Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study

Asato Morita; Yasunori Morishima; David W. Rackham

doi:10.1155/2015/189060

Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study

Morita, Asato;Morishima, Yasunori;Rackham, David W. 2015-05-27 00:00:00 Hindawi Publishing Corporation Asian Journal of Neuroscience Volume 2015, Article ID 189060, 9 pages http://dx.doi.org/10.1155/2015/189060 Research Article Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study 1 2 2 Asato Morita, Yasunori Morishima, and David W. Rackham Graduate School of Arts and Sciences, International Christian University, Tokyo 181-8585, Japan Department of Psychology, International Christian University, Tokyo 181-8585, Japan Correspondence should be addressed to Asato Morita; asamorita@gmail.com Received 17 January 2015; Revised 12 April 2015; Accepted 30 April 2015 Academic Editor: Jinsung Wang Copyright © 2015 Asato Morita et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate time estimation is crucial for many human activities and necessitates the use of working memory, in which the dorsolateral prefrontal cortex (DLPFC) plays a critical role. We tested the hypothesis that the DLPFC is activated in participants attempting time estimations that require working memory. Specifically, we used functional near-infrared spectroscopy (fNIRS) to investigate prefrontal cortical activity in the brains of individuals performing a prospective time production task. We measured cerebral hemodynamic responses in 26 healthy right-handed university students while they marked the passage of specified time intervals (3, 6, 9, 12, or 15 s) or performed a button-pressing (control) task. eTh behavioral results indicated that participants’ time estimations were accurate with minimal variability. The fNIRS data showed that activity was significantly higher in the right DLPFC during the time estimation task compared to the control task. Theoretical considerations and the results of this study suggest that DLPFC activation resulting from time estimation indicates that the working memory system is in use. 1. Introduction It posits that time estimation is based on three information- processing stages: clock, memory, and decision [6]. In the The ability to estimate time plays an important role in adap- SET, a hypothetical internal pacemaker emits pulses that are tation to the environment. For example, when a student gated by a switch during the current to-be-timed interval takes a test, accurate estimation of time intervals is necessary andthensenttoanaccumulator [7]. The content in the to ecffi iently solve many problems within the time limit. accumulator (number of pulses) corresponds to the current Specicall fi y, time estimation refers to appraising temporal time. eTh accumulator’s content is transferred and stored in duration without using information from a clock. Whereas the working memory, which is compared with learned time the number of time estimation studies has increased in recent labels for known intervals previously stored in the reference years [1], the underlying mechanisms remain unclear. Time memory. eTh comparison between these accumulated pulses estimation in the range of seconds to minutes is considered in working memory and learned temporal representations in to be controlled by diverse cognitive systems [2], whereas reference memory determines the time estimation response. time estimation in the millisecond range is thought to be According to this model, individual differences in time associated with the motor system [3]. As our interest is estimation may be attributable to alterations in pacemaker accurate time estimation in daily activities, we focused on the speed, memory efficiency, and comparator function [ 5]. biological substrates and cognitive systems of time estimation Despite the effectiveness of the SET in explaining various in the range of seconds. behavioral and physiological results, its relevance to the Several models have been developed to account for time neural substrates involved in accurate time estimation is not estimation [4, 5]. The scalar expectancy theory (SET) is the fully clear [8]. Many lines of evidence suggest that separate most popular model of time estimation (see Figure 1)[6, 7]. brain mechanisms are responsible for different stages of 2 Asian Journal of Neuroscience of posture on time estimation is unknown. During fMRI Accumulator Pacemaker Clock stage: scanning, participants lie in a supine position, whereas time estimation experiments using behavioral techniques are usu- Switch/gate ally performed while the participant is sitting upright in front of a monitor [14]. Muehlhan and colleagues [14] investigated Reference Working theeeff ctofbodypostureoncognitiveperformance,andtheir Memory stage: memory memory results indicate that sleep quality strongly affected reaction times when participants performed a working memory task in thesupineposture,but theseeeff ctswerenot observed in the sitting position. It has also been reported that differences Decision stage: Comparator in orthostatic load between sitting and supine positions lead to physiological changes [14–16]. Additionally, there are beneficial effects of the sitting position over the supine position on cognitive performance [14]. Time estimation In the present study, we employed functional near-infra- red spectroscopy (fNIRS) to investigate prefrontal activity Figure 1: Scalar expectancy theory (SET) applied to the time pro- during the time production task. This noninvasive neu- duction task. In SET, a pacemaker emits pulses to an accumulator, and the number of pulses is transferred to the working memory roimaging technique enables the measurement of relative by way of an accumulator. eTh corresponding number of pulses changes in concentrations of oxygenated and deoxygenated is stored in the working memory and compared with that of the hemoglobin (oxy- and deoxy-Hb, resp.) in the superficial reference intervals stored in reference memory. When the numbers layer of the cortex [17]. Although fNIRS can only record at of pulses match, the participant responds. the brain surface and has low spatial resolution (3 cm), it can tolerate movement and is suitable for use in seated partici- pants [18]. Neurologically normal adults are relatively accurate in the SET [8]. In this framework, a memory stage is functionally separated from other processing stages [9], andaccuratetime estimating time (e.g., [19, 20]). We were interested in partic- estimation capacity is heavily dependent on working memory ipants’ capacity for accurate time estimation of conventional duration units (i.e., we wanted to determine how accurately efficiency. Several recent reviews and meta-analyses of neuroimag- a student estimates 15 seconds), for which there are few ing studies have shown that many parts of the brain con- studies. Various tasks have been employed to investigate tribute to time estimation. Macar and colleagues [10] defined individuals’ time estimation. eTh present study employed a the dorsolateral prefrontal cortex (DLPFC), anterior cingu- prospective time production task to quantify time estimation; late, right inferior parietal lobe (IPL), supplementary motor this methodology is widely used [21]because theparticipants’ area (SMA), cerebellum, and basal ganglia (caudate and load is minimal, and the experimental procedure is easy. This putamen) as thecoretimeestimationnetwork.Lewis and task is suitable for our research objective because it relies on the scaling of subjective time by units used in daily life. In the Miall [11] reviewed many neuroimaging studies of timing and concluded that suprasecond timing tasks most commonly time production task, participants are asked to indicate when activated the bilateral prefrontal cortex, bilateral parietal a stated time has elapsed. In particular, the task requires the participants to mark the “start” and “stop” when they thought cortex, and cerebellum. In their studies, the right DLPFC was the most frequently activated area. In contrast, a rel- anidentiefi dtimeperiodhadpassed.Weusedtheprospective atively recent meta-analysis reported that the SMA and time estimation paradigm [22] where participants know in right inferior frontal gyrus were part of the core network advancethattheywillbeasked to produceagiventime mediating time estimation in the brain, whereas the DLPFC duration. eTh control task was pressing a button twice in a manner similar to the time production task, but without a was less important for time estimation [1]. u Th s, there have been inconsistencies regarding the neural correlates of time specified target time. This task was equivalent to the motor estimation in previous studies, probably because different requirement of the time production task. We consider that accurately producing a particular duration depends on the brain structures are activated depending on the paradigm, temporal task, and duration range used [1, 3]. memory stage of the SET; the participant should make a Most studies have used functional magnetic resonance comparison between accumulated pulses in working memory imaging (fMRI) to examine brain activity during time estima- and a learned temporal representation of reference memory. tion, as it can noninvasively measure brain activity, does not When participants must accurately produce target dura- involve radiation, and has high spatial resolution. However, tion in conventional time units, the SET [6, 7] would predict several shortcomings are associated with fMRI, including that they compare the content of time units accumulated and confined spaces, scanner noise, and the loss of situational stored in working memory with the representation of time stored in reference memory. Working memory is important control experienced by the participant, which can elicit anx- iety and stress (e.g., [12]) that in turn could affect behavioral for a wide range of high-level cognitive activities and is and neurological data [13]. In addition to these disadvantages commonly dene fi d as the system used to temporarily store information and then manipulate the information online dur- of fMRI, the time estimation may also be aeff cted by the dieff renceinthe body posture. However, theactualeeff ct ing cognitive activities [23]. Previous studies have shown that Asian Journal of Neuroscience 3 the DLPFC plays a critical role in working memory. Pattern A Pattern B Patients with traumatic brain injury or Parkinson’s disease Start Start generally show working memory deficits and inaccurate time Rest: 30 s Rest: 30 s estimation [3]. Workingmemorycapacityisthought to be (1) Time production task (1) Button-pressing task indispensable for time estimation, as the frontal lobe would Rest: 60 s Rest: 60 s (2) Button-pressing task (2) Time production task be needed to store the current interval in working memory, Rest: 60 s Rest: 60 s recall a sense of time in reference memory, and compare (3) Button-pressing task (3) Time production task both values. Rest: 60 s Rest: 60 s The