Example 2 - Replicating figure 5D from Momi et al., 2023

doc/index.rst

@@ -38,4 +38,5 @@ Welcome to WhoBPyT documentation!
    :glob:
 
    auto_examples/eg__tmseeg
+   auto_examples/eg__vary_b_param
 

examples/eg__vary_b_param.py

@@ -0,0 +1,165 @@
+"""
+.. _ex-momi2023-part2:
+
+========================================================
+Exploring the Effects of the Inhibitory Rate Constant Parameter (b)
+========================================================
+
+This script replicates the findings of the paper :footcite:`MomiEtAl2023`:
+
+Momi, D., Wang, Z., Griffiths, J.D. (2023).
+ "TMS-evoked responses are driven by recurrent large-scale network dynamics."
+# eLife, [doi: 10.7554/eLife.83232](https://elifesciences.org/articles/83232)
+
+This code loads up a previously-fit whobpyt model, varies a specific model parameter (the inhibitory rate constant; b), and simulates TEPs to visualize what effect this model parameter has on the output.
+
+
+"""
+
+# sphinx_gallery_thumbnail_number = 1
+#
+# %%
+# Setup
+# --------------------------------------------------
+# Importage:
+
+# os stuff
+import os
+import sys
+sys.path.append('..')
+
+
+# whobpyt stuff
+import whobpyt
+from whobpyt.datatypes import Parameter as par, Timeseries
+from whobpyt.models.jansen_rit import JansenRitModel,JansenRitParams
+from whobpyt.run import ModelFitting
+from whobpyt.optimization.custom_cost_JR import CostsJR
+
+# python stuff
+import numpy as np
+import pandas as pd
+import scipy.io
+import gdown
+import pickle
+import warnings
+warnings.filterwarnings('ignore')
+
+#neuroimaging packages
+import mne
+
+# viz stuff
+import matplotlib.pyplot as plt
+
+# %%
+# Download and load necessary data for the example
+download_data = True 
+url = 'https://drive.google.com/drive/folders/1DTdF_xR78DxB6kzxqY3SVYBAcdU9IkAB?usp=drive_link'
+if download_data: gdown.download_folder(url, quiet=True)
+data_dir = os.path.abspath('eg__tmseeg_data')
+
+
+# # %%
+# # load in a previously completed model fitting results object
+full_run_fname = os.path.join(data_dir, 'Subject_1_low_voltage_fittingresults_stim_exp.pkl')
+F = pickle.load(open(full_run_fname, 'rb'))
+
+
+# %%
+# Define relevant variables for whobpyt fititng/simuations
+
+# Load EEG data from a file
+file_name = os.path.join(data_dir, 'Subject_1_low_voltage.fif')
+epoched = mne.read_epochs(file_name, verbose=False);
+evoked = epoched.average()
+
+
+# %%
+# define options for JR model
+eeg_data = evoked.data.copy()
+time_start = np.where(evoked.times==-0.1)[0][0]
+time_end = np.where(evoked.times==0.3)[0][0]
+eeg_data = eeg_data[:,time_start:time_end]/np.abs(eeg_data).max()*4
+node_size = F.model.node_size
+output_size = eeg_data.shape[0]
+batch_size = 20
+step_size = 0.0001
+num_epochs = 20 #2 # num_epochs = 20
+tr = 0.001
+state_size = 6
+base_batch_num = 20
+time_dim = 400
+state_size = 6
+base_batch_num = 20
+hidden_size = int(tr/step_size)
+
+
+# %%
+# prepare data structure of the model
+data_mean = Timeseries(eeg_data, num_epochs, batch_size)
+
+# stimulation info
+u = np.zeros((node_size,hidden_size,time_dim))
+u[:,:,80:120]= 1000
+
+
+# %%
+
+# Run simulation 
+F.evaluate(u = u, empRec = data_mean, TPperWindow = batch_size, base_window_num = 20)
+
+# Visualizng the original fit
+ts_args = dict(xlim=[-0.1,0.3])
+ch, peak_locs1 = evoked.get_peak(ch_type='eeg', tmin=-0.05, tmax=0.01)
+ch, peak_locs2 = evoked.get_peak(ch_type='eeg', tmin=0.01, tmax=0.02)
+ch, peak_locs3 = evoked.get_peak(ch_type='eeg', tmin=0.03, tmax=0.05)
+ch, peak_locs4 = evoked.get_peak(ch_type='eeg', tmin=0.07, tmax=0.15)
+ch, peak_locs5 = evoked.get_peak(ch_type='eeg', tmin=0.15, tmax=0.20)
+times = [peak_locs1, peak_locs2, peak_locs3, peak_locs4, peak_locs5]
+
+simulated_EEG_st = evoked.copy()
+simulated_EEG_st.data[:,time_start:time_end] = F.lastRec['eeg'].npTS()
+times = [peak_locs1, peak_locs2, peak_locs3, peak_locs4, peak_locs5]
+simulated_joint_st = simulated_EEG_st.plot_joint(ts_args=ts_args, times=times)
+
+# %%
+# now we want to vary paramters and simulate again.
+
+# looking at original value of b
+print(F.model.params.b.val)
+
+# defining a range of b to vary and simulate
+b_range = np.linspace(45, 55, 4)
+
+
+# %% 
+# Defining a range for the b paramter to explore
+b_range = np.linspace(45, 55, 5)
+# testing all ranges:
+sims_dict = {}
+for n, new_b in enumerate(b_range):
+    # changing parameter in model
+    F.model.params.b = par(new_b, new_b, 1, True)
+
+    # evaluate the model
+    F.evaluate(u = u, empRec = data_mean, TPperWindow = batch_size, base_window_num = 20)
+
+    # plotting and saving new simulations
+    simulated_EEG_st.data[:,time_start:time_end] = F.lastRec['eeg'].npTS()
+    simulated_joint_st = simulated_EEG_st.plot_joint(ts_args=ts_args, times=times, title=f'b param = {new_b}')
+    sims_dict[str(new_b)] = simulated_EEG_st.copy().crop(tmin=0, tmax=0.2)
+
+# %%
+# comparing conditions
+fig, ax = plt.subplots(figsize=(12,4))
+mne.viz.plot_compare_evokeds(sims_dict, picks='eeg', combine='gfp', axes=ax, cmap='viridis',);
+fig.show()
+
+# %%
+# Results Description
+# ---------------------------------------------------
+#
+#
+# Here we replicate the results of Momi et al. 2023 (Fig.  5D). As the inhibitory synaptic rate constant b increases (or equivalently, the time constant decreases), 
+# we observe an increase in the amplitude of the first, early, and local TEP components; and a decrease of the second, 
+# late, and global TEP components.