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Simple connectome inference from partial correlation statistics in calcium imaging

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In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the methodology that led us to win the Connectomics Challenge, proposes a simplified version of our method, and finally compares our results with respect to other inference methods.

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Simple connectome inference from partial correlation statistics in calcium imaging

  1. 1. 1/11 Simple connectome inference from partial correlation statistics in calcium imaging Antonio Sutera, Arnaud Joly, Vincent Fran¸cois-Lavet, Zixiao Aaron Qiu Gilles Louppe, Damien Ernst and Pierre Geurts (aka The AAAGV Team).
  2. 2. 2/11 Introduction From time-series of the neuron activity, the goal is to infer the directed connections between neurons. i j
  3. 3. 3/11 Introduction But this problem is difficult because of... masking effects low sampling rate ⇒ slow decay of fluorescence. Time [s]0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
  4. 4. 4/11 Framework of our solution Table of contents How-to : 1. Deal with calcium fluorescence signals : signal processing. 2. Find the network : the inference method. 3. Improve the method : averaging and tuning. One step further : Improvement of each stage of our solution. Comparison with other methods. Even further : The “full method” in a nutshell.
  5. 5. 5/11 Signal processing 1 2 3 4 Signal processing 0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 10 0.5 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 10 1.0 1.5 2.0 2.5 3.0 1. Applying a low-pass filter 2. Applying a high-pass filer 3. Applying a hard-threshold filter 4. Magnify the importance of spikes that occur in case of low global activity
  6. 6. 6/11 Connectome inference from partial correlation statistics pi,j partial correlation = degree of association between neurons i and j known known Partial correlation coefficient pi,j between neurons i and j defined by : pi,j = − Σ−1 ij Σ−1 ii Σ−1 jj , Advantages and drawbacks : Only direct associations Filter out spurious indirect effects Easy to compute (with lots of data) Edge orientation Sensitive to the value of parameters in the filtering process
  7. 7. 7/11 Improvements Approximation for partial correlation statistics using only the 800 first principal components. Averaged partial correlation statistics over various values of the parameters Improve robustness (over all networks) Reduce variance of its prediction Decrease the sensitivity to the filtering process
  8. 8. 8/11 Each stage of our solution brings some improvement Average scores on normal-1,2,3,4 datasets.
  9. 9. 9/11 Comparison with other methods Average scores on normal-1,2,3,4 datasets.
  10. 10. 10/11 An optimized version to win the challenge : “Full method” A tuned signal processing, Weighted average of partial correlation statistics, Prediction of edge orientation.
  11. 11. 11/11 For every details of our method... The description of the method : Sutera, A., Joly, A., Fran¸cois-Lavet, V., Qiu, Z. A., Louppe, G., Ernst, D., Geurts, P. (2014). Simple connectome inference from partial correlation statistics in calcium imaging. Code available at : https://github.com/asutera/kaggle-connectomics

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