Commit a09d7348 authored by Sean Fitzgibbon's avatar Sean Fitzgibbon
Browse files

updated docs

parent 06ddfe08
......@@ -37,32 +37,6 @@
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function will be used to plot ICs later:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def map_plot(d):\n",
"\n",
" N = d.shape[-1]\n",
"\n",
" fig, ax = plt.subplots(int(np.ceil((N/2))),2, figsize=(12, N))\n",
"\n",
" for img, ax0 in zip(image.iter_img(d), ax.ravel()):\n",
" coord = plotting.find_xyz_cut_coords(img, activation_threshold=2.3)\n",
" plotting.plot_stat_map(img, cut_coords=coord, vmax=10, axes=ax0)\n",
" \n",
" return fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -72,7 +46,7 @@
"\n",
"Create a directory in the users home directory to store the downloaded data:\n",
"\n",
"`expanduser` will expand the `~` to the be users home directory:"
"> **NOTE:** `expanduser` will expand the `~` to the be users home directory:"
]
},
{
......@@ -91,7 +65,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Download the data (if not already downloaded). We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data:"
"Download the data (if not already downloaded):\n",
"\n",
"> **Note:** We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data"
]
},
{
......@@ -110,7 +86,13 @@
"<a class=\"anchor\" id=\"clean-the-data\"></a>\n",
"## Clean the data\n",
"\n",
"We use methods from [`nilearn`](https://nilearn.github.io/index.html) to regress confounds from the data (```clean_img```) and to spatially smooth the data with a gaussian filter of 10mm FWHM (```smooth_img```):"
"Regress confounds from the data and to spatially smooth the data with a gaussian filter of 10mm FWHM.\n",
"\n",
"> **Note:**\n",
"> 1. We use `clean_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to regress confounds from the data\n",
"> 2. We use `smooth_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to spatially smooth the data\n",
"> 3. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through four lists simultaneously\n",
"> 4. We use list comprehension to loop through all the filenames and append suffixes\n"
]
},
{
......@@ -135,7 +117,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI. Here we use [`nilearn`](https://nilearn.github.io/index.html) methods to load the MNI152 mask (```load_mni152_brain_mask```), resample to the resolution of the fMRI (```resample_to_img```), and binarize (```math_img```):"
"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"
]
},
{
......@@ -165,7 +153,12 @@
"<a class=\"anchor\" id=\"run-melodic\"></a>\n",
"### Run ```melodic```\n",
"\n",
"Generate a command line string and run group ```melodic``` on the smoothed fMRI with a dimension of 10 components:"
"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`"
]
},
{
......@@ -179,6 +172,15 @@
"print(melodic_cmd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **Note:** \n",
"> 1. Here we use the `!` operator to execute the command in the shell\n",
"> 2. The `{}` will expand the contained python variable in the shell"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -198,7 +200,14 @@
"\n",
"Now we can load and plot the group ICs generated by ```melodic```.\n",
"\n",
"This function will be used to plot ICs:"
"This function will be used to plot ICs:\n",
"\n",
"> **NOTE:**\n",
"> 1. Here we use `plot_stat_map` from the `nilearn` package to plot the orthographic images\n",
"> 2. `subplots` from `matplotlib.pyplot` creates a figure and multiple subplots\n",
"> 3. `find_xyz_cut_coords` from the `nilearn` package will find the image coordinates of the center of the largest activation connected component\n",
"> 4. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through two lists simultaneously\n",
"> 5. `iter_img` from the `nilearn` package creates an iterator from an image that steps through each volume/time-point of the image"
]
},
{
......
%% Cell type:markdown id: tags:
# Fetch Data
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
from nilearn import datasets
from nilearn import image
from nilearn import plotting
import nibabel as nb
import numpy as np
import os.path as op
import os
import glob
import matplotlib.pyplot as plt
```
%% Cell type:markdown id: tags:
This function will be used to plot ICs later:
%% Cell type:code id: tags:
``` python
def map_plot(d):
N = d.shape[-1]
fig, ax = plt.subplots(int(np.ceil((N/2))),2, figsize=(12, N))
for img, ax0 in zip(image.iter_img(d), ax.ravel()):
coord = plotting.find_xyz_cut_coords(img, activation_threshold=2.3)
plotting.plot_stat_map(img, cut_coords=coord, vmax=10, axes=ax0)
return fig
```
%% Cell type:markdown id: tags:
<a class="anchor" id="download-the-data"></a>
## Download the data
Create a directory in the users home directory to store the downloaded data:
`expanduser` will expand the `~` to the be users home directory:
> **NOTE:** `expanduser` will expand the `~` to the be users home directory:
%% Cell type:code id: tags:
``` python
data_dir = op.expanduser('~/nilearn_data')
if not op.exists(data_dir):
os.makedirs(data_dir)
```
%% Cell type:markdown id: tags:
Download the data (if not already downloaded). We use a method from [`nilearn`](https://nilearn.github.io/index.html) called `fetch_cobre` to download the fMRI data:
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:
<a class="anchor" id="clean-the-data"></a>
## Clean the data
We use methods from [`nilearn`](https://nilearn.github.io/index.html) to regress confounds from the data (```clean_img```) and to spatially smooth the data with a gaussian filter of 10mm FWHM (```smooth_img```):
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
clean = [f.replace('.nii.gz', '_clean.nii.gz') for f in d.func]
smooth = [f.replace('.nii.gz', '_clean_smooth.nii.gz') for f in d.func]
# loop through each subject, regress confounds and smooth
for img, cleaned, smoothed, conf in zip(d.func, clean, smooth, d.confounds):
print(f'{img}: regress confounds: ', end='')
image.clean_img(img, confounds=conf).to_filename(cleaned)
print(f'smooth.')
