Homologous mechanisms of visuospatial working memory maintenance in macaque and human: properties and sources.

Abstract

Although areas of frontal cortex are thought to be critical for maintaining information in visuospatial working memory, the event-related potential (ERP) index of maintenance is found over posterior cortex in humans. In the present study, we reconcile these seemingly contradictory findings. Here, we show that macaque monkeys and humans exhibit the same posterior ERP signature of working memory maintenance that predicts the precision of the memory-based behavioral responses. In addition, we show that the specific pattern of rhythmic oscillations in the alpha band, recently demonstrated to underlie the human visual working memory ERP component, is also present in monkeys. Next, we concurrently recorded intracranial local field potentials from two prefrontal and another frontal cortical area to determine their contribution to the surface potential indexing maintenance. The local fields in the two prefrontal areas, but not the cortex immediately posterior, exhibited amplitude modulations, timing, and relationships to behavior indicating that they contribute to the generation of the surface ERP component measured from the distal posterior electrodes. Rhythmic neural activity in the theta and gamma bands during maintenance provided converging support for the engagement of the same brain regions. These findings demonstrate that nonhuman primates have homologous electrophysiological signatures of visuospatial working memory to those of humans and that a distributed neural network, including frontal areas, underlies the posterior ERP index of visuospatial working memory maintenance.

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Behavioral/Systems/Cognitive
HomologousMechanismsofVisuospatialWorkingMemory
MaintenanceinMacaqueandHuman:Propertiesand
Sources
RobertM.G.Reinhart,RichardP.Heitz,BradenA.Purcell,PaulineK.Weigand,JeffreyD.Schall,
andGeoffreyF.Woodman
DepartmentofPsychology,VanderbiltVisionResearchCenter,CenterforIntegrativeandCognitiveNeuroscience,VanderbiltUniversity,Nashville,
Tennessee37240
Althoughareasoffrontalcortexarethoughttobecriticalformaintaininginformationinvisuospatialworkingmemory,theevent-related
potential (ERP) index of maintenance is found over posterior cortex in humans. In the present study, we reconcile these seemingly
contradictoryfindings.Here,weshowthatmacaquemonkeysandhumansexhibitthesameposteriorERPsignatureofworkingmemory
maintenance that predicts the precision of the memory-based behavioral responses. In addition, we show that the specific pattern of
rhythmicoscillationsinthealphaband,recentlydemonstratedtounderliethehumanvisualworkingmemoryERPcomponent,isalso
present in monkeys. Next, we concurrently recorded intracranial local field potentials from two prefrontal and another frontal cortical
areatodeterminetheircontributiontothesurfacepotentialindexingmaintenance.Thelocalfieldsinthetwoprefrontalareas,butnotthe
corteximmediatelyposterior,exhibitedamplitudemodulations,timing,andrelationshipstobehaviorindicatingthattheycontributeto
the generation of the surface ERP component measured from the distal posterior electrodes. Rhythmic neural activity in the theta and
gammabandsduringmaintenanceprovidedconvergingsupportfortheengagementofthesamebrainregions.Thesefindingsdemon-
strate that nonhuman primates have homologous electrophysiological signatures of visuospatial working memory to those of humans
and that a distributed neural network, including frontal areas, underlies the posterior ERP index of visuospatial working memory
maintenance.
Introduction
Workingmemoryisthecognitivesubstratethatallowsustohold
and manipulate information over relatively brief time intervals
and is critical for complex human behavior (Baddeley, 2003).
Although behavioral studies have often been used to infer the
nature of the representations we store (Luck and Vogel, 1997;
Cowan,2001;ZhangandLuck,2008;PerezandVogel,2012),the
recent discovery of a human event-related potential (ERP) com-
ponentthatindexesthemaintenanceofinformationinvisuospa-
tial working memory has allowed for neuroscientific study of
these representations, individual differences, and the role of
working memory representations in attention tasks (Vogel and
Machizawa,2004;Vogeletal.,2005;WoodmanandVogel,2008;
Carlisle et al., 2011). This component is most commonly known
as the contralateral delay activity (or CDA) and is characterized
by a lateralized posterior potential that is sustained during the
retention interval of a short-term memory task. In addition, re-
centstudiesofEEGoscillationshavesuggestedthattheCDAmay
be due to the suppression of posterior rhythmic activity in the
alphaband,contralateraltotherememberedstimulus(Mazaheri
and Jensen, 2008, 2010; van Dijk et al., 2010). However, if we are
going to understand the neuronal and postsynaptic mechanisms
thatgiverisetothiscriticalneuroscientifictoolforstudyingtem-
porarymemory,weneedtosimultaneouslyrecordactivitywithin
the brain.
Researchspanningspatialscales(e.g.,fromlocaltolarge-scale
electrical fields) is one of the greatest needs for understanding
cognitive brain dynamics (Nunez and Srinivasan, 2006). The ab-
senceofsuchresearchhaslimitedourabilitytodefinitivelyiden-
tify neural generators of ERP components given the ambiguities
involved in inversely modeling neural sources (Helmholtz, 1853;
Luck, 2005; Nunez and Srinivasan, 2006). One solution to this
problem is to establish homology between ERP components ob-
servedinmonkeysandhumansandthenrecordthepostsynaptic
activity in different brain regions of the monkey to determine
whether these local fields contribute to the components of inter-
est (Schroeder et al., 1991, 1992; Lamme et al., 1992; Woodman,
2010). The present study takes this underused approach to an
age-old problem.
ReceivedJan.13,2012;revisedMarch29,2012;acceptedApril8,2012.
Authorcontributions:J.D.S.andG.F.W.designedresearch;R.M.G.R.,R.P.H.,B.A.P.,P.K.W.,andG.F.W.performed
research; R.M.G.R., R.P.H., and B.A.P. contributed unpublished reagents/analytic tools; R.M.G.R. analyzed data;
R.M.G.R.,J.D.S.,andG.F.W.wrotethepaper.
This work was supported by National Institutes of Health Grants R01-EY019882, P30-EY08126, and P30-
HD015052,byNationalScienceFoundationGrantBCS-0957072,andbyRobinandRichardPattonthroughtheE.
BronsonIngramChairinNeuroscience.WethankJohnHaitas,MaryFeurtado,andMichelleYoungfortheirtechnical
assistance.
Theauthorsdeclarenocompetingfinancialinterests.
CorrespondenceshouldbeaddressedtoGeoffreyF.Woodman,PMB407817,2301VanderbiltPlace,Vanderbilt
University,Nashville,TN37240-7817.E-mail:geoffrey.f.woodman@vanderbilt.edu.
DOI:10.1523/JNEUROSCI.0215-12.2012
Copyright©2012theauthors 0270-6474/12/327711-12$15.00/0
TheJournalofNeuroscience,May30,2012 • 32(22):7711–7722 • 7711

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Thememory-guidedsaccadetaskhasbeenthemodalworking
memory paradigm in neuroscience (Gnadt and Andersen, 1988;
Funahashi et al., 1989; Colby et al., 1996; Constantinidis and
Steinmetz,1996;ChafeeandGoldman-Rakic,1998;Constantini-
disetal.,2002;Wangetal.,2011).Thistaskrequiresthatasubject
remember a particular spatial location over a delay period. We
had both humans and macaque monkeys perform the same
memory-guided saccade task to test the hypothesis that the CDA
isfoundinbothprimatespecies.WhilerecordingEEGandERPs
fromthemonkeysusinganarrayofsurfaceelectrodes,wesimul-
taneously recorded local field potentials (LFPs) from the frontal
eye field (FEF), the supplementary eye field (SEF), and the sup-
plementarymotorarea(SMA)(Fig.1).Althoughitisknownthat
neurons in regions of frontal cortex exhibit sustained activity
during the memory-guided saccade task, it is unknown whether
these regions contribute to the generation of the decidedly more
posterior CDA.
