Journal of Neuroscience Methods 239 (2015) 170–182

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Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth

Basic Neuroscience

Optimized protocol for high resolution functional magnetic resonance imaging at 3 T using single-shot echo planar imaging Sebastian Domsch a,∗ , Jascha Zapp a , Lothar R. Schad a , Frauke Nees b , Holger Hill c , Derik Hermann c , Karl Mann c , Sabine Vollstädt-Klein c a

Department of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany c Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Germany b

h i g h l i g h t s • • • •

We transferred ultrahigh-resolution fMRI protocols to clinical 3 T settings. Thereby, the maximal t-value in the sensorimotor cortex (SMC) was increased by 66%. Number of activated clusters in the SMC was increased by a factor of 3.3. In the nucleus accumbens, the cluster number increased with the proposed protocol.

a r t i c l e

i n f o

Article history: Received 10 June 2014 Received in revised form 17 October 2014 Accepted 18 October 2014 Available online 29 October 2014 Keywords: fMRI Parallel imaging Spatial specificity Somatosensori motor cortex Motivation Nucleus accumbens

a b s t r a c t Background: To translate highly accelerated EPI-fMRI protocols as commonly used at ultra-high field strengths to clinical 3 T settings. New method: EPI protocols with increasing matrix sizes and parallel imaging (PI) factors were tested in two separate fMRI studies, a simple motor-task and a complex motivation-task experiment with focus on the sensorimotor cortex (SMC) and the nucleus accumbens (NAcc), respectively. Results: By increasing the matrix size and the PI-factor simultaneously, BOLD-sensitivity in terms of maximal t-values and numbers of activated clusters was uncompromised in single individuals in both fMRI experiments. In the SMC, the multi-subject analysis revealed an increase of 66% of the maximal t-value whereby the number of activated clusters was increased by a factor of 3.3 when the matrix size (PI-factor) was increased from 96 × 96 (R = 2) to 192 × 192 (R = 4). In the NAcc, the number of activated clusters increased from 5 to 7 whereby the maximal t-value remained unaffected when the matrix size (PI-factor) was increased from 96 × 96 (R = 2) to 160 × 160 (R = 3). Comparison with existing method: Using the proposed high-resolution EPI protocol, spatial blurring was clearly reduced. Further, BOLD sensitivity was clearly improved in multi-subject analyses and remained unaffected in single individuals compared to using the standard protocols. Conclusions: Conventionally used matrix sizes (PI-factors) might be non-optimal for some applications sacrificing BOLD spatial specificity. We recommend using the proposed high-resolution protocols applicable in detecting robust BOLD activation in fMRI. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Functional Magnetic Resonance Imaging (fMRI) exploits the blood-oxygenation-level dependent (BOLD) effect (Ogawa et al.,

∗ Corresponding author at: Department of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Tel.: +49 621 383 5120; fax: +49 621 383 5123. E-mail address: [email protected] (S. Domsch). http://dx.doi.org/10.1016/j.jneumeth.2014.10.014 0165-0270/© 2014 Elsevier B.V. All rights reserved.

1990a,b) to detect neuronal activation in the human brain. Singleshot gradient-echo echo planar imaging (EPI) is commonly used in fMRI benefiting from high temporal resolution enabling the detection of transient signal modulations in event-related paradigms with short and random stimuli. However, distortions, blurring and ghosting artifacts (Fellner et al., 2009; Heiler et al., 2010) are among the major factors deteriorating the EPI image quality caused by the long duration of the signal read out. Scans were commonly performed with limited spatial resolution to keep the signal read out short and to mitigate these artifacts. With the development of

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parallel imaging (PI) (Sodickson and Manning, 1997; Pruessmann et al., 1999; Sodickson et al., 1999; Griswold et al., 2002), a technique that exploits the spatial sensitivity of multiple receiver coils to accelerate the image acquisition, higher spatial resolution became feasible. High spatial specificity is desirable to study small brain structures with a dimension of only a few millimeters such as the amygdala composed of multiple nuclei or the nucleus accumbens (NAcc), a subregion of the ventral striatum which can be divided into the NAcc shell and the NAcc core. Both brain areas are associated with reward-learning (Knutson et al., 2001; Diekhof et al., 2008) and drug addiction (Diekhof et al., 2008; Vollstadt-Klein et al., 2010) and are therefore of great interest for cognitive neuroscience. While high spatial specificity in single subjects is important for clinical applications such as preoperative planning and neuronavigation (Vlieger et al., 2004; Sunaert, 2006; Wurm et al., 2008), high spatial specificity on the group level may benefit neuroscience and psychological studies relying on multi-subject analysis. In recent studies, the effects of PI and voxel volume reduction on activation detection in fMRI have been extensively investigated. It was demonstrated that PI improves BOLD spatial specificity due to reduced blurring (Preibisch et al., 2003; Fellner et al., 2009; Heidemann et al., 2012), whereas voxel volume reduction leads to reduced susceptibility artifacts (Bellgowan et al., 2006) and, despite the lower signal-to-noise ratio (SNR), potentially improves the BOLD sensitivity in some voxels due to reduced partial volume (Thulborn et al., 1997; Schweizer et al., 2008) and reduced physiological noise (Triantafyllou et al., 2006). Considering the advantages of higher spatial resolution in fMRI, recent studies carried out at ultra high field strength of 7 T with high SNR and PI performance (de Zwart et al., 2004) used small voxel sizes in the order of 1 mm requiring large matrix sizes (e.g. 192 × 192) in combination with high PI-factors (R ≥ 3) (Moeller et al., 2006; Speck et al., 2008; Moeller et al., 2010). However, most fMRI studies are still carried out at commonly available 3 T scanners with limited spatial resolution using small matrix size (e.g. 96 × 96) and low PI-factor (e.g. R = 2) (Metzger et al., 2013). The goal of the present study was to investigate whether spatial specificity in fMRI carried out at clinical field strength of 3 T can be improved by using a high-resolution EPI protocol as commonly used at ultra-high fields. In a first step, the feasibility of high PI-factors was tested in phantom measurements. Subsequently, simulations were used to analyze the effectiveness of increasing the matrix size and the PI-factor for improving the effective spatial resolution in the presence of T2 *-related blurring. Afterwards, the optimized EPI protocols were tested in two independent fMRI studies, a simple motor-task experiment with focus on the sensorimotor cortex (SMC) and a sophisticated motivation-task experiment with the NAcc being the target region. Based on the raw EPI image data, spatial specificity and time-course SNR (tSNR) were analyzed. Finally, BOLD sensitivity was assessed on the single subject and on the group level by means of maximal t-values and numbers of activated clusters.

