For instance, Higuchi et al. (2009) found an overlap of activity between tasks related to language and tool-use action in Broca's area. The authors suggested a domain-general role of Broca's area in hierarchical processing since language and tool-use action share computational principles for processing hierarchical structures common to these two domains. However, they did not directly test the involvement of Broca's area in the processing of action hierarchy. In order to confirm such an involvement, it is necessary to study whether Broca's area is sensitive to the degree of hierarchical complexity or to the sequential order of action.

Thus far, several patterns of musical action have been adopted to study the involvement of Broca's area in the hierarchical processing of observed action. Musical action consists of hierarchical structures in that the visual processing of structured key-press sequences and the integration of this information with the location of the fingers on the piano keyboard are essential to correctly identify harmony or melody by observing silent hand motion (Hasegawa et al., 2004).

For instance, Sammler et al. (2013) conducted an electroencephalography (EEG) study in which expert pianists watched silent videos of a hand playing either congruent or incongruent 5-chord sequences. The video corresponded to a linguistic task that tested the grammatical sensitivity of Broca's area. The results of the study revealed a higher activation of this area in response to sentences with non-canonical word order compared with sentences with standard word order (Friederici et al., 2006). The authors found positivity that possibly originated from a left inferior frontal source when participants observed hand trajectory toward syntactically incongruent chords. These findings indicate that the syntactical analysis of observed action is represented in Broca's area. However, such a silent harmony abstraction task is difficult to perform because theoretical knowledge of western harmony is necessary. Therefore, participation in this task is restricted to professional musicians.

In another study, using functional magnetic resonance imaging (fMRI), non-musicians watched silent videos of hands playing the piano while trying to identify the melodies (Hasegawa et al., 2004). This task may measure the sensitivity of Broca's area to tone or phoneme segmentation and reveal the activation of Broca's area to learned regularity (Abla and Okanoya, 2008; Karuza et al., 2013). Because less-trained participants could identify some melodies solely through the observation of silent keytouching sequences, this task may be adaptable to a larger number of participants than the silent harmony abstraction task. The authors' target region was the left planum temporale, but they observed higher levels of activation of BA 44 in well-trained participants compared with less-trained and naïve participants when they watched silent videos of hands playing melodies. However, in general, melody identification performance in this study was not good, even among the well-trained participants. Thus, the activation of Broca's area may have reflected an embodied simulation of piano playing rather than hierarchical processing.

In this study, to test whether Broca's area is involved in processing the hierarchical organization of observed action, well-trained and less-trained participants watched silent movies that featured hand movements playing familiar and unfamiliar melodies. The activation of Broca's area was measured using near-infrared spectroscopy (NIRS). The NIRS results for familiar and unfamiliar melodies were compared to reveal the involvement of Broca's area in the hierarchical analysis of observed action. Additionally, the comparison of NIRS results between well-trained and less-trained participants was examined to determine the influence of skill in the observed action on the activation of Broca's area.

All participants in the current study had sufficient knowledge about the familiar melodies preceding the experiments. Thus, tracking hand motions to identify the familiar melodies and even anticipating forthcoming action sequences may be easy. However, no participant had knowledge about the unfamiliar melodies. Thus, the participants had to pay extra attention to the hand motions to detect musical segments from stimulus movies and integrate them into whole melodies. Therefore, if a greater activation of Broca's area was found for unfamiliar than familiar melodies, it could be deduced that this area plays an essential role in the hierarchical processing of an observed action.

Although NIRS cannot detect deep brain activity, this technique offers several advantages. NIRS detects changes in hemoglobin concentration associated with brain activation occurring beneath the optodes. Therefore, this technique enables the estimation of the activation source using fewer sensors than are required for EEG. Additionally, NIRS allows the participants to maintain a comfortable posture in a quiet room during measurement, while body movements of participants are strictly restricted during an fMRI session. Thus, NIRS is a suitable technique to measure activity in Broca's area and is advantageous, especially for the participants.

#### **METHODS**

#### **SUBJECTS**

The subjects were 20 healthy, right-handed adults aged 23–40 years. Handedness scores were determined using the Edinburgh handedness inventory (Oldfield, 1971). The subjects were divided into 2 groups according to the criterion by Hasegawa et al. (2004). The subjects in the well-trained group (10 females; mean age, 28.7 years; range, 23–34 years) had received formal piano lessons for at least 8 years (mean, 11.4 years; range, 8–16 years). The subjects in the less-trained group (2 males and 8 females; mean age, 29.9 years; range, 24–40 years) had received formal piano lessons for less than 8 years (mean, 3.4 years; range, 1–7 years). All subjects were able to identify the familiar melodies by watching the keytouching hand movements. Prior to the experiment, the subjects were informed about the nature of the experimental procedures and gave their written informed consent for the study. This study was approved by the ethics committee of the Primate Research Institute, Kyoto University.

#### **STIMULUS**

First, hand movements playing the familiar melodies "Mary Had a Little Lamb" (M) and "London Bridge is Falling Down" (L) (key, C major; tempo, 120 beats per minute; duration, 8 s) on the keyboard were filmed (640 × 480 pixels) (**Figure 1**). There were fade-in and fade-out periods (1 s) in the movies to prevent the subjects from using the initial hand position as a cue to identify the melody. By making temporally reversed versions of these movies, "musically" unfamiliar stimuli were created. Consequently, four movies (familiar M and L movies and unfamiliar M and L movies) were generated. Two identical movies were combined to make the 16-s stimuli. No auditory signal was included in the stimuli (**supplementary videos**).Stimuliwerepresented ona17-inchliquid crystal display monitor placed ∼70 cm from the subjects' heads.

The unfamiliar stimuli contained spatial and temporal distributions of key-press durations of hand movement that were

**FIGURE 1 | Static example of a stimulus movie.**

identical to those of the familiar stimuli. However, by reversing the temporal order, the sequential structure of hand motions (corresponding to note transitions) was rendered largely different between familiar and unfamiliar stimuli.

### **PROCEDURE**

#### **ACTION OBSERVATION TASK**

An experimental session consisted of 12 trials. Each trial lasted 16 s, during which one of the four stimuli was presented. The length of the inter-trial interval was 25 s. All four movies were presented three times in a pseudorandom order, with each session beginning with a familiar stimulus. Every stimulus was presented once in four successive trials, and the same melody (M and L) and/or familiarity (familiar and unfamiliar) condition was repeated in no more than 3 consecutive trials. The participants were instructed to watch the movies carefully to identify the "melody" performed by the hand. The beginning of each stimulus movie was accompanied by a tone pip. The participants were asked to open their eyes gently when they heard the tone and close their eyes gently when the stimulus movie disappeared. Thus, the individuals' eyes were closed during the rest period. The entire experiment lasted for ∼30 min.

Prior to the beginning of the recording session, the participants practiced for 8 trials (4 stimuli × 2 trials) with experimental stimuli to confirm that they understood the instructions.

#### **NEAR-INFRARED SPECTROSCOPY MEASUREMENTS**

Cortical activity was continuously recorded throughout the experiment. Relative changes in oxy-hemoglobin (oxy-Hb) concentrations were measured using the NIRS system (ETG-100, Hitachi Medical Corporation, Tokyo, Japan). The sampling rate was set to 10 Hz. Nine optodes (in a lattice pattern forming 12 channels) were placed on both hemispheres. Because the main region of interest was Broca's area, a single recording channel was determined as a point between the optode nearest to F7 of the international 10–20 system and its posteriorly adjacent optode. Thus, NIRS results presumably reflected activity from Broca's area since F7 on the scalp has been reported to project onto the cortical surface of the anterior portion of the inferior frontal cortex (BA 45/47) (Okamoto et al., 2004, 2006; Koessler et al., 2009). Typically, the position of the target channel was located ∼3 cm above the level of the supraorbital process and ∼3 cm posterior to the lateral margin of the orbit.

It is true that cranio-cerebral correspondence using the international 10–20 system has been suggested (Okamoto et al., 2004, 2006; Koessler et al., 2009). However, there is substantial variation in the precise location and topographic extent of Broca's area among individuals (Amunts et al., 1999). Therefore, I acknowledge the difficulty in ensuring the degree of contribution of different subdivisions within the inferior frontal cortex to the NIRS signal.

The cortical responses for each trial were stimulus-locked and extracted from the raw oxy-Hb time series data. Pulsatile fluctuations were removed from the results by smoothing the oxy-Hb time series backward in time with a 5-s moving window. Baseline drift was corrected using linear interpolation between the time point of task onset and the time point of stimulus onset of the next trial. The oxy-Hb time series data from familiar and unfamiliar trials were individually averaged over trials, independent of the melody types. Finally, a mean oxy-Hb value per time point within a peristimulus period (a 16-s time window from 5 s after stimulus onset to 5 s after stimulus offset) was calculated for each condition. The averaged oxy-Hb values obtained during the familiar and unfamiliar conditions were then submitted to a paired *t*-test to assess the involvement of Broca's area in the hierarchical processing of observed action. The significance level was set to 5%.

One may assume that the degree of involvement of Broca's area to action hierarchy is influenced by the experience. Therefore, the relationship between this involvement of Broca's area and the duration of piano training was assessed. First, subtraction of the averaged oxy-Hb value in the familiar condition from that in the unfamiliar condition was individually calculated as an index that shows the sensitivity to action hierarchy. Then, the correlation between such contrast and the duration of piano training was evaluated using a Spearman's rank correlation test. The significance level was set to 5%.

#### **RESULTS**

**Figure 2** shows the average activity of Broca's area across time in the familiar and unfamiliar conditions. In the trained group (A), a difference in the patterns of signal change was evident between the two conditions. However, such a contrast was not observed in the less-trained group (B).

The activation of Broca's area within the peristimulus period is shown in **Figure 3**, where the mean signal intensities for individual participants are compared between conditions. In the well-trained group (left), statistical analysis revealed a significant difference in the signal intensities between conditions [familiar condition (mean ± SD): 0.018 ± 0.031 mM·mm, unfamiliar condition: 0.045 ± 0.035 mM·mm] [paired *t*-test, *t(*9*)* = 3*.*167, *p* = 0*.*011]. Contrary to the well-trained group, the less-trained group (right) showed no significant difference in signal intensities between conditions [familiar condition: 0.018 ± 0.038 mM·mm, unfamiliar condition: 0.018 ± 0.036 mM·mm] [paired *t*-test,

*t(*9*)* = 0*.*076, *p* = 0*.*941]. Thus, the involvement of Broca's area in the hierarchical processing of observed action was observed in the well-trained participants.

The relationship between the degree of such involvement of Broca's area and the duration of piano training were then assessed. A correlation analysis of the entire population revealed that the between-condition difference in mean signal intensity strongly correlated with the duration of piano training [**Figure 4**; Spearman's rank correlation test, *rho(*18*)* = 0*.*593, *p* = 0*.*006]. When a correlation analysis was applied to individual groups, however, no significant correlation was found both in the welltrained group (red dots; α = 0*.*05*/*2, *rho(*8*)* = 0*.*654, *p* = 0*.*040) or the less-trained group (blue dots; α = 0*.*05*/*2, *rho(*8*)* = 0*.*092, *p* = 0*.*800).

#### **DISCUSSION**

#### **GENERAL CONCLUSION**

The primary goal of the present study was to determine the role of Broca's area in the hierarchical processing of observed action.

**unfamiliar conditions and duration of piano training.** Red and blue dots represent the results of well-trained and less-trained participants, respectively.

To address this issue, well-trained and less-trained participants were exposed to silent hand motions that played familiar and unfamiliar melodies. A key finding was that well-trained participants showed increased activation in Broca's area in response to hand motions associated with unfamiliar rather than familiar melodies. Thus, the current data indicate that this region plays an essential role in the hierarchical processing of an observed action. Previous fMRI and magnetic stimulation studies that showed that Broca's area is involved in hierarchical sequence of action planning (Koechlin and Jubault, 2006; Clerget et al., 2011, 2013) may support the current findings.

Additionally, a correlation analysis of the entire population exhibited that the Broca's area of participants who had longer piano training experience exhibited a stronger sensitivity to the hierarchical structure of observed action. However, because there was no such correlation within each participant group, sensitivity to action hierarchy may not develop monotonically as a function of the duration of piano training. Rather, the current results imply that Broca's area becomes sensitive to perceived motor hierarchy only after sufficient motor training.

#### **WAS THE HAND MOTION MEANINGLESS TO THE LESS-TRAINED PARTICIPANTS?**

In the current study, the less-trained participants showed no significant difference in the activation of Broca's area between the familiar and unfamiliar melodies. It has been shown that the activation of Broca's area is associated with an action that is meaningful or possible to the observer (Decety et al., 1997; Grèzes et al., 1998; Costantini et al., 2005; Wakita and Hiraishi, 2011). Therefore, it may be assumed that the observed actions were meaningless to the less-trained participants. The debriefing after the recording session revealed that neither the well-trained nor less-trained participants could report what the unfamiliar melodies were like, but both well-trained and less-trained participants were able to identify the familiar melodies by the observation of key-touch. Therefore, the task performance was comparable between both groups of participants, and the hand movements featuring familiar and unfamiliar melodies were meaningful actions for both groups of participants. How can the difference in the activation of Broca's area between well-trained and less-trained participants explained?

