Session 9 - fMRI Flashcards
In this session, we will start looking at how to
to manipulate neuroimaging data in Python.
We are going to be using a module called
nibabel to load MRI and fMRI data to Python and save out overlays and statistical maps in the same format - old way of doing it
Going to be using Spyder
which is used for scientific programming in Python and can run python directly from machine (as compared to Colab - running it from cloud) and can produce interactive plots
All of the files for this section can be found in the - (2)
course materials repository.
They are in the s7_fmri directory.
Obtaining the data files needed for this lecture which is found in s7_fMRI directory by running this code:
What does this code mean?
git is accessing remote reposities and borrows data from YNiC (s7_fMRI directory) to local content directory
Output of this code:
After running the code:
We can check the current working directory
List the contents inside the current working directory
List what is inside pin-materials and s7_fMRI
What are these files when listening what is inside s7_fMRI? (last part) - (4)
These are all data files
‘highres.nii.gz’ is a anatomical file - 3D high-resolution structural image (T1) of someone’s brain
‘highres_brain.nii.gz’ - word brain means we have stripped away the skull and just looking at the brain (skull-stripped image of the brain) - using FSL command called BET
There are two functional imaging data files here/datasets which are 3D snapshots of the brain taken every 2 seconds (TR) while its doing a task: 1) filtered_func_data.nii.gz and func_data.nii.gz
What does
‘ls-l’ mean?
Listening contents of files and directories in long format (“l” at bottom)
The ‘Babel’ part of nibabel refers to
the ‘Tower of Babel’.
The nibabel module has
reading and writing routines for various neuroimaging formats - so that we can all speak the same neuroimaging ‘language’ or something.
Most MRI data are taken off scanners in a format known as
DICOM
What does DICOM stand for?
Digital Imaging and Communications in Medicine)
Most MRI data are taken off the scanners in a format known as DICOM - this format is
very complex so scientists almost always convert the DICOM images from the scanner into NIFTI
What does NIFTI stand for?
Neuroimaging Informatics Technology Initiative
Most MRI data are taken off scanners in a format known as DICOM which is complex and scientist always conert to NIFTI and at YNIC this is done
automatically
NIFTI files end in
nii.gz, gz means the file is compressed so does not take up much space on disk
What are the two parts of NIFTI files, and what does each part contain? - (3)
NIFTI files consist of a ‘header,’ which contains information about
the image (e.g., how big the image is, how big the voxels are, many voxels there are in each direction, number of volumes, TR),
and the actual imaging data which is just an array of numbers: usually integers for raw data that has come from scanner; often floating point for processed data
The first dataset we will look at is in the folder is a high-resolution anatomy file called ‘highres.nii.gz’ which contains - (2)
a single T1-weighted scan of a brain
It was acquired slice by slice : 176 slices running >across< the head from Left to Right in steps of 1mm with 256x256 1mm x 1mm voxels in each slice.
Let’s move into the s7_fMRI which contains ‘highres.nii.gz’ directory for rest of tutorial by doing this which means
This code changes the current working directory to ‘/content/pin-material/s7_fmri’
So we are going to examine the ‘highres.nii.gz’ by loading it using nibabel
Thus, we need to import the module first:
This is common practice, analogous to importing numpy as np.
We will now use nibabel to load an fMRI file into Python and examine it of ‘highres.nii.gz’ using nibabel:
Explain this code:
anatImage = nib.load(‘highres.nii.gz’) - (2)
This code utilizes the ‘nibabel’ library to load a structural NIFTI file named ‘highres.nii.gz’.
The function ‘nib.load()’ reads the file and stores its contents in the variable ‘anatImage’, which includes both the header information and the data of the image - two partsof NIFITY
After using nibabel nib.load to load the structural NIFITI file (‘highres.nii.gz’) we can check the type of object returned by file and what type of object is returned which prints out…
this prints out: <class ‘nibabel.nifti1.Nifti1Image’>
We can also make a variable and store header info using anatImage.header and check its type which prints out…
this prints out:
We can also look at the keys of the header using
this print(hdr.leys()) is - (2)
This code prints out the keys present in the header of a NIFTI file.
Think of these keys as analogous to columns in a table. They provide information about various attributes and properties of the NIFTI image data.