present study compared fNIRS-measured frontal (4) Time production task (4) Button-pressing task cortex activity during the time production task with that Rest: 60 s Rest: 60 s measured during the control task. Specicfi ally, we tested the (5) Time production task (5) Button-pressing task hypothesis that the DLPFC would be activated in participants Rest: 60 s Rest: 60 s attempting time estimations that required the working mem- (6) Button-pressing task (6) Time production task ory. Rest: 30 s Rest: 30 s End End This study was designed to assess the role of the working memory system and identify the brain structures involved Figure 2: Schematic representation of the two sets of instruction in accurate time estimation. We intended to separate the sequences (Patterns A and B) that comprised the estimation of memory and decision-stage functions from clock-stage func- time duration test. All participants were assigned to one of two tion, which merely predicts the time interval by comparing groups. Each group was then assigned to one of two patterns to the time production and button-pressing tasks. eTh time balance the two conditions. Each condition was repeated three times production task uses clock, memory, and decision stages that in a predetermined pattern; thus, the experiments consisted of six rely on both working and reference memories [3], whereas blocks, with a 60 s break between blocks. eTh time production task the button-pressing task does not require memory processes. consisted of marking the start and end of perceived times of specified The results were interpreted within the SET theoretical intervals by pressing a button on the response box. eTh intervals (3, framework. To our knowledge, this is the rfi st report of the 6, 9, 12, or 15 s) were randomized for each participant. eTh button- assessment of prefrontal activity using fNIRS during a time pressing task was a control task. production task. the onset of the next trial (Figure 3)). The participants did not 2. Materials and Methods receive any feedback. 2.1. Participants. Twenty-six right-handed, healthy volun- teers (7 males and 19 females, mean age = 20.58 years, SD = 2.3. Apparatus and Stimuli. The experiment was pro- 2.00, and range: 19–27 years) participated in the study. grammed and run using SuperLab Pro 4.5 for Windows, Handedness was assessed using the Edinburgh Handedness with a Cedrus RB–540 response box used to record the par- Inventory [24]. All participants provided written informed ticipant responses (Cedrus Corporation, San Pedro, CA). For consent prior to participating in the experiment, for which alltasks,the stimuliwerepresented on alaptopcomputer they received a coupon worth 500 Japanese yen at the end (Let’s NOTE CF-R5, Panasonic Corporation, Osaka, Japan) of the experiment. This study was conducted in accordance with a display area of 21.1× 15.8 cm and a screen resolution of with the Declaration of Helsinki [25]and wasapprovedby 1024× 768 pixels. eTh words were presented in the center of the relevant ethics committee. the computer screen in black MS Gothic 48-point font on a white background. The distance from the laptop screen to the 2.2. Experimental Design. The design of the fNIRS exper- participant’s head was approximately 60 cm. A second laptop iment was a simple block design with one experimental computer (VOSTRO-3750, Dell Inc., Round Rock, TX) was and one control task condition. eTh 26 participants were used to record and analyze fNIRS data. divided into 2 groups of 13 participants. Each group was then assigned to one of two patterns to balance the two conditions 2.4. Time Estimation and Control Tasks. The time production (Figure 2). Cerebral activations measured with fNIRS were and button-pressing task protocols are shown in Figure 2. then compared between the two conditions. Each condition The experiment session began with 30 s of rest (no body was repeated three times in a predetermined pattern; thus, movement). Subsequently, a fixation cross was displayed, the experiments consisted of six blocks with a 60 s break and an auditory cue (a pure sine wave of 800 Hz) was between each block (Figure 2). The time production task played for 100 ms between trials to arouse attention. eTh n, block consisted of five trials, each of which contained the the instructions were given to the participant. A marking vfi e intervals (3, 6, 9, 12, and 15 s). The order of trial stimulus was synchronously presented at the start and end of presentation was counterbalanced across participants. eTh each trial and externally fed into the fNIRS device. button-pressing task consisted of five trials to match the time In the time production task, participants were instructed interval required for the time production task. er Th e was a to subjectively estimate the presented length of time. eTh 500 ms interval when the cue was displayed on the screen specified length of time to produce was displayed on the hor- between each trial (i.e., between the participant response and izontal axis of the computer monitor for 1700 ms. eTh screen 4 Asian Journal of Neuroscience Produced interval Cue: 500 ms Instruction: 1700 ms Displayed until push Displayed until push Start Cue: 500 ms Stop 3 s (a) Time production task Time Cue: 500 ms Instruction: 3400 ms Displayed until push Displayed until push 1st + Cue: 500 ms Press the button 2nd twice (b) Button-pressing task Figure 3: Sequences of events during the time production and button-pressing tasks. (a) Time production task. eTh number of seconds to be produced wasshown on thescreen, andtimeproductionbegan andended with theparticipant’s first andsecondbuttonpress,respectively. All instructions were given in Japanese. (b) Button-pressing task. In contrast to the time production task, the time instructions were not displayed. Rather, the following instructions were shown in Japanese: “Press the button twice.” Right hemisphere Left hemisphere then changed to display “Start” and the participant pressed thebuttoninthe response boxtobegin time production. 2 5 8 11 14 When the participant felt that the speciefi d length of time hadpassed, theparticipant againpressed thebuttoninthe 1 4 7 10 13 16 response box. The sum of the time perception intervals was 45 s. Summing the ve fi 1700 ms instructed delay cues and the 3 6 9 12 15 delays associated with starting the task resulted in a time production interval that should last at least 55 s. In the button-pressing task, we asked participants to press Fpz the button twice; this task’s motion was equivalent to that of Figure 4: Arrangement of incident, detection, and measurement the time production task. eTh length of the interval between positions (channels). Cortical responses were obtained from 16 the button presses was not specified by the experimenter. locations. The center of the probe matrix was placed at Fpz (the However, a gap of a certain duration existed between the first midpoint between Fp1 and Fp2) in accordance with the International andsecondpresses,and we couldadjustthe length of the 10–20 system. Red, light emitter; white, light receiver; number, block duration. Participants were asked to press the button channel number. twice at their own preferable interval, but without an interval that was too short and without overthinking. eTh task began with a 3400 ms display of instructions to “press the button lightat770 and840nm wasmeasuredatascanning rate of twice at a random duration, where the duration is not too 650 ms. short, without overthinking.” eTh participants were also told to perform this task in a manner that was comfortable. eTh 2.6. fNIRS Channel Positions. Each channel was constructed control task involved a 3400 ms display of the instructions; by a pair of emitter and detector probes at a distance of 3 cm coupled with the associated intertrial delays and movement from each other. The forehead region under measurement delays, it was expected to last approximately 40 s. was 3 cm long and 15 cm wide, and sensor placement was in accordance with the Fpz standard of the International 10–20 system [26]. All16channelswereusedfor data collection. 2.5. fNIRS Instruments. A multichannel fNIRS system Figure 4 indicatesthe typesofarraysand landmarks. (OEG-16, Spectratech Inc., Tokyo, Japan) equipped with six near-infrared light sources and six detectors was used to monitor changes in oxy-Hb concentrations in 16 channels 2.7. Experimental Procedures. Each participant performed (Figure 4). For each channel, the absorption of near-infrared thetasks whilesitting on acomfortable chairinadimlylit Asian Journal of Neuroscience 5 silent room. Participants were asked to sign a consent form leftDLPFC.Themeanvaluesfor each of thefive areaswere indicating their willingness to participate in the experiment used for all analyses. eTh data were stored in the fNIRS aer ft the experimenter had provided them with a general machine and further analyzed using OEG-16 software. description of the purpose of the experiment. Next, the head Although previous fNIRS studies have used various circumference, the distance from nasion to inion, and Fpz methods to detect motion artifacts, a standard method for of the participants were measured. Participants were then this process remains to be established [32]. Some studies have tfi ted with the fNIRS probe headband and given a brief employed subjective methods based on visual inspection [33]. practice sessiontoensurethattheyunderstood thetwo tasks. In this study, sharp noises detected on visual inspection were After relatively stable Hb signals were confirmed, the time regarded as motion artifacts. production or button-pressing task was started. We examined eTh high-frequency portions of the signals were removed hemodynamic changes in the prefrontal cortex using fNIRS by calculating a moving average with a 4.55-s time window. when participants were performing the time production eTh n, OEG-16 software was used to separately average signals and button-pressing tasks. All participants were then fully in response to each trial across the three blocks for the time debriefed regarding the study’s purpose and thanked for their production and button-pressing tasks. To exclude slow drifts time following experiment completion. The amount of time in the signals, a linear trend was removed from the data based for the fNIRS measurement was approximately 15 minutes, on themeanbaselinesignals 10sbeforeand 30saeft rthe and the whole experiment required approximately 30 minutes task blocks.Theaveragedoxy-Hblevel in response to the to complete. time estimation and button-pressing tasks was exported by the OEG-16 software in CSV data format. The oxy-Hb data for a 30-s period from 5 s aer ft stimulus onset was defined 2.8. Data Analysis. Two scores were derived from the time as a cerebral reaction change related to the task. Mean signal production task: the ratio scores and the coecffi ient of changes for 30-s periods were calculated for each participant variance. es Th e are classical indices of performance in timing for each channel. We wished to view each block of the time studies (e.g., [3, 19, 27]).Theaccuracyand variabilityoftime estimation task relative to each block of the button-pressing estimation can also vary according to the conditions in which task. eTh refore, we adjusted the block length in the control the particular tasks or paradigms were used. eTh ratio score task to match the block length in the time estimation task. was calculated by taking the ratio of the duration estimated However, because it would be dicffi ult for the participants to by the participants to the target duration, and it reflects equalize the block length in the time production task with the accuracy of the size of the standard interval [3, 19]. A that in the button-pressing task, we did not use the entire perfect estimation, according to the ratio score, would be 1.00, time estimation period; rather, we chose to analyze the data whereas scores below and above 1.00 reflect underestimations from 5s to 35softhisperiod(the5sdelay wascausedby and overestimations, respectively. The coefficient of variance hemodynamic lag); another reason why only part of the block index was computed by taking the ratio of the standard devi- was used for analysis was that participants became habituated ation to the production mean and represents the variability in the later part of the block. of time estimation for each participant [3, 19, 27]. This index enabled the consistency of the participants in estimating the same target duration to be evaluated [3]. 