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Network Extraction and Analysis in CARMEN . Jennifer Simonotto Marcus Kaiser Evelyne Sernagor Stephen Eglen. Outline. Research question Types of analysis done B urst analysis: ISI, IBI E pisode extraction: CDF N etwork analysis: CC, APL, CD

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network extraction and analysis in carmen
Network Extraction and Analysis in CARMEN

Jennifer Simonotto

Marcus Kaiser

Evelyne Sernagor

Stephen Eglen

outline
Outline
  • Research question
  • Types of analysis done
    • Burst analysis: ISI, IBI
    • Episode extraction: CDF
    • Network analysis: CC, APL, CD
  • Services available on CARMEN Portal (w/ live demo if network behaves!)
    • DUDE for spike detection and noise clean-up (spikes file output)
    • Fourplot to visualize data (plots output)
    • Burst detection and analysis (w/plots and .csv files output)
    • Episode detection and analysis (w/plots and .csv files output)
    • Network extraction and analysis (w/plots and .csv files output)
  • Results
  • Future work
research question overview
Research Question Overview
  • We investigated developmental changes in spontaneous activity patterns recorded from the ganglion cell layer (GCL) in the mouse retina using a 60-channel MEA, spanning the first 15 postnatal days.
  • In this period, we see changes from large waves to more broken un-coordinated activity. How do the properties of these waves change in terms of bursting properties, episode properties (defined later), and network properties?
types of analysis done
Types of Analysis done
  • Burst Detection and analysis
    • Inter-Burst Interval Cumulative Density Functions
    • Inter-Spike Interval CDFs
  • Episode Detection and analysis
    • Episode Duration CDFs
  • Network Extraction and analysis
    • Normalized Clustering Coefficient
    • Normalized Average Path Length
live demo on portal
Live Demo on Portal
  • DUDE
  • Fourplot
  • MEA analysis: movie generator
  • Burst analysis
  • Episode Detection (hopefully!)
  • Network Analysis (hopefully!)
slide6
DUDE
  • P6 data simply threshold spike-detected (left) and after DUDE clean-up and spike detection (right).
  • P11 data simply threshold spike-detected (left) and after DUDE clean-up and spike detection (right).
burst analysis
Burst Analysis

Burst Duration shortens over development.

Inter-Burst Interval lengthens, then shortens over development, as spontaneous activity dissipates.

episode detection and analysis
Episode Detection and Analysis

Spontaneous episode at P6. For each burst detected on a channel, the start and end times are recorded; an episode is simply a continuous chain of bursting events across the array. The red line on the raster plot indicates the Episode duration.

Episode Duration (ED) CDF: we see a decrease in ED over development.

network extraction
Network Extraction

After an episode has been detected, all participating channels are correlated and correlation strength values sorted so that the 4 % strongest connections are retained.

network thresholding
Network Thresholding

Raster plots of data at developmental days: P2 (top), P6 (middle), P11 (bottom); each with different network thresholds shown to the right.

network analysis
Network Analysis

Characteristic Path Length over development, plotted against episode duration for networks extracted at 4% threshold. We see that over developmental age, L gets longer, then shorter than random networks over the development period (though the episode duration gets much shorter, as it is largely spontaneous activity at this point).

Clustering Coefficient over development, plotted against episode duration for networks extracted at 4% threshold. We see that over development, the Clustering Coefficient gets lower, then higher than random networks over the development period (though the episode duration gets much shorter, as it is largely spontaneous activity at this point).

conclusions
Conclusions
  • Burst Duration decreases with retinal maturation, but Inter-Burst Interval is more complex (increase, then decreases as spontaneous activity becomes more dominant).
  • Episodes are defined as co-incident bursting activity, useful for both wave-type activity, and also the more spontaneous activity later in development. The episode duration progressively shortened over the development period.
  • Networks extracted from spontaneous events revealed the presence of highly clustered, small networks at early developmental stages, changing to larger and less tightly clustered networks in the late development period.
  • Past P11, the networks have very short episode duration, yet are highly clustered and have a very short path length, but this is due to the very local nature of the episodes at this development point.
future work i human epilepsy utah data
Future Work I: Human Epilepsy Utah data
    • Adding on from Mark’s talk:
    • Some of this data is already available on the Portal.
  • We are working on adding Utah array epilepsy analysis tools to Portal
    • ripple detection already available for use on portal
    • extracting frequency band-passed data (highpass, already available on portal, check bandpass, put in FIR one if needed)
    • phase synchronization and correlation analysis
    • network analysis based on phase- and amplitude-extracted networks

Jennifer Simonotto, Matt Ainsworth, Marcus Kaiser, Miles Whittington, Mark Cunningham

future work ii human epilepsy eeg data
Future Work II: Human Epilepsy EEG data
  • At present, we have 15 pediatric control and 5 pediatric epilepsy subjects, with DTI, fMRI, and EEG (64 channels) recordings for each. In the next week or so, will get data from another 30 subjects!
  • Working on adding EEG analysis tools to Portal as well
    • extracting epoch (Eyes Open, Eyes Closed paradigm) data for analysis
    • extracting frequency band-passed data
    • phase synchronization and correlation analysis
    • network analysis based on phase- and amplitude-extracted networks

Jennifer Simonotto, Cheol Han, Jose Marcelino, Lars Michels, Marcus Kaiser

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