the hemodynamic response function at …...abstract functional magnetic resonance imaging (fmri)...
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Ghent University
Faculty of Psychology and Educational Sciences
Second Master Theoretical and Experimental Psychology
Year: 2015-2016
Second semester exam period
THE HEMODYNAMIC RESPONSE FUNCTION AT REST AND ITS
RELATIONSHIP TO BRAIN FUNCTIONING:
AN EXPLORATORY STUDY
Nigel Colenbier
Student number: 01105278
Masterproef II
Promotor: Daniele Marinazzo
Department: Data-Analysis
17-05-2016
Abstract
Functional magnetic resonance imaging (fMRI) time series can be modeled as the
convolution between the explicit timings of presented events and a hemodynamic
response function (HRF). The HRF is a model of the underlying hemodynamic response
that indirectly reflects neuronal activity in response to an event. Using the HRF, a lot
has been learned about the functional segregation or activation in the brain. However,
functional segregation is rarely investigated in a new popular neuroimaging technique
called resting-state fMRI (rfMRI) which measures spontaneous brain activity. The
reason functional segregation is rarely investigated is due to the fact there are no explicit
events where the HRF can be modeled too. By using a recently developed blind HRF
retrieval method by Wu et al. (2013), it is possible to retrieve the HRF at rest and thus
investigate functional segregation in rfMRI. In this study, we used the blind HRF
retrieval method to retrieve the HRF that is characterized by three parameters: response
height, time to peak, full-width at half-max. By using behavioral measures that have
shown to modulate the brain during cognitive tasks, we can explore if they also regulate
the brain during spontaneous activity by doing correlational analyses between the
retrieved HRF and the measures. There were no indications that the selected behavioral
measures correlated with the HRF parameters. As a result we conclude that they do no
modulate the hemodynamics during spontaneous activity.
Nederlandse samenvatting
De tijdsopname van functionele beeldvorming (fMRI) kan geconstrueerd worden door
de convolutie tussen aangeboden items en een hemodynamische respons functie (HRF).
De HRF is een model van de onderliggende hemodynamische respons wat een indirecte
weergave is van de neuronale activiteit als antwoord op aangeboden items. Door het
gebruik van de HRF is er al veel geleerd over de functionele segregatie of activiteit van
de hersenen. Functionele segregatie wordt daarentegen bijna niet onderzocht in
functionele beeldvorming bij personen in rust (rfMRI). De reden daarvoor is het feit dat
er geen items zijn waarnaar de HRF kan geconstrueerd worden. Door een recent
ontwikkelde methode door Wu et al. (2013) kan functionele segregatie in rfMRI
onderzocht worden door de HRF op een blinde manier te reconstrueren. In deze studie
is de blinde HRF methode gebruikt om de HRF te reconstrueren die wordt weergegeven
door drie parameters. Door het gebruik van gedragsmaten waarvan er al is aangetoond
dat ze de activiteit van de hersenen moduleren tijdens cognitieve taken, kunnen we
onderzoeken of ze ook de activiteit van de hersenen in spontane activiteit modeleren
door correlationele analyses uit te voeren tussen de gereconstrueerde HRF en de
gedragsmaten. Er was geen bewijs dat de geselecteerde gedragsmaten gecorreleerd
waren met de HRF parameters. Als gevolg concluderen we dat de gedragsmaten de
hemodynamiek tijdens spontane activiteit niet moduleren.
Foreword
Working on this project for the past two years has been an adventure. Starting from
knowing nothing about the field to and ending up with a finished thesis as a result has
given me great satisfaction. I've come from struggling understanding the material, to
finding pleasure in finally understanding concepts or finishing a piece of code that does
what it's supposed to do.
I'd like to especially thank my promotor Daniele Marinazzo for setting up the research
question and guiding me throughout the whole two years. Writing and helping me write
code where necessary, bringing up interesting ideas to test, sending relevant literature
,giving feedback and helping re-analyze all the data after I forget adjusting a parameter.
I couldn't have wished for a better promotor and mentor to finish this project.
Furthermore, I'd like to thank my mom for everything she has ever done for me and my
brother after everything she has been through. Words can't describe how much I own
you. You've put all your energy and effort into making sure me and my brother got a
good youth and are able to go to university and follow our aspirations.
Additionally, I'd like to thank my dad for being my biggest source of inspiration in life.
The endless passion and work ethic you showed for your field of study is something that
is rarely encountered. Always trying to help out other people before looking out for
yourself. I wish you could've seen me graduate, seven years feels like an eternity.
Finally, I'd like to thank my friends, especially Thibault and Thomas for sticking with
me through everything. You've never given up on me and showed me life is still worth
living despite everything.
Contents
Introduction ...................................................................................................... 1
The hemodynamic response function ......................................................................... 1
Resting-state fMRI .................................................................................................... 6
Analyzing rfMRI ....................................................................................................... 8
Functional Integration. ...................................................................................................... 8
Functional Segregation. ..................................................................................................... 9
Current study .................................................................................................. 11
Materials and Methods ................................................................................... 11
Data......................................................................................................................... 11
Preprocessing .......................................................................................................... 13
Spatial preprocessing pipeline. ........................................................................................ 14
Temporal preprocessing pipeline. .................................................................................... 16
Additional preprocessing. ................................................................................................ 17
Spontaneous event and HRF retrieval ...................................................................... 17
Statistical Analysis .................................................................................................. 18
Results ............................................................................................................. 19
Spatial distribution maps of resting HRF parameters ............................................... 19
Correlation between HRF and Age .......................................................................... 21
Correlation between HRF and Morphology of the mid-cingulate cortex ................... 22
Correlation between HRF and Handedness .............................................................. 25
Correlation between HRF and Motor task performance ........................................... 26
Discussion and Conclusion ............................................................................. 27
References ....................................................................................................... 31
1
Introduction
The hemodynamic response function
In recent decades, the use of functional magnetic resonance imaging (fMRI) has
gained much popularity. This neuroimaging technique allows us to relate human
behavior to brain functioning, by producing activations maps showing which brain
regions are active during particular behaviors. The fMRI technique is a noninvasive
neuroimaging technique that is based on the so-called blood oxygen level-dependent
(BOLD) contrast that was discovered by Seiji Ogawa (Ogawa, Lee, Kay, & Tank,
1990). In response to an event, there will be an increase of neuronal activity in given
brain areas of the brain. This increase in neuronal activity will elicit an increase in
oxygen and glucose consumption that is supplied by the vascular system. As a result of
the relative ratios in local oxygenated blood between oxygenated hemoglobin and
deoxygenated hemoglobin changes (Figure 1a). Because the deoxygenated hemoglobin
is paramagnetic, it causes disruptions in the magnetic field of the MRI scanner while
oxygenated hemoglobin is diagmetic and doesn't affect the magnetic field. This effect
will be reflected in the MRI images, allowing us to dissociate active regions from
inactive regions as a result of the BOLD contrast. It's important to note that the BOLD
contrast does not reflect neuronal activity in a direct way, but is rather an indirect way
to measure neuronal activity through the changes in local oxygenated blood ratios
supplied by the vascular system (Logothetis, 2003, 2008; Nair, 2005; Rees, Friston, &
Koch, 2000).
