Commit 737b6c76 authored by Arvid Lundervold's avatar Arvid Lundervold
parents 9820a808 44d55f30
......@@ -138,6 +138,33 @@
"</iframe>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/rAbNbpcUNdY\" \n",
"frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\n",
"</iframe>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%HTML\n",
"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/rAbNbpcUNdY\" \n",
"frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\n",
"</iframe>"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -147,7 +174,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
......@@ -165,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
......@@ -178,7 +205,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
......@@ -195,18 +222,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/arvid/opt/anaconda3/envs/bmed360/lib/python3.8/site-packages/nilearn/datasets/__init__.py:87: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
" warn(\"Fetchers from the nilearn.datasets module will be \"\n"
]
}
],
"outputs": [],
"source": [
"import gdown\n",
"import shutil\n",
......@@ -232,7 +250,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
......@@ -241,7 +259,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
......@@ -261,7 +279,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 78,
"metadata": {},
"outputs": [
{
......@@ -293,7 +311,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 79,
"metadata": {},
"outputs": [
{
......@@ -327,7 +345,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 80,
"metadata": {},
"outputs": [
{
......@@ -361,7 +379,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 81,
"metadata": {},
"outputs": [
{
......@@ -706,74 +724,74 @@
{
"data": {
"text/plain": [
"{'subject': <bids.layout.models.Entity at 0x7fb0ea5b9400>,\n",
" 'session': <bids.layout.models.Entity at 0x7fb0c8924f10>,\n",
" 'task': <bids.layout.models.Entity at 0x7fb0d836af40>,\n",
" 'acquisition': <bids.layout.models.Entity at 0x7fb0d836afd0>,\n",
" 'ceagent': <bids.layout.models.Entity at 0x7fb0d836e100>,\n",
" 'reconstruction': <bids.layout.models.Entity at 0x7fb0d836e160>,\n",
" 'direction': <bids.layout.models.Entity at 0x7fb0d836e1c0>,\n",
" 'run': <bids.layout.models.Entity at 0x7fb0d836e220>,\n",
" 'proc': <bids.layout.models.Entity at 0x7fb0d836e280>,\n",
" 'modality': <bids.layout.models.Entity at 0x7fb0d836e2e0>,\n",
" 'echo': <bids.layout.models.Entity at 0x7fb0d836e340>,\n",
" 'flip': <bids.layout.models.Entity at 0x7fb0d836e3a0>,\n",
" 'inv': <bids.layout.models.Entity at 0x7fb0d836e400>,\n",
" 'mt': <bids.layout.models.Entity at 0x7fb0d836e460>,\n",
" 'part': <bids.layout.models.Entity at 0x7fb0d836e4c0>,\n",
" 'recording': <bids.layout.models.Entity at 0x7fb0d836e520>,\n",
" 'space': <bids.layout.models.Entity at 0x7fb0d836e580>,\n",
" 'suffix': <bids.layout.models.Entity at 0x7fb0d836e5e0>,\n",
" 'scans': <bids.layout.models.Entity at 0x7fb0d836e640>,\n",
" 'fmap': <bids.layout.models.Entity at 0x7fb0d836e6a0>,\n",
" 'datatype': <bids.layout.models.Entity at 0x7fb0d836e700>,\n",
" 'extension': <bids.layout.models.Entity at 0x7fb0d836e760>,\n",
" 'participant_id': <bids.layout.models.Entity at 0x7fb0d836e7c0>,\n",