2.9. Statistical Analysis. The ratio score and coefficient of The fNIRS data were first preprocessed before the func- variance index were compared across the ve fi different tional localization analyses, and then the relationship time intervals (3, 6, 9, 12, and 15 s) to determine whether between the behavioral data and brain activity was examined. the averages for these intervals were significantly different. The relative hemoglobin concentration (oxy-Hb and deoxy- Separate one-way repeated measures analyses of variance Hb) for each of the 16 fNIRS channels was calculated using (ANOVAs) were conductedfor theratio scores andcoe-ffi lightsignalstransmittedatthetwowavelengthsonthebasisof cientofvariancedatausing thetimeinterval(3, 6, 9, 12, the modied fi Beer-Lambert law [ 28] and expressed as concen- and 15 s) as a within-participant variable. eTh Greenhouse- tration (in mM) per unit path length (in mm) traversed by the Geisser correction procedure was used to adjust the degrees near-infrared light through the brain surface (mM/mm). eTh of freedom when appropriate [34]. For these analyses, the exact differential path length factor was not measurable [ 18]. alpha level for significance was set to 0.05, and all post hoc Raw hemoglobin data are measured as relative values that tests were Bonferroni-corrected. cannot be quantiefi d and thus cannot be compared between To determine whether significant changes in the oxy- participants or between channels within a given participant Hb signal occurred in the frontal cortex, paired Student’s𝑡 - [29]. Oxy-Hb is highly correlated with changes in regional test (two-tailed) was used to compare the signal between the cerebral blood flow that reflect synaptic activity [ 30, 31]; time estimation and button-pressing tasks. eTh se statistical therefore, only oxy-Hb was used in the analysis. analyses were performed for five brain areas. In this analysis, To avoidanincreaseinthe familywise errorrate, the the levels of significance were adjusted using the Bonferroni 16 channels were divided into 5 areas. Channels 1–3 corre- correction, which is regarded as conservative, as it involves sponded to the right DLPFC, channels 4–7 corresponded to dividing the alpha level by the number of brain areas (0.05/5 = the right medial frontal lobe, channels 8 and 9 corresponded 0.01). We conducted all statistical analyses using the Statistical to the frontal pole, channels 10–13 corresponded to the left Package for Social Sciences (SPSS) for Windows, Version 15.0 medial frontal lobe, and channels 14–16 corresponded to the (SPSS Inc., Chicago, IL). 6 Asian Journal of Neuroscience 0.1 0.1 0.05 0.05 0 0 −0.05 −0.05 −0.1 −0.1 −10 0 102030405060 −10 0 102030405060 Time (s) Time (s) Oxy-Hb Oxy-Hb Deoxy-Hb Deoxy-Hb (a) (b) Figure 5: Time course for concentrations of oxy- and deoxy-hemoglobin in the right dorsolateral prefrontal cortex (DLPFC) during the (a) time production and (b) button-pressing tasks (control). fNIRS data recorded in channels 1–3 (corresponding to the right DLPFC) were filtered, and the averaged values of the (a) time production and (b) button-pressing tasks are plotted. eTh boxed area (the 30 s period from 5 to 35 s) indicates the data used in the analyses. Table 1: Demographic data (means and standard deviations). each channel recorded during the two tasks (Table 2). In channels 1–3 (right DLPFC), the mean relative change in the Time Raw score Ratio score Coefficient of variance concentration of oxy-Hb during the time production task was intervals Mean SD Mean SD Mean SD signica fi ntly higher than that during the button-pressing task, 3s 3.04 0.91 1.01 0.30 0.11 0.12 𝑡(25) = 2.896 ,corrected𝑝 = 0.040 ,𝑑 = 0.57 (Figure 5). 6s 6.34 1.29 1.06 0.22 0.07 0.04 As shown in Table 2, there were no signicfi ant differences 9.69 1.66 1.08 0.18 9s 0.05 0.03 in themeanrelativechangeinthe concentrationofoxy-Hb 13.06 2.09 1.09 0.17 12 s 0.04 0.03 between the time production and button-pressing tasks in other positions (𝑝>0.05 ). 15 s 16.14 2.34 1.08 0.16 0.07 0.13 3. Results 4. Discussion 3.1. Behavioral Data. Table 1 shows the mean time required The present study used fNIRS to examine activity in the to estimate the different intervals in the time estimation task. frontal brain regions associated with the accurate estimation Differences in the ratio score and coefficient of variance index of time using conventional time units. We compared the among the different time estimation intervals were examined. frontal cortex activities of participants estimating a specified A one-way repeated measures ANOVA for the ratio score of time interval with those measured during the control task. time production did not reveal a significant difference among To focus on time estimation accuracy in daily life, we the intervals, F(1.69, 42.26) = 2.71, MSE = 0.019,𝑝 = 0.086 , instructed the participants to produce a particular duration partial𝜂 =0.098. eTh ANOVA for the coefficient of variance using conventional time units (e.g., seconds). eTh protocol index revealed that response consistency did not differ among was designed to test the hypothesis that working memory is the vfi e intervals, F(1.99, 49.65) = 2.73, MSE = 0.011,𝑝=0.076 , required to accurately estimate time, that is, that the DLPFC partial𝜂 =0.098. wouldbeactiveduringthe time production task. Asstatedabove,theindicesofaccuracyandresponsecon- The mean hemodynamic response of oxy-Hb in the right sistency remained almost identical among the vfi e intervals. DLPFC during the time estimation task was higher than that Hence, we did not classify the vfi e intervals separately based during the control task, which supports our hypothesis. As on the fNIRS data analysis; rather, we averaged them as a noted in the introduction, we compared the time production single overall time estimation condition. and button-pressing tasks to separate memory and decision- stage functions from the clock-stage function that is respon- 3.2. fNIRS Data. The difference in prefrontal cortex activa- sible for estimation of time intervals. In particular, we sought tion was evaluated in terms of oxy-Hb changes during the to assess theroles of theworking memory andspecicfi frontal time estimation and button-pressing tasks. Paired-samples cortex structures involved in accurate time estimation. We 𝑡 -tests were performed to compare the fNIRS data for could not directly measure reference memory, as the activated Hb signal change (mM·mm) Hb signal change (mM·mm) Asian Journal of Neuroscience 7 Table 2: Oxy-Hb activity of time production task and button-pressing task. Time production task Button-pressing task Position 𝑡 value 𝑝 value (corrected) Mean SD Mean SD ch1–ch3 0.020 0.077 −0.027 0.051 2.896 0.040 ch4–ch7 −0.021 0.093 −0.052 0.051 1.768 0.445 ch8-ch9 −0.058 0.108 −0.082 0.059 1.243 0.999 ch10–ch13 −0.037 0.098 −0.058 0.056 1.104 0.999 ch14–ch16 0.009 0.086 −0.027 0.051 2.243 0.170 Note.ch: channel. The different channels (ch) correspond to different regions of the prefrontal cortex as follows: ch1 to ch3, right dorsolateral prefrontal cortex (DLPF C); ch4 to ch7, right medial frontal lobe; ch8 and ch9, central regions including the frontal pole; ch10 to ch13, left medial frontal lobe; and ch14 to ch16, left DLP FC. area is subcortical [3, 9], and the fNIRS instrument used in Coull and colleagues found that attention modulates the the present study could only measure the frontal cortex. subjective perception of time [36]. In single-task paradigms Theoretically, both working and reference memories such as the one used in this study, participants focus on wouldberequiredfor thetimeproductiontask[8], which the time estimation task itself, whereas paradigms with time in contrast to the control task requires associating a given estimation and a concurrent task require processing attention duration with a representation of intervals or knowledge to be shared between temporal information and nontemporal of conventional time units [3]. The representation of con- information, and fewer pulses are gated into the accumulator ventional time units is stored in the reference memory [3]. andtransferredintotheworkingmemoryintheclockstageof According to Perbal-Hatif [3], reference memory is assimi- the SET [3].aTh tis,lessattentionpaidtothetemporalproper- lated with semantic memory, which stores general knowledge ties of a stimulus results in shorter experienced intervals [36]. of the world including time representation. In the present As silently counting time increases the degree of attention task, rfi st, participants had to maintain a target duration given allocated to time, time estimation performance in the present in conventional units of time in their mind until the trial was study was generally accurate with minimal variability. In over. en, Th the participants had to retrieve information based addition, there were no significant differences in the mean on previous experience from their reference memory and values of ratio scores or coefficient of variance indexes simultaneously monitor the experienced duration using the among the vfi e time-interval conditions, indicating that time working memory system. The participants had to continually estimation parameters such as accuracy and variability were compare the elapsing duration in the working memory to identical in the range between 3 and 15 s. representations stored as the target duration in the reference The primary contribution of this study is that we were able memory.Thus,accuratetimeestimationasmeasuredbythe to use fNIRS to clarify