Neuronal Interactions Related to Working Memory Processes in the Primate Prefrontal Cortex Revealed by Cross-correlation Analysis

Shintaro Funahashi1,2,3 and Masato Inoue2

1 Laboratory of Neurobiology, Faculty of Integrated Human Studies, , 2 Department of Cognitive Sciences, Graduate School of Human and Environment Studies, Kyoto University, Kyoto 606-8501 and , 3 PRESTO, Japan Science and Technology Corporation, Japan


    Abstract
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
To understand neuronal mechanisms for manipulating and/or integrating information in working memory processes, we examined functional interactions among prefrontal neurons exhibiting various task-related activities by cross-correlation analysis. Among 168 neuron pairs isolated, 84 (50%) had significant peaks in cross-correlograms (CCGs); 30 had excitatory central peaks at time 0, 38 had excitatory peaks displaced from time 0, 13 had inhibitory central peaks, and three had both excitatory and inhibitory peaks displaced from time 0. Although significant interactions were observed among prefrontal neurons having various task-related activities, the information flow is present from prefrontal neurons having cue-period activity to neurons having oculomotor activity through neurons having delay-period activity. In addition, neuron pairs both having delay-period activity tended to have significant excitatory peaks in CCGs. Further, neuron pairs that had excitatory central peaks in CCGs tended to have similar directional preferences in task-related activities, and this similarity was the highest in neuron pairs both having cue-period activity. Neuron pairs that had displaced peaks in CCGs also showed similarity in directional preferences in task-related activities, and this similarity was also higher in neuron pairs both having cue-period activity. Interactions between neurons exhibiting task-related activity with different directional preferences increase as the temporal sequence of the task progresses. These results suggest that these interactions play an important role for manipulating and integrating information.


    Introduction
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
The prefrontal cortex has been considered an important structure for working memory processes (Goldman-Rakic, 1987Go, 1996Go; Funahashi and Kubota, 1994Go; Petrides, 1994Go, 1996Go). This has been confirmed by various experiments, including recent neurophysiological studies (Funahashi et al., 1989Go; di Pellegrino and Wise, 1993Go; Wilson et al., 1993Go; Miller et al., 1996Go; Carlson et al., 1997Go; Rao et al., 1997Go), positron emission tomography (PET) studies (Jonides et al., 1993Go; Petrides et al., 1993Go) and functional magnetic resonance imaging (fMRI) studies (McCarthy et al., 1996Go; Cohen et al., 1997Go; Courtney et al., 1997Go). Working memory is usually defined as a mechanism for short-term active storage of information (Baddeley, 1986Go). Working memory is also considered to be an important mechanism for various mental activities, including language comprehension, mental calculation, thinking and reasoning (Baddeley, 1986Go). Since processing and manipulation of stored information are necessary to achieve these mental activities, working memory can not only be considered as a mechanism for temporary active storage of information but also includes a mechanism for processing, manipulating or integrating information.

Using spatial working memory tasks such as the delayed-response task, neurophysiological studies revealed that the tonic activation during the delay period (delay-period activity) is considered to be a neuronal correlate for the temporary active storage process of information (Fuster, 1973Go; Niki, 1974Go; Niki and Watanabe, 1976Go; Kojima and Goldman-Rakic, 1984Go; Funahashi et al., 1989Go; Carlson et al., 1997Go). Delay-period activity is prolonged or shortened depending upon the length of the delay period (Fuster, 1973Go; Kojima and Goldman-Rakic, 1982Go; Funahashi et al., 1989Go). This activity was observed only when monkeys performed correct responses (Fuster, 1973Go; Funahashi et al., 1989Go, 1997Go). A great majority of delay-period activity exhibits directional or positional preferences (Funahashi et al., 1989Go; Carlson et al., 1997Go; Rao et al., 1997Go). In addition, experiments using non-spatial working memory tasks such as delayed matching-to-sample tasks or delayed conditional tasks revealed that delay-period activity also reflected active retention of non-spatial information, such as faces (Wilson et al., 1993Go; O'Scalaidhe et al., 1997Go), object's shapes, patterns or colors (Quintana et al., 1988Go; Yajeya et al., 1988Go; Watanabe, 1990Go; Sakagami and Niki, 1994Go; Miller et al., 1996Go; Rao et al., 1997Go). Thus, delay-period activity observed in prefrontal neurons can be considered as a neuronal correlate for the temporary active storage mechanism of information (Funahashi and Kubota, 1994Go; Funahashi, 1996Go; Goldman-Rakic, 1996Go; Fuster, 1997Go).

Some neurophysiological works also suggest a mechanism for manipulating or integrating information in the prefrontal cortex. Funahashi et al. showed that most of saccade-related activity observed in the prefrontal cortex were post-saccadic and could reflect a feedback information from oculomotor centers (Funahashi et al., 1991Go). The characteristics of post-saccadic activity suggest that this activity manipulates delay-period activity, because the termination of delay-period activity coincided with the initiation of post-saccadic activity (Goldman-Rakic et al., 1990Go). Since erasing the unnecessary information is an important process for working memory, feedback inputs from motor centers could play such a role as erasing the unnecessary information by terminating delay-period activity. In addition, using a delayed-response task with sequential hand reaching behavior or sequential saccades, new characteristics of delay-period activity have been observed in the prefrontal cortex, such as delay-period activity holding information regarding a pair of different spatial positions and/or a temporal order of the cue presentation (Barone and Joseph, 1989Go; Funahashi et al., 1993aGo, 1997Go). Since most of these neurons exhibited similar delay-period activity as that observed previously when monkeys performed a conventional delayed-response task with a single spatial cue (Funahashi et al., 1993aGo, 1997Go), delay-period activity with complex characteristics could be constructed by interactions among neurons each of which shows different spatial preference. Thus, the interactions among prefrontal neurons that exhibited various task-related activities with different directional preferences could play an important role for integrating and manipulating information.

In the present experiment, we examined functional interactions between prefrontal neurons exhibiting various task-related activities by cross-correlation analysis. Cross-correlation analysis of simultaneously isolated single-neuron activities is a method to elucidate functional interactions between two cortical neurons (Perkel et al., 1967aGo,bGo; Aertsen and Gerstein, 1985Go; Gerstein and Aertsen, 1985Go). This analysis has been applied in various brain areas, including the visual cortex (Toyama et al., 1981aGo,bGo; Tanaka, 1983Go; Krüger and Aiple, 1988Go; Hata et al., 1991Go, 1993Go), the auditory cortex (Espinosa and Gerstein, 1988Go; Ahissar et al., 1992Go), the hippocampus (Sakurai, 1996Go), the motor cortex (Murphy et al., 1985aGo,bGo; Kwan et al., 1987Go) and the striatum (Bergman et al., 1998Go). Recently, Rao et al. has applied this analysis to examine interactions between pyramidal and non-pyramidal neurons in the prefrontal cortex (Rao et al., 1999Go). However, this analysis has not been applied fully to prefrontal neurons in order to examine functional interactions between prefrontal neurons exhibiting task-related activities at different task events. Since the characteristics of task-related prefrontal activities have been analyzed fully under the oculomotor delayed-response (ODR) condition (Funahashi et al., 1989Go, 1990Go, 1991Go), we applied the cross-correlation analysis to prefrontal neuronal activities recorded under ODR performances. Parts of this experiment have been published in abstract form (Funahashi et al., 1996Go; Hara et al., 1996Go).


    Materials and Methods
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
Materials and Equipment

Three rhesus monkeys (Mm 1079, 6.5 kg; Mm 1102, 5.6 kg; and Mm 1234, 3.7 kg) served as subjects. Each was housed individually in a home cage. The monkeys were deprived of water in their home cages, but could obtain thier daily requirement of water in the laboratory as a reward. To ensure each monkey's condition, body weight and the amount of water intake were measured daily. All these monkeys were also used for our other experiment (M. Inoue and S. Funahashi, in preparation). All experiments were conducted according to the Guide for The Care and Use of Laboratory Animals by the National Institutes of Health and the Guide for The Care and Use of Laboratory Primates by the Primate Research Institute of Kyoto University.

Each monkey sat in a primate chair in a dark room during training and recording sessions. The monkey's head was fixed by a head-restraining instrument that was attached to the chair. The monkey faced a 21 inch color TV (GVM-2100, Sony), on which a fixation target and visual cues were presented. Eye movements were monitored by a high-speed monitoring system using an infra-red camera (R-21C-A, RMS Hirosaki), which sampled horizontal and vertical eye positions at 250 Hz with a minimal accuracy of 0.2° in the visual angle. Two computers (PC-9801VM and PC-9801DA, NEC) were used to present visual stimuli on the TV monitor, to store neuronal activity with event signals in magnetic media and to monitor the monkey's eye positions. Amplified raw neuronal activity, event signals, and horizontal and vertical eye positions were also stored on magnetic tape by a data recorder (PC-108M, Sony Precision Technology) for cross-correlation analysis.