MaterialsandMethods
Subjects
We recorded surface ERPs and intracranial activity from four macaque
monkeys. All procedures and care of the monkeys were performed with
supervisionandapprovalfromtheVanderbiltInstitutionalAnimalCare
and Use Committee in accordance with the Public Health Service Policy
onHumaneCareandUseofLaboratoryAnimals.HumanERPdatawere
collected from 10 neurologically normal subjects (age range, 19–30; six
were females). Each participated in both Experiments 1 and 2 after we
obtained informed consent. All procedures were approved by the Van-
derbilt University Institutional Review Board.
Experimental task and recording
Monkeysandhumansbothperformedthesamememory-guidedsaccade
task (Fig. 2, Experiment 1). Each trial began with subjects fixating a
central 0.4° square fixation point (800–1200 ms duration randomly jit-
teredwitharectangulardistribution).A1?1°circletargetflashedalone
for 100 ms at one of eight isoeccentric locations equally spaced 8–10°
around the fixation point. Trials with different target locations were
randomly interleaved to maximize spatial uncertainty. The critical fea-
ture of this task is that subjects were required to maintain fixation for
500–1000msaftertargetonset(i.e.,thedelayperiodorretentioninterval
length;monkeyS,500ms;monkeysFandZ,600ms;monkeyQ,750ms;
humans, 1000 ms). Different lengths of delay periods were used across
the macaques because of individual differences in their ability to remain
on task with longer retention intervals. When the fixation point center
extinguished, a 2 s window began in which subjects made a saccadic eye
movement to the remembered location of the target, and then main-
tainedgazeatthatrememberedlocationfor750–1000ms.Monkeyswere
rewarded for correctly performed trials. For a response to count as qual-
itatively correct, the saccade needed to be made to a location within 2.5°
of the center of the memory target. All stimuli were isoluminant on a
black background. The intertrial interval was 600 ms (1000 ms for hu-
mans and monkey Z). To precisely identify the onset of the CDA in
humans, an additional memory-guided saccade task was devised (i.e.,
Fig. 2, Experiment 2). The difference between Experiments 1 and 2 was
that the latter contained the presentation of one distractor stimulus in
isoeccentric space directly opposite the location of the target. Target and
distractor were distinguished by isoluminant colors (red and green),
counterbalanced across human subjects. This second version of the
memory-guidedsaccadetask,withstimuliinbothhemifields,allowedus
to distinguish the CDA from lateralized sensory responses that are unre-
latedtoworkingmemorymaintenanceofthetask-relevantinformation.
The FEF LFPs were recorded from both hemispheres of two male
bonnetmacaques(Macacaradiata;identifiedasQ,11yearsofage,?7kg;
and S, 11 years of age, ?8 kg). SEF LFPs were recorded from the right
hemisphereofonemalebonnetmacaque(identifiedasF,18yearsofage,
?15kg).TheSMALFPswererecordedfromtherighthemisphereofone
male rhesus macaque (Macaca mulatta; identified as Z, 8 years of age,
?12.5 kg). Simultaneously, we recorded ERPs from skull electrodes lo-
catedatapproximatelyOL/OR(inmonkeysQ,S,andF),andFpFz,FCz,
Cz, Pz, POz, Oz, F3/4, C3/4, T5/6, O1/2 (in monkey Z) according to the
human 10–20 system scaled to the macaque skull (Fig. 3) (Woodman et
al., 2007; Godlove et al., 2011). LFPs were recorded from 2–5 M? im-
pedance electrodes, sampled at 1 kHz, and filtered at 0.7–170 Hz, using
Plexon head-stage HST/8 050-G20 with an input impedance of 38 M?.
Monkey EEGs were sampled at 1 kHz and filtered between 0.7 and 170
Hz. A guide tube in contact with the surface of the dura was used as
reference for the LFP signals. The frontal EEG electrode was used as
reference for the EEG signals (approximately human Fz for monkeys Q,
S, and F, and FpFz for monkey Z) (Woodman et al., 2007). Eye position
was monitored using an eye coil or video-based infrared eye-tracking
system (EyeLink; SR Research). Eye traces were recorded at 1 kHz. All
surgical procedures were performed under aseptic conditions with gen-
eral anesthesia.
Human EEG was recorded (250 Hz sampling rate; 0.01–100 Hz band-
pass filter) using an SA Instrumentation amplifier connected to 21 tin
electrodes,including3midline(Fz,Cz,Pz),8lateralpairs(F3/F4,C3/C4,
P3/P4, PO3/PO4, T3/T4, T5/T6, O1/O2), and 2 nonstandard sites: OL
(midway between O1 and T5) and OR (midway between O2and T6),
arrayed according to the International 10–20 System and embedded in
an elastic cap (Electro-Cap International). The right mastoid electrode
Figure1.
nial ERPs. Monkey LFP data were acquired from the FEF, SEF, and SMA. Monkey ERPs were
acquiredfromcranialsurfaceelectrodes(reddiscs).
ElectrodelocationsforsimultaneousrecordingofintracranialLFPsandextracra-
Figure2.
performedbybothhumansandmonkeys,eachtrialbeganwithsubjectsholdingfixationona
centralfixationpoint.Atargetappearedbrieflyatoneofeightpossiblelocationsatrandomat
an 8–10° radius. The disappearance of the fixation point center cued subjects to make a
memory-guidedsaccadiceyemovementtotherememberedspatiallocation.InExperiment2,
performed only by humans, a two-stimulus hemifield-balanced memory array was used to
disambiguatesensoryevokedpotentialsfromtheCDAcomponent.ThetargetstimulusinEx-
periment2waseitherredorgreen,counterbalancedacrosssubjects.Thedottedcircleindicates
gazeposition.
Schematicrepresentationofthememory-guidedsaccadetasks.InExperiment1,
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served as the on-line reference for these active electrode sites. Signals
wererereferencedoff-linetotheaverageoftheleftandtherightmastoids
(Luck, 2005). Horizontal and vertical eye positions were monitored by
recording electro-oculogram (EOG) from bipolar electrodes located at
theoutercanthiofeacheye,andaboveandbelowtheorbitofthelefteye,
respectively. All electrode impedances were kept under 5 k?.
Data analysis
Behavior. A custom MATLAB function (MathWorks) automatically
identified saccade initiation and termination using the EOG and eye
trackersignals.Thisfunctionmeasuredwhentheinstantaneoussaccadic
velocity became elevated above 30°/s and then calculated the beginning
and end of the monotonic change in eye position (Hanes and Schall,
1995). To measure the precision of the saccadic responses beyond the
coarse binary distinction of correct versus incorrect, we adopted the
methodforestimatingvariablesaccadeerrorasimplementedinprevious
work (White et al., 1994), which has demonstrated that increasingly
heavy memory demands in this spatial working memory task increase
saccadic errors best captured with this metric. Briefly, saccade error esti-
mates were based on measures of the amplitude and endpoint of the
horizontal and vertical components of the first saccade after the eyes left
centralfixation.Vectorialeyevelocitywascomputedfromthehorizontal
and vertical eye position data using the central-difference differential
algorithm (Bahill and McDonald, 1983). Trials with premature saccades
tothememorystimulusorduringthedelaywereexcludedfromanalysis.
