Age-related spatiotemporal reorganization during response inhibition

International Journal of Psychophysiology 93 (2014) 371–380

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International Journal of Psychophysiology

j ourna l homepage: www.e lsev ie r .com/ locate / i jpsycho

Age-related spatiotemporal reorganization during response inhibition

Xiangfei Hong a,b, Junfeng Sun a, Jesse J. Bengson b, Shanbao Tong a,⁎a School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, Chinab Center for Mind and Brain, University of California-Davis, Davis CA 95618, USA

⁎ Corresponding author at: Shanghai Jiao Tong UnivShanghai 200240, China. Tel.: +86 21 34205138; fax: +8

E-mail address: [email protected] (S. Tong).

http://dx.doi.org/10.1016/j.ijpsycho.2014.05.0130167-8760/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

Article history:Received 19 March 2014Received in revised form 22 May 2014Accepted 27 May 2014Available online 4 June 2014

Keywords:AgingEvent-related potentialsGo/NoGoResponse inhibitionSource localization

As a key high-level cognitive function in human beings, response inhibition is crucial for adaptive behavior.Previous neuroimaging studies have shown that older individuals exhibit greater neural activation than youngerindividuals during response inhibition tasks. This finding has been interpreted within a neural compensationframework, in which additional neural resources are recruited in response to age-related cognitive decline.Although this interpretation has received empirical support, the precise event-related temporal course of thisage-related compensatory neural response remains unexplored. In the present study, we conducted sourceanalysis on inhibition-related ERP components (i.e., N2 and P3) that were recorded while healthy younger andolder adults participated in a visual Go/NoGo task. We found that older adults showed increased source currentdensities of the N2 and P3 components than younger adults, which support previous hemodynamic findings.Further, such age-related differences in neural activation were successfully separated between the N2 and P3periods by source localization analysis. Interestingly, the increased activations in older adults were primarilylocalized to the right precentral and postcentral gyri during the N2 period, which shifted to the right dorsolateralprefrontal cortex and the right inferior frontal gyrus during the P3 period. Taken together, our results clearlyillustrate the spatiotemporal dynamics of age-related functional brain reorganization, and further specify theexact temporal course at themillisecond scale bywhich age-related compensatory neural responses occur duringresponse inhibition.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

The ability to inhibit a prepotent tendency in reaction to changingtask demands is a core cognitive function of human beings. Go/NoGoand Stop-Signal paradigms have been widely used in combinationwith neuroimaging techniques in order to investigate the neuralmechanisms associated with response inhibition. In a typical Go/NoGotask, a overt or covert response (i.e., button press or silent counting) ismade to one stimulus type (Go) and withheld to another (NoGo).Difference waves of event-related potentials (ERPs) between correctlyperformed NoGo-trials and Go-trials (NoGo minus Go) consistentlyreveal a frontocentral negative component around 200–400 ms post-stimulus onset (N2), followed by a frontocentral positive componentaround 300–600 ms post-stimulus onset (P3) (Albert et al., 2013;Eimer, 1993; Falkenstein, 2006; Falkenstein et al., 1999; Kok, 1986;Pfefferbaum and Ford, 1988). In the Stop-Signal task, where subjectsperform a speeded choice and occasionally receive a stop signal that

ersity, Dongchuan Road 800,6 21 34204717.

instructs them to withhold a response, the ERP difference wavesbetween correctly performed Stop-trials and Go-trials (Stop minusGo) also show prominent N2 and P3 components (Huster et al., 2010;Kok et al., 2004; Ramautar et al., 2006).

In spite of years of research, the precise functional significance ofthese two components is still under debate. Early studies interpretedboth N2 and P3 components as neural markers of response inhibition(Eimer, 1993; Falkenstein et al., 1999; Jodo and Kayama, 1992). How-ever, the relationship between N2 and response inhibition has beenquestioned by recent studies, which proposes that P3 primarily reflectsinhibitory process, whereas N2 seems to reflect “response conflict mon-itoring” rather than “response inhibition” (Albert et al., 2013; Donkersand van Boxtel, 2004; Enriquez-Geppert et al., 2010; Falkenstein,2006; Nieuwenhuis et al., 2003). Along with these findings, functionalmagnetic resonance imaging (fMRI) and ERP source localization studieshave suggested that response inhibition is subserved by a distributedbrain network, including superior frontal gyrus, middle frontal gyrus,medial frontal gyrus, inferior frontal gyrus, precentral gyrus, anteriorcingulate, insula, precuneus and inferior parietal lobule (Albert et al.,2013; Huster et al., 2010; Liddle et al., 2001; Rubia et al., 2001;Simmonds et al., 2008; Swick et al., 2011; Zheng et al., 2008). Interest-ingly, ERP source localization studies reported anterior frontal regionsand central regions as the primary neural generators for N2 and P3

372 X. Hong et al. / International Journal of Psychophysiology 93 (2014) 371–380

respectively, indicating a neuroanatomical segregation, which also sup-ported the functional dissociation of these two components (Husteret al., 2010; Kok et al., 2004; Lavric et al., 2004; Nieuwenhuis et al.,2003).

