RESEARCH ARTICLE

Tamami Fukushi Æ Toshiyuki Sawaguchi

Neural representation of response category and motor parametersin monkey prefrontal cortex

Received: 6 July 2004 / Accepted: 19 December 2004 / Published online: 13 May 2005� Springer-Verlag 2005

Abstract Conditional motor behavior, in which therelationship between stimuli and responses changesarbitrarily, is an important component of cognitivemotor function in primates. It is still unclear how cog-nitive processing for conditional motor control deter-mines movement parameters to directly specify motoroutput. To address this issue, we studied the neuronalrepresentation of motor variables relating to conditionalmotor control and also directly to the metrics of motoroutput in prefrontal cortex (PFC). Monkeys were re-quired to generate a force that fell within one of twocategories (‘‘small’’ and ‘‘large’’). We found that mostPFC neurons were activated as a function of force cat-egory, suggesting a role in conditional motor control. Atthe same time, we found that activity in many PFCneurons varied continuously with the force that waseventually produced, suggesting they participated inspecifying the metrics of movements as they were exe-cuted. The results suggest that the PFC neural popula-tion encodes both ‘‘what’’ motor response should beperformed and ‘‘how’’ the selected movement should berealized immediately after the visual instruction.

Keywords Prefrontal cortex Æ Conditional behavior ÆPrimates

Introduction

In non-human primates the cortical control of condi-tional motor behavior has been extensively studied inprefrontal cortex (PFC), which plays an important rolein executive function (see Miller 2000; Miller and Cohen2001; Tanji and Hoshi 2001, for reviews). Past studieshave suggested that two different types of motor infor-mation may be processed to execute conditional motortasks. The first is categorical information associated witha motor response instructed by a sensory stimulus, suchas the ‘‘motor significance’’ or ‘‘behavioral rule’’ asso-ciated with the cue. Neural activity coding categoricalmotor information of this type has been extensively re-ported in PFC and dorsal part of premotor cortex(PMd) (Asaad et al. 2000; Boussaoud and Wise 1993a, b;di Pellegrino and Wise 1991, 1993; Hoshi et al. 2000;Wallis et al. 2001; Wallis and Miller 2003; Watanabe1986a, b; White and Wise 1999). The second type ofmotor information necessary to perform conditionalmotor tasks is parametric information directly linked tothe motor output. Neural activity coding movementvelocity, acceleration, and muscle force has been fre-quently reported in primary motor cortex (MC) andPMd (Ashe and Georgopoulos 1994; Fu et al. 1993,1995; Hepp-Reymond et al. 1999; Kalaska and Cram-mond 1992; Schwartz 1992; Smith et al. 1975), and hasrecently been reported in PFC (Averbeck et al. 2003) aswell. Other studies have demonstrated dual representa-tion of abstract rules and selected motor responses byPFC neurons, though they did not focus on the quan-titative relationship between motor parameters and PFCcell activity (Hoshi et al. 2000; Wallis and Miller 2003).For example it is not clear from past studies whethersingle prefrontal neurons code both parametric andcategorical motor information in the context of condi-tional motor behavior. In this study, we used a delayedisometric force task to examine information processingin PFC neurons during conditional motor behavior. Thetask design enabled us to investigate the relationship

T. FukushiDepartment of Neuroscience,University of Minnesota,Brain Sciences Center (11B) VAMC,One Veterans Drive, Minneapolis,MN 55455, USA

T. Fukushi (&) Æ T. SawaguchiDepartment of Psychology,Hokkaido University,Sapporo 060-0810, JapanE-mail: fukus001@tc.umn.eduTel.: +1-612-4675588Fax: +1-612-7252283

Exp Brain Res (2005) 164: 472–483DOI 10.1007/s00221-005-2268-z

between neuronal activity and both a categorical motorvariable (a ‘‘small’’ versus a ‘‘large’’ target force) and acontinuous motor variable, the actual force exerted bythe subject after a delay.

Materials and methods

Subjects and behavioral procedures

Two male rhesus monkeys (Macaca mulatta, 5.5 and7.0 kg, ‘‘PO’’ and ‘‘TA’’) were used for the presentstudy. Throughout the experiment, the monkeys werecared for in accordance with the Principles of LaboratoryAnimal Care (National Institute of Health) and theGuidelines for Care and Use of Laboratory Primates(Primate Research Institute, Kyoto University, Japan).