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enables higher SNR and PI performance (de Zwart et al., 2004) and was used for the complex motivation-task study. 2.2. Development of a high-resolution protocol 2.2.1. Phantom study A phantom filled with distilled water and NiSO4 was used to analyze the effects of increasing matrix sizes and PI-factors on image quality. Thereby, matrix size and the PI-factor was stepwise increased from 96 × 96 (R = 2) to 192 × 192 (R = 6). The phantom T2 * relaxation time was 120 ms. Therefore, no significant T2 * -related blurring artifacts were expected and the major focus was on spatial aliasing and noise due to PI. Further imaging parameters of the different EPI protocols used are given in Table 1. 2.2.2. Point-spread function simulation To investigate the effects of T2 * -related blurring on spatial resolution, the spatial point-spread function (PSF) was simulated as a function of the T2 * -relaxation time for different matrix sizes and PI-factors (Fig. 1). First of all, k-space weighting functions were simulated for matrix sizes of 96 × 96, 128 × 128, and 192 × 192 pixels and the T2 * -relaxation times were varied between 10 ms and 225 ms. Thereby, T2 * was continuously increased in steps of 1 ms. The bandwidth in PE direction was set to 1000 Hz/Px and the PI-factor was varied between R = 1 and R = 4. The total read out duration was given as the ratio of the number of sampled phase encoding lines (i.e. matrix size in PE direction divided by the PI-factor) and the bandwidth. For example, using a matrix size (PI-factor) of 96 × 96 (R = 2), the total signal read out duration was supposed to be 48 ms (Fig. 1a). For all matrix size and PI-factors yielding a total signal read out shorter than 20 ms, the echo time (TE) was set to 40 ms (Fig. 1a). For longer signal read outs (i.e. larger matrix sizes in combination with low PI factors), the echo time was set to the minimal possible value. The simulated k-space trajectory is shown in Fig. 1b. In PE direction, k-space is traversed from +ky max to −ky max . In real MR measurements using PI, multiple lines in k-space are actually skipped depending on the PI-factor. However, in this simulation it was assumed that each single k-space line (in RO direction) is sampled and that in RO direction k-space is traversed from −kx max to +kx max for all odd lines and from +kx max to −kx max for all even lines. Hence, it was assumed that PI only affected the duration of the signal read out but not the actual k-space trajectory. Based on the kspace weighting function, the PSF was calculated along the RO and PE direction by applying a two dimensional Fourier Transformation. The effective spatial resolution was measured by the full width at half maximum (FWHM) of the simulated PSF. To convert the FWHM measured in pixels to a distance measured in milimeters, a FOV of 210 mm × 210 mm was assumed. 2.3. Functional MRI All participants of this study provided written informed consent according to the Declaration of Helsinki. The study was approved by the local ethics committee. 2.4. Finger-tapping study

2. Materials and methods 2.1. Hardware All imaging was performed at a 3 T whole-body MR-system (Magnetom Trio, Siemens Healthcare, Erlangen, Germany) using a 12 and a 32 channel head coil, respectively. The standard 12 channel head coil, suitable for most fMRI setups including earphones and goggles, was used for the finger-tapping study. The 32 channel coil with a reduced diameter compared to the 12 channel coil

Six healthy subjects (24 ± 3 years, 3 females) performed three runs of a bilateral finger-tapping task with a total duration of 4 min 30 s. The block designed paradigm consisted of 10 active and 10 rest periods with a duration of 13.5 s each. Keeping the sequence timing constant, the EPI matrix size (PI-factor) was alternated randomly. The matrix sizes (PI-factors) were 96 × 96 (R = 2), 128 × 128 (R = 3), and 192 × 192 (R = 4). To avoid ordering effects, the matrix size (PIfactor) was alternated pseudo randomly. In total, each subject was scanned with three different EPI protocols acquiring 105 images

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Fig. 1. Single shot EPI signal read out. (a) Signal decay during EPI read out shown for different T2 * -relaxation times. (b) Sampling function in k-space assuming a T2 * -relaxation time of 35 ms and a matrix size (PI-factor) of 192 × 192 (R = 4). (c) Spatial PSF at different T2 * -relaxation times.

volumes per run. Additionally, a high-resolution T1-weighted 3D image was acquired for each subject to detect potential morphological abnormalities in individuals. Further imaging parameters of the different EPI protocols used are given in Table 1. 2.5. Motivation-task study Initially, 19 subjects were recruited and measured. Data from four participants had to be discarded because of heavy head movement or incompliance with the task instruction. The final sample consisted of 15 healthy right-handed subjects (age = 26 ± 6 years, 3 females). The participants were measured using a standard and a high-resolution EPI protocol in randomized order. The motivation-task comprised 48 trials with a pseudorandom order of presentation and a total duration of 16 min and 3 s. Each trial consisted of an anticipation phase, an instrumental response phase, and an outcome phase (Fig. 2). During the anticipation interval, participants were informed if they could win money in the actual trial (condition “money win”) or not (condition “no win”). In the following instrumental response phase of 3 s duration, participants had to press a button as often as possible where each button press was rewarded with money in the condition “money win” only. In the outcome phase, feedback regarding the money amount gained in the actual trial was provided. To get familiar with the task, participants completed a practice session outside the scanner. Then, participants’ individual performance was assessed in the scanner environment before the fMRI task started. This information was used to calculate the individual gain per button press to standardize the total win (average gain per run: 15 D ). The motivation-task was adapted from Bühler and colleagues (2010). In addition to the fMRI protocols highresolution T1-weighted 3D image data were acquired to detect potential morphological abnormalities in individuals. The standard and the high-resolution EPI datasets were acquired at matrix sizes

