Functional MRI User Guide
Functional MRI User's Guide
Michael A. Yassa
● The Division of Psychiatric Neuroimaging ●
● Department of Psychiatry and Behavioral Sciences ●
●
The Johns Hopkins School of Medicine ●
● Baltimore, MD ●
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Document written in OpenOffice.org Writer 2.0 by Sun Microsystems
Publication date: June 2005 (1st edition)
Online versions available at http://pni.med.jhu.edu/intranet /fmriguide/
Acknowledgments:
This document relies heavily on expertise and advice from the following individuals
and/or groups: John Ashburner, Karl Friston, and Will Penny (FIL-UCL: London), Kalina
Christoff (UBC: Canada), Matthew Brett (MRC-CBU: Cambridge), and Tom Nichols
(SPH-UMichigan, Ann Arbor). Some portions of this document are adapted or copied
verbatim from other sources, and are referenced as such.
Supplemental Reading:
Frackowiak RS, Friston K, Frith C, Dolan RJ, Price CJ, Zeki S, Ashburner J, & Perchey G
(2004). Human Brain Function, 2
nd
edition, Elsevier Academic Press, San Diego, CA.
Huettel SA, Song AW, McCarthy, G. (2004) Functional Magnetic Resonance Imaging.
Sinaur Associates, Sunderland, MA.
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Table of Contents
Magnetic Resonance Physics..............................................................................6
How the MR Signal is Generated.............................................................................6
The BOLD Contrast Mechanism..............................................................................8
Hemodynamic Modeling.........................................................................................10
Signal and Noise in fMRI........................................................................................12
Thermal Noise.......................................................................................................12
Cardiac and respiratory artifacts............................................................................12
N/2 Ghost..............................................................................................................12
Subject motion.......................................................................................................12
Draining veins........................................................................................................13
Scanner drift..........................................................................................................13
Susceptibility artifacts............................................................................................13
Experimental Design...........................................................................................14
Cognitive subtractions ...........................................................................................14
Cognitive Conjunctions...........................................................................................14
Parametric Designs................................................................................................14
Multi-factorial Designs............................................................................................15
Optimizing fMRI Studies.........................................................................................15
Signal Processing..................................................................................................15
Confounding Factors.............................................................................................15
Control task............................................................................................................16
Latent (hidden) factor.............................................................................................16
Randomization and Counterbalancing...................................................................16
Nonlinear Hemodynamic Effects............................................................................16
Epoch (Blocked) and Event-Related Designs .......................................................17
Spatial and Temporal Pre-Processing...............................................................18
Overview................................................................................................................18
Raw Data ...............................................................................................................18
Getting Started.......................................................................................................18
Requirements.........................................................................................................19
Hardware Requirements........................................................................................19
Software Requirements.........................................................................................19
Software Set-up......................................................................................................19
The SPM Environment...........................................................................................20
Data Transfer from Godzilla...................................................................................20
Volume Separation and Analyze headers .............................................................21
Buffer Removal.......................................................................................................24
Slice Timing Correction (For event-related data)...................................................24
To Correct or Not to Correct..................................................................................24
Philips Slice Acquisition Order...............................................................................25
Which Slice to Use as a Reference Slice...............................................................25
Timing Parameters.................................................................................................26
Rigid-Body Registration (Correction for Head Motion)...........................................26
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Creating a Mean Image.........................................................................................26
Realignment...........................................................................................................27
Anatomical Co-registration (Optional)....................................................................29
Co-registering Whole Brain Volumes.....................................................................30
Co-registering Partial Brain Volumes.....................................................................30
Spatial Normalization to Standard Space..............................................................30
Correcting Scan Orientation..................................................................................31
Normalization Defaults...........................................................................................31
Normalization to a Standard EPI Template............................................................32
Gaussian Smoothing.............................................................................................33
Summary of Pre-processing Steps........................................................................34
Statistical Analysis using the General Linear Model.......................................35
Modeling and Inference in SPM.............................................................................35
Model Specification and the SPM Design Matrix ..................................................35
Setting Up fMRI Defaults.......................................................................................36
Model Specification................................................................................................36
Estimating a Specified Model................................................................................39
Global Intensity Normalization................................................................................40
Temporal Filtering...................................................................................................40
Results and Statistical Inference............................................................................42
Contrast Specification............................................................................................42
Thresholding and Inference ..................................................................................43
Rejecting the Null Hypothesis.................................................................................43
Type I Error (Multiple Comparison Correction).......................................................44
Spatial Extent Threshold (Cluster analysis) ...........................................................46
Viewing Results using Maximum Intensity Projection ...........................................46
Small Volume Correction and Regional Hypotheses.............................................48
Extracting Results and Talairach Labeling.............................................................48
Time-Series Extraction and Local Eigenimage Analysis .......................................49
Plotting Responses and Parameter Estimates......................................................50
Anatomical Overlays..............................................................................................53
Editing, Printing and Exporting SPM output...........................................................55
Region of Interest (ROI) Analyses......................................................................56
Anatomical vs. Functional ROIs ............................................................................56
MarsBaR (MARSeille Boîte À Région d'Intérêt) ....................................................57
Overview of the Toolbox........................................................................................57
ROI Definition........................................................................................................57
Running an ROI Analysis ......................................................................................59
Group-Level Analysis and Population-level Inferences...................................63
Inter-subject Analyses............................................................................................63
Fixed-Effects Analysis............................................................................................63
Random-Effects Analysis.......................................................................................64
Conjunction Analysis .............................................................................................66
Nonparametric Approaches....................................................................................67
False Discovery Rate.............................................................................................68
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Special Topics.....................................................................................................68
Cost Function Masking for Lesion fMRI.................................................................68
Advanced Spatial Normalization Methods.............................................................69
Using a Subject-Specific HRF in analysis .............................................................70
Guidelines for Presenting fMRI Data......................................................................73
5
Magnetic Resonance Physics
How the MR Signal is Generated
The magnetic resonance (MR) signal arises from hydrogen nuclei, which are the only
dipoles abundant enough to be measured with reasonably high spatial resolution. The human
body is made up mostly of water (mainly hydrogen atoms). Hydrogen atoms possess a
magnetic property called spin which can be thought of as a small magnetic field. Spin is a
fundamental property of some nuclei (not all nuclei possess spin) and has two important
parameters: (1) size; spin comes in multiples of ½ and (2) charge; spin can be positive or
negative. Paired opposite-charged particles, e.g. protons and electrons can eliminate each
other's spin effects. An unpaired proton (e.g. in the case of hydrogen) has a spin of +½.