The vascular response to neuronal activity follows a stereotypical shape referred to
as a hemodynamic response that can last up to 20 seconds (Friston, Jezzard, & Turner,
1994a). The hemodynamic response is characterized by a delayed time to peak
approximately 4-8s after stimulation and followed by a post-stimulus undershoot which
slowly returns to baseline (Lee, Glover, & Meyer, 1995; Figure 1b). This temporal
delay reflects the interval between an increase in blood oxygenation following neuronal
activity. The fact that a hemodynamic response lasts up to 20 seconds before returning
to baseline limits the amount of presented events in a short period in task-based fMRI
studies, if the goal is to capture the hemodynamic response fully in response to every
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event. As a result, waiting until the hemodynamic response has returned to baseline
every time constrains the ability to infer relationships between behavior and brain
functioning.
Typically researchers use task-based fMRI designs that use a stream of succeeding
events to more efficiently model neuronal activity, which offers more flexibility in the
type of experimental tasks that can be used to study brain function compared to block or
slow event-related designs. In this kind of design, a general linear model (GLM) is used
to find and detect time series of brain regions that fit the pattern of the hemodynamic
responses to the rapid succeeding event stream. The acquired BOLD signal is a
dependent variable while the independent variables of the model try to capture the
BOLD signal as accurately as possible with GLM predictors. The model of the expected
BOLD signal is fitted against the acquired time BOLD signal in all brain regions to
detect whether these brain regions were active during presented events.
Figure 1. (a) Changes in the ratio between oxygenated and deoxygenated hemoglobin
following neuronal stimulation (figure adapted from Huettel et al., 2009). (b) Example of a
hemodynamic response evoked by neural activity in response to an event presented at t=0s.
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However, a critical assumption is required to be able to use the GLM method to
analyze fMRI data. Namely, it assumes that the relationship between the neuronal
activity and the BOLD signal (the hemodynamic response) exhibits linear time-
invariant properties (Boynton, Engel, Glover, & Heeger, 1996). Linearity implies that if
there is knowledge what the response is for separate events, and they both occur close
together in time, the resulting signal would be the sum of the independent signals. Time
invariant means that if an event is shifted by t seconds, the BOLD response will also be
shifted by the same amount (Poldrack, Mumford, & Nichols, 2011). If these
assumptions hold, an expected BOLD signal y(t) of fMRI data can be modeled as the
convolution between an invariant hemodynamic response function (HRF) h(t) which
reflects the hemodynamic response and the theoretically expected neuronal events in the
design s(t) (see Equation 1, where (t) is the time and denotes convolution). Choosing
an appropriate HRF function is crucial in capturing the shape of the hemodynamic
response, as it will ensure a good fit of the GLM predictors to the BOLD time series
when a hemodynamic response is present in the signal.
𝒚(𝒕) = 𝒔(𝒕) 𝒉(𝒕) (1)
Equation 1. Estimation of fMRI BOLD time series.
Several HRF functions reflecting the shape of the hemodynamic response have been
used in fMRI analyses. Based on observations that the hemodynamic response has a
similar shape across early sensory regions in the brain regions S1 (Zarahn, Aguirre, &
D'Esposito, 1997), A1 (Josephs, Turner, & Friston, 1997), and V1 (Boynton et al.,
1996), allowed the development of canonical HRF models. The canonical HRF is a
popular model that exists out of the difference between two gamma functions. One
gamma function to model the peak, the other one to model the undershoot (Friston et
al., 1998a; Glover, 1999). Only the height, or amplitude, of the canonical HRF, which is
taken to be an estimate of the strength of activation in a given brain area, is allowed to
vary and is estimated in the linear model (Worsley & Friston, 1995). As the canonical
HRF, can only vary in height, hemodynamic responses that deviate from this function in
shape or timing-to-peak will not be found in the signal. Thus, even though the canonical
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HRF does a good job at modeling the true underlying hemodynamic response, it does
not capture the variation that the hemodynamic response can show. Studies have shown
that the hemodynamic response is different across subjects (Aguirre, Zarahn, &
D'Esposito, 1998) and is even variable across brain regions within a subject
(Handwerker, Ollinger & D'Esposito, 2004; Schacter, Buckner, Koutstaal, Dale, &
Rosen, 1997). For example, Aguirre and colleagues in 1998 showed that when
participants performed a simple sensorimotor task, the hemodynamic responses from
the primary motor cortex varied significantly across subjects (see figure 2). It has been
postulated that these differences in hemodynamic responses might be due to multiple
factors including neural activity differences, global magnetic susceptibilities,
vasculature differences, slice timing differences and baseline cerebral blood flow
(Handwerker et al., 2004).
As a result not taking the variation of the hemodynamic response into account in the
HRF function will have an influence and might result in invalid and biased conclusions
on brain functioning. This is indeed the case as Calhoun and colleagues in 2004 showed
that when there is a difference in delay between the hemodynamic response and the
canonical HRF, the amplitude of the hemodynamic response is underestimated. To cope
Figure 2. Individual variation of the hemodynamic response across 10 subjects.
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with the observed inhomogeneity, an expanded model of the canonical HRF is used that
to some extent takes the variation between the underlying hemodynamic responses into
account. The canonical HRF is expanded by including either a temporal derivative or
dispersion derivative or both (Friston, Josephs, Rees, & Turner, 1998b; Figure 3).
Adding these derivatives to the canonical HRF model allows capturing variation
regarding differences in the latency (time) of the peak response and differences in the
duration (width) of the peak response. The temporal derivative captures the former
differences while the dispersion derivative captures the latter differences. So, by adding
these derivatives to the canonical HRF, the underlying variable hemodynamic responses
are taken better into account and this is reflected in the estimation of the HRF.
Moreover, as this expanded model has fewer problems with amplitude biases compared
to the canonical HRF without derivatives, there will be fewer biases and incorrect
conclusions on brain functioning. Thus, modeling the underlying hemodynamic
response is crucial for analyzing fMRI data to learn more about brain functioning.
Figure 3. The canonical HRF with its expansion derivatives (temporal, dispersion).
6
Resting-state fMRI
Much of what we currently know about brain function comes from studies utilizing
task related fMRI designs in which a task or stimulus is presented, and the changes in
BOLD contrast and behavior are measured. In recent years, another rapidly expanding
approach called Resting-state fMRI (rfMRI) is being used to understand the neuronal
organization of our brain by investigating the spatial and temporal structure of
spontaneous neural activity (Fox & Raichle, 2007; Power, Schlaggar, & Petersen,
2014). The goal of a rfMRI design is to acquire spontaneous brain activity without
presenting any external task or stimuli i.e. measuring brain activity at "rest". Subjects
are instructed to lie still in the scanner, stay awake and don't think of anything specific.
