NeuroImage 47 (2009) Contents lists available at ScienceDirect. NeuroImage. journal homepage:

NeuroImage 47 (2009) 1797 1808 Contents lists available at ScienceDirect NeuroImage journal homepage: www.elsevier.com/locate/ynimg Continuous performance of a novel motor sequence leads to highly correlated striatal and hippocampal perfusion increases María A. Fernández-Seara a,, Maite Aznárez-Sanado a, Elisa Mengual b,c, Francis R. Loayza a, María A. Pastor a a Functional Neuroimaging Laboratory, Center for Applied Medical Research (CIMA), University of Navarra, Pio XII, 55, 31008 Pamplona, Spain b Neuroanatomy of Basal Ganglia Laboratory, Division of Neuroscience, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain c Department of Anatomy, Medical School, University of Navarra, Spain article info abstract Article history: Received 15 December 2008 Revised 14 May 2009 Accepted 15 May 2009 Available online 28 May 2009 Keywords: Arterial spin labeling 3D GRASE Motor sequence learning Procedural memory Putamen Hippocampus The time course of changes in regional cerebral perfusion during a continuous motor learning task performed with the right hand was monitored using the arterial spin labeling (ASL) technique at high field (3 T). ASL allowed measuring explicit learning related effects in neural activity elicited throughout a 6 minute task period. During this time learning took place as demonstrated by performance improvement. Comparing the initial and final learning phases, perfusion decreases were detected in most of the cortical regions recruited during early learning. More interestingly however perfusion increases were observed in a few cortical and subcortical regions of the contralateral hemisphere: the supplementary motor area, the primary somatosensory area, the posterior insula and posterior putamen, the hippocampus and bilaterally the retrosplenial cortex. Moreover, perfusion increases in the posterior putamen and hippocampus were highly correlated during the learning period. These results support the hypothesis that the striatum and hippocampus form interactive memory systems with parallel processing. 2009 Elsevier Inc. All rights reserved. Introduction Arterial spin labeling (ASL) has been introduced as an alternative to the BOLD technique in functional magnetic resonance imaging (fmri) studies (Detre and Alsop, 1999). ASL can measure changes in cerebral blow flow (CBF) directly during the performance of a task. Using the ASL technique a perfusion-weighted image is obtained by subtraction of a labeled from a control image, where the labeled image is acquired after magnetically altering the signal from the protons in the arterial blood water through a radiofrequency pulse applied in a plane proximal to the brain region of interest. The subtraction of labeled and control images successively acquired yields a signal that is stable over long time scales (Aguirre et al., 2002). ASL has been indicated as a suitable approach in the study of cognitive functions that involve slow changes in neural activity, such as learning processes, since it allows studying tasks presented in low frequency paradigms or in a continuous fashion (Wang et al., 2003). While BOLD data suffer from increased noise power at low frequencies, the power spectrum of the noise in perfusion data is flat, which means that presentation of low frequency tasks is feasible. Previous reports have demonstrated that ASL activation is better localized than BOLD activation and that perfusion fmri is more Corresponding author. E-mail address: mfseara@unav.es (M.A. Fernández-Seara). powerful than BOLD for group analysis in studies using sensorimotor tasks (Aguirre et al., 2002; Wang et al., 2003). Furthermore, the ASL technique is less susceptible to signal loss and distortion due to susceptibility artifacts (Detre and Wang, 2002). However, ASL suffers from low intrinsic signal to noise ratio (SNR) due to the small fraction of arterial blood to tissue in the human brain (1 2%) (Blinkov and Glezer, 1968), which has hampered the application of this technique in fmri studies due to lack of sensitivity (Detre and Wang, 2002). Previous perfusion fmri studies of motor learning have not been able to detect learning effects significant at a mapwise level due to insufficient power (Olson et al., 2006). In this particular study, the authors emphasized the problems encountered due to the low SNR of the perfusion fmri data obtained with their ASL technique. Recent technical improvements in ASL have yielded significant increases in SNR (Fernández-Seara et al., 2007; Wang et al., 2005a). In stroke imaging, an optimized ASL technique that combined pseudo-continuous labeling (Dai et al., 2008) with a single shot 3D sequence for readout (Feinberg et al., 1995) allowed reducing the acquisition time of resting perfusion to less than 1 min, which was crucial to obtain good quality data in poorly cooperative patients (Fernández-Seara et al., 2008). The main purpose of the work presented here was to assess whether this SNR enhancement would translate into increased power of the perfusion fmri data to demonstrate gradual and continuous changes in neural activity. Learning is a mental operation that implies slow changes in neural activity (Nissen and Bullemer, 1987). Motor sequence learning 1053-8119/$ see front matter 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2009.05.061

