Frontal and Parietal Networks for Conditional Motor Learning: A Positron Emission Tomography Study
M.-P. Deiber1, 4,
S. P. Wise3,
M. Honda1,
M. J. Catalan1,
J. Grafman2, and
M. Hallett1
1 Human Motor Control Section and 2 Cognitive Neuroscience Section, Medical Neurology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, 20892-1428; 3 Laboratory of Systems Neuroscience, National Institute of Mental Health, Poolesville, Maryland 20837; and 4 Institut National de la Santé et de la Recherche Médicale, Centre d'Exploration et de Recherche Medicales par Emission de Positrons, 69003 Lyon, France
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ABSTRACT |
Deiber, M.-P., S. P. Wise, M. Honda, M. J. Catalan, J. Grafman, and M. Hallett. Frontal and parietal networks for conditional motor learning: a positron emission tomography study. J. Neurophysiol. 78: 977-991, 1997. Studies on nonhuman primates show that the premotor (PM) and prefrontal (PF) areas are necessary for the arbitrary mapping of a set of stimuli onto a set of responses. However, positron emission tomography (PET) measurements of regional cerebral blood flow (rCBF) in human subjects have failed to reveal the predicted rCBF changes during such behavior. We therefore studied rCBF while subjects learned two arbitrary mapping tasks. In the conditional motor task, visual stimuli instructed which of four directions to move a joystick (with the right, dominant hand). In the evaluation task, subjects moved the joystick in a predetermined direction to report whether an arrow pointed in the direction associated with a given stimulus. For both tasks there were three rules: for the nonspatial rule, the pattern within each stimulus determined the correct direction; for the spatial rule, the location of the stimulus did so; and for the fixed-response rule, movement direction was constant regardless of the pattern or its location. For the nonspatial rule, performance of the evaluation task led to a learning-related increase in rCBF in a caudal and ventral part of the premotor cortex (PMvc, area 6), bilaterally, as well as in the putamen and a cingulate motor area (CM, area 24) of the left hemisphere. Decreases in rCBF were observed in several areas: the left ventro-orbital prefrontal cortex (PFv, area 47/12), the left lateral cerebellar hemisphere, and, in the right hemisphere, a dorsal and rostral aspect of PM (PMdr, area 6), dorsal PF (PFd, area 9), and the posterior parietal cortex (area 39/40). During performance of the conditional motor task, there was only a decrease in the parietal area. For the spatial rule, no rCBF change reached significance for the evaluation task, but in the conditional motor task, a ventral and rostral premotor region (PMvr, area 6), the dorsolateral prefrontal cortex (PFdl, area 46), and the posterior parietal cortex (area 39/40) showed decreasing rCBF during learning, all in the right hemisphere. These data confirm the predicted rCBF changes in premotor and prefrontal areas during arbitrary mapping tasks and suggest that a broad frontoparietal network may show decreased synaptic activity as arbitrary rules become more familiar.
 |
INTRODUCTION |
A burgeoning brain-imaging literature has focused to a large extent on higher brain functions restricted to our species, such as speech, semantic information processing, and other aspects of language (Buckner et al. 1995
; Demonet et al. 1994
; Kapur et al. 1994a
,b
; Paulesu et al. 1993
; Petersen et al. 1988
, 1989
; Tulving et al. 1994a
,b
; Wise et al. 1991
). Fewer efforts have been directed toward those advanced behavioral capabilities that we share with other primates. Conditional motor learning typifies one such faculty; one allowing the flexibility to map any stimulus onto any motor response. This form of motor learning depends on the integrity of premotor and prefrontal areas in both humans (Halsband and Freund 1990
; Petrides 1987
, 1990
) and monkeys (Gaffan and Parker 1997
; Halsband and Passingham 1982
, 1985
; Murray and Wise 1997
; Passingham 1985
; Petrides 1982
, 1985
). Moreover, learning-related evolution in single-cell activity has been observed in the monkey premotor cortex during conditional motor learning (Chen and Wise 1995a
,b
, 1996
; Mitz et al. 1991
).
It had been predicted on these and other grounds that increases in regional cerebral blood flow (rCBF) should be observed in premotor cortex and other frontal areas during the performance of conditionally instructed versus fixed responses. However, when that experimental approach was first attempted, the predicted rCBF contrasts were not statistically significant anywhere in frontal cortex (Deiber et al. 1991
). In view of the finding by Chen and Wise (1995a)
that many premotor cortical cells are only active during conditional motor learning (as opposed to stable performance), we reexamined the prediction by studying rCBF as subjects improved their performance according to such rules. Because both spatial and symbolic forms of information are important in conditional motor learning, we used two different conditional rules, one relying on visuospatial information and the other on nonspatial visual information. Further, to accentuate the explicit aspects of conditional information processing, we required subjects to recognize and discriminate potential movement directions specified by a cue, as well as to generate directional movements as instructed by the same cues. These data have been reported previously in abstract form (Deiber et al. 1996
).
 |
METHODS |
Subjects
We studied seven normal volunteers, five males and two females, aged 22-50 yr (mean, 31 ± 11 yr). One female subject was eliminated from the analysis because she made a large number of errors and showed no improvement in performance over the scanning sessions. All subjects were right-handed as measured by the Edinburgh Inventory (Oldfield 1971
). The protocol was approved by the Institutional Review Board, and all subjects gave written informed consent.