#### **PROCESSING OF OBSERVED ACTION IN BROCA'S AREA OF WELL-TRAINED AND LESS-TRAINED PARTICIPANTS**

The current study implied that Broca's area is also responsible for the analysis of hierarchical structure in a wide range of functional domains. Therefore, it must be significant to survey how native and non-native languages are processed in Broca's area prior to considering the effects of motor expertise on the activity of this area.

Rüschemeyer et al. (2006) showed that BA 44 of a native speaker becomes active only when the processing of a structurally complex sentence is demanded; BA 44 is less active when processing a simple sentence. However, BA 44 of non-native speakers is consistently active, regardless of the degree of complexity of sentences (see also Rüschemeyer et al., 2005; Yokoyama et al., 2006). Moreover, Jeon and Friederici (2013) demonstrated that the syntactical analysis of complex non-native sentence recruited BA 47. Thus, only BA 44 of native speakers showed complexitydependent activation. However, the less-proficient syntactical processing of non-native speakers is compensated for by the semantic information in the sentences.

When well-trained native and less-trained foreign language processing in Broca's area is taken into account, the difference in the activation of this area between well-trained and less-trained participants may be plausibly explained. The current results indicated that Broca's area in well-trained participants was sensitive to the hierarchical organization of hand motion. This effect was likely due to the extensive piano experience of the participant and the concurrent acquisition of neuronal mechanisms in Broca's area that represent action hierarchy. Based on the findings by Rüschemeyer et al. (2006), as well as Jeon and Friederici (2013), it is conceivable that Broca's area is not necessarily activated by processing a simple hand motion action. Rather, Broca's area may come into play only when processing an unfamiliar hand motion sequence.

However, Broca's area in less-trained participants was not sensitive to the hierarchical organization of hand motion. Again, based on the aforementioned linguistic studies, less-trained participants most likely employed neuronal mechanisms that identified "melodies" by relying on the meaning of the melody [mediated by BA 47 (Platel et al., 1997)] rather than by relying on the hierarchical organization of hand motions. Consequently, the appearance of hand motion may induce an equivalent degree of activation in Broca's area regardless of the familiarity of the stimulus. Future studies may compare the activation from the posterior part of the inferior frontal region (BAs 44 and 45) and the anterior part of the inferior frontal region (BAs 45 and 47) to investigate the effects of expertise on the development of the representation of action hierarchy in Broca's area.

#### **ROLE OF BROCA'S AREA AS A DOMAIN-GENERAL HIERARCHICAL PROCESSOR**

Previous studies have reported that familiar body movements evoked increased activation of several brain regions of the mirror neuron system (MNS) than unfamiliar movements (see Rizzolatti and Craighero, 2004; Fadiga et al., 2005 for review). For instance, Calvo-Merino et al. (2005) found a stronger recruitment of MNS including left ventral premotor cortex (vPM) close to Broca's area and bilateral intraparietal sulcus in expert ballet and capoeira dancers for the observation of the dance style of a familiar genre when compared with the unfamiliar genre. The authors argued that the activation of MNS reflected an action resonance processes. However, as far as observation-related brain activation is explained by an uncertain notion, i.e., "action resonance," we do not currently understand the differential processing of information within and between brain regions among MNS.

Alternatively, considering the shared function of Broca's area in a wide range of functional domains must be helpful to understand the role of this area in MNS. Musso et al. (2003) indicated that Broca's area is centrally involved in the processing of grammatical rules. They trained a native speaker of German to learn the grammatical rules of a non-native language with different grammatical rules (e.g., Italian and Japanese). Consequently, they found a significant correlation between the degree of activation of Broca's area and the performance of the judgment of syntactical correctness of language. Thus, Broca's area became active only when regularity of hierarchical structures was successfully abstracted from the sentence.

Given that the acquisition of hierarchical structures is an essential factor for Broca's area to be active, the vPM activation in Calvo-Merino et al. (2005) may be explained in terms of the ability to process action hierarchy. Because classical ballet and capoeira do not share poses and motion sequences with each other, it is plausible to assume that ballet dancers could abstract the regularity of a ballet dance sequence but could not abstract action hierarchy from capoeira. Consequently, the inferior frontal region of ballet dancers may have been more thoroughly recruited for the observation of ballet compared with capoeira and vice versa. Thus, if the notion that Broca's area process hierarchical structures in a wide range of functional domains is correct, this may provide insights into the neural basis of action understanding.

In conclusion, Broca's area was shown to be necessary for the hierarchical processing of observed action. Therefore, the notion that hierarchical processing in Broca's area is a common function shared between the language and music domains may help to explain the role of Broca's area in action perception. In support of these findings, previous studies have reported that the effect of expertise in one domain leads to the improvements in tasks of untrained domains such as enhanced linguistic performance after musical training (Moreno et al., 2009; Strait et al., 2012; François et al., 2013). The effect of auditory rhythm on motor functioning has also been suggested (de Dreu et al., 2012; Hardy and LaGasse, 2013). In summary, the notion of Broca's area as a domain-general hierarchy processor may provide insight into the beneficial transfer across language, music and action abilities.

#### **ACKNOWLEDGMENTS**

This study was supported by grants from the Nakayama Foundation for Human Science. The author thanks Reiko Sawada for technical support.

#### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnhum. 2013.00937/abstract

**Supplementary Video 1 | Example of a familiar stimulus.** This video shows a familiar stimulus generated from "Mary Had a Little Lamb."

#### **Supplementary Video 2 | Example of an unfamiliar stimulus.** This video

shows an unfamiliar stimulus generated from "Mary Had a Little Lamb."

#### **REFERENCES**

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 02 October 2013; accepted: 27 December 2013; published online: 17 January 2014.*

*Citation: Wakita M (2014) Broca's area processes the hierarchical organization of observed action. Front. Hum. Neurosci. 7:937. doi: 10.3389/fnhum.2013.00937 This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 Wakita. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Replication of the correlation between natural mood states and working memory-related prefrontal activity measured by near-infrared spectroscopy in a German sample

#### *Hiroki Sato1,2\*, Thomas Dresler 1,3, Florian B. Haeussinger 1, Andreas J. Fallgatter 1,4 and Ann-Christine Ehlis <sup>1</sup> \**

*<sup>1</sup> Psychophysiology and Optical Imaging, Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany*

*<sup>2</sup> Hitachi, Ltd., Central Research Laboratory, Hatoyama, Japan*

*<sup>3</sup> LEAD Graduate School, University of Tuebingen, Tuebingen, Germany*

*<sup>4</sup> Center of Integrative Neuroscience, Excellence Cluster, University of Tuebingen, Tuebingen, Germany*

#### *Edited by:*

*Nobuo Masataka, Kyoto University, Japan*

#### *Reviewed by:*

*Yukiori Goto, Kyoto University, Japan Hirokazu Doi, Nagasaki University, Japan*

#### *\*Correspondence:*

*Hiroki Sato and Ann-Christine Ehlis, Psychophysiology and Optical Imaging, Department of Psychiatry and Psychotherapy, University of Tuebingen, Calwerstraße 14, Tuebingen 72076, Germany e-mail: hiroki.sato@ med.uni-tuebingen.de<EMAIL_ADDRESS>ann-christine.ehlis@ med.uni-tuebingen.de*

Previous studies have suggested complex interactions of mood and cognition in the lateral prefrontal cortex (PFC). Although such interactions might be influenced by various factors such as personality and cultural background, their reproducibility and generalizability have hardly been explored. In the present study, we focused on a previously found correlation between negative mood states and PFC activity during a verbal working memory (WM) task, which had been demonstrated by using near-infrared spectroscopy (NIRS) in a Japanese sample. To confirm and extend the generalizability of this finding, we conducted a similar experiment in a German sample, i.e., participants with a different language background. Here, PFC activity during verbal and spatial WM tasks was measured by NIRS using a delayed match-to-sample paradigm after the participants' natural mood states had been evaluated by a mood questionnaire (Profiles of Mood States: POMS). We also included control tasks to consider the general effect of visual/auditory inputs and motor responses. For the verbal WM task, the POMS total mood disturbance (TMD) score was negatively correlated with baseline-corrected NIRS data mainly over the left dorsolateral PFC (i.e., higher TMD scores were associated with reduced activation), which is consistent with previous studies. Moreover, this relationship was also present when verbal WM activation was contrasted with the control task. These results suggest that the mood–cognition interaction within the PFC is reproducible in a sample with a different language background and represents a general phenomenon.

**Keywords: near-infrared spectroscopy (NIRS), fNIRS, prefrontal cortex, optical topography, mood, POMS, working memory, hemodynamics**

#### **INTRODUCTION**

While we feel that our mood can influence our everyday activities to some extent, researchers have revealed a wide range of cognitive functions to be modulated by mood (Mitchell and Phillips, 2007). An example of such a mood–cognition interaction has been suggested for working memory (WM), as behavioral studies have shown that WM performance is affected by some emotional states such as withdrawal-related negative states (Gray, 2001; Shackman et al., 2006). Furthermore, the prefrontal cortex (PFC), which plays a crucial role in WM function (Kubota and Niki, 1971; Petrides et al., 1993; D'Esposito et al., 1995), has been suggested to be a brain region where cognition and emotion interact (Pessoa, 2008). In fact, while the PFC has usually been thought to be the central region underlying cognition, Nauta already suggested in 1971 that it is also related to affective and motivational responses to the person's environment based on its close association with the limbic system (Nauta, 1971; Pessoa, 2008).

Recent neuroimaging studies have investigated the impact of mood states on WM-related cortical activity. For example, a functional magnetic resonance imaging (fMRI) study showed that activity in the dorsolateral PFC (DLPFC) during a verbal (number) WM task was reduced after the participants were exposed to negative mood-inducing acute psychological stress (viewing aversive movie clips) (Qin et al., 2009). On the other hand, another fMRI study revealed that an unpleasant emotional state (induced by video clips) resulted in enhanced DLPFC activity during a verbal (letter) WM task and reduced activity during a non-verbal (face) WM task, whereas a pleasant emotional state exhibited the completely opposite pattern (Gray et al., 2002). These contradictory findings suggest a complex mood–cognition interaction in the PFC; however, an integrated theory has not been established yet. Moreover, most of the previous studies used mood-induction methods, which differ in their respective effects (Martin, 1990) and which might be different from natural mood and its effect on cognition (Parrot and Sabini, 1990). To summarize, only few aspects about the relationship between "natural moods" <sup>1</sup> and WM-related PFC activity have been clarified.

To better understand the mood–cognition interaction in the PFC, we previously introduced a new approach which focused on the natural mood state in healthy participants without using a mood-induction method (Aoki et al., 2011, 2013; Sato et al., 2011). In these studies, a psychological questionnaire (Profiles of Mood States, POMS) (McNair et al., 1971; Yokoyama et al., 1990) was administered to assess the participants' natural moods during the past week, and near-infrared spectroscopy (NIRS) a non-invasive and less body-constraint neuroimaging technique (Maki et al., 1995; Koizumi et al., 2005; Ehlis et al., 2014)—was used to measure their PFC activity during an emotionally-neutral delayed match-to-sample verbal/spatial WM paradigm (Smith et al., 1996). A correlation analysis among 29 healthy participants revealed that subjects reporting higher levels of negative mood showed lower levels of PFC activity during the verbal, but not the spatial WM task (Aoki et al., 2011). In a next step, we tried to dissociate the state-dependent effect from trait-dependent factors. Using a within-subjects design (Sato et al., 2011), experimental sessions were repeated three times at 2-week-intervals to investigate time-to-time fluctuations. The results indicated that changes in the depressed-mood score between successive sessions were negatively correlated with changes in left PFC activation for the verbal WM task (Sato et al., 2011), which is well in line with the former study (Aoki et al., 2011). Another previous study examined the contribution of personality effects (measured with the NEO Five-Factor Inventory and the Behavioral Inhibition/Activation scales) and replicated the negative correlation between negative mood scores and PFC activity for the verbal WM task even after controlling for the participants' personality traits (Aoki et al., 2013). Together, these studies strongly suggest that PFC activity during verbal WM tasks reflects the participant's natural mood-state independent of various trait factors. Furthermore, the validity of NIRS for measuring PFC activation in response to a verbal WM task was demonstrated in a simultaneous NIRS-fMRI measurement (Sato et al., 2013): A significant correlation between the NIRS signals and blood oxygenation level-dependent (BOLD) signals for the WM-related PFC activity was shown—not only for the temporal changes, but also for the amplitude of the signal response (Sato et al., 2013).