Most of the rfMRI studies focus on spontaneous low-frequency fluctuations (<0.1 Hz)
in the BOLD signal (Lee, Smyser, & Shimony, 2013). At the beginning investigating
the spontaneous activity of the brain was met with skepticism, as it was suspected that
this signal was driven by noise and wasn't explicitly organized as it is in response to
tasks. In 1995, a publication by Biswal and colleagues quickly overturned this
skepticism. They showed that spontaneous BOLD fluctuations, in the absence of a task,
were highly and specifically correlated among functionally related brain regions. The
same brain regions that were active and correlated during finger tapping were also
correlated with the spontaneous BOLD fluctuations in the absence of overt motor
behavior (Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995). Other groups have since
then replicated this finding, and it has been found in other brain regions as well (Cordes
et al., 2000; Hampson, Peterson, Skudlarski, Gatenby, & Gore, 2002; Lowe, Mock, &
Sorenson, 1998; Stein et al., 2000; Xiong, Parsons, Gao, & Fox, 1999). Since these
discoveries rfMRI has rapidly quick become a new avenue for studying brain
functioning and neuroimaging research, next to task-based fMRI.
One reason rfMRI is so attractive is because it requires little to no participation of
subjects and it is not cognitively demanding. Therefore, it is a useful method to use with
subjects who may not otherwise be able to cooperate with task-based paradigms such as
patients with altered mental status, young children or those with brain tumors (Liu,
2009; Kokkonen et al., 2009; Shimony et al., 2009). Studies have already shown that
analyzing networks observed at rest, the so-called resting state networks (RSNs), have
7
clinical value as sensitive markers of diseases (Filippini et al., 2009; Greicius,
Srivastava, Reiss, & Menon, 2004; Lee et al., 2013). For example, Greicius and
colleagues in 2004 showed that there was decreased activity in an RSN network called
the Default Mode Network (DMN) in a group of subjects with early Alzheimer Disease
compared to an age-matched healthy control group. Suggesting that spontaneous
activity of the brain can be used as a biomarker to identify pathological conditions such
as Alzheimer Disease. Clinical applications and our knowledge about brain function
will continue to grow with developments and advancements that make use of rfMRI
datasets. One of such advancement are recent studies that show that individual task-
evoked activity patterns measured with task-related fMRI designs can be predicted
based on individual resting-state data, and resting-state connectivity can predict levels
of fluid intelligence (Finn et al., 2015; Tavor et al., 2016). These findings suggest that
there is a closer relationship between spontaneous activity and activity evoked by tasks
than was initially thought. Earlier evidence already suggested this relationship as RSNs
and task-evoked networks are quite similar to each other (Cole, Bassett, Power, Braver,
& Petersen, 2014; Smith et al., 2009). While other evidence also shows that RSNs are
modified due to prior induced task-evoked activity compared to no prior induced task-
evoked activity (Hasson, Nusbaum, & Small, 2009; Tailby, Masterton, Huang, Jackson,
& Abbott, 2015). Based on these findings it is argued that functional networks are
continuously interacting with each other at rest, with the same functional hierarchy as
during action and cognition.
Thus, spontaneous activity in rfMRI designs has grown from being considered as an
observation of noise into a major area of human neuroimaging. The discoveries in
rfMRI research have had tremendous implications for the clinical field, as rfMRI data
structures (RSN) are predictive biomarkers for pathological conditions. Additionally,
recent evidence has been able to confirm the relationship between spontaneous activity
at rest and task-evoked activity as new methods can predict individual task activity from
resting state data. However, all the described work above on rfMRI research has mostly
focused on mapping the connectivity of spontaneous BOLD fluctuations (integration).
On the other hand, research studying the local features of spontaneous BOLD
fluctuations (segregation) such as the hemodynamic response in the BOLD signal in
response to spontaneous neuronal activity has been lacking.
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Analyzing rfMRI
In general, there are two main categories of analyzing rfMRI data which reflect two
fundamental aspects of brain function: functional integration and functional segregation
(Friston, 1994b). Functional integration is used to describe how different brain areas
interact with each other while functional integration implies that a function can be
localized in a cortical area.
Functional Integration. Functional integration in rfMRI refers to the interactions or
connectivity between the spontaneous BOLD fluctuations that are measured. Its aim is
to study the dynamics and interconnectivity of the brain by examining the correlations
among activity in different brain areas, or trying to map causal relationships between
various brain regions. There're two main kinds of methods to study functional
integration: functional connectivity and effective connectivity. Functional connectivity
refers to the temporal dependence of neuronal activity patterns of spatially separated
brain regions. While effective connectivity refers explicitly to the influence that one
neuronal system exerts over another, trying to reveal the directionality of the functional
connections (Friston, 2011).
One of the simplest measures to calculate functional connectivity is using the
Pearson correlation coefficient, which measures the linear dependence between two
signals. Commonly a seed "voxel" signal is correlated with the signal at every other
voxel to map out pairwise relationships with the rest of the brain (Biswal, Kylen, &
Hyde, 1997; Cordes et al., 2000).
On the other hand, effective connectivity tries to fits models that explain the
observed dependencies in the data. Typically various models that include these
dependencies are compared and fitted against each other to discover the best fitting
model which contains the directionality of the connections. Commonly used methods
used to investigate the directionality in dynamical interactions include dynamic causal
modeling (DCM) (Friston, Harrison, & Penny, 2003) and Granger causality analysis
(GC) (Bressler & Seth, 2011). GC is a data-driven exploratory method which purely
relies on statistical prediction and temporal precedence. It tests whether the past of a
time series of one neuronal system (X) can help to predict the future of another time
series of another neuronal system (Y). If the prediction error of the model including the
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past of X and Y is smaller than the error of the model with only past information of X, it
is said that Y G-causes X.
As noted earlier fMRI BOLD signal is not a direct measure of neural activity. As
such, early interpretations of directed connectivity by GC assumed that the
hemodynamic response is a homogenous process over the whole brain, to make
inferences of neuronal causality from fMRI data (Friston, 2011). However, several
studies have pointed out that the hemodynamic response is variable across physiological
processes and distinct brain regions (Handwerker et al., 2004, 2012; Roebroeck,
Formisano, & Goebel, 2011). As a result in GC, the temporal precedence is confounded
by the hemodynamic response, influencing the estimates of directed dynamical
influences. The problem of the inhomogeneous hemodynamic response confounding the
temporal precedence in effective connectivity can be dealt with by performing HRF
deconvolution (undoing the effect of hemodynamics trying to obtain an estimate of the
input s(t); see Equation 1). If HRF deconvolution is performed in a correct way, GC
provides reliable estimates of directed dynamical influences. A recently developed
method by Wu et al. (2013) deals with the temporal precedence issue for GC in rfMRI
by retrieving an HRF in rest and deconvolving it from the time-series.
