1 Department of Physiology, Juntendo University, School of Medicine; and 2 Department of Neurology, Division of Neuroscience, Graduate School of Medicine, University of Tokyo, Tokyo 113, Japan
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Nakamura, Kae, Katsuyuki Sakai, and Okihide Hikosaka. Neuronal activity in medial frontal cortex during learning of sequential procedures. J. Neurophysiol. 80: 2671-2687, 1998. To study the role of medial frontal cortex in learning and memory of sequential procedures, we examined neuronal activity of the presupplementary motor area (pre-SMA) and supplementary motor area (SMA) while monkeys (n = 2) performed a sequential button press task, "2 × 5 task." In this paradigm, 2 of 16 (4 × 4 matrix) light-emitting diode buttons (called "set") were illuminated simultaneously and the monkey had to press them in a predetermined order. A total of five sets (called "hyperset") was presented in a fixed order for completion of a trial. We examined the neuronal activity of each cell using two kinds of hypersets: new hypersets that the monkey experienced for the first time for which he had to find the correct orders of button presses by trial-and-error and learned hypersets that the monkey had learned with extensive practice (n = 16 and 10 for each monkey). To investigate whether cells in medial frontal cortex are involved in the acquisition of new sequences or execution of well-learned procedures, we examined three to five new hypersets and three to five learned hypersets for each cell. Among 345 task-related cells, we found 78 cells that were more active during performance of new hypersets than learned hypersets (new-preferring cells) and 18 cells that were more active for learned hypersets (learned-preferring cells). Among new-preferring cells, 33 cells showed a learning-dependent decrease of cell activity: their activity was highest at the beginning of learning and decreased as the animal acquired the correct response for each set with increasing reliability. In contrast, 11 learned-preferring cells showed a learning-dependent increase of neuronal activity. We found a difference in the anatomic distribution of new-preferring cells. The proportion of new-preferring cells was greater in the rostral part of the medial frontal cortex, corresponding to the pre-SMA, than the posterior part, the SMA. There was some trend that learned-preferring cells were more abundant in the SMA. These results suggest that the pre-SMA, rather than SMA, is more involved in the acquisition of new sequential procedures.
Many behaviors rely on learning of a sequence of movements. Studies using primates have revealed that several brain regions are involved in the performance of sequential movements. Neurons that change their activity with particular transitions or combinations of movement sequences rather than movements per se have been found in the prefrontal cortex (Joseph and Barone 1987 Experimental animals
We used two male Japanese monkeys (Macaca fuscata): monkey GA (5.5 kg) and monkey BO (10.5 kg). A total of four hemispheres were surveyed. The monkeys were kept in individual primate cages in an air-conditioned room where food was always available. At the beginning of each experimental session, they were moved to the experimental room in a primate chair. The monkeys were given restricted amounts of fluid during periods of training and recording. Their body weight and appetite were checked daily. Supplementary water and fruit were provided daily. Throughout the experiment the monkeys were treated in accordance with the Guiding Principles for Research Involving Animals and Human beings by the American Physiological Society.
Surgery
The experiments were carried out while the monkey's head was fixed and its eye movements were recorded. For this purpose, a head holder, a chamber for unit recording, and an eye coil were implanted under surgical procedures. The monkey was sedated by intramuscular injections of ketamine (4.0-5.0 mg/kg) and xylazine (1.0-2.0 mg/kg). General anesthesia then was induced by intravenous injection of pentobarbital sodium (5 mg·kg Apparatus
A detailed description of the apparatus and behavioral paradigm was presented in the previous paper (Hikosaka et al. 1995 Behavioral paradigms
2 × 5 TASK.
The monkeys' task, the 2 × 5 task was to press five consecutive pairs of buttons in the correct order, which they had to discover by trial and error in a block of trials. Figure 1A shows an example of the sequence of events in a single task trial. The whole sequence was called a hyperset; each pair was called a set. Time line showing task periods and events are illustrated in Fig. 1B. At the start of a trial, the home key was illuminated. After the monkey pressed the home key for 1 s, 2 of the 16 target LEDs, the first set, turned on simultaneously. The monkey had to press the illuminated buttons in the correct order; the animal had to choose one of two buttons as a first press followed by the second press. If successful, the two buttons were extinguished one by one as they were pressed, and another pair of LEDs, a second set, was illuminated and the monkey had to press them in the correct order again. Each hyperset consisted of five sets, presented in a fixed order. Liquid reward was given after successful completion of each set. The amount of reward was increased toward the final (5th) set, which encouraged the monkey to complete the whole hyperset. Also we gave additional reward to encourage the animal to do the task as quickly as possible; the amount of given water was increased as the performance time was shorter. If the wrong button was pressed at any point in the hyperset, the trial was regarded to be unsuccessful and was aborted, and the monkey had to start again from the home key to initiate a new trial. Each hyperset was presented repeatedly in a block until 10-20 successful trials had been performed. A different hyperset then was used for the next block. Successive trials were separated by an interval of 0.5-3 min by inactivating the panel.