The subjects were trained to perform a visuallyguided isometric force-exertion task during which thesubject exerted two different ranges of isometric force(force categories) based on different visual cues. Theapparatus ensured that wrist flexion force would beisometric, and a force sensor (FSR-S100, Denki-ke-isoku, Tokyo, Japan) attached to a hinged, verticalplate measured the magnitude of force (Fig. 1A).Throughout the trial, the subject was required to fixateon a fixation spot (green square, 1� by 1�) presented atthe center of a computer display within a circularwindow (5� diameter). Eye movements were monitoredand controlled by an infrared eye-camera system (R-

21C-A, RMS), and the trial was cancelled immediatelyif eye movements occurred and the monkey broke fix-ation. The rigid control of eye movements meant thatoculomotor factors were not likely to account forchanges in neural activity. Figure 1B shows the se-quence of events in the visually guided isometric force-exertion task. The task began when the monkey fixatedthe fixation spot. After a 1-s fixation period duringwhich the subject maintained wrist force at a restinglevel (0–0.2 N), a visual cue (4� by 4�) was presentedfor 1 s (cue-period) at the center of the display. Thevisual cue instructed one of two relatively broad non-overlapping force ranges (‘‘small’’, 0.5–2.5 N for TAand 0.3–1.3 N for PO; ‘‘large’’, 2.8–5.8 N for TA and1.5–3.5 N for PO). After 3-s delay period, the fixationspot changed from green to red to indicate a ‘‘Go’’signal, and the monkey generated an isometric forcepulse within 1 s. If the peak of the force pulse waswithin the target range, the subject was rewarded witha drop of water (�0.2 mL per trial). There was a ‘‘gap’’between the ‘‘small’’ and ‘‘large’’ force categories sothat the subjects were required to discriminate the peakforce to get reward. To dissociate the effects of thevisual properties of the cue stimulus and the categoricalassociation with force on neural activity, three differentvisual stimuli were used to instruct each force categoryas follows: a yellow cross, purple triangle, or redpolygon cued the ‘‘small’’ range; a blue circle, sky-bluesquare, or white star cued the ‘‘large’’ range. In onebehavioral block (10–20 trials in all, 5–10 trials per

Fig. 1 Schematicrepresentations of, A, theexperimental apparatus and, B,the visual stimuli displayed insubsequent epochs of the task

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force category) one of three specific pairs of cue stimuliappeared. These were either the combination of yellowcross (‘‘small’’) and blue circle (‘‘large’’), purple triangleand sky-blue square, or red polygon and white star.The ‘‘small’’ and ‘‘large’’ cues were presented randomlyduring the block.

Surgical procedures

After the training period, surgery for electrophysiologi-cal recording was performed under aseptic conditions.Monkeys were anesthetized with sodium pentobarbital(�25 mg kg�1 iv), and a stainless steel head-holdingdevice (Nakazawa Works) was implanted on the skullwith dental acrylic cement for head fixation duringrecording sessions. An oval opening was made in theskull to expose the dura over the frontal cortex contra-lateral (right) to the experimental arm (left), and an ovalstainless steel cylinder (20·40 mm ID, NakazawaWorks) was positioned over the opening and fixed inplace with dental acrylic cement. To prevent bacterialinfection, prophylactic antibiotics were injected intra-muscularly on the day of the surgery and daily for1 week after surgery.

Neuronal recording procedure

After a 10-day recovery period, recording sessions be-gan. In each daily experimental session, the monkeysperformed up to 400 trials for each force category with acorrect response level of �80–90%. The activity of singleneurons from the PFC contralateral to the experimentalhand was recorded extracellularly using glass-insulatedelgiloy microelectrodes with an impedance of 1–4 MX at1 kHz. The microelectrodes were advanced through theintact dura by a hydraulic microdrive (MO-950, Na-rishige) that was attached to the recording cylinder.Neuronal activity was recorded for more than 10 trialsfor each stimulus. Spikes of each neuron were isolatedby a window discriminator (DIS-1, BAK Electronics),and assigned a time of occurrence with a 1-ms timeresolution. Isometric force measured by the force sensorwas sampled and digitized by an A/D converter (AB 98-05A/4, ADTEK) with a 1-ms time resolution. Eventsignals were also sampled with the same time resolution.The spikes of each neuron, isometric force, and eventsignals were processed and displayed by a personalcomputer (PC-9821, NEC).