Behavioral Task

Monkeys performed an ODR task (Fig. 1Go). The task used in this experiment was the same as was used previously (Funahashi et al., 1989Go). After a 5 s inter-trial interval, a fixation target was presented at the center of the TV monitor. The monkey was required to look at the fixation target and maintain fixation. After the 1.5 s fixation period, a visual cue (a white square, 0.4 x 0.4° in visual angle) was presented for 0.5 s at one of the eight predetermined peripheral positions (Fig. 1Go, bottom). The eccentricity of cue positions was 17° from the central fixation target. The position of the visual cue was selected randomly from trial to trial. The monkey was required to maintain fixation at the central target for the 0.5 s cue period and subsequent 3 s delay period. At the end of the delay period, the fixation target was extinguished. This was the Go signal for the monkey to perform a saccadic eye movement to where the visual cue had been presented. If the monkey performed a correct saccade within 0.4 s after the Go presentation, it was rewarded by a drop of water. Correct saccades were defined as saccades that were terminated within the 6° square zone around the cue position. If the monkey broke fixation at the central target during either the fixation period, the cue period or the delay period, the trial would be terminated and the next trial began immediately.



View larger version (23K):
[in this window]
[in a new window]
 
Figure 1.  Temporal sequence of the events in an ODR task (upper figure) and the configuration of the central fixation target and eight peripheral visual cues (lower figure).

 
Surgical Preparation

To monitor the monkey's eye movements, we first performed surgery to attach a head-restraining device to the skull under aseptic conditions. The monkey was first given ketamine (50 mg) i.m. and then an i.v. injection of sodium pentobarbital (30 mg/kg). The skull was partially exposed. The position for attaching the head-restraining device was estimated by stereotaxic coordinates. Stainless steel screws were used to attach the device firmly to the skull. These screws and the head-restraining device were then fixed with dental acrylic resin.

After each monkey reached a criterion level of performance (>80% correct performance per daily session), a second surgical procedure was performed under aseptic conditions to attach a stainless steel chamber to the skull for recording neuronal activity. The monkey was first given ketamine (50 mg) i.m. and then an i.v. injection of sodium pentobarbital (30 mg/kg). The center of the recording area on the prefrontal cortex was estimated by stereotaxic coordinates (30 mm anterior from the interaural plane and 15 mm lateral from the midline). A hole (20 mm in diameter) was made by trephine over this estimated recording area, and a stainless steel chamber (20 mm in diameter) was then placed over the hole. Stainless steel screws were used to attach the recording chamber firmly to the skull. These screws, the recording chamber and the previously attached head-restraining device were then fixed with dental acrylic resin. Monkeys were given systemic antibiotics just before each surgical operation and for 3–4 days after surgery. Monkeys were also given ad libitum fruit, water and chow for at least 1 week after surgery. After monkeys had completely recovered from surgery, the recording of neuronal activity began.

Recording and Isolating Neuronal Activity

Neuronal activity was collected from the cortex within and surrounding the posterior half of the principal sulcus by glass-coated Elgiloy microelectrodes (1–2 M{Omega} at 1 kHz). Raw activity was amplified and monitored using an oscilloscope (5112, Tektronix) and an audio-monitor. During the experiment, raw multiple-neuron activity was stored on magnetic tape together with horizontal and vertical eye positions and event signals by an eight-channel data recorder (PC-108M, Sony Precision Technology). At the same time, we isolated one single-neuron activity from raw activity using a window discriminator (DIS-1, BAK Electronics) and stored isolated activity with event signals in magnetic media as a data file by a computer (PC-9801VM, NEC).

During an off-line analysis, up to four single-neuron activities were isolated from raw multiple-neuron activity by window discriminators. One recording session usually lasted for 30–50 min, and the recording condition usually changed gradually as the recording session progressed. Therefore, we first examined the recording condition throughout one recording session. We then manipulated the parameters of window discriminators (the trigger point level, the upper and lower levels of the window, and the time delay) to examine how many single-neuron activities could be isolated independently from multiple-neuron activities. We also examined how long each single neuron's activity could be isolated without any contamination from other neurons' activities. Finally, we determined the parameters of each window discriminator to isolate each single-neuron activity and the maximum length of time that we could isolate this activity without any contamination from other neurons' activities. During the isolation of single-neuron activity, we fixed the trigger point level, the upper and the lower levels of the window, and the time delay, and we continuously monitored outputs of window discriminators by oscilloscopes and by audio-monitors. We terminated this analysis at the predetermined point. To confirm the constancy in shape and amplitude of single-neuron's action potentials, shapes of each neuron's action potentials were displayed on a digital storage oscilloscope (DS-8606C, Iwatsu Electronics), and some of these shapes were stored in magnetic media and also output to a plotter (DXY-1200, Roland). Figure 2Go shows four examples of isolated single-neuron activities. In records x09701 and x10901, two single-neuron activities were isolated from the same records, whereas in records x07201 and x12001, three single-neuron activities were isolated from the same records. Each isolated single-neuron activity was input to a computer (PC-486HX, Epson) at the 1 kHz sampling rate with task events to store as a data file for later analysis and constructing CCGs.



View larger version (37K):
[in this window]
[in a new window]
 
Figure 2.  Waveforms of simultaneously isolated single-neuron activities from multiple-neuron activities. Two single-neuron activities were isolated simultaneously in neurons x09701 and x10901, and three single-neuron activities were isolated simultaneously in neurons x07201 and x12001. Thick vertical bars in figures indicate the positions of the window.

 
Data Analysis

To investigate whether the isolated neuron exhibited task-related activity, we examined rasters and histograms for each cue condition in the ODR task. Rasters and histograms were constructed using four alignment points: the onset of the visual cue, the start and the end of the delay period, and the reward delivery. If we found excitatory or inhibitory responses in rasters and histograms in relation to at least one task event by visual inspections, further statistical analysis were performed.

To estimate a neuron's baseline discharge rate for each cue condition, we calculated a mean discharge rate during a 1 s interval just before the cue period (the fixation period). When the neuron exhibited an excitatory or inhibitory response during the cue period (cue-period activity) by visual inspection, discharge rates were calculated during the 0.5 s cue period or during the 0.2 s interval started from 0.1 s after the onset of the visual cue for each trial. When the neuron exhibited an excitatory or inhibitory response during the delay period (delay-period activity) by visual inspection, mean discharge rates were calculated during the 3 s delay period for each trial. Finally, when the neuron exhibited an excitation or suppression response during the response period (response-period activity), discharge rates were calculated during the 0.5 s response period or during the 0.5 s interval started from 0.2 s after the Go signal presentation for each trial. These discharge rates were then compared with baseline discharge rates for each cue condition by the Mann–Whitney U-test. If the difference was statistically significant (P < 0.05), we considered that the neuron had either cue-period, delay-period or response-period activity.

To examine whether cue-, delay- or response-period activity exhibited a directional preference, a difference in this activity across different cue conditions was first examined by ANOVA. If the difference was significant (P < 0.05), we considered that the neuron had directional cue-, delay- or response-period activity. Then, to estimate the neuron's best tuned position quantitatively, a tuning curve was made from the discharge rates by their best fit to the Gaussian function

where f (d) is the discharge rate as a function of the cue position d. Constants were interpreted as follows: B is the neuron's baseline discharge rate, D is the direction where the neuron exhibited the best response, Td is an index of the tuning width and R is the discharge rate in the neuron's best direction. We constructed a tuning curve for all neurons that exhibited directional activity during the cue, delay or response period. Based on these tuning curves, we estimated values of D and Td as the best direction and the tuning index, respectively, for each activity. The same method to construct tuning curves has been used in frontal eye field neurons (Bruce and Goldberg, 1985Go) and prefrontal neurons (Funahashi et al., 1989Go, 1990Go, 1991Go).