In the present study, we aimed to replicate our previous findings on a mood–cognition interaction in the PFC for a verbal WM task (Aoki et al., 2011) in a German sample, i.e., participants with a different language background while also striving to improve previous methodical weaknesses: First, we find it necessary to confirm the reproducibility of the findings when the PFC activity is derived from a contrast with a suitable control condition in an improved task design. Because the previous studies only used activation values relative to baseline, possible confounds (input of visual stimuli, reaction by button press, etc.) could not be ruled out. Second, it is important to determine whether the phenomenon can be replicated in other samples with a different cultural and ethnical background. In particular, because a verbal function is the object of our study, the replication in a sample with a different language background is necessary to show the robustness of this phenomenon. Indeed, the Japanese letters used in our previous studies represent a minority in the world; besides, it is still difficult to determine the genetic affiliation of Japanese language to other languages or language families (Teruya, 2004). In this study, we conducted an experiment in a German sample whose language belongs to the major language family of "Indo-European languages" using Latin alphabet letters. Although we usually assume that there are no significant cultural/language-related differences in cognitive neuroimaging data, it is worth replicating the results in a sample with a different language background to make our knowledge more practical.

#### **MATERIALS AND METHODS PARTICIPANTS**

Thirty-one healthy German volunteers participated in this study (16 females and 15 males, mean age ±*SD* = 28*.*0 ± 7*.*1 years). All of them reported no history of neurological or psychiatric disease and gave their written informed consent to participate in this study, which was approved by the Ethics Committee of the University Hospital Tuebingen. All procedures were in line with the Declaration of Helsinki in its latest version. Both males and females were included in this study because our previous study did not reveal any gender differences for the main result of a correlation between negative mood and PFC activity (Aoki et al., 2011). After we excluded data from four participants with increased depression scores (BDI scores *>* 14) and one participant with poor task performance (i.e., accuracy *<*60%), data from 26 participants (13 females and 13 males, all right-handed except for one male) with a mean age of 25*.*9 ± 4*.*6 years (age range: 20–42) were further analyzed. All of them had a higher education (of at least 13 years; "Abitur" in German).

#### **WORKING MEMORY TASKS**

WM refers to the temporary retention of information to perform cognitive activities (Baddeley, 1983; D'Esposito, 2007). Here, we used verbal and spatial delayed match-to-sample WM tasks (Smith et al., 1996) as previously applied (Aoki et al., 2011, 2013; Sato et al., 2011) (**Figure 1A**).

In the verbal WM task, four lowercase letters were presented at four peripheral locations as a target stimulus (Target), and a capital letter was presented at one of the other four peripheral locations as a test stimulus (Test) (see Target and Test stimuli for verbal WM condition in **Figure 1A**). We pseudo-randomly selected and arranged four sets from eight letters (b, f, g, h, m, n, p, t) for the Target, not to make up any meaningful words. The participants were asked to judge whether the character presented as Test corresponded to any of the Target characters and to respond as quickly as possible by pressing a button ("yes"—right index finger, "no"—right middle finger). Since the characters in Target and Test were presented in different letter-types (lowercase/capital), participants had to decide by using the phonetic information (i.e., the phonological loop). In the verbal control (v-control) task, four "x"s were presented at the four fixed locations as Target, and either an "X" or "O" was presented at one of the other four

<sup>1&</sup>quot;Natural moods" are defined as relatively low-intensity and diffuse affective states without a striking preceding cause (Forgas, 1995), being different from emotion and experimentally induced moods.

locations as Test. The participant had to decide whether the Test character was "X" (Yes) or "O" (No). This v-control task was designed to match the verbal WM task in visual/auditory inputs and motor response with less WM load.

In the spatial WM task, the Target was given by the locations of four squares irregularly arranged at eight peripheral locations. The arrangement of squares was organized in a complex way (e.g., no more than three successive squares, no meaningful arrangement like large square or rhombus). After an arrangement was presented as Test, the participant was asked to judge whether the Test square location was identical to any of the Target square locations. In the spatial control (s-control) task, four circles arranged to make a regular square were presented as Target, and a circle was presented at a location among the eight peripheral locations as Test. The participant had to decide whether the location of the Test circle was identical to any of the Target circle locations. Responses were indicated as in the verbal task.

The software *Presentation* (Neurobehavioral Systems, Inc., U.S.A.) was used for stimuli presentation and reaction time (RT) recording. Each WM task trial was as follows (see **Figure 1**): First, a central fixation cross (30% black) turned white 500 ms before the Target onset with an auditory cue (1000 Hz sine wave of 100 ms duration) as a warning signal. Then the Target was presented for 1500 ms, followed by a delay period of 7000 ms with the white fixation cross. During the delay period for the spatial WM task, positions of the eight peripheral squares were also shown in dark-gray (75% black). In the same way, positions of the four peripheral circles were shown for the delay period in the s-control task. Test, indicated by an 800 Hz sine wave of 100 ms duration, was presented until the participant responded (maximum: 2000 ms). All stimuli in Target and Test were shown in light-gray (20% black) at a location among the eight peripheral locations, and the inter-trial-intervals with the central fixation cross (30% black) were randomly varied between 15 and 18 s. A dark-gray background (70% black) was shown throughout.

Participants performed the verbal and spatial WM tasks in different runs. In both verbal WM and spatial WM runs, 8 WM task trials and 8 control trials were presented in a pseudo-randomized order (16 task trials in total for each run). In addition, "yes" and "no" trials were pseudo-randomized to be the same number (i.e., 4 trials for "yes" and the other 4 for "no" in each condition). The order of runs (verbal WM run and spatial WM run) was counterbalanced across participants.

#### **PROCEDURE**

Before the NIRS measurement, the participants' natural mood was assessed by using a short form of the POMS (McNair et al., 1971) in its German version (Bullinger et al., 1990). The participants rated 35 mood-related adjectives on a 7-point scale ranging from 0 ("not at all") to 6 ("extremely") based on how they had been feeling in the past week including the measurement day. While the German version of the POMS consists of four identifiable mood states (depression/anxiety, vigor, fatigue, and discontent), we defined the total mood disturbance (TMD) score as a general negative mood score calculated as the sum of the negative mood scores (depression/anxiety, fatigue, and discontent) minus the positive mood score (vigor). As previous studies showed that PFC activity correlated negatively with negative moods while tending to correlate positively with positive moods (Aoki et al., 2011, 2013), TMD would be an appropriate mood index which is expected to correlate negatively with PFC activity.

Thereafter, PFC activity during the performance of the WM tasks was measured by using a multi-channel NIRS (optical topography) system (ETG-4000, Hitachi Medical Corporation, Japan). The system uses continuous wave laser diodes with two wavelengths (695 and 830 nm) as light sources. Optical fibers are used both for irradiation and for detection of near-infrared light. Seventeen irradiation positions and 16 detection positions were arrayed in a 3 × 11 lattice pattern with 30 mm separation, forming 52 measurement channels (**Figure 1B**). The average power of each wavelength light was 1.5 mW at the irradiation point, which was modulated at different frequencies to be separated for wavelengths and sources in the detection process. The reflected light at a position 30 mm apart from the irradiation position was detected by avalanche photodiodes and recorded in the computer with a sampling rate of 10 Hz.

To obtain the anatomical position of the NIRS channels, we measured the three-dimensional coordinates of the optode positions in four German volunteers by using a neuro-navigation system (LOCALITE GmbH, St. Augustin, Germany). The obtained optode coordinates were transferred from the volunteers' native MRI space to the standard Montreal Neurological Institute (MNI) space by applying normalization procedure of Statistic Parametric Mapping 8 (SPM8, http://www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/software/ spm8/). The normalized coordinates were used in the probabilistic registration method (Okamoto et al., 2004; Okamoto and Dan, 2005) to estimate the brain region for each measurement channel and to generate 3-D topographical maps (**Figures 1**–**3**). The averaged MNI coordinates on the brain are listed with the corresponding Brodmann Area (BA) number (Rorden and Brett, 2000) and the anatomical label determined by Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) in the Supplementary Material (**Table S1**).

#### **DATA ANALYSIS**

For NIRS data analysis, we used MATLAB (The MathWorks, Inc., U.S.A.) and the plug-in-based analysis software Platform for Optical Topography Analysis Tools (developed by Hitachi, CRL).

For each NIRS channel, the optical data for the two wavelengths were transformed into a time series of hemoglobin (Hb) signals (oxy-Hb and deoxy-Hb signals) on the basis of the modified Beer–Lambert law (Delpy et al., 1988; Maki et al., 1995). These signals were expressed as the product of the changes in hemoglobin concentration (mM) and optical path length (mm) in the activation region (effective optical path length). A butterworth band-pass filter (0.013–0.80 Hz) was then applied to the Hb signals to remove low-frequency drift/oscillation and high-frequency system noise. The time-continuous data of Hb signals were divided into 25.5-s task blocks, which consisted of a 1-s pre-task period (starting 1 s before Target onset), an 8.5 s task period (during the 1.5-s Target presentation and the 7-s delay period) and a 16-s post-task period (starting after Test onset). Each task block was baseline-corrected by subtracting the average value of the pre-task period. Here, we especially focused on the oxy-Hb signal in the analysis as previous studies underlying the present work found their main effects in this chromophore (Aoki et al., 2011, 2013; Sato et al., 2011). Note that we did not include data from error-response trials for the further analysis.

To evaluate WM-associated PFC activity, we calculated two kinds of activation values for each channel and participant. The first activation value (Act\_base) contrasts the mean oxy-Hb signal values during a 6-s "activation period" (starting 5 s after Target onset) with the baseline. The activation period was determined to investigate the signal change induced by the encoding and maintenance processes in WM function, avoiding confounding effects related to the motor response and proactive interference (D'Esposito et al., 2000). These oxy-Hb signal values were then averaged across correct-response trials and divided by their standard deviation across trials. The second activation value (Act\_ctl) contrasts WM activation with the control task: The differences between the mean values for a WM task minus those for the control task were averaged across correct-response trials and divided by the standard deviations across trials. These activation values, which take the within-participant reproducibility into consideration, should be more reliable than a mean value from a simply-averaged waveform which is easily influenced by an accidental large response-like change in a single trial.

In the subsequent group analysis, we performed channel-wise statistical tests on the activation values to determine the activation regions. First, significant PFC activation relative to the baseline was assessed by using one-sample *t*-tests (one-tailed) of Act\_base in all 52 channels. Next, significant PFC activation relative to the control task was assessed in a similar way using Act\_ctl for the

**FIGURE 2 | Hemodynamic responses to the verbal and spatial WM tasks. (A)** Activation t-maps of oxy-Hb signal increase during WM tasks compared to the baseline (zero level). **(B)** Activation t-maps of oxy-Hb signal increase during WM tasks compared to the signal during the control task. In both maps, the Student's *t*-value is indicated by a color scale for channels which

meet the *p*-value criteria described below each map. **(C)** Time courses of Hb-signal in a representative channel (Ch 25). These time courses represent the grand average across all participants (*N* = 26) with standard error (transparent color region). The green vertical lines show the time of (a) Target onset and (b) Test onset, respectively.

activated channels determined by the former analysis in Act\_base. In these channel-wise analyses, we used the false discovery rate (FDR) method to correct for multiple comparisons among channels (Benjamini and Hochberg, 1995; Singh and Dan, 2006). Based on the activation map for Act\_ctl, we selected significantlyactivated channels as regions of interests (ROIs). In each ROI, correlation coefficients between mood states (TMD scores) and activation values were calculated using Spearman rank correlation coefficients (*rho*) as the association may not be necessarily linear. Here, we tested significance of the negative correlation (onetailed) with the FDR correction for multiple comparisons for the number of ROIs. To further examine whether the correlations were influenced by confounding factors such as age, gender, and task performance (accuracy and reaction time), we performed partial correlation analyses. In addition, correlation coefficients between behavioral data (accuracy and reaction time) and the TMD score were also examined to consider mood effects on the behavioral level.

#### **RESULTS**

#### **BEHAVIORAL DATA**

The accuracy and RT data are shown in **Table 1**. Two-Way repeated-measures ANOVAs were used to examine the effects of WM load (WM task/control task) and content (verbal/spatial). For accuracy, a main effect of WM load was found [*F(*1*,* <sup>25</sup>*)* = 11*.*82, *p <* 0*.*01], which indicates that the control tasks were easier to solve, as expected. In contrast to that, no significant results were obtained for content [*F(*1*,* <sup>25</sup>*)* = 0*.*41, *p* = 0*.*53] or the interaction of WM load × content [*F(*1*,* <sup>25</sup>*)* = 0*.*37, *p* = 0*.*55]. For RT, a main effect of WM load was found [*F(*1*,* <sup>25</sup>*)* = 49*.*11, *p <* 0*.*001] without a main effect of content [*F(*1*,* <sup>25</sup>*)* = 0*.*38, *p* = 0*.*54], which indicates that both WM tasks were associated with longer reaction times than the control tasks. A significant interaction was also found [*F(*1*,* <sup>25</sup>*)* = 10*.*53, *p <* 0*.*01], indicating that the RT difference between the WM and control tasks was larger for the verbal WM condition (mean difference in the RTs: 161 ms) compared to that for the spatial WM condition (mean difference in the RTs: 65 ms).

The correlation coefficients between the accuracies and TMD scores were not significant (verbal WM: *rho* = 0*.*09, *p* = 0*.*65; spatial WM: *rho* = 0*.*25, *p* = 0*.*22). The correlation coefficients between the RTs and TMD scores were also not significant, but displayed a tendency toward a negative association (verbal WM task: *rho* = −0*.*33, *p* = 0*.*10; spatial WM task: *rho* = −0*.*34, *p* = 0*.*09), i.e., participants who had higher TMD scores showed lower RTs (faster responses) for both WM tasks.