In an external magnetic field, a particle with non-zero spin will experience a torque which
aligns the particle with the field, by precessing (wobbling) around
the magnetic field axis (see figure on the left). The particle develops
an angular momentum, which is empirically related to its
gyromagnetic ratio (γ) (the ratio of the magnetic dipole moment to
the angular momentum of the particle). This value is unique to the
nucleus of each element (For Hydrogen, γ = 42.58 MHz/T). The
value's derivation is too complex to explain here. Instead we will
describe its relationship to the precession angular frequency (ω)
of a proton. Angular frequency is a scalar measure of how fast a
particle is rotating around an axis (see figure on the right)
ω
Larmor
The above is known as the Larmor Equation named
after Joseph Larmor, an Irish physicist (1857-1942). It
describes the relationship between the angular frequency (ω)
of precession and the strength of the magnetic field B. There
= γ Β
are two possible configurations for
proton alignment; one configuration
possesses higher energy than the
other (see figure on the left). A
proton can undergo a transition
between the two energy states by
absorbing a photon that has
enough energy to match the energy
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difference between the two states. This energy E is related to the photon's frequency ν by
Planck's constant h (6.626 x 10
-34
J-sec)
E = h ν
This frequency is associated with a spin flip and is often used to describe the Larmor frequency
as well.
Larmor
= ν
ω
In the context of MRI, a radio-frequency (RF) pulse is applied perpendicular to the static
magnetic field (B
). This pulse, which has a frequency equal to the Larmor frequency, shifts
0
protons into a higher energy state. When the RF pulse (BRF) stops, the protons return to
equilibrium such that their magnetic moment is parallel again to B0. During this process of
nuclear relaxation, the nuclei lose energy by emitting their own RF signal. This is referred to as
a free-induction decay (FID) response signal. The FID response signal is measured by a field
RF coil, and has the characteristic shape shown in the figure below.
The Rf coil measure the relaxation
of the dipoles in two dimensions. The
Time-1 (T1) constant measures the
time for the longitudinal relaxation in
the direction of the B0 field (shown
below on the left). It is referred to as
spin-lattice relaxation.
The Time-2 (T2) constant
measures the time it takes for the
transverse relaxation of the dipole in
the plane perpendicular to the B0 field
(shown below on the right). It is
referred to as spin-spin relaxation.
The T
combined time constant (in physiological tissue) is called T
relaxation process is affected by molecular interactions and variations in B0. The
2
* (T2 star). In the case of MRI, we
2
take advantage of the fact that physiological tissue does not contain not a homogeneous
magnetic field, and thus the transverse relaxation is much faster. The size of these
inhomogeneities depends on physiological processes, such as the composition of the local
blood supply.
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The BOLD Contrast Mechanism
This mechanism is employed in most fMRI studies. The idea is that neural activity
changes the relative concentration of oxygenated and deoxygenated hemoglobin in the local
blood supply. Deoxyhemoglobin (dHb) is paramagnetic (changes the MR signal), while
oxyhemoglobin is diamagnetic (does not change the MRI signal). An increase in dHb causes the
T2* constant to decrease. This was first noticed by Ogawa et al. In 1990 1 in the rodent brain,
and over the following few years became the mainstay of functional MRI. The BOLD Contrast
refers to the difference in T2* signal between oxygenated (HbO2) and dexoygenated (dHB)
hemoglobin.
The above figure illustrates the physiological events that underlie our recording of the MR
signal. Upon stimulation, neural activation occurs, which pulls oxygen from the local blood
supply. Theoretically, as the paramagnetic dHb increases, the field inhomogeneities are
enhanced and the BOLD signal is reduced. However, the dHb increase is tightly coupled with a
surge in cerebral blood flow (CBF) which compensates for the decrease in oxygen, delivering a
larger supply of oxygenated blood. The result is a net increase in cerebral blood volume (CBV)
and in Hb oxygenation, which decreases the susceptibility-related dephasing, increasing T2*
signal and in turn enhancing the BOLD contrast.
1 Ogawa S., Lee T.M., Nayak A.S., Glynn P. (1990). Oxygenation-sensitive contrast in magnetic resonance image
of rodent brain at high magnetic fields. Magn Reson Med 14:68-78.
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The BOLD response can be thought of as the combination of four processes:
(1) An initial decrease (dip) in signal caused by a combination of a negative metabolic and
non-metabolic BOLD effect. The local flow change as a result of the immediate oxygen
extraction leads to a negative metabolic BOLD effect, while the vasodilation leads to a
non-metabolic (or volumetric) negative BOLD effect.
