update tutorial voxel.

gallantlab · Mar 8, 2024 · 659b811 · 659b811
1 parent 59968f8
commit 659b811
Showing 1 changed file with 10 additions and 10 deletions.
diff --git a/index.html b/index.html
@@ -62303,42 +62303,42 @@
 content:"<p>We fit a predictive model for each of ~60,000 voxels in the cerebral cortex and in two separate presentation modalities, listening and reading. These models predict the timecourse of the brain response based on different language timescales. Here we show the estimated timescale based on brain responses during the listening experiment. The selected voxel represents relatively long language timescales during listening.</p>",
 view:[{state:'camera.target', idx:tour_anim_speed, value:[0,15,-15]},
 {state:'mix', idx:tour_anim_speed, value:0.5},
-{state:'camera.azimuth', idx:tour_anim_speed, value:65},
-{state:'camera.altitude', idx:tour_anim_speed, value:61},
+{state:'camera.azimuth', idx:tour_anim_speed, value:119},
+{state:'camera.altitude', idx:tour_anim_speed, value:72},
 {state:'camera.radius', idx:tour_anim_speed, value:239}],
 call:function (v) {
 // Set the correct dataset
 dataset_actions['selectivity_listening'].action();
 // Pick the voxel
-v.surfs[0].surf.picker.process_pick({x:67, y:40, z:15}, 'left', 87375);
+v.surfs[0].surf.picker.process_pick({x:68, y:73, z:12}, 'left', 65051);
 v.schedule();
 }},
 {title:"Voxel-wise models",
 content:"<p>The same voxel represents a similar language timescale during reading.</p>",
 view:[{state:'camera.target', idx:tour_anim_speed, value:[0,15,-15]},
 {state:'mix', idx:tour_anim_speed, value:0.5},
-{state:'camera.azimuth', idx:tour_anim_speed, value:65},
-{state:'camera.altitude', idx:tour_anim_speed, value:61},
+{state:'camera.azimuth', idx:tour_anim_speed, value:119},
+{state:'camera.altitude', idx:tour_anim_speed, value:72},
 {state:'camera.radius', idx:tour_anim_speed, value:239}],
 call:function (v) {
 // Set the correct dataset
 dataset_actions['selectivity_reading'].action();
 // Pick the voxel
-v.surfs[0].surf.picker.process_pick({x:67, y:40, z:15}, 'left', 87375);
+v.surfs[0].surf.picker.process_pick({x:68, y:73, z:12}, 'left', 65051);
 v.schedule();
 }},
 {title:"Similarity of estimated voxel weights",
 content:"<p>To quantify the similarity of timescale representations between modalities for each voxel, we correlated the estimated selectivity for each timescale between reading and listening. Voxels that are well-predicted in both modalities represent similar timescales between the two modalities.</p>",
 view:[{state:'camera.target', idx:tour_anim_speed, value:[0,15,-15]},
 {state:'mix', idx:tour_anim_speed, value:0.5},
-{state:'camera.azimuth', idx:tour_anim_speed, value:65},
-{state:'camera.altitude', idx:tour_anim_speed, value:61},
+{state:'camera.azimuth', idx:tour_anim_speed, value:119},
+{state:'camera.altitude', idx:tour_anim_speed, value:72},
 {state:'camera.radius', idx:tour_anim_speed, value:239}],
 call:function (v) {
 // Set the correct dataset
 dataset_actions['timescale_correlations'].action();
 // Pick the voxel
-v.surfs[0].surf.picker.process_pick({x:67, y:40, z:15}, 'left', 87375);
+v.surfs[0].surf.picker.process_pick({x:68, y:73, z:12}, 'left', 65051);
 v.schedule();
 }},
 {title:"Validating voxel-wise models",
@@ -79621,7 +79621,7 @@
 }
 figure = new jsplot.W2Figure();
 viewer = figure.add(mriview.Viewer, "main", true);
-dataviews = dataset.fromJSON({"views": [{"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__e5c561fa83258f08"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["turbo_matplotlib"], "vmin": [3], "vmax": [8], "name": "selectivity_listening"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__61c60f690e421123"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["turbo_matplotlib"], "vmin": [3], "vmax": [8], "name": "selectivity_reading"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__1c9f7b302690ba77"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["BuWtRd"], "vmin": [-1], "vmax": [1], "name": "timescale_correlations"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__3e159fac2cbbc242"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["hot"], "vmin": [0], "vmax": [0.5], "name": "performance_listening"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__84742622600b7a5a"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["hot"], "vmin": [0], "vmax": [0.5], "name": "performance_reading"}], "data": {"__3e159fac2cbbc242": {"name": "__3e159fac2cbbc242", "subject": "S0", "min": -0.42722976207733154, "max": 