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The present functional magnetic resonance imaging study investigated the instruction-based learning of novel arbitrary stimulus–response mappings in order to understand the brain mechanisms that enable successful behavioral rule implementation in the absence of trial-and-error learning. We developed a novel task design that allowed the examination of rapidly evolving brain activation dynamics starting from an explicit instruction phase and further across a short behavioral practice phase. As a first key result, the study revealed that different sets of brain regions displayed either decreasing or increasing activation profiles already across the first few practice trials, suggesting an impressively rapid redistribution of labor throughout the brain. Furthermore, behavioral performance improvement across practice was tightly coupled with brain activation during the practice phase (caudate nucleus), the instruction phase (lateral midprefrontal cortex), or both (lateral premotor cortex bordering prefrontal cortex). Together, the present results provide first important insights into the brain systems involved in the rapid transfer of control from initially abstract rule representations induced by explicit instructions toward pragmatic representations enabling the fluent behavioral implementation.
One of the outstanding human abilities is to instantaneously implement novel, completely arbitrary behavioral instructions. This type of instruction-based learning stands in stark contrast to the rather time-consuming learning by trial and error, which is required when no clear instruction is given or when the complexity of the instruction is beyond working memory capacity (Petrides 1997). Experimental psychologists naturally exploit this remarkable learning ability when they instruct human participants for the most arbitrary tasks. Indeed, 2 observations can be commonly made in experimental psychology laboratories. First, an attentive participant has typically no difficulty in implementing the task instructions right from the first trial. Second, it takes only a few more practice trials before the participant seems to start feeling comfortable with the task. The present study sought to characterize the neurocognitive processes that are supposed to be taking place in the initial phase of instruction learning. Our main focus was on the first few practice trials that required the actual behavioral implementation of newly instructed rules, but we also examined the relevance of the initial rule encoding during the preceding instruction phase. We were particularly interested in this early phase of practice led by the theoretical assumption that the behavioral expression of a newly instructed task should crucially rely on the successful transfer of control from of a highly abstract, possibly verbally coded task representation formed during the instruction phase toward a more "pragmatic" task representation required for actual motor implementation during behavioral practice trials (Nakayama et al. 2008). In fact, it might be this transfer of control from abstract stimulus–response (S–R) rules toward a pragmatic code that does not work properly in patients with prefrontal damage who are reportedly unable to behaviorally implement a novel instruction while being able to repeat the instruction verbally (Luria 1973).
We created an experimental task in which participants were required to sequentially implement 20 different behavioral instructions each involving 4 unique stimuli mapped onto 2 manual responses (Fig. 1A). Following each novel instruction, there were 32 practice trials, during which each S–R association was repeated 8 times in a randomly intermixed fashion. This task design enabled us to directly examine the brain activation dynamics associated with rapid incremental adaptation processes following the explicit instruction of novel S–R associations (see Materials and Methods). By contrast, previous neuroscientific research has almost entirely focused on the acquisition of novel S–R mappings based on trial-and-error learning, including neuroimaging studies on human subjects (Deiber et al. 1997; Toni et al. 2001; Eliassen et al. 2003; Boettiger and D'Esposito 2005; Brovelli et al. 2008) and studies based on intracranial neuronal recordings or lesions in monkeys (Asaad et al. 1998; Wang et al. 2000; Bussey et al. 2001; Wirth et al. 2003; Brasted and Wise 2004; Pasupathy and Miller 2005). Such studies suggest important roles of premotor, lateral prefrontal, striatal, and mediotemporal areas in the acquisition of arbitrary S–R mappings (Murray et al. 2000; Passingham et al. 2000; Wise and Murray 2000; Petrides 2005a, 2008; Suzuki 2007).
Although learning by trial-and-error seems profoundly different from instruction-based learning, neuropsychological studies based on human patients with lesions across the lateral prefrontal cortex (LPFC) tentatively suggest at least a partial overlap in the cerebral implementation of these different types of learning (Petrides 1990, 1997). Thus, albeit far from facing a clear picture with regard to the commonalities and differences of both types of learning, some conclusions from the trial-and-error learning literature appear sufficiently general to warrant predictions regarding the functional neuroanatomy of instruction-based learning. In particular, results from lesion studies in monkeys suggest that the midventrolateral prefrontal cortex (mid-VLPFC) might be necessary for the learning of novel S–R mappings but not for the implementation of well-learned associations (Murray et al. 2000; Wang et al. 2000; Bussey et al. 2001). In contrast, an area at the border of the anterior premotor cortex and the adjacent posterior PFC (from hereon termed "lateral premotor cortex–prefrontal cortex" or "lateral PMC–PFC") needs to be intact during both rule learning and continued future rule implementation (Petrides 2005). The lateral PMC–PFC region has been referred to as "periarcuate region" in monkeys, but because the present study investigated humans, we will use the terminology for human neuroanatomy when referring to this region. Such findings corroborate the interpretation that mid-VLPFC might be relevant for rule extraction during trial-and-error learning, whereas the lateral PMC–PFC might be relevant for representing the learned S–R associations. Yet, based on trial-and-error learning procedures, it seems difficult, if not impossible, to discern the distinct contributions of multiple, temporally intermingled subprocesses, including 1) the application of generalized strategies for rule extraction, 2) the formation of abstract (i.e., verbal) rule representations, and 3) the formation of pragmatic rule representations. For example, it is not clear whether the involvement of the mid-VLPFC during learning reflects the application of generalized rule extraction strategies or rather the buffering of incrementally extracted abstract S–R rules within working memory. Similarly, abstract rule formation and pragmatic rule implementation are difficult to discern as these 2 processes are, at least partially, overlapping in time.
In instruction-based learning, some of the above-mentioned subprocesses are seemingly irrelevant, whereas others appear to be much more clearly defined. First of all, rule extraction is, by definition, not relevant in instruction-based learning—at least as long as S–R mappings are sufficiently simple to be kept in working memory. Second, the formation of abstract rule representations should be completed by the end of the instruction period—as should be evidenced by low error rates right from the first practice trial. By contrast, the other relevant aspect of instruction-based learning, that is, the recoding of abstract task rules into pragmatic S–R representations, was assumed to start from the first practice trial, followed by a consolidation period during which the pragmatic representation becomes further strengthened and thus less and less dependent on support from the abstract rule representation. Accordingly, we hypothesized that the transfer of action control from an abstract to a pragmatic task representation should be indicated by a rapid redistribution of labor among brain regions across the first few practice trials following the instruction phase. Specifically, we expected a strong initial engagement of brain areas that support the retrieval and implementation of novel abstract task rules, including the mid-VLPFC (Wang et al. 2000; Bussey et al. 2001), possibly in cooperation with mediotemporal brain structures (Yanike et al. 2009). Such brain regions were expected to become less important with increasing stability of the newly formed pragmatic representation, reflected by an increasing engagement of other brain regions including the lateral PMC–PFC possibly in cooperation with striatal subregions (Brasted and Wise 2004).
Most importantly, we reasoned that the true significance of such activation dynamics would eventually need to stand the test against its implications for actual behavioral performance—even more so, as our theoretical emphasis was put on the pragmatics of explicitly instructed rules. Therefore, we first examined the relationship between practice-related brain activation dynamics and behavioral performance. The rationale was that a more efficient transfer of control toward a pragmatic task representation would lead to a stronger performance improvement across the course of practice. Thus, practice-related activation dynamics in brain regions that are specifically involved in the hypothesized transfer of action control from an abstract to a pragmatic task representation were expected to covary with practice-related performance improvement. Second, we exploited one of the strengths of the cognitive neuroscience approach, that is, to be able to investigate the neural correlates of cognitive processes in the absence of overt behavior. Specifically, we determined the correlation between brain activation during the instruction phase and the subsequent practice-related behavioral performance improvement. This analysis allows the identification of brain regions that are essential for the encoding of task rules in a way that is suited to successfully guide future behavioral rule implementation.
Materials and Methods
Experimental Procedures
Sixteen right-handed human participants with no evidence of neurological or psychiatric compromise took part in this study (mean age = 26 years; age range: 21–30 years; 11 females and 5 males). The experimental protocol was approved by the Ethics Committee of the Technische Universität Dresden. All participants gave written informed consent prior to taking part in the experiment and were paid € 8 per hour for their participation.