Experimental design
For each subject, positron emission tomography (PET) scans of rCBF were performed sequentially using H215O as the tracer. During scanning, the subject moved a joystick, constrained by a grooved template that restricted joystick movement to four directions from center. Visual cues were presented on a video screen 58 cm from the subject. The display was masked to a 9 × 9° square (visual angle). The joystick moved 6.5 cm to the endpoint of the groove, and the movement was recorded as completed and the response time (RT) measured at the midpoint of its trajectory.

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| FIG. 1.
Conditional motor task, nonspatial rule. Largest squares represent 9 × 9° masked video screen, with 0.5° center fixation spot ( ), not to scale. Small squares represent the 2 × 2° patterned stimuli. For simplicity, each pattern is illustrated as appearing in upper left corner, but could appear near any of 4 corners. On each trial, a pattern is displayed for 200 ms. Then pattern disappeared (leaving the central spot). Subject had a limit of 1.7 s from stimulus offset to move joystick (bottom, ) in 1 of 4 directions, corresponding to diagonals of square display. Direction of joystick movement instructed by each pattern is represented by . For conditional motor task's spatial rule, same stimuli were presented but were associated with joystick movement responses as listed in Table 1.
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Our behavioral paradigm consisted of two tasks, each with three rules. The two tasks were termed the conditional motor and evaluation tasks. Each task consisted of nonspatial, spatial, and fixed-response rules. For convenience, the nonspatial and spatial rules, together, will be termed conditional rules, in contrast with the fixed-response rule, which was unconditional.
For all tasks and rules, the subject was asked to fixate a 0.5° spot, located in the center of the video display, throughout each block of trials. Each behavioral trial began when a 2° square visual stimulus appeared near one corner of the video display. There were four easily discriminated pattern stimuli (Fig. 1), and each stimulus instructed one and only one of the four possible joystick movements. No feedback was given concerning the correctness of response.
In the conditional motor task, the correct direction for a joystick movement was instructed by the visual stimulus, presented for 200 ms (Table 1). The subject had to choose a response according to one of three rules: a nonspatial rule in which the pattern within the stimulus determined which movement to make (Fig. 1), regardless of its location; a spatial rule in which the pattern's location instructed the direction of movement, regardless of which pattern was presented; and a fixed-response rule in which movement direction was constant regardless of the pattern or its location (Table 1). The subject was required to respond within a 1.7-s response window from stimulus offset, which was 1.9 s from stimulus onset (Fig. 1). If the response had not been completed by the end of the response window, it was classified as late. There were three additional classes of errors: incorrect, if a response was made within the required time constraints, but its direction was wrong; early (seen only in the fixed-response rule), if a response was made before the beginning of the response window; and no response, if the subject failed to move the joystick to any target on a trial. Subsequently, the presentation of another square stimulus marked the beginning of the next trial. The duration of each trial was the same regardless of the RT.
In the evaluation task, the same three rules applied. The initial visual stimulus was presented for 250 ms (Fig. 2). Then, after a delay period of 500 ms, an arrow originating from screen center and extending 80% of the distance to one of the corners of the video display was presented for 250 ms. Immediately thereafter, a screen appeared in which the word NO was located in the lower left corner and YES appeared in the upper right. The subjects used the joystick to report whether the arrow was pointing in the direction of the correct response to that stimulus (YES) or elsewhere (NO). A response was required within a 1.7-s response window from the onset of the YES/NO screen.

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| FIG. 2.
Evaluation task, nonspatial rule. On each trial, the following sequence of screens was presented: pattern plus fixation spot (250 ms), fixation spot only (500 ms, not illustrated), an arrow, which pointed to 1 of 4 corners (250 ms), and a YES/NO screen, in which words no and yes were displayed in lower left and upper right corner, respectively (1,700 ms). Subject had a response limit of 1,700 ms to move joystick to upper right to report that arrow matched the direction associated with pattern or to lower left to report that it did not. For simplicity of illustration, each pattern is displayed in upper left corner and only a selection of the possible combinations are shown. Patterned stimuli could appear near any of 4 corners and were followed by a "correct" or "incorrect" arrow (representing a potential response) in a balanced sequence. For evaluation task's spatial rule, same sequence of screens was presented, but subjects evaluated whether arrow pointed in direction associated with location of stimulus as listed in Table 1.
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Each subject was scanned in two separate sessions, which occurred on different days. The average interval between the scans was 21 days, ranging from 1 to 74 days. Within each session, the subjects performed either the conditional motor or evaluation task for 10 scanning blocks in the following sequence: one block of the fixed-response rule, four consecutive blocks of one of the two conditional rules (i.e., either the spatial or nonspatial rule), four consecutive blocks of the other conditional rule, followed by a final block of the fixed-response rule. Before each session, subjects were presented with the rules in written and graphic form, similar to Figs. 1 and 2, but without the temporal information. Additionally, before the first scanning block, each subject received a five-trial block of practice trials for each rule. Thus during this pre-PET training session, the subjects were instructed explicitly about the response location associated with each stimulus pattern and each place that it might appear. Feedback concerning response accuracy was given. The order of tasks was balanced across subjects, as was the order of the two conditional rules during a session.
A Macintosh IIfx computer (Apple, Cupperton, CA) was used to monitor and store the behavioral data. Stimuli were presented with SuperLab (Cedrus, Wheaton, MD). Joystick position was sampled at 200 Hz. RT was calculated on-line and measured from the onset of the visual stimulus in the Conditional Motor Task and from the onset of the YES/NO screen in the evaluation task.
