Dataset Information

Database analysis of simulated and recorded electrophysiological datasets with PANDORA's toolbox.

ABSTRACT: Neuronal recordings and computer simulations produce ever growing amounts of data, impeding conventional analysis methods from keeping pace. Such large datasets can be automatically analyzed by taking advantage of the well-established relational database paradigm. Raw electrophysiology data can be entered into a database by extracting its interesting characteristics (e.g., firing rate). Compared to storing the raw data directly, this database representation is several orders of magnitude higher efficient in storage space and processing time. Using two large electrophysiology recording and simulation datasets, we demonstrate that the database can be queried, transformed and analyzed. This process is relatively simple and easy to learn because it takes place entirely in Matlab, using our database analysis toolbox, PANDORA. It is capable of acquiring data from common recording and simulation platforms and exchanging data with external database engines and other analysis toolboxes, which make analysis simpler and highly interoperable. PANDORA is available to be freely used and modified because it is open-source (http://software.incf.org/software/pandora/home).

SUBMITTER: Gunay C

PROVIDER: S-EPMC2786174 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Database analysis of simulated and recorded electrophysiological datasets with PANDORA's toolbox.

Günay Cengiz C Edgerton Jeremy R JR Li Su S Sangrey Thomas T Prinz Astrid A AA Jaeger Dieter D

Neuroinformatics 20090528 2

Neuronal recordings and computer simulations produce ever growing amounts of data, impeding conventional analysis methods from keeping pace. Such large datasets can be automatically analyzed by taking advantage of the well-established relational database paradigm. Raw electrophysiology data can be entered into a database by extracting its interesting characteristics (e.g., firing rate). Compared to storing the raw data directly, this database representation is several orders of magnitude higher ...[more]

PMID: 19475520

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Project description:BackgroundIn a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.ResultsHere, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.ConclusionsWith the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.

Project description:BackgroundSimulation-based education (SBE) affords learners opportunities to develop communication skills, including those related to pediatrics. Feedback is an integral part of SBE, and while much research into feedback from multiple sources exists, the findings are mixed. The aim of this comparative study was to replicate some of this work in a novel area, pediatric medical education, to better understand how multisource feedback (self, educator, and simulated parent) may inform learning and curriculum design.MethodsDuring their pediatric rotation, medical students participated in a consultation with a simulated parent, engaged in video-assisted self-reflection, and received feedback from both an educator and the simulated parent through an e-learning platform. The Pediatric Consultation Skills Assessment Tool (PCAT) was used for self-assessment and educator feedback, and the Consultation and Relational Empathy (CARE) measure was used for simulated parent feedback.ResultsOur results showed that high-performing students underrated their performance, and low-performing students overrated their performance. Feedback from multiple sources helps to identify both areas of weakness in student performance and areas of weakness in student self-appraisal. Overall, general areas of weakness identified for the learners related to making contingency plans and providing easy-to-understand explanations for simulated parents. Some simulated parent feedback did not align with educator and student ratings, highlighting the value of including the simulated parent perspective. Our findings question whether a third party can reliably judge the simulated parent's level of understanding.ConclusionMultisource feedback allows students to develop layered insights into their performance and supports self-appraisal. Aggregating feedback through an e-learning platform allows educators to gain greater insights into the strengths and weakness of students and design a more tailored teaching plan to support student needs.

Dataset Information

Database analysis of simulated and recorded electrophysiological datasets with PANDORA's toolbox.

Publications

Database analysis of simulated and recorded electrophysiological datasets with PANDORA's toolbox.

Similar Datasets

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