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Abstract

The Synaptic Gains Tracking Toolbox (SGTT), a SPM EEG synaptic gains tracking toolbox, allows to track the synaptic gains from clinical iEEG data with a fine temporal resolution using a fully automatic method described in [Fan2018a] and [Fan2018b]. The toolbox allows to: 1. track the average excitatory Ae, slow and fast inhibitory synaptic gains, B and G, by automatically fitting EEG data to a neural mass model; 2. compute four synaptic gain ratios (excitatory/slow inhibitory Ae/B, excitatory/fast inhibitory Ae/G, excitatory/(slow + fast) inhibitory Ae/(B+G), and slow/fast inhibitory ratio B/G, and smooth them using moving average filter; 3. display the path to epilepsy through the synaptic gain space. The synaptic gains were estimated in four steps: 1. Pre-processing. The EEG first go through a bandpass filter and notch filter after zero-score normalization. 2. The EEG is segmented into fixed-size windows. 3. Search the synaptic gain space exhaustively and find the top N (defined by the user) fits as candidates. 4. Cluster the candidates and compute the means of the cluster with the smallest average error as the most representative fit. SGTT outputs SPM EEG files. We highly recommend you to install the EEGAnalyzer toolbox (website), which is also a toolbox for SPM 12, for a better visualization. This toolbox was co-developed by Xiaoya Fan, Rudy Ercek and Antoine Nonclercq. If you use this toolbox for a publication (in a journal, in a conference, etc.), please cite it by including as much information as possible from the following: Xiaoya Fan, Rudy Ercek & Antoine Nonclercq, SGTT: a SPM EEG Synaptic gain tracking toolbox, Université Libre de Bruxelles, (WEBSITE) 2018. Please also cite related publications: [Fan2018a] and [Fan2018b]. [Fan2018a] Fan, X., Gaspard, N., Legros, B., Lucchetti, F., Ercek, R. & Nonclercq, A. (2018). Dynamics underlying interictal to ictal transition in temporal lobe epilepsy: insights from a neural mass model. European Journal of Neuroscience, 47(3), 258-268. doi: 10.1111/ejn.13812 [Fan2018b] Fan, X., Gaspard, N., Legros, B., Lucchetti, F., Ercek, R. & Nonclercq, A. (2018). Seizure evolution can be characterized as path through synaptic gain space of a neural mass model. European Journal of Neuroscience, doi: 10.1111/ejn.14142. You can download here: • the Synaptic Gains Tracking Toolbox (SGTT), • the SGTT user manual,

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