The output of this code is
Explain this output:
These are all the stuff you would acquire as you run the MRI scanner such as extent of the data, how big the head was, what is sequence is, how tiny voxels are, coverage of brain, how was imaging planes oriented to acquire data
There are many items in the header but we are interested in
dim and pixdim entries
We can also look at the dim entries in which treating header like a dictionary and requesting ‘dim’ key from it using
print(hdr[‘dim’]) and see as output
What does this mean from print(hdr[‘dim’]) - (3)
When we look at dim, we find an array of integers
The 0th element (3 in this case) refers to how many dimensions of data there are; as this is a structural or anatomical dataset, there are 3-dimensions.
The remaining values tell us the number of voxels in each of the dimensions. As we only have three dimensions, only elements 1 to 3 (inclusive) have any meaning; the rest can be ignored.
When we show pixel dimensions header print(hdr[‘pixdim’]) - explain - (6)
In this code snippet, we’re accessing the ‘pixdim’ header in the NIFTI file, which provides information about the size of each voxel in millimeters.
These numbers are floats because they refer to the size in mm:
The printed array, [1. 1. 1. 1. 2.3 0. 0. 0.], contains float values representing voxel sizes.
Positions 1 to 3 (1.0) are relevant, indicating the voxel sizes in each dimension.
In this example, the numbers are all 1.0 which makes sense because the voxel sizes are 1mm in all dimensions
Additionally, the value in position 4 (2.3) refers to the ‘repetition time’ (TR) of the image, which might be relevant depending on the type of MRI data.
More detail on this output - (2)
We have taken 176 slices in sagittal plane/direction (going through middle of brain)
Each sagittal section is 256 by 256 voxels
Explain this output in more detail - (5)
Position 1: Width of the image in mm.
Position 2: Height of the image in mm.
Position 3: Depth or slice thickness of the image in mm.
For example, if positions 1 to 3 contain [1.0, 1.0, 1.0], it indicates that each voxel (3D pixel) in the image is 1mm x 1mm x 1mm in size.
So, if we’re considering the array [1. 1. 1. 1. 2.3 0. 0. 0.], positions are typically indexed starting from 0.
Next, we can load the actual image data of ‘highres.nii.gz’ using nib.looad instead of its header
Explain this code - (7)
import nibabel as nib: Importing the Nibabel library, used for reading and working with neuroimaging data.
anatImage = nib.load(‘highres.nii.gz’)`: Loading a structural NIFTI image from a file called ‘highres.nii.gz’.
data = anatImage.get_fdata(): Extracting the data from the loaded NIFTI image, storing it as floating-point numbers.
print(data.shape)`: Printing the dimensions of the data array, indicating the size of the image - (176 by 256 by 256)
print(data.dtype)`: Printing the data type of the elements in the data array, indicating they are floating-point numbers - float64
print(data[88, 127, 127]): Printing the value of a voxel located at coordinates (88, 127, 127) within the image. - 274.0 - saying how big the voxel is
print(data[1, 1, 1])`: Printing the value of a voxel located at coordinates (1, 1, 1), typically near the edge of the image (more likely to be small number as most black space near the head) - 4.0
Output of this code
Instead of looking at the value at one voxel, we can instead peek at one slice of data and to do this we need to
import pyplot using import matplotlib.pyplot as plt
Code producing one slice of data instead of one voxel
Explain this code - (4)
import matplotlib.pyplot as plt: Importing the Matplotlib library for visualization.
aSlice = data[124,:,:]: Extracting a single sagittal slice of brain data from a 3D volume stored in the variable ‘data’. This slice is located at index 124 (pass midpoint off the edge) along the first axis, which typically represents the sagittal plane (out of 176 slices) if we say index 88 its more or else down the middle
plt.grid(True): Configuring the plot to display grid lines for better visualization.
- plt.imshow(aSlice)
: Displaying the extracted sagittal slice using Matplotlib'simshow()` function, which visualizes a 2D array as an image. In this case, it displays the 2D slice of brain data.
Output of this code:
matplot lib has a function called
plt.imshow() which if you give a two-dimensional slice of data then it will make a picture of it
First of all the output of this is messy as it
t is standing up on its nose and the colour map is weird.