the neural basis of time estimation task of the present study requires both working and reference in seated participants. eTh results of this study are similar memories in the memory stage of the SET. to those of previous fMRI studies. It has been suggested The present results are consistent with those reported by that cognitive function, arousal level, autonomic nervous Rao and colleagues [20], who indicated that brain activation system function, and fatigue differ between supine and sitting in relation to time estimation occurs in the right hemisphere postures (e.g., [12, 16]). Even though the results of the present of the cortical network, primarily in the right DLPFC; they study may have been aeff cted by the difference in body foundthatthe rightDLPFC wasengaged during thecom- posture compared to previous fMRI studies, the same results parison of temporal information. Furthermore, we replicated were obtained using a different brain imaging machine, and the predominant involvement of the right hemisphere that irrespective of the body posture, we believe that the present has been previously observed; patients with right prefrontal results support previous findings of DLPFC activation during cortex lesions show impaired time estimation. Collectively, time estimation, indicating that the working memory system the evidence demonstrates the importance of right prefrontal is in use. The greatest advantage of fNIRS compared with cortex activation in time estimation [35]. fMRI is that recordings can be obtained without having to xfi eTh results of our study imply that time estimation and theparticipant’sheadandbody.Becausetheparticipantswere working memory depend on the same neural networks. This not excessively restrained, it was possible to obtain measure- evidence is consistent with our hypothesis, as the ndin fi gs ments while they were in a sitting position, which is advan- generally show that the frontal system is directly responsible tageous because it is not necessary to consider the eeff ct of for storing temporal information in the memory stage of the body posture on time estimation as required for conventional SET. fMRI in a supine position. Nevertheless, additional studies The behavioral results indicated that participants’ time are needed to further clarify whether this method could be estimation was accurate with low variability, indicating that used to assess time estimation in pediatric participants. the protocol was successful. These findings are also consis- Our ndin fi gs should be interpreted in the context of sev- tent with previous reports that time estimation is generally eral limitations. eTh rfi st is that three trials per time estima- accurate when participants can count the required time using tion experiment may not have been sufficient to obtain clear conventional time units for the stimulus duration [3, 5, 19, 36]. results. Other research results indicate that the mechanism 8 Asian Journal of Neuroscience of time production differs depending on the target duration Acknowledgments [3, 37]. Even though we pooled vfi etimeintervals (3,6,9, eTh authors thank Akifumi Morita, Fumina Tsukiji, Kota 12, and 15 s) to form a single condition and there were 15 Tamai, Kunitake Suzuki, Masanori Kobayashi, and Nobuko trials in total, this still seems insufficient to overcome the low Kemmotsu for helpful comments. eTh authors gratefully signal-to-noise ratio in the brain activation signal obtained by acknowledge Ryunosuke Oka for his proficient technical fNIRS. Future studies should increase the number of trials assistance. per condition to more stringently determine which specific frontal region is involved in timing estimation. The second limitation associated with this study is that we were unable References to consider the mutual relationships between brain regions [1] M. 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Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2015 Asato Morita et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 2314-7482
DOI: 10.1155/2015/189060
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Hindawi Publishing Corporation Asian Journal of Neuroscience Volume 2015, Article ID 189060, 9 pages http://dx.doi.org/10.1155/2015/189060 Research Article Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study 1 2 2 Asato Morita, Yasunori Morishima, and David W. Rackham Graduate School of Arts and Sciences, International Christian University, Tokyo 181-8585, Japan Department of Psychology, International Christian University, Tokyo 181-8585, Japan Correspondence should be addressed to Asato Morita; asamorita@gmail.com Received 17 January 2015; Revised 12 April 2015; Accepted 30 April 2015 Academic Editor: Jinsung Wang Copyright © 2015 Asato Morita et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate time estimation is crucial for many human activities and necessitates the use of working memory, in which the dorsolateral prefrontal cortex (DLPFC) plays a critical role. We tested the hypothesis that the DLPFC is activated in participants attempting time estimations that require working memory. Specifically, we used functional near-infrared spectroscopy (fNIRS) to investigate prefrontal cortical activity in the brains of individuals performing a prospective time production task. We measured cerebral hemodynamic responses in 26 healthy right-handed university students while they marked the passage of specified time intervals (3, 6, 9, 12, or 15 s) or performed a button-pressing (control) task. eTh behavioral results indicated that participants’ time estimations were accurate with minimal variability. The fNIRS data showed that activity was significantly higher in the right DLPFC during the time estimation task compared to the control task. Theoretical considerations and the results of this study suggest that DLPFC activation resulting from time estimation indicates that the working memory system is in use. 1. Introduction It posits that time estimation is based on three information- processing stages: clock, memory, and decision [6]. In the The ability to estimate time plays an important role in adap- SET, a hypothetical internal pacemaker emits pulses that are tation to the environment. For example, when a student gated by a switch during the current to-be-timed interval takes a test, accurate estimation of time intervals is necessary andthensenttoanaccumulator [7]. The content in the to ecffi iently solve many problems within the time limit. accumulator (number of pulses) corresponds to the current Specicall fi y, time estimation refers to appraising temporal time. eTh accumulator’s content is transferred and stored in duration without using information from a clock. Whereas the working memory, which is compared with learned time the number of time estimation studies has increased in recent labels for known intervals previously stored in the reference years [1], the underlying mechanisms remain unclear. Time memory. eTh comparison between these accumulated pulses estimation in the range of seconds to minutes is considered in working memory and learned temporal representations in to be controlled by diverse cognitive systems [2], whereas reference memory determines the time estimation response. time estimation in the millisecond range is thought to be According to this model, individual differences in time associated with the motor system [3]. As our interest is estimation may be attributable to alterations in pacemaker accurate time estimation in daily activities, we focused on the speed, memory efficiency, and comparator function [ 5]. biological substrates and cognitive systems of time estimation Despite the effectiveness of the SET in explaining various in the range of seconds. behavioral and physiological results, its relevance to the Several models have been developed to account for time neural substrates involved in accurate time estimation is not estimation [4, 5]. The scalar expectancy theory (SET) is the fully clear [8]. Many lines of evidence suggest that separate most popular model of time estimation (see Figure 1)[6, 7]. brain mechanisms are responsible for different stages of 2 Asian Journal of Neuroscience of posture on time estimation is unknown. During fMRI Accumulator Pacemaker Clock stage: scanning, participants lie in a supine position, whereas time estimation experiments using behavioral techniques are usu- Switch/gate ally performed while the participant is sitting upright in front of a monitor [14]. Muehlhan and colleagues [14] investigated Reference Working theeeff ctofbodypostureoncognitiveperformance,andtheir Memory stage: memory memory results indicate that sleep quality strongly affected reaction times when participants performed a working memory task in thesupineposture,but theseeeff ctswerenot observed in the sitting position. It has also been reported that differences Decision stage: Comparator in orthostatic load between sitting and supine positions lead to physiological changes [14–16]. Additionally, there are beneficial effects of the sitting position over the supine position on cognitive performance [14]. Time estimation In the present study, we employed functional near-infra- red spectroscopy (fNIRS) to investigate prefrontal activity Figure 1: Scalar expectancy theory (SET) applied to the time pro- during the time production task. This noninvasive neu- duction task. In SET, a pacemaker emits pulses to an accumulator, and the number of pulses is transferred to the working memory roimaging technique enables the measurement of relative by way of an accumulator. eTh corresponding number of pulses changes in concentrations of oxygenated and deoxygenated is stored in the working memory and compared with that of the hemoglobin (oxy- and deoxy-Hb, resp.) in the superficial reference intervals stored in reference memory. When the numbers layer of the cortex [17]. Although fNIRS can only record at of pulses match, the participant responds. the brain surface and has low spatial resolution (3 cm), it can tolerate movement and is suitable for use in seated partici- pants [18]. Neurologically normal adults are relatively accurate in the SET [8]. In this framework, a memory stage is functionally separated from other processing stages [9], andaccuratetime estimating time (e.g., [19, 20]). We were interested in partic- estimation capacity is heavily dependent on working memory ipants’ capacity for accurate time estimation of conventional duration units (i.e., we wanted to determine how accurately efficiency. Several recent reviews and meta-analyses of neuroimag- a student estimates 15 seconds), for which there are few ing studies have shown that many parts of the brain con- studies. Various tasks have been employed to investigate tribute to time estimation. Macar and colleagues [10] defined individuals’ time estimation. eTh present study employed a the dorsolateral prefrontal cortex (DLPFC), anterior cingu- prospective time production task to quantify time estimation; late, right inferior parietal lobe (IPL), supplementary