This lesson is in the early stages of development (Alpha version)

Introduction to Working with MRI Data in Python

Before we start

Overview

Teaching: 30 min
Exercises: 0 min
Questions
  • What is Python and why should I learn it?

Objectives
  • Present motivations for using Python.

  • Organize files and directories for a set of analyses as a Python project.

  • How to work with Jupyter Notebook.

  • Know where to find help.

This overview have been adapted from the Data Carpentry Python Lessons with Ecological Data.

Why Python?

Python is rapidly becoming the standard language for data analysis, visualization and automated workflow building. It is a free and open-source software that is relatively easy to pick up by new programmers and is available on multiple operating systems. In addition, with Python packages such as Jupyter one can keep an interactive code journal of analysis - this is what we’ll be using in the workshop. Using Jupyter notebooks allows you to keep a record of all the steps in your analysis, enabling reproducibility and ease of code sharing.

Another advantage of Python is that it is maintained by a large user-base. Anyone can easily make their own Python packages for others to use. Therefore, there exists a very large codebase for you to take advantage of for your neuroimaging analysis; from basic statistical analysis, to brain visualization tools, to advanced machine learning and multivariate methods!

Knowing your way around Anaconda and Jupyter

The Anaconda distribution of Python includes a lot of its popular packages, such as the IPython console, and Jupyter Notebook. Have a quick look around the Anaconda Navigator. You can launch programs from the Navigator or use the command line.

The Jupyter notebook is an open-source web application that allows you to create and share documents that allow one to create documents that combine code, graphs, and narrative text. If you have not worked with Jupyter notebooks before, you might start with the tutorial from Jupyter.org called “Try Classic Notebook”.

Anaconda also comes with a package manager called conda, which makes it easy to install and update additional packages.

Organizing your working directory

Using a consistent folder structure across your projects will help you keep things organized, and will also make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, you may wish to create separate directories for your scripts, data, and documents.

This lesson uses the following folders to keep things organized:

How to learn more after the workshop?

The material we cover during this workshop will give you an initial taste of how you can use Python to analyze MRI data for your own research. However, you will need to learn more to do advanced operations such as working with specific MRI modalities, cleaning your dataset, using statistical methods, or creating beautiful graphics. The best way to become proficient and efficient at Python, as with any other tool, is to use it to address your actual research questions. As a beginner, it can feel daunting to have to write a script from scratch, and given that many people make their code available online, modifying existing code to suit your purpose might make it easier for you to get started.

Seeking help

Finally, a generic Google or internet search “Python task” will often either send you to the appropriate module documentation or a helpful forum where someone else has already asked your question.

I am stuck… I get an error message that I don’t understand. Start by googling the error message. However, this doesn’t always work very well, because often, package developers rely on the error catching provided by Python. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”). If the message is very generic, you might also include the name of the function or package you’re using in your query.

You can also check Stack Overflow. Most questions have already been answered, but the challenge is to use the right words in the search to find the answers.

Another helpful resource specific to the neuroimaging community is NeuroStars.

Key Points

  • Python is an open source and platform independent programming language.

  • Jupyter Notebook is a great tool to code in and interact with Python.

  • With the large Python community it is easy to find help on the internet.


From the scanner to our computer

Overview

Teaching: 20 min
Exercises: 10 min
Questions
  • What are the main MRI modalities?

  • What’s the first step necessary to start working with MRI data?

Objectives
  • Understand how different MRI modalities differ and what each one represents

  • Become familiar with converting MRI data from DICOM to NIfTI

Types of MR scans

Anatomical

Sourced from https://case.edu/med/neurology/NR/MRI%20Basics.htm

Functional

Sourced from Wagner and Lindquist, 2015

Diffusion

Sourced from http://brainsuite.org/processing/diffusion/tractography/

Neuroimaging file formats

Format Name File Extension Origin
DICOM none ACR/NEMA Consortium
Analyze .img/.hdr Analyze Software, Mayo Clinic
NIfTI .nii Neuroimaging Informatics Technology Initiative
MINC .mnc Montreal Neurological Institute
NRRD .nrrd  

From the MRI scanner, images are initially collected in the DICOM format and can be converted to these other formats to make working with the data easier.

