diff --git a/talks/matlab_vs_python/migp/fetch_data.ipynb b/talks/matlab_vs_python/migp/MIGP.ipynb
similarity index 82%
rename from talks/matlab_vs_python/migp/fetch_data.ipynb
rename to talks/matlab_vs_python/migp/MIGP.ipynb
index 6778e404ee079e8b4f132865f8ff52a2b7307a33..5dd73e87ea5807a15363c0e439f1eea4a5727f47 100644
--- a/talks/matlab_vs_python/migp/fetch_data.ipynb
+++ b/talks/matlab_vs_python/migp/MIGP.ipynb
@@ -4,15 +4,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Fetch Data\n",
+    "# MIGP\n",
     "\n",
-    "This notebook will download an open fMRI dataset (~50MB) for use in the MIGP demo. It also regresses confounds from the data and performs spatial smoothing with 10mm FWHM.\n",
+    "For group ICA,  `melodic` uses multi-session temporal concatenation. This will perform a single 2D ICA run on the concatenated data matrix (obtained by stacking all 2D data matrices of every single data set on top of each other).\n",
     "\n",
-    "This data is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html), originally released under Creative Commons - Attribution Non-Commercial.\n",
+    "![temporal concatenation](concat_diag.png)\n",
     "\n",
-    "It comprises 10 preprocessed resting-state fMRI selected from 72 patients diagnosed with schizophrenia and 74 healthy controls (6mm isotropic, TR=2s, 150 volumes).\n",
+    "Resulting in **high dimension** datasets!\n",
+    "\n",
+    "Furthermore, with ICA we are typically only interested in a comparitively low dimension decomposition so that we can capture spatially extended networks.\n",
+    "\n",
+    "Therefore the first step is to reduce the dimensionality of the data.  This can be achieved in a number of ways, but `melodic`, by default, uses `MIGP`.\n",
+    "\n",
+    "> MIGP is an incremental approach that aims to provide a very close approximation to full temporal concatenation followed by PCA, but without the large memory requirements *(Smith et al., 2014)*.\n",
     "\n",
-    "* [Download the data](#download-the-data)\n",
+    "Essentially, MIGP stacks the datasets incrementally in the temporal dimension, and whenever the temporal dimension exceeds a specified size, a PCA-based temporal reduction is performed.\n",
+    "\n",
+    "> MIGP does not increase at all in memory requirement with increasing numbers of subjects, no large matrices are ever formed, and the computation time scales linearly with the number of subjects. It is easily parallelisable, simply by applying the approach in parallel to subsets of subjects, and then combining across these with the same “concatenate and reduce” approach described above *(Smith et al., 2014)*.\n",
+    "\n",
+    "## This notebook\n",
+    "\n",
+    "This notebook will download an open fMRI dataset (~50MB) for use in the MIGP demo, regresses confounds from the data, performs spatial smoothing with 10mm FWHM, and then runs group `melodic` with `MIGP`.\n",
+    "\n",
+    "* [Fetch the data](#download-the-data)\n",
     "* [Clean the data](#clean-the-data)\n",
     "* [Run `melodic`](#run-melodic)\n",
     "* [Plot group ICs](#plot-group-ics)\n",
@@ -42,7 +56,11 @@
    "metadata": {},
    "source": [
     "<a class=\"anchor\" id=\"download-the-data\"></a>\n",
-    "## Download the data\n",
+    "## Fetch the data\n",
+    "\n",
+    "This data is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html), originally released under Creative Commons - Attribution Non-Commercial.\n",
+    "\n",
+    "It comprises 10 preprocessed resting-state fMRI selected from 72 patients diagnosed with schizophrenia and 74 healthy controls (6mm isotropic, TR=2s, 150 volumes).\n",
     "\n",
     "Create a directory in the users home directory to store the downloaded data:\n",
     "\n",
@@ -248,13 +266,6 @@
     "# plot\n",
     "fig = map_plot(ics)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -273,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,