influence of muscle activity on brain oxygenation during verbal fluency

Neuroscience 171 (2010) 434 – 442

INFLUENCE OF MUSCLE ACTIVITY ON BRAIN OXYGENATION DURING VERBAL FLUENCY ASSESSED WITH FUNCTIONAL NEAR-INFRARED SPECTROSCOPY M. SCHECKLMANN,a,b* A. C. EHLIS,b,c M. M. PLICHTAb,d AND A. J. FALLGATTERb,c

Functional near-infrared spectroscopy (fNIRS) is an optical approach to measure brain activity via concentration changes of oxygenated (O2Hb) and deoxygenated haemoglobin (HHb) (Obrig and Villringer, 2003). A major advantage of fNIRS over functional magnetic resonance imaging seems to be the low susceptibility to movement artefacts— especially artefacts due to overt speech—as it is continuously postulated in fNIRS literature (Fallgatter et al., 2004; Okamoto et al., 2004; Suto et al., 2004). This assumption led to a multitude of investigations of overt verbal or letter fluency during fNIRS (Matsuo et al., 2000, 2002, 2004, 2005; Herrmann et al., 2003, 2004, 2005, 2006; Suto et al., 2004; Kameyama et al., 2006; Ehlis et al., 2007; Schecklmann et al., 2007, 2008a,b). Brain activity as assessed by fNIRS was reported at dorso-lateral prefrontal, inferior frontal, and superior temporal brain areas which is in line with functional magnetic resonance imaging literature (Fu et al., 2002; Basho et al., 2007). Therefore, fNIRS seems to be quite a good instrument for detecting brain activation during overt speech. For most of these investigations, the fNIRS measurement array was also positioned laterally to cover temporal and inferior frontal brain areas. However, this positioning also covers the temporalis muscle. The main function of this muscle is closing the jaw (Ferrario et al., 2000), but it has also been found to be active during speech (Burnett et al., 2000). Muscle activity can be measured by electromyography (EMG) and is indicated by an increase of the root mean square or the rectified EMG signal (Ferrario et al., 2002; Castroflorio et al., 2005; Okura et al., 2006). Several NIRS investigations of muscle activity found an increase of oxygen consumption during continuous exercise or continuous isometric contractions (Ferrari et al., 2004) as well as an increase in the concentration of O2Hb during fast and short muscle contraction (Cettolo et al., 2007). Therefore, temporalis muscle activity during overt verbal fluency might influence or even mask concentration changes of O2Hb and HHb as indicators of brain activity. Thus, the aim of our study was to investigate whether the overt verbal fluency test—as it is often used in fNIRS studies—actually measures brain activity. In other words, does concurrent activation of superficial muscle interfere with fNIRS measurement of cerebral hemodynamics? For this purpose, we measured changes of O2Hb and HHb over fronto-temporal areas with fNIRS, while activity of the temporalis muscle was simultaneously assessed with EMG, in two experiments. In a first experiment, subjects completed a phonological version of the common verbal fluency test as it is used

a University of Regensburg, Department of Psychiatry, Psychosomatics and Psychotherapy, Germany b University of Wuerzburg, Department of Psychiatry, Psychosomatics and Psychotherapy, Germany c University of Tuebingen, Department of Psychiatry and Psychotherapy, Germany d Central Institute of Mental Health, Department of Psychiatry, Germany

Abstract—A large part of the literature of functional nearinfrared spectroscopy (fNIRS) deals with overt verbal fluency. It has been claimed that fNIRS has a low susceptibility to movement related artefacts as, for example, associated with overt speech. However, so far, no study has investigated this assumption in an experimental design. Therefore, we examined a group of 16 healthy subjects during performance of two verbal fluency tasks (experiment 1: phonological fluency; experiment 2: semantical fluency, paced answers, pronouncing vs. writing). We measured changes of oxygenated (O2Hb) and deoxygenated haemoglobin (HHb) over fronto-temporal (brain) areas via fNIRS, while temporalis muscle activity was simultaneously assessed by means of electromyography (EMG). Statistical analyses indicated comparable word production, higher increases of O2Hb and higher decreases of HHb over fronto-temporal areas during word fluency in contrast to the control task weekday reciting. This fNIRS pattern indicates fluency related activation and was found for pronouncing and for writing in both experiments. Regarding the EMG data, fluency related activity was only found for pronouncing, not for writing. Thus, muscle activity cannot account for fluency related fNIRS activity during writing. Additionally, correlation analyses showed no systematic associations of fNIRS and EMG signals. In conclusion, we found arguments that fNIRS actually allows for the measurement of brain activity over fronto-temporal areas during verbal fluency. Nonetheless, further studies should evaluate more direct associations between fNIRS and EMG signals by specific experimental manipulations and data analysing approaches that allow dealing fNIRS and EMG raw data simultaneously. © 2010 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: functional near-infrared spectroscopy, fNIRS, temporalis muscle, electromyography, verbal fluency, frontotemporal oxygenation. *Correspondence to: M. Schecklmann, University of Regensburg, Department of Psychiatry, Psychosomatics and Psychotherapy, Universitaetsstraße 84, 93053 Regensburg, Germany. Tel: ⫹49-941-9412054; fax: ⫹49-941-941-2025. E-mail address: [email protected] (M. Schecklmann). Abbreviations: AC, activation; ANOVA(s), analyse(s) of variance; BL, baseline; EMG, electromyography; (f)NIRS, (functional) near-infrared spectroscopy; HHb, deoxygenated haemoglobin; K–S, Kolmogorov– Smirnov; O2Hb, oxygenated haemoglobin; RE, rest.