#### **PFC ACTIVITY DURING WM TASKS**

Activation maps in the group analysis are shown in **Figure 2**. For the verbal WM task, the increase in oxy-Hb signal relative to baseline was significant in 11 channels in the bilateral PFC (*p <* 0*.*05, FDR corrected for 52 channels). Four of these channels also showed significant activation in contrast to the v-control task (*p <* 0*.*05, FDR corrected for 12 channels). The representative time courses (ch25) shown below the activation map (**Figure 2**) indicate clear responses in the oxy-Hb signal after the presentation of Target stimuli, where the responses for the WM task were larger than those for the control task.

This activation pattern was not evident for the spatial WM task, where no significant increases in oxy-Hb signal relative to the baseline were found when the threshold was corrected for the number of channels. Although the uncorrected activation maps indicated oxy-Hb signal increases in the PFC region relative to the baseline (similar to the result for the verbal WM task), no significant differences were found when contrasted with the s-control task (**Figure 2**). While the time courses (ch25) showed a similar response to the Target stimuli as shown in the verbal WM condition, the standard error (inter-participant variation) was larger and there was no clear difference from the response to the control task. As activation did not differ between WM and control task, we did not include the spatial WM data in the subsequent correlation analysis.

#### **CORRELATION OF MOOD STATES WITH PFC ACTIVITY FOR THE VERBAL WM TASK**

TMD scores measured by POMS ranged from −37 to 51 across participants (Mean = −3*.*65, *SD* = 19*.*36). To examine the relationship between the negative mood state and PFC activity for the verbal WM task, the correlation coefficients between TMD scores and activation values were calculated for the ROIs. Here, we defined the WM-related activation channels determined by contrasting to the control task (**Figure 2B**) as the ROIs (**Figure 3A**), and the mean activation values of the two channels (channels 18 and 29) were used in the correlation analysis for ROI-1.

The results of the correlation analysis are summarized in **Table 2**. In ROI-1 in the left PFC, which consists of channels 18 ("Frontal\_Mid\_L," BA45) and 29 ("Frontal\_Inf\_Tri\_L," BA45), both Act\_base (activation value relative to baseline) and Act\_ctl (activation value relative to the v-control task) showed significant negative correlations with TMD scores (*p <* 0*.*05, FDR corrected for 3 ROIs) (**Table 2** and **Figures 3B,C**). These correlations remained significant even when the control variables (gender, age, accuracy and RT for the verbal WM task) were entered into a partial correlation analysis (Act\_base: *rho* = −0*.*50, *p* =*<* 0*.*01; Act\_ctl: *rho* = −0*.*42, *p <* 0*.*05). Further, when analyzed separately by gender, females and males showed equivalent correlation coefficients for Act\_ctl

**Table 2 | Correlation coefficient (***rho***) between TMD scores and activation values in ROI.**

*Uncorrected p-values are shown for the correlation coefficients. Significant rho- and p-values (p < 0.05 with FDR correction) are indicated by bold letters.*

(female: *rho* = −0*.*45, *p* = 0*.*12; male: *rho* = −0*.*43, *p* = 0*.*15) although they did not reach statistical significance due to small sample sizes. On the other hand, ROI-2 in the right PFC (channel 25, "Frontal\_Mid\_L," BA46) also showed a significant negative correlation for Act\_base (*p <* 0*.*05, FDR corrected for 3 ROIs) but not for Act\_ctl (*p >* 0*.*05, FDR corrected for 3 ROIs). In addition, ROI-3 in the left temporal pole (channel 51, "Temporal\_Pole\_Sup\_L," BA38) did not show any significant correlation for either Act\_base or Act\_ctl (*p >* 0*.*05, FDR corrected for 3 ROIs).

As the waveform of oxy-Hb signals showed a bi-phasic pattern (**Figure 2C**), we additionally conducted the same correlation analysis using different activation values for the second peak (using activation period: 11–17 s after Target onset) (**Table S2**). As a result, the activation value relative to the baseline (Act2\_base) in the left PFC (ROI-1) still showed a significant negative correlation (*p <* 0*.*01, FDR corrected for 3 ROIs) while the activation value relative to the control task (Act2\_ctl) did not reach statistical significance (*p >* 0*.*05, FDR corrected for 3 ROIs). Activation values in ROI-2 and ROI-3 showed the same tendency of negative correlations with no statistical significance.

#### **DISCUSSION**

In the present NIRS study, we aimed to confirm and extend the generalizability of findings about an association between negative mood states and PFC activity for a verbal WM task as demonstrated in Japanese samples (Aoki et al., 2011, 2013). We could show that negative mood (TMD score evaluated by POMS) was negatively correlated with left-hemisphere PFC activity during a verbal WM task in a German sample. This was also evident when subtracting activation in the v-control task from the WM task. That is, participants with higher TMD scores showed less PFC activity during the verbal WM task, which is consistent with the above-mentioned findings. To our knowledge, this is the first study that replicates this relationship in a sample with a different language background, suggesting that the mood–cognition interaction in the PFC represents a general phenomenon that is reproducible across samples with different cultural and ethnical background.

#### **PFC ACTIVITY DURING WM TASKS**

The oxy-Hb signals increased during the verbal WM task in the bilateral PFC (**Figure 2**). The activated cortical region and the shape of Hb-signal changes do not only resemble those in the published studies (e.g., Aoki et al., 2011), but also those in corresponding fMRI studies (D'Esposito, 2007). The contrast with the control task showed more localized activation around the bilateral middle frontal gyrus (BA46) and the triangular part of left inferior frontal gyrus (BA45) consistent with available PET/fMRI studies (Petrides et al., 1993; D'Esposito et al., 1995; Swartz et al., 1995; Smith et al., 1996; Smith and Jonides, 1999; Walter et al., 2003). This result suggests that the control task was well-chosen to capture the WM relevant PFC regions. Although some studies suggested that extracranial hemodymanic changes such as skin blood flow considerably influence the forehead NIRS signals (Takahashi et al., 2011; Kirilina et al., 2012), a simultaneous NIRS-fMRI study demonstrated that in a comparable WM task Hb-signals were significantly correlated with gray matter BOLD signal rather than with soft tissue or skin blood flow signals (Sato et al., 2013).

In contrast to the verbal WM task, the increase in the oxy-Hb signal for the spatial WM task was not significant in a conservative analysis with FDR correction, although we observed a similar activation region when compared to the baseline without multichannel correction (**Figure 2**). This result was not consistent with our previous NIRS studies which showed the same activation for the spatial as in the verbal WM task (Aoki et al., 2011). As the behavioral accuracy and RT data did not reveal differences between tasks, a difference in difficulty cannot account for this discrepancy. However, the RT data indicates that the difference between WM and control task was less pronounced for the spatial WM task, which could explain the absence of a significant finding for spatial WM. In addition, as the four circle positions in the Target image were fixed for the s-control task to minimize WM load, it may have been seen as a totally different task by the participant. That is, both the WM task and the control task could activate regions involved in "set-shifting" (Konishi et al., 1999).

#### **CORRELATION OF MOOD STATES WITH PFC ACTIVITY FOR THE VERBAL WM TASK**

We found a significant negative correlation of the TMD score with both Act\_base (activation relative to the baseline) and Act\_ctl (activation relative to the v\_control task) for the verbal WM task in the left PFC (around BA45), as found in the aforementioned NIRS studies, demonstrating the reproducibility of the relationship between negative mood and WM-dependent PFC activity. This result extends our knowledge for the following reasons: First, though to a lesser extent, it was also found for Act\_ctl, which suggests that the main component of the correlated NIRS signal indeed stems from a verbal WM function rather than general task demands (e.g., visual/auditory inputs and execution of the motor response). Second, the significant correlation was demonstrated in a localized activation area within the left PFC, which overlaps with the main verbal WM-related regions identified by other modalities (Petrides et al., 1993; Smith et al., 1996; Walter et al., 2003). This can be interpreted as evidence against a critical distortion of our findings by confounding skin blood flow changes which are supposed to appear across large parts of the measurement array (Kohno et al., 2007). According to the "prefrontal asymmetry" hypothesis (Davidson, 1992), positive (approach-related) and negative (withdrawal-related) affect are predominantly associated with the left (verbal function) and right (spatial) hemisphere, respectively (Gray, 2001; Gray et al., 2002). Based on this idea, our results (i.e., decreased left PFC activity) might reflect a reduced positive mood state rather than increases in negative mood. Third, we could confirm the replication of our previous findings in a sample with a different language background. This suggests that the relationship between mood and verbal WM function is general regardless of language. Previous studies have shown cultural influences on the brain activation related to some social cognitive tasks (Chiao et al., 2010; Chiao and Immordino-Yang, 2013). However, up to now, no studies have shown such cultural influences on the lateral PFC during emotionally-neutral cognitive tasks such as our WM tasks, as far as we know. This might indicate that PFC activity for emotionally-neutral cognitive tasks is independent of cultural backgrounds. Although we could not directly compare the activation pattern between Japanese and German samples, cultural differences in PFC activity during emotionally-neutral cognitive tasks might be an important topic to determine the range of cultural effects on the brain. In addition, it should be emphasized that we could also replicate the significant correlation using partial correlation analysis with control variables of gender, age, and task performance. This indicates that the mood–cognition relationship in the PFC cannot be explained by any of the control variables.

Our results are basically in line with an fMRI study by Qin et al. (2009) who demonstrated that experimentally-induced acute stress was accompanied by a decrease in verbal WM-related DLPFC activity. In addition, both studies did not reveal correlational patterns with behavioral data, possibly due to ceiling effects of the task performance. This means that it is not clear whether the negative mood states hinder the PFC function or improve its efficiency. To increase the number of task trials or to make the task more difficult would be a possible solution to reveal the relationship between behavioral measures and mood states. Actually, the previous study showed that participants with the strongest stress response slowed down the most (supporting a hindrance of the PFC) (Qin et al., 2009), while our results indicated a tendency for participants with higher negative mood scores to show faster responses (supporting an improvement of PFC function). Nevertheless, these results suggest that decreased PFC activity can be a neural marker of heightened stress or increased negative mood states and seems to be more sensitive than behavioral performance.

#### **LIMITATION AND FUTURE PERSPECTIVES**

The first limitation of the present study concerns a failure to detect PFC activation for the spatial WM task using a corrected significance threshold. As the previous NIRS study showed a significant correlation between mood and PFC activity only for the verbal WM task but not for the spatial WM task while the two tasks induced almost equivalent PFC activities (Aoki et al., 2011), it would be worth investigating the difference of the two tasks in order to further identify the cognitive factors underlying the mood–cognition interactions.

Next, we could not directly compare the basic NIRS results between German and Japanese samples, because some differences in methods and theoretical considerations did not allow such a procedure. First, differences in the applied paradigms such as the control task and the repetition time restrict comparability and would make it difficult to attribute any potential group differences to a specific factor. Additionally, results from different NIRS machines should not be directly compared as general equivalence in technical parameters, etc. of the machines had not been tested. Second, a direct comparison was not intended as no hypotheses exist why and how these samples differ. Indeed, the main purpose of this study was to replicate the relationship between PFC activity and mood scores, which was successfully done regardless of some cultural/ethnical differences and task modifications. Only testing the samples using exactly the same paradigm parameters and the same NIRS machine would help to systematically explore potential differences between both samples.

Finally, the present study aimed at replicating previous NIRS findings and was not specifically designed to further explain the cognitive or physiological mechanisms underlying the observed mood–PFC relationship. Indeed, we found a similar correlation pattern for the second peak of PFC activation corresponding to the retrieval phase (**Table S2**), while focussing on the activity for encoding and maintenance phases. This might suggest the necessity to resolve the mood-related WM function into cognitive sub-processes in the future. Furthermore, it is necessary to investigate the effects of some other physiological parameters on the mood–PFC correlation to advance our knowledge about the causal relationship between natural mood and PFC activity.

#### **CONCLUSION**

The present study aimed at replicating a previous finding on an interaction between mood states and PFC activity for a verbal WM task, which was found by a NIRS study in a Japanese sample. Conducting a comparable experiment in a German sample, we could indeed confirm this finding; negative mood scores were negatively correlated with verbal WM-related PFC activity across participants, even when the activation values were contrasted with an adequate control task. Although we failed to observe reliable PFC activity for the spatial WM task, it is important to note that the relationship between negative mood and PFC activity for the verbal WM task could be confirmed in a sample with a different language background. Our results suggest that the mood–cognition interaction within the PFC is reproducible and represents a general phenomenon.

#### **ACKNOWLEDGMENTS**

We thank Betti Schopp and Ramona Taeglich for their proficient technical assistance. We also thank Agnes Kroczek, Beatrix Barth, Florian Metzger, Katja Hagen, Sabrina Schneider, and Saskia Deppermann for their helpful support. Thomas Dresler was partly supported by the LEAD graduate school [GSC1028], a project of the Excellence Initiative of the German federal and state governments.

#### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www*.*frontiersin*.*org/journal/10*.*3389/fnhum*.* 2014*.*00037/abstract

**Table S1 | Estimated location of each NIRS channel on normalized brain image. Mean MNI coordinates across participants are shown with the corresponding Brodmann area (BA) number (Rorden and Brett, 2000) and the anatomical label determined by Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002).**

**Table S2 | Correlation coefficient (rho) between TMD scores and activation values (Act2\_base: contrasted to baseline; Act2\_ctl: contrasted to the control task) for the second peak (activation period: 12–18 s after the Target onset).**

#### **REFERENCES**

Aoki, R., Sato, H., Katura, T., Matsuda, R., and Koizumi, H. (2013). Correlation between prefrontal cortex activity during working memory tasks and natural mood independent of personality effects: an optical topography study. *Psychiatry Res.* 212, 79–87. doi: 10.1016/j.pscychresns.2012.10.009

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 22 October 2013; accepted: 17 January 2014; published online: 06 February 2014.*

*Citation: Sato H, Dresler T, Haeussinger FB, Fallgatter AJ and Ehlis A-C (2014) Replication of the correlation between natural mood states and working memoryrelated prefrontal activity measured by near-infrared spectroscopy in a German sample. Front. Hum. Neurosci. 8:37. doi: 10.3389/fnhum.2014.00037*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 Sato, Dresler, Haeussinger, Fallgatter and Ehlis. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Negative emotion modulates prefrontal cortex activity during a working memory task: a NIRS study

#### *Sachiyo Ozawa\*, Goh Matsuda and Kazuo Hiraki\**

*Graduate School of Arts and Science, The University of Tokyo, JST, CREST, Tokyo, Japan*

#### *Edited by:*

*Nobuo Masataka, Kyoto University, Japan*

#### *Reviewed by:*

*Satoru Otani, Ryotokuji University, Japan Shintaro Funahashi, Kyoto University, Japan*

#### *\*Correspondence:*

*Sachiyo Ozawa and Kazuo Hiraki, Graduate School of Arts and Science, The University of Tokyo, Komaba 3-8-1, Meguro-ku, Tokyo 153-8902, Japan e-mail: ozawa@ ardbeg.c.u-tokyo.ac.jp<EMAIL_ADDRESS>
This study investigated the neural processing underlying the cognitive control of emotions induced by the presentation of task-irrelevant emotional pictures before a working memory task. Previous studies have suggested that the cognitive control of emotion involves the prefrontal regions. Therefore, we measured the hemodynamic responses that occurred in the prefrontal region with a 16-channel near-infrared spectroscopy (NIRS) system. In our experiment, participants observed two negative or two neutral pictures in succession immediately before a 1-back or 3-back task. Pictures were selected from the International Affective Picture System (IAPS). We measured the changes in the concentration of oxygenated hemoglobin (oxyHb) during picture presentation and during the n-back task. The emotional valence of the picture affected the oxyHb changes in anterior parts of the medial prefrontal cortex (MPFC) (located in the left and right superior frontal gyrus) and left inferior frontal gyrus during the n-back task; the oxyHb changes during the task were significantly greater following negative rather than neutral stimulation. As indicated in a number of previous studies, and the time courses of the oxyHb changes in our study, activation in these locations is possibly led by cognitive control of emotion, though we cannot deny it may simply be emotional responses. There were no effects of emotion on oxyHb changes during picture presentation or on n-back task performance. Although further studies are necessary to confirm this interpretation, our findings suggest that NIRS can be used to investigate neural processing during emotional control.

**Keywords: NIRS, prefrontal activity, DLPFC, cognitive control, emotion regulation, working memory, n-back task, IAPS**

#### **INTRODUCTION**

A number of previous studies have suggested that emotion is regulated by cognitive processing (Dolcos et al., 2011; Cromheeke and Mueller, 2013). Studies of the cognitive control of emotion or emotion regulation have utilized neuroimaging methods, which are a useful tool that provide objective and precise indices of subjective emotional experiences. A large number of neuroimaging studies have recently been conducted in order to examine the neural functions underlying the cognitive control of emotion (Hare et al., 2005; Erk et al., 2006; Goldin et al., 2008; Adrian et al., 2011). According to these neuroimaging studies, the neural function of emotion regulation involve interactions between the *hot* emotional system in the limbic regions that involves the amygdala and *cold* higher order systems in prefrontal regions (Gray et al., 2002; Dolcos et al., 2011). More concretely, emotional control involves neural interactions that decrease the activity in the amygdala while increasing prefrontal activity. For example, Erk et al. (2006) have shown that working memory tasks downregulate amygdala activity induced by the anticipation of subsequent negative emotions. Ochsner et al. (2002) have indicated that amygdala activity is downregulated by reappraisals, when participants willfully no overrode their negative feelings toward the pictures. Van Dillen et al. (2009) have demonstrated that arithmetic tasks with a higher cognitive load downregulate the increased amygdala activity induced by a prepresented negative stimulus, and release negative emotions compared to arithmetic tasks with a lower cognitive load. Thus, cognitive processing associated with prefrontal enhancements has been shown to decrease activity in the limbic system. At the same time, the negative emotional state is thought to be relieved.

The cold, higher order, system includes brain regions typically associated with executive functions (Dolcos et al., 2011). One of these executive functions is working memory, which contributes to the maintenance of information relevant to current tasks over short periods of time (Baddeley and Della Sala, 1996). During the engagement of a working memory task, brain activity increases in frontal regions that include the dorsolateral prefrontal cortex (DLPFC) (Owen et al., 2005). Moreover, a number of studies have indicated that the cognitive control of emotion also activates various prefrontal regions, such as the orbitofrontal cortex (OFC; Banks et al., 2007), the medial prefrontal cortex (MPFC; Ochsner et al., 2002), and the ventrolateral prefrontal cortex (VLPFC; Lévesque et al., 2003). In particular, activity in the DLPFC has been consistently observed in a number of studies (Beauregard et al., 2001; Lévesque et al., 2003). DLPFC activity has been thought to be indirectly and reciprocally associated with activity in limbic systems through connections in the OFC (Cavada et al., 2000). The DLPFC appears to be the shared region for both working memory and emotional regulation.

In this study, we investigated whether task-irrelevant emotional stimuli elicited differential neural activity during a subsequent working memory task in order to investigate the prefrontal activity underlying the cognitive control of emotion. Previous findings (Dolcos et al., 2011) have suggested that task-irrelevant emotional stimuli can interfere with the maintenance of goalrelevant information and thus serve as a distracter in a cognitive task. A distracter can be presented during (Dolcos and McCarthy, 2006) or prior to (Deckersbach et al., 2008; Van Dillen et al., 2009; Hart et al., 2010) a working memory task. According to Dolcos and McCarthy (2006), when an emotional distracter is presented during a working memory task, DLPFC activity may be attenuated by the distracter and cognitive performance may be impaired. Only a few studies have been conducted with a prepresented distracter. For example, Van Dillen et al. (2009) have found greater activity in the right DLPFC in response to negative pictures than neutral pictures during an arithmetic task following 4 s of emotional picture presentation. A higher cognitive load also elicited greater activity in the right DLPFC. In addition, Hart et al. (2010) have examined the effects of emotional priming on a subsequent Stroop task. In that experiment, a negative or neutral picture was primed for 150 ms, and a Stroop task was then presented. Consequently, an attenuating effect was observed in response to the negative pictures during a congruent condition, but it was not observed during an incongruent condition. Hart et al. (2010) have explained that the need for cognitive processing overrode the deactivating effect of negative stimuli. As mentioned above, Van Dillen et al. (2009) have reported prefrontal activation during a cognitive task after emotional stimulation, and this was in contrast to Hart et al. (2010). The discrepancy of their results may partially depend on the duration of emotional stimulation. On the other hand, both of these studies have reported more activation in the prefrontal cortex during a task with a high cognitive load than that with low cognitive load. Therefore, emotional stimulation for at least 4 s, as in Van Dillen et al. (2009), and a task with a high cognitive load seem to be necessary to investigate the neural correlates of the cognitive control of emotion.

In order to measure prefrontal activity, we employed nearinfrared spectroscopy (NIRS). The attachment of the NIRS system is easy, and it imposes little strain on the participants. If the cognitive control of emotion can be captured by NIRS, emotion regulation studies will be easily conducted in a wide variety of participants, including psychiatric patients and children, without imposing a strain on them. Although NIRS is unable to measure deep activity in the brain, such as in the limbic system, several NIRS studies have recently attempted to capture emotion-related activity in prefrontal regions (Herrmann et al., 2008; Dieler et al., 2010; Hoshi et al., 2011). For example, Hoshi et al. (2011) have observed that strong unpleasant pictures activate the bilateral VLPFC, while strong pleasant pictures deactivate the left DLPFC. Dieler et al. (2010) have shown more activation in the right DLPFC and right VLPFC during attempts to suppress thoughts with negative content than those with neutral or positive contents.

In our experiment, negative and neutral pictures were selected from the International Affective Picture System (IAPS; Lang et al., 1998). In order to induce strong emotional response, a series of two pictures of the same emotional valence were presented in succession for approximately 10 s. For cognitive processing, an n-back working memory task was employed. The n-back task is one of the most prevalent paradigms used in the assessment of working memory. We employed two levels of cognitive load (a high cognitive load or a low cognitive load) because cognitive load can modulate the processing of negative emotion (Erk et al., 2007; Van Dillen et al., 2009). Thus, in the experiment, participants observed two negative or two neutral pictures in succession and then performed a 1-back or 3-back task. Analyses of hemodynamic responses were conducted independently for the picture presentation and n-back task.

We anticipated that, during picture presentation, prefrontal activation would be significantly greater in response to negative pictures than in response to neutral pictures in line with the results of Hoshi et al. (2011). For the n-back task period, the prefrontal region that includes the DLPFC would be activated after negative stimulation relative to neutral stimulation. As mentioned above, the DLPFC and other prefrontal regions have been shown to exhibit general activation during the cognitive control of emotion (Beauregard et al., 2001; Lévesque et al., 2003). In addition, because a previous study has reported that a high cognitive load downregulates amygdala activity significantly more than a low cognitive load (Van Dillen et al., 2009), we anticipated that cognitive processing with a high cognitive load (3-back) would increase prefrontal activity significantly more than that with a low cognitive load (1-back). The cognitive performance may be impaired in response to the distraction of negative emotional stimulation (Dolcos and McCarthy, 2006).

#### **MATERIALS AND METHODS**

#### **PARTICIPANTS**

Twenty healthy male undergraduate students (mean age: 19.38 ± 0.79 years) participated in this study. All participants were righthanded. None of them had with any history of psychiatric or neurological disorders. The study conformed to the Declaration of Helsinki and was approved by the Ethics Committee of the University of Tokyo. All participants were given a comprehensive explanation of the experimental procedures by an experimenter, and they provided written informed consents for participation.

#### **EXPERIMENTAL ENVIRONMENT**

The experiment was performed in a shielded room.  The distance between a participant and the monitor was set to around 80 cm. The NIRS equipment (Spectratech OEG-16; Spectratech Inc., Yokohama, Japan) was attached to their heads. The experimental task was implemented in dim light.

#### **EMOTIONAL STIMULI**

Thirty-two neutral and 32 negative pictures were selected from the IAPS (Lang et al., 1998) based on a 9-point rating scale of the IAPS normative data. The average emotional valence rating of the selected pictures was 5.10 for the neutral pictures and 2.00 for the negative pictures, and the average arousal rating was 3.18 for the neutral pictures and 6.00 for the negative pictures. The arousal ratings of all of the selected negative pictures were above 5.5. Examples of the neutral pictures selected were humans, plants, food, and materials, and examples of the negative pictures were victims, mutilations, insects, and dirty toilets. Pictures with obscure content were not selected.

#### **n-BACK TASK**

The 1-back and 3-back tasks were implemented with the digits 1–9. White-colored digits with a size of 12.70 mm were individually displayed in the middle of the black background, in sequence. The participants were instructed to press the Enter key as quickly as possible only when the current digit matched the one from one step or three steps earlier for the 1-back or 3-back, respectively. A total of 13 digits were presented in a sequence with 3 matches included.

#### **EXPERIMENTAL PROCEDURES**

At the beginning of the experiment, participants were instructed to avoid head and body movements and deep breathing as much as possible during the NIRS measurement. The state of the participants was video-monitored and no salient head and body movement or deep breathing was detected.

The experiment consisted of two successive sessions: a NIRS measurement session and a valence rating session. First, the NIRS equipment was attached to the participants' heads. Each trial in the NIRS measurement session included five periods (**Figure 1**). In the first resting period, a white fixed cross was displayed in the middle of the screen for 9.8 s. The participant was instructed to gaze at it calmly. In the picture presentation period, two IAPS pictures with the same emotional valence (neutral or negative) were sequentially presented for 5.2 s per picture without an interval. Soon after the picture presentation, white letters that read *1-back task* or *3-back task* were presented on the black background for 1.3 s (instruction period). In the n-back period, a sequence of digits was presented for 26.0 s. Each digit was presented on the screen for 300 ms, which was followed by a 1,700-ms interstimulus interval. After the n-back period, a fixed cross was presented for 5.2 s (the last resting period). There were six practice trials with only neutral pictures and 32 experimental trials with both neutral and negative pictures in the NIRS measurement session. At the end, the NIRS equipment was removed, and the participant rated the emotional valence of all of the pictures used in the experiment with a digital scale with nine grades from 1 (unpleasant) to 9 (pleasant).