Data acquisition
Subjects lay in a supine position in a dimly lit, sound-attenuated room. The subject's head was immobilized with an individually fitted thermoplastic face mask. A small plastic catheter was placed in the left cubital vein for injection of radioisotope. PET of the brain was performed using a GE Advance system (General Electric, Schenectady, NY). Data were acquired in three-dimensional mode and reconstructed into 35 contiguous transaxial planes separated by 4.25 mm (center-to-center), which covers the both the vertex and the entire cerebellum. In-plane and axial resolution are 5.2 and 4.6 mm full-width half-maximum, respectively. Emission scans were attenuation corrected with a transmission scan collected before each session during the exposure of a 68Ge/68Ga external rotating source. After a 10-mCi bolus injection of H2 15O, scanning was started when the brain radioactive count rate reached a threshold value, and continued for 60 s. Integrated radioactivity accumulated in the 60 s of scanning was used as an index of rCBF. Ten minutes elapsed between each injection. No arterial blood sampling was performed, and thus the images collected are those of tissue activity. Subjects began performing the task at the time of the injection, i.e., 15-20 s before the beginning of the scan and continued for a total of ~105 s.
Data analysis
Calculations and image matrix manipulations were performed in PROMATLAB (Mathworks, Sherborn, MA) on a SPARC 20 computer (Sun Microsystems, Mountain View, CA) with software for image analysis (SPM, MRC Cyclotron Unit, London, UK). Statistical parametric maps are spatially extended statistical processes that are used to characterize regionally specific effects in imaging data (Friston et al. 1991
, 1994
; Worsley et al. 1992
). The scans from each session of each subject were realigned using the first scan as a reference. The six parameters of this rigid body transformation were estimated using a least squares approach (Friston et al. 1995a
). After realignment, all images were transformed into the standard space of a brain atlas (Talairach and Tournoux 1988
). The spatial normalization involves linear and nonlinear three-dimensional transformations to match each scan to a reference image that already conforms to the standard brain space (Friston et al. 1995a
). Images then were smoothed with an isotropic Gaussian kernel (15 mm full-width half-maximum). The effect of global differences in rCBF between scans was removed by scaling activity in each pixel proportional to the global activity so as to adjust the mean global activity of each scan to 50 ml·100 g
1·min
1.
After the appropriate design matrix was specified, the conditions were estimated according to the general linear model at each voxel (Friston et al. 1995b
), which removes a systematic difference among subjects as a confounding effect. To test hypotheses about the specific regional effects of the condition, the estimates were compared using linear contrasts. The resulting set of voxel values for each contrast constitutes a statistical parametric map of the t statistic (SPM{t}). The SPM{t} were transformed to the unit normal distribution (SPM{Z}) and threshold was set at 2.33. The resulting foci then were characterized in terms of peak activation height and spatial extent. The significance of rCBF changes in each region was estimated using the probability that the peak activation observed could have occurred by chance and/or that the observed number of contiguous voxels could have occurred by chance over the entire volume analyzed (Friston et al. 1994
). A corrected P value of 0.05 was used as a final threshold for significance.
To characterize the predominant patterns of the variance observed in the data, principal component analysis (eigenimage analysis) was applied to the mean-adjusted rCBF images averaged across subjects (Friston et al. 1993
). The data were decomposed into two sets of orthogonal vectors using singular value decomposition (SVD) as follows
M = U*S*VT; where M is the original data matrix with 10 rows (1 for each block) and one column for each voxel, U and V are unitary orthogonal matrices denoting pattern across conditions and in space, respectively, T denotes transposition, and S is a diagonal matrix of decreasing singular value. Each column of V and U can be interpreted as a spatially distributed pattern and the corresponding profile over different conditions, respectively, associated with each principal component.
To examine changes in rCBF over the four consecutive scans that the subject performed for each conditional rule, two kinds of linear contrasts were examined. First, we compared the first scanning block within a rule with the fourth. Second, we used a linear contrast model that can characterize regionally specific monotonic linear changes during learning. These two approaches yielded similar results, and unless otherwise noted, contrasts of the first kind are presented. If they differed, which was the case only for the cerebellum, we report the region as showing significant rCBF differences if found to do so with either method. For an examination of rule-guidance versus fixed-response rule (see Fig. 5), all eight blocks for the spatial and nonspatial rules were pooled and contrasted with the two fixed-response rule scans.

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| FIG. 5.
Contrast analysis for rule-guided (spatial and nonspatial) versus fixed-response scanning blocks. Left: conditional motor task; right: evaluation task. Voxels having Z values exceeding significance threshold of 2.33 with Bonferroni correction for multiple comparisons (P < 0.05) are displayed on a gray scale, with lower Z scores represented in light gray and higher ones in dark gray. SPMs are displayed in anatomic space of (Talairach and Tournoux 1988 ) as a maximum intensity projection viewed from right side (sagittal view), back (coronal view), and top (transverse view) of brain.
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We do not report on contrasts between the evaluation and conditional motor tasks or on those between spatial and nonspatial rules. We performed all of those comparisons in a number of varieties. However, the former comparisons produced obviously artifactual results, probably due to uncompensated day-to-day variations, and the latter produced few significant results, none of which were noteworthy.