Let’s download some example DICOM data to see what it looks like. This data was generously shared publicly by the Princeton Handbook for Reproducible Neuroimaging.

wget https://zenodo.org/record/3677090/files/0219191_mystudy-0219-1114.tar.gz -O ../data/0219191_mystudy-0219-1114.tar.gz
mkdir -p ../data/dicom_examples
tar -xvzf ../data/0219191_mystudy-0219-1114.tar.gz -C ../data/dicom_examples
gzip -d ../data/dicom_examples/0219191_mystudy-0219-1114/dcm/*dcm.gz
rm ../data/0219191_mystudy-0219-1114.tar.gz

dicom-to-nifti

NIfTI is one of the most ubiquitous file formats for storing neuroimaging data. If you’re interested in learning more about NIfTI images, we highly recommend this blog post about the NIfTI format. We can convert our DICOM data to NIfTI using dcm2niix.

We can learn how to run dcm2niix by taking a look at its help menu.

dcm2niix -help

Converting DICOM to NIfTI

Convert the Princeton DICOM data to NIfTI

Solution

mkdir -p ../data/dicom_examples/nii
dcm2niix -z y -o ../data/dicom_examples/nii ../data/dicom_examples/0219191_mystudy-0219-1114/dcm

Key Points


Anatomy of a NIfTI

Overview

Teaching: 20 min
Exercises: 5 min
Questions
  • How are MRI data represented digitally?

Objectives
  • Introduce Python data types

  • Load an MRI scan into Python and understand how the data is stored

  • Understand what a voxel is

  • Introduce MRI coordinate systems

  • View and manipulate image data

In the last lesson, we introduced the NIfTI. We’ll cover a few details to get started working with them.

Reading NIfTI images

NiBabel is a Python package for reading and writing neuroimaging data. To learn more about how NiBabel handles NIfTIs, check out the Working with NIfTI images page of the NiBabel documentation, from which this episode is heavily based.

import nibabel as nib

First, use the load() function to create a NiBabel image object from a NIfTI file. We’ll load in an example T1w image from the zip file we just downloaded.

t1_img = nib.load("../data/dicom_examples/nii/0219191_mystudy-0219-1114_anat_ses-01_T1w_20190219111436_5.nii.gz")

Loading in a NIfTI file with NiBabel gives us a special type of data object which encodes all the information in the file.Each bit of information is called an attribute in Python’s terminology. To see all of these attributes, type t1_img. followed by Tab. There are three main attributes that we’ll discuss today:

1. Header: contains metadata about the image, such as image dimensions, data type, etc.

t1_hdr = t1_img.header
print(t1_hdr)
<class 'nibabel.nifti1.Nifti1Header'> object, endian='<'
sizeof_hdr      : 348
data_type       : b''
db_name         : b''
extents         : 0
session_error   : 0
regular         : b''
dim_info        : 0
dim             : [ 3 57 67 56  1  1  1  1]
intent_p1       : 0.0
intent_p2       : 0.0
intent_p3       : 0.0
intent_code     : none
datatype        : uint8
bitpix          : 8
slice_start     : 0
pixdim          : [1.   2.75 2.75 2.75 1.   1.   1.   1.  ]
vox_offset      : 0.0
scl_slope       : nan
scl_inter       : nan
slice_end       : 0
slice_code      : unknown
xyzt_units      : 2
cal_max         : 0.0
cal_min         : 0.0
slice_duration  : 0.0
toffset         : 0.0
glmax           : 0
glmin           : 0
descrip         : b''
aux_file        : b''
qform_code      : mni
sform_code      : mni
quatern_b       : 0.0
quatern_c       : 0.0
quatern_d       : 0.0
qoffset_x       : -78.0
qoffset_y       : -91.0
qoffset_z       : -91.0
srow_x          : [  2.75   0.     0.   -78.  ]
srow_y          : [  0.     2.75   0.   -91.  ]
srow_z          : [  0.     0.     2.75 -91.  ]
intent_name     : b''
magic           : b'n+1'

t1_hdr is a Python dictionary. Dictionaries are containers that hold pairs of objects - keys and values. Let’s take a look at all of the keys. Similar to t1_img in which attributes can be accessed by typing t1_img. followed by Tab, you can do the same with t1_hdr. In particular, we’ll be using a method belonging to t1_hdr that will allow you to view the keys associated with it.