Experimental Design
We devised an experimental procedure that required participants to sequentially implement 20 different, explicitly instructed arbitrary S–R mappings. Each mapping involved 4 unique abstract shapes, 2 of which required a left index finger button press and the other 2 required a right index finger button press (Fig. 1, upper panel). Each mapping was instructed visually on the computer screen for 10 s. During this instruction phase, 2 stimuli were displayed left of the center (indicating that a left response was demanded), and the other 2 stimuli were displayed right of the center (indicating that a right response was demanded). The instruction was followed by 32 practice trials, each trial comprising one stimulus that was displayed until the participants' response or until the maximum response time window of 1500 ms had elapsed. Error trials (or misses due to time-out) were repeated in the following trial, and both trials were excluded from the analysis. Participants received feedback about the correctness of a response that was displayed for 500 ms immediately after both correct responses and erroneous responses or after time-out. Because there were only 2 possible responses available, an erroneous response indicated unambiguously that the alternative response would have been the correct choice. Unique random sequences of the 4 stimuli were generated for each mapping and for each participant such that each individual stimulus occurred 8 times during practice, not including the additional presentations after erroneous responses. The intertrial interval varied randomly between 0.8 and 3.5 s. The total experiment duration was approximately 35 min, depending on response speed and the number of erroneous responses.
Imaging Procedure
Whole-brain images were acquired on a Siemens 3-T whole-body Trio System (Erlangen, Germany) with a 16 channel circularly polarized head coil. Headphones (NordicNeuroLab, Bergen, Norway) and earplugs dampened scanner noise. Both structural and functional images were acquired for each participant. High-resolution structural images (1.0 × 1.0 × 1.0 mm) were acquired using an magnetization-prepared rapid gradient echo T1-weighted sequence (time repetition [TR] = 1900 ms, time echo [TE] = 2.26 ms, time to inversion = 900 ms, flip = 9°). Functional images were acquired using a gradient echo-planar sequence (TR = 2000 ms, TE = 30 ms, flip = 80°, interleaved slice acquisition, slice gap = 0). Each volume contained twenty-six 5.0-mm thick slices (in-plane resolution, 4.0 × 4.0 mm). The experiment was controlled by Presentation 12.0 software (Neurobehavioral Systems, San Francisco, CA) running on a Windows-XP PC. Stimuli were projected to participants via Visuastim digital goggles (Resonance Technology, Inc.; Northridge, CA) simulating a viewing distance of 100 cm. A fiber optic, light-sensitive key press was used to record participants' behavioral responses.
Data Analysis
The empirical data set were analyzed with SPM5 based on MATLAB 7.1. Preprocessing included slice-time correction, rigid body movement correction (3 translation and 3 rotation parameters), normalization of the functional images by directly registering the mean functional image to the standard Montreal Neurological Institute echo-planar-imaging template image provided by SPM5 (the resulting interpolated spatial resolution was 4 × 4 × 4 mm), and smoothing of the functional images (Gaussian Kernel, full width half maximum = 8 mm).
Event-Related Analysis
The preprocessed imaging data were analyzed using the general linear model (GLM) approach as implemented in the SPM5 software package. Model regressors were created by convolving neural input functions for the different event types with the assumed canonical hemodynamic response function used by SPM5. The analysis aimed at estimating blood oxygen level–dependent (BOLD) activation associated with 1) the 10-s instruction phase and 2) the subsequent practice phase.
Instruction-related activation was captured by 2 different types of regressors both synchronized to the onset of the instruction phase, which were estimated within 2 separate GLMs. One regressor was constructed to capture BOLD activation associated with transient neural activity at the start of the instruction phase (duration parameter = 0). The other regressor was constructed to capture BOLD activation associated with sustained neural activity throughout the instruction phase (duration parameter = 10). Note that implementing an "integrated" GLM solution, in which both instruction-related regressors were estimated within the same model, yielded highly similar results (not reported).
The successful extraction of practice-related BOLD activation relied on an important property of the task design. Specifically, the presentation order of the 4 instructed stimuli during the practice phase was randomly intermixed for each of the 20 S–R mappings. Thus, it was possible to transform the series of 32 successive practice trials (represented on the x-axis in Fig. 1, lower panel) into an intermixed sequence of 8 stimulus repetition levels for each of the 4 stimuli (represented on the y-axis in Fig. 1, lower panel). Thus, instead of trying the impossible, that is, to estimate the highly correlated BOLD responses for each of the 32 successive practice trials, it was possible to analyze practice-related dynamics in terms of the incremental number of repetitions of the 4 S–R associations yielding a highly reduced correlation among BOLD responses for the 8 stimulus repetition levels (Eliassen et al. 2003; Brovelli et al. 2008). Given 20 different S–R mappings, for each of the 8 stimulus repetitions, there were 80 (20 × 4) correct trials available for analysis. We included one additional model regressor which captured activation elicited by error and posterror trials.
The activation dynamics across the 8 stimulus repetition levels was modeled differently within 3 separate GLMs, whereas the model regressors for the instruction phase and the error and posterror trials were always the same. In GLM-1 and GLM-2, stimulus repetition was modeled via parametric designs, whereas GLM-3 realized a factorial design. In GLM-1, we included a single regressor for all practice trials (i.e., 20 × 32 event occurrences) and captured practice-related changes of activation by adding a parametric variable that varied with stimulus repetition level, assuming a linear relationship between stimulus repetition level and BOLD activation change (i.e., parameter values ranged between 1 and 8 according to stimulus repetition level). GLM-2 was identical to GLM-1 except that the relationship between stimulus repetition level and BOLD activation change was assumed to follow an exponential decay function (i.e., parameter values from the linear model were transformed via an exponential decay function). In both GLMs, the presence of practice-related BOLD activation change was evaluated by performing t-tests for the respective stimulus repetition parameter. In contrast, the factorial design of GLM-3 comprised 8 regressors, one for each stimulus repetition level. Within this model, the presence of practice-related activation change was evaluated based on an F statistic testing for significant differences among the 8 stimulus repetition levels. The advantage of the parametric GLMs is that practice-related activation dynamics that resemble either a linear function or an exponential decay function can be captured more reliably due to fewer degrees of freedom. The clear disadvantage is that detailed information about activation at each stimulus repetition level gets lost. Thus, conclusions can only be drawn regarding the direction of practice-related activation change (i.e., increasing or decreasing activation with increasing number of stimulus repetitions). For instance, it is impossible to determine whether a voxel that exhibits decreasing activation is still significantly activated above baseline at the end of practice (i.e., at stimulus repetition 8). Another potential disadvantage of the parametric model as compared with the factorial model is that other relationships between stimulus repetition and BOLD signal change (e.g., U shaped) might be overlooked. Yet, it seems that, at least in this study, the relevant practice-related activation dynamics were fully captured by the 2 parametric models as the factorial model did not identify voxels exhibiting significant practice-related activation that were not also identified by either of the parametric models. Notably, also the 2 parametric models yielded quite similar results.
We used GLM-1 and GLM-2 to identify voxels that showed significant practice-related increase or decrease of BOLD activation (parametric contrast p(t) < 0.001; uncorrected). These voxels were further assessed by the factorial GLM-3 for 2 reasons. First, GLM-3 provides BOLD activation estimates for each of the 8 stimulus repetition levels, thus allowing for a detailed description of the actual practice-related activation dynamics. Second, we used the BOLD response estimates for stimulus repetitions 1 and 8 in several inclusive and exclusive masking procedures to subdivide the previously identified practice-related voxels into 4 mutually exclusive categories. Those included 1) voxels exhibiting practice-related activation decrease terminating significantly above baseline, that is, a significant negative slope for either parametric contrast [p(t)< 0.001] and a significant positive amplitude for the factorial contrast at stimulus repetition 8 (p(t) < 0.05); 2) voxels exhibiting practice-related activation decrease terminating at zero activation, that is, a significant negative slope for either parametric contrast (p(t)< 0.001) and no significant positive activation for the factorial contrast at stimulus repetition 8 [p(t)> 0.05]; 3) voxels exhibiting practice-related activation increase starting at or above baseline, that is, a significant positive slope for either parametric contrast [p(t) < 0.001] and no significant negative activation for the factorial contrast at stimulus repetition 1 [p(t)> 0.05]; and 4) voxels exhibiting practice-related activation increase below baseline, that is, a significant positive slope for either parametric contrast [p(t)< 0.001], a significant negative activation for the factorial contrast at stimulus repetition 1 [p(t)< 0.05], and no significant positive activation for the factorial contrast at stimulus repetition 8 [p(t)> 0.05]. Finally, we included a fifth category for voxels exhibiting significant activation during the practice phase at a constant level, that is, a significant effect for the overall "trial" regressor estimate in either GLM-1 or GLM-2 [p(t)< 0.001] and no significant effects of stimulus repetition [p(t)> 0.05] for either parametric contrast. Finally, we excluded voxels from this fifth category that were located within an 8-mm radius of voxels showing either a significant activation increase or a significant activation decrease. This was done to avoid erroneous classification due to the mutual cancellation of increasing and decreasing activation profiles from closely spaced foci, which are likely to merge due to spatial smoothing and averaging across interindividually variable brain morphologies.