image.smooth_img(img, 10).to_filename(smoothed)
```
%% Cell type:markdown id: tags:
To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI. Here we use [`nilearn`](https://nilearn.github.io/index.html) methods to load the MNI152 mask (```load_mni152_brain_mask```), resample to the resolution of the fMRI (```resample_to_img```), and binarize (```math_img```):
To run ```melodic``` we will need a brain mask in MNI152 space at the same resolution as the fMRI.
> **Note:**
> 1. We use `load_mni152_brain_mask` from the [`nilearn`](https://nilearn.github.io/index.html) package to load the MNI152 mask
> 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
> 3. We use `math_img` from the [`nilearn`](https://nilearn.github.io/index.html) package to binarize the resample mask
> 4. The mask is plotted using `plot_anat` from the [`nilearn`](https://nilearn.github.io/index.html) package
%% Cell type:code id: tags:
``` python
# load a single fMRI dataset (func0)
func0 = nb.load(d.func[0].replace('.nii.gz', '_clean_smooth.nii.gz'))
# load MNI153 brainmask, resample to func0 resolution, binarize, and save to nifti
mask = datasets.load_mni152_brain_mask()
mask = image.resample_to_img(mask, func0)
mask = image.math_img('img > 0.5', img=mask)
mask.to_filename(op.join(data_dir, 'brain_mask.nii.gz'))
# plot brainmask to make sure it looks OK
disp = plotting.plot_anat(image.index_img(func0, 0))
disp.add_contours(mask, threshold=0.5)
```
%% Cell type:markdown id: tags:
<a class="anchor" id="run-melodic"></a>
### Run ```melodic```
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`
%% Cell type:code id: tags:
``` python
# generate melodic command line string
melodic_cmd = f"melodic -i {','.join(smooth)} --mask={op.join(data_dir, 'brain_mask.nii.gz')} -d 10 -v -o cobre.gica "
print(melodic_cmd)
```
%% Cell type:markdown id: tags:
> **Note:**
> 1. Here we use the `!` operator to execute the command in the shell
> 2. The `{}` will expand the contained python variable in the shell
%% Cell type:code id: tags:
``` python
# run melodic
! {melodic_cmd}
```
%% Cell type:markdown id: tags:
<a class="anchor" id="plot-group-ics"></a>
### Plot group ICs
Now we can load and plot the group ICs generated by ```melodic```.
This function will be used to plot ICs:
> **NOTE:**
> 1. Here we use `plot_stat_map` from the `nilearn` package to plot the orthographic images
> 2. `subplots` from `matplotlib.pyplot` creates a figure and multiple subplots
> 3. `find_xyz_cut_coords` from the `nilearn` package will find the image coordinates of the center of the largest activation connected component
> 4. `zip` takes iterables and aggregates them in a tuple. Here it is used to iterate through two lists simultaneously
> 5. `iter_img` from the `nilearn` package creates an iterator from an image that steps through each volume/time-point of the image
%% Cell type:code id: tags:
``` python
def map_plot(d):
N = d.shape[-1]
fig, ax = plt.subplots(int(np.ceil((N/2))),2, figsize=(12, N))
for img, ax0 in zip(image.iter_img(d), ax.ravel()):
coord = plotting.find_xyz_cut_coords(img, activation_threshold=3.5)
plotting.plot_stat_map(img, cut_coords=coord, vmax=10, axes=ax0)
return fig
```
%% Cell type:markdown id: tags:
Hopefully you can see some familiar looking RSN spatial patterns:
%% Cell type:code id: tags:
``` python
# Load ICs
ics = nb.load('cobre.gica/melodic_IC.nii.gz')
# plot
fig = map_plot(ics)
```
%% Cell type:code id: tags:
``` python
```
......
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