0306-4522/10 $ - see front matter © 2010 IBRO. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.neuroscience.2010.08.072

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in our laboratory, that is subjects were instructed to name as many nouns as possible with a certain initial letter. The common control task of weekday reciting should control for simple mnemonic recall and processes in fNIRS and EMG activity related to language generation. In a second experiment we experimentally manipulated the output of words (pronouncing vs. writing). We used a semantical fluency version expected to result in similar oxygenation changes as phonological fluency (Ehlis et al., 2007; Schecklmann et al., 2007, 2008b) to avoid habituation effects between fluency tasks of the first and second experiment. Based on previous findings, the number of pronounced words is expected to be different for fluency versus control tasks (Schecklmann et al., 2007, 2008b). In order to obtain a similar number of words in fluency and control tasks, we paced the subjects’ answers by verbal instructions. As experiment 2 was a paced semantical fluency task with a pronouncing and a writing condition, it enables us to control for effects of differences in output and overt pronouncing. Although there is limited literature regarding the influence of muscle activity on fNIRS signals (muscle NIRS studies with other dependent variables than changes in O2Hb and HHb, investigator instructions in fNIRS studies to avoid strong biting) we posit certain predictions. These predictions would affirm the consideration that fNIRS actually measures brain activity. We expected fronto-temporal fNIRS activity (increase of O2Hb, decrease of HHB) during all verbal fluency conditions in contrast to the control conditions. We expected no verbal fluency related temporalis muscle activity (higher EMG activity during fluency in contrast to the control conditions), as we tried to control for differences in the number of produced words between the fluency and the control condition. Moreover, for the writing condition we did not expect EMG activity for the fluency and control condition, but fNIRS activity during verbal fluency in contrast to the control task. Fluency related fNIRS activity despite missing EMG activity would argue for a dissociation of fNIRS and EMG signals. We did not expect significant correlations between fNIRS and EMG activity; moreover we expected correlations that were distributed around zero by chance.

EXPERIMENTAL PROCEDURES Subjects We examined 16 volunteers (nine females, seven males), whose physical and mental health was ensured by interview and questionnaire for somatic and neurological diseases as well as psychiatric symptoms according to the DSM-IV classification system. All subjects were right handed, non-smokers or rare smokers, free of medication (except for one male: regular asthma medication containing a sympathomimetic drug and cortisone), between 23 and 28 years old, and with a high education level (secondary school examination, 13 years school attendance). The study was approved by the Ethics Committee of the University of Wuerzburg, and all procedures involved were in accordance with the latest version (fifth revision) of the Declaration of Helsinki. All participants gave written informed consent after comprehensible explanation of the experiment and the fNIRS technique.

435

Verbal fluency task Participants sat in a comfortable chair and were instructed to relax and to avoid any major body movements. In experiment 1, we used the phonological version of the verbal fluency task, that is participants were instructed to name as many nouns as possible beginning with a certain letter without using repetitions and proper names. The control condition followed the fluency condition and consisted of reciting weekdays in consecutive manner. The examiner adjusted the number of generated weekdays to the number of generated words in the fluency conditions by pacing the participants with brief verbal orders (“slower” or “faster”) to slow down or to accelerate. The activation phases of both the fluency and the control condition lasted 30 s and were separated by 30 s resting phases. In resting phases the subjects were told to stop pronouncing nouns or weekdays and not to think about the fluency or control task instruction. Participants were instructed to keep their eyes closed during the entire task, which contained three fluency and three control condition blocks, beginning with the fluency condition that was preceded by at least 15 s resting phase. We used E, G, and P as initial letters in randomized order. We chose the mean number of produced words and weekdays as dependent variables. In experiment 2, we used the semantical version of the verbal fluency task, that is participants were instructed to produce nouns from a certain category. In contrast to the phonological task, subject responses were paced by certain time intervals. The examiner called on the participants to produce one noun or weekday every 3.75 s, that is eight times within one 30 s block or activation phase respectively. Additionally, participants should either vocalize or write down the words on a draft with eight blank lines within a block. The control condition followed the fluency condition with the same output condition. The output conditions (writing vs. pronouncing) alternated, and the first output condition was counterbalanced between subjects. If subjects were unable to produce a word, they were instructed to say or write down the phrase “Keine Ahnung” meaning “no idea.” This happened very infrequent (12 times out of 768 trials over the whole sample and both fluency categories). Categories were animals, fruits, and flowers for one output condition, and garments, sports, and professions for the other condition. Assignment to the output condition was randomized. Experiment 2 was conducted with eyes open. Experiment 1 lasted about six, and experiment 2 about 12 minutes. Experiment 1 was succeeded by experiment 2 after a short break of a few minutes. This design may be resulted in habituation effects and diminished brain activation for experiment 2. However, we used a different fluency task in experiment 2 (semantical vs. phonological), experiment 1 and 2 were analysed in distinct examinations, and output conditions within experiment 2 were alternated and counterbalanced between subjects.