#### **BEHAVIORAL ANALYSIS**

The accuracy and reaction time of the n-back task were used as parameters in the behavioral analysis. The accuracy was determined by the percentage of correct responses. The reaction time was the time from the disappearance of a digit to the participants pressing the Enter key.

In order to examine the effects of emotion on the performance of the cognitive task, a 2 (emotional valence: neutral or negative) × 2 (cognitive load: 1-back or 3-back) repeated measures ANOVA with within-subjects factors was performed.

#### **NEAR-INFRARED SPECTROSCOPY RECORDING AND ANALYSIS** *Near-infrared spectroscopy recording*

We used the Spectratech OEG-16, which consisted of six emission probes and six detection probes. This system is able to obtain three parameters of the concentration changes of hemoglobin: oxygenated (oxyHb), deoxygenated (deoxyHb), and total (totalHb) changes. The measures of the hemoglobin changes were obtained from the 16 channels. The emission probe and detection probe were 3 cm apart from each other, and all of the probes were set in a 15 × 3-cm matrix area. The Spectratech OEG-16 employs two wavelengths, which are approximately 770 and 880 nm, to record the absorption changes of hemoglobin at a depth approximately 2 cm below the scalp (Watanabe et al., 2000). The sampling interval was 655 ms. The center of the probe holder was placed on Fpz in the International 10/20 System (**Figure 2**). Our measurement area covers Fp1, Fpz, Fp2, F7, and F8, and regions a little inferior to F3 and F4. According to Okamoto et al. (2004), Fp1 and Fp2 are located on the left and right superior frontal gyrus, respectively. F7 and F8 are placed in the left and right inferior frontal gyrus, respectively. F3 and F4 are located in the inferior part of the DLPFC.

#### *Near-infrared spectroscopy analysis*

We examined the concentration changes of oxyHb in the analysis because it is the most sensitive measure to changes in regional cerebral blood flow (Hoshi et al., 2001), and it has the

strongest correlation with blood-oxygen-level-dependent signals (Strangman et al., 2002).

The raw oxyHb data were high-pass filtered (0.0076 Hz). Each channel was analyzed individually. All sampling data was segmented to each trial and converted to a z-score with the mean value and the standard deviation (*SD*) of oxyHb change during the last 4.59 s (7 samples) of the first resting period used as a baseline. Thus, the mean and *SD* of the baseline were changed into the z-scores of 0 and 1 (Matsuda and Hiraki, 2006). For artifact rejection, all difference values between the sampling data and their *SD*s were calculated. The trials that included at least one difference value over ±8 *SD* were excluded from the statistical analysis.

The prefrontal activities during the picture presentation and n-back task were separately analyzed by conducting both individual and group analyses. In the picture presentations, the oxyHb changes resulting from the neutral or negative picture presentation were analyzed. For the n-back task, the oxyHb changes of the 1-back and 3-back tasks after neutral picture presentation (neutral 1-back, neutral 3-back, respectively) and 1-back task and 3-back task after negative picture presentation (negative 1-back, negative 3-back, respectively) were analyzed independently.

In individual analysis, the means and *SD*s of the oxyHb change in all trials were calculated in each of the picture presentations and the n-back task. Then, the mean oxyHb values were compared with the baseline, in which the average value was 0, with a onesample *t*-test in order to determine any significant activation.

In the group analysis of picture presentation, the means and the *SD*s of the oxyHb changes of all participants were calculated. The mean oxyHb values were compared with baseline, in which the average value was 0, with a one-sample *t*-test. In addition, Student's *t*-test was performed between the mean oxyHb values of negative and neutral picture presentations to

examine the differences in prefrontal activity between emotional valences.

In the group analysis of the n-back task, the means and *SD*s of oxyHb change of all participants were calculated. A 2 (emotional valence: neutral or negative) × 2 (cognitive load: 1-back or 3-back) repeated measures ANOVA with within-subjects factors was performed.

#### **RESULTS**

#### **BEHAVIORAL RESULTS**

#### *Rating of the emotional valence*

In order to confirm whether the pictures used for the emotional stimulation in this study had significantly different emotional valence ratings, *t*-tests were performed. Neutral pictures were significantly different from negative pictures in their emotional valence ratings [mean valence scores: neutral, 4.21; negative, 1.96; *t(*19*)* = 10*.*07, *P <* 0*.*001].

#### *Behavioral performance*

A 2 (emotional valence: neutral or negative) × 2 (cognitive load: 1-back or 3-back) repeated measures ANOVA revealed no main or interaction effect for accuracy (**Table 1**) and a significant main effect of cognitive load for reaction times. A *post-hoc* analysis showed that reaction times in the 1-back task were significantly faster than those in the 3-back task (mean reaction time: 1-back, 246.32 ms; 3-back, 363.77 ms, *P <* 0*.*001). There was no other main or interaction effect for reaction times.

#### **NEAR-INFRARED SPECTROSCOPY**

#### *oxyHb changes during picture presentation*

The result of the individual analysis is shown in **Table 2**. For neutral picture presentations, significant oxyHb increases were observed in seven of 20 participants (35%) while significant oxyHb decreases was found in three participants (15%). For the negative picture presentations, there were nine of 20 participants (45%) who showed significant oxyHb increases while six participants (30%) showed significant oxyHb decreases. Channels that showed significant oxyHb changes were also inconsistent between participants.

**Table 3** displays the results of the group analysis. It represents the means and *SD*s of oxyHb changes of all participants in each channel. A one-sample *t*-test revealed significant activation in three channels during the negative picture presentation compared to baseline. In contrast, significant activation was found in 12 channels in response to the neutral picture presentation.

There were no significant differences found in any of the channels between the mean oxyHb changes during neutral and negative picture presentation.

#### **Table 1 | Means and standard deviations of accuracy and reaction time of response.**

#### **Table 2 | Channels that showed significant oxyHb changes during picture presentation (individual analysis).**

*Numerical numbers indicate channel numbers. The mark "-" indicates no significant oxyHb changes observed in any of the channels.*

*\*P < 0.05; \*\*P < 0.01; \*\*\*P < 0.001.*

#### *oxyHb changes during the n-back task*

The results of the individual analysis of the n-back task were as follows. In the negative 1-back, 11 of 20 participants (55%) exhibited a significant oxyHb increase while one participant (5%) showed a significant oxyHb decrease. In the negative 3-back, a significant oxyHb increase was also observed in 11 of 20 participants (55%) while significant oxyHb decreases occurred in two participants (10%). **Table 4** displays the results of the negative 1-back and negative 3-back tasks. In contrast, there were no participants who

**Table 4 | Channels that showed significant oxyHb changes during the n-back task (individual analysis).**

*Numerical numbers indicate channel numbers. The mark "-" indicates no significant oxyHb changes observed in any of the channels. The results of the neutral 1-back and neutral 3-back were omitted because there were no significant oxyHb changes observed.*

showed significant oxyHb increases or decreases in either neutral n-back task.

In the group analysis of the n-back task, a 2 (emotional valence: neutral or negative) × 2 (cognitive load: 1-back or 3-back) repeated measures ANOVA indicated significant main effects of emotional valence in channels 6 [*F(*1*,* <sup>19</sup>*)* = 6*.*31; *P* = 0*.*021], 9 [*F(*1*,* <sup>19</sup>*)* = 5*.*84; *P* = 0*.*026], 12 [*F(*1*,* <sup>19</sup>*)* = 6*.*35; *P* = 0*.*021], and 15 [*F(*1*,* <sup>19</sup>*)* = 12*.*05; *P* = 0*.*003] (**Table 5**; **Figure 3**). A Bonferroni *post-hoc* analysis revealed significantly more activation in the n-back task after negative picture presentations than in the n-back task after neutral picture presentation in all of the channels. No effects of cognitive load were found in any of these channels. In addition, no interaction effect of emotional valence or cognitive load was found.

**Figure 4** shows the time courses of oxyHb changes in channels 6, 9, 12, and 15 where the significant main effects of emotional valence were observed during the n-back task period. Each waveform represents a time course of one trial. It was constructed by calculating a grand average of oxyHb changes in the neutral 1-back, neutral 3-back, negative 1-back, and negative 3-back. A large difference in activation was observed between emotional valences around 6 s after the n-back task in all channels.

#### **DISCUSSION**

The present NIRS study examined the neural correlates of the cognitive control of emotion in prefrontal regions. In the experiment, task-irrelevant emotional stimuli were presented prior to a working memory task in order to produce an emotional distraction. Participants observed a series of two negative or two neutral pictures and then conducted a 1-back or 3-back task. The oxyHb changes during the picture presentation period and during the n-back period were individually analyzed in order to separate cognitive and emotional processing.

During picture presentation, there was no significant difference in brain activations between the neutral and negative stimulations though more channels showed significant activation in the neutral compared to the negative stimulation compared to baseline in group analysis (**Table 3**). This was probably because the variances of the oxyHb change of channels were relatively larger in negative compared to neutral stimulation though there was no large difference in the mean oxyHb values between the emotional valences (mean ± *SD*; neutral 0.82 ± 1.29, negative 0.83 ± 2.21). A large individual difference of prefrontal activation in response to negative (unpleasant) pictures is also observed in the findings of Hoshi et al. (2011). In their NIRS study, participants were exposed to a negative or positive (pleasant) IAPS picture for 6 s following a resting period of 14 s in order to examine the prefrontal processing of emotion. In response to negative pictures, seven of 14 participants (50%) indicated an oxyHb increase and six participants (42.9%) showed an oxyHb decrease. In a similar way, our findings showed that 45% of participants showed an oxyHb increase and 30% of the participants showed an oxyHb decrease in response to negative pictures (**Table 2**). In Hoshi et al. (2011), they then conducted group analysis by

**Table 5 | The results of 2 (emotional valence) × 2 (cognitive load) repeated measures ANOVAs of the oxyHb changes during the n-back task (group analysis).**

*\*P < 0.05; \*\*P < 0.01.*

using only the data that were rated as "1" (the most unpleasant) for the pictures in order to extract responses of strong negative emotion. Consequently, the group analysis revealed significant activation in the bilateral VLPFC. By adopting only the data rated as "1," individual differences in the oxyHb changes for the negative picture probably became smaller as the mean oxyHb values became larger. Doing this would mean that a statistically significant activation might be observed for negative pictures. In this way, by reducing individual differences in the responses for emotional pictures, prefrontal processing of emotion can be captured by NIRS. A large individual difference is presumably caused by differences of personality traits and brain structures and so on. Especially, in response to negative pictures, various personality traits seem to be related to a brain activity such as amygdala reactivity to negative picture presentation and amygdala-prefrontal connectivity. For example, it is reported that the right amygdala-DMPFC connectivity for angry and fearful compared to neutral faces was positively correlated with neuroticism scores (Cremers et al., 2010). Further research regarding individual differences in emotional processing caused by variations in personality traits is necessary.

Though no significant differences in oxyHb changes between neutral and negative stimulations were exhibited during the picture presentation, significant differences by emotional valence were observed during n-back task in channel 6, 9, 12, and 15 (**Table 5**). These channels were placed close to Fp1 (channel 12), Fpz (channel 9), Fp2 (channel 6), and F7 (channel 15) in the International 10/20 System. According to Okamoto et al. (2004), Fp1, Fpz, and Fp2 are located in the left and right superior frontal gyrus and F7 is in the left inferior frontal gyrus. Among these regions, the activation in the left inferior frontal gyrus

is frequently observed as the result of the cognitive control of emotion (Cromheeke and Mueller, 2013). Thus, activation in channel 15 was presumably led by emotion regulation. Greater activation in the left hemisphere, particularly the left inferior frontal gyrus, was observed in our study. However, we did not observe the right DLPFC activation which is also relatively frequently activated by emotion regulation (Lévesque et al., 2003; Dieler et al., 2010; Cromheeke and Mueller, 2013). For example, in Van Dillen et al. (2009), the right DLPFC activation was found after the onset of the complex arithmetic task following negative picture presentations. The reason why we observed greater activation in the left hemisphere is unclear. However, it may be partially because we employed a verbal n-back task, processing of which

is known to be associated mainly with the left hemisphere (Owen et al., 2005). The difference in the cognitive task may be one factor in determining which regions are susceptible to emotional effects.

The NIRS system is unable to measure the entire region of MPFC. However, channel 12 (Fp1), 9 (Fpz), and 6 (Fp2) possibly measure the oxyHb change in the anterior part of the MPFC. MPFC (Phan et al., 2002) are the regions commonly activated in emotional responses. Thereby, the additional activations during the n-back task after negative stimulation in these channels might simply represent emotional responses induced by picture presentation, considering the delay of hemodynamic responses. On the one hand, the MPFC is also known to be associated with emotional regulation (Davidson et al., 2000). Thereby, it is possible that the NIRS system captured the cognitive control processing occurred in the anterior part of MPFC in our study. Moreover, looking at the time courses of the oxyHb changes (**Figure 4**), during the picture presentation period, both the neutral and negative picture presentation increased the oxyHb change in these channels. Only when participants performed the n-back task, prefrontal responses in these channels began to differentiate: even more rapid oxyHb increase was occurred in negative 3-back and negative 1-back compared to neutral 3-back and neutral 1-back. This rapid oxyHb increase in negative 3-back and negative 1-back was reached approximately at 6 s after the n-back task onset. This rapid activation seems to represent the effect of the n-back task implementation and not a simple emotional response. This activation appears to include both emotional and cognitive processing and implies the possibility of the cognitive control processing of emotion.

