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 ●

1 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, 2nd edition, Elsevier Academic Press, San Diego, CA.

Huettel SA, Song AW, McCarthy, G. (2004) Functional Magnetic Resonance Imaging. Sinaur Associates, Sunderland, MA.

2 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

3 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

4 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)

ωLarmor = γ Β

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

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

6 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. ωLarmor = ν

In the context of MRI, a radio-frequency (RF) pulse is applied perpendicular to the static magnetic field (B0). This pulse, which has a frequency equal to the Larmor frequency, shifts 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 T2 relaxation process is affected by molecular interactions and variations in B0. The combined time constant (in physiological tissue) is called T2* (T2 star). In the case of MRI, we 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.

7 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 1 T2* constant to decrease. This was first noticed by Ogawa et al. In 1990 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.

8 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.

9 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

10 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 responses from the individual events. Empirically, it has been found that for SOA3 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.

11 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.

13 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 multi- factorial 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.

14 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 wide- band. 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.

15 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-by- condition 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 event- related 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 pre- processing 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 ([email protected]). Data is usually saved as a 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).

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.

18 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).

19 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 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 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. Open Secure Shell (SSH) File Transfer Window, and connect to Godzilla (godzilla.kennedykrieger.org) using your username and password. Once connected, in the top menu bar go to and Select . In the folder window enter the folder

20 name e.g. “/g1/studyPI” and press . 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 pre- process. 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

21 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

Here is an explanation of each of these arguments: ✗ - 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. ✗ - 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… ✗ - 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. ✗ - 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 parameter, which in this case is 638976 voxels. ✗ - 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. ✗ - 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

22 product of your XY matrix, e.g. 128 x 128 which is 4096. ✗ - we do not use this parameter. Enter the number 0 ✗ - 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 . 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

23 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.

39650001_1_*.img. In this case click on the filename root, and you should see that files 1-396 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 inter- leaving) 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-

24 related data using HRF derivatives (more information on this in the analysis section).

Philips Slice Acquisition Order In order to perform slice timing correction, click on the button in the SPM fMRI switchboard. Select all images in the series you would like to correct. Under select . The Philips scanner acquires slices in an odd-evens interleaved pattern (i.e. 1, 3, 5, 7, … 2, 4, 6, 8, …). In the empty box enter the correct slice order from your acquisition. For example, if you acquire 20 slices, enter: 1 3 5 7 9 11 13 15 17 19 2 4 6 8 10 12 14 16 18 20 . Numbers should be separated by a single space, and all slices in the acquisition should be included. Once you’re done click enter.

Note regarding slice acquisition order: At the point of scanning, you can specify and let the MR technician know that you would like to acquire the scans in a sequential order (this is the Kirby center default). If you do not change it, then they will be acquired according to the Philips default (interleaved, odds then evens).

Which Slice to Use as a Reference Slice The next prompt will be for the . Enter the slice you want to consider as a reference point. All other slices will be corrected to what they would have been if they were acquired when the reference slice was acquired. The default is the middle slice (although, please make sure the default value given is indeed the middle slice for the number of slices you have). The logic behind selecting the middle slice as a reference point for slice timing correction is that this way there will be a minimum total shifting in time required, and therefore any interpolation introduced by the correction procedure would be minimized. Some may argue that in a perfect slice timing correction, the interpolation to any slice in the temporal sequence is the same, and thus it doesn’t make any difference which slice you choose (even if it is in the space outside the brain). However, SPM’s algorithm is not perfect and is worse for longer TR’s (more

10 as the reference slice. Note: When the first slice in time is NOT used as a reference during correction, the default sampled bin must be adjusted prior to analysis. More details in the analysis section.

25 Timing Parameters Once you’ve specified the reference slice, SPM will prompt you for . This parameter is in your .par file, and is quite simply the amount of time it takes the scanner to acquire a full volume. SPM will suggest a suitable TR by default, but this may not be the correct TR. You must specify the correct TR for slice timing correction to work properly; otherwise temporal artifacts may be induced. Next, SPM will ask you to input , which is the time between the beginning of acquisition of the first slice and the beginning of acquisition of the last slice of one scan. Typically, this is calculated using the formula TA = (TR/#slices)*(#slices – 1). For example, if TR = 2, #slices = 39, TA = 1.949. This default value is calculated by SPM for you, and is displayed in the input box. You may accept this default value, but you may want to confirm that it is indeed correct. This step will produce a* files, which are acquisition corrected. It typically takes 20 minutes or so to correct a typical session (300 scans).