1798 M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 (MSL) is one type of procedural memory that has been extensively studied using functional imaging techniques albeit with results that are sometimes inconsistent, mostly due to differences in methodology, variations of the task, whether learning is implicit or explicit and the phase of motor learning. MSL takes place through repeated practice, as demonstrated by improvements in performance. A greater improvement is usually reported during the initial training or fast learning phase, followed by slow improvements achieved through additional practice leading to automaticity (Doyon and Benali, 2005). Despite the discrepancies found in the literature, it is well established that cortico-striatal and cortico-cerebellar circuits are engaged in MSL during the early, fast learning phase (see for review Doyon et al., 2009; Halsband and Lange, 2006; and Hikosaka et al., 2002). In addition, recent work has revealed the implication of the limbic system in this form of procedural memory (Schendan et al., 2003). Brain imaging findings and lesion studies in animals and humans have been compiled into plausible neurobiological models (Doya, 2000; Doyon and Benali, 2005; Hikosaka et al., 2002; Shadmehr and Krakauer, 2008). Although these models agree on the main players intervening in the acquisition of motor skills, they differ on the functional interactions between these systems. A further aim of the present study was to analyze whether the measurement of dynamic changes in brain activation during MSL through continuous practice using ASL perfusion fmri in a single scanning session, would contribute to better understanding the role of the brain areas implicated in this type of learning. We used an explicit MSL task consisting in the execution of a visually triggered memory guided alternating finger sequence that was designed to imitate learned procedures that are often composed of a sequence of movements. The acquisition of this particular motor skill involved visuo-spatial learning required to build a new and arbitrary sensorimotor association during sequence encoding, and motor sequence processing and execution. We show that learning effects can be reliably detected at a map-wise level in the group analysis, thus demonstrating the potential of ASL fmri to measure slow changes in neural activity. Our data also throw some light on the interaction between areas implicated in the early phase of motor sequence learning. Materials and methods Subjects Fourteen right-handed healthy volunteers (mean age=31±6 [standard deviation], 7 females) participated in the study, approved by the Ethics Research Committee of the University of Navarra, after signing a written informed consent. Experimental paradigm The scanning session consisted of 5 blocks (Fig. 1a): rest (3 min), task (alternating finger movements, sequence 1, 6 min), control (sequential finger movements sequence, 6 min), task (sequence 2, 6 min) and rest (3 min). During the task blocks, subjects were asked to reproduce a sequence of six alternating key presses in a four-key response box (Current Designs, Inc. Philadelphia, USA) with four right hand fingers (index, middle, ring, little). The sequence was visually presented at a frequency of 2.5 Hz at the beginning of each trial for a total of 50 trials. The visual stimuli were projected on a screen situated at the rear of the scanner that was visualized by the subject through a mirror placed on top of the head coil. The visual presentation consisted of 8 circles representing fingers 2 to 5 from each hand over a black background. The circles corresponding to the right hand fingers illuminated one by one with a color present on the response box. The spatial location of the colors on the screen corresponded with the location of the keys in the response box (see Fig. 1b). Fig. 1. fmri paradigm: (a) experimental block design; (b) visual presentation, consisting of 8 circles representing fingers 2 to 5 in each hand over a black background. The circles corresponding to the right hand fingers illuminated one by one with a color present on the response box. The spatial location of the colors on the screen corresponded with the location of the keys in the response box. Sequence one: index, ring, little, ring middle, little Sequence two: middle, ring, index, little, index, ring Control sequence: little, ring, middle, index, little, ring The visual presentation in both the task and control blocks was followed by a black screen with a white central fixation cross that indicated the beginning of the reproduction period of 4.6 s duration. Each trial consisted of sequence presentation and reproduction. No feedback was provided. In the task blocks, the order in which the two different sequences (1 and 2) were presented was alternated for half of the subjects. They were instructed to reproduce the ordinal pattern of the sequences without making mistakes. No instructions were given regarding the sequence rhythm or reproduction speed. The subjects did not see or practice the alternating sequences before the scanning session. During the control block they were asked to perform a sequence of 6 finger movements in order, which had been shown to them prior to the scanning session. During the rest periods, the subjects were asked to stay awake with eyes open though no visual stimuli were presented. The accuracy and timing of the key presses inside the scanner was recorded in a response file. Sequence presentation and performance recording was realized using COGENT 2000 and Matlab. Scanning protocol Studies were performed on a 3 T Trio TIM (Siemens AG, Erlangen, Germany) using the 8-channel head array. Anatomical images were acquired with a MPRAGE sequence, with the following imaging parameters: resolution =1 mm isotropic, FOV=192 256 mm 2, matrix=192 256, 160 axial slices, TR/TE/TI=1620/3.87/950 ms, flip angle=15. Perfusion fmri data were acquired using an optimized ASL technique that combined pseudo-continuous labeling (PCASL) with a background-suppressed (BS) single shot 3D GRASE sequence for readout. One label and control pair was acquired every trial, with imaging parameters: resolution=4 4 6 mm 3, FOV=250 187 96 mm 3, 16 nominal partitions with 13% oversampling, 5/8 partial Fourier, measured partitions =11, matrix size=64 49, BW=2790 Hz/pixel, GE spacing=0.4 ms (with ramp sampling), SE spacing=26 ms, readout time=270 ms, TE=52 ms and TR=3.5 s. Two inversion pulses (15.35 ms duration and 220mG amplitude) were added for BS with inversion times TI 1 =1800 ms