Designation of anatomic structures
As described above, the procedure used for group analysis of the PET data was based on the resizing of the PET scans to a standard anatomic space (Talairach and Tournoux 1988
). This procedure allowed us to relate coordinates to cytoarchitectonic labels as depicted in that atlas. In recognition of the limitations of this technique, we also have described the localized contrasts in rCBF in terms of general regions of the frontal lobe, taking into account the general organizational schemes of Passingham (1993)
, Petrides and Pandya (1994)
, and others (He et al. 1995
; Wise et al. 1996b
). In so doing, we have taken into account both the primary and subsidiary contrast peaks as detected through SPM and illustrated the contiguous voxels that exceed a Z statistic of 2.33 for regions showing significant rCBF contrasts. In the nomenclature used in this report, area 46 is equated with the dorsolateral prefrontal cortex (PFdl), area 9 with the dorsal prefrontal cortex (PFd), and area 47 (area 47/12 of Petrides and Pandya) with the ventro-orbital prefrontal cortex (PFv). Subdivisions of premotor cortex (PM) are recognized on the basis of their relative locations within area 6 (Wise et al. 1996b
). PM be divided into rostral (PMr) and caudal zones (PMc) based on the vertical anterior commissure (VAC) frontal plane. We also made distinctions between dorsal (PMd) and ventral (PMv) zones, based on the range of horizontal coordinates (z-axis) reported for the frontal eye field (Paus 1996
): any z < 40 was taken as PMv, whereas any z >50 was taken as PMd. Coordinates between those levels were not designated as either PMd or PMv.
 |
RESULTS |
Behavior
There were 54 responses (in 102 s) for each behavioral block in the conditional motor task; 34 (in 108 s) in the evaluation task. The number of responses made per scan was not highly variable, but there was sometimes a one-response difference from scan-to-scan. As shown in Fig. 3, A and B, RT decreased over the four consecutive scans for both tasks and both conditional rules. For each task-rule combination, a separate repeated-measures analysis of variance (ANOVA) was performed with RT over scanning blocks as a within-subject factor (Greenhouse-Geisser corrected). RT was always significantly shorter in scan 4 than in scan 1 (conditional motor task, nonspatial rule: F = 5.24, P < 0.05; spatial rule: F = 31.03, P < 0.002; evaluation task, nonspatial rule: F = 125.44, P < 0.001; spatial rule:F = 7.99, P < 0.04). Three-way ANOVA showed a significant interaction between task and scan number (F = 5.736, P < 0.05), showing a greater learning effect in the evaluation task.

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| FIG. 3.
Movement performance over 6 subjects. A and B: mean response time and standard deviation for each block of behavior. Nonspatial and spatial rules labeled with bars over data points. C and D: total number of errors in each scan summed over all subjects. Response time shortens and number of errors drops from scan 1 to scan 4 in each rule. Note that because order of scanning blocks 2-5 and 6-9 (both as a group) were counterbalanced among subjects, placement of data for nonspatial rule before those for spatial rule is arbitrary. Fixed-response rules (F) were always first and last (10th) scans of session. Note also that total number of trials per behavioral block differed for each task (i.e., 54 and 34 trials for conditional motor and evaluation tasks, respectively).
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The number of errors also decreased as the scans progressed (Fig. 3, C and D). However, in these simple conditional tasks, with prior explicit instructions about the mappings, subjects made very few errors. Note that the error totals presented in Fig. 3, C and D, are summed, not averaged, over the six subjects. More errors were made initially in the evaluation task, nonspatial rule than for the other tasks and rules, but this difference was not statistically significant. Three-way ANOVA showed a significant main effect of scan number (F = 6.895, P < 0.05), i.e., a learning effect, but no significant interactive term.
After each scan and before the next one, subjects were asked to bisect a line to report their perceived ability to remember the rule, response accuracy, time to task mastery, as well as their energy level and degree of distraction during the scan. Their responses correlated closely with their behavior as measured by RT or errors. Subjects were asked at the same time whether they were able to fixate the central spot throughout each performance block, and each replied affirmatively.
Neuroimaging
PRINCIPAL COMPONENT ANALYSIS.
Figure 4 shows the loadings for each of the 10 scans per session on the first (Fig. 4, A and B) and second (Fig. 4, C and D) principal components, divided by task. Note that the order of scanning blocks 2-5 (as a group) and 6-9 (also as a group) were counterbalanced among subjects. Accordingly, the placement of the data for the nonspatial rule before those for the spatial rule in Fig. 4 is arbitrary. Together, the first and second principal components (PC1 and PC2) accounted for ~67-70% of the variance in the data. PC1 distinguishes between all scanning blocks in which the subjects had to attend to the visual stimuli and use that information to select a conditional response (spatial or nonspatial rules) versus scans of the other, unconditional (fixed-response) rule. The trend for PC2 loading corresponds, inversely, with changes in RT (Figs. 3, A and B) over the four consecutive scanning blocks for each rule. Both PC1 and PC2 also reveal a smaller within-session trend, as indicated by the differential loadings of the first and last scanning block for the fixed-response rule. To distinguish overall within-session time trends from learning-related trends, PC2 was recalculated for the 10 scans in the order they occurred. This analysis confirmed that the loading inverted between blocks 5 and 6, i.e., at the transition between the two rules (Fig. 4, C and D), regardless of which rule was presented first.

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| FIG. 4.
Principal component (PC) analysis. A and B: first principal component (PC1). C and D: second principal component (PC2). , fixed-response rule; , nonspatial rule; , spatial rule. F, fixed-response rule. Note that, as in Fig. 3, order of spatial versus nonspatial rules in arbitrary for this figure. Percentage of total variance accounted for by each PC is indicated.
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CONDITIONAL RULES VERSUS FIXED-RESPONSE RULE.