NEW AND LEARNED SEQUENCES.
We hypothesized that there are separate neural mechanisms underlying procedural learning and memory, one for acquisition of new procedures and the other for storage of long-term memories and their retrieval. The prerequisite for testing this hypothesis was the experimental situation in which the monkey had acquired long-term procedural memories and at the same time had opportunities to learn new procedures repeatedly.
SIMPLE REACTION TASK.
In addition to 2 × 5 task, we used a visually guided, simple reaching task ("simple reaction task"). After pressing the home key for 1 s, 1 of 16 buttons turned on. The monkey was required to press the illuminated button to obtain reward. The location of the target was chosen pseudorandomly such that each 1 of 16 targets appeared once for 16 consecutive trials.
Intracortical microstimulation
To determine the locations of the pre-SMA and SMA, we performed intracortical microstimulation before a series of extracelluar recording experiments. The stimuli were trains of cathodal pulses generated by a constant current stimulator. The following parameters were used: trains of 20-60 cathodal pulses (duration: 200 µs), at 300 Hz, 20-80 µA. The current strength was controlled on an oscilloscope measuring the voltage drop across a 10-k
Single-unit recording
For monkey BO, glass-coated, elgiloy electrodes (1.0-2.0 M Eye-movement recording
Eye movements were recorded using the search coil method (Enzanshi Kogyo MEL-20U) (Judge et al. 1980 Control of experiments and data acquisition
The behavioral tasks as well as storage and display of data were controlled by a computer (PC 9801RA, NEC, Tokyo). The time and nature of task-related events (e.g., onset and offset of LED targets, pressing and releasing of buttons, neuronal activation) were stored into an event file for off-line analysis. Eye positions were digitized at 500 Hz and stored into an analog file continuously during each block of trials.
Experimental procedures
To record neuronal activity, the electrode was advanced while the monkey performed the 2 × 5 task until task-related activity was found. We were careful to examine the task-relation of neuronal activity for both new hypersets and learned hypersets because there were cells that were activated for only one of them. If the neuron exhibited any change in activity at any period during a trial by visual inspection, data acquisition was initiated. Otherwise, the electrode was advanced to find the next neuron.
Data analysis
TASK RELATIONS.
We focused our analysis on the period from the onset of the stimuli in set 1 to the second button press in set 5 (see Fig. 1B), which will be called "movement period." We compared the neuronal activity during the movement period with "baseline activity," which was obtained for a 1-s period between hypersets (blocks) during which the monkey was not performing any task while resting his hand on the home key gently.
DIFFERENCE BETWEEN NEW AND LEARNED HYPERSETS.
To determine whether a recorded neuron showed preferential activity for new hypersets or learned hypersets, we first calculated the discharge rates during the movement period for each trial. A statistical comparison was made for each cell between the pooled data for the first five successful trials of new hypersets and learned hypersets (Mann-Whitney U test, P < 0.01). Only the first five trials were examined because the neuronal activity often changed rapidly while the monkey was learning the new hypersets; i.e., learning-dependent change (see next section).
LEARNING-DEPENDENT CHANGE.
A learning-dependent change for new hypersets was first assessed by visual inspection. A statistical comparison was performed for the discharge rates during the movement period between the initial five successful trials and the following five successful trials (Mann-Whitney U test, P < 0.05). A cell showing a change in activity for at least one hyperset was categorized as showing learning-dependent change.
Histology
After recording and injection were completed, monkey BO was anesthetized with an overdose of pentobarbital sodium and perfused through the heart with 4% Formalin. The brain was blocked and equilibrated with 30% sucrose. Frozen sections were cut at 50 µm in the planes parallel to the electrode penetrations so that complete tracks were visible in single sections. The sections were stained with thioneine. Reconstruction of the location and extent of SMA and pre-SMA was based on microlesions (5 µA for 200 s) made at every 2 mm within the chamber, 1-3 mm deep from the surface of the cortex. Individual recording and injection sites were estimated based on these microlesions. Monkey GA is still being used for further experiments.
MRI
After the implantation of the recording/injection chambers, we obtained magnetic resonance images (MRI) for both monkeys (Hitachi Laboratory MRIS, 2.11 tesla for monkey BO; Hitachi AIRIS, 0.3 tesla for monkey GA) using the procedure described by Kato et al. (1995) We found neurons in the medial frontal cortex that were activated selectively during performance of either new or learned hypersets. The following sections describe the locations at which we found such neurons and their differential responses during learning of new hypersets and performance of well-practiced hypersets.
