioral record to the onset or offset of the relevant event, divide the record into before and after windows, and calculate the selected measure. The resulting event-linked values can then be compared across trials, conditions, or experimental groups.
It is especially useful when a study asks how behavior changes in relation to a specific stimulus, environmental cue, decision-making event, or learning-related event. In behavioral research, the method can expose temporal patterns that session-wide summaries miss, helping investigators evaluate stimulus-response relationships and interactions between environmental cues and actions.