At the end of daily recording sessions, conventionalintracortical microstimulation (ICMS, 11 cathodal pul-ses of 0.2 ms in duration at 333 Hz, see Asanuma 1975)was applied through the tip of the electrode to examinethe cortical representation of the body and ocularmovement at the recorded site. In addition, the receptivefield of each neuron was examined by palpation of theskin, passive joint movement, and taps on the musclebellies.

Electromyographic recording

In several experimental sessions (12 sessions for monkeyPO, and 15 for monkey TA), which were separately donefrom the single cell recordings, electromyographic(EMG) recording using fine enamel-coated copper wireelectrodes (60 lm in diameter) were carried out. Theelectrodes were implanted transcutaneously at thebeginning of the daily recording session with localanesthesia. In each daily recording session, the EMGactivities of four muscles were recorded simultaneouslyusing a four-channel AC differential amplifier (NoiseBusters, Sapporo, Japan), and EMG potentials thatwere more than �120% of the amplitude of the baselinenoise level were transformed into single pulses andprocessed, as with neuronal activity. We recorded EMGof the following muscles ipsilateral to the experimentalhand: the flexor digitorum superficialis (FDS), extensordigitorum communis (EDC), biceps, triceps, deltoid,pectoralis major, trapezius, latissimus dorsi, and quad-riceps. In addition, EMG of the contralateral FDS,EDC, and quadriceps were recorded to clarify that thesubject did not use the muscles of contralateral side toperform any conditions of the task. The combination ofrecorded muscles was altered in each recording sessionto confirm the stability and consistency of muscleactivity throughout the recording sessions.

Data analysis

For off-line analysis of neural and behavioral data, allstatistical procedures were done by the S-plus 2000software package (MathSoft). Neurons were eliminatedfrom the current analysis if the response force differedsignificantly across different stimuli within the sameforce category, (t-test, P<0.05). A neuron was identi-fied as exhibiting task-related activity if the neuraldischarge rate in any one of several task epochs wassignificantly different from the discharge rate during thefixation period at the beginning of the trial (paired t-test, P<0.05). The task epochs tested included the cueperiod, each 1-s period during the delay interval (D1,D2, and D3 periods, respectively), the reaction time(RT, between the ‘‘Go’’ signal and force onset), and themovement time (MT, between force onset and the peakof the force curve). We calculated the mean and stan-dard deviation (SD) of the force value across the fixa-tion, cue, and delay periods, and defined the time offorce onset as the point following the go signal at whichforce increased by more than two SD from the meanand remained above this level for at least five successivebins with 10-ms width. To examine neuronalrepresentation of the categorical information, one-wayanalysis of covariance (ANCOVA) was used to test theeffect of categorical association on neural activity dur-ing each of cue, D1, D2, D3, RT, and MT periods,with the neuronal discharge during the fixation periodincluded as a covariate (P<0.05). To examine the

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neural representation of parametric force information,multiple linear regression analysis was used to test thequantitative relationships between neuronal dischargesand both the peak magnitude and rate of changeof isometric force. We calculated the proportion ofvariance (R2) in firing rate accounted for by thefollowing model:

f ðdÞ ¼ b0 þ b1 � PkFþ b2 � DF =Dt þ e

where f(d) is an average discharge rate of a given neuronduring each behavioral epoch, b0 to b2 are regressioncoefficients, � is an error term, PkF is the peak magni-tude of the isometric force pulse (N), and DF/Dt is themean velocity of the force curve before the peak (N s�1)(Hepp-Reymond and Diener 1983; Smith et al. 1975).We also calculated multiple regression equations withstandardized regression coefficients to obtain partialcorrelation coefficients for each force variable (Ashe and

Georgopoulos 1994; Fu et al. 1993, 1995). The multipleregression analysis was applied to data pooled acrosstrials with different stimuli cueing the same force cate-gory in each behavioral period.