Neuroimaging studies using fMRI or near-infrared spectroscopy(NIRS) have shown that older adults had greater brain activationsthan younger adults when performing the Go/NoGo task (summarizedin Table 1), and these hyper-activations in the older brain wereinterpreted within a neural compensation framework (Heilbronnerand Munte, 2013; Langenecker and Nielson, 2003; Nielson et al.,2002). However, due to the low temporal resolution, neither fMRI norNIRS can characterize the precise time course of this age-related func-tional reorganization (Table 1) at the scale of milliseconds. Thus, thewithin-trial temporal and anatomical evolution of howolder individualsexhibit a compensatory inhibition-related neural response during N2and P3 periods is still unclear.

We examined this question in a visual Go/NoGo paradigm. Subjectswere required to direct their attention to the cued (left or right) visualfield, discriminate the forthcoming target at the attended location, andrespond to one type of target (Go) but withhold response to another(NoGo). We recorded behavioral performances and scalp electro-encephalography (EEG) from both younger and older adults duringthe experiments. Scalp ERP and source localization analyses werecarried out to investigate the spatiotemporal brain activations duringresponse inhibition. We expected to observe the delayed latencies ofN2 and P3 components due to normal aging. Also, we expected thatolder adults would show increased brain activations than youngeradults during N2 and P3 periods. Finally, since the N2 and P3 com-ponents were suggested to be neuroanatomically segregated and func-tionally dissociated,we hypothesized that the spatiotemporal pattern ofsuch age-related hyper-activations might differ between N2 and P3periods. The confirmation of these hypotheses would enrich our under-standing of the complicated neural processes involved in response inhi-bition, as well as the temporal course of functional brain reorganizationduring normal aging.

2. Material and methods

2.1. Participants

Twenty-three healthy younger students from Shanghai Jiao TongUniversity (mean age: 21.4 years; range: 18–25 years; 7 females; allright-handed) and eighteen healthy older adults from a neighboringcommunity (mean age: 61 years; range: 50–70 years; 11 females; allright-handed)were recruited in this study. There was no significant dif-ference of gender ratios between the two groups (Fisher's Exact Test, pN 0.05). Each participant had 9 years of minimum school education(mean ± standard deviation; younger: 14.1 ± 1.7 years vs. older:11.1 ± 2.7 years; t(27.040) = 4.223, p b 0.001). All participants reportednormal or corrected-to-normal vision, and no history of neurological orpsychiatric disorders. All older participants were evaluated to be cogni-tively healthy based on the Mini-Mental Status Examination (MMSE;

Table 1A summary of significantly increased brain activations (corrected p b 0.05) in older adults tapproaches (L: left hemisphere; R: right hemisphere).

Study Method NoGo stimuli

(Heilbronner and Munte, 2013) NIRS Fixed

(Nielson et al., 2002) and(Langenecker and Nielson, 2003)for a replication

fMRI Varied

mean: 28; range: 26–29), which is consistent with prior aging studies(Langenecker and Nielson, 2003; Nagamatsu et al., 2011; Nielsonet al., 2002). Each participant gave a written informed consent prior tothe experiment. The experimental protocol complied with the Dec-laration of Helsinki and was approved by the institutional ethicalcommittee.

2.2. Stimuli and procedures

A commercially available software (E-Prime 2.0, Psychology Soft-ware Tools, Inc., Sharpsburg, USA) was used to present stimuli and re-cord responses. All stimuli were presented on a 19 inch LCD display(Dell: P190SB) 60 cm in front of the participant. A black central crosshair(1.38° by 1.38° visual angle) and two black location marks (2.39° by2.39° visual angle, 9.05° from the vertical meridian, 7.2° below the hor-izontalmeridian)were presented on awhite background on thedisplay.Subjects were instructed to always maintain fixation on the centralcrosshair in each trial. Trial sequences and timing are illustrated inFig. 1. In each trial, a spatial cue (black arrow pointing left or right,2.24° by 1.62° visual angle) was first presented in the central for 200ms, directing the subject to covertly attend either the lower-left orlower-right square with equal probability, and totally ignore the otherlocation. After a random cue-target interval (CTI, 1000–1200 ms fromcue offset to target onset), a target (1.67° by 1.67° visual angle) was pre-sented for 200 ms inside either the attended or ignored square withequal probability. The target was either the letter “x” or plus sign,which was randomized across trials with equal probability. Subjectswere required to discriminate the target at the attended location, andrespond to the plus sign (Go-target) while refrain from responding tothe letter “x” (NoGo-target). Response was made by pressing a buttonof the response boxwith the right indexfinger as quickly and accuratelyas possible. Correctly responded Go-targets with response time be-tween 200 and 1800 ms were considered as valid trials. A fixed delayof 2600 ms was presented between the target offset and the onset ofnext trial.

Each block consisted of 60 trials for about 5 min, with a 2–3 minbreak between two successive blocks. Subjects were first given the ex-perimental instructions, and trained for at least one block to get familiarwith the task. For each experiment, there were 8 blocks in the youngergroup, and 6 blocks in the older group, given that older adults weremore likely to develop visual fatigue during the experiment. In total,480 and 360 trials were recorded for each younger and older adultrespectively.