M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 1799 Table 1 Reaction time and accuracy (i.e. number of correct key presses per trial) across learning phases (1,2,3) for the task and control. Learning phase Task Control RT [ms] Correct key presses RT [ms] Correct key presses Global 617.9±171.1 5.38 ±0.77 450.2±111.6 5.93 ±0.17 1 728.2 ±185.7 4.85 ±0.90 412.3 ±100.9 5.94 ±0.14 2 569.3±132.0 5.60 ±0.67 472.5±132.4 5.93 ±0.23 3 556.3±143.7 5.68±0.41 465.7±96.0 5.92 ±0.14 F-ratio/p-value 9.23/0.001 13.99/b0.0001 3.77/0.04 NS Values are expressed as mean±standard deviation (n=14). The last row shows the result of the comparison across learning phases, carried out using RM-ANOVA. (selective); TI 2 =500 ms (non-selective). The PCASL pulse, played between the BS pulses, consisted of 1184 selective radio-frequency (RF) pulses (Hanning window, peak B 1 =53 mg, duration=500 μs and G=0.6 G/cm, 1.2 s labeling duration). For the control, the RF phase alternated from 0 to 180. The post-labeling delay was 600 ms. Bipolar gradients (b=5 s/mm 2 ) were added to suppress intravascular signal. The inversion plane was offset 8 cm from the center of the FOV in the HF direction, so that it was located at the base of the cerebellum to achieve good labeling efficiency. The imaging slab covered the cerebrum and the superior part of the cerebellum. After the functional runs a short scan of 5 label/control pairs was performed using the same sequence without background suppression to acquire control images needed for calculation of CBF. Raw data were saved and reconstructed off-line using Matlab. Data analysis Performance data analysis Performance variables (reaction time and accuracy) were evaluated for each trial. The reaction time (RT) was defined as the time interval between presentation of the fixation cross that indicated the beginning of the response period and onset of the first key press. Accuracy was evaluated as the number of correct key presses per trial obtained from the response files data. A key press was considered correct when the corresponding key was pressed in the right order during the time allotted for sequence reproduction. Differences in RT and accuracy between task and control were assessed using paired t- test, corrected for multiple comparisons. Differences in performance across learning phases (as defined in the Results section) were evaluated using repeated-measures ANOVA (RM-ANOVA) and post hoc comparison tests were done using a Bonferroni correction. In addition, two-factor ANOVAs were used to test the effect of sequence and order of execution on accuracy and RT, during the two task blocks. measured and reported in Fernández-Seara et al. (2007). It has been adjusted here taking into account the increase in RF pulse spacing, required by the limitations in duty cycle of the body coil. M o is the image intensity of the mean control image acquired without background suppression. The temporal SNR (tsnr) of the CBF time series was calculated for the central slice of the 3D volume (an axial slice through the superior half of the lateral ventricles) as the mean of the gray matter CBF over its temporal standard deviation. The CBF images were then normalized to the Montreal Neurological Institute standard template brain and spatially smoothed using an 8 mm isotropic Gaussian kernel. For the statistical analysis, each task block was divided into three learning phases of 2 minute duration. Learning phase 1 comprised trials 2 to 17 in each block (since images acquired during trial 1 were discarded), during which learning of the ordinal structure of the sequence took place (see Results section). Learning phase 2 comprised trials 18 to 33 and learning phase 3 included trials 34 to 50. Voxel-wise statistical analysis was performed for each subject, in a block design that modeled five conditions: rest, control, learning phase 1, learning phase 2 and learning phase 3. In this analysis a δ function was used as Perfusion data analysis All data analysis was performed using SPM5 (Wellcome Department of Imaging Neuroscience) except for the computation of CBF images that was carried out using Matlab. Raw ASL images were realigned to the first image and coregistered to the anatomical dataset. 200 perfusion-weighted images were obtained by subtraction of label and control. The first subtraction image in each run was discarded because the signal had not reached the steady state. CBF images were computed from the perfusionweighted images using Eq. 1 based on a single compartment CASL perfusion model, as described in Wang et al. (2005a). CBF = ΔM M o λ 2α expð wr 1a R 1a Þ expð ðτ + w ÞR 1a Þ ð1þ where ΔM is the signal difference between label and control images, λ (=0.9 ml/g) is the blood/tissue water partition coefficient, R 1a (=0.67 s 1 ) is the longitudinal relaxation rate of blood (Wang et al., 2002), τ (=1.2 s) is the duration of the labeling pulse, w (=0.6 s) is the post-labeling delay and α (=77%) is the labeling efficiency. The labeling efficiency for this implementation of PCASL was previously Fig. 2. Performance variables measured during the task ( ) and control ( ) blocks. (a) Reaction time; (b) number of correct key presses per trial. Each data point has been obtained as the mean of 14 subjects. Error bars represent the standard deviation.

1800 M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 response function, since the perfusion data do not have any substantial temporal autocorrelation (Aguirre et al., 2002). No temporal filtering was applied. At the individual level, the control and the three learning phases were compared to rest. Contrast maps of parameters and t- statistics maps corresponding to these contrasts were obtained. The first level analysis was followed by a group inference using the random effects model (Penny et al., 2003). Random-effects group t-maps comparing the control and each learning phase to rest were generated by applying the one-sample t-test for the contrast parameter values of all the subjects at each voxel. Coverage of the cerebellum was variable from subject to subject, for this reason the cerebellum was excluded from the group analysis by masking. The significance level was set at a p-valueb0.005 after correction for multiple comparisons using the false discovery rate (FDR) (Genovese et al., 2002). In addition, a group map of CBF changes due to learning was obtained using a one-way within subject ANOVA to compare learning phases 1 and 3. The SPM(F) map was thresholded at p-fdrb0.05. The asymmetry of CBF changes was assessed by computing a lateralization index using the LI_toolbox (version 1.1) (Wilke and Lidzba, 2007). In order to determine the magnitude and direction of the perfusion changes due to learning in anatomical structures of interest, CBF changes normalized to resting CBF were computed from the normalized CBF images for each subject. Percent CBF changes were averaged to obtain a group mean and correlated to reaction time measurements. Regions of