Histology

At the end of the physiological recording sessions,electrolytic lesions (25 lA in DC, 10 s) were made tomark selected recording sites on the cortical surface.Each subject was deeply anesthetized with an overdoseof pentobarbital sodium (Nembutal, 50 mg kg�1 intra-peritoneal) and perfused with 0.9% physiological saline(�200 mL) followed by 10% formalin (�500 mL). Thebrains were then removed and photographed to deter-mine the surface distribution of neurons examined in thepresent study

Results

Behavioral results

Monkeys significantly discriminated two different rangesof target force during 184 recording sessions for TA and56 sessions for PO. Figure 2A shows the time course of

Fig. 2 A Time course of isometric force exerted by the monkeys TA(left) and PO (right). Superimposed force curves were aligned at cueonset for each force category in each monkey. The cue stimulidrawn at the left upper corners indicate the visual cue presentedduring the trials. B Superimposed traces of eye-movements formonkeys TA (left) and PO (right) during the same session asFig. 2A. The traces for x and y eye position were aligned at the cueonset

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the isometric force exerted by both monkeys duringblocks of trials that lasted for �10 min in a typicalrecording session. In general, the force was stable duringthe fixation, cue, and delay periods at<0.1 N, and thelevel of static force during these periods was similarbetween the trials with different target force categories.The force increased abruptly after the onset of the gosignal, reached a peak at �400 ms and returned to thecontrol level within 1 s. As indicated in Table 1, thevalue of the peak force for the ‘‘large’’ condition wassignificantly greater than that for the ‘‘small’’ conditionfor each pair. Furthermore, the mean force values pro-duced in response to each stimulus cueing the same forcecondition did not differ significantly. Across therecording sessions, the mean force produced on largeand small force category trials differed significantly inboth monkeys (Table 1). Figure 2B shows examples ofthe eye movements of each monkey. We did not observeeither an obvious difference between eye movementsamong conditions or consistent relationships betweeneye movements and task events throughout the trials.Therefore, the possible effects of gaze and eye move-ments on the modulation of neuronal activity during thecue period, or any other task periods, are not consideredfurther.

Response of single cells representing force categoriesand parameters

The activity of single neurons was recorded from PFC oftwo monkeys. The PFC was defined as a cortical regionsurrounding the principal sulcus. Within this corticalregion, we did not evoke any eye-movements with ICMS

Table 1 Mean±SD (N) of peak force and statistical results asso-ciated with the sessions described in Fig. 2A and across allrecording sessions

Large Small

TAPair 1 3.8±0.6 (n=27) 1.7±0.7*** (n=21)Pair 2 3.9±0.7 (n=21) 1.5±0.6*** (n=19)Statistics between pairs NS NSAcross 184 sessions 4.1±0.9 1.2±0.6***POPair 1 2.3±0.6 (n=19) 0.5±0.2*** (n=17)Pair 2 2.3±0.6 (n=21) 0.6±0.2*** (n=19)Statistics between pairs NS NSAcross 56 sessions 2.3±0.8 0.7±0.2***

The numbers in parentheses are the number of trials for eachstimulus***P<0.001 (t-test)

Fig. 3 Example of ‘‘Category-specific’’ activity of a PFCneuron. A Rasters andhistograms for each forcecategory were aligned at the cueonset. Red lines on each rowindicate the timing of forceonset, peak force, and the timeat which force decreased to15% of the peak, respectively.Black bars indicate the range oftiming of force onset and peakforce across trials. D1, D2, andD3 indicate each epoch of delayperiods. B Neuronal dischargerate for each trial across forcecategories during the cue, eachepoch of the delay (D1– D3),RT, and MT periods. C Scatterplot of neuronal dischargeagainst the peak magnitude ofisometric force (PkF) during thecue period

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in either monkey, so the frontal eye field (FEF), which islocated caudal to the PFC and rostral to the arcuatesulcus, was not included. Because the number of re-corded sites and neurons was much smaller for theventral part of the principle sulcus than for the dorsalpart, we combined the neuronal data obtained in thesetwo subdivisions of the PFC.

We found a total of 456 task-related neurons in thetwo monkeys (n=355 for TA, and n=101 for PO,respectively). All neurons were recorded during trialswith two or more stimulus pairs. We examined the ef-fects on neural firing rate of the instructed force category(‘‘small’’ and ‘‘large’’) and parametric variation in theforce pulse ultimately produced (PkF and DF/Dt). Morethan half of the task-related neurons (n=256/456, 56%)were significantly influenced by one or both of thesevariables during at least one task period. Neuronal re-sponses were classified into two types, as follows.‘‘Category-specific’’ cells were those in which firing raterelated significantly to the categorical force instructionby ANCOVA, but did not relate significantly to con-tinuous force parameters as tested by the multiple-regression analysis. In the second type of neuron weencountered, firing rate related significantly to both

categorical and continuous force parameters in both theANCOVA and regression analyses (‘‘Category+Forceparameters’’ neurons).