It is these functional- and effective connectivity methods that have been mainly used
in rfMRI research to learn more about the functional integration of spontaneous BOLD
fluctuations, while studying functional segregation in rfMRI has been widely ignored.
Functional Segregation. As mentioned before functional segregation refers to the
fact that functions can be localized in certain brain regions. Task-based fMRI is one of
the most common techniques to study this, as we can localize functions by looking at
brain regions that display a hemodynamic response during the stimulation of externally
presented stimuli. However in rfMRI functional segregation has received little attention.
Only a few studies have depicted features of the spontaneous BOLD fluctuations. One
example focuses on the frequency characteristic of the BOLD signal and uses a
measurement of the amplitude of low-frequency fluctuations (ALFF). In this case,
ALFF reflects the average of the amplitude of the low-frequency band which is an index
of magnitude for the spontaneous neuronal activity (Zang et al., 2007).
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However, none of these have focused on the hemodynamic response of the BOLD
signal in response to spontaneous neuronal events. This gap in research might largely be
because even though a hemodynamic response is always present in the brain, we just
don't know when it activates. In task-based fMRI, this problem is easily solved by
modeling the HRF to explicit inputs and assumptions made based on an event stream
(Riera et al., 2004). However, in rfMRI, the investigation of the hemodynamic response
becomes problematic as there are no explicit inputs where the HRF can be modeled too,
unless when relying on some specific physiological hypothesis (Havlicek, Friston, Jan,
Brazdil, & Calhoun, 2011).
A new methodology proposed by Wu and colleagues in 2013 was able for the first
time to overcome this problem and retrieve the HRF at rest for every voxel in the brain.
Their blind HRF retrieval methodology was initially designed to deal with the
confounding effect the hemodynamic response has on temporal precedence in lagged
models of directed connectivity by GC described above (David et al., 2008). Their
approach to retrieve the HRF at rest is based on the idea that resting-state BOLD spikes
in the signal can be seen as the response to spontaneous neuronal events. This idea is
supported by increasing evidence that non-random spontaneous events regulate the
dynamics of the brain at rest (Deco & Jirsa, 2012; Petridou, Gaudes, Dryden, Francis, &
Gowland, 2013). By applying a point-by-point analysis (PPA) these spontaneous events
can be captured from typically relatively large fluctuations in BOLD amplitude
(Tagliazucchi et al., 2010, 2011, 2012). Because these detected spontaneous BOLD
point process events are assumed to be induced by the spontaneous neural events, it is
possible to retrieve the HRF of the spontaneous neural events at rest. The shape of the
retrieved hemodynamic response at rest is characterized by the canonical HRF with its
temporal and dispersion derivatives and is estimated by three parameters, namely
response height, time to peak (T2P) and Full Width at Half Maximum (FWHM). These
parameters could be interpreted in terms of potential measures for response magnitude,
latency and duration of neuronal activity (Lindquist & Wager, 2007).
The blind HRF retrieval method originally designed to deal with confounding
effects of the hemodynamic response in effective connectivity methods also allows the
study of functional segregation in rfMRI data by investigating the retrieved HRF, which
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depicts the local features of the spontaneous neuronal activity. Additionally, just as in
functional integration where retrieved RSNs are used as biomarkers, the HRF in rest can
also be used as a biomarker as it has been shown that the HRF in rest is modulated by
pathophysiological states such as chronic pain (Wu & Marinazzo, 2015a) and
consciousness (Wu et al., 2015b, 2015c).
Current study
In the current study, we will make use of the blind HRF retrieval method developed
by Wu and colleagues in 2013, which will allow the investigation of the HRF at rest in
rfMRI data. As already described earlier the HRF is not a homogenous process but has
much variability to it across brain regions, as a consequence of multiple factors
including neural activity differences, global magnetic susceptibilities, vascular
differences, baseline cerebral blood flow, and slice timing differences (Handwerker et
al., 2004). The goal of this study is to explore the inhomogeneity of the HRF further, as
we want to investigate if there is variability in the parameters of the HRF at rest. And
even further, if the observed variability can be related to measures of brain functioning.
To obtain this goal, we performed independent correlation analyses between the
estimated parameters of the HRF and four measures: (a) age, (b) morphology of the
mid-cingulate cortex, (c) handedness and (d) motor task performance. There's evidence
that these measures regulate the brain activity during cognitive tasks. If we find any
correlations between these measures and the HRF parameters at rest, it would mean they
regulate the hemodynamics during spontaneous activity.
Materials and Methods
Data
Resting-state data. The data set used in this work is from the open-source data set
the Human Connectome Project (http://www.humanconnectome.org). The Human
Connectome Project (HCP) aims to map the human brain connectivity and function as
accurately as possible by scanning a healthy population of 1200 adults using cutting-
edge methods of noninvasive neuroimaging techniques (Van Essen et al., 2013). The
four main neuroimaging techniques that are used to acquire data are: diffusion MRI
12
(dMRI), resting-state fMRI (rfMRI), task-based fMRI (tfMRI) and structural MRI.
Additionally, behavioral data is gathered to capture individual differences in cognition,
perception, demographics and personality. The behavioral data is collected to
investigate how it covaries in interesting ways with brain connectivity and function.
In this study we used data from the HCP 500 subjects release, which includes the
release of behavioral and 3 Tesla (T) Magnetic Resonance (MR) imaging data from
over 500 healthy adult participants. As our study focuses on rfMRI, we selected a subset
of 92 healthy subjects for whom rfMRI datasets and demographic information (age, sex,
handedness) were available (n=92; 55 females, age 22-35 with mean 29). rfMRI scans
were acquired through whole-brain echo-planar imaging (EPI) using a 32 channel head
coil on a modified 3T Siemens Skyra with a repetition time (TR) of 720 milliseconds,
an echo time (TE) of 33.1 milliseconds, 72 slices per volume, acquiring 8 slices
simultaneously using a multi-band (mb) acceleration factor of 8 and 2.0 millimeter
isotropic voxels (the same size for voxels in each dimension). For more detailed
information on the HCP scanning acquisition and rfMRI scanning protocols see Uğurbil
et al. 2013, and Smith et al. 2013 respectively.
Behavioral data. The available behavioral data measures are used to explore the
relationship between the spontaneous activation at rest and measures that have shown to
modulate the brain during function. Three behavioral data measures were selected and
available from the HCP database. Additionally, one measure of the morphology cortex
of the mid-cingulate cortex was self-identified.
(a) Age: the exact age of subjects was used in this study as a continuous
variable. The age range from the selected subjects goes from 22 to 35.
(b) Handedness: handedness was assessed using the Edingburgh Handedness
questionnaire (EHI; Oldfield, 1971). Handedness using the Edinburgh scores
goes from a continuous scale from -100 (strongest left-handed) to 100 (strongest
right-handed) and was used in the study as a continuous variable.
