HRF-GIMME
HRF-GIMME is an extension of GIMME that allows for the modeling of
task-related effects in fMRI. Use of HRF-GIMME enables the modeling of
direct task effects as well as how the presence of a task may modulate
the relations between brain regions. Doing so requires the modeling of
the hemodynamic response function (HRF), a function that arrives at the
expected psychophysiological response in the fMRI data (i.e. the
expected BOLD signal that will be measured). Due to previous research
demonstrating that the HRF may vary more between individuals than
between brain regions (Handwerker, Ollinger, & D’Esposito, 2004),
HRF-GIMME provides individual estimates of the HRF. To do so, HRF-GIMME
uses the smoothed finite impulse response function (sFIR; Gouette et
al., 2000), a flexible basis set that is able to model the HRF with no
assumptions about its shape, and has been shown to effectively model the
HRF when there are individual variations in shape (Lindquist, 2009). The
(binary) stimulus onset/task vector is then convolved with the
individual HRF, allowing for the investigation of the direct effects of
the task on the various brain regions. The multiplication of this
convolved task vector with selected brain regions allows for the
investigation of how the relations between brain regions may vary in the
presence of a task. These modulatry effects in combination with the
intrinsic connections among brain regions enable the use of HRF-GIMME in
task-based data.
Note that while HRF-GIMME was designed with fMRI data in mind, the
algorithm can be applied to examine the direct and modulating effects of
any exogenous variable on a set of other variables (e.g. stress) with a
response curve of any shape, as HRF-GIMME makes no assumptions about the
form.
Running HRF GIMME
To begin, we will load in simulated data with a task onset vector.
This data has 25 people, each with 500 time points for 5 variables (4
brain regions and 1 task onset vector). This data is available as part
of the GIMME package.
library(gimme)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
data(HRFsim, package="gimme")
I will provide more details on the arguments relevant to HRF-GIMME.
For further information on the other arguments, please see the GIMME
vignette, or the documentation of the gimmeSEM function using
HRF-GIMME can be run on this simple example of simulated data with
the code shown below:
HRF.fit <- gimme(data = HRFsim,
ar = TRUE,
standardize = TRUE,
exogenous = "V5",
conv_vars = "V5",
conv_length = 16,
conv_interval = 1,
mult_vars = "V3*V5",
mean_center_mult = TRUE,
groupcutoff = .75)
standardize: Whether all variables will be standardized to
have a mean of zero and a standard deviation of one. We recommend
setting standardize = TRUE so that all variables will be standardized,
to prevent issues that may occur from variables with large differences
in variance (common in fMRI).
exogenous: Vector of variable names to specify all exogenous
variables (variables that can predict other variables, but cannot be
predicted). Note that this should include any variables that will be
convolved (e.g. the task onset vector), which are always exogenous. Note
that exogenous variables are currently not lagged.
conv_vars: Vector of variable names to be convolved (e.g. the
task onset vector) via the smoothed Finite Impulse Response
(sFIR).
conv_length: The expected response length in seconds. Defaults
to 16 seconds, which is typical for the HRF.
conv_intervals: Interval between data acquisition. For fMRI
studies, this is the repitition time (TR). Currently must be a
constant.
mult_vars: Vector of variable names to be multipled to explore
bilinear/modulatory effects. Within the vector, multiplication of the
variables should be indicted with an asterik. If a header is used,
variables should be referred to by their variable names. If no header is
used, variables should be referred to as V followed by the column number
(as shown above). If lagged variables are desired, variable names can be
appended with “lag”, no separation (e.g. “V3lagV5”). Remember that
exogenous/convolved variables are not lagged.
mean_center_mult*: Logical indicating whether variables should be
mean-centered before multiplication. We recommend setting
mean_center_mult = TRUE, although the default is FALSE.
Output
Output of the GIMME object is not different from a standard GIMME
object, except that multiplied variables are included and can be
explored, named with “by” separating the variable names. A plot
is shown below for illustration.
When output is saved to an object, you will also obtain the response
function estimates for each individual (e.g., HRF.fit$ref_est using the
example above). This provides the shape of the HRF for each individual.
For more information on output, please see the GIMME vignette.