An example of ‘‘Category-specific’’ activity is shownin Figure 3A. In this neuron, discharge rate increasedduring the cue and RT periods for both force categories.However the activity was greater when the stimulus cuedthe ‘‘large’’ force category compared with that of the‘‘small’’ condition (Fig. 3B). In the ANCOVA, thisneuron showed a significant main effect of force categoryduring the cue and RT periods (F=13.2, df=1, 84,P<0.001 for cue period; F=10.5, df=1, 84, P<0.01 forRT period), whereas no significant effect was foundduring any part of the delay period. Figure 3C showstrial-by-trial variability of the neuronal discharge andthe peak force during the cue period. This neuron clearlyshowed a larger discharge rate on those trials with the‘‘large’’ force category, but the discharge did not haveany significant correlation with the trial-to-trial vari-ability in the force the monkey produced within thiscategory at the end of the trial as assessed by multipleregression analysis.

An example of ‘‘Category+Force parameters’’activity is shown in Fig. 4A. The activity of this neuron

Fig. 4 Example of‘‘Category+Force parameters’’activity of a PFC neuron.Format and abbreviations arethe same as in Fig. 3. A Rastersand histograms for each forcecategory were aligned at the cueonset. B Neuronal dischargerate for each trial across forcecategories during the cue, eachepoch of delay (D1– D3), RT,and MT periods. C Scatter plotof neuronal discharge againstthe peak magnitude of isometricforce (PkF) during the D2period

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showed a significant effect of force category during thedelay periods and a linear dependence on force magni-tude and mean velocity of the force curve in theregression analysis for the ‘‘large’’ force category. Incontrast, neither category nor force parameters signifi-

cantly influenced activity in the RT or MT periods(Fig. 4B). Figure 4C shows the relationship betweendischarge rate and peak force during the D2 period ontrials with ‘‘small’’ and ‘‘large’’ force categories. A sig-nificant effect of force category was observed (ANCO-VA, F=6.5, df=1, 90, P<0.05), and the multipleregression equations expressed with standardized re-gression coefficients was as follows:

fsmallðdÞ ¼ �0:02 � PkF� 0:23 � DF =Dt

flargeðdÞ ¼ 0:46 � PkFþ 0:14 � DF =Dt

The R2 of each multiple regression equation was 0.06(NS) and 0.32 (P<0.001) for ‘‘small’’ and ‘‘large’’ ca-tegories, respectively.

Quantitative and qualitative properties of classifiedneurons

Figure 5a shows the distribution of significant neuronsassociated with categorical association and actual forceparameters for each cortical area. In general, the ‘‘Cat-egory+Force parameters’’ neurons were more frequentthan ‘‘Category-specific’’ neurons. There was a differ-ence in the distribution of significant neurons dependingon the task period. The total number of significantneurons was largest during the cue period (n=165/256,64%) and decreased abruptly during the followingbehavioral epochs (v2=28.2, df=5, P<0.001). This wastrue for both ‘‘Category-specific’’ and ‘‘Categor-y+Force parameters’’ types. We further examinedwhether single neurons that encoded force category andforce parameters during the early phase of the task trialmaintained this information throughout the remainderof the trial. Tables 2 and 3 show the time of onset andthe duration of categorical and parametric effects onneural activity throughout the trial. Most neurons didnot maintain information regarding the force categoryor force parameters throughout the entire trial so thatthis information seemed to be processed by differentneuronal populations across task periods. As shown inTable 2, none of the ‘‘Category-specific’’ neuronsexhibited this effect continuously from the cue throughMT periods. Similarly, only 3% of ‘‘Category+Forceparameters’’ neurons (n=5/126, 4%) maintained theeffect continuously from the cue to the MT periods.

