GAM models for Neuroscience - Code 101

GAMS FOR NEURODEGENERATIVE DISEASES

Okay, this is a summary of the first part of the thesis, or at least of the original idea we had in mind and which is now slightly on hold. In very brief terms, the idea is to create the initial database from the PET and MRI scans already normalised with Matlab and SPM. Once this is a stable data.frame containing all our data (as it will also be for the SCCs), we will be able to make non-parametric regression models in which to plot functions joining points with the same estimated brain-activity intensity.

The initial aim is to obtain images (gam.vis and gam.check) similar to those shown below. The code I present here summarises the process for obtaining them, although there is still an extra stage to carry out: comparing these results with the native results obtained with SPM, software implemented in Matlab that is the market standard in the world of medical imaging diagnosis (at least in the case of neuroimaging). Until I can reliably demonstrate that my methodology is better than SPM’s, they will only be very pretty images.

#install.packages(c("cowplot","magick"))
library(magick);library(cowplot);library(ggplot2)

p1 <- ggdraw() + draw_image("GAMexample1.png")
p2 <- ggdraw() + draw_image("GAMexample2.png")
p3 <- ggdraw() + draw_image("GAMexample3.png")
p4 <- ggdraw() + draw_image("GAMexample4.png")

plot_grid(
  p1, p2,p3, p4,
  ncol = 2)

Of course, this is the same problem as with the SCCs: comparability at the mathematical level. With SCCs, of course, I am one step ahead, since I can obtain images that are practically identical in format at a visual level, and at least visually demonstrating that the results are similar is already a positive result. It may not be the desired final result, but at least I would be showing that there is another, simpler way to obtain similar results, using a functional-data approach.

But what concerns us now is obtaining these models over different slices and seeing their usefulness at a visual level and what they tell us. How to compare them with each other and how to compare them with SPM is a very different story.

PREAMBLE:

#install.packages(c("mgcv","gamair","oro.nifti","memsic"))
library(mgcv);library(gamair);library(oro.nifti);library(memisc)

BASIC VISUALISATIONS:

For visualising PET or MRI images, I would not recommend using R at all. There are a number of packages for working with images, but it is clear that their aim is to work with the data underlying the image, rather than so much with the image itself. To work with images at a visual level, I would instead recommend using SPM (the classic option), MRIcro or Mango. Any of them is more like a standard image editor, something like a Photoshop for brains. Even so, it may sometimes be necessary simply to check that everything is correct, extract a particular pixel value, make an orthogonal visualisation…

Although the number of available functions is limited, here are some of the most useful:

READING IMAGES AND GENERAL DATA:

img_003_S_1059 <- readNIfTI("003_S_1059", verbose = FALSE, warn = -1, reorient = TRUE,
                 call = NULL, read_data = TRUE)
img_003_S_1059

## NIfTI-1 format
##   Type            : nifti
##   Data Type       : 16 (FLOAT32)
##   Bits per Pixel  : 32
##   Slice Code      : 0 (Unknown)
##   Intent Code     : 0 (None)
##   Qform Code      : 2 (Aligned_Anat)
##   Sform Code      : 2 (Aligned_Anat)
##   Dimension       : 79 x 95 x 79
##   Pixel Dimension : 2 x 2 x 2
##   Voxel Units     : mm
##   Time Units      : sec

MOST USEFUL VISUALISATIONS

A list of the different visualisations we can obtain, although, as I have said, they are much more complete and interactive in other programs.

Axial:

image(img_003_S_1059) #axial por defecto

Coronal:

image(img_003_S_1059,plane="coronal") #coronal

Sagital:

image(img_003_S_1059,plane="sagittal") #sagital

Orthographic plane:

orthographic(img_003_S_1059,text="Plano Ortografico PET") #plano ortografico

## Warning in min(x, na.rm = na.rm): ningún argumento finito para min; retornando
## Inf

## Warning in max(x, na.rm = na.rm): ningun argumento finito para max; retornando -
## Inf

ASSOCIATED DATA:

These images, of course, have a series of associated data (otherwise, you tell me what kind of data science I was going to do here). In general, these data are only useful to me in bulk, in order to obtain a useful data.frame. Nevertheless, I can potentially request the value of a specific datum at a specific coordinate, and this can be useful for checking at certain stages of code development that everything is working correctly. Just in case, here it is:

Data associated with the image:

img_data=img_data(img_003_S_1059)
qplot(as.vector(img_data), 
      geom="histogram",
      main = "Histogram for PET values", 
      xlab = "PET levels",  
      fill=I("blue"), 
      col=I("red"), 
      alpha=I(.2))

negatives=img_data[img_data<0]
length(negatives) # de hecho hay muchos valores inferiores a 0 por lo que lo más rápido será, en la database, igualarlos a 0 (MRIcro los identifica como 0's d ehecho)

## [1] 7590

Here, for example, we can already see in the histogram that there are many values close to zero or even negative, so this will have to be taken into account later. This probably derives from a normalisation process that has reduced null values below 0.

Specific coordinates in the image:

#Diferentes coordenadas de la imagen con valores negativos (comprobacion)
img_data[1,2,3]

## [1] 0

img_003_S_1059[45,45,16]

## [1] 0.5555722

img_003_S_1059[10,70,40];img_data[10,70,40] # valdría cualquiera de los dos comandos

## [1] 0.3873922

## [1] 0.3873922

img_data[66,34,24]

## [1] 0.8744069

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