fmri_stimulus_detect: fMRI data stimulus detection

In TCIU: Spacekime Analytics, Time Complexity and Inferential Uncertainty

fMRI data stimulus detection

Description

This function takes a real/complex valued fMRI data and detects locations where stimulus occurs

Usage

fmri_stimulus_detect(
  fmridata,
  mask = NULL,
  stimulus_idx = NULL,
  rest_idex = NULL,
  method,
  fdr_corr = NULL,
  spatial_cluster.thr = NULL,
  spatial_cluster.size = NULL,
  ons = NULL,
  dur = NULL
)

Arguments

fmridata	an array or a vector which contains the spatial and/or temporal record of fMRI result
mask	a 3d array indicating the spatial location of the brain
stimulus_idx	a vector that specifies when motion happens
rest_idex	a vector that specifies when study participant does not move
method	a string that indicates which testing method is to be used. There are 5 options: 'HotellingT2', 'Wilks-Lambda' and 'gLRT'(likelihood ratio test) for complex fMRI data and 't-test', 'wilcoxon-test' for real fMRI data. For 4D real-valued fMRI data, two more options: 'on_off_diff' and 'HRF' method can be applied.
fdr_corr	a logical variable. True if FDR correction is to be applied
spatial_cluster.thr	threshold p-value to be used for spatial clustering
spatial_cluster.size	number of spatially connected voxels to be tested for spatial clustering
ons	a vector with the first time points of the time periods when the fMRI data receives stimulation. The default is NULL. Need to specify when choose the method 'gLRT' or 'HRF'.
dur	a vector of the time length of each stimulated period. The default is NULL. Need to specify when choose the method 'gLRT' or 'HRF'.

Details

The function fmri_stimulus_detect is used to conduct motor area detection. It first takes in a real or complex valued fMRI data, and then users can choose to use various methods to find the spatial regions where motor area is located inside the brain. User can either input the 4d fMRI data and get a 3d array storing p-values or input the fMRI data with smaller dimension (e.g. fix the x,y axis) and get a vector storing p-values. Besides, one can use this function to just calculate raw p-values, and we also provide options so that users can do FDR correction and spatial clustering to get a more accurate result.

Value

If input fMRI data is 4d, return a 3d array storing p-values for the 4d fMRI data. If input fMRI data is less than 4d, return a vector storing p-values for the fMRI data.

Author(s)

SOCR team <http://socr.umich.edu/people/>

Examples

fmri_generate = fmri_simulate_func(dim_data = c(64, 64, 40), mask = mask)
fmridata = fmri_generate$fmri_data
stimulus_idx = fmri_generate$on_time
ons = fmri_generate$ons
dur = fmri_generate$dur


# p-values using t-test for 4d fMRI data
p_value1 = fmri_stimulus_detect(fmridata = fmridata, mask = mask,
                                stimulus_idx = stimulus_idx,
                                method = 't-test')
dim(fmridata)
dim(p_value1)

# p-values using t-test for 2d fMRI data
p_value2 = fmri_stimulus_detect(fmridata = fmridata[40,41,,], mask = mask,
                                stimulus_idx = stimulus_idx,
                                method = 't-test')
dim(fmridata[40,41,,])
dim(p_value2)

TCIU documentation built on Sept. 15, 2024, 9:07 a.m.