" 'gender': <bids.layout.models.Entity at 0x7fb0d836e820>,\n",
" 'age': <bids.layout.models.Entity at 0x7fb0d836e880>,\n",
" 'group': <bids.layout.models.Entity at 0x7fb0d836e8e0>,\n",
" 'pre_soup_pain': <bids.layout.models.Entity at 0x7fb0d836e940>,\n",
" 'pre_soup_nausea': <bids.layout.models.Entity at 0x7fb0d836e9a0>,\n",
" 'pre_soup_fullnes': <bids.layout.models.Entity at 0x7fb0d836ea00>,\n",
" 'pre_soup_total': <bids.layout.models.Entity at 0x7fb0d836ea60>,\n",
" 'pre_soup_full': <bids.layout.models.Entity at 0x7fb0d836eac0>,\n",
" 'post_soup_pain': <bids.layout.models.Entity at 0x7fb0d836eb20>,\n",
" 'post_soup_nausea': <bids.layout.models.Entity at 0x7fb0d836eb80>,\n",
" 'post_soup_fullness': <bids.layout.models.Entity at 0x7fb0d836ebe0>,\n",
" 'post_soup_total': <bids.layout.models.Entity at 0x7fb0d836ec40>,\n",
" 'post_soup_full': <bids.layout.models.Entity at 0x7fb0d836eca0>,\n",
" 'num_glasses': <bids.layout.models.Entity at 0x7fb0d836ed30>,\n",
" 'ConversionSoftware': <bids.layout.models.Entity at 0x7fb0d836edc0>,\n",
" 'SeriesNumber': <bids.layout.models.Entity at 0x7fb0d836ee50>,\n",
" 'SeriesDescription': <bids.layout.models.Entity at 0x7fb0d836eee0>,\n",
" 'ImageType': <bids.layout.models.Entity at 0x7fb0d836ef70>,\n",
" 'Modality': <bids.layout.models.Entity at 0x7fb0d836ee80>,\n",
" 'AcquisitionDateTime': <bids.layout.models.Entity at 0x7fb0d836efa0>,\n",
" 'RepetitionTime': <bids.layout.models.Entity at 0x7fb0d8377160>,\n",
" 'PhaseEncodingDirection': <bids.layout.models.Entity at 0x7fb0d83771f0>,\n",
" 'EchoTime': <bids.layout.models.Entity at 0x7fb0d8377280>,\n",
" 'InversionTime': <bids.layout.models.Entity at 0x7fb0d8377310>,\n",
" 'PatientName': <bids.layout.models.Entity at 0x7fb0d83773a0>,\n",
" 'PatientAge': <bids.layout.models.Entity at 0x7fb0d8377430>,\n",
" 'PatientWeight': <bids.layout.models.Entity at 0x7fb0d83774c0>,\n",
" 'PatientPosition': <bids.layout.models.Entity at 0x7fb0d8377550>,\n",
" 'SliceThickness': <bids.layout.models.Entity at 0x7fb0d83775e0>,\n",
" 'FlipAngle': <bids.layout.models.Entity at 0x7fb0d8377670>,\n",
" 'Manufacturer': <bids.layout.models.Entity at 0x7fb0d8377700>,\n",
" 'SoftwareVersion': <bids.layout.models.Entity at 0x7fb0d8377790>,\n",
" 'MRAcquisitionType': <bids.layout.models.Entity at 0x7fb0d8377820>,\n",
" 'InstitutionName': <bids.layout.models.Entity at 0x7fb0d83778b0>,\n",
" 'DeviceSerialNumber': <bids.layout.models.Entity at 0x7fb0ea5e5820>,\n",
" 'ScanningSequence': <bids.layout.models.Entity at 0x7fb0c8924550>,\n",
" 'SequenceVariant': <bids.layout.models.Entity at 0x7fb0d8377940>,\n",
" 'ScanOptions': <bids.layout.models.Entity at 0x7fb0d83779a0>,\n",
" 'SequenceName': <bids.layout.models.Entity at 0x7fb0d8377a00>,\n",
" 'DiffusionBValue': <bids.layout.models.Entity at 0x7fb0d8377a90>,\n",
" 'DiffusionGradientOrientation': <bids.layout.models.Entity at 0x7fb0d8377b20>,\n",
" 'TotalReadoutTime': <bids.layout.models.Entity at 0x7fb0d8377bb0>,\n",
" 'EffectiveEchoSpacing': <bids.layout.models.Entity at 0x7fb0d8377c40>,\n",
" 'TaskName': <bids.layout.models.Entity at 0x7fb0d8377cd0>,\n",
" 'SliceTiming': <bids.layout.models.Entity at 0x7fb0d8377d60>}"