For the ‘‘Category+Force parameters’’ type of neu-rons, we examined whether single neurons encoded forceparameters during trials in both force categories or onlyfor a specific category. Most of the neurons showed asignificant regression coefficient in one force categorybut not the other (Fig. 5B). The distribution of casesselective for the ‘‘small’’ and ‘‘large’’ categories wassimilar across task periods. In addition, we examined thedistributions of R2 values associated with the multipleregression models for each neuron across cortical re-gions for each task period. An R2 ranged between 0.1and 0.5 except during the RT period when it increased to

Fig. 5 A Distribution of neurons showing significant effect(s) offorce category and parametric force output. The numbers inparentheses on the chart indicate the number of active neurons foreach task period. B Distribution of neurons selectively showing asignificant correlation with force output during trials of the ‘‘small’’force category, the ‘‘large’’ force category, or both, for ‘‘Cate-gory+Force parameters’’ neurons. The numbers in parenthesesindicate the total number of significant cells for each task period.C Average values of R2 of ‘‘Category+Force parameters’’ neuronsacross task periods

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0.9 in some neurons. There were significant differencesbetween average values of R2 depending on the taskperiod (F=4.8, df=5, P<0.001 in one-way analysis ofvariance (ANOVA) without replication). The averagevalues of R2 ranged from 0.18 to 0.26 across the cue and

delay periods, and increased to more than 0.3 during theRT period (Fig. 5C).

Finally, we examined the distribution on the corticalsurface of each type of neuron for the different taskperiods (Fig. 6). In both monkeys, larger numbers ofrecording sites contained neurons significant for cate-gorical and parametric association during the cue periodthan during the D2 and RT periods.

EMG activity

Examples of EMG activities for monkey TA are shownin Fig. 7. Activities of the FDS, EDU, and pectoralis

Fig. 6 Cortical distribution of ‘‘Category-specific’’ and ‘‘Cate-gory+Force parameters’’ neurons during the cue, D2 and RTperiods for Monkeys TA (top) and PO (bottom). Circles indicate therecording sites at which ‘‘Category+Force parameters’’ neuronswere solely observed; triangles indicate recording sites at which‘‘Category-specific’’ neurons were solely observed; crosses indicaterecording sites at which both types of neuron were observed

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major of the performing hand, and of the FDS andEDU of the non-performing hand were aligned to theonset of the cue and go signals. The activities of thesemuscles were stable during the fixation, cue, and delayperiods. The ipsilateral FDS and EDU were activatedafter the appearance of the go signal, and their activitiesdiffered during the exertion of force under the ‘‘small’’and ‘‘large’’ force categories. These data indicate thatco-contraction of the agonist and antagonist musclesoccurred during the exertion of isometric force, and theiractivities reflected different force categories but not dif-ferent visual stimuli. The EMG of other muscles did notshow obvious changes in activity related to the cate-gorical association or visual stimulus. Thus, the subjectused the same forearm/hand muscles to exert isometric

force during the trials with different force categories, i.e.,‘‘small’’ and ‘‘large’’. We further examined the quanti-tative relationship between muscle activity and isometricforce. We examined a total of 20 cases (some muscleswere recorded more than once) from the arm and handmuscles of the performing limb in both monkeys. Inmonkey TA we examined the FDS, EDC, biceps, triceps,deltoid, pectoralis major, and trapezius. In monkey POwe examined the FDS (three cases), EDC (1), biceps (2),triceps (2), deltoid (2), pectoralis major (1), trapezius (1),and latissimus dorsi (1). In each case, we applied thesame statistical analysis to the EMG data as we appliedto the neural data. None of the muscles showed a sig-nificant effect of response category or force parametersduring the cue and delay periods. Only three cases

Fig. 7 Examples of EMGactivity of Monkey TA. Rastersand histograms of muscleactivity for each cue stimuluscondition are aligned at the cueonset. The abbreviations of themuscles are explained in the text

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(triceps, latissimus dorsi, and FDS) were classified as‘‘Category-specific’’ and two (FDS and Deltoid) as‘‘Category+Force parameters’’ during the RT period.In addition, during the MT period, seven cases [FDS,EDC, biceps, triceps (2), trapezius, and deltoid] had asignificant effect of ‘‘Category-specific’’ and seven (FDS,EDC, biceps, triceps, trapezius, latissimus dorsi, anddeltoid) were significant for ‘‘Category+Force param-eters’’.