t1_hdr.keys()
['sizeof_hdr',
 'data_type',
 'db_name',
 'extents',
 'session_error',
 'regular',
 'dim_info',
 'dim',
 'intent_p1',
 'intent_p2',
 'intent_p3',
 'intent_code',
 'datatype',
 'bitpix',
 'slice_start',
 'pixdim',
 'vox_offset',
 'scl_slope',
 'scl_inter',
 'slice_end',
 'slice_code',
 'xyzt_units',
 'cal_max',
 'cal_min',
 'slice_duration',
 'toffset',
 'glmax',
 'glmin',
 'descrip',
 'aux_file',
 'qform_code',
 'sform_code',
 'quatern_b',
 'quatern_c',
 'quatern_d',
 'qoffset_x',
 'qoffset_y',
 'qoffset_z',
 'srow_x',
 'srow_y',
 'srow_z',
 'intent_name',
 'magic']

Notice that methods require you to include () at the end of them whereas attributes do not. The key difference between a method and an attribute is:

When you type in t1_img. followed by Tab, you’ll see that attributes are highlighted in orange and methods highlighted in blue.

The output above is a list of keys you can use from t1_hdr to access values. We can access the value stored by a given key by typing:

t1_hdr['<key_name>']

Extract values from the NIfTI header

Extract the value of pixdim from t1_hdr

Solution

t1_hdr['pixdim']
array([1.  , 2.75, 2.75, 2.75, 1.  , 1.  , 1.  , 1.  ], dtype=float32)

2. Data

As you’ve seen above, the header contains useful information that gives us information about the properties (metadata) associated with the MR data we’ve loaded in. Now we’ll move in to loading the actual image data itself. We can achieve this by using the method called t1_img.get_fdata().

t1_data = t1_img.get_fdata()
t1_data
array([[[ 8.79311806,  8.79311806,  8.79311806, ...,  7.73574093,
          7.73574093,  7.38328189],
        [ 8.79311806,  9.1455771 ,  8.79311806, ...,  8.08819997,
          8.08819997,  8.08819997],
        [ 9.1455771 ,  8.79311806,  8.79311806, ...,  8.44065902,
          8.44065902,  8.44065902],
        ...,
        [ 8.08819997,  8.44065902,  8.08819997, ...,  7.38328189,
          7.38328189,  7.38328189],
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.73574093,
          7.38328189,  7.38328189],
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.38328189,
          7.38328189,  7.03082284]],

       [[ 8.79311806,  9.1455771 ,  8.79311806, ...,  7.73574093,
          7.38328189,  7.38328189],
        [ 8.79311806,  9.1455771 ,  9.1455771 , ...,  8.08819997,
          7.73574093,  8.08819997],
        [ 8.79311806,  9.49803615,  9.1455771 , ...,  8.44065902,
          8.44065902,  8.44065902],
        ...,
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.38328189,
          7.38328189,  7.03082284],
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.38328189,
          7.38328189,  7.38328189],
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.38328189,
          7.38328189,  7.73574093]],

       [[ 9.1455771 ,  9.1455771 ,  8.79311806, ...,  7.73574093,
          7.38328189,  7.03082284],
        [ 9.1455771 ,  9.49803615,  9.1455771 , ...,  8.08819997,
          7.73574093,  7.38328189],
        [ 9.1455771 ,  9.49803615,  9.1455771 , ...,  8.08819997,
          8.08819997,  8.08819997],
        ...,
        [ 8.08819997,  8.44065902,  8.44065902, ...,  7.73574093,
          7.38328189,  7.38328189],
        [ 8.44065902,  8.08819997,  8.44065902, ...,  7.38328189,
          7.38328189,  7.38328189],
        [ 8.08819997,  8.08819997,  8.08819997, ...,  7.38328189,
          7.38328189,  7.38328189]],

       ...,

       [[ 9.49803615,  9.85049519,  9.85049519, ...,  7.38328189,
          7.38328189,  7.03082284],
        [ 9.85049519,  9.85049519,  9.85049519, ...,  7.38328189,
          7.38328189,  7.73574093],
        [ 9.85049519,  9.85049519, 10.20295423, ...,  8.08819997,
          8.08819997,  8.08819997],
        ...,
        [ 9.49803615,  9.1455771 ,  9.49803615, ...,  7.73574093,
          7.73574093,  7.73574093],
        [ 9.49803615,  9.49803615,  9.1455771 , ...,  7.73574093,
          8.08819997,  7.73574093],
        [ 9.49803615,  9.1455771 ,  8.79311806, ...,  8.08819997,
          7.73574093,  7.73574093]],