"{'subject': <bids.layout.models.Entity at 0x7ff2414b3970>,\n",
" 'session': <bids.layout.models.Entity at 0x7ff241487220>,\n",
" 'task': <bids.layout.models.Entity at 0x7ff2c160caf0>,\n",
" 'acquisition': <bids.layout.models.Entity at 0x7ff2c160cb50>,\n",
" 'ceagent': <bids.layout.models.Entity at 0x7ff2c160cbb0>,\n",
" 'reconstruction': <bids.layout.models.Entity at 0x7ff2c160cc10>,\n",
" 'direction': <bids.layout.models.Entity at 0x7ff2c160cc70>,\n",
" 'run': <bids.layout.models.Entity at 0x7ff2c160ccd0>,\n",
" 'proc': <bids.layout.models.Entity at 0x7ff2c160cd30>,\n",
" 'modality': <bids.layout.models.Entity at 0x7ff2c160cd90>,\n",
" 'echo': <bids.layout.models.Entity at 0x7ff2c160cdf0>,\n",
" 'flip': <bids.layout.models.Entity at 0x7ff2c160ce50>,\n",
" 'inv': <bids.layout.models.Entity at 0x7ff2c160ceb0>,\n",
" 'mt': <bids.layout.models.Entity at 0x7ff2c160cf10>,\n",
" 'part': <bids.layout.models.Entity at 0x7ff2c160cf70>,\n",
" 'recording': <bids.layout.models.Entity at 0x7ff2c160cfd0>,\n",
" 'space': <bids.layout.models.Entity at 0x7ff2c160cf40>,\n",
" 'suffix': <bids.layout.models.Entity at 0x7ff2c16100d0>,\n",
" 'scans': <bids.layout.models.Entity at 0x7ff2c1610130>,\n",
" 'fmap': <bids.layout.models.Entity at 0x7ff2c1610190>,\n",
" 'datatype': <bids.layout.models.Entity at 0x7ff2c16101f0>,\n",
" 'extension': <bids.layout.models.Entity at 0x7ff2c1610250>,\n",
" 'participant_id': <bids.layout.models.Entity at 0x7ff2c16102b0>,\n",
" 'gender': <bids.layout.models.Entity at 0x7ff2c1610310>,\n",
" 'age': <bids.layout.models.Entity at 0x7ff2c1610370>,\n",
" 'group': <bids.layout.models.Entity at 0x7ff2c16103d0>,\n",
" 'pre_soup_pain': <bids.layout.models.Entity at 0x7ff2c1610430>,\n",
" 'pre_soup_nausea': <bids.layout.models.Entity at 0x7ff2c1610490>,\n",
" 'pre_soup_fullnes': <bids.layout.models.Entity at 0x7ff2c16104f0>,\n",
" 'pre_soup_total': <bids.layout.models.Entity at 0x7ff2c1610550>,\n",
" 'pre_soup_full': <bids.layout.models.Entity at 0x7ff2c16105b0>,\n",
" 'post_soup_pain': <bids.layout.models.Entity at 0x7ff2c1610610>,\n",
" 'post_soup_nausea': <bids.layout.models.Entity at 0x7ff2c1610670>,\n",
" 'post_soup_fullness': <bids.layout.models.Entity at 0x7ff2c16106d0>,\n",
" 'post_soup_total': <bids.layout.models.Entity at 0x7ff2c1610730>,\n",
" 'post_soup_full': <bids.layout.models.Entity at 0x7ff2c1610790>,\n",
" 'num_glasses': <bids.layout.models.Entity at 0x7ff2c1610820>,\n",
" 'ConversionSoftware': <bids.layout.models.Entity at 0x7ff2c16108b0>,\n",
" 'SeriesNumber': <bids.layout.models.Entity at 0x7ff2c1610940>,\n",
" 'SeriesDescription': <bids.layout.models.Entity at 0x7ff2c16109d0>,\n",
" 'ImageType': <bids.layout.models.Entity at 0x7ff2c1610a60>,\n",
" 'Modality': <bids.layout.models.Entity at 0x7ff2c1610af0>,\n",
" 'AcquisitionDateTime': <bids.layout.models.Entity at 0x7ff2c1610b80>,\n",
" 'RepetitionTime': <bids.layout.models.Entity at 0x7ff2c1610c10>,\n",
" 'PhaseEncodingDirection': <bids.layout.models.Entity at 0x7ff2c1610ca0>,\n",
" 'EchoTime': <bids.layout.models.Entity at 0x7ff2c1610d30>,\n",
" 'InversionTime': <bids.layout.models.Entity at 0x7ff2c1610dc0>,\n",
" 'PatientName': <bids.layout.models.Entity at 0x7ff2c1610e50>,\n",
