"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",
"\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",
"* [Download 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",
"\n",
"Firstly we will import the necessary packages for this notebook: "
"To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI. \n",
"\n",
"> **Note:**\n",
"> 1. We use `load_mni152_brain_mask` from the [`nilearn`](https://nilearn.github.io/index.html) package to load the MNI152 mask\n",
"> 2. We use `resample_to_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to resample the mask to the resolution of the fMRI \n",
"> 3. We use `math_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to binarize the resample mask\n",
"> 4. The mask is plotted using `plot_anat` from the [`nilearn`](https://nilearn.github.io/index.html) package"
"Generate a command line string and run group ```melodic``` on the smoothed fMRI with a dimension of 10 components:\n",
"\n",
"> **Note**: \n",
"> 1. Here we use python [f-strings](https://www.python.org/dev/peps/pep-0498/), formally known as literal string interpolation, which allow for easy formatting\n",
"> 2. `op.join` will join path strings using the platform-specific directory separator\n",
"> 3. `','.join(smooth)` will create a comma seprated string of all the items in the list `smooth`"
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.
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.
It comprises 10 preprocessed resting-state fMRI selected from 72 patients diagnosed with schizophrenia and 74 healthy controls (6mm isotropic, TR=2s, 150 volumes).
*[Download the data](#download-the-data)
*[Clean the data](#clean-the-data)
*[Run `melodic`](#run-melodic)
*[Plot group ICs](#plot-group-ics)
Firstly we will import the necessary packages for this notebook:
%% Cell type:code id: tags:
``` python
fromnilearnimportdatasets
fromnilearnimportimage
fromnilearnimportplotting
importnibabelasnb
importnumpyasnp
importos.pathasop
importos
importglob
importmatplotlib.pyplotasplt
```
%% Cell type:markdown id: tags:
<aclass="anchor"id="download-the-data"></a>
## Download the data
Create a directory in the users home directory to store the downloaded data:
> **NOTE:** `expanduser` will expand the `~` to the be users home directory:
%% Cell type:code id: tags:
``` python
data_dir=op.expanduser('~/nilearn_data')
ifnotop.exists(data_dir):
os.makedirs(data_dir)
```
%% Cell type:markdown id: tags:
Download the data (if not already downloaded):
> **Note:** We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data
%% Cell type:code id: tags:
``` python
d=datasets.fetch_cobre(data_dir=data_dir)
```
%% Cell type:markdown id: tags:
<aclass="anchor"id="clean-the-data"></a>
## Clean the data
Regress confounds from the data and to spatially smooth the data with a gaussian filter of 10mm FWHM.
> **Note:**
> 1. We use `clean_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to regress confounds from the data
> 2. We use `smooth_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to spatially smooth the data
> 3. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through four lists simultaneously
> 4. We use list comprehension to loop through all the filenames and append suffixes
%% Cell type:code id: tags:
``` python
# Create a list of filenames for cleaned and smoothed data
Generate a command line string and run group ```melodic``` on the smoothed fMRI with a dimension of 10 components:
> **Note**:
> 1. Here we use python [f-strings](https://www.python.org/dev/peps/pep-0498/), formally known as literal string interpolation, which allow for easy formatting
> 2. `op.join` will join path strings using the platform-specific directory separator
> 3. `','.join(smooth)` will create a comma seprated string of all the items in the list `smooth`
"Generate a command line string and run group ```melodic``` on the Matlab MIGP dimension reduced data with a dimension of 10 components. We disable MIGP because it was already run separately in Matlab.\n",
"\n",
"> **Note**: \n",
"> 1. Here we use python [f-strings](https://www.python.org/dev/peps/pep-0498/), formally known as literal string interpolation, which allow for easy formatting\n",
"> 2. `op.join` will join path strings using the platform-specific directory separator"