motor this methodology is widely used [21]because theparticipants’ area (SMA), cerebellum, and basal ganglia (caudate and load is minimal, and the experimental procedure is easy. This putamen) as thecoretimeestimationnetwork.Lewis and task is suitable for our research objective because it relies on the scaling of subjective time by units used in daily life. In the Miall [11] reviewed many neuroimaging studies of timing and concluded that suprasecond timing tasks most commonly time production task, participants are asked to indicate when activated the bilateral prefrontal cortex, bilateral parietal a stated time has elapsed. In particular, the task requires the participants to mark the “start” and “stop” when they thought cortex, and cerebellum. In their studies, the right DLPFC was the most frequently activated area. In contrast, a rel- anidentiefi dtimeperiodhadpassed.Weusedtheprospective atively recent meta-analysis reported that the SMA and time estimation paradigm [22] where participants know in right inferior frontal gyrus were part of the core network advancethattheywillbeasked to produceagiventime mediating time estimation in the brain, whereas the DLPFC duration. eTh control task was pressing a button twice in a manner similar to the time production task, but without a was less important for time estimation [1]. u Th s, there have been inconsistencies regarding the neural correlates of time specified target time. This task was equivalent to the motor estimation in previous studies, probably because different requirement of the time production task. We consider that accurately producing a particular duration depends on the brain structures are activated depending on the paradigm, temporal task, and duration range used [1, 3]. memory stage of the SET; the participant should make a Most studies have used functional magnetic resonance comparison between accumulated pulses in working memory imaging (fMRI) to examine brain activity during time estima- and a learned temporal representation of reference memory. tion, as it can noninvasively measure brain activity, does not When participants must accurately produce target dura- involve radiation, and has high spatial resolution. However, tion in conventional time units, the SET [6, 7] would predict several shortcomings are associated with fMRI, including that they compare the content of time units accumulated and confined spaces, scanner noise, and the loss of situational stored in working memory with the representation of time stored in reference memory. Working memory is important control experienced by the participant, which can elicit anx- iety and stress (e.g., [12]) that in turn could affect behavioral for a wide range of high-level cognitive activities and is and neurological data [13]. In addition to these disadvantages commonly dene fi d as the system used to temporarily store information and then manipulate the information online dur- of fMRI, the time estimation may also be aeff cted by the dieff renceinthe body posture. However, theactualeeff ct ing cognitive activities [23]. Previous studies have shown that Asian Journal of Neuroscience 3 the DLPFC plays a critical role in working memory. Pattern A Pattern B Patients with traumatic brain injury or Parkinson’s disease Start Start generally show working memory deficits and inaccurate time Rest: 30 s Rest: 30 s estimation [3]. Workingmemorycapacityisthought to be (1) Time production task (1) Button-pressing task indispensable for time estimation, as the frontal lobe would Rest: 60 s Rest: 60 s (2) Button-pressing task (2) Time production task be needed to store the current interval in working memory, Rest: 60 s Rest: 60 s recall a sense of time in reference memory, and compare (3) Button-pressing task (3) Time production task both values. Rest: 60 s Rest: 60 s The present study compared fNIRS-measured frontal (4) Time production task (4) Button-pressing task cortex activity during the time production task with that Rest: 60 s Rest: 60 s measured during the control task. Specicfi ally, we tested the (5) Time production task (5) Button-pressing task hypothesis that the DLPFC would be activated in participants Rest: 60 s Rest: 60 s attempting time estimations that required the working mem- (6) Button-pressing task (6) Time production task ory. Rest: 30 s Rest: 30 s End End This study was designed to assess the role of the working memory system and identify the brain structures involved Figure 2: Schematic representation of the two sets of instruction in accurate time estimation. We intended to separate the sequences (Patterns A and B) that comprised the estimation of memory and decision-stage functions from clock-stage func- time duration test. All participants were assigned to one of two tion, which merely predicts the time interval by comparing groups. Each group was then assigned to one of two patterns to the time production and button-pressing tasks. eTh time balance the two conditions. Each condition was repeated three times production task uses clock, memory, and decision stages that in a predetermined pattern; thus, the experiments consisted of six rely on both working and reference memories [3], whereas blocks, with a 60 s break between blocks. eTh time production task the button-pressing task does not require memory processes. consisted of marking the start and end of perceived times of specified The results were interpreted within the SET theoretical intervals by pressing a button on the response box. eTh intervals (3, framework. To our knowledge, this is the rfi st report of the 6, 9, 12, or 15 s) were randomized for each participant. eTh button- assessment of prefrontal activity using fNIRS during a time pressing task was a control task. production task. the onset of the next trial (Figure 3)). The participants did not 2. Materials and Methods receive any feedback. 2.1. Participants. Twenty-six right-handed, healthy volun- teers (7 males and 19 females, mean age = 20.58 years, SD = 2.3. Apparatus and Stimuli. The experiment was pro- 2.00, and range: 19–27 years) participated in the study. grammed and run using SuperLab Pro 4.5 for Windows, Handedness was assessed using the Edinburgh Handedness with a Cedrus RB–540 response box used to record the par- Inventory [24]. All participants provided written informed ticipant responses (Cedrus Corporation, San Pedro, CA). For consent prior to participating in the experiment, for which alltasks,the stimuliwerepresented on alaptopcomputer they received a coupon worth 500 Japanese yen at the end (Let’s NOTE CF-R5, Panasonic Corporation, Osaka, Japan) of the experiment. This study was conducted in accordance with a display area of 21.1× 15.8 cm and a screen resolution of with the Declaration of Helsinki [25]and wasapprovedby 1024× 768 pixels. eTh words were presented in the center of the relevant ethics committee. the computer screen in black MS Gothic 48-point font on a white background. The distance from the laptop screen to the 2.2. Experimental Design. The design of the fNIRS exper- participant’s head was approximately 60 cm. A second laptop iment was a simple block design with one experimental computer (VOSTRO-3750, Dell Inc., Round Rock, TX) was and one control task condition. eTh 26 participants were used to record and analyze fNIRS data. divided into 2 groups of 13 participants. Each group was then assigned to one of two patterns to balance the two conditions 2.4. Time Estimation and Control Tasks. The time production (Figure 2). Cerebral activations measured with fNIRS were and button-pressing task protocols are shown in Figure 2. then compared between the two conditions. Each condition The experiment session began with 30 s of rest (no body was repeated three times in a predetermined pattern; thus, movement). Subsequently, a fixation cross was displayed, the experiments consisted of six blocks with a 60 s break and an auditory cue (a pure sine wave of 800 Hz) was between each block (Figure 2). The time production task played for 100 ms between trials to arouse attention. eTh n, block consisted of five trials, each of which contained the the instructions were given to the participant. A marking vfi e intervals (3, 6, 9, 12, and 15 s). The order of trial stimulus was synchronously presented at the start and end of presentation was counterbalanced across participants. eTh each trial and externally fed into the fNIRS device. button-pressing task consisted of five trials to match the time In the time production task, participants were instructed interval required for the time production task. er Th e was a to subjectively estimate the presented length of time. eTh 500 ms interval when the cue was displayed on the screen specified length of time to produce was displayed on the hor- between each trial (i.e., between the participant response and izontal axis of the computer monitor for 1700 ms. eTh screen 4 Asian Journal of Neuroscience Produced interval Cue: 500 ms Instruction: 1700 ms Displayed until push Displayed until push Start Cue: 500 ms Stop 3 s (a) Time production task Time Cue: 500 ms Instruction: 3400 ms Displayed until push Displayed until push 1st + Cue: 500 ms Press the button 2nd twice (b) Button-pressing task Figure 3: Sequences of events during the time production and button-pressing tasks. (a) Time production task. eTh number of seconds to be produced wasshown on thescreen, andtimeproductionbegan andended with theparticipant’s first andsecondbuttonpress,respectively. All instructions were given in Japanese. (b) Button-pressing task. In contrast to the time production task, the time instructions were not displayed. Rather, the following instructions were shown in Japanese: “Press the button twice.” Right hemisphere Left hemisphere then changed to display “Start” and the participant pressed thebuttoninthe response boxtobegin time production. 2 5 8 11 14 When the participant felt that the speciefi d length of time hadpassed, theparticipant againpressed thebuttoninthe 1 4 7 10 13 16 response box. The sum of the time perception intervals was 45 s. Summing the ve fi 1700 ms instructed delay cues and the 3 6 9 12 15 delays associated with starting the task resulted in a time production interval that should last at least 55 s. In the button-pressing task, we asked participants to press Fpz the button twice; this task’s motion was equivalent to that of Figure 4: Arrangement of incident, detection, and measurement the time production task. eTh length of the interval between positions (channels). Cortical responses were obtained from 16 the button presses was not specified by the experimenter. locations. The center of the probe matrix was placed at Fpz (the However, a gap of a certain duration existed between the first midpoint between Fp1 and Fp2) in accordance with the International andsecondpresses,and we couldadjustthe length of the 10–20 system. Red, light emitter; white, light receiver; number, block duration. Participants were asked to press the button channel number. twice at their own preferable interval, but without an interval that was too short and without overthinking. eTh task began with a 3400 ms display of instructions to “press the button lightat770 and840nm wasmeasuredatascanning rate of twice at a random duration, where the duration is not too 650 ms. short, without overthinking.” eTh participants were also told to perform this task in a manner that was comfortable. eTh 2.6. fNIRS Channel Positions. Each channel was constructed control task involved a 3400 ms display of the instructions; by a pair of emitter and detector probes at a distance of 3 cm coupled with the associated intertrial delays and movement from each other. The forehead region under measurement delays, it was expected to last approximately 40 s. was 3 cm long and 15 cm wide, and sensor placement was in accordance with the Fpz standard of the International 10–20 system [26]. All16channelswereusedfor data collection. 