(2) A sustained signal increase or positive BOLD effect due to the significantly increased
blood flow and the corresponding shift in the deoxy/oxy hemoglobin ratio. As the blood
oxygenation level increases, the signal continues to increase.
(3) A sustained signal decrease which is induced by the return to normal flow and normal
deoxy/oxy hemoglobin ratios.
(4) A post-stimulus undershoot caused by the slow recovery in cerebral blood volume.
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Hemodynamic Modeling
The BOLD response is very complex. The signal depends on the total of dHb, which
means that the total blood volume is also a factor. Another factor is the amount of oxygen
leaving the blood to enter the tissue (metabolic changes), which also changes the blood
oxygenation level. Finally, due to the elasticity of vascular tissue, increasing blood flow, changes
blood volume. All these factors have to be modeled adequately in order for us to estimate the
neural signal. The model currently employed in research and literature uses a canonical
hemodynamic response function that linearly transforms neural activity to the observed MR
signal. However, being able to get the true neural signal based on the hemodynamic counterpart
is a bigger problem.
Ideally, we would like to evaluate how well our linear transform model allows us to
estimate the actual neural signal. This can be done using simultaneous measurements of the
neural and BOLD signals.
Source: Logothetis and Wandell 2004
2
The above figure shows these simultaneous measurements in a monkey brain, using
extracellular field potential recording, together with fMRI. (a) the black trace is the mean
extracellular field potential (mEFP) signal; the red trace is the BOLD response. (b) spike activity
2 Logothetic NK, Wandell BA. (2004). Interpreting the BOLD signal. Ann Rev Physiol 66:735-69
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derived from the mEFP. (c) frequency band separation of the mEFP (d) estimated temporal
pulse response function relating the neurophysiological and BOLD measurements in monkeys.
Even though these recordings are problematic due to their invasive nature (cannot be done in
humans) and due to sampling bias, they provided useful evidence for the coupling of the neural
signal and the hemodynamic response.
In human fMRI, we can estimate the hemodynamic response function, using known tasks
with known and expected specific neural activation, e.g. visual, motor, etc... Results that are
consistent with what we already know about specific structures' involvement in cognitive
processes may provide some insight (even though it is at best speculative) into the neural
activation and the related hemodynamic
response. Over recent years, a more
descriptive canonical hemodynamic
response function has been developed that
accounts for the timing delay (temporal
derivative) as well as the duration (dispersion
derivative) of the response. This set of
functions is what SPM uses to estimate the
neural signal. The mathematics behind the
hemodynamic model are too complicated to
explain here, but more details are given in
the fMRI analysis section.
It is important to understand however that
this model is a 'best fit' model, which means
it does a good job of explaining variance in
the hemodynamic response after neural
stimulation. However, it does not explain all
the parameters. The metabolic and neural
processes that couple action potentials to
blood flow are still not well understood, and
are the subject of much of today's fMRI
research.
Animal research is attempting to carry out
more multi-modal experiments to produce empirical data to support or reject this model, and
human research is getting better at the deconvolution of the neural impulse using higher order
mathematical modeling.
From the above we can see the entire cycle takes about 30 seconds to complete. Early
event-related studies were limited by this, and thus had to use very long inter-stimulus intervals
to allow the response to return to baseline before another one started. If the hemodynamic
responses were perfectly linear, then they should not have been hindered by this, as the linear
summation of HRFs can be deconvolved easily. However, BOLD response non-linearities exist,
and pose a problem. This non-linearity can be thought of as a “saturation” effect where the
response to a series of events is smaller than would be predicted by the sum of the BOLD
3
responses from the individual events. Empirically, it has been found that for SOA
of below ~8
seconds, the degree of saturation increases as the SOA decreases. However, for SOA of 2-4
3 SOA: Stimulus Onset Asynchrony – This is the amount of delay between the presentation of one experimental
stimulus to another.
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seconds, the magnitude of saturation is small. This is important to think about in designing an
fMRI experiment, and is particularly of importance in discussing rapid event-related fMRI.
To summarize, the general shape of the hemodynamic response is the same across
individuals and cortical areas. However, the precise shape varies from individual to individual
and from area to area. Canonical modeling however offers us a powerful tool to be able to
reasonably estimate the neural signal, based on the observed changes in regional cerebral
blood flow.
Signal and Noise in fMRI
The magnitude of the BOLD response signal we are trying to measure in fMRI is very
small compared to the overall MR signal. We can improve our signal detection ability by
increasing the amplitude of the signal or reducing the amplitude of the noise. The type of control
is referred to as signal-to-noise ratio or SNR. There are many different sources of noise that
produce artifacts in the scanner. Here is a brief description of some of the most common
problems:
Thermal Noise
Thermal noise is produced due to the thermal motion of electrons
inside the subject's body and in the large electronic circuits of the MRI
scanner. This type of intrinsic scanner noise is uncorrelated to the task
and the hemodynamic signal, and therefore can be described as “white”
noise. This type of noise increases with increased resolution (smaller
voxels). Therefore controlling it is a trade-off with the resolution of the
images.
Cardiac and respiratory artifacts
The pulsation of the blood and changes connected to breathing can change blood flow
and oxygenation. These factors create high frequency signal artifacts, for example, the cardiac
cycle is too fast (500 ms) to be sampled with a relatively average TR (2000 ms). However, when
this is the case, the variabilities become attributed to a lower frequency (aliasing), creating an
even larger problem.
