1 Department of Physiology, Tohoku University School of Medicine, Sendai 980-8575, Japan, 2 Department of Neurology, Tohoku University School of Medicine, Sendai 980-8575, Japan, 3 The Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, Kawaguchi 332-0012, Japan and 4 Research Institute of Electrical Communication, Tohoku University, Sendai 980-8577, Japan
Address correspondence to Jun Tanji, Department of Physiology, Tohoku University School of Medicine, 2-1 Seiryo-Machi, Aobaku, Sendai 980-8575, Japan. Email: tanjij{at}mail.tains.tohoku.ac.jp.
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Abstract |
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Key Words: action planning behavioral goals maze monkey problem solving single-cell recording
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Introduction |
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Materials and Methods |
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Two male Japanese monkeys (Macaca fuscata), weighing 6.8 and 7.5 kg, were used in this study. The care and treatment of the animals were in accordance with National Institutes of Health guidelines and the Guidelines for Animal Care and Use published by our institute. Each animal was seated in a primate chair. The animal's head was restrained, and it faced a 15'' color monitor that was positioned at a distance of 43 cm from its eyes. Two manipulanda in the chair could be operated by supination and pronation of either forearm with one degree of freedom. A computer system controlled the behavioral task. Eye position was monitored using an infrared eye camera system (R21-C-AC; RMS, Hirosaki, Japan) with a 250 Hz sampling rate.
Behavioral task
The monkeys were trained to perform a path-planning task that required the planning of multiple movements of a cursor to reach a goal within a maze (Fig. 1A). A checkerboard-like maze and a 1.5° cursor were displayed on the monitor. The movement of the cursor was linked to the movement of the manipulanda.
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To dissociate the movements of the arms and cursor, we trained the monkeys to perform the task described above with three different armcursor assignments (Fig. 1B). The assignment was changed every 48 trials, and the monkeys were required to adapt to new assignments without further instruction. Both monkeys required relatively few trials to become accustomed to each new armcursor assignment. Data collected during these transitional behavioral states were excluded from the analysis.
We used one of two sets of final goals for each recording session (either set 14 or 58, represented in red in Fig. 1C). To force the monkeys to select more than one immediate goal to reach a final goal, one of four possible paths (AD, black in Fig. 1C) was removed randomly during delay 2, 1 s after the beginning of delay 1, as illustrated in Figure 1D. The removal of a path was referred to as a path-block and was scheduled in such a way that four final goals and four positions of the path-block were selected equally and randomly within each trial, which prevented a one-to-one association between the position of the path-blocks and the immediate goals. If the animal attempted to move the cursor in the direction in which the path had been blocked, the cursor movement was blocked and an error signal was given, which required the monkey to restart the trial. In the present study, we analyzed only data collected while an animal moved the cursor from the start position to the final goal by moving the cursor in three steps.
Surgery and Data Acquisition
After completing the behavioral training, an area of the skull (20 x 25 mm) over the right principal sulcus was removed and an acrylic recording chamber (25 x 30 mm) was mounted on the skull over the cavity. All surgical procedures were performed under aseptic conditions using pentobarbital sodium anesthesia (30 mg/kg i.m.) with ketamine hydrochloride (10 mg/kg i.m.) and atropine sulfate. Antibiotics and analgesics were administered to prevent postsurgical infection and pain.
Following surgery, cortical sulci were identified using a magnetic resonance imaging (MRI) scanner (OPART 3D-System; TOSHIBA, Tokyo, Japan). Prior to recording neuronal activity within the PFC, we first defined the frontal eye field (FEF) using intracortical microstimulation (ICMS; Bruce et al., 1985). The recording sites covered the expanse of the PFC extending 14 mm rostrocaudally in sites at which ICMS with currents of <80 µA did not evoke saccades. We sampled neuronal activity rostral to the FEF, including the banks of the principal sulcus and the adjacent cortical convexity.
Neuronal activity was recorded extracellularly using glass-insulated Elgiloy microelectrodes (1.02.5 M at 333 Hz), which were inserted through the dura while the monkeys were performing the behavioral task. The electrodes were manipulated with an electrode positioning system (EPS; Alpha-Omega, Nazareth, Israel). Single-unit potentials were amplified with a multi-channel processor and were discriminated using a multi-spike detector (MCP plus 8, MSD; Alpha-Omega). We advanced the electrodes into the cortex until discriminable action potentials were obtained, after which we recorded the activity of all cells without preselection. Behavioral events and neuronal activity were displayed online on computer screens and oscilloscopes and were stored for offline analysis.
Data Analysis
Behavioral Performance
To evaluate the behavioral performance of each monkey, we measured success rates and response times (RT). The RT was defined as the time that elapsed between the appearance of the first GO signal and the execution of a required movement. We determined whether the RT was influenced by the armcursor assignment (TukeyKramer multiple comparison test, = 0.01) or by the location of the path-block (t-test,
= 0.01). To examine the effect of the path-block, we compared the RT between two types of trials, namely effective and ineffective path-block trials (trials in which the path-block did and did not interrupt a direct path to the final goal, respectively). For example, for final goal 1 or 5 as depicted in Figure 1C, a path-block that interrupted A or B was defined as an effective path-block, while a path-block that interrupted C or D was ineffective.
Statistical Analysis of Neuronal Activity
Our database included neurons from which activity was recorded during more than two blocks of trials for each armcursor assignment. In this report, we analyzed neuronal activity during the delay 1 and delay 2 periods that preceded the first GO signal. If neuronal activity (discharge rate) during either of the two delay periods (01000 ms in each period) was significantly different (Wilcoxon signed-ranks test, = 0.05) from that during a control period (500 ms in the initial hold period, starting 300 ms after its onset), we defined the activity as delay-related.
Initially, to examine whether delay-related activity reflected motor responses, we performed simple linear regression analysis using the following formula
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A separate analysis was performed for neuronal activity during the goal display period. In the same way as the two delay periods, we performed a multiple regression analysis to examine the effect of the positions of the final goal on neuronal activity.
Quantification of Selectivity for Final and Immediate Goals
In the next step of the analysis, we calculated the selectivity index (SI) of behavioral goal-selective neurons to quantify the selectivity of such neurons for the final and immediate goals. The SI was defined as (Vf Vi)/( Vf + Vi), where Vf was the F-value for the final goal and Vi was the F-value for the immediate goal that were derived from the regression analysis using formula 2. Positive values reflected selectivity for the final goal, whereas negative values reflected selectivity for the immediate goal.
Statistical Analysis for Eye Movements
Although the monkeys were not required to control their gaze while performing the task, we nevertheless analyzed eye position and movement extensively. First, we calculated the average horizontal and vertical eye positions in 10 ms bins for each trial and performed multiple linear regression analysis to examine relationship between eye positions and locations of the final and immediate goals by using the following formula
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Results |
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Both monkeys moved the cursor stepwise from a start position to reach to a briefly cued goal. Although during training the start and goal positions were specified at various positions within the maze, the start position was always at the center of the maze in trials during which neuronal activity was measured. The animals performed the task at a success rate of >95%. In >94% of successful trials, the animals reached the remembered goal with three movements of the cursor and avoided the path-blocks (Table 1). Performance errors resulted mainly from premature initiation of supination/pronation during the hold period, except for a transitional period during which the armcursor assignment was altered and during which the animals committed the error of approaching the path-block with the cursor. The RT varied with the type of arm movement, but was independent of the position of the immediate goal (TukeyKramer multiple comparison test, P > 0.05). The RT was not influenced by the armcursor assignment (Table 2). We also examined whether the RT differed according to the location of the path-block and found no significant differences for the RT of effective and ineffective path-block trials (t-test, P > 0.1; Table 3).
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A total of 1311 PFC neurons (898 and 413 from monkeys 1 and 2, respectively) exhibited significant changes in activity during delay 1, delay 2 or both delay periods (Wilcoxon signed-ranks test, P < 0.05). We first performed a linear regression analysis for 1096 neurons that were active during delay 1 and for 1136 neurons that were active during delay 2 to determine whether delay-related activity reflected arm movements ( = 0.01). Surprisingly, we found that only 1.3% (14 of 1,096) and 2.9% (33 of 1,136) of the delay-related neurons reflected the prepared arm movements during delay 1 and delay 2, respectively (see Table 4). An example of PFC neurons that exhibited properties that reflected non-motor attributes is shown in Figure 2. In this example, neuronal activity appeared to be initiated exclusively during delay 2, the period during which the animal prepared to move the cursor by supinating the left arm in armcursor assignment A1 (top panels in Fig. 2). However, in assignment A2, the same neuron was active only when the animal prepared to respond by pronating the left arm (middle panels in Fig. 2). In assignment A3, the same neuron appeared to be active while the animal prepared to respond by pronating the right arm (bottom panels). What were common factors that led to the activation of this neuron? As illustrated in Figure 2, neuronal activity commenced each time the animal prepared to move the cursor upward to attain the first immediate goal, which was the first step in the task.
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As noted above, activity that reflected the position of the final goal was observed during both delay 1 and delay 2. This begged the question: did individual neurons exhibit continuous activity across both periods, or were neurons active during only one of the two delay periods? In addition, we sought to determine whether final goal-selective activity during the delay periods was a continuation of activity that had commenced in response to the presentation of the visual signal indicating the position of the final goal. To this end, we explored the distribution of final goal-selective neurons that were active during the three task periods (the goal display period, delay 1 and delay 2). For individual neurons, we examined the presence or absence of final goal selectivity in each of the three periods using the regression analysis described in the Material and Methods, and we subsequently classified each neuron into one of seven groups according to the period(s) during which final goal selectivity was detectable, as illustrated by the Venn diagram in Figure 8. We found that few neurons (9.2% or 42 of 456 final goal-selective neurons) were continuously active throughout the three periods, as exemplified in Figure 9C. Similarly, few neurons were active throughout delay 1 and delay 2 (12.1%, or 55/456). The distribution of neurons categorized according to the seven categories (bottom panel in Fig. 8) revealed that the majority (63.8%, or 291/456) was active during only one of the three task periods. Typically, final goal-selective activity during the goal display period decreased during the subsequent delay periods (Fig. 9A). A substantial proportion of neurons exhibited final goal selectivity that appeared de novo either during delay 1 (14.9%, or 68/456; Fig. 9B) or delay 2 (23.5%, or 107/456; Fig. 5).
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Cortical sites of electrode entry (Fig. 10B) were reconstructed based on the results of the MRI, which revealed the location of the recording chamber with reference to the cortical sulci. Although the exact localization of neurons from which we obtained recordings awaits detailed histological study, three points are worth reporting at this stage. First, delay-related neurons that reflected behavioral goals were located within the lateral PFC, dorsal and ventral to the principal sulcus with a possible predominance in the dorsal area (Fig. 10C). Second, neurons that responded to the visual cue with goal-selective activity during the goal display period were located primarily ventral to the principal sulcus (Fig. 10D). The distribution of delay-related, behavioral goal-selective neurons was significantly different from that of neurons that responded to the visual cue (2 test, P < 0.001), which indicated that the former were located more dorsal to the latter. Finally, neurons that exhibited selective responses to the path-block were located primarily ventral to the principal sulcus (Fig. 10E). The distribution of goal-selective neuron was studied based on the number of selective neurons per recording site. We also performed the aforementioned statistical analysis using the proportion of selective neurons per total sample of neurons at a given site, and found results pointing to the same regional differences.
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We examined whether there was any relationship between eye positions and the location of the final or immediate goal. For this purpose, we calculated the average horizontal and vertical eye positions in 10 ms bins for each trial, and then obtained eight sets of quantified data for eye positions that were sorted according to locations of the final and immediate goals (Fig. 11). We then performed regression analysis on these data using the location of either the final or immediate goal as a factor. We found that the locations of neither the final goal nor the immediate goal significantly influenced the eye positions throughout the task periods preceding the first GO signal, as shown with sequential P-value displays at the bottom of Figure 11 (P > 0.05). In the next step of the analysis, we investigated whether neuronal activity that was found related to the location of behavioral goals, as described above, had any relation to either eye positions or eye movements during any task phases. For this purpose, we performed multiple regression analysis using three sets of regressors: vertical and horizontal eye positions, vertical and horizontal components of saccade vector, as well as the locations of final and immediate goals. Results of this analysis are shown in Figure 12 that displays sequential plots of coefficients of determination (R2) for examples of six PFC cells. As typically shown in Figure 12, none of PFC neurons tested revealed apparent relation to eye positions or movements.
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We examined a possibility that the aforementioned behavioral goal-selective neurons could code the position of goals relative to a retinocentric frame of reference. To examine this possibility, neuronal activity was regressed on to the horizontal and vertical positions of the goal specified in the retinal coordinates. For this purpose, the retinal locations of the final and immediate goals were reconstructed based on their positions in the maze and the position of the eyes. We then applied four independent variables (horizontal and vertical eye position of final and immediate goals in retinal coordinates) to a regression model. This analysis revealed that the regression coefficients never reached a significant level (P > 0.05).
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Discussion |
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Paucity of Activity Reflecting Motor Responses
We found that only a small minority of PFC neurons (1.3 and 2.9% during delays 1 and 2, respectively) reflected forthcoming motor responses. This finding is at variance with previous reports on PFC activity during planning or an instructed delay period. Although it was reported that PFC neuronal activity reflected primarily the visuospatial information that was provided as a visual cue rather than the direction of intended motion (Niki and Watanabe, 1976; Boussaoud and Wise, 1993
; Funahashi et al., 1993
), the proportion of the population of task-related neurons that reflected an instructed action ranged from one-third to one-fifth. The discrepancy between our data and those of previous studies might be explained (at least in part) by the fact that we dissociated the direction of the animal's movements and the direction of the movement of visual objects that were acted upon in the present study. It is conceivable that the apparent correlation between PFC activity and direction of motion that was described in previous reports might have included neuronal activity reflecting the motion of visual objects that were produced as an outcome of intended action.
Neural representation of the target of visually guided arm movements has been reported before in several motor areas by Alexander and colleagues (Alexander and Crutcher, 1990a,b
; Crutcher and Alexander, 1990
; Shen and Alexander, 1997a
,b
). They found that neurons in cortical motor areas and in the putamen responded during a motor planning period as a function of the direction in which a visual cursor would move in response to a forthcoming arm movement. However, as compared with the PFC, these motor areas contained a higher proportion of neurons reflecting the direction of limb movement itself rather than the spatial target of movements. This suggests that there may be a gradient in coding movement goals to movement metrics in moving from the PFC to motor areas, although absolute boundaries in the types of information coded in these areas may not exist.
Neuronal Activity Reflecting the Immediate Goal
During the delay periods that preceded the first GO signal, monkeys were required to select a position to which the cursor was to be moved (an immediate goal) or the direction of the first of three cursor movements necessary to reach the position of the final goal. This information was not provided by cues, because neither the presentation of the goal nor the path-block provided a visual cue that would indicate the direction of the first cursor movement. Thus, the animals were required to generate the information for the first movement without the aid of cues. Consequently, neuronal activity that reflected the immediate goal did not reflect currently available or remembered visual signals. We propose that the neuronal activity that was observed to be selective for the immediate goal reflected PFC activity that represented an immediate behavioral goal generated during planning. The immediate goal-selective activity was most prominent during delay 2. A small fraction of the activity during delay 1, however, also reflected the immediate goal, which suggested that the monkeys had already initiated the planning of the first cursor movement during delay 1. Nevertheless, because the PFC is involved in monitoring (Petrides, 1995) and anticipating future events (Sakagami and Niki, 1994
; Watanabe, 1996
; Rainer et al., 1999
), an alternative interpretation of the findings is that internal monitoring or expectation of the cursor motion (or the outcome of the forthcoming action) produced the immediate goal-selective activity.
Neuronal Activity Reflecting the Final Goal
We found that the activity of a large proportion of PFC neurons reflected the position of the final goal (61 and 41% during delays 1 and 2, respectively). To interpret this finding, we should first consider the possibility that this activity might reflect the visual cue that was presented during the goal display, because previous studies have established that PFC neurons maintain sensory information provided by visual cues during an instructed delay period (Goldman-Rakic, 1987). This explanation is plausible for activity that persisted during the delay periods after being initiated during the goal display period. However, we found that for half of the final goal-selective neurons, activity during the delay period commenced only after the disappearance of the visual cue. This finding is in line with previous reports that prefrontal neurons build up cue-instructed activity throughout a delay period, in the absence of apparent cue-evoked activity (Kojima and Goldman-Rakic, 1982
; Chafee and Goldman-Rakic, 1998
). On the other hand, for 24% of final-goal selective neurons, activity started de novo during delay 2; for these neurons, activity may be generated in neural networks, including the PFC. Furthermore, final goal-selective activity coded the location of the goal in a spatial reference frame defined in the maze, rather than in a retinocentric reference frame. These observations suggest that information about the final goal would appear to be provided internally through the activity of neuronal networks that involve the PFC. Such information is likely to be an expression of the prospective memory of the achievement of the final goal, rather than the reflection of the retrospective memory of the visual signal. This view is supported by our finding in the present study that the final goal-selective activity during the delay periods was attributable to neurons that were distributed dorsal to the principal sulcus, whereas activity that reflected sensory responses to the presentation of the goal during the goal display was attributable to neurons that were distributed ventral to the principal sulcus in the lateral PFC (Petrides, 1991
, 1995
; Owen et al., 1996
; Hoshi and Tanji, 2004
).
Interpretation of the Present Findings with Reference to Previous Reports
In previous reports on PFC activity during an instructed delay period, the properties of PFC neurons were described as being representative of either the visual information that was provided with an instruction cue or the direction of forthcoming motion (Fuster and Alexander, 1971; Fuster, 1973
; Kojima and Goldman-Rakic, 1982
, 1984
; Funahashi et al., 1989
; Wilson et al., 1993
; Miller et al., 1996
; Rao et al., 1997
; Rainer et al., 1998
). In the present study, we report a novel aspect of information representation by PFC neurons, namely behavioral goal representation. Our findings indicate that neuronal activity related to the immediate goal was neither a sensory nor a motor representation but instead represented the objective of the forthcoming behavior. It is important to note that, in the paradigm used in the present study, the animals were required to create information that specified an immediate goal. In view of the gradual shift of information from that representing the final goal to that representing the immediate goal, which occurred during the transition from delay 1 to delay 2, it is likely that the information related to the immediate goal was transformed from the final goal-related information during the process of behavioral planning. This process of transformation resembles the transformation process observed by Fukushima et al. (2004)
, who reported that the representation of an updated target was generated internally according to a nonspatial instruction.
Recent studies have revealed that the activity of PFC neurons during an instructed delay period represents a variety of behavioral factors that are more abstract in nature than the sensorial representation of instruction signals or the direction of future movements. Such behavioral factors include task conditions or rules (Hoshi et al., 1998; White and Wise, 1999
; Wallis et al., 2001
), behavioral monitoring (Petrides, 2000
), multiple motor planning (Averbeck et al., 2002
) and the coding of abstract information (Freedman et al., 2001
; Nieder et al., 2002
; Ninokura et al., 2003
). The results of the present study suggest that an additional factor, namely behavioral goals (specifically, a planned immediate goal and a prospective final goal), should be considered to be part of the repertoire of PFC representations during an instructed delay period.
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Acknowledgments |
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References |
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![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Alexander GE, Crutcher MD (1990b) Neural representations of the target (goal) of visually guided arm movements in three motor areas of the monkey. J Neurophysiol 64:164178.
Averbeck BB, Chafee MV, Crowe DA, Georgopoulos AP (2002) Parallel processing of serial movements in prefrontal cortex. Proc Natl Acad Sci USA 99:1317213177.