Several brain regions showed greater rCBF for the two conditional rules than for the fixed-response rule (Fig. 5, Table 2). These regions correspond closely with the positively loaded voxels for PC1 (not illustrated) and included a number of cerebellar and visual areas. Few frontal areas showed significant contrasts, although a left ventrocaudal premotor area (PMvc, area 6) did so in both tasks. Right PMvc showed similar rCBF increases but only in the evaluation task.
NONSPATIAL RULE: EVALUATION TASK.
Several areas showed a decrease in rCBF as subjects improved performance on the nonspatial rule in the evaluation task (Fig. 6; Table 3), a trend that corresponds to PC2 (see above). These decreases paralleled the improvement in RT (Fig. 3B) and the decrease in error rate (Fig. 3D). The most dramatic of these decreases was seen in the ventral part of the left prefrontal cortex (Fig. 6A), extending onto the orbital surface at its most lateral extent (PFv, area 47/12). In the right hemisphere, a rostral aspect of the dorsal premotor cortex (PMdr, area 6) showed a less dramatic, but significant rCBF decrease, extending rostrally into area 8, with all or nearly all suprathreshold voxels rostral to the VAC line (Fig. 6C). A subsidiary contrast peak was found in the PFd (area 9), also on the right side (Fig. 6D). In the posterior parietal cortex (areas 39/40), a similar rCBF decrease was found (Fig. 6B). When using a monotonic linear model only (but not when contrasting the first and last scans of the 4-scan sequence), a broad area of the left, lateral cerebellar hemisphere showed a significant decrease in rCBF (Table 3; not illustrated in Fig. 6).

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| FIG. 6.
Evaluation task, nonspatial rule: voxels with rCBF decreases from 1st to 4th scan. VAC, vertical line passing through the anterior commissure; VPC, vertical line passing through the posterior commissure. A-D: cerebral areas with significant rCBF changes are labeled, and for each of them, mean adjusted rCBF and standard deviations are plotted at voxel of maximum Z score in each of 4 scans.
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Increases in rCBF from the first to final (fourth) scan of the nonspatial rule in the evaluation task were less common (Fig. 7; Table 4). A broad bilateral area with increasing blood flow had its peak contrast in the PMvc (area 6), bilaterally, and extended caudally into the hand representation of the primary motor cortex (M1, area 4). In the right hemisphere, this region extended to the cingulate cortex (area 24), and in the left hemisphere to the putamen and insula. Note that the aspect of right PM showing rCBF increases across the four scans (Fig. 7B) was both more caudal and more ventral than the part of PM showing decreases (Fig. 6C).

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| FIG. 7.
Evaluation task, nonspatial rule: voxels with rCBF increases from 1st to 4th scan. Format as in Fig. 6.
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NONSPATIAL RULE: CONDITIONAL MOTOR TASK.
Significant contrasts for the nonspatial rule of the conditional motor task were much less extensive than for the evaluation task (Fig. 8A; Table 3) with no significant contrasts occurring in the frontal cortex. A posterior parietal region (area 39/40) showed a rCBF decrease as performance improved and overlapped to a large extent with that showing a similar change in the evaluation task (Fig. 6B).

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| FIG. 8.
Conditional motor task, nonspatial rule: voxels with rCBF decreases from first to fourth scan. Only 1 cerebral area has significant rCBF changes, right parietal cortex (A). Format as in Fig. 6.
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SPATIAL RULE: EVALUATION TASK.
There were no significant rCBF contrasts in either parietal or frontal cortex for the evaluation tasks' spatial rule, although there were increases in the left putamen and insula (Table 4, not illustrated).
SPATIAL RULE: CONDITIONAL MOTOR TASK.
The right PFdl (area 46) and the caudally adjacent right PMvr (area 6) showed significant rCBF decreases as performance improved (Fig. 9, B and C). A posterior parietal region (area 39/40) did so, as well (Fig. 9A), also on the right side, and it overlapped with the parietal region described above for the nonspatial rule (Figs. 6B and 8A). Contiguous voxels above threshold extended ventrocaudally into the occipital cortex.

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| FIG. 9.
Conditional motor task, spatial rule: voxels with rCBF decreases from first to fourth scan. Format as in Fig. 6.
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DISCUSSION |
Comparisons among tasks and rules
Several motor regions, including PMvc (area 6) and anterior cingulate (area 24) areas, as well as the cerebellum, showed greater rCBF for conditional-rule scans (spatial and nonspatial rules, combined) than for fixed-response scans (Fig. 5; Table 2). It is possible that these regions are especially important in the guidance of behavior by concrete visuomotor rules. Alternatively, these contrasts might be attributed to the selection of an action on each trial (Deiber et al. 1991
) rather than guidance by conditional rules per se. Further, because the fixed-response rule did not require subjects to attend to either the visual patterns or their locations, rCBF differences might reflect the increased attentional demands in the conditional-rule scans. Indeed, visual areas in both hemispheres show rCBF enhancements during the conditional-rule scans (Fig. 5, Table 2). Such increases may result from the orientation of attention to those stimuli, the importance of frontal-visual cortex interactions in conditional motor learning (Gaffan and Parker 1997
), or both.