interests (ROIs) were defined from the thresholded SPM(F) map in two ways: for small clusters, the data from the whole cluster was extracted, however for anatomical areas enclosed within a large cluster, a spherical ROI (radius=5 mm) was built using a local maximum as center. The ROI in the supplementary motor area (SMA proper) was defined after lowering the threshold of the SPM(F) map to pb0.05 uncorrected. To further investigate the role of the putamen in motor sequence learning, ROIs were delineated in four different putaminal regions: left and right anterior (LA, RA) or rostral putamen and left and right posterior (LP, RP) or caudal putamen. The LP putamen ROI was defined from the SPM(F) map. The LA and RA putamen ROIs were defined from the SPM(t) map obtained in the comparison of learning phase 1 versus rest, thresholded at p-fdrb0.01 (TN3.12); the RP putamen ROI was defined from the SPM(t) map obtained in the comparison of learning phase 3 versus rest (thresholded at TN3.12), since these regions did not appear in the thresholded SPM(F) map. For each ROI, differences in percent CBF change across learning phases were evaluated using repeated-measure ANOVA (RM-ANOVA) and post hoc comparison tests were done using a Bonferroni correction. Differences in % CBF change between task and control were assessed using paired t-test, corrected for multiple comparisons. Changes in effective connectivity during the learning process between the LP putamen and other brain areas were assessed by the psychophysiological interactions (PPI) method (Friston et al., 1997). For each subject the local maximum at the LP putamen was determined using the SPM(t) map obtained from the contrast learning phase 3 vs. rest. The individual time series were obtained by extracting the first principal component from the raw CBF time series in a Table 2 Main regions showing increased CBF in learning phase 1 (first 2 min of practice) versus rest (second-level analysis, p-fdrb0.005, kn10, TN3.58). Left hemisphere Right hemisphere Region (BA) MNI coordinates T Region (BA) MNI coordinates T x y z x y z Frontal lobe Precentral gyrus, primary motor (BA 4p, 4a) 34 24 56 7.56 Precentral gyrus, premotor (BA 6) 36 16 58 8.04 Precentral gyrus, premotor (BA 6) 46 2 46 8.35 SMA (BA 6) 6 10 52 4.36 Pre-SMA (BA 6) 6 12 50 6.55 Pre-SMA (BA 6) 6 10 50 4.28 Middle frontal gyrus, DLPFC (BA 9) 42 38 32 6.14 Middle frontal gyrus, DLPFC (BA 9) 38 42 40 6.84 Inferior frontal gyrus (p. triangularis) (BA 45) 46 36 26 5.97 Inferior frontal gyrus (p. triangularis) (BA 45) 50 30 30 8.51 Inferior frontal gyrus (p. opercularis, BA 44) 44 12 30 7.69 Inferior frontal gyrus (p. orbitalis) (BA 47) 22 14 22 4.05 Inferior frontal gyrus (p. orbitalis) (BA 47) 18 20 20 4.36 Anterior cingulate gyrus 10 18 38 5.32 Anterior cingulate gyrus 14 14 40 6.84 Insular cortex (anterior) 28 20 12 8.48 Insular cortex (anterior) 32 30 8 7.65 Parietal lobe Cuneus 8 82 44 9.80 Precuneus 10 64 58 8.72 Precuneus 12 54 50 7.57 Superior parietal lobule 20 70 42 10.14 Superior parietal lobule 24 68 44 6.59 Inferior parietal lobule Primary somatosensory 42 44 56 9.83 Inferior parietal lobule 30 54 48 13.87 cortex (BA 2) Supramarginal gyrus (BA 1) 60 20 40 7.06 Anterior intra-parietal sulcus (hip1) 32 48 42 9.72 Anterior intra-parietal sulcus (hip1) 36 52 41 19 Anterior intra-parietal sulcus (hip2) 44 40 34 9.83 Temporal lobe Middle temporal gyrus 44 52 6 7.51 Middle temporal gyrus 52 42 2 14.50 Inferior temporal gyrus associative visual cortex (BA 19) 50 58 2 7.02 Inferior temporal gyrus associative visual cortex (BA 19) 54 62 6 8.47 Occipital lobe Lingual gyrus, primary visual cortex V1 (BA 17) 6 74 4 5.77 Lingual gyrus, primary visual cortex 14 92 14 7.13 V1 (BA 17) Inferior occipital gyrus, secondary visual cortex 28 96 6 6.92 Lingual gyrus, secondary visual cortex 14 78 2 6.01 V2 (BA 18) V2 (BA 18) Middle occipital gyrus, V5/MT+ 46 74 6 6.24 Middle occipital gyrus, V5/MT+ 46 74 6 5.62 Subcortical Thalamus (medial dorsal nucleus) 10 18 12 5.88 Thalamus (ventral lateral nucleus) 10 12 12 6.28 Putamen (anterior) 30 10 4 6.59 Putamen (anterior) 30 18 2 5.09 Caudate nucleus 18 12 16 4.52 Caudate nucleus 12 2 12 5.85 MNI coordinates (in mm) locate the local maximum within the region. Anatomical labeling was performed using the SPM Anatomy Toolbox v1.5 (Eickhoff et al., 2005).

M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 1801 spherical ROI (5-mm radius) centered on the coordinates of the subject specific local maximum. The interaction term (PPI regressor) was computed as the element-by-element product of the LP putamen time series and a vector coding for the main effect of the task. Two PPI analyses were carried out. For the first one, the task regressor was equivalent to the task vs. rest contrast (1 for task, 0 for control and 1 Fig. 3. Group activation maps superimposed on axial anatomical T 1 -weighted images: (a) learning phase 1 versus rest SPM(t); (b) learning phase 3 versus rest SPM(t); (c) SMP(F) showing the CBF changes between learning phases 1 and 3.

1802 M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 for rest). For the second one, the task regressor was equivalent to the contrast of task vs. control (1 for task, 1 for control and 0 for rest). The PPI models included in both cases four regressors: the PPI regressor, the task regressor (the psychological variable), the LP putamen time series (the physiological variable) and a constant. At the individual level a t-contrast was created that was 1 for the PPI regressor and 0 elsewhere. These contrast images were entered into a random effects model, followed by a one-sample t-test. The SPM(t) maps were thresholded at p b0.005, uncorrected for multiple comparisons, kn10. The first model was used to identify brain areas in which the degree of coupling with the LP putamen increased during the task relative to rest, while the second model identified areas of increased coupling with LP putamen during the task relative to the control. Results Performance data The order in which the two novel alternating finger sequences were presented during the task blocks was varied for half of the subjects. No significant effects of sequence or order of execution were found either on accuracy or reaction time comparing the two task blocks, therefore data from the two