N/2 Ghost
EPI scans in general suffer from ghosting artifacts in the phase
encoding direction. During acquisition, k-space data are sampled by an
alternating positive/negative read gradient. This results in a single ghost
shifted by half a FOV, known as the “Nyquist” or N/2 ghost. Using
readout gradient with the same polarity eliminates this problem at the
expense of lengthened data acquisitions.
Subject motion
Subject motion is the single most common source of series artifacts. Even relatively small
motion (of the range much smaller than a voxel size e.g 1.6-3.2 mm) can create serious artifacts
12
due to the partial volume effects. Typically motion of about half a voxel in size will render the
data useless. Subjects should be instructed not to move, with their heads restrained securely.
The task design should also minimize the possibility of task related movements.
Draining veins
Large vessels draining in the brain could induce a hemodynamic signal, that may not be
easily differentiated from the hemodynamic responses related to the neural signal. This is hard
to control, thus caution should be taken in considering activation occurring close to visible large
vessels.
Scanner drift
Drift is created most probably by the small instability of scanner gradients. It can create
slow changes in voxel intensity over time. Even though the magnet contains huge
superconducting coils to maintain its magnetic field, the stability of this magnetic field is
occasionally drifts. This type of spatial distortion can also be caused by non-system factors, e.g.
the subject's head slowly moving downwards due to a possible leak in the vacuum pack holding
the head in place.
Susceptibility artifacts
The EPI images are very sensitive to the changes of the
magnetic susceptibility. In effect the signal from regions close to sinuses
and bottom of the brain may disappear. This can also be caused by the
presence of magnetic material in proximity of the gradients, e.g.
Implants, braces, buttons, or even another human body moving in the
room.
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Experimental Design
This section deals with the different designs that can be employed in neuroimaging
studies. Designs in general can be subdivided into categorical (or parametric) designs and multifactorial designs, with the latter being more complicated than the former.
Cognitive subtractions
These are one type of categorical design, which rely on the premise that the difference
between two tasks can be qualified as a separate cognitive components that is distinct in space
and therefore can be separated as an individual component of the hemodynamic response. An
example is a study in which visual and motor stimulation are combined in the experimental task
or condition, while the control task or condition consist of only the visual or only the motor
stimulation. Subtracting the activation in one condition from the other is expected to show only
the activation relevant to the specific type of stimulation. The problem with these designs is the
underlying assumption that the neural processes underlying behavior are additive in nature. Due
to the complexity of neural responses and the significant functional integration between various
brain structures, this assumption may not always hold true.
Cognitive Conjunctions
These designs can be thought of as a series of subtractions. Instead of testing a single
hypothesis pertaining to the activation in one task over the other, conjunctions test several
hypotheses at a time, asking whether all activations are jointly significant. For example, if we are
interested in verbal working memory, then we can use a series of tasks that have that cognitive
component in common, but nothing else in common. The conjunction of these tasks should
show only the structures that are involved in verbal working memory. Conjunction analyses
allow us to demonstrate neural responses independent of context.
Note: Testing joint significance using conjunctions is a notion that we will return to when we
discuss group fMRI analysis.
Parametric Designs
The underlying premise in these designs is that regional activation will vary systematically
with the degree of cognitive processing. For example, an fMRI study of hemodynamic
responses and performance on a cognitive task illustrates the utility of this design. Correlations
or neurometric functions may or may not be linear. Clinical neuroscience can use parametric
designs by looking for neuronal correlates of clinical ratings over subjects (e.g. symptom
severity, IQ, performance on QNE, etc..). The statistical design then can be viewed as a multiple
linear regression model. However, if one needed to investigate several clinical scores that are
correlated, we have a problem with running the regression model, since variables are not
orthogonal. In this case, factor analysis, or principal components analysis (PCA) is used to
reduce the number of possible explanatory variables, and render them orthogonal to each other.
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Multi-factorial Designs
These designs are more prevalent than single factor designs, because they offer more
information and allow us to investigate interesting interactions between variables, e.g. time by
condition interactions. For example pharmacological activation studies assess evoked
responses before and after the administration of a drug. Interaction terms would reflect the
pharmacological modulation of task-dependent activation. Interaction effects can be interpreted
as (a) the integration of cognitive processes or (b) the modulation of one cognitive process by
another.
Optimizing fMRI Studies
Signal Processing
An fMRI time series can be thought of as a mixture of signal and noise. Signal
corresponds to neurally mediated hemodynamic changes, while noise can be the result of many
contributions that include scanner artifacts, subject drift, motion, physiological changes (e.g.
breathing), in addition to neuronal noise (or signal mediated by neural activity that is not
modeled by explanatory variables). Noise in general can be classified as either white
(completely random), or colored (e.g. the pulsatile motion of the brain caused by cardiac cycles
and modulation of the static magnetic field by respiratory movement.
These effects are typically low-frequency or wideband. Thus in order to optimize an fMRI study, one
should place stimuli and the expected neural stimulation
in a narrow-band or higher frequency than the
physiological noise that is expected. This makes the
process of filtering and hemodynamic deconvolution
easier. For example, the dominant frequency of the
canonical HRF bandpass filter in SPM is ~0.03 Hz. In
order to maximize the signal passed by this filter, the
most efficient design would then be a sinusoidal
modulation of neural response with period ~32 s. In terms
of design, this means a blocked design using a box-car
function with 16s ON and 16 OFF epochs would be
optimal. The objective here is to comply with the natural
constraints of the hemodynamic response and ensure
that the experimental variance is detected in the
appropriate frequencies.