We can then load the data again
To change the colour map of the output - (2)
We can do this
plt.imshow(slc, cmap=’gray’) - # change colour map to grey
What does this code mean in more detail? - (6)
take 124 elemens through one dimension, : take every row and column through 124 slices of the head
We pick slice 124
The dimensions here (see Advanced material below) are
L-R, P-A and I-S
Slice 124 from the first dimension is more or less through the middle of the right hemisphere
The remaining dimensions are P-A and I-S
What does this output display - white stuff and grey stuff - (2)
- white stuff is fat
- grey stuff is neurons - convey communication between different parts of grey matter
Highlight positions on the head
We would like to flip the image the right way around - (3)
plt.show() then using tranpose function which takes a data set and spins it around the axis - it swaprs the dimensions so swaps vertical and horizontal axis
It would need anterior and posterior running along x axis - be upside down
so to fix then take that slice and resample in opposite direction [::-1,:] - pick all rows and columns but pick them in reverse order
Flipping image on right way around - first tranpose
What does this code do first? - (7)
It selects a single slice (slc) from the 3D brain data (data), specifically the slice at index 124 along the first axis.
It plots the selected slice using plt.imshow() with a grayscale colormap (cmap=’gray’).
It adds a title ‘Original’ to the plot.
It displays the plot using plt.show().
After plotting the original slice, the code recognizes that the image orientation needs adjustment for proper display.
It transposes the dimensions of the slice (slc.transpose(1, 0)) to swap the axes, changing the orientation from [I-S, P-A] to [P-A, I-S].
It plots the transposed slice using plt.imshow() with the same grayscale colormap.
After tranposing we need to reverse the image
Explain the reverse part - (7)
Remember how we can use the -1 operator to reverse the order in which we choose numbers from a list? - resample to opposite direction so all row and columns to minus 1
list[<start>:<stop>:<step>]</step></stop></start>
if you leave out an argument it just assumes ‘the start’ or ‘the end’ or ‘1’
So slc[::-1,:] means ‘I want the 2D matrix from slc but give me the first dimension (the rows) stepping ‘backwards’ in steps of 1,
and then leave the other dimension (the columns) unchanged’
his operation effectively flips the image vertically, changing the orientation from inferior-superior (I-S) to superior-inferior (S-I).
remeber dimensions are : # L-R, P-A and I-S
Explain rest of this code: after tranposing we want to we would like to have the axes labelled correctly in mm.
# So we need to know how big
# the image is on each side. That is found from the header:
# pixelSize * numberOfPixels - (8)
ap_npix = anatData.header[‘dim’][2]`: Retrieves the number of pixels along the anterior-posterior (A-P) direction from the image header.
-
si_npix = anatData.header['dim'][3]: Retrieves the number of pixels along the inferior-superior (I-S) direction from the image header. -
ap_pixdim = anatData.header['pixdim'][2]: Retrieves the pixel dimensions (in millimeters) along the A-P direction from the image header. -
si_pixdim = anatData.header['pixdim'][3]: Retrieves the pixel dimensions (in millimeters) along the I-S direction from the image header. -
Setting Image Extent:
-
extent = (0, si_npix*si_pixdim, 0, ap_npix*ap_pixdim): Calculates the extent parameter for the image plot, which defines the spatial dimensions in millimeters. It specifies the extent of the image along the x-axis (I-S) and y-axis (A-P) based on the number of pixels and pixel dimensions.
-
-
Plotting the Image:
-
plt.imshow(slc, cmap='gray', extent=extent): Plots the image (slc) using Matplotlib’simshow()function with a grayscale colormap (cmap='gray') and the calculated extent parameter. This ensures correct scaling and spatial representation of the image in millimeters. -
plt.title('Final image'): Sets the title of the plot to ‘Final image’ usingplt.title(). -
plt.show(): Displays the final image plot.
-
Output after tranposing, reversing and setting image extent:
The dimensions of our image are: [L-R, P-A, I-S]. We have taken a single slice in the left-right direction which means that we are left with data which is [P-A, I-S].
We are using the matplotlib imshow() routine whch
This takes data as if it were a photograph, i.e. the bottom left hand entry in our data matrix will be at the bottom left of the displayed image.