Location of recording sites in SMA and the pre-SMA
We distinguished the SMA and the pre-SMA as previously proposed (Luppino et al. 1991 General characteristics of medial frontal cortical cells
Among 2,098 cells recorded from 4 hemispheres, 728 cells showed task-related activity. Among them, we analyzed neuronal activity for 345 cells for which we recorded complete data sets (Table 1). We determined that 116 cells were from the pre-SMA and 69 cells were from the SMA (monkey BO), and 99 cells were from the pre-SMA and 61 cells were from the SMA (monkey GA). In the present study, we analyzed the spike activity of single cells during the movement period (period from the onset of the stimuli in set 1 to the 2nd pressing in set 5, see Fig. 1B). There were some cells that showed spike activity specifically during the intertrial intervals, but we did not analyze them.
Neurons preferentially activated for new sequences
COMPARISON BETWEEN NEW AND LEARNED SEQUENCES.
We found that a group of neurons that were activated more strongly for performance of new hypersets than learned hypersets. Figure 4 shows a typical example obtained from one left pre-SMA cell of monkey GA. For new hypersets (Fig. 4A), the neuron started discharging after the illumination of stimuli for each set. The discharge rate increased gradually until the monkey pressed the first button. In contrast, the same neuron showed almost no spike discharge for four learned hypersets (Fig. 4B) except for the first set in the first trial.
Learning-dependent decrease of neuronal activity
The new-preferring cells decreased their activity as learning proceeded. The raster display in Fig. 4A shows that activity decreased as the monkey became familiar with the new hyperset. This is shown graphically in Fig. 5A, which included the data for unsuccessful trials as well. The neural activity was high initially when the monkey was still unable to complete the whole hyperset (5 sets) and then decreased gradually as performance was improved. The decrease in cell activity continued even after the monkey could complete the whole hyperset; note, however, that the performance time continued to decrease. Such a learning-dependent decrease in cell activity was repeated every time a new hyperset was introduced (as shown in Fig. 4).
RESTORATION OF NEURONAL ACTIVITY BY PARTIAL CHANGES IN LEARNED SEQUENCES.
Our previous study suggested that the monkey learned a hyperset as a single unique sequence (Rand et al. 1998
Neurons preferentially activated for learned sequences
A group of neurons showed preferential activity for learned hypersets (learned-preferring cells). Figure 7 shows one example. This neuron was recorded in the left SMA of monkey GA. A statistical analysis (Mann-Whitney U test) indicated that the activity of this neuron was significantly higher for learned hypersets than for new hypersets (Mann-Whitney U test P < 0.01).
Distribution of new- and learned-preferring cells
Figure 9 shows the proportion of new- and learned-preferring cells separately for the pre-SMA and SMA for monkey BO and GA. The proportion of new-preferring cells was larger in the pre-SMA in both monkeys: pre-SMA, 22.4% (BO) and 39.4% (GA); SMA, 5.8% (BO) and 14.8% (GA) (
First-trial effect
As evident in the cell shown in Fig. 4, many of the new-preferring cells (n = 27 in monkey BO, n = 40 in monkey GA) showed vigorous activity for learned hypersets but only in the first trial (or even for only set 1). In contrast, only one learned-preferring cell (1 pre-SMA cell of monkey GA) showed this first-trial effect. Such cells were found mostly in the pre-SMA (85% for BO, 85% for GA). The data might suggest that the first-trial activity is related to retrieval of information from long-term memory. However, when a simple reaction task (reaching and pressing of one illuminated button, requiring no memory retrieval) was tested, many of the first-trial active cells (61%) again showed activity only for the first trial (data not shown).
Population data
In Fig. 10, we calculated population activity and learning curves separately for new-preferring cells (n = 25) and learned-preferring cells (n = 6). The data confirmed the findings described above. Activity of new-preferring cells was greater for new hypersets than for learned hypersets, according to the definition; the reverse is true for the learned preferring cells. However, new-preferring cells tended to be active in the first trial of learned hypersets (see First-trial effect) (analysis of variance, P < 0.0003). For new hypersets, activity of new-preferring cells decreased as learning proceeded, while activity of learned-preferring cells tended to increase.