(c) Morphology of the mid-cingulate cortex: the mid-cingulate cortex shows
structural variability. Depending on the structure, subjects could have a
paracingulate sulcus and a cingulate sulcus (pcgs/cgs complex) or only a
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cingulate sulcus (cgs) in the mid-cingulate cortex (Petrides, 2012). The
morphology measure was used as a discrete variable in the study as subjects
were divided in four categories according to the morphological organization of
their mid-cingulate cortex. The categories were the following: (1) subjects with
cgs in both hemispheres, (2) subjects with a cgs and pcgs in both hemispheres,
(3) subjects with a cgs in the left hemisphere and a cgs/pcgs complex in the right
hemisphere, and (4) subjects with a cgs/pcgs complex in the left hemisphere and
a cgs in the right hemisphere.
(d) Motor performance task: a measure of Dexterity (9-hole pegboard task) from
the NIH toolbox was used as an indication of motor performance
(http://www.nihtoolbox.org/). This simple test of manual dexterity records the
time required for the participant to accurately place and remove 9 plastic pegs
into a plastic pegboard. The protocol includes 1 practice and 1 timed trial with
each hand. Raw scores are recorded as time in seconds that it takes the
participant to complete the task with the dominant hand. The variable was used
in this study as a continuous variable.
Preprocessing
In this project, we used the available preprocessed rfMRI data of the HCP. The
functional raw BOLD time series have gone through optimal preprocessing pipelines
specifically designed by the HCP for the collected data. In the first "spatial (minimal)
preprocessing" pipeline issues such as movement of the head of the participant are dealt
with, while the "temporal preprocessing" pipeline aims to remove confounds such as
structured non-neuronal artifacts (cardiac pulsations). Furthermore, we applied two
more preprocessing steps on the result images of the temporal pipeline, as the HCP
takes a more minimal approach to the removal of aspects of data. Only aspects of the
data that are verified as artifacts with strong specificity are removed while a more
minimal approach to removal of ambiguous aspects is taken (Smith et al., 2013). For
example, we apply an additional band-pass filter (filtering out high frequencies of the
data) to the resting state BOLD time series, while the HCP does not apply temporal
low-pass filtering as they consider high frequencies to not only contain artifacts but also
important relevant data. In the following section the preprocessing pipelines (spatial and
14
temporal) and additional preprocessing steps are briefly touched. For a more thorough
overview of the pre-processing pipelines of the HCP, view Glasser et al. (2013) for a
detailed overview of the spatial preprocessing pipeline and Smith et al. (2013) for an
overview of the temporal preprocessing pipeline. The HCP data analysis pipelines are
primarily built on using tools from FMRIB Software Library (FSL; Jenkinson,
Beckmann, Behrens, Woolrich, & Smith, 2012) and FreeSurfer (Fischl, 2012). While an
additional detrending preprocessing step is performed in the MATLAB extension
Statistical parametrical mapping (SPM12; Friston et al., 1994c) and a band-pass filter
preprocess step is performed using the Resting-State Data Analysis Toolkit (REST;
Song et al., 2011).
Spatial preprocessing pipeline.
Gradient Unwarping. By the use of passing current through the gradient coils (x-, y-
, and z-gradients) of an fMRI scanner, a secondary magnetic field is created. This
gradient field slightly distorts the main magnetic field in a predictable pattern in the x-,
y- or z-direction. The function of these gradients is therefore to allow spatial encoding
of the signal. Ideally, these gradients should produce an incremental magnetic field
whose intensity varies linearly with distance from the magnet isocenter (the center of
the magnetic field, the origin of the coordinate system (x,y,z) = (0,0,0)). However, this
is not the case, and gradient linearity falls off significantly the farther one gets from the
isocenter. This causes non-linear gradient distortions in the signal which can cause
spatial distortions in the images. These spatial distortions are corrected by using a
gradient unwarping correction technique, which calculates a warp field that represents
the spatial distortions of the images. In the HCP gradient unwarping is done with a
customized version of the gradient_nonlin_unwarp package available in FreeSurfer
(Jovicich et al., 2006).
Realignment. Subjects do not lie still in the scanner but will move their heads, and
this motion can have drastic effects on the fMRI data. The goal of the realignment, or
motion correction, is to reduce the misalignment between the images in an fMRI time
series that occur due to head motion. Motion correction models assume that the shape of
the head cannot change during scanning, but that the position of the head can change.
Thus, head motion is a problem of rigid body transformation that can be described by
15
translation or rotation along each of the three axes (Poldrack et al., 2011). Each image in
the fMRI time series is co-registered to a reference scan using an image registration
method, and later the images are resliced to create realigned versions of the original
images. In the HCP motion correction is accomplished using the FSL FLIRT tool
(Jenkinson, Bannister, Brady, & Smith, 2002) with a 6 degrees of freedom (3 translation
and 3 rotation parameters) registration to the single band image as the reference image
(SBRef). The SBRef is a scan that is acquired at the beginning of the multi-band rfMRI
data run collection without acquiring multiple slices at the same time. The SBRef is
preferred as the reference scan for motion correction, as it will have a superior signal-to
noise-ratio (SNR) compared to the multi-band time series collected with short TR's
(Smith et al., 2013).
EPI Distortion Correction. Next to the earlier described non-linear gradient
distortions that arise in the images, another source of spatial distortions is found with
the use of the common fMRI acquisition method gradient-echoplanar imaging (EPI).
These spatial distortions are most prominent near regions where air and tissue meet,
such as sinuses or ear canals. These artifacts are due to the inhomogeneity of the main
magnetic field (B0) and occur along the phase of encoding direction that is used by the
pulse sequence, which is generally the Y (anterior-posterior) axis (Poldrack et al.,
2011). The distortions make it difficult to align fMRI images with structural images and
therefore need to be resolved to correct possible errors in the location of structures. This
distortion can be corrected with a pair of spin echo scans with opposite phase encoding
directions (e.g. top-down and bottom-up). This results in two images with identical
magnitude distortions in opposite directions. An estimation of the distortion field can
then be made and used to undistort the fMRI images. In the HCP, this correction is done
by calculating a distortion field from the spin echo scans using the FSL toolbox "topup"
(Andersson, Skare, & Ashburner, 2003).
Spatial Normalization. Even though the human brain shows remarkable consistency
in structure across individuals, it can vary widely in its size and shape. If we want to be
able to compare brains across subjects, they all need to be transformed, so they are
aligned with one another, reducing the substantial variability between structures across
individuals. Images are warped into a standard brain atlas allowing meaningful group
16
analyses. This process of spatially transforming data in a common space for analysis is
known as spatial normalization. In the HCP individual brains are nonlinearly warped
into the MNI 152 template using FSL tool FNIRT. The Montreal Neurological Institute
(MNI) template is derived from 152 structural images, which are averaged together after
high-dimensional nonlinear registration into the common MNI152 co-ordinate system.
Intensity Normalization. In the last step all the transformations (gradient unwarping,
realignment, EPI distortion and spatial normalization) are concatenated into a single
nonlinear transformation that can be applied to the raw fMRI BOLD time series in a
single spline interpolation step. Every frame from the raw fMRI BOLD time series is
transformed into MNI space and corrected for the above described problems. Finally to
be able to better compare across voxels the data are normalized to a global mean for the
whole brain. The resulting images of this spatial preprocessing pipeline are then entered
through the temporal preprocessing pipeline taking care of non-neuronal artifacts in the
data.
Temporal preprocessing pipeline.
The output of the rfMRI images from the spatial preprocessing pipeline is entered
through another temporal preprocessing pipeline. This additional temporal pipeline is
particularly important for resting-state analyses, which rely fundamentally on
correlations between different voxels' time series, as these can be corrupted by artifacts
that span across multiple voxels (Smith et al., 2013). The main focus is to remove
strong structured non-neuronal artifacts out of the data. A promising approach involving
the application of independent component analysis (ICA) is used to identify and remove
these structured components from the spatial preprocessed data. The MELODIC tool
from FSL (Beckmann & Smith, 2004) is used to decompose the data in multiple
components with each component having a spatial map and an associated time course.
These components are said to represent artifacts such as cardiac pulsation, while others
can represent important neuronal-related information. To be able to classify the
components that have to be regressed out of the data, a new tool called 'FIX' (FMRIB's
ICA-based X-noiseifier; Salimi-Khorshidi et al., 2014). is used to automatically classify
the components in "bad" versus "good". The unique variance associated with the bad
components is then regressed out of the data. Also part of this cleanup is the removal of
17
time series derived from the motion estimation parameters (see section realignment),
these are regressed out of the data aggressively as they don't contain any variance of
interest (Smith et al., 2013). This means that all the variance associated with these
components is regressed out completely, and not only the unique variance associated
with the components.
Additional preprocessing.
As mentioned previously the HCP takes a minimalistic approach to the removal of
data aspects. Therefore we added two additional preprocessing steps on the temporal
preprocessed data before applying the blind HRF retrieval method. In a first step we
band-pass filter the data while in a second step the data is detrended.
Filtering. A filtering step is used to remove unwanted temporal frequencies from the
time series. Usually filtering is applied to remove noise or to remove variability in a
range of temporal frequencies that are a priori not of interest. We are interested in
detecting spontaneous events in the BOLD signal in rfMRI that govern the brain
dynamics at rest. Evidence suggest that these spontaneous fluctuations in the BOLD
signal are represented in the low frequencies ranges (<0.1 Hz) (Cole, Smith &
Beckmann, 2010; Fox & Raichle, 2007). As a result, a band-pass filter from 0.01 Hz to
0.1 Hz is applied. Thus, only frequencies in this range of the time series pass the filter
and all other frequencies are rejected for further use.
Detrend. fMRI time series can contain some noise that manifests itself as gradual
changes in the BOLD signal over the course of the session. Detrending seeks to
attenuate these non-neuronal fluctuations in the BOLD signal that are not of interest.
These "drifts" are modeled and removed from the time series.
Spontaneous event and HRF retrieval
Until recently, no method existed to retrieve the HRF from rfMRI. Here we
employed a blind-HRF retrieval method technique specially developed for resting-state
BOLD-fMRI signal, to reconstruct the canonical HRF with its temporal and dispersion
derivatives from the resting-state BOLD fMRI time series (Wu et al., 2013, 2015b). The
method assumes that there are spikes in the rfMRI BOLD signal that reflect
18
'spontaneous point events' who drive the rfMRI BOLD signal. There's growing evidence
that these spontaneous events in the BOLD signal regulate the dynamics of the brain at
rest (Deco & Jirsa, 2012; Petridou et al., 2013). By applying a point-by-point analysis
(PPA) these spontaneous events can be captured from typically relatively large
fluctuations in BOLD amplitude (Tagliazucchi et al., 2010, 2011, 2012). These large
resting-state BOLD spikes/transients are defined as the time points exceeding a given
threshold µ around a local peak, which are detected by the PPA, where µ = (i.e.
Standard Deviation of BOLD signal, 1 in this study). As the peak of the BOLD signal lags
behind the peak of the underlying neuronal activity (i.e. k seconds), it is reasonable to
assume that the BOLD spikes are generated from the spontaneous point process events.
If this is true, a HRF can be retrieved if the onsets of the BOLD spikes are adjusted to
the onsets of the underlying neuronal events. To adjust the onsets to the underlying
neuronal events, there is a time lag estimation to obtain an optimal value for k. After k is
estimated the onsets are adjusted to the onsets of the underlying neuronal events. In the
last step a GLM is fitted to the stored onsets of the neuronal events and the canonical
HRF with its temporal and dispersion derivatives to retrieve an HRF for every voxel. As
a result, the HRF elicited by spontaneous point events is characterized by three
interpretable parameters of the HRF that were estimated. Namely response height, time
to peak (T2P), Full Width at Half Maximum (FWHM), which could be interpretable in
terms of potential measures for response magnitude, latency and duration of neuronal
activity (Lindquist & Wager, 2007). The MATLAB code for the blind HRF retrieval
method is publicly available at http://users.ugent.be/dmarinaz/code.html.
The procedure described above is sketched in Figure 4.
Statistical Analysis
After estimating the parameters for the HRF in rest, the relationship between the
parameters and behavioral measures can be investigated. The HRF parameters (Height,
T2P, FWHM) for each subject were entered individually into a voxel-based multiple
regression analysis in SPM 12 to investigate linear correlations with (a) age, (b)
morphology of the mid-cingulate cortex, (c) handedness and (d) motor task
performance. Type I error due to multiple comparisons across voxels was controlled by
familywise error rate (FWE; p-value<0.05)
19
Results
Spatial distribution maps of resting HRF parameters
The HRF parameters at rest for every voxel in the brain are estimated and mapped on
a brain template. The mean maps across 92 subjects of the parameters response height,
T2P and FWHM, are plotted in figure 5, with values normalized between 0 and 1. The
retrieved HRF at rest exhibits spatial heterogeneity in all three estimated parameters.
Figure 4. Scheme of the resting state HRF retrieving procedure (figure adapted from Wu & Marinazzo, 2015b).
20
Axial Coronal Sagital
Response
Height
FWHM
T2P
Figure 5. Spatial distribution maps of resting HRF parameters.
21
Correlation between HRF and Age
Several studies have already compared the relationship between the HRF and age
using task-based fMRI (Buckner et al., 2000; Mohtasib et al., 2012; Morsheddost,
Asemani, & Shalcy, 2015). These studies all investigated whether there are differences
in the shape of the HRF between young adults and elderly people. For example,
Morsheddost et al. (2015) found that with a simple visual and motor task, the HRF peak
amplitude of the motor cortex was significantly larger for young adults compared with
elderly ones while this was not the case for the visual cortex. From these earlier
observations that suggest age regulates brain function during tasks, we explored if age
also regulates the brain dynamics during spontaneous activity.
Age as a continuous variable was used to investigate the correlations with HRF
parameters at rest. In a first multiple regression, age was used as a covariate in a whole
brain analysis applied independently to all parameters. The multiple regression did not
reveal that age was correlated with the HRF parameters in the brain. In a second
multiple regression, age was used as a covariate of interest with additional covariates of
no interest (gender & handedness). This regression showed as well that age was not
correlated with the HRF parameters in the brain. Based on the observation that task-
based fMRI studies describe that age modulates the HRF in motor and visual areas, a
final multiple regression was run on the visual network (VN) and somatomotor (SMN)
functional networks of Yeo et al. (2011) obtained at rest. In the multiple regression, age
was used a covariate, with additional covariates of no interest (gender, handedness) and
applied independently on the VN and SMN. The regression revealed that age was not
correlated with the HRF parameters in either the VN or SMN. Based on the
observations that the HRF at rest does not correlate with age, we conclude that age does
not seem to regulate the hemodynamics during spontaneous activity (Table 1).
22
Applied to
Multiple Regression (Age)
HRF
Height T2P FWHM
Whole Brain - - -
Whole Brain* - - -
VN* - - -
SMN* - - -
Correlation between HRF and Morphology of the mid-cingulate cortex
Subjects show structural variability in the mid-cingulate cortex. Depending on the
local morphology there might be a double cingulate sulcus in the mid-cingulate cortex
in one of the hemispheres. If this is the case, the dorsal sulcus is referred to as the
paracingulate sulcus (pcgs) and the ventral sulcus as the cingulate sulcus (cgs), the latter
being located close to the corpus callosum (Petrides, 2012; Figure 6). Amiez and
colleagues in 2013 showed that depending on the local morphology of the mid-cingulate
cortex in subjects; there was a difference in feedback-related activity in the human
brain. Activity in the mid-cingulate cortex was strongly related to the particular
morphology of the cingulate and paracingulate complex. When a pcgs was present,
feedback-related activity was located in the pcgs and not in the cgs, but when the pcgs
was absent, feedback-related activity was located in the cgs. Based on the observation
that the morphology of the mid-cingulate cortex regulates the brain during function, we
explored if the morphology also regulates the brain dynamics during spontaneous
activity.
Note. *indicates extra covariates of no interest in the regression (gender & handedness);
- indicates no correlation. FWE corrected, p-value<0.05.
Table 1. Results of Age - HRF parameters correlations.
23
Morphology of the mid-cingulate cortex was used a discrete variable to investigate
the correlations with HRF parameters at rest. The categories of the variable were the
following: (1) subjects with cgs in both hemispheres, (2) subjects with a cgs and pcgs in
both hemispheres, (3) subjects with a cgs in the left hemisphere and a cgs/pcgs complex
in the right hemisphere, and (4) subjects with a cgs/pcgs complex in the left hemisphere
and a cgs in the right hemisphere. In a first multiple regression, the morphology of the
mid-cingulate cortex was used as a covariate of interest with the above described four
levels in a whole brain analysis with additional covariates of no interest (gender, age,
handedness) applied independently to all parameters. The multiple regression did not
reveal that the morphology of the mid-cingulate cortex was correlated with the HRF
parameters in the brain.
Figure 6. Structural variability in the mid-cingulate cortex (figure adapted from Shackman et al., 2011).
2011)
24
As a follow-up analysis, the same multiple regression with morphology of the mid-
cingulate cortex as covariate of interest and covariates of none interest (gender, age,
handedness) but now only limited to the mid-cingulate cortex region was done
independently on the left and right hemisphere. As a result the covariate of morphology
of the mid-cingulate cortex now had two levels, that was different for the regression for
the left hemisphere and right hemisphere. The covariate of morphology of the mid-
cingulate cortex now had two levels: (1) subjects with a cgs in the hemisphere, (2)
subjects with the pcgs and cgs complex in the hemisphere. The multiple regression
applied to the right and left hemisphere did not reveal that the morphology of the mid-
cingulate cortex was correlated with the HRF parameters in the brain. Based on the
observations that the HRF at rest does not correlate with the morphology of the mid-
cingulate cortex, we conclude that the morphology of the mid-cingulate cortex does not
seem to regulate the hemodynamics during spontaneous activity (Table 2).
Applied to
Multiple Regression (Morphology mid-cingulate cortex)
HRF
Height T2P FWHM
Whole Brain* - - -
Left Hemisphere** - - -
Right Hemisphere** - - -
Note. * indicates covariate of interest with four levels; ** indicates covariate of interest with
two levels ; - indicates no correlation. FWE corrected, p-value<0.05
Table 2. Results of Morphology mid-cingulate cortex - HRF parameters correlations.
25
Correlation between HRF and Handedness
According to the body specificity hypothesis (BSH) how subjects their bodies
interact with their physical environments in systematically different ways should have
an influence on the brain (Casasanto, 2009). Based on this hypothesis subjects who
prefer one hand over the other, right- or left-handers, should show different brain
functioning during cognitive tasks. Several studies have confirmed that handedness
regulates brain functioning during several experimental paradigms. Tasks such as motor
imagery (Willems, Toni, Hagoort, & Casasanto, 2009), action verb understanding
(Willems, Hagoort, & Casasanto, 2010), emotional valance (Casasanto, 2009) and
effective motivation (Brookshire & Casasanto, 2012) show body specific brain
activation according to subjects their handedness. These studies mainly showed that the
sensorimotor network (SMN), dorsal attention network (DAN), and visual network
(VN) are regulated by handedness during cognitive tasks. Based on this evidence that
suggests that handedness shapes brain functioning during tasks, we explored if
handedness also regulates the brain dynamics during spontaneous activity.
The Edinburgh score as a continuous scale of handedness was used to investigate the
correlations with HRF parameters at rest. In a first multiple regression, handedness was
used as a covariate in a whole brain analysis applied independently to all parameters.
The multiple regression did not reveal that handedness was correlated with the HRF
parameters in the brain. In a second multiple regression, handedness was used as a
covariate of interest with additional covariates of no interest (gender & age). This
regression showed as well that handedness was not correlated with the HRF parameters
in the brain. Based on earlier evidence that suggest handedness regulates in specific
networks, the same multiple regression was independently applied on the SMN, DAN
and VN functional networks obtained at rest from Yeo et al. (2011). The regression
revealed that handedness was not correlated with the HRF parameters in either the
SMN, DAN or VN. Based on the observations that the HRF at rest does not correlate
with handedness, we conclude that handedness does not seem to regulate the
hemodynamics during spontaneous activity (Table 3).
26
Applied to
Multiple Regression (Handedness)
HRF
Height T2P FWHM
Whole Brain - - -
Whole Brain* - - -
VN* - - -
SMN* - - -
DAN* - - -
Correlation between HRF and Motor task performance
In an exploratory analysis we investigated whether a motor task performance on
dexterity taken from the NIH toolbox regulates the brain dynamics during spontaneous
activity. The motor performance score as a continuous scale was used to investigate the
correlations with HRF parameters at rest. In a first multiple regression, motor task
performance was used as a covariate in a whole brain analysis applied independently to
all parameters. The multiple regression did not reveal that motor task performance was
correlated with the HRF parameters in the brain. In a second multiple regression, motor
task performance was used as a covariate of interest with additional covariates of no
interest (gender, age and handedness). As a final regression, based on the fact that it's a
measure of motor performance, the same multiple regression was applied on the SMN
network obtained at rest from Yeo et al. (2011). This regression showed as well that
motor task performance was not correlated with the HRF parameters in the brain. Based
on the observations that the HRF at rest does not correlate with motor task performance
Note. * indicates extra covariates of no interest in the regression (gender & age); - indicates no
correlation. FWE corrected, p-value<0.05
Table 3. Results of Handedness - HRF parameters correlations.
27
here as a proxy for dexterity, we conclude the measure does not seem to regulate the
hemodynamics during spontaneous activity (Table 4).
Applied to
Multiple Regression (motor task performance)
HRF
Height T2P FWHM
Whole Brain - - -
Whole Brain* - - -
SMN* - - -
Discussion and Conclusion
The aim of this work was to use the blind HRF retrieval method from Wu et al.
(2013), to retrieve the HRF at rest for every voxel in the brain to study the
inhomogeneity of the HRF at rest, and how this is related to functional segregation in
rfMRI. In a first step, the HRF at rest for every voxel in the brain was retrieved, and the
mean maps of the corresponding parameter estimates were mapped on a brain template.
As expected by work studying the HRF during task-based fMRI (Handwerker et al.,
2004, 2012), the HRF shows variability across the brain in all three parameters (see
figure 5). In order to investigate whether the variability was related to behavioral
measures that have been shown to regulate brain functioning during cognitive tasks,
correlational analyses were performed between the estimated HRF parameters and the
behavioral measures. Age, the morphology of the mid-cingulate cortex, handedness, and
motor task performance measures that are related to modulating brain functioning
during cognitive tasks were correlated with the three estimated parameters (Height,
Table 4. Results of Motor task performance - HRF parameters correlations.
Note. * indicates extra covariates of no interest in the regression (gender, age and handedness); -
indicates no correlation. FWE corrected, p-value<0.05
28
T2P, FWHM) in separate multiple regressions. Results indicated that none of the
measures were correlated with the parameters of the HRF (see Table 1, 2, 3, 4).
Suggesting that the observed HRF variability is not explained by the selected behavioral
measures. Moreover, this means that these behavioral measures do not regulate the
hemodynamics during spontaneous activity observed in rfMRI. Additional correlational
analyses with different measures than the ones employed here might show correlations
with the HRF parameters. For example, Wu and Marinazzo (2016), showed that heart
rate variability (HRV) a popular non-invasive method for assessing the activity in the
autonomic nervous system (ANS) correlates with the HRF parameters retrieved at rest,
especially in the brainstem. Suggesting that the ANS modulates the hemodynamics of
the brainstem at rest. In this study, we were not able to verify the results of Wu and
Marinazzo (2016) as the used HCP dataset does not provide measures of cardiac
activity.
Additionally, some limitations in the study might have prevented to find correlations
between the behavioral measures and the HRF parameters. The task-based fMRI studies
which investigated the differences in the HRF with increasing age were all based on the
comparison between young adults and elderly people (Buckner et al., 2000; Mothasib et
al., 2012; Morsheddost et al., 2015). The age of the HCP data subjects ranges from 21-
36 while the task-based fMRI studies compared groups of young adolescents (age 20-
40) versus elderly people (age 67+). As a result, it might not be that surprising no
correlations were found between age and the retrieved HRF parameters. Additional
research with other fMRI datasets that include young and elderly people is needed to
test the hypothesis that age modulates spontaneous brain dynamics.
Furthermore, the discrete variable for the morphology of the mid-cingulate cortex
were subjects were divided into a level according to their morphology of the region was
based on non-expertise knowledge of the region in question. Identifying if subjects have
an additional paracingulate sulcus is sometimes difficult as there are different patterns
possible that indicate when an additional paracingulate sulcus is present (see Amiez et
al., 2013). Ideally, a collaboration with experts in the field of the anatomy of this region
would have been more suitable to investigate the relationship between the morphology
of the mid-cingulate cortex and the estimated HRF parameters.
29
For the selected measure handedness, even though we had selected a sample of 92
subjects only few could be considered left-handed (Edinburgh score smaller than 0).
This could have potentially influenced the correlations, as it might not have been a
sufficient sample size to test the correlation between handedness and the HRF
parameters. As a result, further research with a more representative sample size of left-
handed subjects might give different results. At the start of the study not all handedness
scores were available for the 500 subjects, we selected subjects that already had
available handedness scores. With the new HCP data releases additional information
might have become available and might give different results.
The last limitation might be due to the stance that the HCP takes towards
preprocessing the data. As mentioned in the section preprocessing, the HCP has a
minimal stance on preprocessing the data. Meaning that only signal is removed when
there is strong evidence for it that it is an artifact, whereas ambiguous signals are not
removed from the data. Wu and Marinazzo (2016) suggest that motion artifacts might
influence the point-process events that are selected during the point-by-point analysis
used in the blind HRF retrieval method. As a result, pseudo-point processes events
might be induced by motion artifacts and selected as the process events that are used to
retrieve and estimate the HRF. Even though the motion-estimates acquired from the
realignment preprocessing step are regressed out of the data (see section temporal
preprocessing pipeline), additional motion-related signal influencing the point-process
event selection might still be in the data due to the minimal preprocessing approach
from the HCP. One way to remove extra motion-related signal that is not controlled for
by realignment is using a scrubbing method (Power, Barnes, Snyder, Schlaggar, &
Petersen, 2012). Thus, in a future step, this effect of remaining motion-signal in the data
could be tested by doing an additional scrubbing preprocess step on the HCP data,
retrieve the HRF at rest from the new preprocessed data and rerun the correlational
analyses between the estimated HRF parameters and the behavioral measures.
In conclusion, this study was meant to be an exploratory study capitalizing on the
opportunity of the blind retrieval HRF method developed by Wu et al. (2013) to study
functional segregation in rfMRI through the retrieved HRF at rest. Based on the
30
obtained results we conclude that the selected behavioral measures (age, handedness
and morphology of the mid-cingulate cortex) do not seem to explain the observed
variability in the HRF parameters and thus do not modulate the hemodynamics during
spontaneous activity. However, other measures such as the HVR employed by Wu and
Marinazzo (2016) might explain the observed variability, and regulate the brain
dynamics at rest. Additionally, some limitations to this study were discussed that might
have influenced the obtained results and some improvements that could be tested in the
future. Further research with other datasets, improved preprocessing, and other
behavioral measures are needed to investigate the relationship between behavioral
measures and spontaneous brain activity.
31
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