blocks were pooled and averaged for all subsequent analyses. Performance data for the task and control conditions are shown in Table 1 and Fig. 2. During the task, accuracy increased and RT decreased with trial number. During the control, the execution of the sequence was virtually error-free and RT remained approximately constant. These results indicate that the control condition did not require learning. Statistical comparisons between task and control conditions showed that accuracy was significantly lower (t(41) = 4.59; pb0.0001) and RT was significantly longer (t(41)=6.54; pb0.0001) during the task than during the control trials. During the task, learning of the ordinal structure of the sequence (i.e. the correct sequence of required key presses) was accomplished on average during the first 17 trials. Therefore in order to detect any possible changes within the task block in relation to performance, it was divided into three learning phases of 2 minute duration each, in which learning phase 1 comprised trials 2 to 17, learning phase 2 trials 18 33, and learning phase 3, trials 34 50. Performance improved with practice across learning phases as demonstrated by a significant increase in accuracy (RM-ANOVA, F(2,26) =13.99, pb0.0001), accompanied by a reduction of the reaction time (RM- ANOVA, F(2,26) =9.23, pb0.001) (Table 1). Most of the changes in performance took place during learning phase 1, which corresponded to the first 2 min of practice. Post-hoc comparisons among phases demonstrated that RT in learning phase 2 was significantly shorter than in learning phase 1 (p=0.004) and accuracy was significantly higher (p=0.001). Improvements in performance from learning phase 2 to 3 were also observed but did not reach a significant level (p-valuesn0.05). During the control block the same division was established and three phases of 2 min were statistically compared. There was no significant change in accuracy (RM-ANOVA, F(2,26)=0.06, pn0.9). However, there was a significant change in RT (RM-ANOVA, F(2,26) = 3.77, pb0.04). Post-hoc comparisons among phases revealed that the RT of phase 1 was shorter than RT of phases 2 and 3, almost reaching significance (p=0.056). This behavior was opposed to that observed in the task condition and it was probably due to attentional load. Finally, comparing accuracy and RT between task and control for each learning phase, we obtained the following results: accuracy was significantly lower for the task than the control during the first learning phase only (t(13) =4.86; pb0.0006, corrected for multiple comparisons). However, RT was significantly longer for each learning phase (phase 1: t(13)=7.44; p b0.0001, phase 2: t(13)=2.91; pb0.02; phase 3: t(13) =3.23; pb0.01, all p-values corrected for multiple comparisons). It is worth noticing that our fmri paradigm used a fixed scanning order for rest, task and control blocks, so a contribution of scan order effects cannot be excluded in the comparisons between task and control conditions. Perfusion fmri data CBF quantification The calculated mean gray matter CBF was 41.8±9.7 ml/100 g/min (mean±standard deviation for all subjects, n=14). These values are slightly lower than results reported in the literature (Wang et al., 2005a; Wong et al., 1998; Ye et al., 2000a). This underestimation is due to the reduction of the perfusion signal magnitude caused by the background suppression pulses and to the short post-labeling delay of 600 ms. This short post-labeling delay is preferable for ASL activation imaging (Gonzalez-At et al., 2000). The tsnr of the gray matter CBF time series was 7.9±1.5. This value is in agreement with results reported previously for the application of this technique (Fernández- Seara et al., 2007) and it is higher than values obtained with the most commonly used ASL sequences that still rely on echoplanar imaging (Wang et al., 2005b). Increase in CBF during task execution Areas showing increased CBF in motor learning phase 1 versus rest are listed in Table 2 and depicted in Fig. 3a (p-fdrb0.005, kn10, TN3.6). Even though the task was performed with the right hand, the areas of increased perfusion were distributed in both hemispheres, as confirmed by the calculated lateralization index, which was close to zero (Table 3). Increases in CBF were found in the left primary motor cortex and in secondary motor areas bilaterally: premotor cortex and pre-sma. Additional areas of bilateral CBF increase were: dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex, insular cortex, inferior frontal gyri, cuneus, precuneus, superior parietal lobules, intra-parietal sulci and inferior parietal lobules. Increased perfusion was also found in the temporal and occipital lobes, including the primary visual cortex (BA 17), associative visual cortices (BA 18 and 19) and visual area MT. At the subcortical level, CBF increased bilaterally in the anterior putamen, globus pallidus, caudate nucleus and thalamus. In learning phase 2 versus rest (data not shown) the area of increased CBF in the associative and sensory motor cortical circuits decreased overall and the clusters became more lateralized to the left hemisphere, with a lateralization index of 39.7% (see Table 3). At the subcortical level, areas of increased CBF were confined to the left thalamus and putamen (anterior and posterior). A similar evolution of the activation map was found in learning phase 3 versus rest (Fig. 3b), with a further decrease in the area of activation in sensory motor cortical regions and sustained left lateralization (see Table 3). However, there was an increase of the activation area in the left SMA and posterior putamen. Additionally increased CBF was found in the left hippocampal area. Table 3 Laterality index of the SPMs obtained at the group level analysis computed using the LI_toolbox (version 1.1) (Wilke and Lidzba, 2007). Contrast LI (voxel count) LI (voxel value) t (phase 1 rest) 0.044 0.066 t (phase 2 rest) 0.349 0.397 t (phase 3 rest) 0.318 0.395 t (control rest) 0.188 0.218 F (phase 1 vs. phase 3) 0.083 0.13 The LI index describes the asymmetry of the thresholded SPM map. It yields values between 1 and 1, with +1 representing purely left activation and 1 purely right activation. The SPM(t) maps were thresholded at TN3.6 and the SPM(F) map was thresholded at FN7.6.

