# Lab 11 for the University of Tulsa's CS-6643 Bioinformatics Course
# Introduction to fMRI Analysis and ICA
# Professor: Dr. McKinney, Fall 2022
# Noah L. Schrick - 1492657

## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

#### Part A: Haemodynamic response functions (HRF) and block design
## Plot Basic HRF from 0-20s
hq <- function(t,q=4){
  # q=4 or 5, where 5 has more of a delay
  return (t^q * exp(-t)/(q^q * exp(-q)))
}

# use seq to create vector time and use hq to create hrf vectors
time <- seq(0,20)
hrf1 <- hq(time)
hrf2 <- hq(time, 5)

# plot      
plot(time,hrf1,type="l") 
lines(time,hrf2,col="red")

## Deconvolve with task onset times
# grabbed from afni c code
# basis_block_hrf4 from 3dDeconvolve.c
HRF <- function(t, d){
  if (t<0){
    y=0.0
  }else{
    y = 1/256*exp(4-t)*(-24-24*t-12*t^2-4*t^3-t^4 + exp(min(d,t))*(24+24*(t-min(d,t)) + 12*(t-min(d,t))^2+4*(t-min(d,t))^3+(t-min(d,t))^4))
  }
  return(y)
}

t=seq(0,360,len=360)
onsets=c(14,174,254)
blocks.model = double()
for (curr_t in t){
  summed_hrf=0.0
  for (start in onsets){
    summed_hrf=summed_hrf+HRF(curr_t-start,20)
  }
  blocks.model = c(blocks.model,summed_hrf)
}

plot(blocks.model,type="l")


## Plot voxel time series data and the block design curve
voxel.data <- read.delim("059_069_025.1D")
plot(seq(1,2*dim(voxel.data)[1],by=2),t(voxel.data),
     type="l", xlab="time",ylab="intensity")
# normalize the height of the blocks model
blocks.normal <- max(voxel.data)*blocks.model/max(blocks.model)
lines(blocks.normal,type="l",col="red")

# regression
length(blocks.normal) # too long
dim(voxel.data)[1]
# grab elements from blocks.normal to make a vector same as data
blocks.norm.subset <- blocks.normal[seq(1,length(blocks.normal),len=dim(voxel.data)[1])]
length(blocks.norm.subset)

voxel.data.vec <- matrix(unlist(voxel.data),ncol=1)

# use lm to create voxel.fit <- lm...
voxel.fit <- lm(voxel.data.vec ~ blocks.norm.subset)


plot(blocks.norm.subset,voxel.data.vec,
     xlab="block model",ylab="voxel data",main="regression fit")

# use abline(voxel.fit) to overlay a line with fit coefficients 
abline(voxel.fit)