Confounding Factors
Any variable that co-varies with the independent variable is a confounding factor. These
can be due to variety of sources. For the most part, exerting experimental control on the task
can help resolve these issues. Optimized fMRI designs are generally more successful at
minimizing these factors.
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Control task
The control task is very important in a subtraction design. The idea is to make the control
condition very similar to the experimental condition, except for the variable we are trying to
assess. For example, in a study of face perception, one can use the control condition of simple
fixation. However, the two conditions would differ in more than one aspect, e.g. brightness,
edges, etc... If we use this design, we may not be able to make inferences about the activation
of interest, since it could have been solely due to the perception of a picture in general, and not
a face in particular. We can optimize this design by making the control task stimuli out of the
same faces, but transformed somehow, so that are no longer perceptible as faces, but rather as
images of noise (with a similar intensity histogram).
Latent (hidden) factor
This is one of the most dangerous confounding factors, and is due to the fact that
correlation does not imply causation. For example, you can give a group of Parkinson's disease
patients as well as a group of controls a motor activity task (repeated finger tapping) to
investigate activation in the motor cortex. You find that motor cortex activity is diminished in PD
patients compared to controls. This may lead one to conclude that PD patients under-activate
their motor cortex during motor movement. However, other explanations should also be
considered. In this case, it is possible that PD patients pressed the buttons less often, and
performed poorly on the task, which would explain the diminished activation. Here the latent
factor is performance, while our mis-interpretation of the data makes it seem like the diseased
state was really the causal factor.
Randomization and Counterbalancing
Trials and subjects should be sufficiently randomized, not to induce any confounding
effects. For example, if you test both patients and controls by day and night. You should
randomize whether night subjects are patients or controls. If you have two versions of the task
(or two conditions), you might want to randomize subjects to conditions, so that your subject-bycondition interaction is not a confounding factor. In the case where certain variables cannot be
adequately randomized, the investigator may choose to use a counterbalanced design. For
example if gender is randomly assigned to groups, it is possible that one group will have twice
as many men as the other. Counterbalancing ensures that this is not case, by balancing the
number of men and women in each group. Whether you randomize or counterbalance may
depend on your sample size (for example, in a small sample, randomization may not yield a
perfectly balanced design).
Nonlinear Hemodynamic Effects
This is manifested as a hemodynamic refractoriness or saturation effect at high stimulus
presentation rates. This means that the simple addition of hemodynamic responses is not
enough to deconvolve the individual events. This effect has an important implication for eventrelated fMRI, in which trials are usually presented in quick succession. This issue will be
addressed in detail in the following section.
16
Epoch (Blocked) and Event-Related Designs
Typically, fMRI experimental design can be classified into two types: a blocked design
(epoch-related) and a single event design (event-related). Blocked designs are the more
traditional type and involve the presentation of stimuli as blocks containing many stimuli of the
same type. For example, one may use a blocked design for a sustained attention task, where
the subject is instructed to press the button every time he or she sees an X on the screen.
Typically blocks of stimulation are separated from each other by equivalent blocks of rest (where
the subject may be instructed to passively attend to a fixation cross on the screen. This type of
design is depicted below.
Blocked designs are simple to design and implement. They also have the added
advantage that we can present a large number of stimuli, and thus increase our signal to noise
ratio. It has excellent detection power, but is insensitive to the shape of the hemodynamic
response. We also have to assume a single mode of activity at a constant level during
stimulation. In other words, we cannot infer any information regarding the individual events. This
precludes us from being able to investigate interesting questions, such as the relationship of
activation to accuracy and performance or reaction time. We use blocked designs if we plan to
use a cognitive subtraction or conjunction to analyze our data.
The alternative to epoch designs is a more powerful estimation method. Event-related
fMRI has emerged as a much more informative method that allows for a number of other
analyses to be conducted. Rapid, randomized, event-related fMRI is the newest improvement
on this concept. The idea is to present individual stimuli of various condition types in randomized
order, with variable stimulus onset asynchrony (SOA). This provides us with enough information
for time-series deconvolution using a canonical or individual-derived HRF, and allows us to
conduct post-hoc analyses with trial sorting (accuracy, performance, etc...). This design is more
efficient, because the built-in randomization (jittering) ensures that preparatory or anticipatory
effects (which are common in blocks designs) do not confound event-related responses. A
typical event-related design is depicted below.
Mixed designs are also possible (combining aspects of blocked and event-related
designs, however they are much more complicated to design and analyze. They usually contain
blocks of control and experimental stimuli, however within each block are multiple types of
stimuli. It allows us to simultaneously examine state-related processes (best evaluated using a
block design) and item-related processes (best evaluated using an event-related design).
17
Spatial and Temporal Pre-Processing
Overview
Functional MRI (fMRI) pre-processing is designed to accomplish several purposes. It
corrects for head motion artifacts during the scan (realignment), adjusts the data to a standard
anatomical template (normalization) and convolves the data with a smooth function suitable for
analysis (smoothing). The pre-processing is done within the Statistical Parametric Mapping
(SPM) environment which is a MATLAB package with a graphical user interface (GUI).
Additional MATLAB functions will be used and will be described in detail. Depending on the
computer speed and dataset size, pre-processing can take several hours or days.
Pre-processing also requires a lot of hard drive space, for example if a single subject’s
dataset is 1000 MB (1GB) in size, you will need 5000 MB (5GB) of space to pre-process the
subject’s data. Of course once the pre-processing is done, a lot of the data generated in the
intermediate steps can be deleted, and this can be used to save hard drive space. The preprocessing directory should be either (1) an internal drive at 7200 RPM or more (RAID-0 SATA
or 10-15K SCSI preferred) or (2) an external drive at high throughput rates. FireWire is the
recommended medium, due to its reliability and high throughput rates (800 Mbps on machines
that support 1394b). Pre-processing, in general should not be done over the network (i.e. writing
images to a mapped network drive), as it takes longer, and makes the process more prone to
crashing (this is severely affected by network traffic). However, you may run pre-processing on
another computer on the network, using remote desktop (and the pre-processing computer's
native Matlab/SPM). For instructions on how to set up the remote desktop, please see
http://www.microsoft.com/windowsxp/using/mobility/default.mspx
Raw Data
fMRI datasets are saved at the point of origin (Philips scanner) as combinations of
.par/rec files. This data is saved on Godzilla (large capacity UNIX-based server, maintained by
the F.M. Kirby Research Center: for questions about Godzilla or to set up a user account,
please contact its administrator, Joe Gillen (jgillen@jhu.edu
combination of the subject’s last name and the reverse date of the scan, followed by the scan
number (scans are numbered in the same order in which they were acquired), e.g.
“yassa050103_3.rec”. You may let the technicians know to save the files using a different name
(HIPAA regulations somewhat preclude saving these files with the subject last name).
). Data is usually saved as a
Getting Started
To start a new analysis on your computer, first you must create a new working directory
for storing all of the data files in your dataset. You have to make sure the drive on which you
save the data has enough space to contain all the images. Then you should create a directory
(without spaces in the directory name), e.g. “C:\fmri\subjID\” to contain all of the subject’s fMRI
data. It is a good idea to keep your imaging data organized by project and by subject. fMRI data
involves potentially thousands of files and thousands of data points, so it is essential to keep
everything organized and document this organizational structure somewhere safe.
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Requirements
Hardware Requirements
You must have the following hardware requirements before you begin:
- Windows XP Professional or Windows 2000 or Redhat Linux 9.0 and above.
- At least 20 GB of free space (60 recommended)
- At least 1 GB of RAM (2 – 4 GB recommended)
- 4 GB of swap space (also known as paging file on Windows)
- Dual processors recommended.
Software Requirements
You must have the following software on your computer, before you begin:
- Matlab 6.0 or higher with SPM99 and its latest updates (download)
- Secure Shell SSH Software
If you do not have any of these requirements, you should contact Arnold Bakker or Mike Yassa
to make sure you have the correct setup.
Software Set-up
Install Matlab 6.1 (or above) in its default directory. If you’re using a network installation
of Matlab, you may need to be on an enabled Matlab client (we have a limited number of client
licenses). We also have a personal licensed version of Matlab which is more convenient and
can be installed without the need for network setup.
Download SPM99 from http://www.fil.ion.ucl.ac.uk/spm/ and extract it in a suitable
directory, e.g. “C:\spm99” or “C:\Matlab6p1\spm99”. Find the file “r2a.m” under
\\Soma\Matlab_functions . If you do not have access to Soma, contact Mike Yassa or Arnold
Bakker to get a copy of r2a. Copy and paste the file in your SPM99 directory.
Open Matlab 6.1 and add SPM99’s directory to the Matlab path, by going to File> Set
Path, and adding the SPM99 folder. Save the appended path, and close the “Set path“ window.
To check that everything has been installed correctly, type “spm fmri” in the Matlab console and
wait for the SPM windows to pop up. If you get error messages at this point, then your
installation was unsuccessful or your options are not set correctly.
Note regarding SPM use: SPM is a very resource-hungry program that can be very
temperamental. Make sure you close other open windows and other “memory hogging”
programs, before you start pre-processing or analyzing using SPM. At times it may also
spontaneously suffer from an internal error and indicate this by printing a verbose and cryptic
output to the Matlab command window. It may also crash or lock up your Windows system
entirely. If this happens, then shut down SPM and restart Matlab (restarting Matlab clears its
cache memory, and is necessary before you start the same process again).
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The SPM Environment
Statistical Parametric Mapping (SPM)
main panel allows you to select between two
interfaces, one for fMRI and one for
PET/SPECT modeling. In order to bring up this
screen, type >spm at the Matlab console. Click
on <fMRI Time-series> to bring up the fMRI
interface. If you are running spm2 as well, make
sure that the spm99 directory is prepended to
the top of the Matlab path. Matlab will run
whichever instance of spm it finds in its path
first.
Three SPM windows should appear. The
Upper window will be referred to as the fMRI
switchboard. The lower left window is the SPM
input window, and the right window is the SPM
graphics output window. The switchboard
consists of a spatial preprocessing panel with
option for processing fMRI data. The statistical
analysis panel containing the different linear
models that can be applied to the data. And
finally, the bottom panel contains useful tools for
displaying images, changing directories,
creating means, changing defaults, writing
headers, and running different toolbox options.
Toolboxes are installed in \\spm99\toolbox. The
<Defaults> button changes the defaults only for
the current session. If you close and restart
SPM or Matlab, those changes will be lost. You
can make permanent changes to fMRI defaults
by editing the spm_defaults.m file (or creating
an alternate version for your lab, and placing it
in the Matlab path before the spm directory.
Data Transfer from Godzilla
Godzilla is a large RAID array, acting as a storage server at the F.M. Kirby Research
Center at Kennedy Krieger Institute. It is the default image repository. We use this server to
transfer subject data from the scanner to our laboratory. Once a subject's data is acquired, it is
exported from the scanner database to a specific directory on Godzilla. Usually this is under one
of the two main disks (g1 or g2). Each investigator has a directory for storage and transfer, e.g.
\\g1\myassa
(godzilla.kennedykrieger.org) using your username and password. Once connected, in the top
menu bar go to <Operation> and Select <Go to Folder>. In the folder window enter the folder
. Open Secure Shell (SSH) File Transfer Window, and connect to Godzilla
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name e.g. “/g1/studyPI” and press <Enter>.
This is shown on the left.
In the left window, change the
local folder to the data folder you set up for the
study/ subject. In the right window, navigate
through the remote directories and find the
subject whose data you would like to preprocess. Click and drag the directory with the
correct subject name/date to your local folder.
The individual files will be queued for transfer
sequentially. This process takes quite a bit of
time, and depends on network speed and
traffic. Wait for the transfer to be completed
before you close Secure Shell SSH.
Volume Separation and Analyze headers
This step involves the conversion of the Philips REC/PAR file format to the conventional
3D Analyze format (SPM can only handle Analyze images). The REC file contains all of the time
series images, and the PAR file is the text file containing all the parameters necessary to
separate the REC file into Analyze volumes. Rename the directories and par/rec combinations
to names that identify the subject ID and the session number, e.g. replace
“lastname051112_10_1.par” with “50100_4.par” where “50100” is the subject ID and “4” is the
session number. One way to separate the volumes uses the executable file “separate.exe”
which can be copied from \\Soma\Software\. If you do not have access to Soma, contact Mike
Yassa or Arnold Bakker to get a copy of the file. Separate uses a command line (DOS-like)
interface and requires you to know and/or calculate some of the parameters of your scan
acquisition. First you need to open your .par file. Right click the .par file and select “Open
With…”. Select Wordpad from the list of programs. The header file should look like this:
. Patient name : Yassa,Michael
. Examination name : #-#/g1/myassa/yassa050131
. Protocol name : Bold396 SENSE
. Examination date/time : 2005.01.31 / 10:12:59
. Scan Duration [sec] : 798
. Max. number of slices/locations : 39
. Max. number of dynamics : 396
. Image pixel size [8 or 16 bits] : 16
. Scan resolution (x, y) : 80 80
. Scan percentage : 100
. Recon resolution (x, y) : 128 128
. Number of averages : 1
. Repetition time [msec] : 2000.00
. FOV (ap,fh,rl) [mm] : 230.00 117.00 230.00
. Slice thickness [mm] : 3.00
. Slice gap [mm] : 0.00
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The header file above has been truncated to only show the parameters of interest. The
Recon resolution is the reconstructed image matrix, and is what defines the image space. In the
case above, the matrix is 128 x 128 voxels (in the “x” and “y” planes). The plane of acquisition is
plane “z” and is determined by the Number of Slices
parameter, which in this case is 39. Thus
the image matrix is 128 x 128 x 39.
The Number of dynamics
parameter determines the number of functional scans or time
points in your series, for example 396 dynamics, means your rec file will be separated into 396
Analyze volumes.
The FOV (ap, fh, rl) parameter describes the field of view in three dimensions (“ap” is
anterior-posterior, “fh” is foot-head, and “rl” is right-left). Since the direction of acquisition of this
scan is axial (foot-head) that means the “fh” parameter (in this case, it is 117.00) is in the z
orientation.
The voxel dimensions
can be calculated from the image matrix and the field of view using
the following formula:
Voxel size = FOV (mm)
e.g. 230 x 230 x 117 = 1.8 x 1.8 x 3.0 mm
Matrix (voxels) 128 x 128 x 39 voxel
Once you locate the file “separate.exe” copy it to your “C:\Windows” or “C:\WINNT”
directory. Now click on Start>Run and type “cmd” to display the command prompt. Test that the
file is in the right location and works by typing “separate” at the console, then hitting enter. You
should get the following usage notification with a list of the arguments needed to separate
volumes.
Splits a set of volumes into individual files
Usage: separate <input_file_name> <output_filename> <head_bytes>
<volsize> <numvols> <bufsize> <avg value> <swap bytes? 0 or 1>
Here is an explanation of each of these arguments:
✗ <input_file_name> - this is the name of the .rec file you would like to separate. You have
to type the full location of the file e.g. “C:\my_fmri\scan1.rec”. Separate also does not like
spaces in folder or filenames.
✗ <output_file_name> - this is the root filename for the separated scans, for example
“scan1_sess1_”. Output files would be appended with the dynamic number, e.g.
scan1_sess1_0001.img etc…
✗ <head_bytes> - this is the number of bytes preceding the actual scan. Unless you have
specified this for your scan before acquisition, this parameter should be set to zero.
✗ <volsize> - this is the size of each volume in voxels, which is calculated from the
information retrieved from the header. This is the equivalent of the image matrix, e.g. 128
x 128 x 39. The product of those three numbers is the <volsize> parameter, which in this
case is 638976 voxels.
✗ <numvols> - this is the number of volumes in the dataset, which is also the number of
dynamics, e.g. 396. This is the number of volumes your dataset will be split into.
✗ <bufsize> - this is the number of blank “buffer” voxels you may add to the beginning and
end of each dynamic. We mostly do not use this parameter, but if you wanted to buffer
each dynamic with a two-dimensional slice you would enter a number equivalent to the
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product of your XY matrix, e.g. 128 x 128 which is 4096.