More detail what does transpose do and reverse? - (3)
At present, we have [P-A, I-S], so we start by using the .transpose() function to reverse the orders of the axes, ending up with [I-S, P-A].
This looks nearly right, except that we want our data in row 0 to be the most superior data and the data in the last row (255) to be the most inferior: i.e. our I-S axis needs to be S-I.
To do this, we just reverse the data in that dimension using the ::-1 syntax`(see below).
What does plt.show() and extent() do near end of code? - (7)
Now that we have re-ordered our data, we can pass it to imshow().
In the imshow() call, we use an extra argument which we have not seen before: extent.
This allows us to set the pixel dimensions for the image.
This is not actually that important in this case because the MRI image pixel size is 1x1mm, so it makes no difference.
If, however, your image was 1.13x1mm or similar, you would end up with a distorted image if you do not make this correction.
What we do is to multiply the number of pixels in each dimension by the size of the pixels in that dimension.
This makes matplotlib scale the image properly.
What does red plot do near end of code?
To show off, we also show that we can plot points on the same plot - so, we add a red point at a specific point on the image; we could also add text, arrows and anything else which we can do in matplotlib.
Explain this part of the code that tranposes and reverses brain image NIFTI file that it got to right orientation - (18)
-
Loading Data:
-
import os: Imports the os module to interact with the operating system. -
import nibabel as nib: Imports the nibabel library for working with neuroimaging data. -
import matplotlib.pyplot as plt: Imports the matplotlib library for plotting. -
os.chdir('/content/pin-material/s7_fmri'): Changes the current working directory to ‘/content/pin-material/s7_fmri’. -
s = nib.load('highres.nii.gz'): Loads the structural NIFTI file named ‘highres.nii.gz’. -
data = s.get_fdata(): Loads the image data from the NIFTI file as floating point numbers.
-
-
Plotting Original Slice:
-
slc = data[:, :, 100]: The syntax [:, :, 100] selects all rows and columns (full 2D slice) at the specific index 100 along the third axis (I-S direction) of the data volume. -
plt.imshow(slc, cmap='gray'): Plots the selected slice with a grayscale colormap. -
plt.title('Original'): Sets the title of the plot to ‘Original’. -
plt.show(): Displays the plot.
-
-
Adjusting Orientation:
-
slc = slc.transpose(1, 0): Transposes the dimensions of the slice, effectively swapping the axes from inferior-superior (I-S) to posterior-anterior (P-A).- This operation changes the orientation of the slice by exchanging the axes, which can be useful for adjusting the display orientation.
-
slc = slc[::-1, :]:- By reversing the rows, the orientation of the slice is corrected, ensuring proper anatomical orientation. effectively reverses the order of rows in the 2D array slc, flipping the slice vertically. This operation ensures that the slice is properly oriented along the inferior-superior (I-S) axis,
-
-
Plotting Final Slice:
-
plt.imshow(slc, cmap='gray'): Plots the adjusted slice with a grayscale colormap. -
plt.title('Final'): Sets the title of the plot to ‘Final’. -
plt.grid(True): Adds grid lines to the plot for better visualization. -
plt.show(): Displays the final plot.
-
Explain this code - (6)
The code imports the necessary libraries: nibabel (as nib) and matplotlib.pyplot (as plt).
It then loads a structural NIFTI file named ‘highres.nii.gz’ using the ‘nib.load()’ function and stores it in the variable ‘s’.
The ‘s.get_fdata()’ function retrieves the image data from the NIFTI file and stores it in the variable ‘data’.
in floating-point format
Additionally, the ‘s.get_qform()’ function retrieves the affine transformation matrix (qform matrix) associated with the NIFTI file and stores it in the variable ‘qform’.
This matrix contains information about the spatial orientation and voxel dimensions.
Finally, the ‘s.orthoview()’ function is called, which automatically produces three views of the data, labeling them as anterior, posterior, superior, and inferior.