Rigid-Body Registration (Correction for Head Motion)

Image registration is very important in fMRI, since signal changes due to hemodynamic responses can be masked by signal changes resulting from subject movement. Although, the subject’s head is restrained as much as possible in the scanner, head motion cannot be completely eliminated, thus retrospective motion correction (i.e. Realignment in SPM-speak) is an essential pre-processing step. Image registration involves estimating a transformation matrix that maps image A (the source image) onto image B (reference image (or target), which is assumed to be stationary). A rigid-body transformation is defined by six parameters: 3 translations (x, y, z) and 3 rotations (x, y, z). This type of transformation is a subset of the more general affine (linear) transformations.

Creating a Mean Image Motion correction involves registering a source image to a target image. The target image can be the first image in the series or it could be a mean image based on the entire series. Since the subject could undergo some motion at the beginning of the scan session which subsides as the scan goes on, it is better to calculate a mean image for the series and use this image as the realignment target. The output of the function spm_mean_ui.m is written to the current working directory, so you should change this to your fmri directory before you create a mean. In the fMRI switchboard click on the drop down menu and select to change the current working directory. Using the SPM folder selector window, navigate to the correct folder and select it. SPM should display an alert with the new working directory name. Once this is done, you can click on the drop down menu and select . You will be prompted to select the images to be averaged. Select all of your (slice timing corrected if event related) functional images. If you have several sessions, you may want to select all images or a representative subset of images from each session (MATLAB may crash if you try to average more than a few hundred images at the same time). This process has no progress bar, but the output is printed to the MATLAB screen. The mean image is written to the working directory. You can display the mean using to see if it came out OK.

26 Realignment Click on in the spatial pre-processing tab in the fMRI switchboard. Under type 1 (you can also realign more than one subject at once). Under type the correct number of sessions. You will be asked to select the appropriate files for each session. Here you should first select the mean image followed by the rest of the series. This will instruct SPM to realign all images to the first image select (mean). Under Select . This will cause all files to be realigned by creating transformation .mat files that contain the realignment parameters that need to be applied to the corresponding images. Since reslicing causes the images to lose some resolution, it is recommended only after normalization in the next step. Of course, it is still OK to select if you wanted to output motion corrected volumes to be saved or for other pre-processing. The logic here is that normalization will take into account the motion correction parameters (written to .mat files), so that reslicing has to be performed only once. Note that if you select you will be given an option of the reslice interpolation method. Here’s a brief description of these methods: 1. Trilinear Interpolation : this is the process of linearly interpolating points within a 3 dimensional box given the values at the vertices of the box. For example given the intensities at the vertices of the three dimensional grid of voxels, one can interpolate the intensity at a point inside the grid. 2. Sinc Interpolation : This involves convolving the image with a sinc function centered on the point to be resampled. A true sinc interpolation would use every voxel in the image to interpolate a single point, but due to time and speed considerations, an approximation using a limited number of nearest neighbors, 'window' is used instead. 3. Fourier space interpolation : This is an implementation of rigid-body rotations executed as a series of shears, which are performed in Fourier space. This method can only be applied to images with cubic voxels. For more information on this see Eddy et al.4 The best quality interpolation is given by the 'windowed' sinc interpolation (SPM selects this option as the default). You may also use trilinear interpolation; however, the quality will be degraded. Once you select an interpolation mode you will be asked for which images you would like to create. Here you can select or (remember that image 1 was the mean you already created). If you choose not to output resliced files, you can create just the mean image, and leave the other files without reslicing to prevent degradation of image quality. Next, SPM will ask whether or not you want to . This is a dated function that works well with simulated data, but unfortunately not with real data. It is an additional adjustment that is made to the data that removes a tiny amount of movement-related confounds. It is based on the assumption that most of the realignment errors are from interpolation artifacts, which does not appear to be the case. For this option, it is best to select . During realignment, SPM 99 eliminates unnecessary voxels (voxels offering the least information about intensity differences between images), before performing the realignment using the best voxels to resample, i.e. the ones that provide the most information about the registration, e.g. edge information. Realignment is SPM’s most time-consuming step. Depending on the amount of data being realigned, this can take anywhere from an hour to several hours. It also has a tendency to crash MATLAB and occasionally run out of memory. Be sure to shut

4 Eddy, W. F., Fitzgerald, M., & Noll, D. C. (1996) Improved image registration by using Fourier interpolation, Magn Reson Med. 36(6):923-931.

