Labo Neurosciences Sensoriel Comportement Cognition Director : Rmi - - PowerPoint PPT Presentation

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Aug 30, 2023 389 likes •695 views

Labo Neurosciences Sensoriel Comportement Cognition Director : Rmi Gervais Samuel Garcia Data processing ingineer, CNRS Thmes Labo : Olfaction and Audio Goal : mix 2 analysis spikes study oscillations study For who ? 2 levels for users

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Labo Neurosciences Sensoriel Comportement Cognition Director : Rémi Gervais Samuel Garcia Data processing ingineer, CNRS Thèmes Labo : Olfaction and Audio

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Goal : mix 2 analysis

spikes study

scillations study

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For who ? 2 levels for users :

a graphic user interface for non-scripters A lower level : script with python + scipy + matplolib + « simplified database storage »

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Based on :

Scipy : scientific python module MySQL : famous database server

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Avantages :
centralized ( Client-Server design)
dataming (data and meta-data at the same place)
efficient (MySQL queries)
data sharing (you can open your server)
analysis sharing (same database structure)
free !

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Principle

File File File File database Extraction of data and meta data signal display Spike detection Oscillations detection Analysis with the GUI Analysis with python script (or other)

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database acces

MySQL Server Data Web Server Internet Explorer Firefox Excel Statistica Open office Matlab C/C++ R DLL ODBC ... python

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Database Structure

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SQL Example :

Select all electrode nb 5 between 6th of june and 12th of july :

SELECT id_electrode FROM electrode , trial WHERE electrode.id_trial = trial.id_trial AND electrode.num_channel =5 AND trial.thedatetime > 2007-06-10 AND trial.thedatetime < 2007-07-12

take all the gamma

scillations :

SELECT id_oscillation FROM oscillation WHERE

scillation.freq_max > 35

AND oscillation.freq_max < 90

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Graphic User Interface

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Spikes

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Spike detection and spike sorting : methods compilation

Filtering Détection Projection Clustering FFT Médian windowed

(Buterworth ...)

Threshold No ICA PCA

(Wavelet ...)

K-Mean Superparamegnetic

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Spike detection and sorting : Snapshot

Each spike is stored in the database

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Oscillations

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LFP Oscillations analysis : new approach

Classical analysis : Morlet wavelet time frequencie map (scalogram) New approach : Use the scalogram for extracting oscillations Each oscillation is stored in the database Avantage : Quantitative study (length, energy, phase , frequencie ...) Article : A wavelet-based method for local phase extraction from a multi-frequency oscillatory signal J Neurosci Methods Stéphane G. Roux , Tristan Cenier, Samuel Garcia, Philippe Litaudon,Nathalie Buonviso

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Oscillation detection : principle

Morlet scalogram : local maxima extraction Ridge extraction : time-frequencie line Oscillation in time domain

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Oscillation detection : GUI

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Some examples of analysis ...

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Instantaneous windowed Spike rate

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Interval Inter spike Histogram

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Signal averaging

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Scalogram averaging

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Spike phase on oscillation

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Respiration detection

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Link http://neuralensemble.org/trac/OpenElectrophy

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Internal Code Design

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python Class (database_storage) for :

simplifying reading/wrtiting/modifying datas
able to store numpy array
delete recursively hierachic datas
automagic table creation
automagic update database structure

For Each table, you subclass and declare all fields. And you can work immediatly!

MySQL is great for reading and exploring data but heavy for writing and modifying data

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