Preictal and interictal neuronal activity from the medial temporal lobe of patients with focal epilepsy

Annika Hagemann fff15173b9 Update 'README.md' 3 years ago
Data a1a5d7132c Update 'Data/preIct_recordings_patients.txt' 3 years ago
python_code a05dedb4de gin commit from UX410UAK 3 years ago
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README.md fff15173b9 Update 'README.md' 3 years ago

README.md

Preictal and interictal neuronal activity from the medial temporal lobe of patients with focal epilepsy

Summary

This dataset contains the binned activity times series of intracranial recordings from n=20 patients with medically intractable focal epilepsy. The data was recorded at the Department of Epileptology at the University of Bonn Medical Center. For pre-surgical evaluation, patients were implanted with depth electrodes in different regions of the medial temporal lobe, including hippocampus (H), amygdala (A), parahippocampal cortex (PHC) and entorhinal cortex (EC).

For each patient, the dataset contains one 10-minute reference recording (interictal), obtained in a seizure-free interval, as well as several pre-ictal recordings, spanning 10 minutes prior to seizure onset. Pre-ictal recordings end at seizure onset.

The binned activity is defined as the number of recorded active neurons in a certain brain area in discrete time bins of 4 ms. For each recording, we provide the binned activities in left and right hippocampus (LH/RH), amygdala (LA/RA), parahippocampal cortex (LPHC/RPHC), entorhinal cortex (LEC/REC), and the full medial temporal lobe (left/right). For more details on the data acquisition and pre-processing, see [1].

Repository structure

Data

  • patients.txt contains a list of patientIDs, the location of the epileptic focus and the surgery outcome
  • preIct_recordings_patients.txt contains a list of all preictal recordings and the corresponding patientID
  • interictal/* contains all interictal (reference) recordings
  • preictal/* contains all preictal recordings
  • all recordings are provided as pickle files (.pkl) and as textfiles (.txt)

Python Code

  • Example code to read the data (python_code/reading_data/*).
  • Scripts to estimate the distance to criticality using the Multistep Regression estimator [2] and to reproduce the results shown in [1]. (python_code/analysis_PlosCB/*)

Cite as

Hagemann, A., Wilting, J., Samimizad, B., Mormann, F., & Priesemann, V. (2021). Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex. PLoS computational biology, 17(3), e1008773.

Related Publications

  • [1] Hagemann, A., Wilting, J., Samimizad, B., Mormann, F., & Priesemann, V. (2021). Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex. PLoS computational biology, 17(3), e1008773.
  • [2] Spitzner, F. P., Dehning, J., Wilting, J., Hagemann, A., Neto, J. P., Zierenberg, J., & Priesemann, V. (2020). MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity. arXiv preprint arXiv:2007.03367.

Licensing

See LICENSE.txt for the full license.