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#' Determination of Motor Unit Synchronization from Cross Correlation Histograms
#' using the Cumulative Sum Method
#'
#' @export
#' @importFrom stats "median" "sd"
#' @importFrom methods "show"
#' @keywords recurrence, motor unit, synchronization, cumulative sum
#' @description Calculates the time-domain synchronization indices CIS, k',
#' k'-1, S, E, SI (detailed below) between the two input motor unit discharge
#' trains based on the cumulative sum method. Peak boundaries are determined
#' as the bins associated with 10% and 90% of the range (maximum minus
#' minimum) of the cumulative sum. The peak is considered significant if its
#' mean bin count exceeds the sum of the mean and 1.96 * standard deviation of
#' the baseline bins (<= -60 ms and >= 60 ms). If no significant peak is
#' detected, a default +/- 5 ms peak is used.
#' @usage cumsum_mu_synch(motor_unit_1, motor_unit_2, order = 1, binwidth =
#' 0.001, get_data = T, plot = F)
#' @param motor_unit_1,motor_unit_2 Numeric vectors of strictly increasing
#' numbers denoting sequential discharge times of a motor unit or neuron or
#' any strictly increasing point process.
#' @param order Numeric as a positive integer for the number of forward and
#' backward orders for calculating recurrence times. Default = 1.
#' @param binwidth Numeric as a positive for the bin allocation size for
#' computational histogram. Default = 0.001 or 1 ms.
#' @param get_data T/F logical for outputting motor unit data. Default is TRUE.
#' @param plot T/F logical for outputting the cross correlation histogram.
#' Default is FALSE.
#' @return A list of lists containing motor unit data (the names of each
#' discharge train used, number of discharges, the interspike intervals (ISI),
#' mean ISI, and the recurrence times associated with each order) and
#' synchronization indices. #' CIS = frequency of synchronized discharges.
#' k' = ratio of total discharges in peak to expected discharges in peak. k'-1
#' = ratio of synchronized discharges to expected discharges in peak. S =
#' ratio of synchronized discharges to total number of discharges of both
#' motor units. E = ratio of synchronized discharges to non-synchronized
#' discharges. SI = ratio of synchronized discharges to reference motor unit
#' discharges.
#' @examples
#' x <- c(0.035, 0.115, 0.183, 0.250, 0.306, 0.377, 0.455, 0.512, 0.577,
#' 0.656, 0.739, 0.821, 0.866, 0.950, 1.014, 1.085, 1.153, 1.213, 1.279,
#' 1.355, 1.431, 1.482, 1.551, 1.631, 1.692, 1.749, 1.832, 1.897, 1.964,
#' 2.106, 2.149, 2.229, 2.302, 2.384, 2.420, 2.505, 2.592, 2.644, 2.722,
#' 2.801, 2.870, 2.926, 3.011, 3.098, 2.030, 3.183, 3.252, 3.319, 3.395,
#' 3.469, 3.560, 3.589, 3.666, 3.744, 3.828, 3.876, 3.943, 4.020, 4.104)
#' x <- sort(x)
#' y <- sort(jitter(x))
#' y <- round(y, digits = 3)
#' cumsum_mu_synch(x, y, order = 1, binwidth = 0.001, get_data = TRUE,
#' plot = FALSE)
#' @references Keen, D.A., Chou, L., Nordstrom, M.A., Fuglevand, A.J. (2012)
#' Short-term Synchrony in Diverse Motor Nuclei Presumed to Receive Different
#' Extents of Direct Cortical Input. Journal of Neurophysiology 108: 3264-3275
cumsum_mu_synch <- function(motor_unit_1, motor_unit_2, order = 1,
binwidth = 0.001, get_data = T, plot = F) {
recurrence_intervals2 <- function(motor_unit_1, motor_unit_2, order) {
if (!is.vector(motor_unit_1) || !is.vector(motor_unit_2)) {
stop("'motor_unit_1' and 'motor_unit_2' must be vectors.")