Fig. 2. Design of the motivation-task experiment.

of 96 × 96 (R = 2) and 160 × 160 (R = 3), respectively. Further imaging parameters of the different EPI protocols used are summarized in Table 1. The neuronal responses of event-related paradigms are less robust compared to block-designed paradigms. Thus, to ensure to be able to detect robust activation in the NAcc, the voxels sizes and PI-factors were not chosen as high as for the finger-tapping experiment where clearly more robust activation was expected. Thereby, the reduction factor was limited to R = 3 and the matrix size to 160 × 160 pixels. The echo time was also reduced i.e. from TE = 35 ms to TE = 27 ms since the NAcc is located in the limbic system where the optimal echo time is shorter due to stronger magnetic field inhomogeneities compared to cortical brain areas. Moreover, the FOV was actually slightly reduced to increase the

Table 1 EPI imaging protocols used for the phantom, finger-tapping and motivation-task study. Here, the ACS lines refers to the number of auto-calibration signal lines (Griswold et al., 2002) in phase encoding (PE) direction. Sequence parameters

TR [ms] TE [ms] FOV [mm2 ] # Slices Slice thickness [mm] Inter-slice gap [mm] Bandwidth [Hz/Px] Matrix size PI-factor # ACS lines

Phantom study Finger-tapping study

Finger-tapping study

2700 35 210 × 210 35 3 1.5 1002 96 × 96 2 24

2700 35 210 × 210 35 3 1.5 1002 128 × 128 3 36

Motivation-task study Finger-tapping study

2700 35 210 × 210 35 3 1.5 1116 160 × 160 3 36

2700 35 210 × 210 35 3 1.5 1002 192 × 192 4 48

2700 35 210 × 210 35 3 1.5 1002 192 × 192 5 55

2700 35 210 × 210 35 3 1.5 1102 192 × 192 6 84

3000 27 192 × 192 40 2 1.0 1085 96 × 96 2 24

3000 27 192 × 192 40 2 1.0 1085 160 × 160 3 36

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spatial resolution since the NAcc is a rather small brain area. For the same reason, the slice thickness and the inter-slice gap were slightly reduced to increase the spatial coverage in the NAcc. As a consequence of smaller slice thickness and reduced inter-slice gap, the number of slices was increased to 40 to enable whole-brain coverage. Acquiring the morphological images and the functional 96 × 96 (R = 2) and the 160 × 160 (R = 3) datasets required already approximately 1 h total including preparations and breaks. In order to keep the stress for our volunteers to an acceptable level, we resigned from acquiring any more datasets with different matrix sizes (PI-factors). 2.6. Image post-processing For PI reconstruction the Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm (Griswold et al., 2002) was used for robust sensitivity toward functional activation (Preibisch et al., 2008). The commonly used statistical parametric mapping software spm8 (http://www.fil.ion.ucl.ac.uk/spm) was used for further processing. The first five images were excluded from postprocessing to reduce non-steady state signal fluctuations. First, the images were registered and realigned to the mean image of each time series. For the motivation-task datasets, a slice time correction was performed to account for temporal differences in the slice acquisition times important for event-related studies. Afterwards, the images were normalized to the Montreal Neurological Institute (MNI) space (Collins et al., 1993). For the purpose of a fair comparison between the extend of activated brain areas found in the datasets acquired at different spatial resolutions, all EPI images were resampled to the same voxel size of 1.0 mm × 1.0 mm × 1.0 mm for the finger-tapping and 1.0 mm × 1.0 mm × 2.0 mm for the motivation-task study. Thereby, a 4th degree b-spline interpolation was used. Finally, the fingertapping datasets were spatially smoothed with a Gaussian kernel size at FWHM of 3.2 mm × 3.2 mm × 3 mm. The motivation-task datasets were smoothed with a Gaussian kernel size at FWHM of 3.0 mm × 3.0 mm × 8.0 mm. 2.7. Time-course SNR analysis BOLD sensitivity in fMRI experiments depends on tSNR (Parrish et al., 2000). To analyze the effects of increased matrix size and PIfactors, the tSNR was calculated pixel-by-pixel as the mean signal intensity of the time series, , divided by the standard deviation : tSNR = /.