Output of this code:
In this output - (2)
labels left on left and right is on right
However, in FSL this is flipped other way as in radiological convention so be careful looking at fMRI should be labelled
In the notebook this will just print out three static views of the data. That’s fine but we can do better. If you run it in Spyder it will bring up a complete volume browser that you - (2)
you can click around in spyder by simply uploading same code you did in Colab and changing
s = nib.load(‘highres.nii.gz’) which works in Colab to
s = nib.load(‘C:\Users\gs1211 (whatever usename you have)\pin-material\s7_fmri\highres.nii.gz’)
How to tell spyder to install nibabel? - (2)
In the Spyder Console (Bottom Right) type:
pip install nibabel
How to tell spyder we want interactive graphics? - (5)
Go into the Preferences window like this
Tools->Preferences
From the iPython Console settings:
Select Graphics -> Graphics Backend -> Qt5
And hit ‘Apply’
Explain this code -(5)
producing mask
This code snippet performs several operations on a structural NIFTI file to create a mask and save it. Let’s break down each step:
-
Loading the NIFTI File:
- The code starts by loading a structural NIFTI file named ‘highres.nii.gz’ using the
nib.load()function from the nibabel library. This file contains MRI data.
- The code starts by loading a structural NIFTI file named ‘highres.nii.gz’ using the
-
Extracting Structural Data:
- Once the file is loaded, the structural data is extracted using the
get_fdata()method, and it’s stored in a variable nameddata. - The
print(data.shape)statement displays the shape of the extracted data. This provides information about the dimensions of the MRI data. - (176, 256, 256)
- Once the file is loaded, the structural data is extracted using the
-
Modifying Data to Create Mask:
- Next, a specific region within the data is modified to create a mask. The code sets voxels in a box-shaped region (with indices ranging from 100 to 169 along each axis) to the value 255. This operation effectively creates a mask where the selected region is marked with 255, indicating the presence of the mask. - #setting all one partof the data to be the same value of ‘255’ - inserting bick of 255 values inside someone’s head
- This process allows for the creation of a mask that can be used to isolate specific areas of interest within the MRI data.
-
Creating a New NIFTI Image in memory:
- After modifying the data to create the mask, a new NIFTI image is created in memory using the modified data. This is achieved using the
Nifti1Imageconstructor from nibabel. - The constructor requires three arguments: the modified data (mask), the affine transformation (
s.affine), and the header information (s.header) obtained from the original NIFTI file.
- After modifying the data to create the mask, a new NIFTI image is created in memory using the modified data. This is achieved using the
-
Writing/Saving to the Overlay:
- Finally, the newly created NIFTI image (mask) is saved as ‘my_overlay.nii.gz’ using the
nib.save()function. This function takes two arguments: the new image object (new_image) and the desired filename (‘my_overlay.nii.gz’). - #takes 2 arguements, dataset you made and name it
- Finally, the newly created NIFTI image (mask) is saved as ‘my_overlay.nii.gz’ using the
What mask does this code produce? - (2)
The code defines a box-shaped region within the MRI data, with boundaries set along the left-right (L-R), posterior-anterior (P-A), and inferior-superior (I-S) axes, corresponding to the X, Y, and Z dimensions, respectively.
Within this defined region, the voxel intensities are set to 255, effectively creating a binary mask where this specific region is highlighted, while the rest of the voxels remain unchanged.
What is output of this code? (spyder)
change to be smaller e.g., 100:110 to all
Explain this code that loads the fMRI data, extracts header information, and prints some details about the data - (6)
- The code begins by importing necessary libraries: nibabel for working with NIFTI files, matplotlib.pyplot for plotting, and numpy for numerical operations.
- It loads fMRI data from a NIFTI file named ‘func_data.nii.gz’ using the nibabel.load() function.
- The header and data are then extracted from the loaded file using the .header and .get_fdata() methods, respectively.
- The header contains essential information about the data, such as its dimensions and pixel dimensions, which are printed using the hdr[‘dim’] and hdr[‘pixdim’] statements.
- The size and type of the data array are printed using the data.shape and type(data) statements, respectively.
- This provides an overview of the fMRI data, including its dimensions and voxel size, facilitating further analysis or visualization.
Explain the output from this code of loading and extracting info of fMRI data - (6)
The first line [ 4 80 80 64 160 0 0 0] represents the dimensions of the data. Each number corresponds to a specific dimension:
The first number (4) indicates that the data has four dimensions - we have four-dimensional data. This makes sense as we now also have a time dimension.