}
if (length(motor_unit_1) <= 1 || length(motor_unit_2) <= 1) {
stop ("'motor_unit_1' and 'motor_unit_2' must be vectors of length > 1.")
}
if (is.unsorted(motor_unit_1, strictly = T)
|| is.unsorted(motor_unit_2, strictly = T)) {
stop ("'motor_unit_1' and 'motor_unit_2' must be strictly increasing.")
}
if (!is.numeric(order) || order%%1 != 0) {
stop("Order must be whole number.")
}
# reference (ref) and event motor units (MU) assigned according to which MU
# has more firings (length()). ISI = InterSpike Intervals
if (length(motor_unit_1) < length(motor_unit_2)) {
ref.name <- deparse(substitute(motor_unit_1, env = parent.frame()))
event.name <- deparse(substitute(motor_unit_2, env = parent.frame()))
ref.MU <- motor_unit_1
event.MU <- motor_unit_2
ref.MU.ISI <- diff(motor_unit_1)
event.MU.ISI <- diff(motor_unit_2)
mean.ref.ISI <- round(mean(ref.MU.ISI), digits = 3)
mean.event.ISI <- round(mean(event.MU.ISI), digits = 3)
} else {
ref.name <- deparse(substitute(motor_unit_2, env = parent.frame()))
event.name <- deparse(substitute(motor_unit_1, env = parent.frame()))
ref.MU <- motor_unit_2
event.MU <- motor_unit_1
ref.MU.ISI <- diff(motor_unit_2)
event.MU.ISI <- diff(motor_unit_1)
mean.ref.ISI <- round(mean(ref.MU.ISI), digits = 3)
mean.event.ISI <- round(mean(event.MU.ISI), digits = 3)
}
MU.names <- list(Reference_Unit = ref.name,
Number_of_Reference_Discharges = length(ref.MU),
Reference_ISI = ref.MU.ISI,
Mean_Reference_ISI = mean.ref.ISI,
Event_Unit = event.name,
Number_of_Event_Discharges = length(event.MU),
Event_ISI = event.MU.ISI,
Mean_Event_ISI = mean.event.ISI,
Duration = max(ref.MU, event.MU) - min(ref.MU, event.MU))
lags <- vector('list', order)
for (i in 1:length(ref.MU)) {
pre_diff <- rev(event.MU[event.MU < ref.MU[i]])
pre_diff <- pre_diff[1:order]
pre_diff <- pre_diff - (ref.MU[i])
post_diff <- event.MU[event.MU >= ref.MU[i]]
post_diff <- post_diff[1:order]
post_diff <- post_diff - (ref.MU[i])
for (j in 1:order) {
y <- c(pre_diff[j], post_diff[j])
lags[[j]] <- append(lags[[j]], y)
}
}
# remove NA's
lags <- lapply(lags, Filter, f = Negate(is.na))
names(lags) <- paste(1:order)
lags <- append(MU.names, lags)
return(lags)
}
recurrence.data <- recurrence_intervals2(motor_unit_1,
motor_unit_2,
order)
mean.reference.ISI <- recurrence.data$Mean_Reference_ISI
# Create frequency table by binning recurrence times according to specfied bin
# width using the mean reference ISI as the positive and negative boundaries.
frequency.data <- unlist(recurrence.data[paste(1:order)])
frequency.data <- frequency.data[frequency.data >= -mean.reference.ISI &
frequency.data <= mean.reference.ISI]
frequency.data <- as.vector(frequency.data)
frequency.data <- motoRneuron::bin(frequency.data, binwidth = binwidth)