" 'PatientAge': <bids.layout.models.Entity at 0x7ff2c1610ee0>,\n",
" 'PatientWeight': <bids.layout.models.Entity at 0x7ff2c1610f70>,\n",
" 'PatientPosition': <bids.layout.models.Entity at 0x7ff2c1610e80>,\n",
" 'SliceThickness': <bids.layout.models.Entity at 0x7ff2c1610fa0>,\n",
" 'FlipAngle': <bids.layout.models.Entity at 0x7ff2c1617160>,\n",
" 'Manufacturer': <bids.layout.models.Entity at 0x7ff2c16171f0>,\n",
" 'SoftwareVersion': <bids.layout.models.Entity at 0x7ff2c1617280>,\n",
" 'MRAcquisitionType': <bids.layout.models.Entity at 0x7ff2c1617310>,\n",
" 'InstitutionName': <bids.layout.models.Entity at 0x7ff2c16173a0>,\n",
" 'DeviceSerialNumber': <bids.layout.models.Entity at 0x7ff241498190>,\n",
" 'ScanningSequence': <bids.layout.models.Entity at 0x7ff2413a33d0>,\n",
" 'SequenceVariant': <bids.layout.models.Entity at 0x7ff2c1617430>,\n",
" 'ScanOptions': <bids.layout.models.Entity at 0x7ff2c1617490>,\n",
" 'SequenceName': <bids.layout.models.Entity at 0x7ff2c16174f0>,\n",
" 'DiffusionBValue': <bids.layout.models.Entity at 0x7ff2c1617580>,\n",
" 'DiffusionGradientOrientation': <bids.layout.models.Entity at 0x7ff2c1617610>,\n",
" 'TotalReadoutTime': <bids.layout.models.Entity at 0x7ff2c16176a0>,\n",
" 'EffectiveEchoSpacing': <bids.layout.models.Entity at 0x7ff2c1617730>,\n",
" 'TaskName': <bids.layout.models.Entity at 0x7ff2c16177c0>,\n",
" 'SliceTiming': <bids.layout.models.Entity at 0x7ff2c1617850>}"
]
},
"execution_count": 19,
......@@ -2430,7 +2448,7 @@
"\" width=\"600\" height=\"400\" frameBorder=\"0\"></iframe>"
],
"text/plain": [
"<nilearn.plotting.html_connectome.ConnectomeView at 0x7fb08880c880>"
"<nilearn.plotting.html_connectome.ConnectomeView at 0x7ff2c1a491c0>"
]
},
"execution_count": 49,
......@@ -2722,7 +2740,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 28min, sys: 32 s, total: 28min 32s\n",
"CPU times: user 28min 7s, sys: 33.1 s, total: 28min 40s\n",
"Wall time: 1min 51s\n"
]
}
......@@ -2950,8 +2968,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 44.1 s, sys: 1.18 s, total: 45.3 s\n",
"Wall time: 38.5 s\n"
"CPU times: user 45.9 s, sys: 1.37 s, total: 47.3 s\n",
"Wall time: 40.7 s\n"
]
}
],
......@@ -9,7 +9,7 @@
- [**02-Network-based-statistics.ipynb**](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/02-Network-based-statistics.ipynb) <a href="https://colab.research.google.com/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/02-Network-based-statistics.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
- [**03-Resting-state-fmri-explore.ipynb**](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/03-resting-state-fmri-explore.ipynb) <a href="https://colab.research.google.com/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/03-Resting-state-fmri-explore.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
- [**03-resting-state-fmri-explore.ipynb**](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/03-resting-state-fmri-explore.ipynb) <a href="https://colab.research.google.com/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/03-resting-state-fmri-explore.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
- [**Brain connectivity and fMRI**](Connectivity-fMRI.md)
......@@ -18,6 +18,23 @@