We observed the effect of negative stimulation only in brain activations during the cognitive task while there was no emotional effect observed in behavioral performance.  The hypothesis suggests that highly-anxious people require greater cerebral activation, the compensatory effort, for cognitive control in order to maintain the same level of performance as non-anxious people. The impairment of behavioral performance is compensated for if resources in working memory are available (Fales et al., 2008; Basten et al., 2012). Thus, there seems to be a potential ability of a healthy brain to maintain behavioral performance at a high level under an anxious state. In our study, negative pictures yielded an unpleasant emotional state, which is presumably similar to anxiety. Even under such an unpleasant state, the behavioral performance was maintained probably because we employed participants who had sufficient working memory resources. However, it is important to examine if similar tendencies can be observed using other cognitive tasks.

Our findings revealed a significant effect of cognitive load on reaction times, with responses being significantly faster during the 1-back task than during the 3-back task (mean reaction times: 1-back, 246.32 ms; 3-back, 363.77 ms, *P <* 0*.*001). Thus, the two cognitive loads actually differed in difficulty level. However, cognitive load did not significantly affect accuracy on the n-back task, and although changes in oxyHb were greater in negative and neutral 3-back tasks than in negative and neutral 1-back tasks in some channels (**Figure 4**), group analysis did not reveal any effect of cognitive load on changes in oxyHb during the n-back task (**Table 5**). In contrast, Van Dillen et al. (2009) reported that accuracy was better and reaction times faster for a simple arithmetic task than for a complex one. At the same time, brain activity was greater for the complex arithmetic task than for the simple one after negative stimulation. Significant effects of cognitive load on reaction times and brain activity may therefore be observed by employing tasks with cognitive loads that differ more greatly than ours did.

In sum, the present NIRS study clearly indicated additional activation in channels 6, 9, 12, and 15 during the n-back after negative stimulation. It thus indicated the ability of the NIRS system to capture emotion-related processing, though further research is necessary for the interpretation of this activation and the roles of personal traits for individual differences of brain activity, particularly adopting other experimental designs, cognitive tasks, and self-report questionnaires, for example. It is interesting that the emotional effect was observed only in brain activations during the n-back task but not in behavioral performance because it implies that brain activation could be an effective index of emotion regulation. By adopting the NIRS system, the study of emotion regulation can be more easily studied in various participants, such as children and psychiatric participants.

#### **ACKNOWLEDGMENTS**

We would like to thank the participants for their involvement in the experiment that induced unpleasant emotions. This study was supported by grants from JST CREST, Grant-in-Aid from MEXT (21118005) and JSPS (22240026).

#### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 30 September 2013; accepted: 21 January 2014; published online: 10 February 2014.*

*Citation: Ozawa S, Matsuda G and Hiraki K (2014) Negative emotion modulates prefrontal cortex activity during a working memory task: a NIRS study. Front. Hum. Neurosci. 8:46. doi: 10.3389/fnhum.2014.00046*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 Ozawa, Matsuda and Hiraki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

### Sensitivity of fNIRS to cognitive state and load

### *Frank A. Fishburn1, Megan E. Norr 2, Andrei V. Medvedev3 and Chandan J. Vaidya2,4\**

*<sup>1</sup> Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC, USA*

*<sup>2</sup> Department of Psychology, Georgetown University, Washington, DC, USA*

*<sup>3</sup> Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC, USA*

*<sup>4</sup> Children's National Medical Center, Children's Research Institute, Washington, DC, USA*

#### *Edited by:*

*Nobuo Masataka, Kyoto University, Japan*

#### *Reviewed by:*

*Hasan Ayaz, Drexel University, USA Yukika Nishimura, University of Tokyo, Japan*

#### *\*Correspondence:*

*Chandan J. Vaidya, Department of Psychology, Georgetown University, 306 White-Gravenor Hall, Washington, DC 20057, USA e-mail<EMAIL_ADDRESS>
Functional near-infrared spectroscopy (fNIRS) is an emerging low-cost noninvasive neuroimaging technique that measures cortical bloodflow. While fNIRS has gained interest as a potential alternative to fMRI for use with clinical and pediatric populations, it remains unclear whether fNIRS has the necessary sensitivity to serve as a replacement for fMRI. The present study set out to examine whether fNIRS has the sensitivity to detect linear changes in activation and functional connectivity in response to cognitive load, and functional connectivity changes when transitioning from a task-free resting state to a task. Sixteen young adult subjects were scanned with a continuous-wave fNIRS system during a 10-min resting-state scan followed by a letter n-back task with three load conditions. Five optical probes were placed over frontal and parietal cortices, covering bilateral dorsolateral PFC (dlPFC), bilateral ventrolateral PFC (vlPFC), frontopolar cortex (FP), and bilateral parietal cortex. Activation was found to scale linearly with working memory load in bilateral prefrontal cortex. Functional connectivity increased with increasing n-back loads for fronto-parietal, interhemispheric dlPFC, and local connections. Functional connectivity differed between the resting state scan and the n-back scan, with fronto-parietal connectivity greater during the n-back, and interhemispheric vlPFC connectivity greater during rest. These results demonstrate that fNIRS is sensitive to both cognitive load and state, suggesting that fNIRS is well-suited to explore the full complement of neuroimaging research questions and will serve as a viable alternative to fMRI.

**Keywords: working memory, n-back, functional connectivity, resting state, fronto-parietal**

#### **INTRODUCTION**

Unprecedented technical advances in the past 20 years have made functional magnetic resonance imaging (fMRI) the primary neuroimaging modality for cognitive neuroscience. However, there are some notable drawbacks to fMRI that limit its utility in imaging young children and those with developmental disorders. First, head motion leads to substantial artifacts due to its relatively low temporal resolution and minimal constraint on head mobility in the scanning apparatus. Offline motion correction algorithms are effective for small movements, but larger movements necessitate subject exclusion, requiring oversampling as high as 30% in children with disorders such as Autism Spectrum Disorders (ASD), Attention Deficit Hyperactivity Disorder (ADHD), and Epilepsy (Yerys et al., 2009). In healthy young children (e.g., 4–6 years) who can comply with task instructions, the exclusion rate due to head motion was up to 40% (Yerys et al., 2009). Excessive head motion poses even more of a limitation for examining functional connectivity, the temporal co-activation of multiple brain regions, because even very small movements (e.g., *<*1 mm) introduce a systematic bias toward underestimating functional connectivity between distant regions (Power et al., 2012; Van Dijk et al., 2012). As the primary working hypothesis of some developmental disorders (e.g., ASD) is reduced long-distant functional connectivity, use of fMRI for those populations is particularly limiting. Second, the MR scanning environment is intimidating for many children. The enclosed nature of the scanning apparatus often produces feelings of claustrophobia, and the loud noise is fear-inducing for young children and autistic children with sensory hypersensitivity. Thus, despite its robust properties as a neuroimaging modality, fMRI is poorly suited for a large subset of pediatric and clinical populations. Thus, it is imperative to develop alternate neuroimaging modalities for investigating task-based and functional connectivity research questions.

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging modality for recording cortical hemodynamic activity. The method projects near-infrared light through the scalp and records optical density fluctuations resulting from metabolic changes within the brain. Similar to fMRI, cerebral blood flow is used as a proxy for neuronal activity. Both the spatial resolution and penetration depth of fNIRS are dependent upon the distances between light sources and detectors. The result is that the spatial resolution of fNIRS is on the order of 2.5– 3 cm and is capable of imaging depths of 1–2 cm (McCormick et al., 1992), making it well-suited for imaging cortical regions. This technique is particularly resilient to contamination from head motion since the optodes are affixed to the head and thus move with the subject. The silent operation and unenclosed scanning environment make fNIRS more amenable to subjects that have sensory hypersensitivity or claustrophobia. These qualities of fNIRS make it particularly suitable for use with pediatric populations, including those with developmental disorders. While fNIRS has been used in functional neuroimaging for almost 30 years (Ferrari et al., 1985), it remains unclear whether fNIRS has the requisite sensitivity to serve as an alternative to fMRI. To that end, it is important that fNIRS be validated against cognitive phenomena with known neural bases. While fNIRS has potential for use with developmental and clinical populations, it is necessary to first validate its sensitivity for cognitive processes in healthy adults. Furthermore, in order for fNIRS to be considered as a viable alternative to fMRI for examining developmental disorders, it is vitally important that its sensitivity be validated on cognitive processes commonly affected in those disorders. The present study examines the sensitivity of fNIRS to changes in cognitive state (e.g., resting to task) and task load during working memory, a component process of higher cognition that is disrupted in numerous developmental and psychiatric disorders.

Working memory is a temporary buffer for active maintenance and manipulation of goal-relevant information that critically depends upon the integrity of prefrontal cortex and its connections with posterior brain regions (Miller and Cohen, 2001). fMRI studies have consistently shown activation within the dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex, with significant left-hemisphere lateralization in prefrontal cortex for verbal working memory tasks (for a meta-analysis, see Owen et al., 2005). More specifically, studies have shown that activation in left dlPFC scales linearly with working memory load (Braver et al., 1997; Jansma et al., 2000; Veltman et al., 2003), indicating load-dependent recruitment of dlPFC. Common manipulations of load in verbal working memory tasks involve linear increases in the size or temporal lag of to-be-remembered information. For example, on the n-back task, letters are presented serially with instructions to detect target letters that repeat, successively (low load, termed 1-back) or with 2 or 3 intervening trials (higher load, termed 2-back, and 3-back, respectively). Working memory capacity predicts higher cognitive ability indexed by general intelligence (Kane et al., 2005; Oberauer et al., 2005) and reasoning (Süß et al., 2002). It increases during development (Gathercole et al., 2004) and those age-related increases relate to frontal-parietal white-matter maturation (Nagy et al., 2004) and activation (Olesen et al., 2003). Working memory is impaired in several developmental disorders (Alloway et al., 2009) and its training improves higher cognition, such as reasoning (Jaeggi et al., 2008) and attention in ADHD (Klingberg et al., 2005). Further, training-related changes are reflected in frontal and parietal activation (Olesen et al., 2004). Thus, working memory is an optimal candidate for validation of fNIRS as it is crucial for higher cognition, sensitive to developmental pathology and intervention, and depends upon prefrontal and parietal cortices.

A number of fNIRS studies have examined the effect of varying working memory load on activation. The studies have generally found that higher working memory load tends to produce greater activation within dlPFC (Hoshi et al., 2003; Li et al., 2010; Ayaz et al., 2012; Molteni et al., 2012). Further, fNIRS has demonstrated sensitivity to group differences in activation during working memory, based on gender (Li et al., 2010), ADHD diagnosis (Ehlis et al., 2008), schizophrenia diagnosis (Lee et al., 2008), dopamine receptor genotype (Herrmann et al., 2007), and mild cognitive impairment in the elderly (Niu et al., 2013b). However, no fNIRS study has demonstrated dlPFC activation that scales linearly with working memory load. As the utility of fNIRS is contingent upon its robustness as an imaging modality, it is important to demonstrate that fNIRS is sensitive enough to detect the linear relationship between prefrontal activation and working memory load that has been documented with fMRI (Braver et al., 1997; Jansma et al., 2000; Veltman et al., 2003). Additionally, no study has examined functional connectivity during working memory with fNIRS. Thus, it is unknown whether fNIRS is capable of detecting working memory load-dependent changes in functional connectivity. Task-evoked functional connectivity measurement by fNIRS, is itself novel with only a handful of studies to date (Chaudhary et al., 2011; Medvedev et al., 2011; Hall et al., 2013).

It is presently not known whether fNIRS is sensitive to changes in cognitive state. Sensitivity to changes in cognitive state from drowsy to wakeful to cognitively engaged can be detected reliably with scalp-based electroencephalography (Schomer and Da Silva, 2010). Recent fMRI studies have shown how cortical functional networks change as subjects transition from resting/awake to cognitively engaged states (Gordon et al., 2012a,b). Specifically, functional connectivity was greater during working memory than rest between dlPFC and inferior parietal cortex (Gordon et al., 2012b). State-dependent changes are important to understand as they depend upon genetic factors (Gordon et al., 2012b) and can reflect consolidation associated with learning (Lewis et al., 2009). Further, task-free resting state, itself, is sensitive to individual variation in a variety of affective and behavioral traits (Vaidya and Gordon, 2013). Thus, demonstrating that fNIRS is sensitive to cognitive state is important to establish its versatility as a tool that is as suitable for the full complement of research questions as other neuroimaging modalities.