# Calculate mean frequency of baseline bins (all bins outside +/- 60 ms).
# Keen et al 2012
baseline.mean <- frequency.data[frequency.data$Bin <= ((min(frequency.data$Bin))
+ 0.060) | frequency.data$Bin >=
(max(frequency.data$Bin) - 0.060), ]
baseline.mean <- mean(as.numeric(unlist(baseline.mean["Freq"])))
# Calculate sd frequency of baseline bins (all bins outside +/- 60 ms).
baseline.sd <- frequency.data[frequency.data$Bin <= ((min(frequency.data$Bin))
+ 0.060) | frequency.data$Bin >=
(max(frequency.data$Bin) - 0.060), ]
baseline.sd <- sd(as.numeric(unlist(baseline.sd["Freq"])))
# calculate cumulative sum as new data frame
cumsum <- data.frame(Bin = frequency.data$Bin, Cumsum = cumsum(
as.numeric(frequency.data$Freq) - baseline.mean))
cumsum <- cumsum[cumsum$Bin >= ((min(frequency.data$Bin)) + 0.060) &
cumsum$Bin <= (max(frequency.data$Bin) - 0.060),]
# calculate 90th and 10th percentile of max difference in cumsum
ninety.percent <- min(cumsum$Cumsum) + ((max(cumsum$Cumsum) - min(cumsum$Cumsum)) * 0.9)
ten.percent <- min(cumsum$Cumsum) + ((max(cumsum$Cumsum) - min(cumsum$Cumsum)) * 0.1)
# find upper and lower bounds (bins) associated with the 90th and 10th
# percentile
bounds <- vector()
bounds[1] <- cumsum[(which(abs(cumsum$Cumsum - ten.percent)
== min(abs(cumsum$Cumsum - ten.percent)))), 1]
bounds[2] <- cumsum[(which(abs(cumsum$Cumsum - ninety.percent)
== min(abs(cumsum$Cumsum - ninety.percent)))), 1]
# swap upper and lower limits if needed
if(bounds[1] > bounds[2]) {
old.lower <- bounds[1]
bounds[1] <- bounds[2]
bounds[2] <- old.lower
rm(old.lower)
}
# subset out peak from upper and lower bounds
peak <- frequency.data[frequency.data$Bin >= bounds[1] &
frequency.data$Bin <= bounds[2],]
peak <- as.numeric(unlist(peak["Freq"]))
peak.mean <- mean(peak)
peak.zscore <- (peak.mean - baseline.mean) / baseline.sd
# if peak.zscore >= 1.96 default to +/- 5 ms and define new peak
if(peak.zscore < 1.96) {
bounds[1] <- -0.005
bounds[2] <- 0.005
peak <- frequency.data[frequency.data$Bin >= bounds[1] &
frequency.data$Bin <= bounds[2],]
peak <- as.numeric(unlist(peak["Freq"]))
peak.mean <- mean(peak)
peak.zscore <- (peak.mean - baseline.mean) / baseline.sd
}
# Calculate total number of instances in peak
total.peak <- sum(peak)
# Calculate number of instances in peak in excess of what is
# expected (i.e. above baseline mean)
extra.peak <- sum((peak - baseline.mean)[which((peak - baseline.mean) > 0)])
# Calculate total number of instances in frequency data
total.count <- sum(as.numeric(unlist(frequency.data$Freq)))
# Determine bins in peak below baseline mean
q <- as.numeric(vector())
for (m in 1:length(peak)) {
if (peak[m] <= baseline.mean) {
q <- c(q, peak[m])
} else {next}
}
# Calculate number of instances in peak below baseline mean
expected.peak <- baseline.mean *
(length(which(peak > baseline.mean))) +
sum(q)
Cumsum.Synch <- list()
if (get_data) {
Cumsum.Synch[["Data"]] <- recurrence.data
}
if (plot) {
show(plot_bins(frequency.data))
}
Cumsum.Synch[["Indices"]] <- list(CIS = extra.peak / recurrence.data$Duration,
kprime = (total.peak / expected.peak),
kminus1 = (extra.peak / expected.peak),
E = (extra.peak
/ recurrence.data$Number_of_Reference_Discharges),
S = (extra.peak
/ (recurrence.data$Number_of_Reference_Discharges
+ recurrence.data$Number_of_Event_Discharges)),
SI = (extra.peak / (total.count / 2)),
Peak.duration = bounds[2] - bounds[1],
Peak.center = median(c(bounds[2], bounds[1])))
return(Cumsum.Synch)
}
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