kisuya / neuroimaging_python

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Neuroimaging_Python

뉴로해킹 파이썬 파트 스터디 관련 자료를 저장하는 곳입니다.

참고자료:

• PSYCH 214 Fall 2016

본 코스의 수업 구성(Classes and labs)

• [Day 00] Introduction, Python, images (2nd 2017-06-01)
  • [Lab 00] Basic Python exercises
• [Day 01] Arrays, images and plotting
  • [Lab 01] Basic numpy exercises
• [Day 02] 4D arrays, time series and diagnostics
  • [Lab 02] Git walk-through
• [Day 03] Vectors, projection and PCA
  • [Lab 03] Outlier detection and git / github workflow
• [Day 04] Correlation, regression, statistics on brain images
  • [Lab 04] Git / github workflow, the Python path
• [Day 05] Exploring the general linear model
  • [Lab 05] Some git, some multiple regression
• [Day 06] Project pitch, ANOVA with the GLM
  • [Lab 06] More on github workflow
• [Day 07] The HRF, modeling and statistical maps
  • [Lab 07] F tests; convolution; project template
• [Day 08] Multiple comparison correction
• [Day 09] Slice timing and motion correction
  • [Lab 09] Multiple comparison correction, whole brain analysis
• [Day 10] Affine and cross-modality registration
  • [Lab 10] Optimization and image registration
• [Day 11] Cross-subject registration
  • [Lab 11] Affine transformations
• [Day 12] Exploring cross-subject registration
  • [Lab 12] Scripting using nipype

본 코스의 Topic 별 구성 Link

Python

• Brisk introduction to Python. See Introduction, Python, images;
• “for” and “while”, “break” and “else:”;
• Modules and scripts;
• Packages and namespaces;
• List comprehensions;
• Two double underscore variables;
• String literals in Python;
• Inserting values into strings;
• Docstrings;
• Kind-of True;
• Using assert for testing;
• Keyword arguments;
• Making and breaking file paths in Python. See: Git / github workflow,the Python path;
• Where does Python look for modules?. See: Git / github workflow, the Python path;
• Using PYTHONPATH;
• Python coding style.

Numpy, arrays and images

• What is an image?. See Introduction, Python, images;
• NumPy introduction (from scipy lecture notes (SLN);
• numpy array object (SLN);
• array operations (SLN). See: Basic numpy exercises;
• Array reduction operations;
• Arrays as images, images as arrays. See: Arrays, images and plotting;
• Reshaping and three-dimensional arrays. See: Arrays, images and plotting;
• Index ordering and reshape in NumPy and MATLAB;
• Working with four dimensional images, masks and functions. See: 4D arrays, time series and diagnostics;
• Reshaping, 4D to 2D;
• Logical operations on boolean arrays;
• Reshaping 4D images to 2D arrays;
• Slicing with boolean vectors. See: Correlation, regression, statistics on brain images;
• Indexing with boolean masks.
• Vector and matrix dot products, “np.outer”;
• Testing for near equality with “allclose”;
• Numpy arange;
• Methods vs functions in NumPy;
• Subtracting the mean from columns or rows;
• Adding length 1 dimensions with newaxis;
• Diagonal matrices;
• numpy.tranpose for swapping axes;
• Random numbers with np.random;
• Removing length 1 axes with numpy.squeeze;
• Making coordinate arrays with meshgrid;
• Comparing arrays;
• Comparing floats and floating point error;
• Making floating points numbers print nicely.

Matplotlib

• Plotting lines in matplotlib;
• Subplots and axes in matplotlib.

Git

• curious git;
• First steps with git;
• Git walk-through;
• Looking at real git objects;
• curious remotes.

Exercises:

• Git workflow exercises;
• PCA exercise, with some github practice.

General statistics and math

• algebra of sums;
• vectors and dot products;
• vector projection;
• introduction to Principal Component Analysis. See: Vectors, projection and PCA;
• vector angles;
• correlation and projection. See Correlation, regression, statistics on brain images;
• matrix rank
• Inverse of a diagonal matrix;
• introduction to the General Linear Model. See Exploring the general linear model;
• cumulative density functions;
• A worked example of the general linear model;
• Subtracting the mean from a vector;
• Hypothesis tesing with the general linear model;
• tutorial on correlated regressors.
• tutorial on convolution.

Image processing and spatial transformations

• Otsu’s method for binarizing images.
• rotation in 2D;
• Rotations and rotation matrices;
• Encoding zooms (scaling) with a diagonal matrix;
• coordinate systems and affine transforms;
• mutual information;
• The nibabel.affines module;
• Applying coordinate transforms with nibabel.affines.apply_affine.
• Resampling with scipy.ndimage;
• General resampling between images with scipy.ndimage.map_coordinates;
• Making and saving new images in nibabel;
• introduction to smoothing;
• smoothing as convolution.
• optimizing spatial transformations.

Specific to FMRI

• Voxel time courses. See Correlation, regression, statistics on brain images;
• Modeling a single voxel;
• Convolving with the hemodyamic response function.
• Coordinate systems and affine transforms;
• The nibabel.affines module;
• Applying coordinate transforms with nibabel.affines.apply_affine;
• Sometimes, the NIfTI image stores the TR in the header;
• Registration with dipy and the Anterior cingulate exercise;
• Introducing nipype;
• See also: SPM slice timing exercise; Scripting of SPM analysis with nipype exercise.