The overall goal of this Granger Causality Analysis is to study directed functional connectivity in the brain related to Alzheimer's disease progression, thereby establishing a novel objective basis for assessing the severity of the disease. This method can help answer key question in the Alzheimer's disease field, such as the directed connectivity between the PCC and whole brain regions. The main advantage of this technique is that it measures the causal effects of FMRI time series and it shows the dynamics and the directions of poor signal.
The implication of this technique extend towards the diagnosis of progression of Alzheimer's disease because it compares the directed functional connectivity between AD, MCI and the control groups. Through this method can provide insight into scaling directed functional connectivity of Alzheimer's, it can also be applied to other system such as electroying cephalogram studies. Generally, individual who are new to this method will struggle if they're unfamiliar with the software's operation.
We first have add here for the semester, and then we rate the prior literatures about Granger Causality Analysis. While having a real-world demonstration of this method, it's critical because there are multiples to be noted beyond what can be written in the text. Begin by opening RESTplus through the software and left click on Pipeline.
Import the relevant files into RESTplus. Select the work directory and then the starting EPI and T1 directories. Next, to convert Dicom files to Nifti check off the DicomToNifti box in Preprocessing.
And check off the EPI Dicom to Nifiti and the T1 to Nifiti parameters. Remove the first 10 time points by checking off the RemoveFirstTimePoints and setting the end parameter as 10. Then, check off the Slice timing box.
Set the slice number according to the RS FMRI parameters of the study and enter the slice order. Check Realign to correct the time and head motion. Then, perform spacial normalization by checking off Normalize and leave the default parameters at the bottom.
Use the T1 image unified segmentation and all heads standardized to the same space by selecting the parameters. Normalize by using T1 image unified segmentation and European. Next select smooth to perform spacial smoothing using an isotropic Gaussian kernal with a full width at half maximum of 6 mm.
Remove the linear trend by checking off Detrend. Select nuisance covariates regression in the following, six head motion parameters, global mean signal, white matter signal, and cerebrospinal fluid signal in order to increase the signal to noise ratio. Finally select filter to retain signals between 0.01 to 0.08 hertz.
Remove high frequency physiological noise as well as remove low frequency drift. Begin by performing the voxel-wise Granger Causality Analysis, or GCA, by using the REST GCA in the REST toolbox. In the Postprocessing box, check off GCA.
Set the order as one in the default. Select Define ROI and choose the spherical ROI to define the region of interest. Select next.
Identify seed points of interest in the posterior singular cortex, or PCC, by setting the center coordinates and radius of the seed ROI based on the known data and select OK.Then select Run and OK to run the program. Next, find folders named ZGCA and GCA after processing of relevant file data. Sort out the files of ZGCA and classify them into four subfolders, XX, XY, YX, YY accordingly.
Within the software, open RESTplus and left click on Statistical Analysis. Left click on REST Two-Sample T-Test. Name the output result as T1XY and set the output directory.
Left click on Add Group Images to open the XY subfolder in the AD Results folder and the XY subfolder in the NC Results folder. Next, left click to open the BrainMask subfile in the mask folder. Then select Compute to run the program.
Name the output results as T2XY and set the output directory. Left click on Add Group Images to open the XY subfolder in the AD Results folder. And the XY subfolder in the MCI Results folder.
Repeat computation of the brain mask file to obtain outputs for T3XY, T1YX, T2YX, and T3YX for a total of six files. Left click on Viewer of RESTplus to view the resulting files. Import the template named Ch2 in Underlay.
Finally, find the six resulting files in the output directory and fill in the overlay one by one. Create the final graph using the six output files. After the active nodes in the whole brain were first identified, GCA technology was used to determine the directed functional connectivity from the PCC to the whole brain.
And from the whole brain to the PCC in the AD, MCI, and control groups. The directed connectivity from the whole brain to the PCC was enhanced in the AD group as compared to the normal control group and primarily focused in the bilateral cerebellar region outside the DMN. The directed connectivity from the PCC to the whole brain was significantly reduced in the AD group as compared to the controls with the main regions such as the right precuneus and left meta frontal gyrus belonging to the DMN.
While masters, this technique can be done in 14 hours if it is performed properly. While attempting this procedure, it's important to remember that all file and the photo names must be in English and can not have spaces. After it's development this technique paved the way for reserachers in the field of FMRI to explore the directed factional connectivity in the progression of Alzheimer's disease.
After watching this video, you should have a good understanding of how to start it the directed factional connectivity in the brain related to AD progression. Thereby establish a narrow objective basis for assessing the severity of the disease.