Regional difference in learning-related function
In the present study, we found a regional difference within the medial frontal cortex, in terms of acquisition of sequential procedures. There was a clear trend that cells in the rostral part of the medial frontal cortex were activated more for new than learned hypersets; most neurons in the caudal part did not distinguish these kinds of hypersets. The rostral and caudal parts would correspond, respectively, to the pre-SMA and the SMA, the distinction proposed by Rizzolatti et al. (1990) Learning-related activity in pre-SMA neurons
The activity of the pre-SMA neurons differentiated new sequences from learned sequences. Furthermore, their activity for a new sequence tended to decrease as the monkey learned the new sequence. For individual pre-SMA neurons, a similar activity change was observed each time a new sequence was introduced which the monkey had to learn. A possible explanation would be that the pre-SMA cell activity is contingent on the way in which the hand moved: quick and continuous movements for learned sequences versus slow and discontinuous movements for new sequences. We think, however, that this possibility is unlikely. The fact that pre-SMA neurons were active regardless of the side of the performing hand suggests that they carry information remote from motor outputs. Further, the magnitude of neuronal activity does not appear directly related to the kinematic parameters of hand movements (Fig. 4).
NOVELTY DETECTION.
Learning is initiated when the subject encounters a new environment; it is unnecessary in a familiar environment. Therefore, it is very important to differentiate new from familiar environments, which would require the comparison between current sensory inputs and long- or short-term memories. In fact, many pre-SMA neurons behaved like a novelty detector: their activity decreased rapidly as the monkey started learning.
SELECTIVE ATTENTION TO VISUAL CUES.
For the new hyperset, the monkey would move their eyes and hand in response to the visual instruction; for the learned hyperset, the eye and hand movements would be generated in a preprogrammed manner (Miyashita et al. 1996a DECISION MAKING.
In the initial stage of new learning, the monkeys had to choose and press one of the two illuminated buttons. No information was given as to which button was more likely to be correct, and such an uncertain situation was present for each set. Consistent with this idea, many pre-SMA neurons discharged before the first button press for each set when the animal had to make a decision (Fig. 4). This aspect would be supported by human imaging studies (Deiber et al. 1991 ERROR DETECTION.
The detection of errors is crucial in trial-and-error learning. During new learning of a hyperset, the errors occurred at earlier trials and therefore its frequency was high in the early stage, which would correspond to the learning-related decrease in pre-SMA cell activity. We found, however, few neurons in the pre-SMA that fired selectively after errors.
MEMORY ENCODING AND RETRIEVAL.
During learning, the sequence information must be maintained as a memory and at the same time must be retrieved as a motor command. As discussed earlier, the memory in this case would be a short-term one. Human imaging studies have shown that the area corresponding to the pre-SMA was activated when encoding and retrieval of short-term memory is required (Buckner et al. 1996 SHIFT OF MOTOR PLAN.
Once an error is detected, the monkey had to shift or change the motor plan in the next trial. Pre-SMA neurons might be related to this process, as they responded to a sensory signal that instructed the monkey to change the ongoing action to a new one (Matsuzaka et al. 1996 AROUSAL.
The "habituation"-like behavior of pre-SMA neurons would raise the possibility that they encode "arousal" or "vigilance" (Thompson and Spencer 1966 Role of SMA in learning and memory
Many studies have shown that the SMA is related to sequential movements. In the monkey SMA, Tanji and his colleagues found neurons that became active selectively before a particular sequence of movements or at a particular transition of movements; the result implies that the information for learned motor sequences is stored in the SMA (Tanji and Shima 1994 Relation to other animal studies
Learning-dependent changes in neural activity have been shown in the premotor cortex (Germain and Lamarre 1993 Relation to human studies
We also applied the same learning paradigm to a human functional MRI study, with slight modification of task procedures (Hikosaka et al. 1996 Parallel mechanisms for learning of new sequences
Other studies from our laboratory have indicated that different brain regions other than the medial frontal cortex also are related to learning of sequential procedures. Muscimol injections in the anterior part of the striatum disrupted learning of new sequences, not performance of learned sequences (Miyachi et al. 1997
INTRODUCTION
Abstract
Introduction
Methods
Results
Discussion
References
), premotor cortex (Mushiake et al. 1991
), supplementary motor area (Mushiake et al. 1990
), caudate nucleus (Kermadi and Joseph. 1995), globus pallidus (Mushiake and Strick 1995
), and dentate nucleus (Mushiake and Strick 1993
). These studies focused on how sequential movements are controlled because the monkeys already had learned the sequential movements with extensive practice before the experiments started. However, acquisition and control of movement sequences could possibly be mediated by separate neural mechanisms (Keele and Summers 1976
; Shadmehr and Brashers-Krug 1997
; Shadmehr and Holcomb 1997
; Summers 1981
).
) is suitable to test this hypothesis because a large number of motor sequences can be generated. The task was to press five consecutive pairs of target buttons (indicated by illumination), in the correct order for each pair. Each pair was called the "set" and the whole sequence was called "hyperset." This task required the subject to find out correct order in which to press the button by trial and error. In a previous paper (Hikosaka et al. 1995
), we showed that monkeys could learn many hypersets and, with daily practice, acquired excellent procedural skills for 10-20 learned hypersets. Learning occurred for each hyperset repeatedly, suggesting that the long-term memories were generated for individual sequences. Important here was that, even after the mastery of the procedural skills, we could ask the monkeys to learn new hypersets repeatedly that were generated by the computer. These behavioral studies have provided us with a good experimental system to test the separate acquisition/storage mechanisms described above.