![networks](./assets/BMED_360_Lec7_brain_connectivity_networks_man_machine.png)
<!--
[<img src="https://img.youtube.com/vi/rAbNbpcUNdY/maxresdefault.jpg" width="50%">](https://youtu.be/rAbNbpcUNdY)
<iframe width="560" height="315" src="https://www.youtube.com/embed/rAbNbpcUNdY"
frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen>
</iframe>
<div align="center">
[![Watch the session video](https://img.youtube.com/vi/rAbNbpcUNdY/hqdefault.jpg)](https://youtu.be/rAbNbpcUNdY)
</div>
-->
Video from the lecture on _Networks, Graphs, fMRI and brain connectivity_:<br>
--- Zoom recording 2021-05-10 ---> [![Watch the session video](https://img.youtube.com/vi/rAbNbpcUNdY/hqdefault.jpg)](https://youtu.be/rAbNbpcUNdY)
<br>
See also **Neural Networks hands-on** by Peder Lillebostad at the [CBM101](https://github.com/oercompbiomed/CBM101) [_Open Educational Resources in Computational Biomedicine_](https://ec.europa.eu/programmes/erasmus-plus/projects/eplus-project-details/#project/bc4e0bdb-aa64-4d5c-a7f2-26d68ec36647) site: https://github.com/oercompbiomed/CBM101/tree/master/H_Neural_Networks
......
......@@ -2,7 +2,7 @@
## ( with [_ad hoc_](https://github.com/computational-medicine/BMED360-2021/tree/main/Outbreak-Science-Extra#readme) curriculum* on COVID-19 and "outbreak science" )
[Work in progress ver. 2021-04-28]
[Work in progress ver. 2021-05-10]
![BMED360 image](./assets/bmed360_logo.png)
......@@ -54,12 +54,12 @@ Throughout the course you will work with notebooks that contain various material
- **[Lec 0](https://docs.google.com/presentation/d/1aoNyjXZ053yCUbdY5YVAtftGL2yym10QmbnnrlNFr7A/edit?usp=sharing): Course overview; [SW installation](setup.md); Motivation:** [Can a biologist fix a radio?](https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.pdf) Lazebnic (2002); [Why programming?](https://drive.google.com/file/d/1Zss5kTEgVmoF8PxQpho2yvfNviJOksgv); Why top-down? - [teaching "the whole game"](https://www.fast.ai/2016/10/08/teaching-philosophy) (see also https://computingskillsforbiologists.com)
- **[Lec 1](https://docs.google.com/presentation/d/1fZEID0NKFBdtMsQgzMuNwpvaUa-y7LAIihpap0Y3Q20/edit?usp=sharing)**: _Introduction to modelling, MRI, and image processing_; **BROWSE through**: [Tofts (2018) Ch. [1](https://drive.google.com/open?id=1s36p4vEXEEfmL3KZqMaHkbu4e58XMgDj), Ch. [2](https://drive.google.com/open?id=1zWhdLIzTFsk92a-XLWj7eW8pHmDs5VEG), Ch. [17](https://drive.google.com/open?id=19U76zBL2ZQrRnhUT-s_GpFzPM29WpJLB), Ch. [18](https://drive.google.com/open?id=1t9vUUJ6Xc4zmCdCjwNJuqhZHo_CV06ad); McRobbie (2017) Ch. [3](https://drive.google.com/open?id=1igDMEVAnWR3kU1doXTz_X-LygdVLQovJ), Ch. [4](https://drive.google.com/open?id=15_9lHA_6DXZHhQEXH2T-VLNDbV16A1zo), Ch. [5](https://drive.google.com/open?id=14TGT59koVT6ujgYNDwK5G5xOA3LFlcSI), Ch. [8](https://drive.google.com/open?id=1CiCRAslFUb5Q3DZqpbEhkdBJm57vQx4O)]
- **[Lec 2](https://docs.google.com/presentation/d/1qMxwu401az5zgq6Rg5M7kOtygdeosnMeSucvy7SSbNk/edit?usp=sharing)**: _Water diffusion_, dMRI, and tissue microstructure - Part 1 [Tofts (2018) Ch. [8](https://drive.google.com/open?id=1rck9B5qsW0uV49D9588HolNLCRm7g9jy); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 303-310.]