To control for multiple comparisons, all activation maps were additionally assessed via the cluster-level criterion based on the Gaussian-random field theory implemented in SPM5. Given the particular smoothness properties of the present data set this means that an activation cluster (with an initial uncorrected voxel-level threshold set to P < 0.001) needed to comprise at least 26 contiguously activated voxels to reach a cluster-corrected significance level of P < 0.05. For a few cases during the analysis of the relationship between performance improvement and BOLD activation (see next section), we relaxed these demands in order not to miss potentially relevant information. Importantly, each case is explicitly identified.
Performance Improvement × BOLD Activation
Analogously to the analysis of the brain activation data, we examined practice-related changes of behavioral performance as a function of stimulus repetition level with regard to both the mean response times and the percentage of errors. Furthermore, and most importantly, we examined the relationship between practice-related behavioral performance improvement and BOLD activation. To determine the covariance between behavior and brain activation, the behavioral performance improvement based on response times for each participant was entered as a covariate into the statistical parametric mapping (SPM) group-level analysis of 1) practice-related BOLD activation dynamics (based on the parametric stimulus repetition contrasts from GLM-1 and GLM-2) and 2) instruction-related BOLD activation. The improvement of behavioral performance was expressed by a single parameter obtained by fitting an exponential decay function across the mean response times for each stimulus repetition level, with higher positive decay values indicating a stronger performance improvement. To make sure that the SPM-based regression analysis did not produce significant correlations due to possible outliers, we additionally computed the Spearman rank correlation between performance improvement and brain activation from the peak activation voxel in each region identified via the original SPM regression analysis (weighing reduced statistical power against insensitivity to outliers). The scatter plots depicted in Figures 5 and 6 are based on the ranked values for behavior and brain activation that were used for the Spearman correlations.
Behavioral Performance
In a first analysis step, we determined practice-related changes in behavioral performance. The results are summarized in Figure 2 depicting mean response times and error rates as a function of stimulus repetition. Clearly, both curves are characterized by high learning rates, leading to asymptotic performance levels around stimulus repetition 4. According to a repeated measures analysis of variance, the general performance improvement with increasing stimulus repetition was significant for both response time (RT) (F3,49 = 20.9; P < 0.001, Greenhouse–Geisser-corrected) and error rate (F5,70 = 4.2; P = 0.003, Greenhouse-Geisser-corrected). With regard to RT, the pairwise contrasts for adjacent stimulus repetitions were significant for stimulus repetition 2 versus 1 (F1,15 = 25.3; P < 0.001) and for stimulus repetition 3 versus 2 (F1,15 = 15.8; P < 0.001). No other such pairwise contrasts were significant (neither for RT nor for error rate). Thus, the performance data suggest that some rapid adaptation processes might have been completed already halfway through the practice phase. Moreover, the error rate was impressively low right from the first stimulus repetition (mean 6%; ranging between 0% and 15% across participants) and decreased by only 3% until the end of practice. These observations suggest that the instructions were well memorized, and thus, trial-and-error learning seems unlikely to have contributed substantially to the observed performance improvement.
Brain Activation
The imaging data corroborate the conclusion that rapid adaptation processes have occurred across the first few stimulus repetitions. We will first describe the general pattern of practice-related BOLD activation changes (i.e., different types of activation increase and decrease). Thereafter, we will turn to the key analysis investigating the relationship between practice-related performance improvement and brain activation during 1) the practice phase and 2) the instruction phase.
We identified 4 types of practice-related BOLD signal change, including 2 types of BOLD signal decrease (Supplementary Tables S1 and S2) and 2 types of BOLD signal increase (Supplementary Tables S3 and S4). Furthermore, we identified several brain regions that exhibited significant BOLD activation during the practice phase at a constant level, that is, irrespective of the stimulus repetition (Supplementary Table S5; see for a representative brain region, Fig. 4B).
Practice-Related Activation Decrease
Brain areas exhibiting a BOLD signal decrease included the LPFC along the inferior frontal sulcus, caudally transitioning into the inferior frontal junction (IFJ) area (Brass et al. 2005; Derrfuss et al. 2005). The same overall decreasing pattern was also observed along the intraparietal sulcus (IPS) and in the anterior insular cortex. Interestingly, we observed stark differences between voxels with regard to their asymptotic levels of activation. Specifically, some voxels reached asymptotic activation well above baseline level, whereas other voxels reached activation levels close to baseline (i.e., close to zero). To further objectify this observation, we created 2 separate whole-brain activation maps based on the original map for practice-related BOLD signal decrease (see Materials and Methods). Because we were particularly struck by the observation of near-baseline asymptotic activation levels (especially in the LPFC), we chose a very lenient criterion of P < 0.05 (uncorrected) to determine the activation level for the final stimulus repetition 8 in order to avoid misclassifying weak asymptotic activation as nonexistent activation. Thus, only voxels exhibiting close-to-zero activation at stimulus repetition 8 would become classified as nonsignificantly activated above baseline at the end of practice (for a representative brain regions, see Fig. 3C). Conversely, voxels exhibiting only weak activation at stimulus repetition 8 would already be classified as significantly activated above baseline at the end of practice (for a representative brain regions, see Fig. 3B).
This additional subclassification into 2 types of BOLD signal decrease revealed that activation in most of the LPFC declined to baseline level at the end of practice. The only exceptions were the more caudolateral part of the IFJ (bilaterally) and the right mIFS. These regions exhibited significant, though numerically small, above-baseline activation even at the end of practice. Similar to the predominant activation pattern in the LPFC, a BOLD signal decrease down to baseline level was also observed for the lower bank of the IPS. By contrast, the BOLD signal recorded from the upper bank of the IPS remained well above baseline until the end of practice. Notably, as exemplified by the 2 representative brain regions depicted in Figure 3B, significant asymptotic above-baseline activation was consistently observed on a much higher general level of activation in parietal regions as compared with frontal regions.
Practice-Related Activation Increase
Two completely different types of practice-related BOLD signal increase were observed, including 1) above-baseline BOLD signal increase with increasing stimulus repetition (for exemplary brain regions, see Fig. 3D) and 2) below-baseline BOLD signal increase with increasing stimulus repetition (for an exemplary brain region, Fig. 4A).
We identified several regions that exhibited above-baseline BOLD signal increase, including the precentral/postcentral gyri dorsomedially transitioning into the supplementary motor area (SMA) and ventrally transitioning into the rolandic operculum, and the adjacent middle insular cortex and superior temporal gyrus. Additionally, a significant practice-related BOLD signal increase was found in the striatum. As mentioned above, we also observed brain areas exhibiting practice-related BOLD signal increase starting from (and remaining) below baseline (Supplementary Table S4). These regions by and large overlap with the resting state or default-mode network described previously (Raichle et al. 2001; Miall and Robertson 2006). Accordingly, it seems likely that the practice-related activation increase in these regions is rather unspecifically related to a gradual return to the resting state. In other words, the system becomes increasingly less absorbed by the experimental task, thus freeing increasingly more resources for resting state processes.
Relationship between Practice-Related Activation and Behavioral Performance Improvement
So far, our results have strikingly revealed the presence of rapid adaptation processes following the explicit instruction of novel S–R mappings as evidenced by the practice-related dynamics of both behavior and brain activation. The next, most crucial, analysis step determined the extent to which practice-related changes in BOLD activation were directly associated with practice-related performance improvement. This analysis was meant to provide much more direct evidence for the potential involvement of certain brain regions in the formation of pragmatic rule representations (as actual behavior should be closely controlled by this representation). We found 3 brain regions that exhibited significant negative correlations between practice-related changes in performance and practice-related changes in brain activation (Fig. 5; Table 1), including a region at the border of the right lateral PMC and PFC (at a lower significance threshold of P < 0.01, uncorrected, also in the left hemisphere at MNI coordinates: −56, −4, 36), the head of the right caudate nucleus (at a lower significance threshold of P < 0.01, uncorrected, also in the left hemisphere at MNI coordinates: −8, 20, 8), and a region in the right posterior superior parietal lobule (pSPL). Note that only the right PMC activation conforms to the whole-brain cluster-level–corrected statistical criterion equivalent to at least 26 contiguously activated voxels. By contrast, the right pSPL reached only voxel-level significance at P < 0.001 (uncorrected). The right caudate nucleus failed the cluster-level-corrected threshold on the whole-brain level but reached cluster-level-corrected significance after small volume correction selectively including voxels that exhibited a general practice-related activation increase above baseline.
|Brain region
|MNI coordinates
|BOLD (practice) × performance improvement
|BOLD (instruction) × performance improvement
|R. PMC-a
|R. PMC-p
|R. pSPL
|R. caudate nucleus
|Brain region
|MNI coordinates
|BOLD (practice) × performance improvement
|BOLD (instruction) × performance improvement
|R. PMC-a
|R. PMC-p
|R. pSPL
|R. caudate nucleus
Note: Asterisks denote significant t-values and Rho values: *P < 0.05, **P < 0.01, ***P < 0.001, uncorrected at the voxel level. Abbreviations: R, right; a, anterior; p, posterior.