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2.8. Statistical analysis The General-Linear-Model (GLM) was used to model BOLD signal changes on a voxel-by-voxel basis (SPM first level analysis). The movement parameters estimated during spatial realignment were considered in the GLM as six additional regressors of no interest. The hemodynamic responses during the blocks in the finger-tapping task as well as in the motor response phase of the motivation-task were modeled by box-car functions, convolved with a synthetic hemodynamic response. For the anticipation and feedback phases in the motivation-task delta functions convolved with a synthetic hemodynamic response function were used. The conditions of interest were “tapping” for the fingertapping study and “money win” for the motivation-task study, both versus implicit baseline. Brain activation during the different phases of the motivation-task, differential contrasts between “money win” and “no win” as well as associations between instrumental responding and brain activation will be reported elsewhere. For the finger-tapping task datasets, a group analysis (SPM second level analysis) was performed to detect BOLD activation in the SMC. Moreover, for the motivation-task datasets, a group analysis was performed to detect BOLD activation in the NAcc which was shown to be activated during the anticipation of (monetary) reward (Knutson et al., 2001; Buhler et al., 2010). 3. Results 3.1. Phantom and simulation study Fig. 3 shows that the 192 × 192 (R = 4) EPI protocol is feasible for both coil settings without producing obvious PI-artifacts. Using the 12-channel head coil, the standard deviation measured over the whole phantom-slice increased by 20%, 40% and 90% when the matrix size (PI-factor) was increased from 96 × 96 (R = 2) to 128 × 128 (R = 3), 160 × 160 (R = 3) and 192 × 192 (R = 4), respectively. When the PI-factor was further increased, the spatial variance significantly increased by 270% (R = 5) and 530% (R = 6) due to aliasing artifacts. In comparison, better PI-performance was seen when the 32-channel head coil was used enabling a reduction factor of R = 5. Thereby, the standard deviation measured in the whole phantom-slice was only moderately increased by 20% using the 192 × 192 (R = 5) compared to the 96 × 96 (R = 2) EPI protocol. However, clear PI-artifacts accompanied by a significant increase of the spatial variance (i.e. standard deviation increase of 100%) can be observed when the PI-factor was further increased to R = 6.

Fig. 3. Phantom images acquired with different EPI protocols using the 12- and the 32-channel head coil. Each panel shows the spatial standard deviations (sigma), measured in the ROI (red circle), normalized to the standard deviation measured in the 96 × 96 (R = 2) image. To emphasize artifacts, each image is displayed at a different contrast. Obvious PI artifacts are marked by the red arrows. (For interpretation of the references to color in this text, the reader is referred to the web version of the article.)

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Fig. 4. Simulation of the PSF using different EPI protocols. (a) FWHM of the PSF as a function of T2 * shown for increasing PI-factors using a matrix size of 192 × 192 pixels. (b) FWHM in PE direction shown for different matrix sizes (PI-factors). The FOV was assumed to be 210 mm × 210 mm.

For a matrix size of 192 × 192 pixels, the simulation (Fig. 4a) shows that the resolution in PE direction was effectively improved by increasing the PI-factor. At a TE of 40 ms, the FWHM of the simulated PSF decreased from 3.0 mm (R = 1) to 1.7 mm (R = 2), 1.4 mm (R = 3) and to 1.3 mm (R = 4). It can be seen that there was no significant effect on spatial resolution when the PI-factor was further increased to R ≥ 5. From Fig. 4b can be seen that the resolution can be significantly increased by using a 192 × 192 (R = 4) compared to a 96 × 96 (R = 2) or a 128 × 128 (R = 3) protocol. Thereby, the FWHM at TE of 40 ms decreased from 2.8 mm to 2.0 mm to 1.4 mm, respectively.

3.2. In vivo analysis For both coil set-ups, Fig. 5 reveals that the in-plane resolution was clearly improved revealing more structural details in all subjects shown when the high-resolution EPI protocols were used in comparison to a conventional low-resolution protocol. Moreover, the orbito-frontal area, here shown for one representative subject, was apparently less affected by signal voids and image distortions, which was found in all subjects. With the 12-channel head coil, apparently minor PI-artifacts in orbito-frontal and limbic brain areas were observed when the 192 × 192 (R = 4) protocol was used. Fig. 6a shows a comparison between a high-resolution T2 weighted axial imaging slice and the same slice acquired with different EPI protocols when the 12-channel head coil was used. It can be seen that the structural similarity between the highresolution T2 -image and the EPI images continuously increased when the matrix size (PI-factor) was increased. Fig. 6b and c shows line profiles in PE and RO direction, as depicted in Fig. 6a, for the T2 -image and the EPI images acquired at increasing matrix size (PIfactor). Apparently the line profiles in the 192 × 192 (R = 4) dataset were more similarity to the line profiles of the T2 -image compared to the line profiles of the lower resolution EPI images. Thereby, the correlation coefficients between the T2 - and the EPI line profiles continuously increased in PE-direction from 76.3% to 83.7% to 85.2% when the matrix size (PI-factor) was increased. The correlation coefficient between the respective line profiles along the RO direction also increased from 78.4% to 80.4% and to 84.2%. For both coil setups, tSNR was significantly decreased in most brain areas especially in subcortical brain areas when the matrix size (PI-factor) was increased (Fig. 7). However, the pixel-by-pixel tSNR ratio reveals that some region close to tissue/tissue and

tissue/bone interfaces exhibit unaffected tSNR values using the 12channel head coil (Fig. 7a) and slightly increased tSNR values using the 32-channel head coil (Fig. 7b), which was found in all subjects. Tables 2 and 3 show tSNR values for the different EPI protocols used and for both coil setups measured in each individual. With the 12-channel head coil, tSNR in the SMC was on average

Table 2 Temporal SNR measured in the SMC and the NAcc at different matrix sizes (PIfactors) using a 12 channel head coil.

Subject

96 × 96 R=2

96 × 96 R=2

128 × 128 128 × 128 192 × 192 192 × 192 R=3 R=3 R=4 R=4

SMC

NAcc

SMC

NAcc

SMC

NAcc

105 105 89 16 108 49 78.7 37.8

51 54 55 32 53 31 46.0 11.3

41 40 38 14 39 16 31.3 12.7

33 29 28 13 26 14 23.8 8.3

16 15 15 6 16 6 12.3 4.9

1 93 108 2 77 3 23 4 5 110 60 6 78.5 Mean Standard 33.2 deviation

Table 3 Temporal SNR measured in the SMC and the NAcc at different matrix sizes (PIfactors) using a 32 channel head coil. 96 × 96 R=2