2.3. EEG recording

EEG data were continuously recorded during the experiment usingBrainAmp MR Plus amplifier and EasyCap™ (Brain Products GmbH,Gilching, Germany) from 30 scalp locations (Fp1, Fp2, F3, F4, F7, F8, Fz,FC1, FC2, FC5, FC6, C3, C4, Cz, T7, T8, CP1, CP2, CP5, CP6, P3, P4, P7, P8,Pz, O1, O2, Oz, TP9, TP10). The electrodes TP9 and TP10 refer to inferiortemporal locations over the left and right mastoids, respectively. FCz

han younger adults during response inhibition in the literature based on hemodynamic

Lobe Brain structure Hemisphere

Frontal Precentral gyrus RMiddle frontal gyrus R

Parietal Postcentral gyrus RFrontal Middle frontal gyrus L, R

Inferior frontal gyrus LMedial frontal gyrus R

Parietal Inferior parietal lobule LSubcortical Claustrum L

Putamen L

Fig. 1. Experimental paradigm. In each trial, an arrow cue first instructed the subject to shift attention to either the left or the right lower visual field in anticipation of a forthcominglateralized target (the plus sign or letter “x”), while totally ignoring the rest visualfield. Targetswere presented after a randomcue-target interval of 1000–1200ms. Subjectswere requiredto discriminate the targets at the attended location, by making a button response as accurately and quickly as possible to Go-target (the plus sign) while withholding response to NoGo-target (the letter “x”).

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(the 31st electrode) was used as recording reference and AFz (the 32ndelectrode) was used as ground. The EEG signals were amplified and dig-itized at a sampling rate of 1000 Hz, with an online band-pass anti-aliasing filter between 0.016 and 100 Hz. Impedance at each electrodewas maintained below 10 kΩ throughout the recording. In order tomonitor eye movements and blinks, one electrode was placed on theouter left ocular canthus and another electrode was placed above theright eye to record horizontal and vertical electrooculograms (EOGs),respectively.

2.4. EEG analysis

2.4.1. EEG preprocessingEEG preprocessing was performed in the open source MATLAB-

based EEGLAB Toolbox (Delorme and Makeig, 2004) (http://sccn.ucsd.edu/eeglab/) and ERPLAB Toolbox (http://www.erpinfo.org/erplab/).Raw EEG data were first band-pass filtered into 0.1–40 Hz (a two-way, zero phase shift, Butterworth filter with a roll-off slope of 12 dB/oct), then subjected to a Parks McClellan notch filter to remove the re-maining noise at 50 Hz. The noises on EEG signals caused by eye blinksand eye movements were corrected by independent component analy-sis using the Infomax algorithm (Jung et al., 2000), as integrated inEEGLAB. Typically, only one or two independent components relevantto eye blinks or eye movements were removed for each subject. EEGdata were then re-referenced to the algebraic average of two mastoidelectrodes (TP9 and TP10), and segmented into target-related EEGepochs (−200–800 ms post-target). Physical artifacts were detectedin each EEG channel during the whole epoch window following thesteps: (i) a moving window (width: 200 ms; step: 50 ms) peak-to-peak functionwas used to examine themaximal allowed amplitude dif-ference of each EEG channel, with voltage threshold of ±150 μV; (ii) asimple voltage threshold function was used to examine the absolutevoltage value of each EEG channel, with voltage threshold of ±100 μV.Further, EEG epochs with overt eye movements during the wholeepoch window or eye blinks during the target period were marked asbad epochs following the steps: (i) a step function was used to detecteyemovements on the HEOG channel during thewhole epochwindow,with a moving window (width: 400 ms; step: 10 ms) and voltagethreshold of ±40 μV; (ii) a step function was used to detect eye blinks

on the VEOG channel around target period (−200–200 ms post-target), with voltage threshold of ±50 μV. Finally, all EEG epochs werevisually double checked to ensure the quality of the EEG data. Furtheranalysis would only include the correctly performed trials that wereartifact-free in all channels.

Since the targets at the unattended location did not require be-havioral responses, only trials with targets at the attended location(Go, NoGo) were analyzed in the present study. The average number(mean ± SEM [standard error of the mean]; collapsed across the leftand right cue trials) of EEG epochs used for further analysis was112.5 ±1.9 (Go trials) and110.3±1.4 (NoGo trials) for younger adults,77.4 ± 2.5 (Go trials) and 77.2 ± 2.1 (NoGo trials) for older adults.Though the number of EEG epochs is different due to the number ofblocks between the two groups, we did not find any statistical differ-ences in scalp ERP or source localization analyses between using thefirst 6 blocks EEG data and using all 8 blocks EEG data in the youngergroup. Therefore, we only reported results hereafter based on the 8blocks EEG data from younger adults unless otherwise specified. Afterimplementing a criterion of at least 20 EEG epochs per condition (Go,NoGo) per subject needed for a reliable N2 or P3 according to prior stud-ies (Albert et al., 2012; Cohen and Polich, 1997; Leue et al., 2013), oneolder subject was excluded due to excessive eye movements duringthe experiment. As a result, twenty-three younger adults and seventeenolder adults were included in further analyses.