The subsequent numbers (80, 80, 64, 160) represent the size of each dimension: 80 voxels along the first dimension, 80 voxels along the second dimension, 64 voxels along the third dimension, and 160 time points along the fourth dimension.
Looking at the next four numbers, we find that each of our images is (80, 80, 64) and that we have 160 of them (so 160 time-points).
The pixel dimensions tell us that the voxel size in this case is 3x3x3; i.e. we have a 3x3mm acquisition with 3mm slices. The next number in the pixdim array is the TR: in this case that is 2s. - blocks of brain collected every 2s and dimensins of block 3,3,3mm
The third line (80, 80, 64, 160) - this shape specification indicates that the data array comprises 80 voxels in the x-direction, 80 voxels in the y-direction, 64 voxels in the z-direction, and 160 time points, forming a four-dimensional array.
We can add f.orthoview()
at end to make it interactive:
Extending this code to produce graphs of voxel from two different positions - explain the code - (8) with output
This code plots the time-series for two voxel positions using data obtained from fMRI.
It uses the ‘ggplot’ style for visualization and plots the signals from two different voxels in blue and red, respectively.
It also adds labels for the x-axis and y-axis, as well as a legend to distinguish between the two voxel positions.
Because we now have data available as a numpy array, we can plot it.
This time we choose two (very non-accidentally chosen) voxels and plot the timeseries for each of them:
The first thing to note is that this is the raw image-space data from the scanner; it has not been pre-processed in any way.
The second thing is that it looks like we have almost periodic responses in each of these voxels. In Voxel 1 (plotted in blue) we have 10 cycles through the experiment (32 seconds per cycle).
In Voxel 2 (plotted in red) we have 16 cycles through the experiment (20 seconds per cycle). It is almost as though the person was doing two different tasks or having different parts of their brain stimulated at different frequencies.
Explain this code that plots three different voxels - (5)
Imports necessary libraries: nibabel for working with NIFTI files, matplotlib.pyplot for plotting, and numpy for numerical operations.
Loads the fMRI data from the file named ‘filtered_func_data.nii.gz’.
Extracts header information (hdr), voxel data (data), and the qform matrix (qform) from the loaded NIFTI file.
Prints some header information, including the dimensions (hdr[‘dim’]), pixel dimensions (hdr[‘pixdim’]), and shape of the data array (data.shape).
Plots the time-series data for three specific voxels (p1, p2, and p3) on the same graph p1: represents voxel data at position (29, 29, 40), p2 represents voxel data at position (50, 29, 40)., p3 represents voxel data at position (46, 9, 30).
What do the coordinates represent in the context of the code snippet p1=data[29, 29, 40, :], p2=data[50, 29, 40, :], and p3=data[46, 9, 30]? - (4)
In the context of fMRI data, the coordinates represent the spatial location of specific voxels within the brain volume.
Each set of coordinates specifies the position of a voxel along the x, y, and z axes, respectively.
For example, in p1=data[29, 29, 40, :], the voxel is located at coordinates (29, 29, 40), indicating its position along the x-axis (29 units), y-axis (29 units), and z-axis (40 units).
Similarly, p2=data[50, 29, 40, :] and p3=data[46, 9, 30] represent voxels located at different spatial coordinates within the fMRI volume.
Output of this code
Those are the two voxels we looked at before plus another one that doesn’t seem to be active.
We can then produce fourier transform of each voxel using this code
Explain it - (6)
-
fp1=abs(np.fft.fft(p1)),fp2=abs(np.fft.fft(p2)), andfp3=abs(np.fft.fft(p3)): These lines apply the Fourier Transform to each voxel time series (p1,p2, andp3) to compute the amplitude of each frequency component. -
plt.figure(figsize=(15,5)): Creates a new figure for plotting with a specific size. -
plt.bar(np.arange(29)+.8,fp1[1:30],width=0.2),plt.bar(np.arange(29)+1,fp2[1:30],width=0.2),plt.bar(np.arange(29)+1.2,fp3[1:30],width=0.2): These lines generate bar charts for each voxel’s Fourier transform. They plot the amplitudes of the Fourier transform components (from frequencies 1 to 29) along the x-axis, with slight adjustments to the bar positions to avoid overlap. -
plt.xlabel('Frequency (cycles/expt)'),plt.ylabel('Signal amp (au)'): Sets labels for the x-axis and y-axis of the plot. -
plt.legend(['Voxel 1', 'Voxel 2','Voxel 3']): Adds a legend to the plot indicating which voxel each set of bars represents. -
plt.show(): Displays the plot.