Cross-correlation Analysis

Using stored data files of single-neuron activities isolated from the same record, cross-correlograms (CCGs) were calculated using an algorithm described previously (Perkel et al., 1967aGo,bGo). Most of CCGs presented in this report were constructed by the neuronal activities collected during the period from the beginning of the fixation period until the 1 s after the reward delivery. The procedures to construct neural (subtracted) CCGs are illustrated in Figure 3Go. We first constructed a raw (original) CCG using pairs of single-neuron activities isolated from the same record (Fig. 3DGo). However, the original CCG included the effects of the onset and the offset of the visual cue, saccadic eye movements, and the reward delivery. Therefore, to eliminate these effects from the original CCG, we constructed a ‘shuffled' CCG (Fig. 3EGo). Because the cue position was selected randomly from trial to trial, we first sequentially compiled neuronal activities collected only under trials having the same cue position. Secondly, we shifted the sequence of the trial of one neuron's activity to make a new pair with another neuron's activities, such that neuron A's activity at the first trial now made a pair with neuron B's activity at the second trial, and so on, and finally neuron A's activity at the last trial made a pair with neuron B's activity at the first trial. We constructed a shuffled CCG using these new shuffled pairs of activities for one cue condition. Then, we repeated this procedure to construct the shuffled CCG for one record. Finally, we constructed a neural (subtracted) CCG by subtracting the shuffled CCG from the original CCG bin by bin (Fig. 3FGo). Each CCG was constructed using a bin width of 1 ms.



View larger version (27K):
[in this window]
[in a new window]
 
Figure 3.  A method for constructing CCGs. (A, B) Superimposed shapes of spike activities of neuron z03601a (A) and neuron z03601b (B), respectively. (C) Original CCG constructed from neuronal discharges sampled at 2 kHz. This CCG was constructed by cell B's activity triggered by cell A's spikes. The bin width is 0.5 ms. (D) Original CCG constructed from neuronal discharges sampled at 1 kHz. This CCG was constructed by cell B's activity triggered by cell A's spikes. (E) Shuffled CCG. (F) Subtracted (neural) CCG. This CCG was constructed by subtracting the shuffled CCG (E) from the original CCG (D) bin by bin. The method for constructing these CCGs is described fully in the text. The bin width is 1 ms in (D), (E) and (F).

 
In the present experiment, we used a single electrode to collect multiple-neuron activities and isolated multiple single-neuron activities from them based on the difference of wave shapes and amplitudes. Therefore, when spikes of two neurons occurred simultaneously or when one neuron's spike was immediately followed by the other neuron's spike, this system could not detect either spike or even both spikes. As long as one used a single electrode to collect multiple-neuron activities, this could be the case when one used a time-based window discriminator as we did in the present experiment, or even when one used a sophisticated computer-based waveform discriminating system. However, in the present system, we could detect spikes of two neurons even when one neuron's spike was followed by the other neuron's spike within a 1 ms interval. Figure 4Go shows a distribution of spike widths for neurons isolated in the present experiment. In the present measurement, the spike width was defined as the time from the onset of the trigger for the window discriminator (at 0.0 ms on Fig. 2Go) to the point when the wave returned to the baseline. The baseline was defined as the mean voltage of the neuron having the smallest amplitude at 1.0 ms after the onset of the window trigger. As is shown in Figure 4Go, the mean spike width was 0.72 ms and only 9% of isolated neurons had a spike width of >1.0 ms. Although the mean spike width was 0.72 ms in the present definition, the large deflections of spike waves ended within 0.5 ms from the trigger point for the most neurons (see Figs 2, 3GoGo). Therefore, it is possible to detect spikes of two neurons at <1 ms interval. In fact, when we sampled single-neuron activities at 2 kHz (0.5 ms interval) in selected neuron pairs, the central peak at time 0 disappeared in the CCG (see Fig. 3CGo). In the present experiment, the computer checked output pulses from the window discriminator every 1 ms. Therefore, if two isolated neurons fired within 1 ms interval, the program considered that both neurons fired simultaneously.



View larger version (32K):
[in this window]
[in a new window]
 
Figure 4.  Distribution of spike widths for isolated neurons. The spike width was defined as the time from the onset of the window trigger to the point when the wave returned to the baseline.

 
To test whether CCGs had a statistically significant peak, confidence limits were estimated based on the assumption that the spike counts per bin in a CCG can be modulated as the result of a Poisson process if both neurons fired independently. Resulting from a Poisson process, 95% confident limits were equal to two times the standard deviation (SD) of the mean spike count per bin (Abeles 1982Go). Therefore, we used 2SD levels from the mean as a criterion for statistical significance (Hata et al., 1991Go, 1993Go). We counted numbers of spikes bin by bin during the 200 ms period (from 100 ms before time 0 to 100 ms after time 0) in the neural (subtracted) CCG, and calculated the mean and SD of spike counts per bin. We first confirmed that each shuffled CCG had no significantly higher or lower peaks within the 200 ms period. We then considered that a CCG had a significant positive or negative peak if two or more consecutive bins exhibited higher or lower values than the 2SD limit level. The peak time of the CCG was determined as the bin which showed the highest or the lowest value. The peak time was observed within 10 ms from time 0.

In addition to constructing CCGs, we also constructed auto-correlograms (ACGs) based on each neuron's discharges to examine whether the neuron had regular spike activity, such as oscillations or bursts. Two neurons exhibited oscillatory spike activity at 25–35 Hz. However, the neuron pairs including these neurons did not show significant CCGs.

Histology

At the end of the study, several electrolytic lesions were made within the recording area by passing a positive current through Elgiloy microelectrodes to identify and estimate the recording sites during histological examination. All monkeys were sacrificed by injecting an overdose of sodium pentobarbital (45–50 mg/kg). The brain was first perfused with saline and then with 10% formalin solution with 2% potassium ferrocyanide to identify electrolytic lesions by Elgiloy electrodes via the Prussian blue reaction. The brain was cut serially into 100 µm sections in the coronal plane and stained by the Nissl method.


    Results
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
Database

We collected a total of 133 raw records while three monkeys performed the ODR task. Most neurons were recorded from the posterior half of the cortex around the principal sulcus (Walker's area 46), but four neurons were recorded from the frontal eye field, which was defined by the microstimulation through the recording electrode. From these 133 raw records, 283 single-neuron activities were isolated. Among them, 180 (64%) exhibited task-related activity. This was defined as an activity showing a significant increase or decrease (P < 0.05, Mann–Whitney U-test) during at least one task event compared with the baseline discharge rates. Among 180 neurons showing task-related activities, 30 had only cue-period activity, 23 had only delay-period activity, 45 had only response-period activity and six exhibited reward-period activity. Reward-period activity was defined as an excitatory response that was initiated after the reward delivery across all trial conditions. The remaining 76 neurons showed task-related activity during more than two task events (Fig. 5Go). As a result, a total of 90 neurons exhibited cue-period activity, 77 exhibited delay-period activity and 112 exhibited response-period activity.



View larger version (15K):
[in this window]
[in a new window]
 
Figure 5.  Venn diagram indicating numbers of neurons exhibiting task-related activity. Task-related activities were classified into four types: cue-period activity (Cue), delay-period activity (Delay), response-period activity (Response) and reward-related activity (Reward).

 
A large number of neurons that had task-related activity exhibited directional preferences. Such preferences were observed in 82% of neurons having cue-period activity, 68% of neurons having delay-period activity and 74% of neurons having response-period activity. We estimated the best direction (D) of each activity by constructing a tuning curve. In 74 neurons having directional cue-period activity, 45, 16 and 13 had their best directions towards the contralateral hemifield (110–250°), the ipsilateral hemifield (0–70°, 290–360°) and the vertical directions (70–110°, 250–290°), respectively. Similarly, in 52 neurons having directional delay-period activity, 31, 15 and six had their best directions towards the contralateral hemifield, the ipsilateral hemifield and the vertical directions, respectively. Further, in 83 neurons having directional response-period activity, 45, 32 and six had their best directions towards the contralateral hemifield, the ipsilateral hemifield and the vertical directions, respectively. The significant contralateral bias of the best directions was observed in neurons having cue-period activity ({chi}2 = 13.79, df = 1, P < 0.001) and neurons having delay-period activity ({chi}2 = 5.57, df = 1, P < 0.05), but was not observed in neurons having response-period activity ({chi}2 = 0.59, df = 1, P > 0.5). The ratio of task-related activities, the percentages of neurons exhibiting directional preferences and the contralateral bias of directional preferences observed in the present experiment agree with the results obtained previously by Funahashi et al. (1989, 1990, 1991).

Characteristics of CCGs

Using 133 raw records, two single-neuron activities were isolated from 117 records, three single-neuron activities were isolated from 15 records and four single-neuron activities were isolated from one record. As a result, we obtained a total of 168 neuron pairs for calculating CCGs. We considered that a CCG had a significant peak or depth if the values of two or more consecutive bins in the CCG were over or below the 2SD limit level, respectively. The peak of the CCG was defined as the highest or the lowest bin among significant bins. Among 168 neuron pairs, 84 pairs (50%) had significant peaks or depths in CCGs. We observed four types of neuronal interactions (excitatory central peak, excitatory displaced peak, inhibitory peak, and excitatory and inhibitory peaks) based on the shape of the CCG and the time when the peak of the CCG was observed (Fig. 6Go).