2.4.2. Scalp ERP analysisTarget-locked ERPs were computed using the artifact-free EEG

epochs (−200–800ms post-target),with 200mspre-target as baseline.Individual ERPs were derived by averaging across trials of each kindof targets (correctly answered Go-target, correctly withheld NoGo-target) for each EEG electrode, and collapsed across the left and rightcue trials. In addition to the NoGo- and Go-ERPs, the difference waves(NoGo minus Go) were also computed, and averaged across thefrontocentral electrodes (Fz, Cz, FC1, FC2; as shown in Fig. 2 D), giventhat the most prominent N2 and P3 components were observed withinfrontocentral areas during response inhibition (Albert et al., 2013; Chiuet al., 2008; Falkenstein et al., 1999, 2002). To highlight the Go-NoGodifferences, we measured the N2 and P3 components in the difference

Fig. 2. Grand-averaged target-evoked ERPs within frontocentral ROI for younger (A) and older (B) adults. Go-target ERPs, NoGo-target ERPs, and difference waves (NoGo minus Go) areshown separately. N2d and P3dweremeasured on the differencewaves in both groups (C). Frontocentral ROI, including Fz, Cz, FC1 and FC2, is defined inD,where themost prominentN2dand P3d components were observed during response inhibition.

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ERPs, which were called “N2d” and “P3d”, respectively (Falkensteinet al., 2002).

Individual peak latency and amplitude (mean within a 40 ms timewindow around the peak latency) corresponding to N2d (most nega-tive) and P3d (most positive) were obtained within different timewin-dows in the two groups: (i) younger: 250–380 ms (N2d); 380–550 ms(P3d), (ii) older: 250–420 ms (N2d); 420–700 ms (P3d), according tothe observation of grand-averaged ERP waveforms (Fig. 2), whichwere also consistent with prior studies (Albert et al., 2013; Chiu et al.,2008; Leue et al., 2013; Lucci et al., 2013). Since the peak latency issensitive to high-frequency noise, individual ERPs were filtered by atwo-way, zero phase shift, finite impulse response low-pass filter of15 Hz (the eegfilt.m function in EEGLAB) before ERP latency measure-ments (Leue et al., 2013; Luck, 2005). Since the mean amplitude is lesssensitive to high-frequency noise, this filter was not applied to ERPamplitude measurements (Luck, 2005). Individual peak latency andamplitude were submitted to the following statistical analysis.

2.4.3. Source localization analysisStandardized low resolution brain electromagnetic tomography

(sLORETA) was used to compute the cortical three-dimensional distri-bution of current density based on individual topographical distribu-tions of the 28 channels scalp ERPs for each subject (Fuchs et al., 2002;Jurcak et al., 2007; Pascual-Marqui, 2002). Briefly, the intracerebralvolume is partitioned into 6239 voxels at 5 mm spatial resolution, and

sLORETA images represent the standardized electric activity, that is,the exact magnitude of the estimated current density, at each voxel inneuroanatomical Montreal Neurological Institute (MNI) space. Withmore than 25 channels of scalp EEG recordings, LORETA can havegood correspondencewith fMRI or positron emission tomographymea-surements in the same task (Mulert et al., 2004; Pascual-Marqui, 2002;Pizzagalli et al., 2004). The deep brain sources, such as the cingulatecortex (Albert et al., 2012; Huster et al., 2010; Lorenzo-Lopez et al.,2008; Pandey et al., 2012) and the mesial temporal lobe (Zumsteget al., 2006), have been successfully localized with this approach.Further, it is worth emphasizing that sLORETA has shown its successin analyzing the differences in brain source between NoGo and GoERPs in previous studies on healthy younger adults or male alcoholics(Albert et al., 2013; Chiu et al., 2008; Pandey et al., 2012).

In the present study, source localization analysis was conducted onthe N2d and P3d components in the difference waves, which is usefulto reveal activation that is exclusive to Go-NoGo differences during re-sponse inhibition. On the other hand, thismakes our results comparableto prior neuroimaging studies that were based on hemodynamic con-trasts between NoGo and Go conditions (Heilbronner and Munte,2013; Swick et al., 2011; Zheng et al., 2008), or those ERP source locali-zation studies on healthy younger adults that were also based on thedifferential brain sources between NoGo- (or Stop) and Go-ERPs(Albert et al., 2013; Huster et al., 2010; Kok et al., 2004; Lavric et al.,2004; Nieuwenhuis et al., 2003). N2d and P3d sLORETA images for

375X. Hong et al. / International Journal of Psychophysiology 93 (2014) 371–380

each group were computed by averaging across individual sLORETAimages. Individual sLORETA images were submitted to the followingstatistical analysis.

2.5. Statistical analysis

Independent-samples t-test (2-tailed) was used to compare thebehavioral performance and ERP components between the two groups.Levene's test was used to examine the equality of variances andcorrected p-values were provided if appropriate. Statistical analysiswas performed in SPSS 16.0. For the source localization results, weperformed voxel-by-voxel between-group comparisons of the currentdensity power of N2d and P3d sLORETA images to examine the agingeffects on current density distribution, which was implemented in thesLORETA software. Statistical significance was assessed based on theempirical probability distributions and corresponding significancethresholds that were generated by 5000 random permutations. ThisSnPM (Statistical non-Parametric Mapping) approach corrects multiplecomparisons and does not require Gaussian assumption (Nichols andHolmes, 2002). Statistical significance was accepted for values of p b

0.05 and all results were presented as mean ± SEM.