27 down all major programs while realignment is in progress. Realignment works in two stages. First, the first image from each session is realigned to the first file of the first session that you selected (mean.img). Second, within each session, the rest of the images (2..n) images are realigned to the first image. As a consequence, after realignment, all files are realigned to the first file select (mean.img). Realignment produces .mat files that correspond to the realigned volumes. If you asked SPM to reslice at this stage, it will also produce r*.img files that are the resliced realigned volumes. Realignment produces text files with the estimated realignment (or motion) parameters for each session. These are the realignment_params_*mean.txt files stored in each session's directory. They contain 6 columns and each row corresponds to an image. The columns are the estimated translations in millimeters ("right", "forward", "up") and the estimated rotations in radians ("pitch", "roll", "yaw") that are needed to shift each file. These text files can be used later at the statistics stages, to enter the estimated motion parameters as user-specified regressors in the design matrix (see section on motion parameters as confounds in analysis). This stage also produces a spm99.ps postscript file, which contains two plots of the transformations. This file can be viewed using a postscript viewer or can be converted to a PDF using Adobe Acrobat Distiller. The top plot shows x, y, and z translations, and the bottom plot shows x, y, and z rotations. Normally translations should be within 2 mm and rotations should be within a few radians. If there are translations or rotations of more than 10 mm or radians, then you should seriously consider using your motion correction parameters as confounds in the statistical analysis. Otherwise, the large motion artifacts could cause signal changes that affect your model. See example plot below for what to expect. Also, you should NOT see large sets of consistent values. If a set of continuous scans appear to stay the same in translation or rotation (straight line on the plot), that means something has gone terribly wrong. This could indicate a calculation error that resulted in a meaningless loop in SPM computations, or it is possible that

28 all those files are merely copies of the same file. If this happens, you need to diagnose the problem: One potential reason for this problem, is an error during the volume separation (if you are using the old “separate and create headers” routine). You can check if this is the problem, by running separate again, or by using the r2a (rec2analyze) function to separate the volumes. If this doesn’t work, run a short realignment on a smaller subset of volumes to determine if the problem is consistent. It is also possible that data was corrupted either in the export process at the scanner, or in the transfer from Godzilla. Check the data at all stages to make sure this is not the case. If this is not due to a data handling error, it could be due to a scanner error, and the data may be irrecoverable. Of course, you should exhaust all options first. Your realigned volumes at this point (if you elected to reslice) will be saved in the same directory as your raw (or slice-timing corrected) volumes, using the same filenames, except the names will be pre-pended with the letter “r” to indicate that these volumes have been realigned. You can check the quality of the realignment by display a few of the realigned scans (a few from the beginning, middle and end of the series) using the button in SPM. The images will be printed to the SPM graphical output window and you can click around using the left mouse button to check the quality of the registration, you may want to re-run the registration using a higher quality interpolation (e.g. if you used Trilinear interpolation, you should use Sinc interpolation). You can also change the default options in SPM. Under select Realignment. Change the option for registration quality from 0.5 to 1.0 (slowest, but most accurate). You may also choose to adjust for interpolation to see if it improves the quality of the registration. At this point, you may compress and save a copy of your motion-corrected volumes (since this step is the most error-prone and time-consuming) to have a backup in the case of data loss.

Anatomical Co-registration (Optional) This step is recommended for single subject studies, as it offers better anatomical localization of signal differences. It is also recommended for partial brain acquisitions. The idea is to use the subject’s anatomical scan as a template to overlay functional activation and to localize signal differences, instead of using a standard template such as the MNI (Montreal Neurological Institute) or the Talairach 5 During scanning, you should collect three types of scans: 1. EPI functional scans 2. An in-plane T1-weighted scans with the same parameters as the EPI. You can use a 2D sequence like a Spin Echo. 3. A high resolution whole brain T1-weighted scan. Typically this scan has an isotropic (or almost isotropic ~1mm3) resolution and good gray/white contrast. An example is the popular MP-RAGE (Magnetization Prepared Rapid Acquisition Gradient Echo) 6. A good MP-RAGE sequence can be used for structural morphometry and gray/white matter segmentation, but it can also be used as a reference scan for EPI/in-plane T1 co- registration.

5 Talairach, J. & Tournoux, P. (1988) Co-planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme, New York.

6 Mugler, J. P., III & Brookeman, J. R. (1990) Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE), Magn Reson Med 15(1):152-157.

29 Co-registering Whole Brain Volumes In this step, you co-register the in-plane T1 to the high resolution 3 dimensional T1 scan. Click on the button in the fMRI switchboard. Select <1> for . Select under . Select for and