2.5. fNIRS Instruments. A multichannel fNIRS system Figure 4 indicatesthe typesofarraysand landmarks. (OEG-16, Spectratech Inc., Tokyo, Japan) equipped with six near-infrared light sources and six detectors was used to monitor changes in oxy-Hb concentrations in 16 channels 2.7. Experimental Procedures. Each participant performed (Figure 4). For each channel, the absorption of near-infrared thetasks whilesitting on acomfortable chairinadimlylit Asian Journal of Neuroscience 5 silent room. Participants were asked to sign a consent form leftDLPFC.Themeanvaluesfor each of thefive areaswere indicating their willingness to participate in the experiment used for all analyses. eTh data were stored in the fNIRS aer ft the experimenter had provided them with a general machine and further analyzed using OEG-16 software. description of the purpose of the experiment. Next, the head Although previous fNIRS studies have used various circumference, the distance from nasion to inion, and Fpz methods to detect motion artifacts, a standard method for of the participants were measured. Participants were then this process remains to be established [32]. Some studies have tfi ted with the fNIRS probe headband and given a brief employed subjective methods based on visual inspection [33]. practice sessiontoensurethattheyunderstood thetwo tasks. In this study, sharp noises detected on visual inspection were After relatively stable Hb signals were confirmed, the time regarded as motion artifacts. production or button-pressing task was started. We examined eTh high-frequency portions of the signals were removed hemodynamic changes in the prefrontal cortex using fNIRS by calculating a moving average with a 4.55-s time window. when participants were performing the time production eTh n, OEG-16 software was used to separately average signals and button-pressing tasks. All participants were then fully in response to each trial across the three blocks for the time debriefed regarding the study’s purpose and thanked for their production and button-pressing tasks. To exclude slow drifts time following experiment completion. The amount of time in the signals, a linear trend was removed from the data based for the fNIRS measurement was approximately 15 minutes, on themeanbaselinesignals 10sbeforeand 30saeft rthe and the whole experiment required approximately 30 minutes task blocks.Theaveragedoxy-Hblevel in response to the to complete. time estimation and button-pressing tasks was exported by the OEG-16 software in CSV data format. The oxy-Hb data for a 30-s period from 5 s aer ft stimulus onset was defined 2.8. Data Analysis. Two scores were derived from the time as a cerebral reaction change related to the task. Mean signal production task: the ratio scores and the coecffi ient of changes for 30-s periods were calculated for each participant variance. es Th e are classical indices of performance in timing for each channel. We wished to view each block of the time studies (e.g., [3, 19, 27]).Theaccuracyand variabilityoftime estimation task relative to each block of the button-pressing estimation can also vary according to the conditions in which task. eTh refore, we adjusted the block length in the control the particular tasks or paradigms were used. eTh ratio score task to match the block length in the time estimation task. was calculated by taking the ratio of the duration estimated However, because it would be dicffi ult for the participants to by the participants to the target duration, and it reflects equalize the block length in the time production task with the accuracy of the size of the standard interval [3, 19]. A that in the button-pressing task, we did not use the entire perfect estimation, according to the ratio score, would be 1.00, time estimation period; rather, we chose to analyze the data whereas scores below and above 1.00 reflect underestimations from 5s to 35softhisperiod(the5sdelay wascausedby and overestimations, respectively. The coefficient of variance hemodynamic lag); another reason why only part of the block index was computed by taking the ratio of the standard devi- was used for analysis was that participants became habituated ation to the production mean and represents the variability in the later part of the block. of time estimation for each participant [3, 19, 27]. This index enabled the consistency of the participants in estimating the same target duration to be evaluated [3]. 2.9. Statistical Analysis. The ratio score and coefficient of The fNIRS data were first preprocessed before the func- variance index were compared across the ve fi different tional localization analyses, and then the relationship time intervals (3, 6, 9, 12, and 15 s) to determine whether between the behavioral data and brain activity was examined. the averages for these intervals were significantly different. The relative hemoglobin concentration (oxy-Hb and deoxy- Separate one-way repeated measures analyses of variance Hb) for each of the 16 fNIRS channels was calculated using (ANOVAs) were conductedfor theratio scores andcoe-ffi lightsignalstransmittedatthetwowavelengthsonthebasisof cientofvariancedatausing thetimeinterval(3, 6, 9, 12, the modied fi Beer-Lambert law [ 28] and expressed as concen- and 15 s) as a within-participant variable. eTh Greenhouse- tration (in mM) per unit path length (in mm) traversed by the Geisser correction procedure was used to adjust the degrees near-infrared light through the brain surface (mM/mm). eTh of freedom when appropriate [34]. For these analyses, the exact differential path length factor was not measurable [ 18]. alpha level for significance was set to 0.05, and all post hoc Raw hemoglobin data are measured as relative values that tests were Bonferroni-corrected. cannot be quantiefi d and thus cannot be compared between To determine whether significant changes in the oxy- participants or between channels within a given participant Hb signal occurred in the frontal cortex, paired Student’s𝑡 - [29]. Oxy-Hb is highly correlated with changes in regional test (two-tailed) was used to compare the signal between the cerebral blood flow that reflect synaptic activity [ 30, 31]; time estimation and button-pressing tasks. eTh se statistical therefore, only oxy-Hb was used in the analysis. analyses were performed for five brain areas. In this analysis, To avoidanincreaseinthe familywise errorrate, the the levels of significance were adjusted using the Bonferroni 16 channels were divided into 5 areas. Channels 1–3 corre- correction, which is regarded as conservative, as it involves sponded to the right DLPFC, channels 4–7 corresponded to dividing the alpha level by the number of brain areas (0.05/5 = the right medial frontal lobe, channels 8 and 9 corresponded 0.01). We conducted all statistical analyses using the Statistical to the frontal pole, channels 10–13 corresponded to the left Package for Social Sciences (SPSS) for Windows, Version 15.0 medial frontal lobe, and channels 14–16 corresponded to the (SPSS Inc., Chicago, IL). 6 Asian Journal of Neuroscience 0.1 0.1 0.05 0.05 0 0 −0.05 −0.05 −0.1 −0.1 −10 0 102030405060 −10 0 102030405060 Time (s) Time (s) Oxy-Hb Oxy-Hb Deoxy-Hb Deoxy-Hb (a) (b) Figure 5: Time course for concentrations of oxy- and deoxy-hemoglobin in the right dorsolateral prefrontal cortex (DLPFC) during the (a) time production and (b) button-pressing tasks (control). fNIRS data recorded in channels 1–3 (corresponding to the right DLPFC) were filtered, and the averaged values of the (a) time production and (b) button-pressing tasks are plotted. eTh boxed area (the 30 s period from 5 to 35 s) indicates the data used in the analyses. Table 1: Demographic data (means and standard deviations). each channel recorded during the two tasks (Table 2). In channels 1–3 (right DLPFC), the mean relative change in the Time Raw score Ratio score Coefficient of variance concentration of oxy-Hb during the time production task was intervals Mean SD Mean SD Mean SD signica fi ntly higher than that during the button-pressing task, 3s 3.04 0.91 1.01 0.30 0.11 0.12 𝑡(25) = 2.896 ,corrected𝑝 = 0.040 ,𝑑 = 0.57 (Figure 5). 6s 6.34 1.29 1.06 0.22 0.07 0.04 As shown in Table 2, there were no signicfi ant differences 9.69 1.66 1.08 0.18 9s 0.05 0.03 in themeanrelativechangeinthe concentrationofoxy-Hb 13.06 2.09 1.09 0.17 12 s 0.04 0.03 between the time production and button-pressing tasks in other positions (𝑝>0.05 ). 15 s 16.14 2.34 1.08 0.16 0.07 0.13 3. Results 4. Discussion 3.1. Behavioral Data. Table 1 shows the mean time required The present study used fNIRS to examine activity in the to estimate the different intervals in the time estimation task. frontal brain regions associated with the accurate estimation Differences in the ratio score and coefficient of variance index of time using conventional time units. We compared the among the different time estimation intervals were examined. frontal cortex activities of participants estimating a specified A one-way repeated measures ANOVA for the ratio score of time interval with those measured during the control task. time production did not reveal a significant difference among To focus on time estimation accuracy in daily life, we the intervals, F(1.69, 42.26) = 2.71, MSE = 0.019,𝑝 = 0.086 , instructed the participants to produce a particular duration partial𝜂 =0.098. eTh ANOVA for the coefficient of variance using conventional time units (e.g., seconds). eTh protocol index revealed that response consistency did not differ among was designed to test the hypothesis that working memory is the vfi e intervals, F(1.99, 49.65) = 2.73, MSE = 0.011,𝑝=0.076 , required to accurately estimate time, that is, that the DLPFC partial𝜂 =0.098. wouldbeactiveduringthe time production task. Asstatedabove,theindicesofaccuracyandresponsecon- The mean hemodynamic response of oxy-Hb in the right sistency remained almost identical among the vfi e intervals. DLPFC during the time estimation task was higher than that Hence, we did not classify the vfi e intervals separately based during the control task, which supports our hypothesis. As on the fNIRS data analysis; rather, we