Significant activations with P < 0.05, corrected at the cluster level (i.e., at least 26 contiguous voxels).
Significant activations with P < 0.05, corrected at the cluster level after small volume correction selectively including voxels that exhibited a practice-related activation increase. Rho denotes Spearmans correlation coefficient. All reported correlations for BOLD (instruction) × performance improvement are based on the BOLD estimate for transient instruction-related activation.
Importantly, the lateral PMC–PFC region corresponds roughly to the periarcuate region in monkey brains described by Petrides (2005), a region that we specifically predicted to be involved in the formation of pragmatic rule representations. This relatively large right-hemispheric activation cluster transitioned posteriorly into a region that showed practice-related activation increase and anteriorly into a region that exhibited practice-related activation decrease, partially overlapping with the IFJ area defined above. Thus, for these 2 PMC-PFC subregions (from hereon termed as PMC-p and PMC-a) the actual performance-dependent activation dynamics underlying the negative correlations between performance and BOLD activation should be quite different. Specifically, a negative correlation between performance improvement and activation increase as observed in the PMC-p (and in the caudate nucleus) indicates weaker activation increase (i.e., BOLD parameter estimate less positive) with higher performance improvement. By contrast, a negative correlation between performance improvement and activation decrease as observed in the PMC-a (and in the right pSPL) indicates stronger activation decrease (i.e., BOLD parameter estimate more negative) with higher performance improvement. These 2 contrasting patterns of performance-dependent activation dynamics are visualized in Figure 6. Specifically, practice-related BOLD activation dynamics are shown as a function of practice-related performance improvement after implementing a median split separately for each of the 2 PMC subregions. Note that this median split was intended for visualization purposes only. For the statistical assessment of the relationship between performance and brain activation, the results from the regression analyses are preferable (MacCallum et al. 2002). The median split visualization clearly demonstrates the expected differential activation patterns for the anterior and posterior PMC subregions (Fig. 6A,B). BOLD activation decrease in the PMC-a was stronger for high as compared with low performance improvement. By contrast, the PMC-p showed the opposite pattern, that is, stronger activation increase for low as compared with high performance improvement.
It seems tempting to interpret these contrasting patterns of performance-dependent activation dynamics in terms of a direct interaction between both subregions assuming that PMC-a provides abstract information about the instructed rules as long as a stable pragmatic code has not yet been established within PMC-p. Thus, when performance improvement is low, that is, when pragmatic code formation is supposed to be rather inefficient, the PMC-a needs to provide abstract rule information for a longer period of time throughout the practice phase (little PMC-a activation decrease), paralleled by a more pronounced increase of PMC-p recruitment, reflecting the ongoing formation, and strengthening of the pragmatic representation. Conversely, when performance improvement is high, that is, when pragmatic code formation is supposed to be highly efficient, PMC-a activation decreases quickly (as abstract rule information becomes less important), paralleled by little additional increase in PMC-p engagement, reflecting that the pragmatic representation has quickly reached a stable state. Interestingly, based on the median split data, the PMC-p activation was significantly higher for high as compared with low performance improvement already in the initial practice phase at stimulus repetitions 1 and 2 (t = 2.36; P < 0.03). This finding suggests that high performance improvement during practice might be due to an already strong pragmatic code right from the outset of practice. In turn, such an interpretation implies that a stable pragmatic code might have been formed already during the instruction phase. In other words, high performance improvement during physical practice might be related to some form of mentally simulated practice already during the instruction phase, possibly similar to processes described in the context of motor imagery (Jeannerod and Frak 1999; Jeannerod 2001).
Relationship between Instruction-Related Activation and Behavioral Performance Improvement
The "simulated practice" interpretation proposed above is corroborated by findings from our final analysis step, which addressed the relationship between brain activation during the instruction phase and performance improvement during the subsequent practice phase. This analysis was performed to pursue the more general question how participants manage to implement newly instructed S–R mappings from the first trial, that is, without actual behavioral practice and solely based on reading and encoding the instructions. The general expectation was that a better encoding of S–R rules during the instruction phase, as indicated by a stronger engagement of the relevant brain regions, would lead to a stronger performance improvement during the subsequent practice phase. This predicted positive relationship was found in multiple brain areas (Table 2, Fig. 5). Most interestingly, this included the same right lateral PMC–PFC region that exhibited the complex relationship between practice-related dynamics of BOLD activation and performance improvement visualized in Figure 6. For this region, a positive correlation with performance improvement was found primarily for transient instruction-related activation (see results reported in Table 2) but at a more lenient threshold of P < 0.01 (uncorrected) also for sustained instruction-related activation. This finding nicely supports the interpretation developed above, which posits 1) that the formation of pragmatic task representations relies on a complex interplay between anterior and posterior subregions within the lateral PMC–PFC and 2) that this work can be accomplished during the instruction phase via some form of simulated practice.
|Brain region
|MNI coordinates
|BOLD (instruction) × performance improvement
|BOLD (practice) × performance improvement
|L. mMFG
|R. mMFG
|R. Pre-SMA
|Brain region
|MNI coordinates
|BOLD (instruction) × performance improvement
|BOLD (practice) × performance improvement
|L. mMFG
|R. mMFG
|R. Pre-SMA
Note: Asterisks denote significant t-values and Rho values: *P < 0.05, **P < 0.01, ***P < 0.001, uncorrected at the voxel level. Abbreviations: R, right; L, left; mMFG, midportion of the middle frontal gyrus.
Significant activations with P < 0.05, corrected at the cluster level (i.e., at least 26 contiguous voxels). Rho denotes Spearmans correlation coefficient. All reported correlations for BOLD (instruction) × performance improvement are based on the BOLD estimate for transient instruction-related activation.
Importantly though, there were other brain regions that exhibited a positive correlation between instruction-related activation and practice-related performance improvement but did not at the same time also show a correlation between practice-related activation and performance (for a representative brain region, see Fig. 5B). Such a pattern was observed in several brain regions selectively for transient instruction-related activation, including the midportion of the middle frontal gyrus bilaterally and the pre-SMA, though the latter region failed to reach significance based on the Spearman rank correlation. Note that additional caution seems advised as all 3 regions failed to reach the cluster-level-corrected significance criterion equivalent to 26 contiguously activated voxels. This distinct correlation pattern suggests that these regions might not be directly involved in the transfer of control from abstract to pragmatic rule representations as would have been suggested by an additional correlation between practice-related activation and performance. Rather, these regions might be related to the encoding of abstract S–R rules into working memory in a way that is suited to successfully guide future behavioral rule implementation. In other words, the more effort is put into abstract rule encoding during the instruction phase by these regions, the easier becomes the retrieval and implementation during subsequent practice. With regard to the pre-SMA, this interpretation seems in line with a recent study showing stronger pre-SMA activation for merely instructed but not yet implemented S–R rules compared with not-instructed stimuli (Brass et al. 2009).
Finally, we also observed 2 regions, the right caudate nucleus and the pSPL, which exhibited the opposite pattern, that is, correlations between practice-related activation dynamics and practice-related performance improvement (see above) in the absence of a significant correlation between instruction-related activation and performance improvement (Fig. 5C). Thus, different from the lateral PMC–PFC region, the caudate nucleus and pSPL might only be involved in pragmatic encoding processes when true behavioral feedback is generated, that is, during physical practice but not during simulated practice.
The present study aimed to identify the neurocognitive basis of instruction-based learning involving novel arbitrary S–R mappings. Two key features of our study design stick out in comparison to previous research, including 1) the examination of rapid adaptation processes across only a few practice trials and 2) the explicit instruction of S–R rules as compared with trial-and-error learning procedures. We reasoned that a sequence of 2 core processes might be central to the successful implementation of newly instructed S–R rules. First, we postulated that the instructions would initially (i.e., before their first behavioral implementation) be encoded on an abstract, possibly verbal level. Second, for successful behavioral implementation, such an abstract rule representation was hypothesized to be transformed into a pragmatic representation. We reasoned that this transformation process would necessarily start right from the first implementation of each newly instructed S–R association (otherwise no behavior would be possible), followed by further representational strengthening across practice. In other words, with each consecutive behavioral implementation of a given S–R association during the practice phase, the pragmatic representation should become increasingly independent from the scaffolding by the abstract rule representation, which, in turn, should become less and less relevant. Thus, as a result of this incremental transfer of control we expected 1) behavioral performance to improve, 2) brain areas involved in the pragmatic implementation of S–R rules to become increasingly engaged, and 3) brain areas involved in a more abstract representation of rules to become increasingly disengaged.