Learning effects
Early blocks for a given conditional rule were associated with significantly different rCBF rates than later ones. Before concluding that these differences are learning effects, it is important to consider overall within-session trends that may undermine that interpretation. Principal component analysis shows that such within-session effects did, indeed, occur (Fig. 4). However, the second principal component (PC2) reveals the same inversion of loading at the rule transition (from scanning block 5 to 6) regardless of which conditional rule was presented first. This finding indicates that PC2 and the related SPM contrasts (Figs. 6-9; Tables 3 and 4) reflect some aspect of the time within a given rule, presumably learning. Nevertheless, an influence of general rCBF trends over the course of the 10-scan sessions cannot be ruled out.
In the present task, some of the most important aspects of learning probably occurred before the scanning session began. Subjects were informed explicitly about the spatial and nonspatial mappings that needed to be used for either evaluation or movement. In addition, they practiced for a block of five trials on each rule, i.e., approximately one trial per stimulus-response mapping. This brief practice, along with the visual and verbal instructions, was accompanied by immediate feedback about response accuracy, presented on the visual display. (By contrast, no feedback was given during the PET scans). Thus the first phase of learning took place during the pre-PET training session, and much of the explicit knowledge about the rules had been learned before the scans commenced. Accordingly, the subjects made very few errors during the scanning blocks (Fig. 3, C and D). We conclude that the subjects' improvement in performance resulted from an increased strength in stimulus-response mappings over the four scanning blocks as reflected mainly by decreasing RT.
Prefrontal cortex
The prefrontal decreases in rCBF described here resemble those observed for motor sequence learning (Jenkins et al. 1994
) and increasing familiarity with a noun-verb generation task (Raichle et al. 1994
). Similar results have been obtained during skill learning tasks, including mirror drawing (Imamura et al. 1996
) and adaptation to externally imposed force fields (Shadmehr et al. 1996
). During certain kinds of sequence learning, by contrast, rCBF tends to increase during learning, especially as explicit knowledge is gained (Doyon et al. 1996
; Grafton et al. 1992
, 1994
, 1995
; Hazeltine et al. 1996; Hikosaka et al. 1996
; Honda et al. 1996
). One interpretation of a decrease in rCBF, especially as applied to prefrontal cortex, is that it reflects a disengagement of that area as a behavior becomes routine and the task demands become less ambiguous.
PRESENT FINDINGS IN THE CONTEXT OF THEORIES OF THE PREFRONTAL LOBE.
Among the many, diverse theories of frontal lobe function in primates, two general classes can be discerned. One class, which focuses on the temporary storage of information, has been termed the working-memory theory of the frontal lobe (Goldman-Rakic 1988
, 1995
; Owen et al. 1996a
,b
; Pascual-Leone et al. 1996
). That theory emphasizes the role of the frontal cortex in short-term maintenance of information in a variety of sensory domains, including the encoding and retrieval of information about events (Kapur et al. 1994a
,b
; Tulving et al. 1994a
) and language (Fiez et al. 1995
; Petersen et al. 1988
, 1990
; Wise et al. 1991
). A large number of brain-imaging studies have been directed toward verifying the role of frontal cortex in sensory working memory (Buckner and Peterson 1996
; D'Esposito et al. 1995
; Hugdahl et al. 1995
; Kapur et al. 1995
; McCarthy et al. 1996
; Owen et al. 1996a
,b
; Petrides 1996
).
We cannot rule out a working memory component in the rules and tasks studied here. In the evaluation task, for example, pertinent information must be retained during a 500-ms delay period. However, in interpreting the contrasts among sequential PET scans, we are interested primarily in whether there is a systematic change in working memory as conditional associations become stengthened. One point is especially important in this regard. Working memory for stimulus and/or mapping information is essential for learning conditional rules but not for performance according to them. If the mapping and/or stimulus information is not retained until the time that a provisional mapping can be evaluated, then it is impossible to improve performance on arbitrary mapping tasks typified by conditional motor learning. As the mappings become stronger, stimulus information can be transformed quickly into the associated response, and neither the stimulus nor the mapping information needs to be retained in working memory. Thus we cannot rule out the possibility that the rCBF decreases reported for PF (and other areas) reflect the decreasing need for stimulus working memory as subjects become more familiar with arbitrary mappings.
The other class of theory, which centers on the role of the frontal cortex in central executive functions, can be termed the response-management theory. Its variants include an emphasis on the manipulation and use of managerial knowledge (Grafman 1989
, 1995
), which is used to organize behavior thematically in accordance with long-term goals. This idea corresponds to some extent with the view, mainly derived from studies of nonhuman primates, that the frontal lobe functions in learning what response to select based on context (Passingham 1993
). The present view emphasizes this central executive aspect of frontal lobe function (see also Frith et al. 1991
; Jenkins et al. 1994
; Raichle et al. 1994
). From our perspective, the present data are most consistent with the hypothesis that localized frontal areas, as well as the parietal networks associated with them, show potentiated synaptic activity when routine rules need to be rejected and new ones adopted (Wise et al. 1996b
). Thus the decrease in rCBF observed in prefrontal (and rostral premotor) areas may reflect a relaxation of the cortical network subserving a behavior as that behavior becomes routine.
DIFFERENCES AMONG PREFRONTAL AREAS.