3. Results

3.1. Behavioral performance

First, to assess the overall behavioral performance, we analyzed theaccuracy and response time (RT) in both groups. Accuracy was definedas thepercentage of all correctly performed trials, including bothGo andNoGo targets presented at all locations. RTwas calculated as themeanofall correctly performed Go-targets. The younger group performedmarginally better than the older group (younger: 99.52% ± 0.08% vs.older: 98.91% ± 0.31%; t(18.027) = 1.893, p = 0.074), though the abso-lute difference of accuracy was very small (b1%). Also, both groupsshowed high accuracy (N98%), suggesting successful attention deploy-ment and target discrimination in the task. Moreover, younger adultsresponded significantly faster than older adults (younger: 477.56 ±10.74 ms vs. older: 556.49 ± 28.46 ms; t(20.582) = −2.595, p =0.017),suggesting a response slowing during normal aging.

Second, to compare the performance of response inhibition betweenthe two groups, we defined the false alarm rate (FAR) as the percentageof incorrect responses to NoGo-targets at the attended location,collapsed across the left and right cue trials. There was no significantdifference of FARs between the two groups (younger: 0.52% ± 0.13%vs. older: 1.17% ± 0.50%; t(18.115) = −1.250, p N 0.2). The negligibleFARs (b1.5%) suggest that both younger and older adults showedsuccessful response inhibition in the Go/NoGo task.

3.2. ERP results

Fig. 2 A (younger adults) and Fig. 2 B (older adults) show the grand-averaged target-evoked ERP waveforms within frontocentral ROI,collapsed across the left and right cue trials. Fig. 2 C illustrates the differ-ence waves (NoGo- minus Go-ERPs) in the two groups. Fig. 3 A (youn-ger adults, post-target 240–560 ms) and Fig. 3 B (older adults, post-target 280–600 ms) are topographical maps of the difference waves,grand-averaged within every successive 20 ms window in each group.Prominent N2d and P3d components could be observed within thefrontocentral area in both groups.

For the N2d component, therewas no significant difference in eitherlatency (t(22.647)=−1.247, p N 0.2) or amplitude (t(38)= 0.779, p N 0.4)between the two groups (see Fig. 3 C). However, as Fig. 3 D illustrates,older adults showed later latency (t(20.760) = −4.876, p b0.001) andlarger amplitude (t(38) = −3.121, p = 0.003) than younger adults inP3d component. Taken together, our results show that the P3d

component was delayed and enhanced, while the N2d componentwas not significantly influenced during normal aging.

3.3. sLORETA results

The grand-averaged sLORETA images of N2d and P3d for youngeradults are shown in Fig. 4 A and C, respectively. Specifically, the regionsof current density distribution around N2d latency include frontal lobe(medial frontal gyrus, superior frontal gyrus, inferior frontal gyrus,middle frontal gyrus, orbital gyrus and rectal gyrus) and limbic lobe(anterior cingulate). The regions of current density distribution aroundP3d latency include frontal lobe (medial frontal gyrus, precentral gyrus,paracentral lobule and superior frontal gyrus), limbic lobe (cingulategyrus) andparietal lobe (postcentral gyrus). Overall, these brain sourcesfor response inhibition are consistentwith previous neuroimaging stud-ies (Huster et al., 2010; Liddle et al., 2001; Rubia et al., 2001; Simmondset al., 2008; Swick et al., 2011).

The grand-averaged sLORETA images of N2d and P3d for older adultsare shown in Fig. 4 B and D, respectively. Compared with youngeradults, older adults showed larger and more distributed current densi-ties. Specifically, the regions of current density distribution aroundN2d latency include frontal lobe (medial frontal gyrus, superior frontalgyrus, precentral gyrus, middle frontal gyrus, orbital gyrus, rectalgyrus, sub-gyral, paracentral lobule and inferior frontal gyrus), limbiclobe (anterior cingulate) and parietal lobe (postcentral gyrus and supe-rior parietal lobule). The regions of current density distribution aroundP3d latency include frontal lobe (medial frontal gyrus, precentral gyrus,paracentral lobule, superior frontal gyrus, middle frontal gyrus and sub-gyral), limbic lobe (paracentral lobule and cingulate gyrus) and parietallobe (postcentral gyrus and precuneus).