View larger version (28K):
[in this window]
[in a new window]
 
Figure 6.  Four examples of obtained CCGs. (A) Excitatory central peak. (B) Excitatory displaced peak. (C) Excitatory and inhibitory peaks. (D) Inhibitory peak. The bin width is 1 ms.

 
Among 84 neuron pairs having significant peaks in CCGs, 30 pairs (36%) showed an excitatory sharp and symmetrical peak at time 0 (central peak). An example is shown in Figure 6AGo. The shape of CCG indicates that these pairs of neurons tended to fire coincidentally, suggesting that both neurons receive an input from a common afferent. In 38 neuron pairs (45%), CCGs had excitatory sharp but asymmetrical distribution around time 0, and their peaks were displaced from time 0 (displaced peak). An example is shown in Figure 6BGo. This CCG has a sharp excitatory peak at 2 ms before time 0, indicating that neuron B's spike discharges tend to be followed by neuron A's spike discharges with a 2 ms delay. This observation suggests that these two neurons are connected in series. In addition, in three neuron pairs (4%), CCGs showed asymmetrically positive and negative distribution around time 0. The CCG shown in Figure 6CGo has an excitatory broad and asymmetrical distribution before time 0 and an inhibitory asymmetrical distribution after time 0, and the peaks of both excitatory and inhibitory distributions were displaced from time 0. This suggests that one neuron sends an inhibitory output to the other neuron and, at the same time, receives an excitatory input from that neuron. On the other hand, in 13 neuron pairs (15%), CCGs had significant negative peaks around time 0 (Fig. 6DGo), suggesting that, in these neuron pairs, one neuron's firing caused suppression of the other neuron's firing. However, since one of the paired neurons had a wider spike width (>1.0 ms) in eight neuron pairs, this CCG may be an artifact caused by using a single electrode for collecting multiple-neuron activities. Therefore, we excluded these 13 neuron pairs in the following analysis.

Figure 7Go shows the distribution of latencies of the peaks of CCGs observed for neuron pairs having excitatory displaced peaks in CCGs. Most (85%) neuron pairs had their peaks in CCGs within 3 ms from time 0. Therefore, interactions observed between the pair of neurons could be monosynaptic.



View larger version (39K):
[in this window]
[in a new window]
 
Figure 7.  Distribution of latencies of excitatory displaced peaks in CCGs.

 
Interactions among Neurons Having Task-related Activities

Significant interactions were observed in half of neuron pairs both of which had task-related activities at the same or different task events. The results are summarized in Tables 1 and 2GoGo. Neuron pairs in which both neurons showed task-related activity at the same task event tended to have significant peaks in CCGs (35/62, 56%; see Table 1Go), compared with neuron pairs in which each neuron showed task-related activity at different task events (41/94, 44%; see Table 2Go). In particular, significant peaks in CCGs were observed in 71% (10/14) of neuron pairs in which both neurons had delay-period activity. Significant peaks were also observed in 62% (13/21) of neuron pairs in which one neuron exhibited delay-period activity and the other exhibited response-period activity. However, significant peaks were not observed in 65% (35/54) of neuron pairs in which one neuron exhibited cue-period activity and the other exhibited response-period activity. In neuron pairs in which both neurons had the same task-related activity (Table 1Go), the percentage of neuron pairs which had central peaks (27%) was similar to that of neuron pairs which had displaced peaks (29%). However, in neuron pairs in which each showed task-related activity at different task events (Table 2Go), more neuron pairs tended to show displaced peaks in CCGs (27%) than central peaks (17%). This tendency was more obvious in neuron pairs in which one neuron exhibited delay-period activity and the other exhibited response-period activity (displaced peak, 43%; central peak, 19%).


View this table:
[in this window]
[in a new window]
 
Table 1 Relationships between neuron pairs and types of interactions: both neurons exhibited the same task-related activity
 

View this table:
[in this window]
[in a new window]
 
Table 2 Relationships between neuron pairs and types of interactions: each of paired neurons exhibited task-related activity at different task events
 
In neuron pairs which had displaced peaks in CCGs, we observed two directions of information flow (feed-forward and feedback information flow) in functional relationships between a pair of neurons. In 56% (14/25) of neuron pairs, the direction of information flow estimated from the peak position in the CCG was same as the temporal sequence of task events (feed-forward information flow), e.g. a neuron having cue-period activity sent an excitatory output to a neuron having delay-period activity. However, in the remaining 44% of neuron pairs, the direction of information flow was opposite to the temporal sequence of task events (feedback information flow), e.g. a neuron having response-period activity sent an excitatory output to a neuron having cue-period activity.

These results indicate that, although many neuron pairs seem to receive inputs from the common afferent, many serial interactions between adjacent neurons were also observed, and that an information flow was present from neurons having cue-period activity to neurons having response-period activity through neurons having delay-period activity (feed-forward information flow) in the prefrontal cortex. However, a similar number of neuron pairs exhibited the opposite direction of information flow to the temporal sequence of task events, suggesting that this feedback information flow is also important for processing information.

Neuron Pairs Having Excitatory Central Peaks

Among 30 neuron pairs showing excitatory central peaks, both neurons of 11 pairs exhibited task-related activities. In these neuron pairs, both neurons had similar directional preferences when both neurons exhibited task-related activity at the same task event. Figure 8Go is an example of these neuron pairs. Neuron z03401a had directional cue-period activity [F(1,71) = 8.716, P < 0.005] with the best direction (D) at 235° and a tuning index (Td) of 43°, and had directional oculomotor activity [F(1,71) = 13.005, P < 0.001] with the best direction at 265° and a tuning index of 56° (Fig. 8CGo). Similarly, neuron z03401b had directional cue-period activity [F(1,71) = 5.357, P < 0.05] with the best direction at 226° and a tuning index of 41°, and also had directional oculomotor activity [F(1,71) = 7.300, P < 0.01] with the best direction at 170° and a tuning index of 156°. Both neurons exhibited the maximum responses in the same direction in cue-period activity, but somewhat different directional preferences in response-period activity.



View larger version (42K):
[in this window]
[in a new window]
 
Figure 8.  An example of a neuron pair (neuron z03401a and z03401b) having excitatory central peak in CCG. (A) Superimposed shapes of spike activities of each neuron. (B) Subtracted (neural) CCG. The bin width is 1 ms. (C, D) Polar plots showing directional preferences of task-related activities for each neuron. Dotted lines indicate discharge rates during the fixation period (baseline discharge rate) and solid lines indicate mean discharge rates across trial conditions for each-period activity. Neuron z03401a exhibited significant cue-period activity (best direction and tuning index were 235 and 43°, respectively) and significant response-period activity (best direction and tuning index were 265 and 56°, respectively). Neuron z03401b exhibited significant cue-period activity (best direction and tuning index were 226 and 41°, respectively) and significant response-period activity (best direction and tuning index were 170 and 156°, respectively). * indicates statistically significant activation (P < 0.05) from the baseline discharge rates.

 
To examine a similarity or difference in directional preferences for paired neurons which had excitatory central peaks, we constructed scattergrams for the neurons' best directions and tuning indices, and calculated correlation coefficients from the scattergrams. Among 11 neuron pairs, both neurons exhibited cue-period activity in six pairs, delay-period activity in three pairs and response-period activity in five pairs. Figure 9AGo shows scattergrams of the neurons' best directions and tuning indices for the same task-related activity obtained from these neuron pairs. A significant positive correlation coefficient was obtained from the scattergram of neuron's best directions (Fig. 9GoA-1, r = 0.861, P < 0.01). The mean difference of best directions was 41.4° for all neuron pairs. The mean difference of best directions was the lowest in cue-period activity (23.6°, n = 6) compared with other task-related activities (72.6° for delay-period activity, n = 3; 72.6° for response-period activity, n = 5), although these values were not statistically different [ANOVA, F(2,11) = 1.439, P > 0.1]. A weak positive correlation coefficient was obtained from the scattergram of tuning indices (Fig. 9GoA-2, r = 0.355, P > 0.1).



View larger version (36K):
[in this window]
[in a new window]
 
Figure 9.  Scattergrams of best directions and tuning indices for neuron pairs having excitatory central peaks in CCGs. (A) Scattergrams of best directions and tuning indices for the same task-related activity of paired neurons. A significant positive correlation coefficient was obtained for best directions (r = 0.861, P < 0.01), but no significant correlation coefficient was obtained for tuning indices (r = 0.355, P > 0.1). (B) Scattergrams of best directions and tuning indices for task-related activity at different task events of paired neurons. A significant positive correlation coefficient was obtained for best directions (r = 0.594, P < 0.05), but no significant correlation coefficient was obtained for tuning indices (r = 0.324, P > 0.1).