- **[Lec 2](https://docs.google.com/presentation/d/1qMxwu401az5zgq6Rg5M7kOtygdeosnMeSucvy7SSbNk/edit?usp=sharing)**: _Water diffusion_, dMRI, and tissue microstructure - Part 1 [Tofts (2018) Ch. [8](https://drive.google.com/open?id=1rck9B5qsW0uV49D9588HolNLCRm7g9jy); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 303-310]. Video recording from the dMRI session: https://youtu.be/ZkVclYejv54
- **[Lec 3](https://docs.google.com/presentation/d/1pcFZO9H3EOrB3rYgUuIjd9Ja_UfMWjULH8wSZcOYDbw/edit?usp=sharing)**: _Water diffusion_, diffusion tensor imaging and beyond - Part 2 [Tofts (2018) Ch. [9](https://drive.google.com/open?id=1ZRfd4iI8q0VmfuEzPVwgkhlHawvrglf3); [Westin (2002)](https://drive.google.com/open?id=1WkAbJi3Xh4sdDdBMiu9yuXEU9UzGKKSs)]
- **[Lec 4](https://docs.google.com/presentation/d/1C0WGl1qKrQdwrMKOMfWyTnnASLzECRKhvLhZozphJwQ/edit?usp=sharing)**: _Blood perfusion_ and dynamic susceptibility contrast MRI (DSC-MRI) - Part 1 [Tofts (2003) Ch. [11](https://drive.google.com/open?id=1DWhL0B8xGc1EL1Ag7J4I2iCMBdHl-2mA); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 311-314.]
- **[Lec 5](https://docs.google.com/presentation/d/1CTuA2tBCBQaWHL7_k2o0o26E8Lp6G4UaNjlL1y2Qsac/edit?usp=sharing)**: _Blood perfusion_, tracer kinetics, and deconvolution - Part 2 [Tofts (2003) Ch. [11](https://drive.google.com/open?id=1DWhL0B8xGc1EL1Ag7J4I2iCMBdHl-2mA)]
- **[Lec 6](https://docs.google.com/presentation/d/1EYuKHtQM4RIgkvIxrMyAkq02l8KUd9WuCsjCHviBaKI/edit?usp=sharing)**: _Vascular permeability_, compartment modelling, and T1w dynamic contrast-enhanced MRI (DCE-MRI) [Tofts (2018) Ch. [14](https://drive.google.com/open?id=1Wy6ZGurLkV18q6v1XAlhMRfvLttV2f08); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 316-319; Measurement of Renal Perfusion and Filtration with MRI [GitHub](https://github.com/arvidl/functional-kidney-imaging) / [slides](https://docs.google.com/presentation/d/1WS6ODHrXOfYL-fXLw847EkYR4qcvDJA6bRTvqFE_fIY/edit?usp=sharing)]
- **[Lec 7](https://docs.google.com/presentation/d/142Y5wQKkIvRkcmBiSV2INDhuMdRHrpPhxqs2zx17ZCY/edit?usp=sharing)**: _Brain connectivity_ assessed with aMRI, dMRI, fMRI and network (graph) theory [Fornito (2016) Ch. [1](https://drive.google.com/open?id=179E3CAZsV6LzV7Jb37eHmczuzVLJUB4H); [Bassett (2018)](https://drive.google.com/open?id=1PW30HroQMBLPLDiZsnhJZ-obumW5RaEA); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 319-325.]