SnPMwas used to statistically compare the current density distribu-tion between the two groups duringN2d and P3d periods separately. Ascan be observed in Fig. 5, older adults showed increased current densi-ties than younger adults during both N2d and P3d periods. Specifically,as Table 2 lists, the regionswhere older individuals showed significantly(corrected p b 0.05) increased current density than younger individualsare observed mostly in central areas (precentral gyrus and postcentralgyrus) but much less in prefrontal areas (superior frontal gyrus andmiddle frontal gyrus) during the N2d period. In contrast, during theP3d period, the regions showing significantly (corrected p b 0.05)increased current density are primarily observed in prefrontal areas(superior frontal gyrus, middle frontal gyrus, medial frontal gyrus, andinferior frontal gyrus) but much less in central (precentral gyrus) orparietal areas (inferior parietal lobule) (see Table 3). Another interest-ing finding is that all increased activations in the older brain were ob-served in the right hemisphere (Fig. 5). Finally, it should be noted thatthere was no region showing significantly decreased activation duringaging. Taken together, our sLORETA results reveal that older individualsrecruit a significantly larger and more distributed neural networkduring response inhibition, and that these age-related networks dy-namically shift in sequence relative to the ERP timewindow of interest.

4. Discussion

The main objective of the present study was to characterize thespatiotemporal dynamics of age-related brain reorganization during re-sponse inhibition. We found that older adults showed increased andmore distributed brain current densities than younger adults duringboth N2d and P3d periods. Interestingly, these increases were mostlyobserved in precentral and postcentral gyri during the N2d period,which rapidly shifted to dorsolateral prefrontal cortex (DLPFC) and infe-rior frontal gyrus during the P3d period. Taken together, these resultsclearly illustrate the spatiotemporal brain dynamics that serve as theneural substrate of response inhibition, and also illustrate the dynamicfunctional brain reorganization that is a consequence of normal aging.

Fig. 3. Grand-averaged topographical maps of N2d and P3d components for younger (A) and older (B) adults. Between-group comparisons of N2d and P3d are shown in C and D,respectively. The amplitudes and latencies of N2d and P3d were measured within the frontocentral ROI. The error bars indicate SEM.

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4.1. Normal aging delays P3d latency during response inhibition

Our behavioral results support the well-known response slowingduring normal aging, which is accompanied by optimal accuracy andlow FAR (Salthouse, 1996), though older adults showed slightly loweraccuracy than younger adults. Consistently, our ERP results show thatthe P3d is significantly delayed during aging. In contrast, the latenciesof N2d between the two groups are not significantly different. Overall,these results are concordant with previous studies that the N2d wasnot delayed (Lucci et al., 2013) or slightly delayed (Falkenstein et al.,2002) during aging. Such differential effects of aging on the N2d andP3d may result from different cognitive processes engaged, that is,these two components are functionally dissociated in the Go/NoGotask. Specifically, prior work reported that the N2d was elicited byrare or low-frequent stimuli regardless of their association with aNoGo or Go response, which supported that the N2d reflected moreconflict monitoring rather than inhibitory processing (Albert et al.,2013; Donkers and van Boxtel, 2004; Enriquez-Geppert et al., 2010;Nieuwenhuis et al., 2003).Moreover, by analyzing the ERPs of unattend-ed targets (results not included here), we did not observe the NoGo-N2component, but still a prominent NoGo-P3 component for the un-attended targets (always requiring no response). This is not surprisingbecause the cue that preceded all targets always caused the responsepreparation, which however, would be inhibited if the following targets

appeared at the unattended locations. Therefore, our data also supportthe idea that these two components are functionally dissociated (noconflict exists for the unattended targets). In the present study, theGo/NoGo taskwas simplewith equal probability of Go andNoGo targetsat the attended location,which has a lowdemand of conflictmonitoringand results in good accuracy as well as negligible FARs in both groups.Consistent with recent findings, such low involvement of conflict mon-itoring could prevent us observing the aging effect on the N2d (Lucciet al., 2013). It is noted that the simple task in the present aging studywas helpful to minimize the aging effects from other complicatedcognitive processes, such as working memory that could become aninterference if we use complex tasks (Simmonds et al., 2008).

Our results confirmed the frontocentral P3d component as a primaryneural marker of inhibitory processing (Albert et al., 2013; Enriquez-Geppert et al., 2010; Smith et al., 2008, 2013). One concern here isthat the P3d component might be generated by the movement-related(i.e., button press) negativity in Go-ERPs instead of the inhibition-related positivity in NoGo-ERPs (Salisbury et al., 2004). However,other studies argued against this idea by observing the robust P3d com-ponent in the silent counting version of Go/NoGo tasks, where no overtmovement occurred (Bruin andWijers, 2002; Smith et al., 2008, 2013).Another concern in the present study is that the inhibition requirementmight be reduced in such an easy Go/NoGo task, which seems to besupported by the low FARs. However, the Go/NoGo target was always

Fig. 4. Group-averaged sLORETA images of ERP components for younger (N2d: A; P3d: C) and older adults (N2d: B; P3d: D). Each map consists of top, bottom, back, front, left and rightviews (L: left; R: right; A: anterior; P: posterior; S: superior; I: inferior). The scale bars indicate current density values.

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preceded by an instructive cue that led to increased response prepara-tion in order to get a fast response to Go-targets. In this case, a preparedresponse had to be aborted when a NoGo-target appeared at the cuedlocation, which led to a robust response inhibition process and causedthe prominent P3d component (Bruin et al., 2001; Smith et al., 2006,2007). Moreover, the low FARwe found is also consistentwith previousresearch on aging and response inhibition (Falkenstein et al., 2002).Therefore, ourfinding of the delayed P3d latency in the older group sug-gests that the inhibitory processing was significantly delayed duringaging, which agrees with previous studies (Falkenstein et al., 2002;Lucci et al., 2013), and supports a processing speed account of cognitiveaging (Salthouse, 1996).

