diff --git a/functional_parcellation_intro.ipynb b/functional_parcellation_intro.ipynb index ee1e0a5..22c30b4 100644 --- a/functional_parcellation_intro.ipynb +++ b/functional_parcellation_intro.ipynb @@ -163,7 +163,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -315,7 +315,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -375,7 +375,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -423,7 +423,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -547,14 +547,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0 1 2 3 4 5 5 6 7 8]\n" + "[ 0 1 2 3 4 5 5 6 7 8 8 5 9 0 10]\n" ] } ], @@ -562,7 +562,7 @@ "# We can cut the tree at whatever number of clusters we choose (here 17, because 17 is good)\n", "part = np.squeeze(cut_tree(hier, n_clusters=17)) # Cut the hierarchy\n", "# Each entry of the vector part is a parcel, and codes for the number of the network of this parcel\n", - "print(part[0:10]) # This means that partel 3 is in network 3. What is the network of parcel 5?" + "print(part[0:15]) # This means that partel 3 is in network 3. What is the network of parcel 5?" ] }, { @@ -573,7 +573,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -631,7 +631,7 @@ "data": { "text/plain": [ "(,\n", - " ,\n", + " ,\n", " Text(0.5, 1.0, 'part'))" ] }, @@ -679,7 +679,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -718,6 +718,67 @@ "plotting.plot_roi(part_img, title=\"DMN cluster\", cut_coords=(2, -51, 27))" ] }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "

From individual to group parcels

\n", + "
\n", + "\n", + "
\n", + "Adapted from Liu et al., Neuroimage 2014." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's do a crude group analysis: load time series for a few subjects \n", + "tseries_all = []\n", + "for indx, img in enumerate(adhd.func): \n", + " tseries_all.append(masker.transform(img))\n", + " \n", + "# clustering on the average connectome\n", + "conn_all = ConnectivityMeasure(kind='correlation').fit(tseries_all)\n", + "hier = linkage(conn_all.mean_, method='average', metric='euclidean') \n", + "\n", + "# Cut the hierarchy and look at the group-level networks\n", + "part_group = np.squeeze(cut_tree(hier, n_clusters=17)) \n", + "part_group_img = masker.inverse_transform(part_group.reshape([1, 444]) + 1) # note the sneaky shift to 1-indexing\n", + "plotting.plot_roi(part_group_img, title=\"group networks\", colorbar=True, cmap=\"Paired\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -763,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": { "slideshow": { "slide_type": "-" @@ -815,10 +876,10 @@ "\" width=\"600\" height=\"270.0\" frameBorder=\"0\">" ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -844,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -892,10 +953,10 @@ "\" width=\"600\" height=\"270.0\" frameBorder=\"0\">" ], "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -962,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "slideshow": { "slide_type": "subslide" @@ -972,10 +1033,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -1037,7 +1098,7 @@ "
\n", "

Individual parcels

\n", " \n", - " For ten densely sampled individuals (10 runs of 30 mns resting-state over ten days) identify details in individual parcellations that cannot be observed at the level of group parcellations (indicated by arrows, group parcellation at the top). \n", + " For ten densely sampled individuals (10 runs of 30 mns resting-state over ten days), Gordon et al. (2017) identified details in individual parcellations that cannot be observed at the level of group parcellations (indicated by arrows, group parcellation at the top). \n", " \n", "
\n", "" @@ -1064,6 +1125,46 @@ " \n", "" ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adjusted Rand reproducibility: 0.1058196841391461\n" + ] + } + ], + "source": [ + "# Let's compare two parcellations generated from different subjects\n", + "part_ind = []\n", + "for ind in range(2):\n", + " tseries_ind = masker.transform(adhd.func[ind])\n", + " conn_ind = np.squeeze(ConnectivityMeasure(kind='correlation').fit_transform([tseries_ind]))\n", + " hier_ind = linkage(conn_ind, method='average', metric='euclidean') \n", + " part_ind.append(np.squeeze(cut_tree(hier_ind, n_clusters=17))) \n", + "\n", + "# If we were to count the proportion of elements in the two adjacency matrices that are identical \n", + "# (excluding the diagonal which is always 1), we would get a measure of agreement called the Rand index. \n", + "# The adjusted Rand score corrects for chance-level overlap, such that the adjusted Rand will be close to \n", + "# zero for cluster overlap near chance level, and adjusted Rand is 1 for identical cluster solutions.\n", + "# Note that scikit-learn actually does not need the adjacency matrix representation, but works directly \n", + "# from vectors of cluster labels. \n", + "from sklearn import metrics\n", + "repro = metrics.adjusted_rand_score(part_ind[0], part_ind[1])\n", + "print(\"adjusted Rand reproducibility:\", repro)\n", + "# Ouch, this is really low. \n", + "# Inter-subject variability? Not enough data? Bad algorithm? all of the above?\n", + "# Check (Nikolaidis et al., Neuroimage 2020) for systematic evaluation" + ] } ], "metadata": { diff --git a/liu2014.png b/liu2014.png new file mode 100644 index 0000000..2fc09ae Binary files /dev/null and b/liu2014.png differ