- **[Lec 4](https://docs.google.com/presentation/d/1C0WGl1qKrQdwrMKOMfWyTnnASLzECRKhvLhZozphJwQ/edit?usp=sharing)**: _Blood perfusion_ and dynamic susceptibility contrast MRI (DSC-MRI) - Part 1 [Tofts (2003) Ch. [11](https://drive.google.com/open?id=1DWhL0B8xGc1EL1Ag7J4I2iCMBdHl-2mA); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 311-314]. Video recording from this session: https://youtu.be/h1AboyMq7Uw
- **[Lec 5](https://docs.google.com/presentation/d/1CTuA2tBCBQaWHL7_k2o0o26E8Lp6G4UaNjlL1y2Qsac/edit?usp=sharing)**: _Blood perfusion_, tracer kinetics, and deconvolution - Part 2 [Tofts (2003) Ch. [11](https://drive.google.com/open?id=1DWhL0B8xGc1EL1Ag7J4I2iCMBdHl-2mA)]. Video recording from thos session: https://youtu.be/mTCs2MFxEzk
- **[Lec 6](https://docs.google.com/presentation/d/1EYuKHtQM4RIgkvIxrMyAkq02l8KUd9WuCsjCHviBaKI/edit?usp=sharing)**: _Vascular permeability_, compartment modelling, and T1w dynamic contrast-enhanced MRI (DCE-MRI) [Tofts (2018) Ch. [14](https://drive.google.com/open?id=1Wy6ZGurLkV18q6v1XAlhMRfvLttV2f08); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 316-319; Measurement of Renal Perfusion and Filtration with MRI [GitHub](https://github.com/arvidl/functional-kidney-imaging) / [slides](https://docs.google.com/presentation/d/1WS6ODHrXOfYL-fXLw847EkYR4qcvDJA6bRTvqFE_fIY/edit?usp=sharing)]. Video recording from this session: https://youtu.be/X4zGyhid48U
- **[Lec 7](https://docs.google.com/presentation/d/142Y5wQKkIvRkcmBiSV2INDhuMdRHrpPhxqs2zx17ZCY/edit?usp=sharing)**: _Brain connectivity_ assessed with aMRI, dMRI, fMRI and network (graph) theory [Fornito (2016) Ch. [1](https://drive.google.com/open?id=179E3CAZsV6LzV7Jb37eHmczuzVLJUB4H); [Bassett (2018)](https://drive.google.com/open?id=1PW30HroQMBLPLDiZsnhJZ-obumW5RaEA); McRobbie (2017) Ch. [18](https://drive.google.com/open?id=1kAUzG30nGN4a9pPq45jH6pIHPU0EKwyC) pp. 319-325]. Video recording from this session: https://youtu.be/rAbNbpcUNdY
- **Lec 8**: _Outbreak science and COVID-19_ [[README](https://github.com/computational-medicine/BMED360-2021/tree/main/Outbreak-Science-Extra#readme)] (biology, epidemiology, geo-mapping, imaging) [[covid-19-eda](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Outbreak-Science-Extra/epi/covid-19-eda.ipynb)] [[simulitis-outbreak](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Outbreak-Science-Extra/epi/simulitis-outbreak.ipynb)]
......@@ -105,6 +105,7 @@ Throughout the course you will work with notebooks that contain various material
- [[README](https://github.com/computational-medicine/BMED360-2021/tree/main/Lab6-Networks-Graphs#readme)]
- [[Lab6-01-Concepts-in-network-theory](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/01-Concepts-in-network-theory.ipynb)]
- [[Lab6-02-Network-based-statistics](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/02-Network-based-statistics.ipynb)]
- [[Lab6-03-resting-state-fmri-expore](https://nbviewer.jupyter.org/github/computational-medicine/BMED360-2021/blob/main/Lab6-Networks-Graphs/03-resting-state-fmri-explore.ipynb)]
- [[Connectivity-fMRI](./Lab6-Networks-Graphs/Connectivity-fMRI.md)] (brain connectivity and fMRI - concepts, software, and data)
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment