Functional connectivity

Created by: didi-hou

This PR added necessary scripts and documentation for visualizing functional connectivity.

• Added script for computing functional connectivity
• Added script for computing louvain communities
• Added script for computing synaptic input
• Added script for plotting functional connectivity of simulated model and FC of experimental data
• Added section 3.5 in the notebook

figures/MAM2EBRAINS/M2E_compute_fc.py

+import correlation_toolbox.helper as ch
+import numpy as np
+import os
+import sys
+
+from multiarea_model import MultiAreaModel
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
+
+from M2E_compute_synaptic_input import compute_synaptic_input
+
+"""
+Compute the functional connectivity between all areas of a given
+simulation based on their time series of spiking rates or their
+estimated BOLD signal.
+"""
+
+# data_path = sys.argv[1]
+# label = sys.argv[2]
+# method = sys.argv[3]
+
+def compute_fc(M, data_path, label):
+    # compute synaptic input
+    for area in M.area_list:
+        compute_synaptic_input(M, data_path, label, area)
+        
+    method = "synaptic_input"
+    
+    load_path = os.path.join(data_path,
+                             label,
+                             'Analysis',
+                             method)
+    save_path = os.path.join(data_path,
+                             label,
+                             'Analysis')
+
+    # """
+    # Create MultiAreaModel instance to have access to data structures
+    # """
+    # M = MultiAreaModel({})
+    
+    time_series = []
+    for area in M.area_list:
+        fn = os.path.join(load_path,
+                          '{}_{}.npy'.format(method, area))
+        si = np.load(fn)
+        if method == 'bold_signal':  # Cut off the long initial transient of the BOLD signal
+            si = si[5000:]
+        time_series.append(ch.centralize(si, units=True))
+
+    D = pdist(time_series, metric='correlation')
+    correlation_matrix = 1. - squareform(D)
+
+    np.save(os.path.join(save_path,
+                         'functional_connectivity_{}.npy'.format(method)),
+            correlation_matrix)