 
On the other hand, in three neuron pairs which had excitatory central peak in CCGs, one of paired neurons exhibited directional cue-period activity and the other exhibited directional delay-period activity. In eight pairs, one neuron exhibited cue-period activity and the other exhibited response-period activity. In three pairs, one neuron exhibited delay-period activity and the other exhibited response-period activity. Figure 9BGo shows scattergrams of best directions and tuning indices of task-related activities for these neuron pairs. Again, a significant positive correlation coefficient was obtained from the scattergram of the best directions for task-related activities at different task events (Fig. 9GoB-1, r = 0.594, P < 0.05). The mean difference of best directions was 74.1° for all neuron pairs. The mean difference of the best directions between cue-period activity and delay-period activity was 116.9° (n = 3), that between cue-period activity and response-period activity was 48.2° (n = 8), and that between delay-period activity and response-period activity was 100.4° (n = 3), although these differences were not significant [ANOVA, F(2,11) = 2.707, P > 0.1]. Weak positive correlation coefficient was again obtained from the scattergram of tuning indices (Fig. 9GoB-2, r = 0.324, P > 0.1).

The mean difference of the best directions was smaller (41.4°) when task-related activity was observed at the same task event in both of the neurons in a pair than when task-related activity was observed at different task events for each of the neuron of a pair (74.1°), although these values were not statistically significant (unpaired t-test, t = 1.762, df = 26, P > 0.05). Therefore, these results suggest that directional preference of task-related activity is similar in neuron pairs that had excitatory central peaks, and that the similarity of directional preferences is the highest in cue-period activity when both neurons exhibited task-related activity at the same task event.

Neuron Pairs Having Excitatory Displaced Peaks

In 20 out of 38 neuron pairs which had excitatory displaced peaks from time 0 in CCGs, both of paired neurons exhibited task-related activity at the same or different task events. Figure 10Go is an example of these neuron pairs. The CCG had a sharp peak at 2 ms (Fig. 10CGo), suggesting that the firing of neuron x07402d tended to be followed by the firing of neuron x07402e with a 2 ms interval. During ODR performances, neuron x07402d exhibited directional cue-period activity [F(1,78) = 9.322, P < 0.005] with the best direction at 25° and a tuning index of 88°, and also exhibited directional response-period activity [F(1,78) = 20.163, P < 0.001] with the best direction at 43° and a tuning index of 62°. Similarly, neuron x07402e exhibited directional cue-period activity [F(1,78) = 9.475, P < 0.005] with the best direction at 17° and a tuning index of 23°, and directional delay-period activity [F(1,78) = 19.967, P < 0.001] with the best direction at 17° and a tuning index of 24°. These results indicate that, in neuron pairs having excitatory displaced peaks, both neurons again tend to show similar directional preference in task-related activity when both neurons exhibited task-related activity at the same task event.



View larger version (42K):
[in this window]
[in a new window]
 
Figure 10.  An example of a neuron pair (neurons x07402d and x07402e) having excitatory displaced peak in CCG. (A) Superimposed shapes of spike activities of each neuron. (B) Subtracted (neural) CCG. The bin width is 1 ms. (C, D) Polar plots showing directional preferences of task-related activities for each neuron. Dotted lines indicate discharge rates during the fixation period (baseline discharge rate) and solid lines indicate mean discharge rates across trial conditions for each-period activity. Neuron x07402d exhibited significant cue-period activity (best direction and tuning index were 25 and 88°, respectively) and significant response-period activity (best direction and tuning index were 43 and 62°, respectively). Neuron x07402e exhibited significant cue-period activity (best direction and tuning index were 17 and 23°, respectively) and significant delay-period activity (best direction and tuning index were 17 and 24°, respectively. * indicates statistically significant activation (P < 0.05) from the baseline discharge rates.

 
Both neurons of 17 neuron pairs exhibited task-related activity at the same task event. A positive value of the correlation coefficient was obtained from the scattergram of the best directions of the same task-related activity for paired neurons, although this value was not statistically significant (Fig. 11GoA-1; r = 0.411, P > 0.1). The mean difference of the best directions of the same task-related activity between a pair of neurons was 74.2° for all neuron pairs, 57.4° for cue-period activity (n = 4) 77.3° for delay-period activity (n = 6) and 68.6° for response-period activity (n = 5). No significant correlation coefficient was obtained from the scattergram of tuning indices of the same task-related activities for paired neurons (Fig. 11GoA-2; P = –0.162, P > 0.5).



View larger version (42K):
[in this window]
[in a new window]
 
Figure 11.  Scattergrams of best directions and tuning indices for neuron pairs having excitatory displaced peaks in CCGs. (A) Scattergrams of best directions and tuning indices for the same task-related activity of paired neurons. A weak positive correlation coefficient was obtained for best directions (r = 0.411, P > 0.1), but no significant correlation coefficient was obtained for tuning indices (r = –0.162, P > 0.5). (B) Scattergrams of best directions and tuning indices for task-related activity at different task events of paired neurons having feed-forward information flows. A weak positive correlation coefficient was obtained for best directions (r = 0.484, P > 0.1), but no significant correlation coefficient was obtained for tuning indices (r = –0.571, P > 0.5). (C) Scattergrams of best directions and tuning indices for task-related activity at different task events of paired neurons having feedback information flows. A significant positive correlation coefficient was obtained for best directions (r = 0.683, P < 0.05), but no significant correlation coefficient was obtained for tuning indices (r = 0.023, P > 0.5).

 
In 11 neuron pairs, each of paired neurons exhibited task-related activity at different task event. The positions of the significant peaks in the CCGs suggested that these neuron pairs had the feed-forward information flow. A positive value of the correlation coefficient was obtained from the scattergram of the best directions of task-related activity at different task events for these neuron pairs, although the value was not statistically significant (Fig. 11GoB-1, r = 0.484, P > 0.1). The mean difference of the best directions of different task-related activities was 101.7° for all neuron pairs. The mean difference of the best directions between cue- and delay-period activity was 83.1° (n = 2), that between cue- and response-period activity was 101.6° (n = 4), and that between delay- and response-period activity was 109.4° (n = 5). No significant correlation coefficient was obtained from the scattergram of tuning indices between different task-related activities (Fig. 11GoB-2, P = –0.571, P > 0.5). However, task-related activity of a neuron that received excitatory inputs tended to have a wider tuning width than the activity of the paired neuron that sent excitatory outputs (Fig. 11GoB-2).

The positions of the significant peaks in CCGs suggested that another 11 neuron pairs had the feedback information flow. A significant positive correlation coefficient was obtained from the scattergram of the best directions in task-related activity at different task events for these neuron pairs (Fig. 11GoC-1, r = 0.683, P < 0.05). The mean difference of the best directions of different task-related activity was 85.6° for all neuron pairs. The mean difference of the best directions between cue- and delay-period activity was 113.4° (n = 3), that between cue- and response-period activity was 53.5° (n = 5), and that between delay- and response-period activity was 111.3° (n = 3). A significant value of the correlation coefficient was not obtained from the scattergram of tuning indices between different task-related activities (Fig. 11GoC-2, P = 0.023, P > 0.5).

These results indicate that the similarity of directional preferences of task-related activities was higher when both of paired neurons had task-related activity at the same task event than when each of paired neurons had task-related activity at different task event. In addition, the similarity of directional preferences of task-related activities was higher in neuron pairs having excitatory central peaks in CCGs than in neuron pairs having excitatory displaced peaks.

Neuron Pairs Having No Significant Correlation

Among a total of 166 neuron pairs, 84 (51%) pairs had no significant excitatory or inhibitory peaks in CCGs. Among 24 neuron pairs in which both neurons exhibited task-related activity, both neurons of 19 pairs had the same task-related activity. Using these neurons, we also examined whether a similarity or a difference was observed in directional preferences between the same task-related activities of paired neurons. A significant positive correlation coefficient was obtained from the scattergram of the best directions (Fig. 12GoA-1; r = 0.725, P < 0.01). The mean difference of the best directions between the same task-related activities of paired neurons was 45.8° for all neuron pairs, 32.3° for cue-period activity (n = 7), 85.2° for delay-period activity (n = 4) and 29.9° for response-period activity (n = 4). No significant correlation was obtained from the scattergram of tuning indices between the same task-related activities of paired neurons (Fig. 12GoA-2; P = 0.392, P > 0.1). On the other hand, in 27 neuron pairs, both neurons exhibited significant task-related activity, but each neuron exhibited task-related activity at a different task event. In these neuron pairs, no significant correlation coefficient was obtained from the scattergram of the best directions (Fig. 12GoB-1; r = 0.295, P > 0.1). The mean difference of the best directions between different task-related activities of paired neurons was 100.9° for all neuron pairs. The difference of the best directions between cue- and delay-period activity was 119.4° (n = 10), that between cue- and response-period activity was 93.3° (n = 9) and that between delay- and response-period activity was 86.4° (n = 8). Again, no correlation was obtained from the scattergram of tuning indices between different task-related activities of paired neurons (Fig. 12GoB-2; P = 0.044, P > 0.5).



View larger version (35K):
[in this window]
[in a new window]
 
Figure 12.  Scattergrams of best directions and tuning indices for neuron pairs having no significant peak in CCGs. (A) Scattergrams of best directions and tuning indices for the same task-related activity of paired neurons. A significant positive correlation coefficient was obtained for best directions (r = 0.725, P < 0.01), but no significant correlation coefficient was obtained for tuning indices (r = 0.392, P > 0.1). (B) Scattergrams of best directions and tuning indices for task-related activity at different task events of paired neurons. No significant correlation coefficients were obtained for either best directions (r = 0.295, P > 0.1) or tuning indices (r = 0.044, P > 0.5).

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
To examine functional interactions among prefrontal neurons that participate in spatial working memory processes, we examined CCGs of neuronal discharges between a pair of simultaneously isolated neurons while monkeys performed an ODR task. As a result, we found that functional interaction was observed among prefrontal neurons having various task-related activities, and that both feed-forward and feedback information flow between neurons in relation to the temporal sequence of task events were present in the prefrontal cortex. In addition, the mean difference of directional preference in task-related activity was the smallest in neuron pairs both of which exhibited cue- period activity, and became larger in neuron pairs both of which exhibited either delay-period activity or response-period activity. Therefore, this suggests that the interactions between a pair of neurons exhibiting different directional preferences increased as the progress of the temporal sequence of the task and that these interactions play an important role in manipulating or integrating spatial information.

Technical Considerations to Construct CCGs

In the present experiment, we used a single microelectrode to collect multiple-neuron activities and isolated multiple single-neuron activities from these activities by time- and amplitude-based window discriminators. Because of a technical limitation, this system could not detect either spike or even both spikes of two simultaneously isolating neurons when spikes of two neurons occurred simultaneously. However, this technical limitation always happens whenever one uses a single microelectrode to collect multiple-neuron activities, regardless of the methods to isolate multiple single-neuron activities. That is, this is the case when one uses amplitude-based window discriminators, as we did in the present experiment, or when one uses a computer-based waveform discriminating system (DeAngelis et al., 1999Go; Rao et al., 1999Go). Although this technical limitation was present in our experiment, we could detect spikes of two neurons even when one neuron's spike was followed by the other neuron's spike within a 1 ms interval, as explained in Materials and Methods. As was shown in Figure 4Go, the mean spike width was 0.72 ms and only 9% of isolated neurons had a spike width of >1.0 ms in our samples. Although the mean spike width was 0.72 ms in the present definition (see Materials and Methods), the large deflections of spike waves ended within 0.5 ms from the trigger point for most neurons. Our amplitude-based window discriminators generated a detection pulse of spike discharge whenever the wave of spike discharge went over the trigger level and passed between the upper and the lower levels of the window. Therefore, our system could detect spikes of two neurons when one neuron's spike was immediately followed by the other neuron's spike within a 1 ms interval. However, as we could expect from Figure 4Go, the central peak of the CCG would disappear if we sampled single-neuron activities at 0.5 ms intervals. In fact, this was the case, as was shown in Figure 3CGo. Since the computer sampled spike discharge every 1 ms in the present experiment, the program considered that both neurons fired simultaneously, if each detection pulse came to the computer from each of two window discriminators within a 1 ms interval. Therefore, it is possible to get central peaks at time 0 in CCGs. In the present experiment, all CCGs were constructed in 1 ms bin size and 36% of neuron pairs had excitatory central peaks at time 0 in CCGs, indicating that these pairs of neurons have a tendency to fire together within a 1 ms interval.

In the present experiment, half (50%) of prefrontal neuron pairs examined had significant excitatory or inhibitory peaks in CCGs. This ratio is a little higher than those for other cortical areas previously reported. The ratio of neuron pairs having significant peaks in CCGs was, for example, 26% in the adult cat visual cortex (Hata et al., 1991Go), 21% in the kitten visual cortex (Hata et al., 1993Go), and 30% in V1 and 47% in V2 of the monkey visual cortex (Tamura et al., 1996Go). In the primate auditory cortex, 35% of sampled pairs showed significant peaks (Ahissar et al., 1992Go), whereas in the primate motor cortex, 34–35% of neuron pairs showed significant peaks (Murphy et al., 1985aGo; Kwan et al., 1987Go). In the association cortices, Gochin et al. reported that 31% of neuron pairs showed significant peaks in CCGs in the temporal cortex (Gochin et al., 1991Go). In all these experiments, pairs of neuron activities were sampled by inserting multiple electrodes. It has been reported that the ratio of neuron pairs having significant peaks decreased in proportion to the tip distance between two electrodes (Krüger and Aiple, 1988Go; Gochin et al., 1991Go; Hata et al., 1991Go, 1993Go). In the present experiment, we isolated single-neuron activities from multiple-neuron activities collected by a single microelectrode. The pairs of isolated neurons must be located closely from the electrode tip. This could account for the higher ratio of neuron pairs that exhibited significant peaks in CCGs. In fact, Murphy et al. (1985a,b) reported that 51% of neuron pairs (42/82) in the primate motor cortex had significant peaks in CCGs when neuron activities were collected from one electrode, whereas only 26% (40/155) had significant peaks when neuron activities were collected from multiple electrodes. Similar results have been obtained in motor cortex neurons (Kwan et al., 1987Go).

Most of the CCGs obtained in the present experiment had a sharp peak and a short duration, even for the neuron pairs that had excitatory central peaks at time 0 in the CCGs. Usually the CCGs constructed from neuron activities collected by multiple electrodes have broad peaks and a long duration. Murphy et al. (1985a,b) compared CCGs constructed by pairs of neuronal activities collected from one electrode with those collected from two electrodes. They found that the peak duration of the CCG became short and clustered about time ±1 ms when a pair of neuronal activities were collected from one electrode. Our present results are consistent with these observations. In addition, the CCG with asharp peak and of short duration was not an artifact caused by the same neuron's activity being accidentally detected by two window discriminators. The reasons are as follows. (i) The trigger levels of two window discriminators were the same during the off-line sampling of spike discharges. However, the upper and the lower levels of the window were different across different neurons, and never overlapped (see Fig. 2Go). In addition, the upper and the lower levels of the window for each neuron were fixed during the off-line sampling. Therefore, it was impossible to detect the same neuron's spike discharge by two discriminators simultaneously. (ii) Task-related activity was not always observed at the same task event in both of the neuron pairs. (iii) Even if two neurons simultaneously isolated exhibited task-related activity at the same task event, both the best directions and tuning widths of task-related activity were never the same between a pair of neurons.

Prefrontal Neuron Pairs Having Excitatory Central Peaks in CCGs

In the present experiment, half (50%) of the prefrontal neuron pairs examined had significant excitatory or inhibitory peaks in CCGs. Among them, 36% of neuron pairs that showed significant peaks in CCGs exhibited central and symmetrical peaks at time 0. The shapes of the CCGs suggest that both neurons receive excitatory common afferents (Perkel et al. 1967aGo,bGo). The central peaks in the CCGs were observed not only in the neuron pairs in which both neurons exhibited task-related activity at the same task event but also in the neuron pairs in which each neuron exhibited task-related activity at a different task event. In addition, in neuron pairs in which both neurons exhibited task-related activity at the same task event, the directional preference was similar between the same task-related activity obtained from two neurons. This tendency was obvious when both neurons exhibited cue-period activity than when both neurons exhibited response-period activity. Although the central peaks in the CCGs were observed in neuron pairs in which both neurons exhibited response-period activity, the similarity of directional preference in response-period activity between a pair of neurons was much lower than that observed in cue-period activity. The columnar pattern in the cortico-cortical projection has been observed in parietal projections to the prefrontal cortex (Goldman-Rakic and Schwartz, 1982Go; Goldman-Rakic, 1984Go; Andersen et al., 1985Go; Cavada and Goldman-Rakic, 1989Go). The neurons having cue- period activity could receive visuospatial information from the parietal cortex, since visual responses observed in the prefrontal cortex diminished by the cooling of the posterior parietal cortex (Quintana et al., 1989Go). Therefore, a group of adjacent prefrontal neurons may receive similar visuospatial information from the posterior cortex. This notion was supported by the fact that pairs of neurons tended to show similar directional preference in cue-period activity, even in neuron pairs that had no significant peaks in CCGs (see Fig. 10Go).

Prefrontal Neuron Pairs Having Excitatory Displaced Peaks in CCGs

More neuron pairs (45% versus 36%) exhibited excitatory displaced peaks from time 0 in CCGs. The shapes of the CCGs indicate the presence of feed-forward or feedback information flow between a pair of neurons. Feed-forward and feedback information flows were observed in similar ratios in prefrontal neuron pairs. In addition, the mean difference of directional preference in task-related activity was smaller in neuron pairs both of which exhibited cue-period activity (54.7°), compared with neuron pairs both of which exhibited either delay-period activity (77.3°) or response-period activity (68.6°). These results suggest that the interaction between neurons having different directional preferences in task-related activity increases as the temporal sequence of the task progresses, and that such interaction plays a role in integrating or manipulating information.

The majority (71%) of neuron pairs both of which exhibited delay-period activity had significant excitatory peaks in CCGs and many of these (43%) peaks were displaced. The directional preferences of delay-period activity were not always similar between a pair of neurons. Feed-forward information flows between a pair of neurons each of which exhibited different directional preferences may also play a role in manipulating and integrating stored information. Pair-dependent or temporal order-dependent delay-period activity has been observed in the prefrontal cortex when subjects were required to retain a pair of spatial positions and the temporal order of their presentation (Barone and Joseph, 1989Go; Funahashi et al., 1993aGo, 1997Go). Most neurons having pair-dependent or temporal order-dependent delay-period activity exhibited directional preferences when the subjects were required to retain one spatial position on each trial (Funahashi et al., 1997Go). Therefore, pair-dependent or temporal order-dependent delay-period activity might be constructed by feed-forward interactions among neurons that retain information regarding different spatial positions.

Feedback information flows between a pair of neurons were also observed in the prefrontal cortex and could also play an important role in working memory processes. For example, erasing unnecessary information is an important process for working memory. Goldman-Rakic et al. suggested from neurophysiological observations that post-saccadic activity, which was observed in many prefrontal neurons, plays such a role as erasing delay-period activity after saccadic eye movements were performed during ODR performance (Goldman-Rakic et al., 1990Go). Although direct evidence supporting the interaction between neurons having delay-period activity and neurons having post-saccadic activity was not observed in the present experiment, this type of interaction must be present in the prefrontal cortex.

Neuronal Networks Related to Working Memory Processes

Figure 13Go is a diagram showing the possible neuronal circuitry in the prefrontal cortex which would be necessary to perform an ODR task. The present analysis suggests that visuospatial information flows from neurons having cue-period activity to neurons having oculomotor activity through neurons having delay-period activity. Since neuron pairs both of which had cue-period activity tended to show similarity in directional preferences in cue-period activity, a group of these neurons could be organized as a column. The neurons having delay-period activity could receive visuospatial information from the neurons having cue-period activity. Many of the neuron pairs both of which had delay-period activity had significant peaks in CCGs, but the directional preferences of task-related activity for these neurons were different. These results suggest that interactions between the neurons having delay-period activity with different directional preferences is important to construct more complex visuospatial characteristics of neuronal response, such as delay-period activity retaining a pair of spatial positions (Funahashi et al., 1997Go). In addition, it was reported that the majority (70%) of neurons displaying delay-period activity retained information regarding visual cue positions, whereas some (30%) retained information regarding forthcoming motor responses (Niki and Watanabe, 1976Go; Funahashi et al., 1993bGo). This suggests that the prefrontal cortex participates in transformation from visual information to motor information and that delay-period activity seems to play an important role in this transformation. The present results suggest that interactions among neurons having delay-period activity participate in such transformation. The neurons having delay-period activity could provide visuospatial information to the neurons having pre-saccadic activity to initiate an appropriate response. This notion could be supported by the observations that pre-saccadic activity was often accompanied with excitatory delay-period activity (Funahashi et al., 1989Go) and that directional preferences were similar between pre-saccadic activity and delay-period activity in the neurons which had both activities (Funahashi et al., 1991Go). The neurons having post-saccadic activity might receive feedback information from the oculomotor centers (Funahashi et al., 1991Go) and could play a role in the termination of delay-period activity (Goldman-Rakic et al., 1990Go). Since the prefrontal neuronal circuitry necessary to perform the ODR task is not yet fully understood, we need further physiological as well as anatomical experiments.



View larger version (26K):
[in this window]
[in a new window]
 
Figure 13.  A diagram of potential neuronal circuitry in the prefrontal cortex necessary to perform the oculomotor delayed-response task. Arrows indicate directions of information flow which were suggested by the present experiment.

 

    Notes
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
This work was supported by a Grant-in-Aid for Scientific Research (C) (no. 07680871) and a Grant-in-Aid for Scientific Research on Priority Area (no. 08279225), both from the Japanese Ministry of Education, Science, Sports and Culture, and by grants from The Naito Memorial Foundation and The Inamori Foundation. This work was performed as a part of the Cooperation Research Program of the Primate Research Institute, Kyoto University (1994–1996). The authors would like to thank Ms Soyoka Hara for her assistance in data analysis.

Address correspondence to S. Funahashi, PhD, Laboratory of Neurobiology, Faculty of Integrated Human Studies, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan. Email: h50400{at}sakura.kudpc.kyoto-u.ac.jp.


    References
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
Abeles M (1982) Quantification, smoothing, and confidence limits for single-units' histograms. J Neurosci Methods 5:317–325.[ISI][Medline]

Aertsen AM, Gerstein GL (1985) Evaluation of neuronal connectivity: sensitivity of cross-correlation. Brain Res 340:341–354.[ISI][Medline]

Ahissar M, Ahissar E, Bergman H, Vaadia E (1992) Encoding of sound-source location and movement: activity of single neurons and interactions between adjacent neurons in the monkey auditory cortex. J Neurophysiol 67:203–215.[Abstract/Free Full Text]

Andersen RA, Asanuma C, Cowan WM (1985) Callosal and prefrontal associational projecting cell populations in area 7a of the macaque monkey: a study using retrogradely transported fluorescent dyes. J Comp Neurol 232:443–455.[ISI][Medline]

Baddeley A (1986) Working memory. Oxford: Oxford University Press.

Barone P, Joseph J-P (1989) Prefrontal cortex and spatial sequencing in macaque monkey. Exp Brain Res 78:447–464.[ISI][Medline]

Bergman H, Feingold A, Nini A, Raz A, Slovin H, Abeles M, Vaadia E (1998) Physiological aspects of information processing in the basal ganglia of normal and parkinsonian primates. Trends Neurosci 21:32–38.[ISI][Medline]

Bruce CJ, Goldberg ME (1985) Primate frontal eye fields. I. Single neurons discharging before saccades. J Neurophysiol 53:603–635.[Abstract/Free Full Text]

Carlson S, Rämä P, Tanila H, Linnankoski I, Mansikka H (1997) Dissociation of mnemonic coding and other functional neuronal processing in the monkey prefrontal cortex. J Neurophysiol 77:761–774.[Abstract/Free Full Text]

Cavada C, Goldman-Rakic PS (1989) Posterior parietal cortex in rhesus monkey. II. Evidence for segregated corticocortical networks linking sensory and limbic areas with the frontal lobe. J Comp Neurol 287: 422–445.[ISI][Medline]

Cohen JD, Perlstein WM, Braver TS, Nystrom LE, Noll DC, Jonides J, Smith EE (1997) Temporal dynamics of brain activation during a working memory task. Nature 386:604–608.[ISI][Medline]

Courtney SM, Ungerleider LG, Keil K, Haxby JV (1997) Transient and sustained activity in a distributed neural system for human working memory. Nature 386:608–611.[ISI][Medline]

DeAngelis GC, Ghose GM, Ohzawa I, Freeman RD (1999) Functional micro-organization of primary visual cortex: receptive field analysis of nearby neurons. J Neurosci 19:4046–4064.[Abstract/Free Full Text]

di Pellegrino G, Wise SP (1993) Visuospatial versus visuomotor activity in the premotor and prefrontal cortex of a primate. J Neurosci 13: 1227–1243.[Abstract]

Espinosa IE, Gerstein GL (1988) Cortical auditory neuron interactions during presentation of 3-tone sequences: effective connectivity. Brain Res 450:39–50.[ISI][Medline]

Funahashi S (1996) Prefrontal cortex and working memory. In: Brain processes and memory (Ishikawa K, McGaugh JL, Sakata H, eds), pp. 397–410. Amsterdam: Elsevier.

Funahashi S, Kubota K (1994) Working memory and prefrontal cortex. Neurosci Res 21:1–11.[ISI][Medline]

Funahashi S, Bruce CJ, Goldman-Rakic PS (1989) Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. J Neurophysiol 61:331–349.[Abstract/Free Full Text]

Funahashi S, Bruce CJ, Goldman-Rakic PS (1990) Visuospatial coding in primate prefrontal neurons revealed by oculomotor paradigms. J Neurophysiol 63:814–831.[Abstract/Free Full Text]

Funahashi S, Bruce CJ, Goldman-Rakic PS (1991) Neuronal activity related to saccadic eye movements in monkey's dorsolateral prefrontal cortex. J Neurophysiol 65:1464–1483.[Abstract/Free Full Text]

Funahashi S, Chafee MV, Goldman-Rakic PS (1993a) Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task. Nature 365:753–756.[ISI][Medline]

Funahashi S, Inoue M, Kubota K (1993b) Delay-period activity in the primate prefrontal cortex during sequential reaching tasks with delay. Neurosci Res 18:171–175.[ISI][Medline]

Funahashi S, Hara S, Inoue M (1996) Neuronal networks related to working memory processes in the primate prefrontal cortex revealed by cross-correlation analysis. Soc Neurosci Abstr 22:1389.

Funahashi S, Inoue M, Kubota K (1997) Delay-period activity in the primate prefrontal cortex encoding multiple spatial positions and their order of presentation. Behav Brain Res 84:203–223.[ISI][Medline]

Fuster JM (1973) Unit activity in prefrontal cortex during delayed-response performance: neuronal correlates of transient memory. J Neurophysiol 36:61–78.[Free Full Text]

Fuster JM (1997) The prefrontal cortex: anatomy, physiology, and neuropsychology of the frontal lobe, 3rd edn. Philadelphia, PA: Lippincott-Raven.

Gerstein GL, Aertsen AM (1985) Representation of cooperative firing activity among simultaneously recorded neurons. J Neurophysiol 54: 1513–1528.[Abstract/Free Full Text]

Gochin PM, Miller EK, Gross CG, Gerstein GL (1991) Functional interactions among neurons in inferior temporal cortex of the awake macaque. Exp Brain Res 84:505–516.[ISI][Medline]

Goldman-Rakic PS (1984) Modular organization of prefrontal cortex. Trends Neurosci 7:419–429.[ISI]

Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. In: Handbook of physiology. The nervous system, vol. V (Plum F, ed.), pp. 373–417. Bethesda, MD: American Physiological Society.

Goldman-Rakic PS (1996) The prefrontal landscape: implications of functional architecture for understanding human mentation and the central executive. Phil Trans R Soc Lond B 351:1445–1453.[ISI][Medline]

Goldman-Rakic PS, Schwartz ML (1982) Interdigitation of contralateral and ipsilateral columnar projections to frontal association cortex in primates. Science 216:755–757.[ISI][Medline]

Goldman-Rakic PS, Funahashi S, Bruce CJ (1990) Neocortical memory circuit. Cold Spring Harbor Symp Quant Biol 55:1025–1038.[Medline]

Hara S, Funahashi S, Inoue M (1996) Neuronal networks in the primate prefrontal cortex revealed by cross-correlation analysis. Neurosci Res Suppl 20:S242.

Hata Y, Tsumoto T, Sato H, Tamura H (1991) Horizontal interactions between visual cortical neurons studied by cross-correlation analysis. J Physiol 441:593–614.[Abstract]

Hata Y, Tsumoto T, Sato H, Hagihara K, Tamura H (1993) Development of local horizontal interactions in cat visual cortex studied by cross-correlation analysis. J Neurophysiol 69:40–56.[Abstract/Free Full Text]

Jonides J, Smith EE, Koeppe RA, Awh E, Minoshima S, Mintun MA (1993) Spatial working memory in humans as revealed by PET. Nature 363: 623–625.[ISI][Medline]

Kojima S, Goldman-Rakic PS (1982) Delay-related activity of prefrontal neurons in rhesus monkeys performing delayed response. Brain Res 248:43–49.[ISI][Medline]

Kojima S, Goldman-Rakic PS (1984) Functional analysis of spatially discriminative neurons in prefrontal cortex of rhesus monkey. Brain Res 291:229–240.[ISI][Medline]

Krüger J, Aiple F (1988) Multimicroelectrode investigation of monkey striate cortex: spike train correlations in the infragranular layers. J Neurophysiol 60:798–828.[Abstract/Free Full Text]

Kwan HC, Murphy JT, Wong YC (1987) Interaction between neurons in precentral cortical zones controlling different joints. Brain Res 400: 259–269.[ISI][Medline]

McCarthy G, Puce A, Constable RT, Krystal JH, Gore JC, Goldman-Rakic PS (1996) Activation of human prefrontal cortex during spatial and nonspatial working memory tasks measured by functional MRI. Cereb Cortex 6:600–611.[Abstract]

Miller EK, Erickson CA, Desimone R (1996) Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J Neurosci 16:5154–5167.[Abstract/Free Full Text]

Murphy JT, Kwan HC, Wong YC (1985a) Cross correlation studies in primate motor cortex: synaptic interaction and shared input. Can J Neurol Sci 12:11–23.[ISI][Medline]

Murphy JT, Kwan HC, Wong YC (1985b) Cross correlation studies in primate motor cortex: event related correlation. Can J Neurol Sci 12:24–30.[ISI][Medline]

Niki H (1974) Differential activity of prefrontal units during right and left delayed response trials. Brain Res 70:346–349.[ISI][Medline]

Niki H, Watanabe M (1976) Prefrontal unit activity and delayed response: relation to cue location versus direction of response. Brain Res 105: 79–88.[ISI][Medline]

O'Scalaidhe SP, Wilson FAW, Goldman-Rakic PS (1997) Areal segregation of face-processing neurons in prefrontal cortex. Science 278: 1135–1138.[Abstract/Free Full Text]

Perkel DH, Gerstein GL, Moore GP (1967a) Neuronal spike trains and stochastic point processes. I. The single spike train. Biophys J 7: 391–418.[ISI][Medline]

Perkel DH, Gerstein GL, Moore GP (1967b) Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys J 7:419–440.[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, Vol. 9 (Boller F, Grafman J, eds), pp. 59–82. Amsterdam: Elsevier.

Petrides M (1996) Specialized systems for the processing of mnemonic information within the primate frontal cortex. Phil Trans R Soc Lond B 351:1455–1462.[ISI][Medline]

Petrides M, Alivisatos B, Evans AC, Meyer E (1993) Dissociation of human mid-dorsolateral from posterior dorsolateral frontal cortex in memory processing. Proc Natl Acad Sci USA 90:873–877.[Abstract]

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 474:211–221.[ISI][Medline]

Quintana J, Fuster JM, Yajeya J (1989) Effects of cooling parietal cortex on prefrontal units in delay tasks. Brain Res 503:100–110.[ISI][Medline]

Rao SC, Rainer G, Miller EK (1997) Integration of what and where in the primate prefrontal cortex. Science 276:821–824.[Abstract/Free Full Text]

Rao SG, Williams GV, Goldman-Rakic PS (1999) Isodirectional tuning of adjacent interneurons and pyramidal cells during working memory: evidence for microcolumnar organization in PFC. J Neurophysiol 81: 1903–1916.[Abstract/Free Full Text]

Sakagami M, Niki H (1994) Encoding of behavioral significance of visual stimuli by primate prefrontal neurons: relation to relevant task conditions. Exp Brain Res 97:423–436.[ISI][Medline]

Sakurai Y (1996) Hippocampal and neocortical cell assemblies encode memory processes for different types of stimuli in the rat. J Neurosci 16:2809–2819.[Abstract]

Tamura H, Sato H, Katsuyama N, Hata Y, Tsumoto T (1996) Less segregated processing of visual information in V2 than V1 of the monkey visual cortex. Eur J Neurosci 8:300–309.[ISI][Medline]

Tanaka K (1983) Cross-correlation analysis of geniculostriate neuronal relationships in cats. J Neurophysiol 49:1303–1318.[Abstract/Free Full Text]

Toyama K, Kimura M, Tanaka K (1981a) Cross-correlation analysis of interneuronal connectivity in cat visual cortex. J Neurophysiol 46:191–201.[Free Full Text]

Toyama K, Kimura M, Tanaka K (1981b) Organization of cat visual cortex as investigated by cross-correlation technique. J Neurophysiol 46: 202–214.[Free Full Text]

Watanabe M (1990) Prefrontal unit activity during associative learning in the monkey. Exp Brain Res 80:296–309.[ISI][Medline]

Wilson FAW, O'Scalaidhe SP, Goldman-Rakic PS (1993) Dissociation of object and spatial processing domains in primate prefrontal cortex. Science 260:1955–1958.[ISI][Medline]

Yajeya J, Quintana J, Fuster JM (1988) Prefrontal representation of stimulus attributes during delay tasks. II. The role of behavioral significance. Brain Res 474:222–230.[ISI][Medline]