       [[ 9.49803615,  9.85049519, 10.20295423, ...,  7.38328189,
          7.38328189,  7.03082284],
        [ 9.1455771 ,  9.85049519,  9.1455771 , ...,  7.73574093,
          7.38328189,  7.38328189],
        [ 9.85049519,  9.85049519,  9.49803615, ...,  8.08819997,
          7.73574093,  8.08819997],
        ...,
        [ 9.49803615,  8.79311806,  9.1455771 , ...,  8.08819997,
          7.38328189,  7.73574093],
        [ 9.1455771 ,  9.1455771 ,  9.1455771 , ...,  7.73574093,
          8.08819997,  7.73574093],
        [ 8.79311806,  9.1455771 ,  9.1455771 , ...,  7.73574093,
          7.73574093,  7.38328189]],

       [[ 9.1455771 ,  8.79311806,  8.79311806, ...,  7.03082284,
          7.03082284,  6.6783638 ],
        [ 9.49803615,  9.85049519,  9.49803615, ...,  7.73574093,
          7.73574093,  7.73574093],
        [ 9.49803615,  9.49803615,  9.85049519, ...,  7.73574093,
          8.08819997,  7.73574093],
        ...,
        [ 8.79311806,  9.1455771 ,  9.1455771 , ...,  8.08819997,
          8.08819997,  8.08819997],
        [ 9.1455771 ,  9.1455771 ,  9.1455771 , ...,  8.08819997,
          8.08819997,  7.73574093],
        [ 8.79311806,  9.1455771 ,  9.1455771 , ...,  8.08819997,
          7.73574093,  7.73574093]]])

What type of data is this exactly? We can determine this by calling the type() function on t1_data.

type(t1_data)
numpy.ndarray

The data is a multidimensional array representing the image data. In Python, an array is used to store lists of numerical data into something like a table.

Check out attributes of the array

How can we see the number of dimensions in the t1_data array? Once again, all of the attributes of the array can be seen by typing t1_data. followed by Tab.

Solution

t1_data.ndim
3

t1_data contains 3 dimensions. You can think of the data as a 3D version of a picture (more accurately, a volume).

While typical 2D pictures are made out of squares called pixels, a 3D MR image is made up of 3D cubes called voxels.
What about the how big each dimension is (shape)?

Solution

t1_data.shape
(57, 67, 56)

The 3 numbers given here represent the number of values along a respective dimension (x,y,z). This brain was scanned in 57 slices with a resolution of 67 x 56 voxels per slice. That means there are:

57 * 67 * 56 = 213864

voxels in total!

Let’s see the type of data inside of the array.

t1_data.dtype
dtype('float64')

This tells us that each element in the array (or voxel) is a floating-point number.
The data type of an image controls the range of possible intensities. As the number of possible values increases, the amount of space the image takes up in memory also increases.

import numpy as np
print(np.min(t1_data))
print(np.max(t1_data))
6.678363800048828
96.55541983246803

For our data, the range of intensity values goes from 0 (black) to more positive digits (whiter).

How do we examine what value a particular voxel is? We can inspect the value of a voxel by selecting an index as follows:

data[x,y,z]

So for example we can inspect a voxel at coordinates (10,20,3) by doing the following:

t1_data[9, 19, 2]
32.40787395834923

NOTE: Python uses zero-based indexing. The first item in the array is item 0. The second item is item 1, the third is item 2, etc.

This yields a single value representing the intensity of the signal at a particular voxel! Next we’ll see how to not just pull one voxel but a slice or an array of voxels for visualization and analysis!

Working with image data

Slicing does exactly what it seems to imply. Giving our 3D volume, we pull out a 2D slice of our data. Here’s an example of slicing from left to right (sagittal slicing):

This gif is a series of 2D images or slices moving from left to right.

Let’s pull the 10th slice in the x axis.

x_slice = t1_data[9, :, :]

This is similar to the indexing we did before to pull out a single voxel. However, instead of providing a value for each axis, the : indicates that we want to grab all values from that particular axis.

Slicing MRI data

Now try selecting the 20th slice from the y axis.

Solution

y_slice = t1_data[:, 19, :]

Finally, try grabbing the 3rd slice from the z axis

Solution

z_slice = t1_data[:, :, 2]

We’ve been slicing and dicing brain images but we have no idea what they look like! In the next section we’ll show you how you can visualize brain slices!

Visualizing the data

We previously inspected the signal intensity of the voxel at coordinates (10,20,3). Let’s see what out data looks like when we slice it at this location. We’ve already indexed the data at each x, y, and z axis. Let’s use matplotlib.

import matplotlib.pyplot as plt
%matplotlib inline

slices = [x_slice, y_slice, z_slice]

fig, axes = plt.subplots(1, len(slices))
for i, slice in enumerate(slices):
    axes[i].imshow(slice.T, cmap="gray", origin="lower")

Now, we’re going to step away from discussing our data and talk about the final important attribute of a NIfTI.

3. Affine: tells the position of the image array data in a reference space

The final important piece of metadata associated with an image file is the affine matrix. Below is the affine matrix for our data.

t1_affine = t1_img.affine
t1_affine
array([[  2.75,   0.  ,   0.  , -78.  ],
       [  0.  ,   2.75,   0.  , -91.  ],
       [  0.  ,   0.  ,   2.75, -91.  ],
       [  0.  ,   0.  ,   0.  ,   1.  ]])

To explain this concept, recall that we referred to coordinates in our data as (x,y,z) coordinates such that:

Although this tells us how to access our data in terms of voxels in a 3D volume, it doesn’t tell us much about the actual dimensions in our data (centimetres, right or left, up or down, back or front). The affine matrix allows us to translate between voxel coordinates in (x,y,z) and world space coordinates in (left/right,bottom/top,back/front). An important thing to note is that in reality in which order you have:

Depends on how you’ve constructed the affine matrix, but for the data we’re dealing with it always refers to:

Applying the affine matrix (t1_affine) is done through using a linear map (matrix multiplication) on voxel coordinates (defined in t1_data).

The concept of an affine matrix may seem confusing at first but an example might help gain an intuition:

Suppose we have two voxels located at the the following coordinates: $(64, 100, 2)$

And we wanted to know what the distances between these two voxels are in terms of real world distances (millimetres). This information cannot be derived from using voxel coordinates so we turn to the affine matrix.

Now, the affine matrix we’ll be using happens to be encoded in RAS. That means once we apply the matrix our coordinates are as follows:
(Right, Anterior, Superior)

So increasing a coordinate value in the first dimension corresponds to moving to the right of the person being scanned.

Applying our affine matrix yields the following coordinates: $(10.25, 30.5, 9.2)$

This means that:

This covers the basics of how NIfTI data and metadata are stored and organized in the context of Python. In the next segment we’ll talk a bit about an increasingly important component of MR data analysis - data organization. This is a key component to reproducible analysis and so we’ll spend a bit of time here.

Key Points


Data Organization with BIDS

Overview

Teaching: 30 min
Exercises: 15 min
Questions
Objectives

Brain Imaging Data Structure (BIDS) is a simple and intuitive way to organize and describe your neuroimaging and behavioural data. Neuroimaging experiments result in complicated data that can be arranged in several different ways. BIDS tackles this problem by suggesting a new standard (based on consensus from multiple researchers across the world) for the arrangement of neuroimaging datasets.

Using the same structure for all of your studies will allow you to easily reuse all of your scripts between studies. Additionally, sharing code with other researchers will be much easier.

Why convert the community to BIDS?

Share data:

Build better pipelines:

bidsifying your dataset:

Key Points


Exploring Open MRI Datasets

Overview

Teaching: 30 min
Exercises: 15 min
Questions
  • How does standardizing neuroimaging data ease the data exploration process

Objectives
  • Understand the BIDS format

  • Use PyBIDS in to easily explore a BIDS dataset

Tutorial Dataset

In this episode, we will be using a subset of a publicly available dataset, ds000030, from openneuro.org. All of the datasets on OpenNeuro are already structured according to BIDS.

Downloading Data

DataLad

DataLad installs the data - which for a dataset means that we get the “small” data (i.e. the text files) and the download instructions for the larger files. We can now navigate the dataset like its a part of our file system and plan our analysis.

First, navigate to the folder where you’d like to download the dataset.

cd ~/Desktop/dc-mri/data
datalad install ///openneuro/ds000030

Let’s take a look at the participants.tsv file to see what the demographics for this dataset look like.

import pandas as pd

participant_metadata = pd.read_csv('../data/ds000030/participants.tsv', sep='\t')
participant_metadata