A first important aspect of the results is that all 3 indicators of this postulated rapid transfer of control were indeed observed, including 1) the improvement of behavioral performance across the practice phase, 2) an activation increase in the pre- and postcentral gyri and in the striatum, and 3) an activation decrease in the LPFC and along the IPS. This overall pattern of practice-related activation dynamics seems to be at least in part consistent with findings from other neuroimaging studies examining the learning of arbitrary S–R mappings by trial and error (Deiber et al. 1997; Toni et al. 2001; Eliassen et al. 2003; Boettiger and D'Esposito 2005; Brovelli et al. 2008). Thus, despite the apparent procedural differences between instruction-based learning and trial-and-error learning, on a general level, some neurocognitive commonalities seem to exist. An in-depth conceptual analysis regarding the possible functional commonalities and differences among these 2 forms of learning is certainly not warranted based on the current study results alone. Yet, the striking activation decrease in LPFC and parietal cortex observed in the present study is reminiscent of findings from the trial-and-error learning literature reporting strong PFC activation early during learning (Eliassen et al. 2003; Brovelli et al. 2008) which decreases with learning progress (Deiber et al. 1997; Law et al. 2005). These similar activation dynamics might reflect common underlying working memory processes related to the maintenance, monitoring, and selection among S–R rules (Bunge et al. 2003; Postle 2006). As elaborated in more detail at the end of the Discussion, the current results suggest that such executive control processes (especially those supported by the PFC) seem to be disengaged extremely quickly across only a few practice trials when S–R rules are explicitly instructed and sufficiently simple. Furthermore, our findings of practice-related activation increases following the instruction of novel abstract S–R rules are, on a general level, in line with previous reports in both humans and nonhumans, showing activation increases or arguing for the importance of sensorimotor areas (Deiber et al. 1997; Boettiger and D'Esposito 2005; Zach et al. 2008) and the striatum (Winocur and Eskes 1998; Hadj-Bouziane et al. 2003; Boettiger and D'Esposito 2005) during visuo-motor learning tasks.
Importantly though, despite these similar patterns of results, different from trial-and-error learning procedures, the present study suggests that these activation dynamics (both BOLD increases and decreases) are probably not so much reflecting the extraction of novel S–R rules (i.e., figuring out which response belongs to which stimulus) but might rather be related to the formation and strengthening of pragmatic representations of S–R rules. This conclusion extends insights from previous trial-and-error learning studies, which could not disentangle activation changes due to rule extraction processes from those related to the improvement of rule pragmatics. By contrast, in the present design, S–R rules can be encoded successfully already during the explicit instruction phase, and thus, practice-related activation dynamics are likely related to the improvement of rule pragmatics rather than to rule extraction. This conclusion seems to be supported also by the striking lack of reliable practice-related activation dynamics in mediotemporal lobe (MTL) structures in the present study. The MTL has typically been implicated in studies of rule learning, be it arbitrary S–R rules (Yanike et al. 2009), stimulus classification rules (Poldrack et al. 2001), or even implicit rule acquisition which does not rely on explicit feedback processing as in trial-and-error learning (Rose et al. 2002; Schendan et al. 2003). Yet, different from the present study, all these paradigms required the extraction of rules in the first place. Thus, together with the present results, this suggests a specific role of learning-related MTL activation for rule extraction processes.
Maybe the most important and certainly the more specific finding of the present study, however, is that practice-related performance improvement was significantly correlated with practice-related BOLD activation dynamics exclusively in 3 brain regions: the lateral PMC–PFC, the caudate head, and the pSPL (though this latter region failed to reach significance on the cluster level). Because actual overt behavior should be a direct reflection of pragmatic rule implementation, this correlation strongly suggests that practice-related activation dynamics in the identified brain regions might indeed reflect an involvement in the formation of pragmatic representations. Such a conclusion seems particularly warranted with regard to the lateral PMC–PFC, which is exactly the region that we predicted to be involved in the rapid formation and stabilization of pragmatic S–R associations. The involvement of the lateral PMC–PFC in pragmatic rule formation as implicated by the present study results further specifies and differentiates the functional role of this region in the implementation of novel S–R rules as discussed previously (Petrides 2005, 2008). Going beyond existing observations, our study design enabled us to demonstrate this region's extremely rapid adaptation dynamics across only few practice trials. Moreover, our results suggest a functional subdivision into a more anterior, as compared with a more posterior, PMC subregion, which seems consistent with complementary roles during the transfer from an abstract code (more anterior PMC) toward a self-sufficient pragmatic code (more posterior PMC). On a more general level, this functional segregation is consistent with previous theoretical considerations suggesting that more cognitive, or abstract action plans might be supported by anterior PMC subregions, as compared with more concrete motor programs supported by posterior PMC subregions (Picard and Strick 2001).
A further analysis of the lateral PMC–PFC region provided strong evidence that the transfer of control from abstract to pragmatic rule representations takes place already before the first behavioral implementation of novel S–R associations. This conclusion is suggested by the finding that stronger activation of the lateral PMC–PFC during the instruction phase was associated with stronger performance improvement during the subsequent practice phase. Thus, it seems that simulated practice performed during the instruction phase—at least by some subjects—can to some extent contribute to the formation of pragmatic S–R representations, which would otherwise be left for the "real" practice phase. Interestingly, the caudate nucleus does not seem to be involved in such type of simulated practice as it did not show a reliable correlation between behavior and instruction-related activation. Still, the caudate nucleus, like the lateral PMC–PFC, seemed to be involved in the formation of pragmatic S–R representations during "physical" practice as indicated by a significant correlation between behavior and practice-related activation.
The practice-related activation increase in the caudate nucleus following explicit rule instruction seems at odds with previous results from studies that employed probabilistic stimulus classification tasks in which participants had to learn to classify exemplar stimuli into different stimulus categories. Specifically, it has been shown that the caudate nucleus is involved in classification learning only when the rules had to be extracted based on trial-and-error learning but not during the explicit instruction of identical rules (Poldrack et al. 2001; Shohamy et al. 2004, 2006). Yet, other studies suggest that the MTL, but not the caudate nucleus, seems to be related to the extraction of complex rules (Rose et al. 2002, 2004). In these experiments, the caudate nucleus exhibited an activation increase associated with the gradual "proceduralization of simple S–R associations" based on explicitly instructed behavioral rules, which seems similar to the practice of instructed S–R rules in the present study, though on a much slower timescale. Together, across different study types, the exact functional role of the caudate nucleus during learning and practice seems to remain a matter of future research, which needs to further clarify the relevance of rule-type (e.g., S–S vs. S–R), specific learning procedure, and possibly also the timescale of the examined adaptation dynamics. The present study contributes to this quest by demonstrating that the caudate nucleus exhibits a rapidly increasing engagement during the practice of novel, explicitly instructed, and simple S–R rules.
Finally, our results might have interesting implications with regard to notions on the relationship between PFC function and automatization. A classical conceptual framework that might come to mind first, suggests that novel and unpracticed tasks rely on some form of "supervisory attentional system" (i.e., high demands on executive control) localized in the PFC before behavior becomes more automatic and less dependent on this brain structure (Norman and Shallice 1986). Importantly though, such an account of PFC function is typically thought to refer to the transition from controlled to automatic information processing across an extensive practice phase much longer than in the present study (Schneider and Chein 2003; Chein and Schneider 2005). Yet, our results seem to suggest that the engagement of large parts of the PFC decreases extremely quickly down to baseline level within only a few practice trials. This observation suggests that the implementation of novel behavioral rules depends on the PFC only during a very brief initial period and more generally, that the time window of mandatory PFC recruitment might be much smaller than implied by classical notions of controlled versus automatic processing, at least in case of relatively simple S–R rules.
Supplementary Material
Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.
We wish to thank Kerstin Raum for managing the recruitment of participants and for collecting the data. Conflict of Interest: None declared.


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Showing reviews 1-10 of 110 | Next
Posted by Jon on 30th Jan 2017
Similar to some of the other reviews. I had been using branded saws and previous 2 seen over 30 years service. Was very doubtful about how an £80 saw would compare.
Fear not, for the money this can't be beaten. It is a quality product and cuts like a dream. Loads of power and in the week of having it cut about 400kg ash, 400kg elm and 250kg pine. All done like a hot knife through butter.
I would highly recommend this saw. Next day deliver too which was result after the last one packed up.... many thanks.
Posted by Unknown on 16th Nov 2016
It was a little difficult to get started the first time, but once it was running it was great.