96 × 96 R=2

160 × 160 R=3

160 × 160 R=3

Subject

SMC

NAcc

SMC

NAcc

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Mean Standard deviation

36 34 63 63 58 78 48 77 65 100 70 63 41 79 45 61.3 18.3

36 41 38 42 38 44 29 48 40 54 41 42 41 48 35 41.1 6.0

29 24 45 33 35 34 43 43 37 42 32 21 30 43 34 35.0 7.3

13 15 15 15 13 16 14 17 15 17 15 12 15 17 15 14.9 1.5

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Fig. 5. EPI data acquired with different protocols and coil set-ups. (a) Typical EPI images shown for five random subjects acquired with a conventional 96 × 96 (R = 2) and a high-resolution 192 × 192 (R = 4) protocol using a 12-channel head-coil. (b) Typical EPI images shown for five random subjects acquired with a conventional 96 × 96 (R = 2) and a high-resolution 160 × 160 (R = 3) protocol using a 32-channel head-coil. The respective two top rows in (a) and (b) show the whole imaging slice. The respective two bottom rows show enlarged views to emphasize structural differences between the low and the high-resolution images, which are marked by green arrows. Parallel imaging artifacts are marked by red arrows. For the 12-channel head coil, the focus is laid on the SMC brain area activated during finger-tapping. For the 32-channel head coil, the focus is laid on the NAcc brain area activated during the motivation-task. For fair comparison, all images were resampled to a 256 × 256 matrix size using bilinear interpolation. (For interpretation of the references to color in this text, the reader is referred to the web version of the article.)

reduced by 41.4% and 69.7% using the 128 × 128 (R = 3) and the 192 × 192 (R = 4) protocol, respectively, compared to the 96 × 96 (R = 2) protocol (Table 2). In the NAcc region, tSNR loss was more pronounced than in the SMC with an average decrease of 60.2% and 84.4%, respectively. With the 32-channel head coil, tSNR in the SMC was on average reduced by 42.9% using the 160 × 160 (R = 3) compared to the 96 × 96 (R = 2) protocol (Table 3). As observed for the 12-channel coil, tSNR loss in the NAcc brain area was clearly higher than in the SMC with an average decrease of 63.7% when the high-resolution EPI protocol was used. It should be noted that

across all subjects, the variance of tSNR values in the SMC and NAcc was clearly reduced using a high-resolution protocol. 3.3. Functional MRI 3.3.1. Finger-tapping task The single subject fMRI results are presented in Table 4. It shows that on the single subject level, there was a slight increase of the average maximal t-value and the number of clusters of 2% and 18%, respectively, when the matrix size (PI-factor) was increased from

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Fig. 6. Line-profile comparison for EPI data acquired with a 12-channel head coil. (a) Comparison between an axial slice acquired with a high-resolution T2 -weighted imaging sequence and with different EPI protocols. All EPI images were resampled to a 320 × 320 matrix size using bilinear interpolation. Conspicuous structural differences are marked by the red arrows. Line profiles, marked by the green lines, along the PE (b) and RO direction (c). (For interpretation of the references to color in this text, the reader is referred to the web version of the article.)

96 × 96 (R = 2) to 128 × 128 (R = 3). Further increasing the matrix size (PI-factor) to 192 × 192 (R = 4), the average maximal t-value dropped by 9% and the average number of clusters increased by 9% compared to using the 96 × 96 (R = 2) protocol. However, based on a paired t-test there was no significant effect on either the maximal t-value or the cluster number in dependence on which particular EPI protocols was used. Fig. 8 shows that robust BOLD activation was detected on the single subject (Fig. 8a and b) and on the group level (Fig. 8c and d) when the matrix size (PI-factor) was increased. Apparently the BOLD activation was less blurred and spatially more separable in terms of more activated clusters using the 192 × 192 (R = 4) compared to the 96 × 96 (R = 2) protocol. On the group level no significant difference between the maximal t-value in the 96 × 96 (R = 2) and 128 × 128 (R = 3) dataset was found supposing a statistical error of 10% (Fig. 9a). Using the 192 × 192 (R = 4) protocol, the maximal t-value was increased by 41% and 66% compared to the 96 × 96 (R = 2) and 128 × 128 (R = 3) datasets. Fig. 9b shows that the number of clusters continuously increased when the matrix size (PI-factor) was increased. For a

fixed statistical threshold of p ≤ 5 × 10−3 , the number of clusters was increased by a factor of 3.3 when the matrix size (PI-factor) was increased from 96 × 96 (R = 2) to 192 × 192 (R = 4). Fig. 10 shows that the finding of increased numbers of activated clusters detected at higher spatial resolution was threshold independent. 3.4. Motivation-task Robust bilateral NAcc activation was detected using both the 96 × 96 (R = 2) and the 160 × 160 (R = 3) EPI protocol (Fig. 11a and b) whereby the BOLD activation was less blurred in the highresolution dataset. Moreover, only one cluster in each hemisphere was detectable in the coronal slices of the standard 96 × 96 (R = 2) datasets whereas two clusters with separable local maxima in each hemisphere were detectable in the 160 × 160 (R = 3) datasets. Within an error margin of 10%, the maximal t-value was not affected when the matrix size (PI-factor) was increased (Fig. 11c) while the number of clusters was in increased from 5 to 8 (Fig. 11d) using the high-resolution protocol. 4. Discussion

Table 4 Single subject fMRI results in a finger-tapping task using different EPI protocols. The maximal t-value (Tmax ) and the number of clusters (# Clusters) were measured in the sensorimotor cortex. The numbers of clusters were measured at a statistical threshold of p < 5 × 10−3 (uncorrected). The cluster size was set to k > 30 voxels. 96 × 96 (R = 2) max