What does output show of fourier transform of each voxel? - (3)
voxel 1 (red) clearly responding at 10 cycles per second - has 1 input
voxel 3 (lilac) responding at 10 and 16 cycles per second - has 2 inputs
voxe 2 (blue) that is weak has some power at every frequency - call white noise
explain in more detail what line of code does - (5)
plt.bar(np.arange(29)+1.2,fp3[1:30],width=0.2)
np.arange(29): Generates an array of integers from 0 to 28, representing the indices of the frequencies.
+1.2: Adds a small offset to the x-axis values, ensuring that the bars for each frequency are evenly spaced.
fp3[1:30]: Extracts the Fourier transform magnitudes for frequencies 1 to 29 (indexing is zero-based in Python).
width=0.2: Sets the width of the bars to 0.2 units.
Together, this line positions and plots the bars representing the Fourier transform magnitudes of the third voxel’s time-series data, ensuring proper spacing and visualization of the frequency spectrum.
Explain this code- (7)
Fourier Transform (FT) Calculation: The code begins by computing the Fourier transform of the entire fMRI data array (data) using NumPy’s Fast Fourier Transform (FFT) function (np.fft.fft()). The FFT is applied along the first dimension, which represents time. This step decomposes the time series data into its frequency components, providing insights into how different frequencies contribute to the signal.
Magnitude Extraction: After computing the FFT, the code uses the abs() function to extract the magnitude of the resulting complex numbers. This is necessary because the FFT output includes both real and imaginary components. By taking the absolute value, we obtain the magnitude of each frequency component, which represents the strength or amplitude of the signal at that frequency.
Frequency Response Extraction: Next, the code extracts the frequency responses at 10 and 16 times per experiment from the magnitude data (fData). These frequencies are of interest because they may correspond to specific experimental conditions or stimuli. The extracted responses are stored in arrays response10 and response16, respectively, creating 3D maps of response magnitudes across all voxels in the fMRI data.
Slice Selection: To visualize the frequency responses, the code selects specific slices (s10 and s16) from the response maps. These slices represent responses at particular voxels within the brain. However, since the orientation of the data may not be optimal for visualization, the code applies a transpose operation and reverses the order of elements along the second axis to ensure correct orientation ([:, :, ::-1].transpose()).
Plotting: The selected response slices (s10 and s16) are plotted side by side using Matplotlib’s imshow() function. Each subplot displays the response map at either 10 or 16 cycles per second. The ‘jet’ color map is utilized to visualize the intensity of responses, with warmer colors indicating higher response magnitudes. Additionally, titles are added to each subplot to denote the corresponding frequency.
Save Output: Finally, the code saves the generated response maps as NIFTI files (10cycles.nii.gz and 16cycles.nii.gz). These files can be further analyzed or visualized using neuroimaging software.
List Files: As a final step, the code executes a command (!ls -lat) to list the files in the current directory, providing an overview of the saved output files.
What does plt.subplot(1,2,1) mean? - (2)
The code utilizes Matplotlib’s subplot() function to organize the visualization of the frequency response maps.
By specifying (1,2,1) as the argument for the first subplot, it instructs Matplotlib to create a grid of subplots with one row and two columns and select the first subplot for the current plot.
What does response10=fData[:,:,:,10] do in code?
In the given code snippet, response10[29, :, ::-1] reverses the order of elements along the last axis of response10, ensuring proper orientation of the data for visualization
What is output
I have loaded in anantomical nifti file using nibabel like this:
What does the ‘dim’ key in the header represent?
The 3D or 4D dimensions of the image in voxels (and TRs if appropriate)
5:
I have a nifti file and I want to plot two different voxels of data from it. However although the data loads, I only see one line. What is wrong with this code and how can I fix it?
You are plotting p1 twice. Change the second ‘plt.plot’ statement to use p2 not p1