4.2. Age-related functional reorganization showedspatiotemporal dynamics

The present sLORETA results of N2d and P3d in the younger groupare consistent with fMRI studies of response inhibition that have ob-served activations in superior frontal gyrus,middle frontal gyrus,medialfrontal gyrus, inferior frontal gyrus, anterior cingulate, cingulate gyrusand precentral gyrus (Garavan et al., 1999; Liddle et al., 2001; Rubiaet al., 2001; Simmonds et al., 2008; Swick et al., 2011; Zheng et al.,2008). Interestingly, we found that the brain sources for N2d show

more anterior distribution (Fig. 4 A), while the sources for P3d aremore centrally distributed (Fig. 4 C). Such results are concordant withprior ERP source localization studies that the N2dwasmainly generatedin prefrontal regions (Huster et al., 2010; Lavric et al., 2004;Nieuwenhuis et al., 2003), whereas the P3d was mainly generated incentral areas (Huster et al., 2010; Kok et al., 2004; Ramautar et al.,2006). Taken together, our sLORETA results support the view that theneural generators of N2d and P3d components are partially segregatedfrom a neuroanatomical perspective, which also implies that differentcognitive processes, i.e., conflict monitoring and inhibitory processing,are engaged during response inhibition tasks (Albert et al., 2013;Huster et al., 2010; Nieuwenhuis et al., 2003).

Between-group comparisons of sLORETA images reveal significantlyincreased activations in the older brain, including superior frontal gyrus,middle frontal gyrus, medial frontal gyrus, inferior frontal gyrus,precentral gyrus, postcentral gyrus and inferior parietal lobule (Fig. 5).Consistent with the NIRS study (Heilbronner and Munte, 2013), wefound the age-related increase of brain activation only in the righthemisphere, i.e., the right DLPFC and inferior frontal gyrus. This is notsurprising because the right hemispheric dominance is a commonfinding in response inhibition studies (Aron et al., 2003; Garavan et al.,1999; Konishi et al., 1999; Swick et al., 2011; Zheng et al., 2008). Inter-estingly, the age-related differences for the N2d component, which was

Fig. 5. sLORETA-based Statistical non-Parametric Mapping (SnPM) of between-group comparisons (older vs. younger) for the current density values of N2d (A) and P3d (B) components.Each map consists of slice views from axial, sagittal and coronal planes for the same area with maximal t-value, and scalp views from the top, left and right (L: left; R: right; A: anterior;P: posterior). The scale bars indicate t-values (positive: older N younger). The corresponding t-values of statistical significance (corrected p = 0.05) are 3.910 and 3.823 in A and B,respectively.

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suggested to reflect conflict monitoring rather than inhibitory process-ing, were also right-lateralized. According to a recent review paper(Park and Reuter-Lorenz, 2009), the older brain likely recruits comple-mentary activation in the regions that are less dedicated to the task.Therefore, we speculate that the right sensori-motor cortex of theolder brain would be more likely to get involved when the right hand

Table 2Brain areas that showed significantly different activations (corrected p b 0.05) between young

Lobe Brain structure MNI coordinates (X Y Z)

Older N youngerFrontal Precentral gyrus 20 −20

30 −2025 −2015 −2035 −2525 −3025 −3025 −3030 −3035 −2530 −30

Superior frontal gyrus 20 −1515 −15

Middle frontal gyrus 25 −1510 −1510 −1515 −15

Parietal Postcentral gyrus 20 −3020 −3020 −3020 −3520 −3525 −35

Older b younger

is used to respond. In contrast, the aging studies using fMRI revealedincreased activations in both hemispheres (Langenecker and Nielson,2003; Nielson et al., 2002). Such inconsistencies might be due to differ-ent complexity of the Go/NoGo tasks. According to a meta-analysis thatdivided the Go/NoGo tasks into “simple” (fixed NoGo stimuli) and“complex” (varied NoGo stimuli depending on context) versions

er and older adults during the N2d period.

Hemisphere Brodmann area t-Value

70 R 6 4.3165 R 6 4.2860 R 6 4.1970 R 6 4.1470 R 6 4.1070 R 4 4.2165 R 4 4.2060 R 4 4.0570 R 4 4.0365 R 4 3.9865 R 4 3.9770 R 6 4.0570 R 6 4.0465 R 6 4.0065 R 6 3.9970 R 6 3.9960 R 6 3.9965 R 3 4.2470 R 3 4.2360 R 3 4.0965 R 3 3.9770 R 3 3.9570 R 3 3.92No significant areas

Table 3Brain areas that showed significantly different activations (corrected p b 0.05) between younger and older adults during the P3d period.