OUTPUT:

participant_id diagnosis age gender bart bht dwi pamenc pamret rest scap stopsignal T1w taskswitch ScannerSerialNumber ghost_NoGhost
0 sub-10159 CONTROL 30 F 1.0 NaN 1.0 NaN NaN 1.0 1.0 1.0 1.0 1.0 35343.0 No_ghost
1 sub-10171 CONTROL 24 M 1.0 1.0 1.0 NaN NaN 1.0 1.0 1.0 1.0 1.0 35343.0 No_ghost
2 sub-10189 CONTROL 49 M 1.0 NaN 1.0 NaN NaN 1.0 1.0 1.0 1.0 1.0 35343.0 No_ghost
3 sub-10193 CONTROL 40 M 1.0 NaN 1.0 NaN NaN NaN NaN NaN 1.0 NaN 35343.0 No_ghost
4 sub-10206 CONTROL 21 M 1.0 NaN 1.0 NaN NaN 1.0 1.0 1.0 1.0 1.0 35343.0 No_ghost

From this table we can easily view unique diagnostic groups:

participant_metadata['diagnosis'].unique()
array(['CONTROL', 'SCHZ', 'BIPOLAR', 'ADHD'], dtype=object)

Imagine we’d like to work with participants that are either CONTROL or SCHZ (diagnosis) and have both a T1w (T1w == 1) and rest (rest == 1) scan. Also, you’ll notice some of the T1w scans included in this dataset have a ghosting artifact. We’ll need to filter these out as well (ghost_NoGhost == 'No_ghost').

We’ll filter this data out like so:

participant_metadata = participant_metadata[(participant_metadata.diagnosis.isin(['CONTROL', 'SCHZ'])) &
                                            (participant_metadata.T1w == 1) &
                                            (participant_metadata.rest == 1) &
                                            (participant_metadata.ghost_NoGhost == 'No_ghost')]
participant_metadata['diagnosis'].unique()
array(['CONTROL', 'SCHZ'], dtype=object)

From this we have a list of participants corresponding to a list of participants who are of interest in our analysis. We can then use this list in order to download participant from software such as aws or datalad. In fact, this is exactly how we set up a list of participants to download for the fMRI workshop! Since we’ve already downloaded the dataset, we can now explore the structure using PyBIDS, a Python API for querying, summarizing and manipulating the BIDS folder structure.

Getting and dropping data

datalad get datalad drop

from bids.layout import BIDSLayout

layout = BIDSLayout("~/Desktop/dc-mri/data/ds000030")

The pybids layout object indexes the BIDS folder. Indexing can take a really long time, especially if you have several subjects, modalities, scan types, etc. pybids has an option to save the indexed results to a SQLite database. This database can then be re-used the next time you want to query the same BIDS folder.

layout.save("~/Desktop/dc-mri/data/ds000030/.db")

layout = BIDSLayout("~/Desktop/dc-mri/data/ds000030", database_path="~/Desktop/dc-mri/data/ds000030/.db")

The pybids layout object also lets you query your BIDS dataset according to a number of parameters by using a get_*() method. We can get a list of the subjects we’ve downloaded from the dataset.

layout.get_subjects()
['10171',
 '10292',
 '10365',
 '10438',
 '10565',
 '10788',
 '11106',
 '11108',
 '11122',
   ...
 '50083']

We can also pull a list of imaging modalities in the dataset:

layout.get_modalities()
['anat', 'func']

As well as tasks and more!:

#Task fMRI
print(layout.get_tasks())

#Data types (bold, brainmask, confounds, smoothwm, probtissue, warp...)
print(layout.get_types())
['rest']

['bold',
 'brainmask',
 'confounds',
 'description',
 'dtissue',
 'fsaverage5',
 'inflated',
 'midthickness',
 'participants',
 'pial',
 'preproc',
 'probtissue',
 'smoothwm',
 'warp']

In addition we can specify sub-types of each BIDS category:

layout.get_types(modality='func')
['brainmask', 'confounds', 'fsaverage5', 'preproc']

We can use this functionality to pull all our fMRI NIfTI files:

layout.get(task='rest', type='bold', extensions='nii.gz', return_type='file')
TO FILL

Finally, we can convert the data stored in bids.layout into a pandas.DataFrame :

df = layout.as_data_frame()
df.head()

OUTPUT:

path modality subject task type
0 ~/Desktop/dc-mri/data... NaN NaN rest bold
1 ~/Desktop/dc-mri/data... NaN NaN NaN participants
2 ~/Desktop/dc-mri/data... NaN NaN NaN NaN
3 ~/Desktop/dc-mri/data... anat 10565 NaN brainmask
4 ~/Desktop/dc-mri/data... anat 10565 NaN probtissue

Key Points

  • BIDS is an organizational principle for neuroimaging data

  • PyBIDS is a Python-based tool that allows for easy exploration of BIDS-formatted neuroimaging data