; Mushiake et al. 1990
, 1991
; Tanji and Shima 1994
) and human (Lang et al. 1990
; Roland et al. 1980
; Shibasaki et al. 1993
). Recent anatomic and physiological studies indicated that the classical SMA is subdivided into two distinct areas, the presupplementary motor area (pre-SMA), located rostrally, and the SMA proper (SMA), located caudally (Matelli et al. 1991
; Matsuzaka et al. 1992
; Rizzolatti et al. 1990
). These areas have different cortical and subcortical connections (Bates and Goldman-Rakic 1993
; Dum and Strick 1991
; He et al. 1995
; Hutchins et al. 1988
; Lu et al. 1994
; Luppino et al. 1993
, 1994
; Matelli and Luppino 1996
; Matelli et al. 1995
). We wanted to know whether the pre-SMA and SMA have different roles in learning and memory of sequential movements.
METHODS
Abstract
Introduction
Methods
Results
Discussion
References
1·h
1). Surgical procedures were conducted in aseptic conditions. After exposing the skull, 15-20 acrylic screws were bolted into it and fixed with dental acrylic resin. The screws served as anchors by which a head holder and a chamber, both made of delrin, were fixed to the skull. A scleral eye coil was implanted in one eye for monitoring eye position (Judge et al. 1980
; Robinson 1963
). The recording chamber was implanted tangentially to the cortical surface, centered on the midline of the frontal cortex. The monkey received antibiotics (sodium ampicillin 25-40 mg/kg intramuscularly each day) after the operation.
). Briefly, the monkey sat in a primate chair and faced a black panel on which 16 light-emitting diode (LED) buttons were mounted in a 4 × 4 matrix. At the bottom of the panel was another LED button that was used as a home key. To have the monkey use only one hand for a button press, a vertical Plexiglas plate was attached to the chair in an oblique direction between the plate and the unused hand. To reverse the hand that was used, the plate was replaced to the other side. The monkey's head was fixed with a head holder connected to the primate chair. A metallic pipe for supply of reward (water) was positioned in front of the monkey's mouth.
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FIG. 1.
A: sequence of events for a representative trial of the 2 × 5 task. For each set, the monkey had to press the 2 illuminated buttons in the correct order (denoted as 1 and 2) to proceed to the next set. Monkey had to find the correct order by trial and error. B: time line showing task periods and events. After pressing a home key for 1 s, the 2 light-emitting diode (LED) buttons of the 1st set were illuminated simultaneously. Each illuminated button was turned off when the monkey pressed it. We analyzed the neuronal activity for the movement period: from the onset of the 1st set to the 2nd button press in the 5th set.
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FIG. 2.
Monkey's performance for a new hyperset (A) and a learned hyperset (B). Number of completed sets (a) and performance time for a successful trial (b) are plotted against trial number. For the new hyperset, the number of completed sets increased and the performance time decreased as learning proceeded. For the learned hyperset, the monkey completed the whole 5 sets and the performance time was minimum from the initial trial.
resistor in series with the stimulating electrode. The stimuli were applied while the monkey was sitting in the chair, alert, while two investigators were observing evoked body movements. The threshold current was determined at the current intensity by which a body movement was evoked in about half of the stimulation trials.
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FIG. 3.
Distribution of stimulus-evoked body movements (A) and task-related neurons (B) in monkey BO. Data are shown on representative coronal sections as viewed from the caudal end (left and right hemispheres are shown on left and right; distance between sections: 2 mm). Arrows (PA) indicate the rostrocaudal level corresponding to the genu or arcuate sulcus. Border between the presupplementary motor area (pre-SMA) and SMA was determined by the results of intracortical microstimulation. A: kinds of stimulus-evoked body movements are indicated by different symbols, their sizes indicating the threshold currents. Small symbol, >60 µA; medium, 40-60 µA; large, <40 µA. Dots indicate the sites from which no movement was evoked. B: recording sites are indicated along electrode tracks (vertical lines) the entry points of which are indicated by black dots on the surface. Blue rectangles indicate new-preferring cells; red rectangles indicate learned-preferring cells; black short bars indicate other task-related cells. Note that neurons were recorded mostly in the medial wall of the frontal cortex and above the cingulate sulcus. C: penetration sites and distribution of new-preferring (New > Learned) and learned-preferring (Learned > New) cells. The top view of the brains (anterior upward) of 2 monkeys are presented. Ratios of new- and learned-preferring cells were calculated by dividing their number by the total number of task-related cells recorded at each penetration site. Ratios are expressed by the sizes of squares (new preferring) and circles (learned preferring). Sulci were drawn according to the histology (monkey BO) and the magnetic resonance imaging (MRI; monkey GA). PS, principal sulcus; ARC, arcuate sulcus; CS, central sulcus; PA, genu of arcuate sulcus.