0.5717297792434692, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__84742622600b7a5a": {"name": "__84742622600b7a5a", "subject": "S0", "min": -0.41629108786582947, "max": 0.5529628992080688, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__61c60f690e421123": {"name": "__61c60f690e421123", "subject": "S0", "min": 0.0, "max": 8.583460848450338, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__1c9f7b302690ba77": {"name": "__1c9f7b302690ba77", "subject": "S0", "min": 0.0, "max": 0.9999229907989502, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__e5c561fa83258f08": {"name": "__e5c561fa83258f08", "subject": "S0", "min": 0.0, "max": 8.584175443944606, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}}, "images": {"__3e159fac2cbbc242": ["data/__3e159fac2cbbc242_0.png"], "__84742622600b7a5a": ["data/__84742622600b7a5a_0.png"], "__61c60f690e421123": ["data/__61c60f690e421123_0.png"], "__1c9f7b302690ba77": ["data/__1c9f7b302690ba77_0.png"], "__e5c561fa83258f08": ["data/__e5c561fa83258f08_0.png"]}});
+dataviews = dataset.fromJSON({"views": [{"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__e5c561fa83258f08"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["turbo_matplotlib"], "vmin": [3], "vmax": [8], "name": "selectivity_listening"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__61c60f690e421123"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["turbo_matplotlib"], "vmin": [3], "vmax": [8], "name": "selectivity_reading"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__1c9f7b302690ba77"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["BuWtRd"], "vmin": [-1], "vmax": [1], "name": "timescale_correlations"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__3e159fac2cbbc242"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["hot"], "vmin": [0], "vmax": [0.5], "name": "performance_listening"}, {"xfm": [[-0.4456816947032737, -0.02239158932199446, 0.01226493262555982, 53.7623568504377, 0.022871048235938447, -0.44609563882817327, 0.03230636673898953, 61.150284200375474, 0.006168754918175968, 0.013176714286112067, 0.24097662479180523, 3.574049692717037, 0.0, 0.0, 0.0, 1.0]], "data": ["__84742622600b7a5a"], "state": null, "attrs": {"priority": 1}, "desc": "", "cmap": ["hot"], "vmin": [0], "vmax": [0.5], "name": "performance_reading"}], "data": {"__61c60f690e421123": {"name": "__61c60f690e421123", "subject": "S0", "min": 0.0, "max": 8.583460848450338, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__e5c561fa83258f08": {"name": "__e5c561fa83258f08", "subject": "S0", "min": 0.0, "max": 8.584175443944606, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__3e159fac2cbbc242": {"name": "__3e159fac2cbbc242", "subject": "S0", "min": -0.42722976207733154, "max": 0.5717297792434692, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__84742622600b7a5a": {"name": "__84742622600b7a5a", "subject": "S0", "min": -0.41629108786582947, "max": 0.5529628992080688, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}, "__1c9f7b302690ba77": {"name": "__1c9f7b302690ba77", "subject": "S0", "min": 0.0, "max": 0.9999229907989502, "shape": [30, 100, 100], "raw": false, "mosaic": [6, 5]}}, "images": {"__61c60f690e421123": ["data/__61c60f690e421123_0.png"], "__e5c561fa83258f08": ["data/__e5c561fa83258f08_0.png"], "__3e159fac2cbbc242": ["data/__3e159fac2cbbc242_0.png"], "__84742622600b7a5a": ["data/__84742622600b7a5a_0.png"], "__1c9f7b302690ba77": ["data/__1c9f7b302690ba77_0.png"]}});
 legend = new Legend();
 // Create pickers, bind picker
 listeningpicker = new VoxelDataPicker(figure, "#voxeldataaxis_html", 1);