averaged them as a noted in the introduction, we compared the time production single overall time estimation condition. and button-pressing tasks to separate memory and decision- stage functions from the clock-stage function that is respon- 3.2. fNIRS Data. The difference in prefrontal cortex activa- sible for estimation of time intervals. In particular, we sought tion was evaluated in terms of oxy-Hb changes during the to assess theroles of theworking memory andspecicfi frontal time estimation and button-pressing tasks. Paired-samples cortex structures involved in accurate time estimation. We 𝑡 -tests were performed to compare the fNIRS data for could not directly measure reference memory, as the activated Hb signal change (mM·mm) Hb signal change (mM·mm) Asian Journal of Neuroscience 7 Table 2: Oxy-Hb activity of time production task and button-pressing task. Time production task Button-pressing task Position 𝑡 value 𝑝 value (corrected) Mean SD Mean SD ch1–ch3 0.020 0.077 −0.027 0.051 2.896 0.040 ch4–ch7 −0.021 0.093 −0.052 0.051 1.768 0.445 ch8-ch9 −0.058 0.108 −0.082 0.059 1.243 0.999 ch10–ch13 −0.037 0.098 −0.058 0.056 1.104 0.999 ch14–ch16 0.009 0.086 −0.027 0.051 2.243 0.170 Note.ch: channel. The different channels (ch) correspond to different regions of the prefrontal cortex as follows: ch1 to ch3, right dorsolateral prefrontal cortex (DLPF C); ch4 to ch7, right medial frontal lobe; ch8 and ch9, central regions including the frontal pole; ch10 to ch13, left medial frontal lobe; and ch14 to ch16, left DLP FC. area is subcortical [3, 9], and the fNIRS instrument used in Coull and colleagues found that attention modulates the the present study could only measure the frontal cortex. subjective perception of time [36]. In single-task paradigms Theoretically, both working and reference memories such as the one used in this study, participants focus on wouldberequiredfor thetimeproductiontask[8], which the time estimation task itself, whereas paradigms with time in contrast to the control task requires associating a given estimation and a concurrent task require processing attention duration with a representation of intervals or knowledge to be shared between temporal information and nontemporal of conventional time units [3]. The representation of con- information, and fewer pulses are gated into the accumulator ventional time units is stored in the reference memory [3]. andtransferredintotheworkingmemoryintheclockstageof According to Perbal-Hatif [3], reference memory is assimi- the SET [3].aTh tis,lessattentionpaidtothetemporalproper- lated with semantic memory, which stores general knowledge ties of a stimulus results in shorter experienced intervals [36]. of the world including time representation. In the present As silently counting time increases the degree of attention task, rfi st, participants had to maintain a target duration given allocated to time, time estimation performance in the present in conventional units of time in their mind until the trial was study was generally accurate with minimal variability. In over. en, Th the participants had to retrieve information based addition, there were no significant differences in the mean on previous experience from their reference memory and values of ratio scores or coefficient of variance indexes simultaneously monitor the experienced duration using the among the vfi e time-interval conditions, indicating that time working memory system. The participants had to continually estimation parameters such as accuracy and variability were compare the elapsing duration in the working memory to identical in the range between 3 and 15 s. representations stored as the target duration in the reference The primary contribution of this study is that we were able memory.Thus,accuratetimeestimationasmeasuredbythe to use fNIRS to clarify the neural basis of time estimation task of the present study requires both working and reference in seated participants. eTh results of this study are similar memories in the memory stage of the SET. to those of previous fMRI studies. It has been suggested The present results are consistent with those reported by that cognitive function, arousal level, autonomic nervous Rao and colleagues [20], who indicated that brain activation system function, and fatigue differ between supine and sitting in relation to time estimation occurs in the right hemisphere postures (e.g., [12, 16]). Even though the results of the present of the cortical network, primarily in the right DLPFC; they study may have been aeff cted by the difference in body foundthatthe rightDLPFC wasengaged during thecom- posture compared to previous fMRI studies, the same results parison of temporal information. Furthermore, we replicated were obtained using a different brain imaging machine, and the predominant involvement of the right hemisphere that irrespective of the body posture, we believe that the present has been previously observed; patients with right prefrontal results support previous findings of DLPFC activation during cortex lesions show impaired time estimation. Collectively, time estimation, indicating that the working memory system the evidence demonstrates the importance of right prefrontal is in use. The greatest advantage of fNIRS compared with cortex activation in time estimation [35]. fMRI is that recordings can be obtained without having to xfi eTh results of our study imply that time estimation and theparticipant’sheadandbody.Becausetheparticipantswere working memory depend on the same neural networks. This not excessively restrained, it was possible to obtain measure- evidence is consistent with our hypothesis, as the ndin fi gs ments while they were in a sitting position, which is advan- generally show that the frontal system is directly responsible tageous because it is not necessary to consider the eeff ct of for storing temporal information in the memory stage of the body posture on time estimation as required for conventional SET. fMRI in a supine position. Nevertheless, additional studies The behavioral results indicated that participants’ time are needed to further clarify whether this method could be estimation was accurate with low variability, indicating that used to assess time estimation in pediatric participants. the protocol was successful. These findings are also consis- Our ndin fi gs should be interpreted in the context of sev- tent with previous reports that time estimation is generally eral limitations. eTh rfi st is that three trials per time estima- accurate when participants can count the required time using tion experiment may not have been sufficient to obtain clear conventional time units for the stimulus duration [3, 5, 19, 36]. results. Other research results indicate that the mechanism 8 Asian Journal of Neuroscience of time production differs depending on the target duration Acknowledgments [3, 37]. Even though we pooled vfi etimeintervals (3,6,9, eTh authors thank Akifumi Morita, Fumina Tsukiji, Kota 12, and 15 s) to form a single condition and there were 15 Tamai, Kunitake Suzuki, Masanori Kobayashi, and Nobuko trials in total, this still seems insufficient to overcome the low Kemmotsu for helpful comments. eTh authors gratefully signal-to-noise ratio in the brain activation signal obtained by acknowledge Ryunosuke Oka for his proficient technical fNIRS. Future studies should increase the number of trials assistance. per condition to more stringently determine which specific frontal region is involved in timing estimation. The second limitation associated with this study is that we were unable References to consider the mutual relationships between brain regions [1] M. Wiener,P.Turkeltaub, andH.B.Coslett,“eTh imageoftime: involved in time estimation. fNIRS is a useful technology; a voxel-wise meta-analysis,” NeuroImage,vol.49,no.2,pp.1728– however, it canonlybeusedtoestimateprefrontalcortex 1740, 2010. activity. It has recently been suggested that frontal lobe [2] U.A.Kumar,A.V.Sangamanatha, andJ.Vikas,“Eeff cts of connectionsthrough thethalamusand striatum arerespon- meditation on temporal processing and speech perceptual skills sible for time estimation. fMRI studies would be needed in younger and older adults,” Asian Journal of Neuroscience,vol. to clarify which cortical and subcortical structures, such 2013, Article ID 304057, 8 pages, 2013. as the cerebellum and basal ganglia, process and integrate [3] S. 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According to the SET, the mem- Alzheimer’s disease patients,” Behavioral and Brain Functions, ory and comparator stages are considered to be important vol. 5, article 32, 2009. for producing accurate time intervals. eTh refore, the right [10] F. Macar, H. Lejeune, M. Bonnet et al., “Activation of the DLPFC is likely to be the primary cortical region for accurate supplementary motor area and of attentional networks during time estimation in the range of several seconds. Our n fi dings temporal processing,” Experimental Brain Research,vol.142,no. suggest that the right cerebral hemisphere has an advantage 4, pp. 475–485, 2002. over the left with regard to time estimation. fNIRS is a useful [11] P. A. Lewis and R. C. 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Baddeley, “Working memory: looking back and looking forward,” Nature Reviews: Neuroscience,vol.4,no. 10,pp. 829– 839, 2003. [24] R. C. Oldfield, “The assessment and analysis of handedness: the Edinburgh inventory,” Neuropsychologia, vol. 9, no. 1, pp. 97–113, [25] World Medical Association, “World Medical Association Dec- laration of Helsinki: ethical principles for medical research involving human subjects,” eTh Journalofthe American Medical Association,vol.310,no. 20,pp. 2191–2194, 2013. [26] H. H. Jasper, “The ten-twenty electrode system of the Interna- tional Federation,” Electroencephalography and Clinical Neuro- physiology,vol.10, pp.371–375,1958. [27] M.-C. Sevign ´ y, J. Everett, and S. Grondin, “Depression, atten- tion, and time estimation,” Brain and Cognition,vol.53, no.2, pp. 351–353, 2003. [28] M. Cope,D.T.Delpy,E.O.Reynolds, S. Wray,J.Wyatt,and P. van der Zee, “Methods of quantitating cerebral near infrared spectroscopy data,” Advances in Experimental Medicine and Biology, vol. 222, pp. 183–189, 1988. [29] M. Sanefuji, Y. Takada, N. Kimura et al., “Strategy in short-term memory for pictures in childhood: a near-infrared spectroscopy study,” NeuroImage,vol.54, no.3,pp. 2394–2400, 2011. [30] M. Jueptner and C. Weiller, “Does measurement of regional cerebral blood flow reflect synaptic activity? Implications for PET and fMRI,” NeuroImage,vol.2,no. 2, pp.148–156,1995. [31] K. Sakatani, Y. Murata, N. Fujiwara et al., “Comparison of blood-oxygen-level-dependent functional magnetic resonance imaging and near-infrared spectroscopy recording during func- tionalbrainactivationinpatientswithstrokeandbraintumors,” Journal of Biomedical Optics,vol.12, no.6,Article ID 062110, [32] R. Aoki, H. Sato, T. 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References