M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 1803 CBF changes during the learning session A map-wise analysis of CBF changes between learning phases 1 and 3 yielded the areas listed in Table 4 and depicted in Fig. 3c (p-fdrb0.05, kn10, FN7.6). The values of CBF change in Table 4 are normalized to resting CBF, extracted from ROIs defined from the SPM (F), and averaged across subjects for the three learning phases and the control block. The magnitude of measured CBF changes is consistent with reported values for a similar MSL task (Garraux et al., 2005). As expected, perfusion decreased with practice in most of the cortical regions listed in Table 4, reaching values close to those measured during the control block. The perfusion decreases, bilateral in most cases, were larger in the right hemisphere and were correlated with the decreases in RT. Specifically, such decreases in perfusion were found in the premotor cortex, the pre-sma, the DLPFC, the inferior frontal cortex, the precuneus, the superior parietal lobules and the anterior intra-parietal sulci, the middle and inferior temporal gyri, and in occipital cortical areas including a small fraction of the primary visual cortex, approximately 20% of the associative visual cortices and almost 100% of the visual MT area. Subcortical perfusion decreases were significant only in the right caudate nucleus. The most interesting findings, however, were the increases in perfusion from learning phase 1 to learning phase 3, in specific areas both cortical and subcortical. Such changes were located to a small region of the primary somatosensory cortex, the posterior insular cortex, the posterior putamen and the hippocampus in the left (contralateral) hemisphere only and bilaterally in the retrosplenial cingulate cortex. When the threshold was lowered to p b0.05 (uncorrected), increased perfusion was also found in the left SMA. Perfusion data extracted from these ROIs yielded inverse correlations with RT (Table 4). Perfusion increases in the left posterior putamen were directly correlated with perfusion increases in SMA (r=0.61, Fig. 4a) and the hippocampus (r=0.84, Fig. 4c). There was also a high linear correlation between perfusion increases measured in the posterior insular cortex and the primary somatosensory cortex (r=0.85, Fig. 4b). Table 4 Main regions showing learning related CBF changes (p-fdrb0.05, F N7.6, kn10). Region (BA) MNI coordinates F % CBF increase (mean±sd) r p x y z LPH1-R LPH2-R LPH3-R C-R Left hemisphere Frontal lobe SMA a 2 12 52 4.52 9.2 ±10.5 12.3±13.1 14.4 ±12.2 9.1±8.8 0.47 b0.001 Precentral gyrus, premotor (BA 6) 48 2 46 8.5 35.2±31.2 26.1±26.9 24.7±28.9 20.2±34.0 0.24 NS Middle frontal gyrus, DLPFC (BA 9) 42 38 34 21.8 17.4±10.0 7.9±12.0 6.7±13.3 3.0±8.6 0.67 b0.0001 Inferior frontal gyrus (p. triangularis) (BA 45) 46 30 26 19.7 10.7±9.7 4.6±11.6 2.8±10.2 1.5±7.4 0.63 b0.0001 Parietal lobe Precuneus 14 74 48 28.5 27.0±18.4 18.8±14.7 10.8 ±13.3 7.8±12.5 0.60 b0.0001 Superior parietal lobule (BA 7) 32 54 62 13.2 31.0±31.4 23.0±41.6 23.7±45.5 12.9±20.6 0.32 b0.05 Postcentral gyrus, primary somatosensory cortex (BA 2,3) 46 28 48 12.2 19.5±9.0 25.2±13.7 27.2±9.2 20.2±9.2 0.53 b0.0001 Anterior intra-parietal sulcus (hip1) 32 56 32 9.7 25.5±9.4 17.2±16.6 13.5±12.2 8.3±7.3 0.61 b0.0001 Temporal lobe Insular cortex (posterior) 36 4 10 29.2 4.7±4.2 8.4±7.0 9.3±3.6 6.6±5.2 0.53 0.0001 Middle temporal gyrus (BA39) 50 52 12 16.1 9.1±6.4 5.5±7.9 3.6±4.8 3.2±4.8 0.53 b0.0001 Inferior temporal gyrus, associative visual cortex (BA 37,19) 54 58 8 19.9 10.2±7.6 6.4±13.0 1.9±8.5 3.0±6.1 0.47 b0.001 Hippocampus 32 34 8 10.8 0.5 ±6.5 2.9±10.5 6.0 ±4.6 1.7±3.9 0.22 NS Retrosplenial cortex (BA29) 12 44 8 10.9 1.7±8.0 2.8±11.4 6.7±8.5 1.3±5.5 0.40 b0.005 Occipital lobe Middle occipital gyrus, secondary visual cortex V2 (BA 18) 30 96 0 38.1 31.7±17.7 23.0±24.4 8.3±13.0 16.9±13.9 0.60 b0.0001 Middle occipital gyrus, V5/MT+ 46 76 8 37.7 14.5±10.0 6.9±12.7 2.6±9.1 5.5±8.1 0.69 b0.0001 Subcortical Putamen (posterior) 28 6 4 14.1 3.8±5.4 6.6±6.1 8.1±3.0 5.2±5.4 0.35 b0.05 Right hemisphere Frontal lobe Precentral gyrus, premotor (BA 6) 52 10 44 29.5 18.7±13.3 8.7±14.7 3.5±10.1 4.7±9.0 0.65 b0.0001 Pre-SMA (BA 6) 2 12 52 12.3 13.6±11.0 10.3±9.7 7.7±7.5 5.6±10.7 0.41 b0.005 Middle frontal gyrus, DLPFC (BA 9) 50 34 30 30.7 15.7±9.4 9.0±14.8 5.5±9.5 2.8±7.4 0.66 b0.0001 Inferior frontal gyrus (p. opercularis) (BA 44) 42 14 30 27.5 10.6±6.2 8.2±9.2 6.3±6.7 2.7±6.0 0.45 b0.005 Inferior frontal gyrus (p. orbitalis) (BA 47) 46 48 6 24.1 15.6±10.6 6.7±9.5 2.5±6.8 4.1±6.8 0.68 b0.0001 Anterior cingulate gyrus 12 30 34 11.1 11.8±8.2 6.1±10.6 5.4±7.2 0.4±5.7 0.62 b0.0001 Parietal lobe Precuneus 8 80 46 23.8 21.1±9.9 12.7±15.0 10.7 ±10.1 5.4 ±9.4 0.57 b0.0001 Superior parietal lobule (BA 7) 34 68 58 30.8 27.1±39.0 23.3±48.7 22.8±42.1 12.5±24.8 0.31 b0.05 Anterior intra-parietal sulcus (hip1) (BA40) 40 48 32 38.7 24.7±12.9 18.5±11.6 11.5±9.8 8.8±9.6 0.68 b0.0001 Temporal lobe Middle temporal gyrus (BA 39) 42 54 10 24.2 13.1±8.3 6.1±11.1 4.3±5.9 4.7±6.8 0.68 b0.0001 Middle temporal gyrus, associative visual cortex (BA 19) 50 56 4 22.7 14.7±9.9 7.2±11.6 5.7±6.3 4.2±6.6 0.60 b0.0001 Inferior temporal gyrus (BA 37) 66 46 12 30.7 11.9±8.8 6.8±10.1 2.3±7.2 4.5±5.9 0.60 b0.0001 Parahippocampal gyrus, retrosplenial cortex (BA 27,30) 14 44 2 9.8 1.2±5.8 0.9±11.4 5.4±6.8 0.3±5.5 0.27 NS Occipital lobe Lingual gyrus, primary visual cortex V1 (BA 17) 16 92 14 24.7 26.3±14.5 19.9±15.7 11.1±14.5 18.2±12.5 0.60 b0.0001 Middle occipital gyrus, secondary visual cortex V2 (BA 18) 32 92 2 27.2 34.2±20.0 20.1±20.7 10.7±19.1 19.2±13.9 0.72 b0.0001 Middle occipital gyrus, V5/MT+ 40 62 12 24.1 13.4±7.7 7.8±12.5 4.4±7.1 4.7±7.3 0.66 b0.0001 Subcortical Caudate nucleus 14 2 12 16.3 6.1±3.7 3.6±7.6 3.0±4.3 0.2±5.0 0.40 b0.005 MNI coordinates (in mm) locate the local maximum within the region. Anatomical labeling was performed using the SPM Anatomy Toolbox v1.5 (Eickhoff et al., 2005). LPH: learning phase, R: rest, C: control. r and p are the correlation coefficient and significance value for correlations between the % CBF increase and RT. Areas of increased perfusion are shown in bold type. a Perfusion changes in SMA were only significant when the threshold was lowered to pb0.05 uncorrected.