128 × 128 (R = 3) max

192 × 192 (R = 4) max

Subject

T

#Clusters

T

# Clusters

T

# Clusters

1 2 3 4 5 6 Mean Standard deviation

13.9 8.0 8.9 6.4 9.8 8.7 9.3 1.0

12 7 15 8 9 17 11 2

14.0 9.5 8.3 8.9 8.1 8.1 9.5 0.9

19 11 18 8 5 18 13 2

9.4 9.9 8.3 8.3 8.1 7.1 8.5 0.4

13 10 9 5 22 14 12 2

The proposed high-resolution EPI protocol is based on the phantom and PSF simulation study. The phantom measurements revealed that PI-factors up to R = 4 and R = 5 are feasible using the 12- and the 32-channel head coil, respectively, without leading to major aliasing artifacts. The PSF simulation further showed that for a matrix size of 192 × 192 pixels, a PI-factor of R = 4 is highly efficient to reduce T2 * -related blurring artifacts. The maximal reduction factor during fMRI was limited to R = 4 since the PSF simulation indicated that blurring reduction by higher PI-factors becomes ineffective. Moreover, the risk of spatial aliasing increases at higher reduction factors especially when a coil with a smaller number of channels is used as seen in the phantom study using a 12-channel head coil. Thereby, the PI-performance critically depends on the number of coils used (de Zwart et al., 2004). Using a head coil

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Fig. 7. tSNR maps using conventional and high-resolution EPI protocols. Axial, coronal and sagittal tSNR maps acquired with the 12-channel (a) and 32-channel head coil (b). The voxelwise ratio of the low- and high-resolution tSNR maps are shown below in each case. A region of interest analysis was performed in the SMC and NAcc marked by the black ellipse. The time series images were spatially realigned prior to tSNR calculation.

with a smaller number of channels, Preibisch and colleagues (2008) found that the optimal PI-factor was limited to only R = 2 before the incidence of unfolding artifacts increased. In vivo, the benefits for both coil set ups using a high-resolution EPI protocol were improved in-plane resolution revealing more structural details due to reduced blurring artifacts and less image distortions and signal voids in the orbito-frontal brain area. However, minor PI-artifacts occurred in limbic and orbito-frontal brain areas when the 12-channel head coil in combination with the 192 × 192 (R = 4) EPI protocol was used. In contrast, brain areas located closer to the head coil revealed no obvious PI-artifacts probably due to higher SNR enhancing the PI-performance. As expected, no obvious PI-artifacts were observed the high-resolution datasets using the 32-channel head coil enabling better PI-performance

compared to the 12-channel head coil in agreement with the study by Triantafyllou et al. (2011). The line profile analysis revealed that the improved resolution observed in the high-resolution datasets was not only a result from an increased spatial resolution in RO but also in PE direction. Even though BOLD spatial specificity is limited by blurring of the BOLD response itself (Shmuel et al., 2007), draining veins (Yacoub et al., 2003; Olman et al., 2007), movement artifacts, and spatial smoothing, the increased spatial resolution was also visible in the BOLD activation maps of the finger-tapping and the motivation-task experiment. The BOLD activation appeared less blurred and spatially more separable using the high-resolution compared to the standard protocol in consistence with Schmidt et al. (2007).

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Fig. 8. fMRI results using EPI with different matrix sizes (PI-factors). (a and b) Single subject results of a representative subject and (c and d) group analysis (N = 6) showing robust BOLD activation in the SMC during a bilateral finger-tapping task. The zoomed versions of the ROIs are displayed at different statistical threshold levels. The blue crosshair marks the peak voxel in each dataset. The activation maps are depicted in MNI-space and were superimposed on a T1-weighted structural image. (For interpretation of the references to color in this text, the reader is referred to the web version of the article.)

Fig. 9. SMC group activation (N = 6) using different matrix sizes (PI-factors). (a) Maximal t-values and (b) number of activated clusters at p < 5 × 10−3 (k ≥ 30 voxels) found in the SMC.

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Fig. 10. Number of activated clusters in dependence of the statistical threshold using different EPI protocols. The cluster number was measured in the SMC. The error bars denote a supposed error of 10%. The cluster size was k ≥ 30 voxels.

A drawback for fMRI using a high-resolution compared to a conventional EPI protocol is a significant tSNR loss in nearly all brain areas due to reduced voxel size. For both coils there was an average tSNR reduction of more than 40% and 60% in the SMC and NAcc, respectively, using the respective high-resolution EPI protocols. However, across all subjects we also found region with increased tSNR in cortical brain areas near tissue/tissue and tissue/bone

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interfaces in the high-resolution datasets when the 32-channel head coil was used. Whether the increased tSNR values are artificially produced by imperfect image realignment or rather result from reduced signal dephasing due to smaller voxel sizes needs further examination. In the latter case, this effect could be exploited in e.g. somatopy studies relying on high spatial resolution and focusing on only a few cortical layers. Considering the dependency between tSNR and t-values (Parrish et al., 2000), one would expect clearly reduced BOLD sensitivity in the high-compared to the low-resolution fMRI datasets. In fact, in single individuals we observed a mild but still insignificant reduction of maximal t-values in agreement with (Fellner et al., 2009) whereby the number of activated clusters remained unaffected by using a high-resolution EPI protocol. The fMRI study carried out by Schmidt et al. (2007) revealed a small but significant decrease of t-values at higher spatial resolution which might be due to the specific head coil used with only a limited number of channels (i.e. 8-channel head coil) enabling lower SNR and PI-performance compared to our 12- and 32-channel head coils. The cause for rather uncompromised BOLD sensitivity observed in the high-resolution fMRI datasets may be the reduced partial volume which has been discussed in the literature (Frahm et al., 1993; Moeller et al., 2006; Schmidt et al., 2007; Newton et al., 2012). In agreement with the single subject fMRI results also the multisubject analysis revealed that in the high-resolution datasets BOLD activation appeared less blurred and more separable. Moreover, we observed a significant increase of 66% of the maximal t-value in the SMC using the 192 × 192 (R = 4) compared to the 96 × 96 (R = 2) protocol. Furthermore, the group analysis of both the fingertapping and the motivation-task experiment showed an increase of the number of activated clusters in the high-resolution datasets.

