diff --git a/HIBALL-winterschool-2023.ipynb b/HIBALL-winterschool-2023.ipynb
index 4938542..a6ccd9e 100644
--- a/HIBALL-winterschool-2023.ipynb
+++ b/HIBALL-winterschool-2023.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "import siibra\n",
     "from packaging.version import Version\n",
-    "assert Version(siibra.__version__) >= Version('1.0a14')\n",
+    "assert Version(siibra.__version__) >= Version('1.0a17')\n",
     "\n",
     "import matplotlib\n",
     "from nilearn import plotting\n",
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "ecd71f02",
    "metadata": {},
    "outputs": [],
@@ -130,7 +130,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "siibra.parcellations.JULICH_BRAIN_CYTOARCHITECTONIC_ATLAS_V3_0_3"
+    "siibra.parcellations['julich']"
    ]
   },
   {
@@ -271,7 +271,7 @@
    "outputs": [],
    "source": [
     "julich_brain = (\n",
-    "    siibra.parcellations.JULICH_BRAIN_CYTOARCHITECTONIC_ATLAS_V3_0_3\n",
+    "    siibra.parcellations['julich']\n",
     ")\n",
     "julich_mpm = julich_brain.get_map(\n",
     "    space=siibra.spaces.MNI_152_ICBM_2009C_NONLINEAR_ASYMMETRIC\n",
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "1e6834c0",
    "metadata": {},
    "outputs": [],
@@ -827,7 +827,7 @@
    "outputs": [],
    "source": [
     "features = siibra.features.get(\n",
-    "    siibra.parcellations.get('julich 2.9'),\n",
+    "    siibra.parcellations.get('julich'),\n",
     "    siibra.features.connectivity.StreamlineCounts\n",
     ")\n",
     "f = features[0]"
@@ -869,8 +869,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc_hcp_000 = f.get_element('000')\n",
-    "sc_hcp_000"
+    "sc_hcp_0 = f.get_element('210415')\n",
+    "sc_hcp_0"
    ]
   },
   {
@@ -881,7 +881,7 @@
    "outputs": [],
    "source": [
     "# we can also plot the matrix\n",
-    "sc_hcp_000.plot(logscale=True)"
+    "sc_hcp_0.plot(logscale=True)"
    ]
   },
   {
@@ -899,9 +899,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "node_coords = sc_hcp_000.compute_centroids(space='mni152')\n",
+    "node_coords = sc_hcp_0.compute_centroids(space='mni152')\n",
     "plotting.view_connectome(\n",
-    "    adjacency_matrix=sc_hcp_000.data,\n",
+    "    adjacency_matrix=sc_hcp_0.data,\n",
     "    node_coords=node_coords,\n",
     "    edge_threshold=\"99%\",\n",
     "    node_size=3, colorbar=False,\n",
@@ -961,7 +961,7 @@
     "    siibra.get_region('julich', '4p left'),\n",
     "    siibra.features.cellular.BigBrainIntensityProfile\n",
     ")\n",
-    "print(len(features))\n",
+    "print(len(features[0]))\n",
     "f = features[0]"
    ]
   },
@@ -985,6 +985,16 @@
     "f.plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ed9f6a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f[0].plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fc1eacea",
@@ -1029,11 +1039,11 @@
    "outputs": [],
    "source": [
     "# let's fetch the 1 micron section at a lower resolutioin, and display in 3D space.\n",
-    "section1402 = features[3]\n",
+    "section1402 = [f for f in features if \"#1402\" in f.name][0]\n",
     "plotting.plot_img(\n",
     "    section1402.fetch(),\n",
     "    bg_img=bigbrain_template.fetch(),\n",
-    "    title=\"#1402\",\n",
+    "    title=section1402.name,\n",
     "    cmap='gray'\n",
     ")"
    ]
@@ -1045,13 +1055,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Let's fetch a crop inside hoc5 at full resolution.\n",
-    "# we intersect the bounding box of hoc5l and the section\n",
-    "hoc5_bbox = hoc5l.get_boundingbox('bigbrain').intersection(section1402)\n",
-    "print(f\"Size of the bounding box: {hoc5_bbox.shape}\")\n",
+    "# Let's fetch a crop inside hoc5 at full resolution. We intersect the bounding box of hoc5l and the section\n",
+    "# (Since hoc5l is not mapped in bigbrain, we don't restrict the space so that\n",
+    "# siibra gets the bounding box from a map on another space and warps it to BigBrain space automatically)\n",
+    "hoc5_intersection_bbox = hoc5l.get_boundingbox('bigbrain', restrict_space=False).intersection(section1402)\n",
+    "print(f\"Size of the bounding box: {hoc5_intersection_bbox.shape}\")\n",
     "\n",
     "# this is quite large, so we shrink it\n",
-    "voi = hoc5_bbox.zoom(0.1)\n",
+    "voi = hoc5_intersection_bbox.zoom(0.4)\n",
     "crop = section1402.fetch(voi=voi, resolution_mm=-1)"
    ]
   },
@@ -1062,7 +1073,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotting.plot_img(crop, bg_img=None, cmap='gray')"
+    "plotting.plot_img(crop, bg_img=None, cmap='gray', display_mode='y')"
    ]
   },
   {
@@ -1072,7 +1083,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plotting.plot_img(crop, bg_img=bigbrain_template.fetch(), cmap='magma')"
+    "plotting.plot_img(crop, bg_img=bigbrain_template.fetch(resolution_mm=0.8), cmap='magma', display_mode='y')"
    ]
   },
   {
@@ -1100,7 +1111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.9.15"
   }
  },
  "nbformat": 4,