1804 M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 Fig. 4. Scatter plots of group mean CBF values in ROIs where perfusion increased during the learning period: (a) left SMA versus left posterior putamen; (b) left somatosensory cortex (S1) versus left posterior insula; (c) left hippocampus versus left posterior putamen. Each data point represents one trial during the learning period from trial 2 to 50. (d) Psychophysiological interaction results from the left posterior putamen, showing increased coupling with left hippocampus in the MSL task compared with rest. CBF changes in the putamen across learning phases Percent CBF changes normalized to resting CBF during the three learning phases and the control block are depicted in Figs. 5a, b and plotted in Fig. 5c for the following regions of the putamen: LA, LP, RA and RP. The LP region of the putamen experienced increases in perfusion across learning phases that were significant at the voxel level (Fig. 5b, right) and the ROI level (RM-ANOVA, F(2,26) =3.49; pb0.04). The other 3 regions showed changes in perfusion during the learning period that did not reach a significant level but displayed the following trends: perfusion in the LA putamen stayed approximately constant, perfusion in the RA putamen decreased and perfusion in the RP putamen increased. Perfusion values in the LA, RA and RP ROIs were significantly higher during the learning task than during the control (all pb0.05, corrected for multiple comparisons). However, there were no significant differences in CBF between task and control in the LP putamen. In fact, during the control block, only this region of the putamen exceeded a map-wise level of significance (see Fig. 5a), which was corroborated by the quantitative CBF measurements. Effective connectivity analysis The PPI analyses showed the brain regions that had significantly stronger psychophysiological interactions with the LP putamen during the MSL task compared to rest (Table 5) and to control conditions (Table 6). In both cases there was an increase in coupling between the left posterior putamen and left hippocampus (see Fig. 4d). Discussion ASL perfusion fmri for the study of continuous MSL The primary goal of this study was to evaluate the efficacy of ASL perfusion fmri for the assessment of continuous changes in neural activity with learning. The results reported here indicate that increases in SNR of ASL perfusion brought about by recent technical improvements translate in increased statistical power, comparable to that obtained in BOLD fmri studies of motor learning. The ASL technique employed in this work is a combination of pseudo-continuous labeling (Dai et al., 2008) with a backgroundsuppressed 3D single shot GRASE sequence for readout. The nonselective inversion pulse used for background suppression caused a small decrease in the magnitude of the perfusion signal due to pulse imperfections (Duyn et al., 2001; Garcia et al., 2005; Ye et al., 2000b) of approximately 8% (Fernández-Seara et al., 2007). The first inversion pulse, selective to the imaging slab and placed before the ASL pulse, should not affect the ASL signal. Since this attenuation was not taken into account in the CBF calculation, our CBF measurements are slightly underestimated; however this should not affect the statistical analysis of the perfusion data. In spite of this effect, the increase in the perfusion time series temporal SNR achieved with this technique contributed significantly to the sensitivity increase of the perfusion data. This increase in sensitivity facilitated the use of perfusion fmri for the study of motor learning and allowed the detection of changes in activation patterns across learning phases at the group level in a mapwise analysis. Coverage of the cerebellum was variable from subject to subject, thus cerebellar activations are not discussed in this work. This limitation of the current technique can be overcome in future studies by using parallel imaging techniques to increase the thickness of the imaging slab (Fernández-Seara et al., 2005). Dynamic changes in brain perfusion during MSL The learning period was divided into three learning phases. Learning phase 1 can be identified with the early learning phase

M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 1805 Fig. 5. Group activation maps in a slice across the putamen superimposed on an axial T 1 -weighted image, for the following contrasts: (a) from left to right: learning phase 1 rest, learning phase 2 rest, learning phase 3 rest, control rest, thresholded at p-fdrb0.01 (TN3.12); (b) left: learning phase 1 learning phase 3, thresholded at p-fdrb0.05 (TN2.34), right: learning phase 3 learning phase 1, thresholded at pb0.01 uncorrected (TN2.47). (c) Percent CBF changes with respect to rest across learning phases (1=blue, 2=red, 3=yellow) and control (green) for the following regions of the putamen: left anterior (LA), left posterior (LP); right anterior (RA) and right posterior (RP). The error bars represent the SD. defined in Bernstein's (1996) and Fitts' (1964) classical theories. During this early phase, the learner becomes familiar with the correct sequence of movements, performance improves rapidly and the task requires dedicated attention. Learning phase 3 can be identified with the intermediate learning phase, during which there are further performance improvements and a reduction in performance variability. Due to the short duration of the practice session in this study, the subjects never reached the final learning phase or automaticity. During learning phase 1 (within the first 2 min of practice), CBF was bilaterally increased with respect to rest in cortical and subcortical areas involved in sensory motor processing, visual to motor mapping, working memory and motor execution, consistent with results from previous fmri studies that have shown that brief episodes of new learning are mainly associated with bilateral activation in widely distributed cortical regions (Floyer-Lea and Matthews, 2005; Halsband and Lange, 2006; Muller et al., 2002). Cortical and subcortical activation patterns changed across learning phases, leading to the hemispheric lateralization of the cortical activation, which from bilateral became progressively left-shifted, as previously reported (Floyer-Lea and Matthews, 