measured at 1 kHz) were inserted through the exposed dura to record single neurons in the medial frontal cortex. It was difficult, however, using this method to estimate the depths of recorded neurons because the electrode, as passing through the dura, tended to depress the brain surface. For monkey GA, after determining the locations of the pre-SMA and SMA by intracortical microstimulation, we implanted Teflon guide tubes (outer diameter: 0.85 mm, inner diameter: 0.6 mm), which were fixed on the skull using dental acrylic resin so that their tips were positioned below the dura and close to the surface of the brain. They could be removed and re-implanted at different locations within the chamber. The operation was performed under general anesthesia with ketamine and xylazine. Single-unit recordings were performed using tungsten electrodes (diameter: 0.25 mm, 1-5 M
, measured at 1 kHz, Frederick Haer) through these guide tubes. The procedure allowed us to estimate the depths of recorded neurons because the depression of the brain surface was minimized. We found that electrode penetrations performed several weeks apart through the same guide tube yielded neurons with similar responses at similar depths. Single-unit potentials were amplified, filtered with a band-pass of 500 Hz to 5 kHz, and digital-sampled using a window discriminator.
; Matsumura et al. 1992
; Robinson 1963
). Eye positions were digitized at 500 Hz and stored into an analog file continuously during each block of trials. On the computer monitor were presented, as a two-dimensional display, the states of the 4 × 4 target arrays (e.g., whether they are illuminated or pressed) and the current and recent eye positions. Horizontal and vertical eye positions also were displayed, for each set, relative to the time before and after the onsets of target LEDs.
. We confirmed in monkey BO that the recording sites estimated on the MR images well corresponded with the histologically identified microlesions. In monkey GA, the reconstruction of recording sites was based on the MR images.
RESULTS
Abstract
Introduction
Methods
Results
Discussion
References
; Matsuzaka et al. 1992
; Matsuzaka and Tanji 1996
). In the SMA was found a somatotopic representation such that the face-arm-leg regions were arranged rostrocaudally (Luppino et al. 1991
); movements were elicited with low thresholds (20-40 µA) at 20 pulses. Rostral to the face region of the SMA was another focus from which arm movements were evoked, which we determined to be the pre-SMA (Luppino et al. 1993
); larger currents (40-80 µA) and more pulses (40-60 pulses) were needed to evoke movements (Fig. 3). Movements evoked from the SMA tended to be brisk and consistent across trials, whereas movements evoked from the pre-SMA tended to be more complex, slow, or variable across trials. In the rostral part of the pre-SMA, even the strong stimulation was often ineffective; in such cases, the effects of stimulation could be observed only as an arrest of voluntary hand movements. We eliminated the data obtained from the area where eye movements were evoked, which was considered to be the supplementary eye field.
View this table:
TABLE 1.
Activity of new and learned hyperset preferring neurons
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FIG. 4.
Activity of a left pre-SMA cell for new hypersets (A) and learned hypersets (B). Spike activity, shown by rasters and histograms, are aligned at the time when the monkey pressed the 1st button for each set. Only activity for correctly executed trials are shown, the 1st trial at top, the last at bottom. Inverted triangles in the raster indicates other task-related events (stimulus onset and second button press). For the new hypersets, the cell showed phasic activity for every set before the 1st button press; for the learned hypersets, it was nearly silent except for the 1st trial. Note that the activity for the new hyperset decreased as learning proceeded. Top 3 rows: data when the monkey used the hand contralateral to the recording site. Bottom row: data when the ipsilateral hand was used. Correct orders of button presses for the hypersets used are shown below.
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FIG. 5.
Learning-dependent decrease in pre-SMA cell activity (a) in comparison with the monkey's performance [number of completed sets (b) and performance time (c)] (data shown in Fig. 4, top, were analyzed). Abscissa indicates the trial number. Neuronal activity (a) indicates the discharge rate for all performed sets for each trial. For the learned hyperset (B), the neuronal activity rate was negligible except for the 1st trial.
). This suggests that, if part of a learned hyperset is changed, it would be regarded as a new hyperset. We expected, therefore, that a new-preferring cell then would become more active for the partially modified learned hyperset.
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FIG. 6.