Fig. 11. NAcc group activation (N = 15) using different EPI protocols. (a and b) BOLD activation depicted in MNI-space and superimposed on a T1-weighted structural image. Activation outside the NAcc is not shown using a binary mask from the Nielsen and Hansen database (2002). (c) Maximal t-values and (d) number of activated clusters in the NAcc detected at a statistical threshold of p < 1% (uncorrected). The cluster size was k ≥ 20 voxels.

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We suppose that the increased maximal t-values and the increased number of clusters potentially result from an improved spatial alignment of activated brain areas in different subjects which could be the consequence of improved inter-subject image registration due to reduced blurring and distortions artifacts in the high-resolution images. In support of that hypothesis, Fellner and colleagues have shown that geometric distortions in EPI are in the order of a few millimeters even in rather homogeneous brain areas such as the precentral gyrus (Fellner et al., 2009), which the SMC is part of. The authors further show that increasing the PI-factor or the matrix size independently significantly reduces such distortions by up to a few millimeter. Standard unwarping methods were previously proposed to reduce geometric distortions in fMRI. However, the spatial accuracy of these standard methods is limited (Jezzard and Balaban, 1995). More advanced distortion correction methods have been developed to increase the spatial accuracy e.g. by using a dual echo EPI updating the field map with each EPI scan (Hutton et al., 2002) or by using a phase labeling algorithm (Xiang and Ye, 2007). However, both methods either compromise temporal resolution crucial to detect transient signal modulations in event-related fMRI studies or they require sophisticated sequence programming. Hence, besides the general benefits of using higher spatial resolutions, increasing the matrix size in combination with the PI-factor might be more eligible for distortion correction in EPI. An important question is whether lower tSNR values or more false-positives may have caused an increased number of clusters of smaller extend observed in the high-resolution datasets. Using the same statistical threshold, the primary effect of lower tSNR/tvalues should be that truly activated peaks would fall below the threshold causing fewer clusters to be detectable neglecting the partial-volume effect mentioned above. The tSNR maps shown for the representative subject appeared more homogeneous using a high compared to a low-resolution EPI protocol. Moreover, the tSNR analysis of all subjects further revealed that the inter-subject variability was thereby decreased in the SMC and NAcc indicating that the chance of artificially increasing the number of clusters should decrease using a high-resolution protocol. With respect to false-positives, a strong argument for the clusters observed in the high-resolution datasets to be non-false positives is that their spatial location lay within the brain areas expected to be activated and also coincide with the cluster locations seen in the low resolution datasets. Hence, we assume that the increased number of clusters originates from improved spatial specificity which was observed in the raw EPI images. The motivation-task study revealed that robust fMRI at higher in-plane resolution is also feasible in a complex event-related stimulus design using EPI. Considering the increased number of activated clusters detected in the group analysis of both fMRI experiments using a high-resolution compared to a conventional protocol supports the finding that higher in-plane resolutions leads to more distinguishable BOLD activation. Detecting distinguishable brain activations in small brain regions is of great importance in cognitive neuroscience. The target region of our motivation-task study was the NAcc, which is a major part of the ventral striatum and plays a central role in the mesolimbic reward circuit (Knutson et al., 2001) and in the development and maintenance of addiction (Everitt and Robbins, 2005). Animal work suggests that two components of the NAcc, namely the core and the shell, have differential functions (Zahm, 1999; Everitt and Robbins, 2005). Whereas the shell mediates the acute effects of primary reinforcers or unconditioned cues (“motivational valence”), the core seems to be more involved in evaluating the “motivational value” of conditioned cues or secondary reinforcers and is responsible for sensory motor integration and the regulation of goal-directed behavior. In the high-resolution EPI data of our motivation-task study, activation clusters appear more

distinguishable and less blurred compared to the standard EPI data. This can be seen for example in the coronal slices of the group activation maps where the 96 × 96 (R = 2) datasets detected only one cluster in each hemisphere whereas the 160 × 160 (R = 3) datasets revealed two clusters in each hemisphere. The lateral cluster might correspond to the NAcc core region and the medial cluster might be located in the NAcc shell region. However, in humans, the separation of shell and core is not as clear as in animals, on the histological as well as the functional level. To the best of our knowledge, there is only one study in humans examining this topic using fMRI in pain processing (Aharon et al., 2006). However, the authors suggest using higher field strength and a better spatial resolution in future studies. In our motivationtask study, one would expect activation of the NAcc core, because participants viewed a cue announcing the secondary reinforcer money. However, activation of the NAcc shell would be also plausible, since studies in rodents suggest the NAcc shell to be activated during reward anticipation (Ikemoto and Panksepp, 1999). The results of our motivation-task study indicate that with the proposed 160 × 160 (R = 3) EPI protocol, small brain regions might be functionally more separable than with the standard 96 × 96 (R = 2) EPI protocol. However, the motivation-task was not designed to differentially activate the NAcc shell and core. We used it in a first step for this feasibility study, because the task was shown to robustly activate the NAcc in general. In a second step, further studies should elaborate experimental paradigms clearly activating the NAcc shell and core separately during different conditions. Furthermore, in our opinion high-resolution fMRI might support such further research on examining the role of the NAcc shell and core in humans. Subregion of the SMC are extensively studied in monkeys (Muakkassa and Strick, 1979; Macpherson et al., 1982; Mitz and Wise, 1987). Studies in humans suggest a role of the SMC in movement planning and execution (Roland et al., 1980; Halsband et al., 1994) as well as in the coordination of movement and bimanual coordination (Brinkman, 1981; Shima and Tanji, 1998; Serrien et al., 2002). A finger-tapping task includes voluntary movement as well as coordination, which makes it plausible that separable clusters are activated during this task. However, the finger-tapping task used is not appropriate for the differentiation of activations in the functional SMC subregion. Further studies should therefore use tasks activating distinguishable subregion during different conditions, e.g. using different fingers or unimanual versus bimanual task conditions. In this study, a conventional spatial smoothing method was used. However, conventional smoothing can decrease BOLD sensitivity and introduce spatial blurring of the BOLD activation in the case that only a few activated voxels are averaged with many nonactivated voxels which likely occurs at higher spatial resolutions. To avoid this effect, Tabelow and colleagues (2009) proposed an ‘adaptive’ smoothing algorithm where solely activated voxels are averaged. Therefore, in the context of high-resolution fMRI, adaptive smoothing should rather be used than conventional smoothing to reduce extra blurring and to increase BOLD sensitivity without sacrificing spatial specificity. On the group level, both fMRI experiments independently showed increased maximal t-values and increased numbers of clusters in the high-resolution datasets which indicates that BOLD activation can be detected more robustly and appears spatially more distinguishable using the proposed high-resolution protocols. Considering this, it should be tested in future studies whether robust BOLD activation can still be detected in multi-subject fMRI analyses at even higher spatial resolution. Further increasing the matrix size (PI-factor) could be beneficial only if the signal read out is kept short enough to avoid extra blurring and to maintain a BOLD sensitive echo time. This would require PI-factors beyond R = 4, which produces severe aliasing using a standard 12-channel head