Posted by Mr M CORMIER on 31st Oct 2016
Had trouble starting chainsaw.Contacted company , very helpful was told as new need to pull extra hard to start . Got my fit son to start chainsaw for me ,worked a treat .Powerful machine well made ,now starts easy Comes with loads of accessories
Posted by john on 1st Aug 2016
This is a fantastic buy. I received next day and easy to set up. I was a bit concerned about the reviews I had read saying it was hard to start but no problems at all in starting. First time use I cut an 18 inch diameter 30 foot larch tree down which made light work of both the felling and cutting afterwards. Wish I had bought one sooner.
Posted by Unknown on 20th May 2016
Smashing product for the price. A bit tricky to start first time - there is no primer bulb - but great once going.
Posted by Dai on 13th Apr 2016
I'm not an expert on chainsaws or 2-stroke engines in general, so I did have some concerns that I might struggle with assembling and starting this saw, as others have reported.
Yes, the instructions are not very well written and the diagrams are not the best, but it really isn't difficult to put together. Yes, I had a few problems starting it - with hindsight I should have closed the choke a lot sooner than I did, but I got it going in the end and I've no problems starting it since.
It works well, cutting through the trees with ease and I am delighted that I decided to buy it. At the end of the day it's all about cost verses the amount of work you need the tool to do, and for me this one certainly struck the right balance.
For anyone looking to fell the odd tree or cutting up logs for the fire, I would certainly recommend this porduct.
Posted by Alec on 4th Mar 2016
General comment:
I have owned three chainsaws before this one and the experience came in useful in setting up and early use. If you are new to chainsaws then the manual may confuse or disappoint and some YouTube research would seem like a very good idea. A good general introduction is provided here:
Don't expect your first attempts at starting to be necessarily easy-peasy. They are not. Remember this is a powerful machine with strong compression. Take great care to use the choke ONLY until the machine gives some indication that it may start (it gives a little kick) then push the choke button in. If you don't do this you are likely to flood the engine and seriously consider a bad review and return This would be a pity. You are well advised to hold the saw firmly to the ground and expect to fell some strong resistence to your pulling on the starter chord. I found the first few starts were best done with one foot on the handle plate and the other foot on the top of the black sleace of the wrap-over handle. In this way the saw couldn't budge as I used both hands to pull the starter. Another tip is not to over tightten the chain and to make sure it will move easily around the guide bar. Do this wearing a strong pair of gloves (engine OFF of course). The starting will get easier the more you use the machine.
How does it perform in operation?
Pretty well. I have had my saw for a few weeks now and in that time have cut down three trees - around 20 foot tall with a girth of 14 inches or so. I haver also spent several hours cutting firewood from the trunk and branches. The saw hasn't complained at all - although my back complains from time to time (old age approaches I'm affraid).
Would I recommend this saw?
Provided you are realistic in your expectations, spend some time and patience in getting used to it and don't expect it to last a lifetime then the answer is a definite YES. As a pretty good chinese copy of existing saw features it represents extrordinary value for money. My last saw cost more than five times as much - but it did last 12 years. The likely after sales service and spares is a consideration but I will be happy enough if the machine lasts beyond its guaratee period.
A final note.
This saw is no lightweight (which can be seen as a good thing) and If you expect to use a machine mainly for cutting logs and clearing branches then perhaps you should consider a 15" or 16" model - 20" takes a bit more care and management. Just a thought.
If you expect to use you saw
Posted by Jeffrey on 1st Mar 2016
This is the 4th Timberpro item I have bought all great machines and value for money with a good back up and helpful sales/service team
Would strongly recommend.
Posted by john m. on 17th Feb 2016
Saw arrived promptly. Assembly was straightforward. Was a bit worried about the lack of priming bulb but it started after a couple of pulls. Have used the saw fairly constantly over a four day period and it has started easily each time and cuts well. Seems well made so very happy with it so far.
Posted by Andy on 10th Feb 2016
I now have a large pile of logs from a tree that blew down in a storm, but it was quite a struggle to get there. First thing was the order, which the website clearly states 'next working day delivery if ordered by 2pm'. I ordered on a Friday morning but it wasn't even dispatched by Timberpro until late Tuesday afternoon, and then Parcelforce 24 took a further two days to deliver. When I complained I was told that I had placed a "pre-order" rather than an "order", but that is not what it says on the website.
Assembly of the chainsaw was a bit of a challenge as the instruction booklet is hopeless. The drawings are so tiny as to be useless, and the text is not in plain English. I had to use the helpline a couple of times and watched the online video.
Getting it going was also difficult. Partly my fault as I first tried using fuel I had in my cellar, which would not spark. So I bought fresh fuel and even then it took a lot of pulls on the cord (and seriously hefty pulls) before the engine caught.
However, once it got going it was excellent and made quick work of my fallen tree.
Showing reviews 1-10 of 110 | Next

subset: commoncrawl


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Darren Aronofsky
"When I slept under the stars"
Born and raised in Brighton Beach – a Brooklyn neighborhood dubbed Little Odessa for its high concentration of Ukrainian residents – screenwriter and director Darren Aronofsky embodies the true spirit of the American independent filmmaker for the alternative path of his career. "I grew up with Spielberg and Lucas, Raiders of the Lost Ark, E.T. and Star Wars, but I've always had a special regard for alternative films and directors like Spike Lee and Jim Jarmusch".
He debuted at Sundance in 1998 with his first feature-length movie, Pi, a sci-fi thriller which won the Director's Award and a Best First Feature nomination. After spending his life's savings, $60,000, to make it, Pi became a cult movie of the 90s, grossing $5m in U.S. movie theaters alone. Two years later he helmed Requiem for a Dream based on Hubert Selby Jr.'s novel of the same title. This was his first big-budget movie: $4.5m.
In 2006 he directed The Fountain and in 2008 The Wrestler, which won the Independent Spirit Award for Best Film and put Mickey Rourke back on Hollywood's A-list. His latest movie, Black Swan, set in the rarified world of ballet, won the Best Director and Best Film awards at both the Golden Globes and the Independent Spirit Awards. It was also nominated for an Academy Award in the Best Director category and earned Natalie Portman the Academy Award for Best Actress in a Leading Role.
10 Quick Facts
1 – Favorite movies: Pineapple Express produced by Judd Apatow; Superbad, and Frank Capra classics.
2 - Obsessions: "The story of Noah's Ark. I've been working on a script since I was 13. Noah is a fascinating character, the first real environmentalist".
3 - Nickname: "The dark prince of American cinema. I like that".
4 – Most important book: Hubert Selby's Last Exit to Brooklyn. It changed my life, not only because I was born and raised in Brooklyn but also because his writing style is like a fever that burns among the words on every page".
5 – The biggest satisfaction: "Returning to Cannes as a movie director. The first time I saw the film festival was in 1987 and I had $12 in my pocket. I stood with the crowd waiting to see the movie stars arrive at the Palais des Festivals. Not having enough money for a hotel room, I spent the night in a nearby park, looking at the stars in the sky".
6 – The greatest present: "A note from David Fincher during Oscar night. He wrote: "BS. No BS" - "Black Swan. No Bullshit".
7 – Good point: "With every film I direct I manage to lose 20 pounds, even if I eat just pizza and falafel! A movie's creative process is exhausting".
8 – Bad point: "I adore symmetry and organization. I'm very orderly, almost obsessive. I've classified my collection of nearly 2000 DVDs in alphabetic order".
9 – Film you'd like to make: "A movie about hip-hop because the hip-hop culture is in music, dance and the arts, but there's never been a movie about it".
10 – Movies or live theater: "Movies. As a filmmaker, I can show the audience a person's thoughts".
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Complete pipeline for Infinium® Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation
Touleimat N.,French Atomic Energy Commission |
Tost J.,French Atomic Energy Commission |
Tost J.,Center dEtude du Polymorphisme Humain
Epigenomics | Year: 2012
Background: Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium® Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. Materials & methods: We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. Results: We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. Conclusion: We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures. © Future Medicine Ltd. Source
Association of common variants in TNFRSF13B, TNFSF13, and ANXA3 with serum levels of non-albumin protein and immunoglobulin isotypes in Japanese
Osman W.,Tokyo Medical University |
Okada Y.,RIKEN |
Okada Y.,University of Tokyo |
Kamatani Y.,Center dEtude du Polymorphisme Humain |
And 3 more authors.