Modulation of pre-SMA cell activity when parts of a learned hyperset were changed. A: this pre-SMA neuron was preferential for new hypersets as shown in a and b. When the stimuli of sets 2 and 3 of the learned hyperset were changed (c), the neuronal activity increased for the changed sets (sets 2 and 3) together with 2 adjacent sets (sets 1 and 4) and set 5 as well. When the sets 4 and 5 were changed (d), the neuronal activity also increased for sets 1-3. Sequences used are shown in B.
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FIG. 7.
Activity of a left SMA cell that was stronger for learned hypersets (B) than for new hypersets (A). Data for each set were aligned at the time when the stimuli of each set turned on.
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FIG. 8.
Learning-dependent increase in SMA cell activity (A) in comparison with the monkey's performance (B: number of completed sets, C: performance time). Monkey performed 1 new hyperset in 2 consecutive blocks.
2 test,
2 = 19.9, df = 2, P < 0.0001). The proportion of learned-preferring cells was smaller, but there was some trend that learned-preferring cells were more abundant in the SMA: pre-SMA, 4.3% (BO) and 3.0% (GA); SMA, 8.7% (BO) and 6.6% (GA).
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FIG. 9.
Proportions of new-preferring (New > Learned) and learned-preferring cells (Learned > New) relative to the total number of task-related cells in the pre-SMA and SMA in 2 monkeys (BO and GA).
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FIG. 10.
Population activity (a) and performance curves (b and c) for 25 new-preferring cells (A) and 6 learned-preferring cells (B) recorded in monkey GA. We calculated the population activity after normalizing the activity of a given cell to its maximal discharge per trial (which occurred either 1 of the hypersets tested). Trial number was normalized by aligning the activity and behavioral parameters at the 1st time when the monkey achieved a correct trial (indicated by trial number 0). Mean values of the number of completed sets were calculated and plotted for normalized trial numbers (b). Performance times were calculated only for the successful trials (c). Error bars: ±1 SE.
DISCUSSION
Abstract
Introduction
Methods
Results
Discussion
References
, Luppino et al. (1991)
, and Matsuzaka et al. (1992)
according to the result of intracortical microstimulation.
), we suggested that learning of the 2 × 5 task occurred at three levels: short-term and sequence-selective learning that occurred by repeating a particular hyperset during a block of experiment
our monkeys learned, to some degree, to perform a new hyperset within a several minutes; long-term and sequence-selective learning that took place for each hyperset across days
by daily practice, they further improved their skills for performing the particular hyperset; and long-term and sequence-unselective learning that was indicated by the improvement of performance for new hypersets
they performed gradually better with more experiences in the 2 × 5 task. These results, taken together, suggest that the pre-SMA is related to learning, especially short-term sequence-selective learning. This is supported by our experiment which showed that local inactivation of the pre-SMA led to selective deficits in learning of new sequences (Miyashita et al. 1996b
).
(which corresponds to pre-SMA) was whether the object was at a reachable distance, not physical characteristics (the size or the type of grip) nor location of the target object. In other words, area 6a
does not seem to be related to the visuomotor transformation per se but could be involved in higher order recognition process (Rizzolatti et al. 1990
).
). Thus the learning-related decrease in pre-SMA cell activity might reflect the decrease in the monkey's attention to the visual instruction. In fact, Matsuzaka et al. (1992)
showed that cells with visual response were more abundant in the pre-SMA than in the SMA. We also found that a considerable portion of new-preferring cells were active in the simple reaction task, which required attention but not learning.
) showing that the human medial frontal area that would correspond to the pre-SMA becomes active when the subject had to choose one out of four movements voluntarily.
; Fiez et al. 1996
; Paulesu et al. 1993
). The well-documented inputs to the pre-SMA from the dorsolateral prefrontal cortex (Bates and Goldman-Rakic 1993
) might provide the short-term working memory signals.
; Shima et al. 1996
). This result is consistent with our finding that the same pre-SMA neurons became active only at the very first trial of a learned hyperset, at which the monkey was required to update the procedure, that is, to discard the previous sequence and set the new sequence.
; Vinogradova and Sokolov 1975
). However, the learning-related activation was relatively localized in the pre-SMA, as shown in this study and the human fMRI study (Hikosaka et al. 1996
), which is inconsistent with the view that arousal is the general increase in the level of brain activity.
) as well as ventral (Rizzolatti et al. 1988
) premotor areas. In this way, the pre-SMA would control, rather than execute, motor programs. A similar idea has been proposed by Rizzolatti et al. (1996)
. Such a control mechanism would allow efficient learning of a sequential procedure because the performance of the procedure initially is dependent on sensory information but eventually becomes automatic (Anderson 1982
).
). Different lines of research in human subjects would support this view. Impairment of alternating hand movements is an enduring sign after the lesion of the medial frontal cortex including the SMA (Laplane et al. 1977
).