Lobe Brain structure MNI coordinates (X Y Z) Hemisphere Brodmann area t-Value

Older N youngerFrontal Superior frontal gyrus 5 40 55 R 8 4.63

5 35 55 R 8 4.395 40 50 R 8 4.365 35 50 R 8 4.3010 40 50 R 8 4.2610 35 50 R 8 4.235 45 50 R 8 4.1910 45 50 R 8 4.080 35 50 R 8 4.0715 35 55 R 8 4.0515 45 50 R 8 4.015 30 60 R 6 3.945 30 55 R 8 3.9310 30 60 R 6 3.905 30 50 R 8 3.88

Middle frontal gyrus 55 15 35 R 9 4.2150 15 40 R 9 4.0050 15 45 R 9 3.90

Medial frontal gyrus 5 35 45 R 8 4.085 40 45 R 8 3.9610 35 45 R 8 3.910 40 45 R 8 3.8910 30 50 R 8 3.87

Inferior frontal gyrus 55 10 40 R 9 4.1555 5 35 R 9 3.9655 10 35 R 9 4.1050 10 35 R 9 3.9460 10 30 R 9 3.91

Precentral gyrus 60 5 30 R 6 3.9760 5 35 R 6 4.12

Parietal Inferior parietal lobule 60 −35 30 R 40 3.8965 −40 30 R 40 3.8960 −40 30 R 40 4.1165 −40 35 R 40 3.85

Older b younger No significant areas

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(Simmonds et al., 2008), the right DLPFC only showed reliable ac-tivations in the complex tasks which demanded frequent updating ofstimulus–response associations inworkingmemory (see Table 1). Com-pared with the complex task in those fMRI studies (Langenecker andNielson, 2003; Nielson et al., 2002), the NIRS study (Heilbronner andMunte, 2013) and the present study used a simple task with the fixedNoGo stimuli. Therefore, the increased right DLPFC activation in thesimple Go/NoGo task implies a functional compensation in the olderbrain (Heilbronner and Munte, 2013).

Our results extend previous findings (Table 1) by illustrating thespatiotemporal dynamics of functional brain reorganization during re-sponse inhibition. Specifically, we observed increased neural activationin the older brain which rapidly shifted from central areas during theN2d period (precentral and postcentral gyri, ~346 ms post-targetonset, see Fig. 5 A) to prefrontal areas during the P3d period (DLPFCand inferior frontal gyrus, ~554 ms post-target onset, see Fig. 5 B).This age-related dynamic reorganization at the millisecond scale couldnot be revealed by fMRI or NIRS studies (Table 1). Interestingly, the re-gions with increased activation in the older brain (N2d period: centralareas; P3d period: prefrontal areas) differ from those in the youngerbrain (N2d generators: prefrontal areas; P3d generators: centralareas). Such results support a functional plasticity hypothesis thatbrain reorganization occurs in adjacent or homologous cortical regions(Greenwood, 2007). This hypothesis was also supported by lesion stud-ies. For example, the brain area controlling theparetic limbwas found toexpand into adjacent regions in stroke patients (Liepert et al., 1998; Roet al., 2006). Further, these age-related spatiotemporal dynamicsfrom N2d to P3d periods also have implications for the ongoing debateon response inhibition, supporting the idea that these two componentsreflect different cognitive processes, i.e., conflict monitoring and

inhibitory processing, respectively (Albert et al., 2013; Enriquez-Geppert et al., 2010; Huster et al., 2010; Nieuwenhuis et al., 2003).

In the past two decades, neuroimaging studies have provided evi-dence that increased neural activation as a consequence of aging maybe functionally supportive of cognition in older adults (Cabeza et al.,2002; Grady, 2012; Greenwood, 2007; Park and Reuter-Lorenz, 2009;Spreng et al., 2010). Consistently, the increased frontal activations ob-served in older individuals during response inhibition were suggestedto be related to inhibitory performance and interpreted within a neuralcompensation framework (Langenecker and Nielson, 2003; Nielsonet al., 2002). To our knowledge, this is the first study to investigate thetemporal course of this age-related functional reorganization during re-sponse inhibition.We observed not only age-related increases in neuralactivation, but also a dynamic spatiotemporal reorganization of neuralactivity in the older group, with comparable inhibitory ability relativeto the younger group (no significant difference of FARs between thetwo groups). Based on these findings, we infer that age-related neuralcompensation not only occurs as an increase in overall brain activitywithin a modular neural network, but also occurs as a difference inthemillisecond-level activation of brain regions that serve as the neuralsubstrate of response inhibition. One limitation in the present studywas that the relation between brain activation and inhibitory per-formance could not be investigated to test the compensatory inter-pretation, as the older adults showed negligible FARs during theexperiment and RT to Go-targets is not an appropriate measure of in-hibitory performance (Williams et al., 1999). We recommend to usethe stop-signal reaction time in the Stop-Signal paradigm as a mea-sure of inhibitory performance to investigate the brain–behavior re-lationship (Verbruggen and Logan, 2008), and extend the currentfindings.

380 X. Hong et al. / International Journal of Psychophysiology 93 (2014) 371–380

Acknowledgments

This work was partly supported by the National Basic ResearchProgram of China (973 Program) (No. 2011CB013304). X. H. is sup-ported by China Scholarship Council (201206230034). J.S. is partlysupported by Med-X Research Fund of Shanghai Jiao Tong University(No. YG2012MS09). We thanked Yixue Li for assistance with the datacollection.

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