PLoS ONE | Year: 2012
We performed a genome-wide association study (GWAS) on levels of serum total protein (TP), albumin (ALB), and non-albumin protein (NAP). We analyzed SNPs on autosomal chromosomes using data from 9,103 Japanese individuals, followed by a replication study of 1,600 additional individuals. We confirmed the previously- reported association of GCKR on chromosome 2p23.3 with serum ALB (rs1260326, Pmeta = 3.1×10-9), and additionally identified the significant genome-wide association of rs4985726 in TNFRSF13B on 17p11.2 with both TP and NAP (Pmeta = 1.2×10-14 and 7.1×10-24, respectively). For NAP, rs3803800 and rs11552708 in TNFSF13 on 17p13.1 (Pmeta = 7.2×10-15 and 7.5×10-10, respectively) as well as rs10007186 on 4q21.2 near ANXA3 (Pmeta = 1.3×10-9) also indicated significant associations. Interestingly, TNFRSF13B and TNFSF13 encode a tumor necrosis factor (TNF) receptor and its ligand, which together constitute an important receptor-ligand axis for B-cell homeostasis and immunoglobulin production. Furthermore, three SNPs, rs4985726, rs3803800, and rs11552708 in TNFRSF13B and TNFSF13, were indicated to be associated with serum levels of IgG (P<2.3×10-3) and IgM (P<0.018), while rs3803800 and rs11552708 were associated with IgA (P<0.013). Rs10007186 in 4q21.2 was associated with serum levels of IgA (P = 0.036), IgM (P = 0.019), and IgE (P = 4.9×10-4). Our results should add interesting knowledge about the regulation of major serum components. © 2012 Osman et al. Source
The human AIRE gene at chromosome 21q22 is a genetic determinant for the predisposition to rheumatoid arthritis in Japanese population
Terao C.,Kyoto University |
Yamada R.,Kyoto University |
Ohmura K.,Kyoto University |
Takahashi M.,Kyoto University |
And 13 more authors.
Human Molecular Genetics | Year: 2011
Rheumatoid arthritis (RA) is a typical complex trait and the major cause of chronic inflammation worldwide. Although multiple genetic loci have been shown for their association with the onset of RA, they cover only a part of its genetic components and are largely ethnicity-specific. To identify novel genetic factors related to the predisposition and prognosis of RA in Japanese, we conducted a large-scale genome-wide association (GWA) study. We performed a GWA analysis by scanning the genome of 1247 RA cases and 1486 controls for 277 420 single nucleotide polymorphisms (SNPs), followed by replication analysis using two independent sample sets consisting of 1865 cases and 1623 controls, and 2303 cases and 3380 controls. We identified two SNPs, rs2075876 and rs760426, in intron of the autoimmune regulator AIRE gene at chromosome 21q22 that showed strong associations with the disease (P = 3.6 3 10-9 and P = 4.4 3 10-8, respectively). Rs1800250, in exon7 of AIRE, was in strong linkage disequilibrium (r2 = 0.94) with rs2075876 and introduced an amino acid alteration (S278R) in the SAND domain of the AIRE protein. In silico analysis showed the decreased transcription of AIRE by the risk allele of rs2075876 compared with the alternative allele (P = 6.8 3 10-5). No correlation was observed between the rs2075876 genotype and quantitative traits reflecting the progression of RA. As AIRE is a key molecule which regulates the expression and presentation of self-antigens in thymic negative selection, its downregulation by genetic polymorphisms may result in the survival of auto-reactive T cells to trigger auto-inflammation in RA. © The Author 2011. Published by Oxford University Press. All rights reserved. Source
Marrakchi S.,University of Sfax |
Marrakchi S.,University of Paris Descartes |
Guigue P.,University of Paris Descartes |
Renshaw B.R.,Amgen |
And 28 more authors.
New England Journal of Medicine | Year: 2011
BACKGROUND: Generalized pustular psoriasis is a life-threatening disease of unknown cause. It is characterized by sudden, repeated episodes of high-grade fever, generalized rash, and disseminated pustules, with hyperleukocytosis and elevated serum levels of C-reactive protein, which may be associated with plaque-type psoriasis. METHODS: We performed homozygosity mapping and direct sequencing in nine Tunisian multiplex families with autosomal recessive generalized pustular psoriasis. We assessed the effect of mutations on protein expression and conformation, stability, and function. RESULTS: We identified significant linkage to an interval of 1.2 megabases on chromosome 2q13-q14.1 and a homozygous missense mutation in IL36RN, encoding an interleukin- 36-receptor antagonist (interleukin-36Ra), an antiinflammatory cytokine. This mutation predicts the substitution of a proline residue for leucine at amino acid position 27 (L27P). Homology-based structural modeling of human interleukin-36Ra suggests that the proline at position 27 affects both the stability of interleukin-36Ra and its interaction with its receptor, interleukin-1 receptor-like 2 (interleukin-1 receptor- related protein 2). Biochemical analyses showed that the L27P variant was poorly expressed and less potent than the nonvariant interleukin-36Ra in inhibiting a cytokine- induced response in an interleukin-8 reporter assay, leading to enhanced production of inflammatory cytokines (interleukin-8 in particular) by keratinocytes from the patients. CONCLUSIONS: Aberrant interleukin-36Ra structure and function lead to unregulated secretion of inflammatory cytokines and generalized pustular psoriasis. (Funded by Agence Nationale de la Recherche and Société Française de Dermatologie.) Copyright © 2011 Massachusetts Medical Society. Source
Genome-wide association study for C-reactive protein levels identified pleiotropic associations in the IL6 locus
Okada Y.,RIKEN |
Okada Y.,University of Tokyo |
Takahashi A.,RIKEN |
Ohmiya H.,RIKEN |
And 10 more authors.
Human Molecular Genetics | Year: 2011
C-reactive protein (CRP) is a hallmark acute-phase reactant and is widely used as a blood marker for inflammation. Substantial roles of serum CRP levels in the pathogenesis of diseases have been suggested, and investigation of the mechanisms that regulate serum CRP levels would have a substantial clinical impact. Here, through genome-wide association and replication studies performed using 12 854 Japanese subjects, we identified a significant association between serum CRP levels and a single nucleotide polymorphism in the promoter region of interleukin-6 (IL6) (rs2097677, P = 4.1 × 10 -11), a typical pleiotropic pro-inflammatory cytokine. Our study also replicated the associations in the CRP (rs3093059, P = 3.5 × 10 -21) and HNF1A loci (rs7310409, P = 2.7 × 10 -8). Pleiotropic association analysis with hematological and biochemical traits using 30 466 Japanese subjects demonstrated that the CRP-increasing allele of rs2097677 in the IL6 locus was significantly associated with an increased white blood cell count, platelet count and serum globulin and a decreased mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration (P < 5.0 × 10 -4), although no pleiotropic association was observed in the CRP or HNF1A locus (α = 0.01). Our study demonstrated the pivotal role of the IL6 locus in the regulation of serum CRP levels and inflammatory pathways. © The Author 2010. Published by Oxford University Press. All rights reserved. Source


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Influenza, but not HIV-specific CTL epitopes, elicits delayed-type hypersensitivity (DTH) reactions in HIV-infected patients
Ruiz-Riol M.,IrsiCaixa AIDS Research Institute HIVACAT |
Mothe B.,IrsiCaixa AIDS Research Institute HIVACAT |
Mothe B.,Autonomous University of Barcelona |
Gandhi R.T.,Massachusetts General Hospital |
And 5 more authors.
European Journal of Immunology | Year: 2013
The induction of cytotoxic T lymphocytes (CTLs) is believed to be an important defense mechanism against viral infections. The availability of simple, sensitive, specific and physiologically informative in vivo tests, applicable to humans, would greatly elucidate the nature of protective immune responses and facilitate immune monitoring in large vaccine trials. Here we studied the possibility of using defined HLA-A*02:01-restricted CTL epitopes from influenza matrix protein (GL9, GILGFVFTL) and HIV Gag p17 (SL9, SLYNTVATL) to elicit a cutaneous delayed-type hypersensitivity (DTH) reaction. Our results show that the GL9 but not the SL9 epitope was able to induce a DTH reaction. HIV infection status, HIV RNA level and CD4+ T-cell counts were not predictive of the extent of DTH reactions. However, a markedly reduced expression of skin homing markers CD103 and cutaneous lymphocyte associated Ag (CLA) on epitope-specific CTL populations was associated with a lack of SL9 DTH reactivity. These data demonstrate that DTH reactions can be elicited by optimally defined CTL epitopes per se and point towards specific homing markers that are required for such reactions. These data may offer new insights into the immune pathogenesis of HIV infection and provide the basis of novel immune monitoring approaches for large-scale HIV vaccine trials. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source
Mothe B.,IrsiCaixa AIDS Research Institute HIVACAT |
Mothe B.,Autonomous University of Barcelona |
Llano A.,IrsiCaixa AIDS Research Institute HIVACAT |
Ibarrondo J.,IrsiCaixa AIDS Research Institute HIVACAT |
And 35 more authors.