Previous investigations have found that resting-state networks can be detected with fNIRS (White et al., 2009; Lu et al., 2010; Mesquita et al., 2010; Zhang et al., 2010a,b), and are stable across testing sessions (Zhang et al., 2011). Resting-state networks have been shown to be segregated within different frequency bands (Sasai et al., 2011) and correlate with networks detected by simultaneous fMRI (Sasai et al., 2012). Graph theory approaches have also been successfully applied to resting-state fNIRS (Niu et al., 2012), demonstrating its sensitivity to the topological organization of resting-state networks and that these measurements are stable across testing sessions (Niu et al., 2013a). However, no studies have used fNIRS to investigate functional connectivity differences between the resting state and a cognitive task.

The present study addressed two questions: First, is fNIRS sensitive to load-dependent working memory changes in activation and functional connectivity in prefrontal-parietal regions? Second, is fNIRS sensitive to functional connectivity differences between working memory and a task-free resting state? Healthy young adult subjects were imaged during a 10-min task-free resting state followed immediately by a verbal n-back task, with three loads, 1-back, 2-back, and 3-back. We hypothesized that: (1) activation within prefrontal cortex would scale with n-back load, (2) fronto-parietal functional connectivity would scale with n-back load, and, (3) fronto-parietal functional connectivity would be greater during task than rest.

#### **METHOD**

#### **SUBJECTS**

Sixteen Georgetown University undergraduates (6 male; 1 lefthanded) ages 18–24 years (Mean ± *SD* = 20*.*3 ± 1*.*7) participated in the study for payment. Participants were not using psychotropic medication (e.g., stimulants, anti-depressants, anxiolytics) and had no history of neurological injury or disease, seizure disorder, or psychiatric diagnosis. All participants provided informed consent according to guidelines of the Georgetown University Institutional Review Board, which approved the protocol.

#### **TASK PROCEDURE**

fNIRS sessions consisted of a 10-min resting-state run in which the subjects were instructed to close their eyes and remain awake, followed by a 6.5 min n-back task. The sequence of rest and task was not counter-balanced due to previous research showing that task-induced changes in functional connectivity persist after task completion (Evers et al., 2012; Gordon et al., 2012a; Harmelech et al., 2013). During the n-back task, participants were presented with a series of single consonant letters and instructed to press a button with their dominant hand when the presented letter was the same as the one presented *n* letters ago. Subjects were tested on three blocks of each of the three load conditions: 1-back, 2-back, and 3-back. The load condition order was pseudorandomized using a modified Latin square. Each block consisted of 9 trials, each lasting 3000 ms, with the letter exposed for 500 ms followed by a lag of 2500 ms. Each 27-s block was followed by a 14-s interval of fixation to allow the hemodynamic response to return to baseline. Subjects practiced the n-back task prior to the scanning session.

#### **IMAGING PROCEDURE**

Optical signals were recorded on a two-wavelength (690 and 830 nm) continuous-wave CW5 imaging system (TechEn, Inc., Milford, MA). Data were collected from detectors in parallel at a sampling rate of 41.7 kHz, with each laser modulated at a different frequency to allow subsequent offline demodulation and separation of source-detector pairs (i.e., channels). The 40 optical channels were comprised of 12 sources and 29 detectors, arranged into 5 probes, covering bilateral parietal cortex, bilateral prefrontal cortex, and frontal pole. Participants were fitted with a 128-channel HydroCel EEG cap (Electrical Geodesics, Inc., Eugene, OR) prior to probe placement. The cap provided a consistent frame of reference for positioning optical probes. Optode coordinates (provided in **Table 1**) in 10–20 reference space were estimated by triangulation with the three nearest EEG electrodes, using the electrode coordinates provided by the manufacturer. The NFRI software package (Singh et al., 2005) was then used to generate interpolation kernels for projection of channel data onto the brain surface, with interpolation only taking place between channels on the same probe (**Figure 1**).

**Table 1 | Optode positions computed from distances to neighboring EEG electrodes.**

*Source optodes are denoted with S, and detector optodes are denoted with D. Source optodes in PFC were located along the boundary between dlPFC and vlPFC, with detector optodes above and below giving rise to dlPFC and vlPFC channels, respectively.*

#### **BEHAVIORAL DATA ANALYSIS**

Behavioral data were lost for 2 subjects due to computer malfunction. Subject accuracy was computed for each load condition by taking the mean percentage of correct trials. Reaction time was computed by taking the mean across correct trials within each load condition. Repeated-measures ANOVAs and paired *t*-tests were performed for both accuracy and reaction time.

**FIGURE 1 | NIRS probe configuration.** Upper left panel shows the right parietal and right frontal probes and the upper right panel shows the medial frontal probe. Bottom panel is a rendering of estimated channel positions on a template brain. Colors indicate the channel sets used for interpolation.

#### **fNIRS PREPROCESSING**

Data were low-pass filtered with a high-order (400) FIR filter at 0.8 Hz and downsampled to 20 Hz. Raw optical density signals were converted to hemoglobin concentration changes using the modified Beer–Lambert law (Cope and Delpy, 1988) with the HOMer software package (Huppert et al., 2009). The oxy-Hb signal has previously been shown to correlate with blood flow better than the deoxygenated signal (Hoshi et al., 2001), thus interpretations focus on the oxygenated signal. Results for deoxygenated data are provided in the supplementary materials.

#### **fNIRS ANALYSIS—ACTIVATION**

Channel timecourses were modeled with a general linear model (GLM) in NIRS-SPM (Ye et al., 2009). Regressors were generated by convolving the weighted task boxcar function with the canonical hemodynamic response function provided by SPM8 (Friston et al., 1994). Data were corrected for low frequency drift by detrending using the wavelet-MDL algorithm (Jang et al., 2009) and corrected for serial correlations, such as those produced by physiological noise sources, using the HRF precoloring method (Worsley and Friston, 1995) implemented within NIRS-SPM (Ye et al., 2009). In order to separate the effects of load-dependent and load-independent activation, two regressors were generated: (1) a load-independent regressor in which all n-back blocks were weighted equally, and (2) a load-dependent regressor in which each n-back block was weighted by its load (i.e., 1, 2, or 3). Channel-wise beta values were compared across subjects for outliers. Subjects that had two or more adjacent channels with beta values over 3 standard deviations from the group mean were excluded from further analysis. Three subjects were excluded in this manner, thus reported results are from *N* = 13. The channel-wise beta values from the remaining subjects were then interpolated into voxel space. T-contrasts were then used to generate statistical parametric maps of activation for each regressor. A *p*-value correction was applied to control the familywise error rate using the Lipschitz-Killing curvature-based Euler characteristic (EC) approach (Li et al., 2012). Activation maps were thresholded at a corrected threshold of *p <* 0*.*05.

#### **fNIRS ANALYSIS—FUNCTIONAL CONNECTIVITY**

As fMRI studies have primarily found functional resting-state networks in the 0.01–0.10 Hz frequency range, both the restingstate data and n-back data were filtered around this range. This step also prevented high-frequency physiological artifacts from biasing the results. To this end, a band-pass Fourier filter was applied using the publicly available iFilter script for Matlab (Filter parameters: center = 0.035 Hz, width = 0.04 Hz, shape = 10; corresponding to a pass-band of approximately 0.009–0.09 Hz).

#### *Load-dependent*

For each n-back load, the individual block timecourses were concatenated and the Pearson correlation coefficient was computed between all channel-pairs. A Fisher's r-to-Z transformation was then applied to normalize the variance of the correlation values. For each channel-pair, the transformed correlation values were regressed against the corresponding n-back load. The t-statistic of the estimated beta value (i.e., the beta value divided by its standard error) was used in a one-sample *t*-test across subjects. Channels were grouped into 7 anatomical regions: left/right parietal (P), left/right ventrolateral PFC (vlPFC), left/right dorsolateral PFC (dlPFC), and frontal pole (FP). For each region-pair, a one-sample *t*-test was performed on the channel-wise *t*-statistic against the null hypothesis that the mean channel-wise *t*-statistic was less than the corresponding critical value at *p <* 0*.*05. A Bonferroni correction was applied to the region-wise *p*-values to control for multiple comparisons. The final significance threshold was set at *p <* 0*.*05.

#### *State-dependent*

To determine the contribution of cognitive state to functional connectivity, the resting-state and n-back scans were compared. Beginning and ending sections of the resting-state scan were removed to match the duration of the n-back task. In order to mitigate the influence of activation on the state-wise functional connectivity comparison, activation was regressed from the n-back fNIRS data. A GLM was created in NIRS-SPM with each load-level as a separate regressor. This GLM was necessary in order to remove non-linear load effects that would not be captured by the linear GLM used in the activation analyses. The residuals from the estimation were then used in the connectivity analyses. The results from the connectivity analyses without this regression step are provided in **Supplementary Figures 4**, **5**. Regression of activation was not applied in the load-dependent functional connectivity analysis, as different n-back loads are directly comparable and make an interpretable contribution to load-dependent changes in functional connectivity. The Pearson correlation was computed between all pairs of channels and Fisher's r-to-Z transformation was applied. For each channel-pair, a paired *t*-test (n-back *>* rest) was computed across subjects. For each region-pair, a one-sample *t*-test was performed on the channel-wise *t*-statistic. A Bonferroni correction was applied to the region-wise *p*-values to control for multiple comparisons. The *p*-value threshold was set at *p <* 0*.*05.

### **RESULTS**

#### **TASK PERFORMANCE**

One-Way repeated-measures ANOVA with Greenhouse-Geisser correction revealed a main effect of load on accuracy [*F(*1*.*768*,* <sup>17</sup>*.*677*)* = 4*.*043, *p <* 0*.*05]. Paired *t*-tests indicated that mean accuracy for 1-back (98.7 ± 1.9%) and 2-back (98.3 ± 3.0%) were near ceiling and did not differ (**Figure 2A**). Accuracy for the 3-back (94.6 ± 4.8%) condition was significantly lower than the 1-back [*t(*10*)* = 2*.*292, *p <* 0*.*05] and the 2-back [*t(*10*)* = 2*.*236, *p <* 0*.*05].

One-Way repeated-measures ANOVA with Greenhouse-Geisser correction revealed a main effect of load on reaction time [*F(*1*.*272*,* <sup>12</sup>*.*722*)* = 7*.*697, *p <* 0*.*05]. Paired *t*-tests indicated that 1-back (559 ± 106 ms) was performed faster than the 2-back (665 ± 218 ms), *t(*10*)* = 2*.*423, *p <* 0*.*05, and the 3-back (800 ± 207 ms), *t(*10*)* = 4*.*513, *p <* 0*.*005, while the 2-back and 3-back did not differ (**Figure 2B**).

to 3-back trials on the verbal n-back task. Activation for the loadindependent regressor, in which all blocks were weighted equally, did not survive correction.

#### **LOAD-DEPENDENT FUNCTIONAL CONNECTIVITY**

Oxy-Hb functional connectivity increased with increasing n-back loads: (1) between hemispheres for parietal cortex, L-Par— R-Par, and prefrontal cortex, L-vlPFC—R-vlPFC, L-dlPFC—RdlPFC, L-vlPFC—R-dlPFC; (2) between frontal and parietal regions within the left (L-Par—L-dlPFC) and right (R-Par—RdlPFC) hemispheres, across hemispheres (L-Par—R-dlPFC), and between parietal and frontopolar cortex (R-Par—FP, L-Par— FP); (3) between adjacent regions in frontal cortex, L-vlPFC— L-dlPFC, R-vlPFC—R-dlPFC, FP—L-dlPFC, FP—R-dlPFC; and (4) within all regions: L-Par, R-Par, L-vlPFC, R-vlPFC, L-dlPFC, R-dlPFC, FP (**Figures 4A,B**).

### **LOAD-DEPENDENT ACTIVATION**

Load-dependent oxy-Hb activation was significant in bilateral dlPFC (**Figure 3**), showing that the engagement of these regions increased linearly as working memory load increased from 1-back

#### **STATE-DEPENDENT FUNCTIONAL CONNECTIVITY**

Oxy-Hb functional connectivity increased from the resting-state run to n-back task: (1) between frontal and parietal regions, L-Par—R-vlPFC, R-Par—R-vlPFC; (2) between adjacent frontal regions, L-dlPFC—FP, R-dlPFC—FP; and (3) within bilateral

**FIGURE 4 | Channel-wise (A) and region-wise (B) load-dependent functional connectivity matrices.** Warm colors denote a load-dependent increase in functional connectivity, while cool colors denote load-dependent decrease in functional connectivity. Abbreviations: Par, Parietal; vlPFC, ventrolateral prefrontal cortex; dlPFC, dorsolateral prefrontal cortex; FP, frontal pole.

dlPFC, R-dlPFC, L-dlPFC (**Figure 5**). In contrast, functional connectivity decreased from the resting-state run to the n-back task: (1) between homologous frontal regions: L-vlPFC—RvlPFC, L-dlPFC—R-vlPFC. Thus, task-engagement resulted in an increase of functional connectivity between right vlPFC and bilateral parietal cortex, within bilateral dlPFC, and between bilateral dlPFC and FP. Functional connectivity decreased from rest to task between right vlPFC and left PFC (vlPFC and dlPFC).

#### **DISCUSSION**