Replication Data for: Computational Modelling of Cerebellar Magnetic Stimulation: the Effect of Washout

The data are related to the following article: Antonietti A., Casellato C., D'Angelo E. and Pedrocchi A. (2019) Computational Modelling of Cerebellar Magnetic Stimulation: the Effect of Washout Abstract Nowadays, clinicians have multiple tools that they can use to stimulate the brain, by mean of electric or magnetic fields that can interfere with the bio-electrical behaviour of neurons. However, it is not still clear what are the neural mechanisms that are involved and how the external stimulation changes the neural response at network-level. In this paper, we have exploited the simulations carried out using a spiking neural network model inspired to the cerebellar system to shed light on the effects of cerebellar Transcranial Magnetic Stimulation (TMS). Namely, two computational studies have been merged and compared to identify the role of the washout period that follows the TMS stimulation. The two studies employed a very similar experimental protocol: a first session of Pavlovian associative conditioning, the administration of the TMS (effective or sham), a washout period, and a second session of Pavlovian associative conditioning. In one study, the washout period between the two sessions was long (1 week), while the other study foresaw a very short washout (15 minutes). Computational models suggested a mechanistic explanation for the TMS effect on the cerebellum. In this work, we have found that the duration of the washout strongly changes the modification of plasticity mechanisms in the cerebellar network, that is then reflected by the behavioural response. Usage: The code has been tested with MATLAB 2019a 64-bit. With the following files you can reproduce the figures and findings of the referenced paper. All the files need to be in the directory where the simulation is launched. Open with MATLAB the file Analysis_TMS.m and run it. It will generate the three figures and the results reported in the manuscript. The following files are included: - Analysis_TMS.m is the MATLAB script that loads the data and generates figures and results presented in the manuscript. - TMS_Long.mat: includes the CRs in both the experimental study (Monaco et al., 2014) and the computational study (Antonietti et al., 2016) and the optimal parameters of the three different situations (session_1, session_2sham, session_2tms) found by the Genetic Algorithm optimization process. - TMS_Short.mat: includes the CRs in both the experimental study (Monaco et al., 2018) and the computational study (Antonietti et al., 2018) and the optimal parameters of the three different situations (session_1, session_2sham, session_2tms) found by the Genetic Algorithm optimization process. - figureFullScreen.m is used to plot the results. These model files were supplied by Alberto Antonietti. If you have any question/comments/feedback, please contact me.