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coil, as was shown in the phantom study. However, with an optimized coil set up, e.g. using a 32-channel head coil enabling higher PI-factors (de Zwart et al., 2004) and improved SNR (Fellner et al., 2009) potentially facilitates high-resolution fMRI with improved BOLD sensitivity. In summary, we have shown that the proposed high-resolution EPI protocols yield images with only minor aliasing and improved structural information compared to conventional EPI images. The BOLD activation detected in the multi-subject analysis demonstrated less blurring and was more robust whereby the activation sensitivity in single subjects remained unaffected. 5. Conclusion In conclusion, conventionally used matrix sizes (PI-factors) might be non-optimal for some applications sacrificing BOLD spatial specificity. We recommend using the proposed high-resolution protocols turning out to be applicable in detecting robust BOLD activation at higher spatial specificity. Thus, the high-resolution protocols should be suitable for clinical applications such as preoperative planning and neuro-navigation (Vlieger et al., 2004; Sunaert, 2006; Wurm et al., 2008) ideally requiring both high BOLD spatial specificity and sensitivity (Vlieger et al., 2004). Conflict of interest statement None of these authors has any current or potential conflict of interest concerning this paper. Acknowledgements We would like to thank Michael Rieß and Damian Karl for their assistance in data collection and Sebastian Weingärtner for fruitful discussions. The study was supported by the Deutsche Forschungsgemeinschaft (SFB 636). References Aharon I, Becerra L, Chabris CF, Borsook D. Noxious heat induces fMRI activation in two anatomically distinct clusters within the nucleus accumbens. Neurosci Lett 2006;392(3):159–64. Bellgowan PS, Bandettini PA, van Gelderen P, Martin A, Bodurka J. Improved BOLD detection in the medial temporal region using parallel imaging and voxel volume reduction. Neuroimage 2006;29(4):1244–51. Brinkman C. Lesions in supplementary motor area interfere with a monkey’s performance of a bimanual coordination task. Neurosci Lett 1981;27(3):267–70. Buhler M, Vollstadt-Klein S, Kobiella A, Budde H, Reed LJ, Braus DF, Buchel C, Smolka MN. Nicotine dependence is characterized by disordered reward processing in a network driving motivation. Biol Psychiatry 2010;67(8):745–52. Collins DL, Mills SR, Brown ED, Kelly RL, Peters TM. 3D statistical neuroanatomical models from 305 MRI volumes. In: IEEE Conference Record; 1993. p. 1813–7. de Zwart JA, Ledden PJ, van Gelderen P, Bodurka J, Chu R, Duyn JH. Signal-to-noise ratio and parallel imaging performance of a 16-channel receive-only brain coil array at 3.0 Tesla. Magn Reson Med 2004;51(1):22–6. Diekhof EK, Falkai P, Gruber O. Functional neuroimaging of reward processing and decision-making: a review of aberrant motivational and affective processing in addiction and mood disorders. Brain Res Rev 2008;59(1):164–84. Everitt BJ, Robbins TW. Neural systems of reinforcement for drug addiction: from actions to habits to compulsion. Nat Neurosci 2005;8(11):1481–9. Fellner C, Doenitz C, Finkenzeller T, Jung EM, Rennert J, Schlaier J. Improving the spatial accuracy in functional magnetic resonance imaging (fMRI) based on the blood oxygenation level dependent (BOLD) effect: benefits from parallel imaging and a 32-channel head array coil at 1.5 Tesla. Clin Hemorheol Microcirc 2009;43(1–2):71–82. Frahm J, Merboldt KD, Hanicke W. Functional MRI of human brain activation at high spatial resolution. Magn Reson Med 1993;29(1):139–44. Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47(6):1202–10. Halsband U, Matsuzaka Y, Tanji J. Neuronal activity in the primate supplementary, pre-supplementary and premotor cortex during externally and internally instructed sequential movements. Neurosci Res 1994;20(2):149–55. Heidemann RM, Ivanov D, Trampel R, Fasano F, Meyer H, Pfeuffer J, Turner R. Isotropic submillimeter fMRI in the human brain at 7 T: combining reduced

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Optimized protocol for high resolution functional magnetic resonance imaging at 3T using single-shot echo planar imaging.

To translate highly accelerated EPI-fMRI protocols as commonly used at ultra-high field strengths to clinical 3T settings...
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