✗ <avg_value> - we do not use this parameter. Enter the number 0
✗ <swap_bytes> - each voxel is represented by two bytes of data and the swap parameter
specific which order in which those bytes are read in order to form a readable image.
Different operating systems read the bytes in different order. The scanner can be thought
of as a UNIX-based machine. Since we are operating on a Windows PC, we have to
swap the bytes to read the image. Enter the number 1.
Thus in order to separate the session 1 rec file in the example above, you would enter:
separate C:\fmri\50001_1.rec C:\fmri\50001_1_ 0 638976 396 0 0 1
There is no interactive output written to the screen. You will know when the process is
finished because the console will return to input mode with the flashing cursor.
You may want to browse through the directory where all the files have been made to
make sure that things went well. Is there the right number of files, (the "numvols" parameter)?
Are they all the same size? Are they all the correct size? If any of these things seems wrong,
check the original commands that you entered, check for inconsistencies, check for math errors
on your part and then try again.
In our example above, there should be 396 files of size 1.21 MB each in the directory
C:\fmri\50001 and they should be numbered sequentially from 50001_1_0000.img to
50001_1_0396.img. Note that you cannot double-click any of these files to view them, without
first writing Analyze headers for them (the next step). You may now close the command prompt
screen. The next steps will all be handled by SPM99.
Assuming Matlab and SPM99 are already installed and SPM99’s directory was appended
to the Matlab path, you may now create header (.hdr) files using SPM’s HDREdit facility.
Open Matlab and type “spm fmri” at the console. This should bring up the SPM windows.
At the fMRI switchboard window, click on <HDREdit>. The lower left box will contain a
series of options on a pull-down menu that asks you to set various values that describe the
images. Click on the drop down menu and select the first parameter:
Set Image Dimensions
: This is the same as the image matrix which you retrieved from
the .par file. Enter the matrix parameters separated by space, e.g. 128 128 39
Set Voxel Dimensions: This was also calculated using the formula, which in our example
yields 1.8 1.8 3.0 (Entered with spaces as separators again)
Set Scalefactor: Scalefactor is 1 unless otherwise specified.
Set Datatype: Datatype from the Philips scanner is 16-bit integer data. Byte swapping is
optional and depends on the dataset. Try selecting Int16 first. If after header specification and
displaying the images they look incorrect, then you probably need to select byte-swapped Int16.
Set Offset into file
: This is only specified if there is a buffer, otherwise it should be zero. (If
you do not set this option, it is set by default to zero).
Set Origin (x y z)
: This is the mathematical origin of the scan, and it by default set to 0 0
0. (If you do not set this option, it set by default to 0 0 0).
Set Image Description: Here you can type a text description of the images in the series,
e.g. subject ID, or a standard statement like “property of PNI”, etc… You may also leave this
field blank or choose not to set it.
Now select APPLY to images. The “SPMget” file selector window will be invoked. This is
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the standard way of selecting files in SPM. You can change the present directory from
C:\Matlab\work to the directory where your images are kept, and select all the *.img which you
wrote using the separate function. You will notice that SPM does not list all of the files, but
instead it abbreviates the files with similar names and uses only the common root while the
number of files sharing this root are marked with subscript numbers to the left of the name, e.g.
50001_1_*.img. In this case click on the filename root, and you should see that files 1-396
396
were selected (turns blue). You can select more than one file and more than one series to write
headers to. Once you have selected all the files for which you would like to write Analyze
headers, click Done. SPM will create header files for each image file you selected, using the
same filename as the image file, but using the extension .hdr instead. You will see the progress
in the bottom left window.
You can check that the headers were written correctly by double-clicking an image file,
and displaying it in MRIcro. If the images do not display correctly, it is possible that your
datatype should have been byte-swapped or that one or more of your parameters during
separation and/or header creation was incorrect.
Buffer Removal
In most fMRI acquisitions, the first few volumes acquired can be removed from the series
to be excluded from the analysis. This is done for two reasons. We have to make sure that the
net magnetization has reached steady state condition, and we also have to account for possible
hemodynamic effects that may be related to the start of the experiment, e.g. Scanner noise,
shifting stimulus, etc... If these scans are included in the analysis there will be a large change in
signal that is not related to experimental conditions per se, which should be avoided.
Before you remove any volumes, you have to make sure that these volumes were
acquired during rest (or fixation) and be sure that your model or design accounts for the lag that
will result in the timing parameters. If you would rather not use the first few scans as a buffer,
you can also use dummy scans to get magnetization to reach steady state before you start the
actual experiment. This can be specified in your MRI protocol on the Philips scanner. Check
with the MRI technician to make sure that enough dummy scans are included before the trigger.
Slice Timing Correction (For event-related data)
To Correct or Not to Correct
Functional MRI data from the Philips scanner are acquired slice-wise so that a small
amount of time elapses between the acquisition of consecutive (or in the Philips case interleaving) slices. Given a TR of 2000 ms, for example, in a 20-slice acquisition, each slice would
roughly take 100 ms to be acquired. This becomes an issue only in event-related designs where
one typically uses stimulus durations that elicit BOLD responses lasting only a couple of
seconds. For these designs it is critical that an appropriate temporal model is used, as any
difference between the expected and actual onset times may decrease the sensitivity of the
analysis. For short TR's (i.e. less than 3 seconds), slice timing correction can be used to remedy
this problem. Essentially this pre-processing step will determine the midpoint slice in the
acquisition and temporally interpolate all the other slices to this point.
Note: If slice timing correction is used, then one can use a naïve HRF model in the
analysis. If slice timing correction is not possible or is not performed, one can still model event-
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