Discussion

The findings of this study can be summarized as follows.First, the neurons in PFC showed significant effects ofboth the categorical force instruction and the parametersof force output. Second, force category and forceparameters were not represented by the same neuronsthroughout the task epochs, but rather, were representedsequentially across serially active neural populations.The results indicate that PFC processes two differentfactors, one is associated with the visual instruction of acategorical association, and the other is associated withthe actual force parameters that the monkey wouldeventually produce after a delay. In other words, theseneurons process two kinds of information associatedwith different aspects of a conditional motor behavior,one is ‘‘what’’ motor response should be performed, and

the other is ‘‘how’’ the selected movement should berealized.

Functional roles of neurons representing categoricalassociation and force parameters

This study found two types of neural activity repre-senting categorical association and force parameters thathad different profiles across time. A relatively largenumber of neurons in PFC were categorized as ‘‘Cate-gory-specific’’ cells during the cue period. These cellsmay detect the behavioral significance of an upcomingaction, a property that has been extensively studied inPFC (Miller et al. 1996; Watanabe 1986a). ‘‘Cate-gory+Force parameters’’ cells are probably involved inthe selection of the forthcoming response based on theassociation of cue stimuli and an instructed movement.A large proportion of PFC neurons were grouped asbelonging to this cell type. Taken together, the distri-bution of these two cell types suggests that PFC mayplay a prominent role in linking behavioral context andcontinuous motor variables, the ‘‘small’’ and ‘‘large’’categorical association and force parameters in this case.Similar neural response properties, which reflect bothabstract rules associated with hold/release motor re-sponse, have been previously reported (Wallis andMiller 2003). In addition, an integrative process linkingdifferent categories of information represented by PFC

Table 2 Percent of neurons showing a significant effect of force category during each task period that also exhibited this effect insubsequent task periods for ‘‘Category-specific’’ neurons

Maintained to Cue D1 D2 D3 RT MT

Significant from Cue 39 (100%) 8 (21%) 4 (10%) 3 (8%) 0 (0%) 0 (0%)D1 5 (100%) 2 (40%) 1 (20%) 0 (0%) 0 (0%)D2 8 (100%) 7 (37%) 3 (16%) 1 (5%)D3 19 (100%) 7 (37%) 3 (16%)RT 19 (100%) 7 (37%)MT 9 (100%)

Total numberof significant cells

39 13 14 19 8 20

The figure in parentheses is the percentage of neurons maintaining the significant effect compared with the number of significant neuronsfor first appearance. D1, D2, and D3 indicate each epoch of delay period

Table 3 Percent of neurons showing a significant effect of both category and force parameters during each task period that also exhibitedthese effects in subsequent task periods for ‘‘Category+Force parameters’’ neurons

Maintained to Cue D1 D2 D3 RT MT

Significant from Cue 126 (100%) 54 (43%) 34 (27%) 22 (17%) 12 (9%) 5 (4%)D1 19 (100%) 7 (37%) 3 (16%) 1 (5%) 0 (0%)D2 34 (100%) 9 (26%) 5 (15%) 0 (0%)D3 46 (100%) 18 (40%) 7 (15%)RT 64 (100%) 20 (31%)MT 40 (100%)

Total number ofsignificant cells

126 73 74 79 100 73

The figure in parentheses is the percentage of neurons maintaining the significant effect compared to the number of significant neurons forfirst appearance. D1, D2, and D3 indicate each epoch of delay period

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neurons has also been demonstrated by Hoshi and Tanji(2004). In the motor and premotor cortical areas,‘‘context-dependent’’ parametric coding of isometricforce during movement execution has been reported(Hepp-Reymond et al. 1999). One can conceptualize thatthe linking of behavioral context and parametric codingof the forthcoming motor response is first represented byPFC neurons and then further processed by premotorand motor cortical neurons to execute the appropriatemotor response, possibly using cortico-cortical connec-tions between PFC and premotor cortex (Barbas andPandya 1987; Ghosh and Gattera 1995; Lu et al. 1994;Luppino et al. 2003; Takada et al. 2004; Wang et al.2002). Indeed, the fact that different subpopulations ofPFC neurons were associated with either category-spe-cific or motor parameter-specific information acrossdifferent task periods suggests that conditional motorbehavior is not represented by a single, homogeneousneural population, but different populations of neuronsinteract with one another to achieve selected motorgoals.