Boussaoud D, Wise SP (1993) Primate frontal cortex: effects of stimulus and movement. Exp Brain Res 95:2840.[ISI][Medline]
Bruce CJ, Goldberg ME, Bushnell MC, Stanton GB (1985) Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements. J Neurophysiol 54:714734.
Chafee MV, Goldman-Rakic PS (1998) Matching patterns of activity in primate prefrontal area 8a and parietal area 7ip neurons during a spatial working memory task. J Neurophysiol 79:29192940.
Constantinidis C, Franowicz MN, Goldman-Rakic PS (2001) The sensory nature of mnemonic representation in the primate prefrontal cortex. Nat Neurosci 4:311316.[CrossRef][ISI][Medline]
Crutcher MD, Alexander GE (1990) Movement related neuronal activity selectively coding either direction or muscle pattern in three motor areas of the monkey. J Neurophysiol 64:151163.
Draper NR, Smith H (1998) Applied regression analysis, 3rd edn. New York: John Wiley & Sons.
Freedman DJ, Riesenhuber M, Poggio T, Miller EK (2001) Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291:312316.
Fukushima T, Hasegawa I, Miyashita Y (2004) Prefrontal neuronal activity encodes spatial target representations sequentially updated after nonspatial target-shift cues. J Neurophysiol 91:13671380.
Funahashi S, Bruce CJ, Goldman-Rakic PS (1989) Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. J Neurophysiol 61:331349.
Funahashi S, Chafee MV, Goldman-Rakic PS (1993) Prefrontal neuronal activity in rhesus monkeys performing a delayed anti saccade task. Nature 365:753756.[CrossRef][ISI][Medline]
Fuster JM (1973) Unit activity in prefrontal cortex during delayed response performance: neuronal correlates of transient memory. J Neurophysiol 36:6178.
Fuster JM (1997) The prefrontal cortex: anatomy, physiology and neuropsychology of the frontal lobe. Philadelphia, PA: Lippincott-Raven.
Fuster JM, Alexander GE (1971) Neuron activity related to short term memory. Science 173:652654.[ISI][Medline]
Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. In: Handbook of Physiology. Vol.5 (Plum F, Mountcastle V eds), pp. 373417. Bethesda, MD: Am Physiolical Society.
Hasegawa R, Sawaguchi T, Kubota K (1998) Monkey prefrontal neuronal activity coding the forthcoming saccade in an oculomotor delayed matching to sample task. J Neurophysiol 79:322333.
Hoshi E, Tanji J (2004) Area-selective neuronal activity in the dorsolateral prefrontal cortex for information retrieval and action planning. J Neurophysiol 91:27072722.
Hoshi E, Shima K, Tanji J (1998) Task dependent selectivity of movement related neuronal activity in the primate prefrontal cortex. J Neurophysiol 80:33923397.
Kojima S, Goldman-Rakic PS (1982) Delay-related activity of prefrontal neurons in rhesus monkeys performing delayed response. Brain Res 248:4349.[CrossRef][ISI][Medline]
Kojima S, Goldman-Rakic PS (1984) Functional analysis of spatially discriminative neurons in prefrontal cortex of rhesus monkey. Brain Res 291:229240.[CrossRef][ISI][Medline]
Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167202.[CrossRef][ISI][Medline]
Miller EK, Erickson CA, Desimone R (1996) Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J Neurosci 16:51545167.
Mushiake H, Saito N, Sakamoto K, Sato Y, Tanji J (2001) Visually based path planning by Japanese monkeys. Brain Res Cogn Brain Res 11:165169.[CrossRef][ISI][Medline]
Mushiake H, Saito N, Furusawa Y, Izumiyama M, Sakamoto K, Shamoto H, Shimizu H, Yoshimoto T (2002) Orderly activations of human cortical areas during path planning task. Neuroreport 13:423426.[CrossRef][ISI][Medline]
Nieder A, Freedman DJ, Miller EK (2002) Representation of the quantity of visual items in the primate prefrontal cortex. Science 297:17081711.