The most dramatic decreases in rCBF observed in the present study were those in the left PFv, a region Petrides and Pandya (1994)
have termed area 47/12. They argued that it is homologous to part of area 12 in macaque monkeys. The left PFv has been previously reported to show activation during conditional tasks, although the results have not always been discussed in those terms. These tasks include extrinsically cued word finding (Frith et al. 1991
), a newly learned (reversal) of a sensorially cued eye movement (Paus et al. 1993
), word selection contrary to previously learned associations (Paus et al. 1993
), generation of the uses of terms presented visually, i.e., semantic association (Petersen et al. 1988
), and both visual and auditory association tasks involving semantic processing (Petersen et al. 1989
). Passingham (1993)
has postulated that the ventral prefrontal areas, area 47/12 among them, are the principal areas engaged in learning behavioral responses based on context. In accord with this idea, ventro-orbital PF cortex (Murray and Wise 1997
) and its intrahemispheric interactions with visual areas (Gaffan and Parker 1997
) recently have been shown to be essential for conditional (but not unconditional) learning in macaque monkeys. Alternatively, it has long been held, on the grounds that it receives direct projections from inferotemporal cortex, that PFv is specialized for processing or selecting nonspatial rather than spatial visual information (Jones and Powell 1970
; Webster et al. 1994
). Brain imaging (Courtney et al. 1996
; Haxby et al. 1991
; Ungerleider and Haxby 1994
) and neurophysiological (Goldman-Rakic 1995
; Wilson et al. 1993
) data have been presented as supporting this view, although those interpretations of the neuroimaging data have been challenged (Rushworth et al. 1997
). The present data are consistent with both hypotheses.
The notion that PFdl (area 46) is involved in spatial information processing is well accepted (Courtney et al. 1996
; McCarthy et al. 1996
). Accordingly, the finding of right hemisphere decreases in rCBF during spatial rule learning, seen in the conditional motor task, is consistent with prevailing views on PF organization. However, we have no compelling explanation for the failure to observe comparable changes for spatial rule learning in the evaluation task.
In the nonspatial rule of the evaluation task, the right PFd (area 9) showed a decrease in rCBF during learning (as did PFv). A role of PFd in processing some aspects of nonspatial information is consistent with the deficits observed in identifying objects that have been selected previously or the order of objects in a sequence after PFd lesions in macaque monkeys (Petrides 1995
). However, we have no compelling explanation for the failure to observe rCBF changes in PFd (or, for that matter, PFv) during nonspatial rule learning in the conditional motor task. One possibility, for both PFd and PFv, is that the lack of a need for explicit, evaluative information processing in the conditional motor task resulted in less overall engagement of PF in the task, regardless of the subjects' previous exposure to the arbitrary mappings.
Premotor cortex
In both right and left hemispheres, a ventrocaudal aspect of the lateral premotor cortex (PMvc) showed an rCBF increase during conditional motor learning in the evaluation task. Except for small regions in the insula and putamen (see below), it was the only region in any rule or task to do so. This result differs from that of a previous study of conditional motor learning by Deiber et al. (1991)
. They contrasted rCBF during performance of nonspatially instructed movements with that during the performance of a fixed response and found no premotor or prefrontal areas with significant rCBF increases. Similarly, Paus et al. (1993)
did not report significant activation of the lateral premotor areas during a variety of conditional motor tasks. The present result confirms the predictions made on the basis of neuropsychological and neurophysiological studies in monkeys (Halsband and Passingham 1982
, 1985
; Passingham 1985
; Petrides 1982
, 1985
) and humans (Halsband and Freund 1990
; Petrides 1987
, 1990
) pointing to the lateral premotor cortex as an important site for conditional visuomotor learning. This region of premotor cortex with increasing rCBF during learning appears to be near that described by Grafton et al. (1996)
as activated in imagined grasping. They identified this zone with area 44 (coordinates
43, 0, 30), which appears rostral to what we term PMvc (
48,
8, 32, and 42,
10, 32). Note, however, that the activated voxels in our data abut the VAC line (Fig. 7). Because the subjects may have been imagining the instructed movement during the 500-ms delay period, we cannot rule out a role for motor imagery in the present results. However, such imagery would have to change over the consecutive scans within each rule to affect our principal results, and this appears unlikely.
Three factors seem most likely to have contributed to the present, positive result. First, in the nonspatial rule of the evaluation task, subjects were compelled to consider explicitly the correctness or incorrectness of a represented response rather than to simply perform the instructed movement. Second, there was the learning component as reflected in decreasing RTs. And third, the subjects were required to retain the stimulus and/or response information across a 500-ms delay period. Because the learning component is the one that changes most dramatically between the first and fourth scans, we are tempted to focus on learning as the principal cause of the rCBF changes. However, the lack of any significant increases during learning of the same rules in the conditional motor task suggests that the other components also may be important.
Sweeney et al. (1996)
found a similarly located site activated in a conditional oculomotor task, but interpreted it as the frontal eye field. Because our subjects were instructed to fixate the central spot during the scan and reported that they were able to do so, we do not believe that our results reflect overt eye movements.
Rostral (and more dorsal) aspects of premotor cortex (PMdr, area 6) showed a learning-related decrease in rCBF, much like the prefrontal areas. Taken together with the blood flow increases in more caudal premotor areas, these data suggest a functional affinity between PMr and PF. The distinction between PMr and PMc has clear parallels in the connectional organization of the frontal lobe in monkeys, including the distribution of corticospinal neurons (Bates and Goldman-Rakic 1993
; Ghosh and Gattera 1995
; He et al. 1993
; Lu et al. 1994
; Wise et al. 1996a
, 1997
). A similar distinction is reflected in PET data for both the medial (Colebatch et al. 1991
; Deiber et al. 1991
; Matelli et al. 1993
; Picard and Strick 1996
; Stephan et al. 1995
) and lateral (Deiber et al. 1991
) premotor areas.