2004). Decreases in perfusion with learning, that correlated directly with decreasing RT took place in explicit learning circuits, found active primarily in learning phase 1: the DLPFC and caudate nucleus loop that is responsible for building the association between sensory stimuli and motor response and the prefrontal cortex and pre-sma loop that is involved in explicit working memory (Halsband and Lange, 2006). Perfusion decreased also in areas implicated in visual information processing and visuo-spatial integration. More interestingly, the improvement in execution of sequential movements during the learning period required greater perfusion in a few discrete areas. Clusters of increased perfusion were found preferentially on the left hemisphere: SMA, the primary somatosensory cortex (S1), the posterior insula and putamen, the parahippocampal gyrus reaching the hippocampus, and bilaterally the retrosplenial cortex, indicating that these areas play a role in achieving a certain degree of automaticity in the motor performance. Increased perfusion in left SMA proper was only found when the significance level was lowered to pb0.05 uncorrected. Contrary to pre-sma that was more active during learning phase 1, SMA proper has been found to experience increased activation with practice (Grafton et al., 2002; Lehericy et al., 2005). SMA is important in the temporal organization of sequential movements (Tanji, 2001), that in our study became more synchronous as learning progressed (data not shown). Increased activity in S1 due to learning has been previously reported both in studies of long-term training (Floyer-Lea and Matthews, 2005) after extended practice and associated with an increase of activity in the primary motor cortex and in studies of intra-session motor learning (Toni et al., 1998), where it was found early in the learning period. The perfusion increases in S1 measured in this work were not associated with increased activity in the primary motor cortex, however they were strongly correlated with increased perfusion in the posterior insular

1806 M.A. Fernández-Seara et al. / NeuroImage 47 (2009) 1797 1808 Table 5 Main regions showing greater effective connectivity to the LP putamen during the task than during rest (pb0.005 uncorrected, TN3, kn10). Left hemisphere Right hemisphere Region (BA) MNI coordinates T Region (BA) MNI coordinates T x y z x y z Frontal lobe Precentral gyrus (BA 6) 34 8 44 3.78 Precentral gyrus (BA 6) 18 18 64 3.47 Middle frontal gyrus (BA 6) 30 2 52 5.54 Middle frontal gyrus (BA 6) 30 4 46 3.90 Middle frontal gyrus (BA 9) 26 30 28 3.36 Middle frontal gyrus (BA 10) 26 48 2 3.34 Insular cortex (anterior) 30 18 12 3.44 Parietal lobe Posterior cingulate 6 38 16 3.48 Inferior parietal lobule, primary 38 38 46 3.68 somatosensory cortex (BA 2) Anterior intra-parietal sulcus (hip1) 42 46 40 3.15 Temporal lobe Temporal pole 40 6 18 3.21 Medial temporal pole 42 14 32 3.72 Amygdala 30 12 10 4.93 Hippocampus 40 22 14 3.44 Middle temporal gyrus 46 2 18 3.16 Occipital lobe Middle occipital gyrus (hip1) 28 58 32 3.82 Subcortical Globus pallidus 18 2 4 4.89 Caudate nucleus 16 10 24 4.67 Putamen (posterior) 34 10 0 3.61 MNI coordinates (in mm) locate the local maximum within the region. Anatomical labeling was performed using the SPM Anatomy Toolbox v1.5 (Eickhoff et al., 2005). cortex (r=0.85), located on the border of the secondary somatosensory cortex (S2). This finding suggests that somatosensory feedback is increasingly required during these three phases of learning. Perfusion increases across learning phases were also observed in the hippocampus, which can be associated with the formation of consciously accessible memories. This result shows that there is a contribution to MSL from structures of the middle temporal lobe (MTL), in line with other reports regarding also procedural learning (Albouy et al., 2008; Curran and Schacter, 1997; Schendan et al., 2003; Walker et al., 2005). Schendan et al. reported that MTL participates in the acquisition of sequential movements during both explicit and implicit learning. In this report, however MTL activity was greater during the early rather than late learning phase in a serial reaction time task. Albouy et al. using an implicit oculomotor sequence found an increase in BOLD signal in the hippocampus during training that decreased throughout training in a group of fast learners but increased in a group of slow learners. Lehericy et al. (2005) also reported hippocampal activation during the late learning phases in a study that involved extended practice of an explicitly learned finger sequence. These apparently conflicting findings may be due to diverse learning strategies employed in the different tasks and by the different groups of subjects. The task in our study involved additional processes than those related to motor execution, since the subject was required to reproduce the finger sequence from memory following the visual presentation. Other studies of MSL however have not found increased activity in the hippocampus. In addition to task differences, the lack of hippocampal activation in MSL studies can be related to challenges in obtaining good BOLD fmri signal from this relatively small region in the presence of high static susceptibility gradients due to its proximity to the temporal sinuses. In contrast, the ASL perfusion signal is not dependent on susceptibility contrast, therefore images were acquired using a T 2 - insensitive sequence which improved coverage in this region Table 6 Main regions showing greater effective connectivity to the LP putamen during the task than during control (pb0.005 uncorrected, TN3, kn10). Left hemisphere Right hemisphere Region (BA) MNI coordinates T Region (BA) MNI coordinates T x y z x y z Frontal lobe Middle frontal gyrus (BA 9) 26 36 30 3.17 Precentral gyrus (BA 6) 24 18 68 3.66 Parietal lobe Parietal lobe Precuneus 20 86 44 3.31 Posterior cingulate 4 42 20 3.68 Posterior cingulate 6 40 14 3.37 Temporal lobe Hippocampus 32 14 12 3.38 Occipital lobe Superior occipital gyrus (BA 18) 18 102 14 3.66 Cuneus (BA 19) 6 90 28 3.32 Subcortical Caudate nucleus 18 10 24 3.80 MNI coordinates (in mm) locate the local maximum within the region. Anatomical labeling was performed using the SPM Anatomy Toolbox v1.5 (Eickhoff et al., 2005).