The index of variable error (i.e., the scatter in saccadic endpoints) was
obtained by first calculating the average horizontal and the average ver-
tical eye position at the end of the initial saccade for each single target
location (i.e., 0, 45, 90, 135, 180, 225, 270, 315°) and for each human
subjectormonkeyrecordingsession.Then,foreachsaccadeinthistarget
by subject (or session) data matrix, the straight-line distance of each
individual endpoint from the calculated average endpoint was obtained
using the following formula:
di ? ??X?? Xi?2? ?Y?? Yi?2,
where diis the deviation of saccade endpoint from the endpoint average,
X?istheaveragehorizontalendposition,Xiisthehorizontalendpointfor
a particular saccade i, Y?is the average vertical end position, and Yiis the
vertical end position for saccade i. Finally, we computed a Pearson cor-
relation coefficient between mean variable saccade endpoint error and
mean CDA amplitude across trials for each human subject or each re-
cording session from a monkey.
Electrophysiology: amplitudes and onsets. Grand average target-locked
contralateral versus ipsilateral waveforms were generated collapsed
across left and right hemispheres across our electrophysiological mea-
sures. Specifically, ERPs from the human subjects were averaged across
subjects(N?10),ERPsfromthemacaqueswereaveragedacrossrecord-
ingsessions(Q,109;S,31;F,33;Z,39),andLFPsfrommonkeysaveraged
acrossrecordingsites(Q,38;S,62;F,66;Z,78).Waveformswerebaseline
corrected to the average of the activity in the 200 ms window preceding
target onset. Trials were collapsed across the left and right hemifield
stimulus conditions (left: 45, 90, 135°; right: 225, 270, 315°) to increase
the power brought to bear. The number of trials at each target location
wasmatchedbyexcludingrandomtrialsfromtheoverrepresentedtarget
locations.Single-trialtarget-lockedEEGandLFPepochsweretruncated
20 ms before saccade onset to eliminate artifacts arising from possible
temporally smeared saccade activity. Trials in which subjects failed to
executethecorrecteyemovementwithintheallottedtimewereexcluded
from the electrophysiological analyses and counted as incorrect.
The CDA mean amplitude was measured at lateral occipital ERP elec-
trodesitesasthedifferenceinmeanamplitudebetweenthecontralateral
and ipsilateral waveforms using conservative measurement windows
based on approximate mean latency onset and delay interval end time
(humans, 450–1000 ms; monkey S, 350–500 ms; monkey Q, 350–750
ms; monkeys F and Z, 350–600 ms). The same method was used to
calculateLFPpolarizationmeasuredwithinFEF,SEF,andSMA(monkey
S,300–500ms;monkeyQ,300–750ms;monkeyFandZ,300–600ms).
Surface CDA and mnemonic LFP latency onsets were defined by the
following significance test. First, a difference wave was constructed by
subtracting ipsilateral from contralateral ERPs. The variability of the
difference wave was assessed by calculating the SD during the baseline
period (?200 to 0 ms). Significant epochs (shown with gray-shaded
zones in the figures) were defined as periods when the difference wave
deviated from baseline by ?2 SDs for ?50 ms, provided it exceeded 3
SDs in that interval. These are the same analytical methods used in pre-
vious simultaneous recordings of surface ERPs and intracranial LFPs
(Cohen et al., 2009).
The above-mentioned CDA measurement is based on a common ap-
proach for calculating the human CDA component, namely a compari-
sonbetweentargetsinthehemifieldcontralateralversusipsilateraltothe
EEG electrode (Perez and Vogel, 2012). However, for the monkeys, we
also calculated a comparison based on the receptive field (RF) of the
neuronrecordedsimultaneouslywiththeERPsandLFPs(asinCohenet
al., 2009). We measured the difference between the ERPs and LFPs on
trials when the target stimulus was inside the RF of the neuron and the
ERPsandLFPsontrialswhenthetargetfellatthelocation(s)oppositethe
RF of the neuron. ANOVAs were used for statistical tests of amplitude
and latency, and p values were adjusted using the Greenhouse–Geisser ?
correction for nonsphericity, where appropriate (Jennings and Wood,
1976).Forourcorrelationanalysesrelatingtheelectrophysiologicalfind-
ingstobehavior,werecalculatedthecorrelationswhileexcludingoutliers
(?2 SDs). For the correlation coefficients reported, the same pattern of
resultswasobtained.Thatis,thesignificantcorrelationsthatarereported
were significant with or without outliers excluded.
Electrophysiology: spatial distributions. The spatial distribution of cur-
rent densities producing the CDA was computed for all human subjects
and monkey Z (i.e., the animal implanted with the highest density of
surface electrodes) using all of the electrodes across the head, including
frontal sites. These methods were identical with previous analyses of the
spatial distributions of other ERP components observed across primate
species(Godloveetal.,2011;Reinhartetal.,2012).ERPdatafrommon-
key Z were coregistered spatially with anatomical magnetic resonance
images (MRIs) to enable current density analysis using the multimodal
neuroimaging software CURRY 6 (Compumedics Neuroscan). While
under sedation, the monkey was placed in a stereotaxic apparatus cus-
tomizedfortheMRIenvironment(CristInstrument).AnatomicalMRIs
wereacquiredwithaPhilipsInteraAchieva3teslascannerusingaSENSE
Flex-S two-element phased array receive only coil. One element was
placed below the head and the other at the skull vertex. T1-weighted
Figure3.
relatively sparse surface electrode configurations, whereas monkey Z was implanted with a
denseelectrodearray.
MonkeysurfaceEEGelectrodemontages.MonkeyQ,S,andFwereimplantedwith
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gradient-echostructuralimageswereobtainedwitha3Dturbofieldecho
anatomical sequence (TR, 8.729 ms; 130 slices; 0.70 mm thickness). A
high-resolution 3D model of the segmented skull and brain was con-
structedinCURRY6.Thecranialsurfaceelectrodelocationswerecoreg-
istered to the head model guided by stereotaxic coordinates recorded
duringsurgery.Fromthis3Dheadmodel,athree-compartmentbound-
ary element method (BEM) volume conductor geometry was generated.
TheBEMmodelconsistedof2704trianglemeshesoverall,or1358nodes,
whichdescribethesmoothedinnerskull,theouterskull,andtheoutside
of the skin. The mean triangle edge lengths (node distances) were 9 mm
(skin),8mm(skull),and6mm(braincompartment).Standardconduc-
tivity values for the three compartments were used as follows: skin, 0.33
S/m; skull, 0.0042 S/m; brain, 0.33 S/m. Of note, CURRY is designed to
provide solutions from skin surface electrodes; however, we measured
monkey ERPs from the cranial surface. Skin conductance and thickness
values did not contribute to the computed model solutions in monkeys
because electrodes were located on the skull surface.
For humans, the interpolated BEM model was derived from averaged
MRI data from the Montreal Neurological Institute. It consisted of 9300
triangularmeshesoverall,or4656nodes,withmeantriangleedgelengths
of 9 mm (skin), 6.8 mm (skull), and 5.1 mm (brain compartment).
Standard conductivity values for the three compartments were set to the
following: skin, 0.33 S/m; skull, 0.0042 S/m; and brain, 0.33 S/m. The
interpolated BEM model was built using the onboard CURRY 6 MRI
dataset (Fuchs et al., 2002).