The image of time: a voxel-wise meta-analysis,

M. Wiener, P. Turkeltaub, and H. B. Coslett
Effects of meditation on temporal processing and speech perceptual skills in younger and older adults,

U. A. Kumar, A. V. Sangamanatha, and J. Vikas
A neuropsychological approach to time estimation,

S. Perbal-Hatif
Timing and time perception: a review of recent behavioral and neuroscience findings and theoretical directions,

S. Grondin
Neuropsychology of timing and time perception,

W. H. Meck
Scalar expectancy theory and Weber's law in animal timing,

J. Gibbon
Toward a neurobiology of temporal cognition: advances and challenges,

J. Gibbon, C. Malapani, C. L. Dale, and C. R. Gallistel
What makes us tick? Functional and neural mechanisms of interval timing,

C. V. Buhusi and W. H. Meck
Time estimation in mild Alzheimer's disease patients,

L. Caselli, L. Iaboli, and P. Nichelli
Activation of the supplementary motor area and of attentional networks during temporal processing,

F. Macar, H. Lejeune, M. Bonnet et al.
Distinct systems for automatic and cognitively controlled time measurement: evidence from neuroimaging,

P. A. Lewis and R. C. Miall
Patients' perceptions of magnetic resonance imaging,

R. MacKenzie, C. Sims, R. G. Owens, and A. K. Dixon
Enhanced sympathetic arousal in response to fMRI scanning correlates with task induced activations and deactivations,

M. Muehlhan, U. Lueken, J. Siegert, H.-U. Wittchen, M. N. Smolka, and C. Kirschbaum
The effect of body posture on cognitive performance: a question of sleep quality,

M. Muehlhan, M. Marxen, J. Landsiedel, H. Malberg, and S. Zaunseder
Psychophysiology of arterial baroreceptors and the etiology of hypertension,

H. Rau and T. Elbert
The behavioral impact of baroreflex function: a review,

S. Duschek, N. S. Werner, and G. A. Reyes del Paso
Functional near-infrared optical imaging: utility and limitations in human brain mapping,

Y. Hoshi
Functional near-infrared spectroscopy: current status and future prospects,

Y. Hoshi
Recovery of time estimation following moderate to severe traumatic brain injury,

J. W. Anderson and M. Schmitter-Edgecombe
The evolution of brain activation during temporal processing,

S. M. Rao, A. R. Mayer, and D. L. Harrington
Temporal interval production and processing in working memory,

C. Fortin and R. Breton
Time perception, attention, and memory: a selective review,

R. A. Block and R. P. Gruber
Working memory: looking back and looking forward,

A. D. Baddeley
The assessment and analysis of handedness: the Edinburgh inventory,

R. C. Oldfield
World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects,

World Medical Association
The ten-twenty electrode system of the International Federation,

H. H. Jasper
Depression, attention, and time estimation,

M.-C. Sévigny, J. Everett, and S. Grondin
Methods of quantitating cerebral near infrared spectroscopy data,

M. Cope, D. T. Delpy, E. O. Reynolds, S. Wray, J. Wyatt, and P. van der Zee
Strategy in short-term memory for pictures in childhood: a near-infrared spectroscopy study,

M. Sanefuji, Y. Takada, N. Kimura et al.
Does measurement of regional cerebral blood flow reflect synaptic activity? Implications for PET and fMRI,

M. Jueptner and C. Weiller
Comparison of blood-oxygen-level-dependent functional magnetic resonance imaging and near-infrared spectroscopy recording during functional brain activation in patients with stroke and brain tumors,

K. Sakatani, Y. Murata, N. Fujiwara et al.
Relationship of negative mood with prefrontal cortex activity during working memory tasks: an optical topography study,

R. Aoki, H. Sato, T. Katura et al.
Optical brain imaging reveals general auditory and language-specific processing in early infant development,

Y. Minagawa-Kawai, H. Van Der Lely, F. Ramus, Y. Sato, R. Mazuka, and E. Dupoux
On methods in the analysis of profile data,

S. W. Greenhouse and S. Geisser
About hemispheric differences in the processing of temporal intervals,

S. Grondin and C. Girard
Functional anatomy of the attentional modulation of time estimation,

J. T. Coull, F. Vidal, B. Nazarian, and F. Macar
Neural networks engaged in milliseconds and seconds time processing: evidence from transcranial magnetic stimulation and patients with cortical or subcortical dysfunction,

G. Koch, M. Oliveri, and C. Caltagirone

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Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study

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Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study

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