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DecoEtAl2018_Fig3B.py
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# ==========================================================================
# ==========================================================================
# Computes the Fitting of the Serotonin 5-HT2AR Model to the LSD data
#
# For the LSD condition, when using this optimal coupling point of the placebo
# condition and systematically scaling the excitatory gain function in each
# region with the empirical 5-HT2AR data, we find that there is an optimum at
# around (0.2,0.045) (minimum of blue line). In contrast, varying the scaling of the
# neuronal gain for the placebo condition does not yield an optimum (see
# monotonically rising green line), and thus the fit is not improved by changing
# the scaling of the neuronal gain by 5-HT2AR density. This clearly demonstrates
# that the LSD brain activity is dependent on the precise 5-HT2A density distribution
# maps.
# --------------------------------------------------------------------------
#
# Computes simulations with the Dynamic Mean Field Model (DMF) using
# Feedback Inhibitory Control (FIC) and Regional Drug Receptor Modulation (RDRM):
#
# - the optimal coupling (we=2.1) for fitting the placebo condition
# - the optimal neuromodulator gain for fitting the LSD condition (wge=0.2)
#
# Taken from the code (Code_Figure3.m) from:
#
# [DecoEtAl_2018] Deco,G., Cruzat,J., Cabral, J., Knudsen,G.M., Carhart-Harris,R.L., Whybrow,P.C.,
# Whole-brain multimodal neuroimaging model using serotonin receptor maps explain non-linear functional effects of LSD
# Logothetis,N.K. & Kringelbach,M.L. (2018) Current Biology
# https://www.cell.com/current-biology/pdfExtended/S0960-9822(18)31045-5
#
# Code written by Josephine Cruzat [email protected]
#
# Translated to Python by Gustavo Patow
# ==========================================================================
# ==========================================================================
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
from pathlib import Path
def loadFittingData(suffix):
filePath = 'Data_Produced/DecoEtAl2018_fitting'+suffix+'.mat'
print('Loading {}'.format(filePath))
data = sio.loadmat(filePath)
return data['fitting_FCD'].flatten(), data['S_E'].flatten()
fitting_PLA, S_E_PLA = loadFittingData('_PLA')
fitting_LSD, S_E_LSD = loadFittingData('_LSD')
minPLA = S_E_PLA[np.argmin(fitting_PLA)]
minLSD = S_E_LSD[np.argmin(fitting_LSD)]
print("\n\n#####################################################################################################")
print(f"# Max FC({minPLA}) = {np.max(fitting_PLA)} ** Min FCD({minLSD}) = {np.min(fitting_LSD)} **")
print("#####################################################################################################\n\n")
plt.rcParams.update({'font.size': 15})
plotFCDpla, = plt.plot(S_E_PLA, fitting_PLA)
plotFCDpla.set_label("Placebo")
plotFCpla, = plt.plot(S_E_LSD, fitting_LSD)
plotFCpla.set_label("LSD")
plt.title("Whole-brain fitting")
plt.ylabel("FCD Fitting")
plt.xlabel("Exhitatory Gain Modulation")
plt.legend()
plt.show()
# ================================================================================================================
# ================================================================================================================
# ================================================================================================================EOF