Thecurrentdensityanalysiswascalculatedfromthevoltagedifference
waves (contralateral minus ipsilateral) during the memory retention in-
tervalforhumans(450–1000ms)andmonkeyZ(350–600ms).Current
Figure 4.
waveformsareillustratedforExperiments1and2inhumans(A),andforExperiment1inmonkeys(B).Plottedwithnegativevoltageupandtimerelativetotheonsetofthememoryitem.The
waveformswerecollapsedacrosstargetlocations(left:45,90,135°;right:225,270,315°)andacrossleftandrighthemisphereoccipitalelectrodesites.Thegray-shadedregionshighlighttheperiod
duringwhichipsilateralandcontralateralwaveformsrelativetotherememberedlocationsignificantlydiverged,definingtheCDAcomponent.Thedashedverticallinemarksthetimesubjectswere
cuedtorespond.CurrentdensitydistributionsderivedfromthegrandaverageCDAdifferenceswaves(ipsilateralminuscontralateral)areshownforExperiments1and2inhumans(C)andfor
Experiment1inmonkeyZ,whowasimplantedwithadenseelectrodearray(D).ThecolorscaledifferencehighlightsthepolarityreversalinCDAbetweenspecies.Thelightercolorsindicategreater
currentdensitywithdistributionsshownforrightvisualfieldmemoryitems.
Human and monkey CDA waveforms and spatial distributions recorded from the surface EEG electrodes. Grand average target-locked ipsilateral (red) versus contralateral (black)
Monkey QMonkey S Monkey ZMonkey F
-200
Time from Target Onset (ms)
-15 μV
400
0
800
A
B
FrontalCentral Parietal
Figure5.
lateraloccipitalelectrodesareillustratedforeachmacaquemonkey.B,ERPwaveformsacrossfrontal,central,andparietallateralelectrodepairsfrommonkeyZ.Thegray-shadedzonesindicatethe
periodduringwhichipsilateralandcontralateralwaveformssignificantlydiverged(i.e.,CDAcomponent).Thebrokenverticallinemarksthetimesubjectswerecuedtorespond,andthesolidblack
verticallineindicatesmediansaccadicreactiontime.MonkeyQ’searlysensoryevokedpotentialswerecontaminatedbyaCRTmonitorartifact,resultingina0mslatencyofthevisualresponse.
However,monkeyQexhibitedasignificantsurfaceCDAwithsimilaronsetlatency,amplitude,correlationtobehavioralperformance,andcorrespondencetotheLFPpolarizationduringthememory
retentioninterval(Fig.12).Furthermore,monkeyQexhibitedsignificantdelayperiodrhythmicoscillationsrecordedintracranially(Fig.12E)andextracranially(Fig.10).
SurfaceERPwaveformsfromeachindividualmonkey.A,GrandaverageERPstime-lockedtothememory-targetonset.Ipsilateral(red)versuscontralateral(black)ERPwaveformsfrom
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density was estimated using the standardized low-resolution electro-
magnetic tomography-weighted accurate minimum norm method
(SWARM)(Wagneretal.,2007).FormonkeyZ,SWARMwasestimated
using the cranial surface electrode locations mentioned above, whereas
for humans SWARM was estimated using electrode positions based on
the International 10–20 System and a cortical surface obtained from a
segmentation of the CURRY 6 individual reference brain.
Electrophysiology: time–frequency analyses. Time–frequency analyses
were performed using a Morlet wavelet decomposition with FieldTrip
software (Oostenveld et al., 2011). The Morlet wavelet has a Gaussian
envelope that is defined by a constant ratio (?f? f/7) and a wavelet
duration (6?t), where f is the center frequency and ?t? 1/(2??f). After
obtainingcomplextime–frequencydatapoints
foreveryindividualtrial,thesedataweretrans-
formed into a total power measure. For each
frequency in the resulting time–frequency ma-
trix, the baseline period was defined by the av-
erage of the values within a ?200 to 0 ms time
window preceding the time-locking target
event. A simple subtraction of baseline values
fromallthevaluesintheepochwasperformed,
as this is the most common approach to base-
line correction in EEG and ERP research. An
average of total power, single-trial values was
calculated using conservative measurement
windows based on approximate mean latency
onset and delay interval end time, just as with
the CDA (humans, 300–1000 ms; monkey S,
200–500 ms; monkey Q, 200–750 ms; mon-
keys F and Z, 200–600 ms). As with the CDA,
figures were generated from measurements at
lateral occipital electrode sites for EEG, and at
lateral FEF, SEF, and SMA sites for LFPs as the
differenceinmeanamplitudebetweenthecon-
tralateral and ipsilateral waveforms. Similarly,
we statistically compared the power in left and
righttargetconditionsforeachhemispherein-
tracranially at theta (4–8 Hz) and gamma fre-
quencies (30–50 Hz), and extracranially for
electrodes sites OL and OR at the alpha and
low-beta frequencies (8–16 Hz).
Results
Behavior
Using the modal memory-guided saccade task in the neurosci-
ence literature (Gnadt and Andersen, 1988; Funahashi et al.,
1989; Colby et al., 1996; Constantinidis and Steinmetz, 1996;
Chafee and Goldman-Rakic, 1998; Constantinidis et al., 2002;
Wang et al., 2011), we found that monkeys and humans per-
formed at similar levels of accuracy (?90% correct across all
target locations and subjects, with the exception of monkey S,
whose percentage correct was ?75% correct) (for accuracy cri-
teria, see Materials and Methods). For the qualitatively correct
memory-guided saccades made in the vicinity of the memory
target, the saccadic endpoint error for monkeys was 1.5 ? 0.5°
(mean?SD)andforhumanswas1.0?0.3°(Experiment1)and
1.2 ? 0.3° (Experiment 2). Reaction times for monkeys were
248?31ms(mean?SD)andforhumans318?58ms(Exper-
iment 1) and 320 ? 51 ms (Experiment 2) across correct trials.
Within species, the Pearson correlation between saccadic reac-
tion times and positional error was not significant (monkey:
r(211)? ?0.025, p ? 0.891; human Experiment 1: r(9)? ?0.189,
p ? 0.301; human Experiment 2: r(9)? ?0.110, p ? 0.349).
Between species, a two-tailed t test revealed significant differ-
ences between saccadic reaction times (p ? 0.01) and positional
error (p ? 0.01) due to monkeys being faster but less accurate
than humans. This pattern of memory-guided saccade perfor-
mance is consistent with previous findings from humans (Curtis
et al., 2004) and monkeys (Funahashi et al., 1989).
Surfaceevent-relatedpotentialsandoscillatory activity
In humans, a sustained contralateral negativity arose ?450 ms
after memory target presentation over posterior electrode sites
and continued through the memory retention interval (Fig. 4A).
This is the primary defining characteristic of the CDA during
change detection tasks in humans (Vogel and Machizawa, 2004;
Vogel et al., 2005; Perez and Vogel, 2012). To determine which
Figure6.
synchronizedipsilateral(red)versuscontralateral(black)ERPwaveformsareshownusinganaveragemastoidreferenceelectrode
configuration(leftpanel),thestandardinhumanCDAresearch,aswellasafrontocentralreferenceelectrodeconfigurationsimilar
tothatofeachmonkey’sERPsinthepresentstudy(rightpanel).Thegray-shadedzonesindicatetheperiodduringwhichipsilateral
andcontralateralwaveformssignificantlydiverged(i.e.,CDAcomponent).Thebrokenverticallinemarksthetimesubjectswere
cuedtorespond,andthesolidblackverticallineindicatesmediansaccadicresponsetime.B,Currentdensitydistributionsderived
from the grand average CDA difference waves (contralateral minus ipsilateral) are illustrated for humans with both reference
electrodesites.Thelightercolorsindicategreatercurrentdensitywithdistributionsshownforrightvisualfieldmemoryitems.
HumanCDAwaveformsandspatialdistributionsacrossreferenceelectrodeconfigurations.A,Grandaveragetarget-
Figure 7.
behavioral performance. Scatterplots of significantly negative linear correlations be-
tween mean variable error (in degrees of visual angle) and mean surface CDA amplitude
(contralateralminusipsilateral)areshownforExperiments1and2inhumans(A)andfor
Experiment1inmonkeys(B).
The relationship between human and monkey surface CDA amplitude and
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portionsofthewaveformswereduetothepotentiallyconfound-
ing effects of lateralized sensory evoked potentials, and verify the
latencyoftheCDAinhumans,thesamegroupofhumansubjects
performed a second memory-guided saccade task in which a dis-
tractorwaspresenteddirectlyoppositethetask-relevantmemory
targetintheotherhemifield(Fig.2,Experiment2).Experiment2
confirmedtheonsetofthehumanCDAduringthisspatialmem-
ory task was at 450 ms after stimulus (Fig. 4A).
Inmacaques,wefoundthattheCDA,definedbythesustained
difference in lateralized potentials, began at ?350 ms after the
memory target and took the form of a strong contralateral posi-
tivity over posterior electrodes. As in humans, this activity was
sustained throughout the delay period (Fig. 4B). All monkeys
exhibited a clear CDA during the memory retention interval fol-
lowing the initial sensory ERPs (Fig. 5). For monkeys Q and S,
this sustained contralateral positivity rode on top of a bilateral
positivity, whereas for monkeys F and Z this contralateral posi-
tivityappearstooverlapwithabilateralnegativity.Previousstud-
ies of the CDA in humans (Vogel and Machizawa, 2004; Carlisle
et al., 2011) have shown that the CDA commonly overlaps with
boththeP3componentthatisbelievedtoindexworkingmemory
updating (Donchin and Coles, 1988) and it is known to exist in
nonhumanprimates(ArthurandStarr,1984)andlaternegativity
slowwavesthathavebeendocumentedinworkingmemorytasks
(Ruchkin et al., 1992). It appears that, across monkeys, we are
observing some individual differences in the onset and duration
of these overlapping ERP components. Such individual differ-
encesarecommoninhumanERPstudies,althoughthesourceof
suchvariationacrosssubjectsisnotyetwellunderstood(Woodman,
2010).ItisnotablethatmonkeyFexhibitedaCDAthatwassimilar
totheothermacaques,giventhatthismonkeywas?18yearsofage.
Thisfindingappearstochallengearecentreportinwhichthework-
ing memory-related activity of older macaque monkeys appears to
beabnormal(Wangetal.,2011).
As is evident, the CDA latency differed by ?100 ms between
species (Fig. 4). However, a substantial portion of this interspe-
cies latency difference is due to the use of different reference
electrodes. A reanalysis of the human data using a frontocentral
referenceelectrode,asinthemonkeyrecordings,essentiallyelim-
inated this latency discrepancy (Fig. 6). However, when compar-
ing the latency of the CDA in humans and one monkey (i.e., Z)
rereferencedtosimilarmastoidreferencesites,itappearsthatthe
monkey CDA does onset ?75 ms earlier than the same effect in
humans. This latency difference would be similar to previous
studiesofmacaqueERPsinwhichonsetlatenciesofhomologous
components are often ?25% shorter than those observed in hu-
mans(Woodman,2012).TheinterspeciesdifferenceinCDApo-
larity was reliably observed using both the standard average
mastoid and frontocentral reference electrodes. This polarity in-
version of homologous ERP components across species is con-
sistent with previous studies of lateralized attention effects
(Woodmanetal.,2007;Cohenetal.,2009),butwasnotfoundfor
the error-related negativity and positivity in monkeys (Godlove
et al., 2011). Such polarity inversions are most likely due to vari-
ationofcorticalfoldingacrossspecies(Woodman,2010)because
theorientationofthecorticaltissuegeneratingtheelectricalfields
measured in ERP recordings determines the polarity of the com-
ponent. For example, in humans it appears that the same ERP
component (i.e., the C1, which begins at ?50 ms after stimulus)
will flip polarity when the stimulus activates the inferior versus
superior banks of the calcarine sulcus (Clark et al., 1995). Never-
theless,comparedwiththehuman,themonkeyCDAexhibiteda
comparable latency following lateralized sensory transients and
identical persistence throughout the memory retention interval.
In both species, the CDA continued until the saccadic response
was initiated. However, the lateralized ERPs did not exhibit an
increase in amplitude immediately before the saccade, as would
be expected if it reflected saccade preparation (Figs. 4, 5, 6A).
Like all ERP components, the current sources of the CDA can
be modeled from the spatial distribution of voltages on the sur-
Figure 8.
endpointerrorandmeansurfaceCDAamplitude(ipsilateralminuscontralateralwaveforms)areillustratedforeachmonkey(Q:r??0.515,p?0.001;S:r??0.564,p?0.001;F:r??0.506,
p?0.01;Z:r??453,p?0.01).
The relationship between each monkey’s surface CDA and behavioral performance. Scatterplots showing significant negative linear correlations between mean variable saccadic
Figure9.
time-lockedtothetargetstimulusandbyconventionshownasthedifferencebetweenpower
for right and left hemifield stimuli collapsed across right and left hemisphere electrodes for
human Experiments 1 and 2 (A) and monkeys (B). The dashed vertical line marks the time
subjects were cued to respond. The plots show the contralateral suppression of power in the
alphaband.
Time–frequencyplotsoflateralizedsurfacetotalpower.Grandaveragedataare
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face using all of the electrodes on the head. The spatial distribu-
tion of the human CDA component from Experiments 1 and 2
was assessed during the epoch that a significant CDA was found
(i.e., 450–1000 ms). The dense electrode array of monkey Z al-
lowedforthesameanalysisofthemonkeyCDA(from350to600
ms) (Fig. 3 shows electrode configurations for each monkey).
Distributedcurrentdensitieswereprojectedonto3Dreconstruc-
tionsofanatomicalMRIsfromthemonkeyandareferencebrain
forhumans.Currentdensityinhumanswasconcentratedindor-
sal posterior foci explaining 97% (Experiment 1) and 98% (Ex-
periment 2) of the variance of the surface potential distribution
(Fig. 4C). Similarly, current density for the monkey CDA was
concentrated in dorsal posterior areas (96% explained variance)
(Fig. 4D). We verified that a dorsal posterior scalp topography
was also found in humans when using a frontocentral reference,
as was used in the monkeys (Fig. 6B). Thus, the spatial distribu-
tionofcurrentdensityproducingtheCDAinmonkeysmirrored
thatofhumansperformingthememory-guidedsaccadetaskwith
a target presented alone or with a distractor.
A key advantage of the memory-guided saccade task is that it
provides a graded metric of accuracy through the distribution of
saccadic endpoints. Behavioral studies of the memory-guided
saccade task in both humans (Ploner et al., 1998) and monkeys
(White et al., 1994) have shown that measurements of saccadic
endpoint scatter (i.e., the squared variability of saccadic end-
point, sometimes known as variable error) is more directly re-
lated to visual working memory maintenance than coarser
measures of correct versus incorrect, or absolute error. To deter-
mine the relationship between the delay period activity and the
fidelity of the mnemonic representation guiding the behavioral
response,wetestedthepredictionthattheamplitudeoftheCDA
measured during the retention interval predicted the spatial pre-
cision of the subsequent memory-guided saccade. If the CDA
provides a measure of the quality of the memory representation
of the location, then we would expect that a larger CDA would
result in less error due to a higher fidelity
working memory representation. In con-
trast, if the CDA were simply measuring
the eccentricity of the remembered loca-
tion, then the CDA would not predict the
accuracy of the behavioral report of the
location,butinsteadsimplytheamplitude
of the saccadic response, regardless of
what the memory target location was.
We found that the mean amplitude of
the CDA was predictive of the mean end-
point error using subjects as the unit of
analysis for humans and recording ses-
sions for the monkey data (Fig. 7). Signif-
icant negative correlations were observed
between mean CDA amplitude and magnitude of saccadic error
inhumans(Experiment1:r(9)??0.841,p?0.01;Experiment2:
r(9)??0.810,p?0.01)andmonkeys(QandS:r(139)??0.528,
p ? 0.01; F and Z: r(71)? ?0.447, p ? 0.01) (Fig. 8). In contrast,
we found that the amplitude of the CDA did not strongly or
reliablypredicttheeccentricityofthesaccadicresponsesmadein
humans (values of r ? ?0.06 to ?0.35; values of p ? 0.17) or
monkeys (values of r ? ?0.02 to ?0.24; values of p ? 0.06). We
found a similar pattern of results when within-subject correla-
tions were performed. Specifically, within-subject correlations
mirrored those obtained using both measures of saccadic error
(humanandmonkey:valuesofp?0.01)andsaccadicamplitude
(human:valuesofp?0.11;monkey:valuesofp?0.10).Thus,as
predicted above, when CDA amplitude was higher, response er-
ror was lower.
It has recently been shown that systematic modulation of the
lateralized posterior alpha rhythm (8–13 Hz) in humans may
account for the slow and sustained evoked responses of the CDA
(Mazaheri and Jensen, 2008, 2010; van Dijk et al., 2010). This
hemisphere-specificlateralizedEEGalphaactivityoverposterior
cortex is often referred to as contralateral alpha-band suppres-
sion and is found during visuospatial working memory tasks in
humans (Jokisch and Jensen, 2007; Van Der Werf et al., 2008;
Grimaultetal.,2009;Sausengetal.,2009).Toprovideadditional
evidencethatthemacaquehomologofthehumanCDAisdueto
the same EEG dynamics, we spectrally decomposed EEG signals
from both species to assess oscillatory fluctuations in the alpha
band. Beginning ?300 ms after memory target onset, alpha
power in human was suppressed over parietooccipital electrodes
inthehemispherecontralateraltotherememberedstimulus(Fig.
9A).Thatis,whenthetargetwaspresentedintherighthemifield,
the right electrodes showed significantly greater alpha-band
power relative to left electrodes (t(9)? 4.844; p ? 0.002). The
samepatternofactivitywasobservedfortargetspresentedonthe
left(t(9)?2.691;p?0.03).Theseresultsreplicatepreviouswork
Figure10.
shownasthedifferencebetweenpowerforrightandlefthemifieldstimulicollapsedacrossrightandlefthemisphereelectrodesforeachmonkey.Thedashedverticallinemarksthetimesubjects
werecuedtorespond.
Individualmonkeytime–frequencyplotsoflateralizedtotalpowerattheposteriorsurfaceelectrodes.Grandaveragecontralateraldataaretime-lockedtothetargetstimulusand
Figure 11.
contralateral(black)LFPwaveformsrelativetotherememberedlocationareshownfromrecordingssitesinFEF,SEF,andSMA.The
waveformswerecollapsedwithinleftandrighttargetlocations(left:45,90,135°;right:225,270,315°)andacrossrecordingsites.
Thegray-shadedzonesindicatetheperiodduringwhichipsilateralandcontralateralwaveformssignificantlydiverged,andthe
brokenverticallinemarksthetimesubjectswerecuedtorespond.
Monkey intracranial LFPs averaged relative to the remembered location. Grand average ipsilateral (red) versus
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(JokischandJensen,2007;VanDerWerfetal.,2008;Grimaultet
al., 2009; Sauseng et al., 2009) and demonstrate a strong lateral-
ization in human alpha-band activity with respect to the hemi-
field of the remembered stimulus during memory retention.
In macaques, we found a contralateral alpha-band suppression
beginningat?150msfollowingmemorytargetonsetoverposterior
electrodesmirroringtheEEGeffectsfoundinhumans(Fig.9B).All
monkeys exhibited suppressed alpha and low-beta power at poste-
rior electrodes in the hemisphere contralateral to the remembered
stimulus. This resulted in power being significantly greater for the
hemisphereipsilateraltotherememberstimulus(lefthemisphereQ:
t(108)?2.572,p?0.01;S:t(30)?3.158,p?0.004;F:t(32)?2.214,
p ? 0.04; Z: t(38)? 2.613, p ? 0.01; right hemisphere Q: t(108)?
2.704,p?0.009;S:t(30)?3.252,p?0.003;F:t(32)?2.076,p?0.05;
Z:t(38)?2.287,p?0.02;Fig.10).Despitetheinterspeciesdifference
inCDApolaritypreviousnoted,hemisphericlateralizationofalpha
power was reliably observed across species, providing further evi-
dence that the human and monkey CDAs are indexing the same
fundamental neural dynamics. In sum, time–frequency analysis of
oscillatorypowerforbothhumanandnonhumanprimatesrevealed
strongmodulationofalpha-bandactivityduringthedelayperiod.
Intracranialevent-relatedlocalfieldpotentialsand
oscillatory activity
AlthoughestimatingthecurrentdensityproducingtheCDApro-
videsusefulinformation,simultaneousintracranialrecordingsof
LFPsarecriticaltoidentifythesourcesinthedistributednetwork
believed to underlie ERP components. This is because it is be-
lieved that the surface ERPs are generated by a spatially weighted
average of the postsynaptic LFP activity in the brain, although a
number of these sources might not contribute to the spatial dis-
tribution of a component due to cancellation or superposition
(Helmholtz, 1853; Luck, 2005; Nunez and Srinivasan, 2006;
Woodman, 2010). In macaque monkeys performing the
memory-guided saccade task, the LFPs recorded from FEF and
SEF,butnotSMA,exhibitedsustaineddelayperiodactivitywhen
the memory target was in the contralateral hemifield (Fig. 11)
(see also Fig. 12A). This resulted in significantly more positive
LFPs when the remembered location was contralateral to the site
in FEF (F(1,99)? 15.280; p ? 0.01; 94% of sites significant) and
SEF (F(1,65)? 22.160; p ? 0.01; 92% of sites significant), but not
in SMA (F(1,77)? 0.637; p ? 0.427; 4% of sites significant). In
additiontothetraditionalapproachofmeasuringtheCDAcom-
ponentbycomparingpolarizationwhenremembereditemswere
presented in the contralateral versus the ipsilateral hemifield, we
restricted the analysis to the trials when the memory target was
located within the RF of the neuron recorded simultaneously
with the LFPs. This more restrictive analysis yielded the same
pattern of results with significant differences in the amplitude of
thedelayperiodLFPswhenthememoryitemwasinversusoutof
the RF of the neurons in FEF (F(1,99)? 13.770; p ? 0.01; 92%
of sites significant) and SEF (F(1,65)? 17.738; p ? 0.01; 93% of
sites significant), but not in SMA (F(1,77)? 0.116; p ? 0.734; 1%
of sites significant).
Next, we examined how the sustained LFPs were related to
behavioral accuracy in the visuospatial working memory task,
just as we had with the CDA. Figures 12B and 13 show that the
amplitude of the LFPs in FEF and SEF predicted the precision of
the subsequent behavioral report of the memory item at the end
of the trial. Specifically, we found significant negative correla-
tions between the mean amplitude of the LFP during the delay
period(FEF,300–500ms;SEF,300–600ms;SMA,300–600ms)
andmeansaccadic-endpointerrorduringarecordingsessionfor
FEF (r(99)? ?0.415; p ? 0.01) and SEF (r(65)? ?0.372; p ?
0.01),butnotforSMA(r(77)??0.036;p?0.757).Mirroringthe
pattern observed with the CDA, this shows that the greater the
amplitude of the sustained negativity in the LFPs, the more pre-
cise was the behavioral report of the remembered location.
If the frontal areas we recorded from contribute to the gener-
ation of the CDA measured over posterior cortex, then the am-
plitude of the CDA should covary with the LFP polarization in
thesecorticalareas.Wecomputedthecorrelationsbetweenmean
CDA amplitude and the LFP amplitudes during the memory re-
tention intervals in FEF (300–500 ms), SEF (300–600 ms), and
Figure12.
andtrialwiseLFP-ERPcorrelationsinmonkeysQandS.A,GrandaverageLFPsfromFEFtime-
lockedtothememory-targetonsetwiththeipsilateral(red)versuscontralateral(black)poten-
tialsformonkeysQandS.Thegray-shadedzonesindicatetheperiodduringwhichipsilateral
andcontralateralwaveformssignificantlydiverged(i.e.,CDAcomponent),andthebrokenver-
tical line marks the time subjects were cued to respond. Despite different memory retention
intervals(Q,750ms,andS,500msaftermemorytargetonset),bothmonkeysQandSshowed
significantlysustainedLFPpolarizationsduringthedelayperiod.B,Scatterplotsshowingsig-
nificantnegativelinearcorrelationsbetweenmeanvariableerror(indegreesofvisualangle)
andmeansurfaceCDAamplitude(ipsilateralminuscontralateralwaveforms)areillustratedfor
both monkeys (Q: r ? ?0.441, p ? 0.01; S: r ? ?0.408, p ? 0.01). C, Scatterplots of
significant positive linear correlations between mean LFP CDA amplitude in FEF and mean
surface CDA amplitude over posterior electrode sites are illustrated on a session-by-session
basis for monkeys Q (r ? 0.445; p ? 0.01) and S (r ? 0.409; p ? 0.01). D, Trial-by-trial
correlationsbetweenmeanLFPCDAamplitudeinFEFandmeansurfaceCDAamplitudeover
posteriorcortexareillustratedformonkeysQ(meanr?SE,0.307?0.041;29of38LFP-ERP
pairs) and S (0.309 ? 0.027; 50 of 62 LFP-ERP pairs). Significant correlation coefficients are
representedbythedarkbars,andthedashedverticallinesindicateacorrelationofzero.
LFPpolarizations,LFPandbehaviorperformancerelationships,andsessionwise
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SMA (300–600 ms). The correlations between CDA amplitude
and LFP amplitude across the recordings were significant in FEF
(r(99)?0.419;p?0.01)andSEF(r(65)?0.302;p?0.02),butnot
in SMA (r(77)? ?0.103; p ? 0.367) (Fig. 14) (see also Fig. 12C).
Similarly, amplitude relationships be-
tween the lateralized LFPs and the CDA
on a trial-by-trial basis were significant
(Fig. 15; Fisher’s z test, p ? 0.05) for an
overwhelming majority of LFP-ERP pairs
inFEF(meanr?SE,0.309?0.023;74of
100 LFP-ERP pairs) and SEF (0.300 ?
0.025;49of66LFP-ERPpairs),butnotin
SMA (0.019 ? 0.016; 3 of 78 LFP-ERP
pairs; Fig. 12D).
Next, we compared the onset latency
ofthememory-relatedeffectsintheintra-
cranial LFPs and the extracranial ERPs
(i.e., the CDA). If the activity in FEF or
SEFarefeedingbacktotheposteriorareas
that appear to generate the CDA as impli-
cated by the topographic analyses, then
thelateralizedLFPsshouldonsetsimulta-
neously or even before the surface CDA
(Cohen et al., 2009). Using the same sta-
tistical criteria, we found that the mean
onset of the FEF LFPs (310 ? 11 ms) was
not significantly different from the onset
oftheconcurrentlyrecordedsurfaceCDA
(337?11ms)(F(1,99)?1.939;p?0.167),
whereas the mean latency of the lateral-
ized LFP in SEF (267 ? 17 ms) preceded
the onset of the CDA recorded at the sur-
face (F(1,65)? 19.584; p ? 0.01). Finally,
we assessed the frequency content of the
LFPs in the frontal regions during mem-
ory retention. Given that theta and
gamma oscillations are linked to working
memory processes (Lisman, 2010), the
presence or absence of these frequencies
in the frontal structures under study
should provide an additional line of con-
verging evidence that the activity in these
regions underlies memory maintenance.
Time–frequency analysis of delay period
LFPs revealed prominent lateralized
rhythmic activity in the theta (4–8 Hz)
andgammabands(30–50Hz)inFEF,the
theta band (4–8 Hz) in SEF, but no sus-
tained peaks in LFP power spectra were
observed in SMA (Figs. 16, 17). As shown
in Table 1, statistical analysis confirmed
that significant lateralized oscillations
were observed in FEF and SEF, but not
SMA. These results reveal the oscillatory
character of the frontal regions under in-
vestigation and confirm the involvement
of FEF and SEF while further ruling out
SMA in generating the visuospatial work-
ingmemoryeffectsobservedatthesurface
electrodes. The presence of higher fre-
quency activity in the intracranial record-
ings relativeto
modulations during memory mainte-
thelow-frequency
nanceonthesurfaceelectrodesisconsistentwithpreviousstudies
showing that the skull serves as a low-pass filter that sharply
attenuates activity in the gamma band in EEG and ERP record-
ings (Cooper et al., 1965; Nunez and Srinivasan, 2006).
Figure13.
saccadicendpointerror(variableerrorindegreesofvisualangle)againstmeanLFPamplitude(contralateralminusipsilateral)are
shownonasession-by-sessionbasis.
TherelationshipbetweenmonkeyintracranialLFPamplitudeandbehavioralperformance.Scatterplotsofmean
Figure14.
LFPamplitudeagainstmeansurfaceCDAamplitudeareillustratedonasession-by-sessionbasis.
TherelationshipbetweenmonkeyintracranialLFPamplitudeandsurfaceERPacrosssessions.Scatterplotsofmean
Figure15.
tionsbetweenmeanLFPamplitudeandmeansurfaceCDAamplitudeweresignificantforamajorityofrecordingsitesinFEFandSEF,but
notinSMA.Significantcorrelationcoefficientsareillustratedbydarkbars,andthedashedverticallinesindicateacorrelationofzero.
TherelationshipbetweentheamplitudeofthemonkeyintracranialLFPandsurfaceERPacrosstrials.Trial-by-trialcorrela-
Figure16.
FEF, SEF, and SMA are illustrated as the difference between power for right and left hemifield stimuli collapsed across right and left
hemisphericrecordingsites,time-lockedtomemoryitemonset.Thedashedverticallinesmarkthetimesubjectswerecuedtorespond.
Time–frequencytotalpowerplotsoflateralizedintracranialLFPs.GrandaverageLFPpowerdatafromrecordingssitesin
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Discussion
By taking the rare step of recording surface ERPs from humans
andmonkeysperformingthesametask(ArthurandStarr,1984),
we discovered a macaque homolog of the human CDA compo-
nent indexing visuospatial working memory maintenance. The
monkey CDA satisfied multiple criteria for establishing homol-
ogy,includingtiming,spatialdistributionacrosstheskull,scaling
with cognitive demands (i.e., the length of delay), underlying
oscillatory activity, and its relationship to behavioral perfor-
mance. This homology links the scalp potentials recorded from
macaques and humans during visuospatial working memory
maintenance, bridging an empirical gap between two disparate
literatures in neuroscience (Gnadt and Andersen, 1988; Fu-
nahashi et al., 1989; Colby et al., 1996; Constantinidis and
Steinmetz, 1996; Chafee and Goldman-Rakic, 1998; Constan-
tinidis et al., 2002; Pesaran et al., 2002; Vogel and Machizawa,
2004; Wang et al., 2011). Our findings validate the macaque as
a model of human visuospatial working memory maintenance
and allow us to draw stronger conclusions about the neural
mechanisms of humans from the previous electrophysiologi-
cal studies of monkeys.
Our findings show that the LFPs in FEF and SEF, but not in
SMA, contribute to the generation of the CDA indexing working
memory maintenance. This conclusion is motivated by four as-
pects of the results. First, the amplitude of the delay period LFPs
in FEF and SEF predicted the amplitude of the CDA measured at
the surface ERP electrodes. Second, the absence of similar effects
in SMA rules out the hypothesis that the local fields generated in
this area contribute to the surface ERP component. This obser-
vation is also important because it shows that the effects in the
morefrontalregionsarenotsimplyduetovolumeconduction,as
SMA lies closer to the posterior focus of the CDA than FEF or
SEF. The lack of theta and gamma oscillations in SMA provide
further converging evidence that rules out this area as a contrib-
utor to the surface effects as activity in these frequency bands are
knowntoplayakeyroleinphysiologicalprocessesimportantfor
working memory (Lisman, 2010). Third, the mnemonic LFPs in
FEF and SEF were coincident with or preceded the onset of the
surface CDA. Fourth, the FEF and SEF LFPs correlated with be-
havioral accuracy, just as did the CDA recorded over posterior
cortex. These findings show that the frontal areas we recorded
from are part of a distributed neural network that underlies the
CDA and demonstrates the utility of concurrent ERP and inva-
sive microelectrode recordings.
The participation of SEF in CDA generation deserves further
mention. Despite much overlap in neuroanatomy, physiology,
and function, this agranular frontal region differs from FEF in
multiplerespects,includingdensityandtopographyofitsprojec-
tions to oculomotor structures, connectivity to other frontal
structures, and visuospatial encoding (Johnston and Everling,
2011). Furthermore, recent research demonstrates that SEF is
more concerned with executive control settings than the direct
controlofattentionorsaccadeproduction(StuphornandSchall,
2006).OurfindingsalsodemonstratearoleforSEFinanimpor-
tant aspect of executive functioning, the control of visuospatial
working memory maintenance.
As evidence accumulates demonstrating that the CDA is a
reliableelectrophysiologicalmarkerofworkingmemorymainte-
nance(VogelandMachizawa,2004;Vogeletal.,2005;Woodman
and Vogel, 2008; Carlisle et al., 2011), it is important to under-
standitsneuralorigins(PerezandVogel,2012).Thisismotivated
by evidence showing that working memory deficits occur in nu-
merous disorders, including Alzheimer’s disease, attention defi-
cit/hyperactivity disorder, and most prominently schizophrenia
(Green et al., 2000; Braver et al., 2002), with the CDA holding
promiseasadiagnostictoolinclinicalresearch(PerezandVogel,
2012). The intraparietal sulcus is a likely contributor to the CDA
in humans, consistent with the dorsal, posterior scalp distribu-
tion and role in working memory storage (Todd and Marois,
2004;XuandChun,2006).However,itisnotpossibletoinferthe
generatorsofanERPcomponentsimplybasedonitsscalpdistri-
bution (Urbach and Kutas, 2002), and the present findings dem-
onstrate that neural activity can be clearly measured in brain
areasthatisnotobservedatelectrodesdirectlyaboveontheskull.
This is consistent with the expectation that ERP components are
unlikely to be generated by a single cortical source (Luck, 2005;
Nunez and Srinivasan, 2006). The intracranial findings reported
here rule out the hypothesis of a single source.
Ourintracranialresultssupportthehypothesisthatprefrontal
cortex (FEF) plays a significant role in the circuit generating the
CDA (Fuster and Alexander, 1971; Goldman-Rakic, 1987; Fu-
nahashi et al., 1989) and extends our knowledge by implicating a
primarily visuomotor and cognitive control region (SEF) and
excluding a part of the sensorimotor cortex (SMA). Presumably,
the electrical fields generated in FEF and SEF are either actively
canceled or of such a geometry that they do not result in a more
frontally skewed CDA across the surface electrodes (Fig. 4C,D).
One might expect that the presence of ipsilateral receptive fields
intheprefrontalregionsofFEFandSEFmightbethecauseofthe
lateralized CDA being observed above such areas (Schall,
1991a,b). However, our LFP recordings show that lateralized
memory-related field potentials are evident in these cortical re-
gions, even if the geometry of these electrical fields does not vis-
ibly contribute to the distribution of the CDA cross the head. It
seems likely, given the timing of the intracranial effects, that the
frontal regions we recorded from here feedback to posterior re-
gions,likeparietalareasandevenlower-levelareasofextrastriate
and striate cortex (Fig. 4), with this feedback contributing to the
generationofthelocalfieldsinposteriorvisualareasthataccount
Figure17.
andS.GrandaverageLFPpowerdatafromrecordingssitesinFEFareillustratedasthediffer-
encebetweenpowerforrightandlefthemifieldstimulicollapsedacrossrightandlefthemi-
sphericrecordingsitesformonkeysQandS,time-lockedtomemoryitemonset.
Time–frequencytotalpowerplotsoflateralizedintracranialLFPsinmonkeysQ
Table1.StatisticalanalysesofthelateralizeddelayperiodLFPpowerinthetheta
andgammabandsacrossfrontalareasandmonkeys
Theta(4–8Hz)
Area,hemisphere,monkey
Gamma(30–50Hz)
tpvalue
tpvalue
FEF,left,Q
FEF,right,Q
FEF,left,S
FEF,right,S
SEF,right,F
SMA,right,Z
t(37)?2.50
t(37)?2.14
t(61)?2.11
t(61)?2.25
t(65)?2.46
t(77)?0.79
p?0.02
p?0.03
p?0.05
p?0.04
p?0.02
p?0.43
t(37)?3.60
t(37)?3.53
t(61)?2.39
t(61)?2.51
t(65)??0.54
t(77)?0.64
p?0.001
p?0.002
p?0.02
p?0.02
p?0.59
p?0.53
7720 • J.Neurosci.,May30,2012 • 32(22):7711–7722Reinhartetal.•PrimateContralateralDelayActivity

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for the observed CDA distribution at the surface. At this point, it
isunclearwhetherthenatureoftheneuralactivityunderlyingthe
CDAisthatofsustainedfiringofneurons,oscillatoryenhancement,
or relative suppression. However, these are network-dynamic hy-
potheses that future concurrent intracranial recordings will be able
totestinfurtherdetail.
Future work using our combination of ERP and intracranial
recordings will be aimed at distinguishing between competing
models of visuospatial working memory that differ in their pro-
posedrolesthatattentionmechanismsplayinthemaintenanceof
information. For example, one theoretical perspective is that in-
formation is maintained in visuospatial working memory by the
deployment of attention to the remembered location in the
memory-guided saccade task (Cowan, 1999; Awh and Jonides,
2001).ThiscouldexplainwhythelateralizedERPeffectsofcovert
attentional deployment resemble those of working memory
maintenance reported here at the level of the surface ERPs
(Woodman et al., 2007; Cohen et al., 2009). However, other the-
ories propose that the maintenance of information and covert
attentional selection rely upon separate mechanisms (Baddeley,
2003).Thesetofempiricaltoolsthatwehavedevelopedherehold
new promise for definitively distinguishing between cognitive
models of working memory. In addition, our findings will guide
inactivation and microstimulation studies that will be able to
provide causal evidence that characterizes the precise neuroana-
tomical loci and neurophysiological events that give rise to the
macaque homolog of the human CDA.
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