Journal of Translational Medicine | Year: 2011
Background: The efficacy of the CTL component of a future HIV-1 vaccine will depend on the induction of responses with the most potent antiviral activity and broad HLA class I restriction. However, current HIV vaccine designs are largely based on viral sequence alignments only, not incorporating experimental data on T cell function and specificity.Methods: Here, 950 untreated HIV-1 clade B or -C infected individuals were tested for responses to sets of 410 overlapping peptides (OLP) spanning the entire HIV-1 proteome. For each OLP, a "protective ratio" (PR) was calculated as the ratio of median viral loads (VL) between OLP non-responders and responders.Results: For both clades, there was a negative relationship between the PR and the entropy of the OLP sequence. There was also a significant additive effect of multiple responses to beneficial OLP. Responses to beneficial OLP were of significantly higher functional avidity than responses to non-beneficial OLP. They also had superior in-vitro antiviral activities and, importantly, were at least as predictive of individuals' viral loads than their HLA class I genotypes.Conclusions: The data thus identify immunogen sequence candidates for HIV and provide an approach for T cell immunogen design applicable to other viral infections. © 2011 Mothe et al; licensee BioMed Central Ltd. Source
Randomised study to assess the efficacy and safety of once-daily etravirine-based regimen as a switching strategy in HIV-infected patients receiving a protease inhibitor-containing regimen. Etraswitch study
Echeverria P.,Autonomous University of Barcelona |
Bonjoch A.,Autonomous University of Barcelona |
Puig J.,Autonomous University of Barcelona |
Molto J.,Autonomous University of Barcelona |
And 9 more authors.
PLoS ONE | Year: 2014
Background: Etravirine (ETR) was approved for patients with virological failure and antiretroviral resistance mutations. It has also shown antiviral efficacy in antiretroviral-naïve patients. However, data on the switching from protease inhibitors (PI) to ETR are lacking. Methods: HIV-1-infected patients with suppressed viral load (VL) during a PI-containing regimen (>12 months) and no previous virological failure were randomized to switch from the PI to ETR (400 mg/day, dissolved in water) (ETR group, n = 22) or to continue with the same regimen (control group, n = 21). Percentage of patients with VL≤50 copies/mL were assessed at week 48, as well as changes in CD4 T-cell counts and metabolic profile. Results: We included 43 patients [72.9% male, 46.3 (42.2; 50.6) years]. Two patients receiving ETR (grade-1 diarrhea and voluntary discontinuation) and another in the control group (simplification) discontinued therapy early. No patients presented virological failure (two consecutive VL>50 copies/mL); treatment was successful in 95.2% of the control group and 90.9% of the ETR group (intention-to-treat analysis, missing = failure) (p = 0.58). CD4+ T-cell counts did not significantly vary [+49 cells/μL in the ETR group (p = 0.25) and 24 cells/μL in the control group (p = 0.71)]. The ETR group showed significant reductions in cholesterol (p<0.001), triglycerides (p = <0.001), and glycemia (p = 0.03) and higher satisfaction (0-10 scale) (p = 0.04). Trough plasma concentrations of ETR were similar to observed in studies using ETR twice daily. Conclusion: Switch from a PI-based regimen to a once-daily combination based on ETR maintained undetectable VL during 48 weeks in virologically suppressed HIV-infected patients while lipid profile and patient satisfaction improved significantly. Trial Registration: ClinicalTrials.gov NCT01034917. © 2014 Echeverría et al. Source
The HLA-C∗04:01/KIR2DS4 gene combination and human leukocyte antigen alleles with high population frequency drive rate of HIV disease progression
Olvera A.,IrsiCaixa AIDS Research Institute HIVACAT |
Perez-Alvarez S.,IrsiCaixa AIDS Research Institute HIVACAT |
Perez-Alvarez S.,Autonomous University of Barcelona |
Ibarrondo J.,University of California at Los Angeles |
And 14 more authors.
AIDS | Year: 2015
Objective: The objective of this study is to identify human leukocyte antigen (HLA) class I and killer-cell immunoglobulin-like receptor (KIR) genotypes associated with different risks for HIV acquisition and HIV disease progression. Design: A cross-sectional study of a cohort of 468 high-risk individuals (246 HIV-positive and 222 HIV-negative) from outpatient clinics in Lima (Perú). Methods: The cohort was high-resolution HLA and KIR-typed and analysed for potential differences in single-allele frequencies and allele combinations between HIV-positive and HIV-negative individuals and for associations with HIV viral load and CD4+ cell counts in infected individuals. Results: HLA class I alleles associated with a lack of viral control had a significantly higher population frequency than relatively protective alleles (P = 0.0093), in line with a rare allele advantage. HLA-A∗02 : 01 and HLA-C∗04 : 01 were both associated with high viral loads (P = 0.0313 and 0.0001, respectively) and low CD4+ cell counts (P = 0.0008 and 0.0087, respectively). Importantly, the association between HLA-C∗04 : 01 and poor viral control was not due to its linkage disequilibrium with other HLA alleles. Rather, the coexpression of its putative KIR ligand KIR2DS4f was critically linked to elevated viral loads. Conclusion: These results highlight the impact of population allele frequency on viral control and identify a novel association between HLA-C∗04 : 01 in combination with KIR2DS4f and uncontrolled HIV infection. Our data further support the importance of the interplay of markers of the adaptive and innate immune system in viral control. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Source
Ctl responses of high functional avidity and broad variant cross-reactivity are associated with hiv control
Mothe B.,IrsiCaixa AIDS Research Institute HIVACAT |
Mothe B.,Autonomous University of Barcelona |
Llano A.,IrsiCaixa AIDS Research Institute HIVACAT |
Ibarrondo J.,IrsiCaixa AIDS Research Institute HIVACAT |
And 25 more authors.
PLoS ONE | Year: 2012
Cytotoxic T lymphocyte (CTL) responses targeting specific HIV proteins, in particular Gag, have been associated with relative control of viral replication in vivo. However, Gag-specific CTL can also be detected in individuals who do not control the virus and it remains thus unclear how Gag-specific CTL may mediate the beneficial effects in some individuals but not in others. Here, we used a 10mer peptide set spanning HIV Gag-p24 to determine immunogen-specific T-cell responses and to assess functional properties including functional avidity and cross-reactivity in 25 HIV-1 controllers and 25 non-controllers without protective HLA class I alleles. Our data challenge the common belief that Gag-specific T cell responses dominate the virus-specific immunity exclusively in HIV-1 controllers as both groups mounted responses of comparable breadths and magnitudes against the p24 sequence. However, responses in controllers reacted to lower antigen concentrations and recognized more epitope variants than responses in non-controllers. These cross-sectional data, largely independent of particular HLA genetics and generated using direct ex-vivo samples thus identify T cell responses of high functional avidity and with broad variant reactivity as potential functional immune correlates of relative HIV control. © 2012 Mothe et al. Source


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Chalkboard for kids
Explore related topics
Magnetic chalkboard for Ava's playroom
Give your kids another reason to play outside with a chalkboard fence.
Can we make this on one of the walls in your playroom, pleeeaase? I'll help you if you'd like ;-)
How to Build a HUGE Chalkboard for Cheap! Every home could use one of these! | www.overthebigmoon.com!
Like the idea of a chalkboard at kiddo's eye level - maybe in kitchen or mudroom area?
my idea from this....use chalk board or magnet board for our calendar wall (a small angled wall off the hallway) and the hanging containers for the calendar pieces, and chalk. Good place to hand the kids art work also :-)
Kids Chalkboard Art WallCall today or stop by for a tour of our facility! Indoor Units Available! Ideal for Outdoor gear, Furniture, Antiques, Collectibles, etc. 505-275-2825
House shaped Chalkboard for kids playroom/ bedroom
How to Make an Outdoor Chalkboard- What a fun outdoor activity for the kids! Full tutorial includes list of weather proof materials!
How to make a giant outdoor chalkboard for your yard. This is such a perfect outdoor activity for the kids and it has held up for over 2 years!


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From Wikipedia, the free encyclopedia
In mathematics, the y-homeomorphism, or crosscap slide, is a special type of auto-homeomorphism in non-orientable surfaces.
It can be constructed by sliding a Möbius band included on the surface around an essential 1-sided closed curve until the original position; thus it is necessary that the surfaces have genus greater than one. The projective plane has no y-homeomorphism.
See also[edit]
- J. S. Birman, D. R. J. Chillingworth, On the homeotopy group of a non-orientable surface, Trans. Amer. Math. Soc. 247 (1979), 87-124.
- D. R. J. Chillingworth, A finite set of generators for the homeotopy group of a non-orientable surface, Proc. Camb. Phil. Soc. 65 (1969), 409-430.
- M. Korkmaz, Mapping class group of non-orientable surface, Geometriae Dedicata 89 (2002), 109-133.
- W. B. R. Lickorish, Homeomorphisms of non-orientable two-manifolds, Math. Proc. Camb. Phil. Soc. 59 (1963), 307-317.
|This geometry-related article is a stub. You can help Wikipedia by expanding it.


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