Niki H, Watanabe M (1976) Prefrontal unit activity and delayed response: relation to cue location versus direction of response. Brain Res 105:7988.[CrossRef][ISI][Medline]
Ninokura Y, Mushiake H, Tanji J (2003) Representation of the temporal order of visual objects in the primate lateral prefrontal cortex. J Neurophysiol 89:28682873.
Owen AM, Evans AC, Petrides M (1996) Evidence for a two stage model of spatial working memory processing within the lateral frontal cortex: a positron emission tomography study. Cereb Cortex 6:3138.[Abstract]
Passingham RE (1993) The frontal lobes and voluntary action. New York: Oxford University Press.
Petrides M (1991) Monitoring of selections of visual stimuli and the primate frontal cortex. Proc R Soc Lond B Biol Sci 246:293298.[ISI][Medline]
Petrides M (1994) Frontal lobes and working memory: evidence from investigations of the effects of cortical excisions in nonhuman primates. In: Handbook of neuropsychology (Boller F, Grafman J, eds), pp. 5982. Amsterdam: Elsevier Science.
Petrides M (1995) Impairments on nonspatial self ordered and externally ordered working memory tasks after lesions of the mid dorsal part of the lateral frontal cortex in the monkey. J Neurosci 15:359375.[Abstract]
Petrides M (2000) The role of the mid dorsolateral prefrontal cortex in working memory. Exp Brain Res 133:4454.[CrossRef][ISI][Medline]
Quintana J, Fuster JM (1999) From perception to action: temporal integrative functions of prefrontal and parietal neurons. Cereb Cortex 9:213221.
Quintana J, Yajeya J, Fuster JM (1988) Prefrontal representation of stimulus attributes during delay tasks. I. Unit activity in cross temporal integration of sensory and sensory motor information. Brain Res 474:211221.[CrossRef][ISI][Medline]
Rainer G, Asaad WF, Miller EK (1998) Memory fields of neurons in the primate prefrontal cortex. Proc Natl Acad Sci USA 95:1500815013.
Rainer G, Rao SC, Miller EK (1999) Prospective coding for objects in primate prefrontal cortex. J Neurosci 19:54935505.
Rao SC, Rainer G, Miller EK (1997) Integration of what and where in the primate prefrontal cortex. Science 276:821824.
Saito N, Mushiake H, Sakamoto K, Tanji J (2001) Neuronal activity in the dorsolateral prefrontal cortex during a path-planning task. Soc Neurosci Abstr 27.
Saito N, Mushiake H, Sakamoto K, Tanji J (2004) Involvement of the prefrontal cortex in governing goal-oriented behavioral sequence. Soc Neurosci Abstr 30.
Sakagami M, Niki H (1994) Encoding of behavioral significance of visual stimuli by primate prefrontal neurons: relation to relevant task conditions. Exp Brain Res 97:423436.[ISI][Medline]
Shen L, Alexander GE (1997a) Neural correlates of a spatial sensory to motor transformation in primary motor cortex. J Neurophysiol 77:11711194.
Shen L, Alexander GE (1997b) Preferential representation of instructed target location versus limb trajectory in dorsal premotor area. J Neurophysiol 77:11951212.
Tanji J, Hoshi E (2001) Behavioral planning in the prefrontal cortex. Curr Opin Neurobiol 11:164170.[CrossRef][ISI][Medline]
Wallis JD, Anderson KC, Miller EK (2001) Single neurons in prefrontal cortex encode abstract rules. Nature 411:953956.[CrossRef][ISI][Medline]
Watanabe M (1996) Reward expectancy in primate prefrontal neurons. Nature 382:629632.[CrossRef][ISI][Medline]
White IM, Wise SP (1999) Rule dependent neuronal activity in the prefrontal cortex. Exp Brain Res 126:315335.[CrossRef][ISI][Medline]
Wilson FA, Scalaidhe SP, Goldman-Rakic PS (1993) Dissociation of object and spatial processing domains in primate prefrontal cortex. Science 260:19551958.[ISI][Medline]
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