Neural representation of conditional movementduring the delay period

An interesting result in this study concerns the inter-pretation of delay-related activity. The delay-relatedactivity of PFC neurons has been examined extensivelyand it has been suggested that it represents the storage ofinformation associated with the forthcoming motor act(see Fuster 1997, 2000a, b; Funahashi 2001; Funahashiand Kubota 1994; Goldman-Rakic 1995 for reviews). Inthe current study, the number of neurons showing asignificant association to response category or forceparameters during the delay period was almost half thatduring the cue-related period. In addition, significanteffects of force category and force parameters were notmaintained by the same single unit across the wholedelay epoch in most cases (Tables 2, 3). It is possiblethat PFC neurons were not necessarily required forstorage of either response category or motor parametersunder the fixed-delay period in the current study, duringwhich the monkey was over-trained and easily predictedthe response cue to prepare the movement. It is alsoconceivable that the delay-related activity observed inthe current study might encode other factors, not beingspecifically monitored during the task, such as activesuppression of forthcoming motor response, attention,or the prediction of the forthcoming ‘‘Go’’ signal(Constantinidis et al. 2002; see also Funahashi 2001 for areview). In addition, one might consider why we failed tofind a strong association between the delay activity andmovement parameters. Our behavioral task, which justfocused on the one-dimensional isometric force exertedby wrist flexion, did not fully explore directional space,thereby not engaging PFC neurons which have shownstrong directional preferences and respond selectivelyduring the delay period in a variety of tasks such as

multi-directional reaching task, wrist flexion-extensiontask, and a GO/NO-GO task (Funahashi et al. 1997;Hoshi et al. 2000; Hoshi and Tanji 2004; Wallis andMiller 2003; Watanabe 1986b). The current data dem-onstrate, however, that prefrontal neurons participate inmotor control by coding force parameters in addition tothe spatial parameters that define movement. In thepresent context, PFC neurons were found to code force,both as a categorical variable related to an instruction inour conditional task, and as a continuous variablereflecting the actual force generated at the end of thetrial.

Neuronal representation of movement parametersin prefrontal cortex

Multiple regression analysis has been used extensively toexamine the relationships between neuronal activity andvarious parameters of limb movement in the primarymotor and/or premotor cortical areas during goal-di-rected motor tasks (Ashe and Georgopoulos 1994; Fuet al. 1993, 1995) and in PFC during a shape-drawingtask (Averbeck et al. 2003). In addition, the parametersused in the current analysis were also used in previousstudies examining neuronal correlation with force mag-nitude and the rate of isometric force production (Hepp-Reymond and Diener 1983; Smith et al. 1975). Our re-sults show that most cells in PFC have significant rela-tionships with the magnitude and rate of change ofisometric force. This is consistent with past findingsduring isometric force exertion in MC (including cor-tico-motoneuronal cells), premotor and somatosensorycortex (Hepp-Reymond and Diener 1983; Hepp-Rey-mond et al. 1999; Maier et al. 1993; Smith et al. 1975;Wannier et al. 1991). We extend the previous work onparametric muscle force control to more rostral parts ofthe frontal cortex.

One remarkable finding in this study is that neuronsin PFC, in sites where ICMS failed to evoke limbmovements, showed significant correlations with theactual parameter(s) of isometric force. The encoding ofmotor variables by PFC neurons has been reportedduring an oculomotor task, which demonstrated thatPFC neuronal activity encodes the variability of bothsensory input and motor output (Constantinidis et al.2001; Kim and Shadlen 1999). The involvement of PFCin limb motor control has been already reported (diPellegrino and Wise 1991, 1993; Kubota and Funahashi1982), and recent study has documented the quantitativerepresentation of kinematic motor parameters by PFCneural activity during a shape drawing task (Averbecket al. 2003). The current results provide quantitativeevidence that the neuronal encoding of dynamic vari-ables, as well as of kinematic variables, is a property ofPFC neurons.

Acknowledgements Part of this study was done as the Ph.D. re-search of Tamami Fukushi, directed by Toshiyuki Sawaguchi. The

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authors also thank James Ashe, Matthew V. Chafee, and KiyoshiKurata for helpful discussion of the early version of the manu-script, and Matt Gregas for statistical comment. This work wassupported by the Japan Society for the Promotion of Science(H8DC16024) and by the Japan Science Society (9-248/10-248K).The current address of Toshiyuki Sawaguchi is the Laboratory ofNeurobiology, Hokkaido University School of Medicine, N15W7Kitaku Sapporo 060-8638, Japan.

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