, 1994
, 1995
; van Mier and Petersen 1996
) or sequence (Jenkins et al. 1994
; Seitz and Roland 1992
). The SMA is activated by execution of mental imagery of sequential movements (Roland et al. 1980
). Using a trial-and-error sequential movement task, it was shown that activation of the SMA was higher when prelearned sequences were performed than when new sequences were performed (Jenkins et al. 1994
). van Mier and Petersen (1996)
also reached the same conclusion using a maze task.
) in which activation of the SMA, unlike the pre-SMA, was related to sensorimotor processes, not learning processes.
showed that, after extensive practice of a motor task, SMA neurons, which were previously active, no longer showed task-related activity, while M1 neurons did (Aizawa et al. 1991
). Although the monkeys in the study of Tanji and Shima (1994)
were well trained for performing learned sequences, the speed of the performance was controlled, unlike our experiments. If such fast movement sequences are controlled by other brain areas, such as the cerebellum, as suggested by another study from our laboratory (Lu et al. 1998
), the role of the SMA may be less important as a memory site.
and van Mier and Petersen (1996)
, the subjects closed their eyes, whereas our study was strongly dependent on visual stimuli used. The results of the study by Mushiake et al. (1991)
are consistent with this idea. In their study, SMA neurons were usually inactive when the monkey was ready to perform a motor sequence according to explicit instructions, but they may be active when the performance was based on memory.
; Mitz et al. 1991
) and the supplementary eye field (Chen and Wise 1995a
,b
). Chen et al. found that some of neurons in the SEF changed their activity while the monkey learned to associate a new picture with a particular direction of saccade. Although the task used by Chen and Wise and the task we used may appear dissimilar, they may contain common aspects in that the monkey had to associate a visual stimulus with a particular movement pattern. A learning-related decrease in neural activity also has been found in the orbito-frontal cortex (Tremblay and Schultz 1996
).
) and the caudo-neostriatum of birds (Chew et al. 1995
). A critical question remains unsolved how such a habituation-like decrement of neural activity occurs. Neurons in the monkey pre-SMA and other areas listed above were in general broadly tuned to the stimuli or sequences, and yet the decrement of activity occurred selectively to the stimuli or sequences that have been experienced.
; Sakai et al. 1998
). The results were consistent with the conclusion in the present study. We found learning-related activation that was localized in a small region anterior to the SMA, which we regarded as the human homologue of the pre-SMA. In contrast, the human SMA was activated with sensorimotor processes rather than with sequence learning. Furthermore, the activation of the human pre-SMA decreased as the subject learned a visuomotor sequence. These results are virtually the same as the results obtained in monkeys in the present study, suggesting that humans and monkeys share the same learning mechanism in the pre-SMA.
) showed a rCBF (cerebral blood flow) increase when the pianists played an unfamiliar musical piece, but not practiced piece (Sergent et al. 1992
, 1993). This area has shown increased activity when subjects performed remembered sequential saccades immediately after viewing the sequence (Petit et al. 1996
). Additionally, the pre-SMA activity was shown to decrease when subjects had learned to perform a sequential finger-to-thumb opposition task (Friston et al. 1992
).
).
). The pre-SMA is known to project to the anterior part of the striatum (Parthasarathy et al. 1992
), which presumably sends information back to the pre-SMA. A functional MRI study on human subjects, using the same paradigm (Sakai et al. 1998
), indicated that the pre-SMA becomes active during learning of new sequences together with dorsolateral prefrontal cortex, precuneus (medial part of parietal cortex), and intraparietal cortex. Thus multiple brain regions, including the frontal and parietal cortex and basal ganglia, appear to contribute to the acquisition of new sequences in parallel.
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ACKNOWLEDGEMENTS |
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We are grateful to Dr. M. Kato for designing the computer programs. We thank Drs. Longtang Chen, Jeremy Goodridge, and Carol Colby for encouraging and extensive comments on our manuscripts. We also thank Dr. J. Tanji and his collaborators for many important pieces of advice for the experiment.
This study was supported by the Uehara Memorial Foundation, a Grant-in-Aid for Scientific Research on Priority Areas from The Ministry of Education, Science and Culture of Japan, and The Japan Society for the Promotion of Science (JSPS) Research for the Future program.
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FOOTNOTES |
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Present address of K. Nakamura: Center for the Neural Basis of Cognition, Dept. of Neuroscience, University of Pittsburgh, 115 Mellon Institute, 4400 Fifth Ave., Pittsburgh, PA 15213-2683.
Address for reprint requests: O. Hikosaka, Dept. of Physiology, Juntendo University, School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113, Japan.
Received 8 April 1998; accepted in final form 5 August 1998.
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REFERENCES |
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