Cingulate area 24, corresponding to one of the cingulate motor areas described in both humans and nonhuman primates by Picard and Strick (1996)
, also showed an increase in rCBF during learning. Although data from nonhuman primates indicates that cingulate areas are not necessary for performance of conditional motor tasks (Chen, Y., et al. 1995), the cingulate motor areas may be important in acquisition of such behavior.
Parietal cortex
A large, right posterior parietal region (area 39/40) also showed decreasing rCBF during most tasks and rules. A similarly located, predominant right parietal activation was described by Jenkins et al. (1994)
when comparing newly learned with previously learned motor sequences. These authors attribute this finding to spatial attention, as suggested by other PET studies (Corbetta et al. 1993
; Petersen et al. 1994
). It is possible that subjects attend to stimuli less intensely as the mappings become more familiar (Coull et al. 1996
; Paus et al. 1996). However, there are other possibilities. In all of the conditional rules and tasks used here, a conversion of visual information into the spatial and/or motor domain was required. The rCBF changes in posterior parietal cortex therefore may reflect the mapping of visual information into a different coordinate scheme (Andersen 1995
). As these coordinate transformations become routine and relatively automatic, the importance of the posterior parietal cortex may diminish.
Cerebellum
Left lateral cerebellar hemisphere showed a decrease during learning (in the nonspatial rule, evaluation task). However, this decrease only reached statistical significance when all four scans were considered and contrasted with a monotonic linear contrast model. The role of the cerebellum, especially the lateral cerebellar hemisphere, in sensory-sensory conditional learning has been supported in the clinical literature (Bracke-Tolkmitt et al. 1989
; Canavan et al. 1994
), including for tasks involving nonspatial visual stimuli as instructions.
Basal ganglia
The left putamen showed time-dependent increases in rCBF for both the spatial and nonspatial rules, in the conditional motor and evaluation tasks, respectively. Besides the premotor cortex (PMvc, area 6), this was the principal brain region to show such increases during learning. In monkeys, neurons in the striatum show learning-related activity changes (Tremblay et al. 1994
) that resemble those in premotor areas (see below), and interrupting pallido-thalamocortical connections at the thalamic level causes dramatic deficits in conditional motor performance (Canavan et al. 1989
).
Comparison with neuronal activity in monkeys
As monkeys learn novel, nonspatial conditional motor rules, some neurons in the supplementary eye field decrease discharge rates as learning progresses. These cells often become inactive when a mapping rule becomes highly familiar (Chen, L., and Wise 1995a,b). Some of these neurons, termed learning-selective by Chen and Wise, show a monotonic decline in discharge rate, but most have an initial increase in activity followed by a steep decline as performance improves. The decreases in rCBF observed in PMdr (area 6), PFv (area 47/12), PFd (area 9), posterior parietal cortex (areas 39/40), and elsewhere during learning appear more similar to the monotonic pattern. It is important to emphasize, however, that we cannot rule out a rCBF increase during the pre-PET training session. If the learning and processing of explicit knowledge about the task, which occurred before the beginning of the scans, were reflected in a blood flow increase, then the overall pattern would be an initial increase, followed by a decline, as for most of the single-unit data.
In contrast to the learning-selective activity described above, other cells show increasing discharge as conditional motor tasks become more familiar. This activity has been termed learning dependent. The discharge rates of these neurons correlate very closely with the animal's learning curve, much as the rCBF rates resemble the improvements in RT. Learning-dependent activity has been reported for both the dorsal premotor cortex and the supplementary eye field, both parts of area 6 in monkeys (Chen, L., and Wise 1995a; Mitz et al. 1991
). In the supplementary eye field, neurons with learning-dependent and learning-selective activity are intermixed. However, on the present data and the assumption that rCBF increases reflect enhanced excitation, we suggest that learning-selective activity may predominate in prefrontal areas, whereas learning-dependent neurons may be most important in the caudal premotor cortex.
The comparison of rCBF with neuronal activity in nonhuman primates is, of course, problematic. There are the well-known difficulties both in relating changes in rCBF or metabolism to neuronal discharge rates and in inferring homologies between cortical areas. There is evidence that PMd but not PMv is necessary for conditional motor performance in monkeys (Kurata and Hoffman 1994
; Wise 1996
), but here PMvc shows rCBF increases during learning. Further, the measure of conditional motor learning in the monkey experiments cited above was the proportion of correct responses rather than improving RT, as in the present report. It is possible that different or completely distinct neuronal mechanisms underlie these two learning measures, but we find no compelling reason to assume so.
Conclusions
Three main findings emerge from the present study: as subjects become increasingly familiar with conditional rules, a broad frontal-parietal network shows decreased rates of blood flow; the posterior parietal part of this network remains relatively constant from rule-to-rule and task-to-task, but the prefrontal components vary; and, rostral and dorsal premotor areas show learning-related decreases in rCBF, much like prefrontal and parietal cortex, whereas more caudal and ventral premotor areas, along with the putamen and a cingulate motor area, show rCBF increases. The learning-related changes in cortical blood flow resemble the evolution of neuronal activity as monkeys learn conditional motor mappings.
 |
ACKNOWLEDGEMENTS |
The authors thank Dr. Ilsun M. White for comments on an earlier version of this manuscript.
 |
FOOTNOTES |
Address for reprint requests: S. P. Wise, Laboratory of Systems Neuroscience, National Institute of Mental Health, PO Box 608, Poolesville, MD 20837. E-mail: spw{at}codon.nih.gov
Received 30 December 1996; accepted in final form 1 May 1997.
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