Functional near-infrared spectroscopy We used a continuous wave system (ETG-4000 Optical Topography System; Hitachi Medical Co., Japan) working with two different wavelengths (695⫾20 and 830⫾20 nm) and a time resolution of 10 Hz to measure relative changes of absorbed nearinfrared light. Transformation of these changes into concentration changes of O2Hb and HHb as indicators for brain activity was performed by means of a modified Beer-Lambert law (Obrig and Villringer, 2003). The unit is mmol⫻mm, that is changes of chromophore concentration depend on the path length of the nearinfrared light, which is unknown in our examination. We used two identical probe sets (plastic panels) of optodes for each side of the head. One probe set consisted of eight light emitters and seven detectors with an inter-optode distance of 30 mm. A measuring point of activation (channel) was defined as the region between one emitter and one detector. Thus one probe set consisted of 22 channels and covered an area of 12⫻6 cm2 on the scalp. The

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M. Schecklmann et al. / Neuroscience 171 (2010) 434 – 442

Fig. 1. Schematic arrangement of the functional near-infrared spectroscopy (fNIRS) probe set (dark squares: emitter; bright squares: detectors; numbers: measurement channels) and electromyography (EMG) electrodes. The inferior row of the left probe set was oriented towards T3 and Fp1 (T4 and Fp2 for the right side) according to the international 10 –20 system (Jasper, 1958). EMG electrodes were placed just below the F7 and F8 electroencephalography (EEG) positions close to the bellies of the temporalis muscles and about 2 cm in ventral direction on the zygomatic bones.

panels were fastened to the head by elastic straps. The probe sets were placed on the head with regard to the relevant standard positions of the international 10 –20 system for electroencephalography (EEG) electrode placement (Jasper, 1958; Okamoto et al., 2004). The placement of the probe sets is shown in Fig. 1.

Electromyography We amplified and recorded the EMG signals with a 32-channel amplifier (BrainAmp MR and Vision Recorder, respectively; Brain Products, Germany) working with a sampling rate of 5000 Hz and a band pass filter of 0.1 and 350 Hz. Surface electrode pairs (silver, 12 mm diameter, 1.5 mm height) were placed just below the F7 and F8 EEG positions close to the bellies of the temporalis muscles and about 2 cm in ventral direction on the zygomatic bones. The monopolar electrodes were enclosed in plastic housings (15 mm diameter, 6 mm height) with a blank round area with 7.3 mm diameter. A reference electrode (silver/silver chloride) was placed on Cz EEG position and a wet ground strap was wrapped around the forearm. The skin under the electrodes was cleaned with abrasive emulsion and conductive paste was brought up. The placement of the electrodes is shown in Fig. 1. Before measurement it was assured that EMG signals mirror temporalis muscle activity by visual inspection of the EMG signal while subjects were briefed to clench their teeth.

Data and statistical analysis The high frequency portion of the fNIRS signal was removed by calculating a moving average with a time window of 5 s. EMG data were filtered with a low-pass filter of 250 Hz and a high-pass filter of 25 Hz. Signals from the electrodes at the zygomatic bone were subtracted from the signals of the electrodes at the bellies of the temporalis muscles, for each hemisphere, respectively. The subtraction of the signal of the spatial adjacent, but neutral electrode should control signal changes not associated with the temporalis muscle activity. Thereafter the EMG data were rectified. For fNIRS data, we used a linear fitting procedure resulting from the calculation of the mean of the 10 s before the activation phase and the mean of the interval between the 10th and 20th second after the activation phase, that is the difference of the raw signal and the line through the two calculated means, subsequently averaging the three repetitions of each condition. We defined six time segments of 10 s each (baseline BL, activation 1–3 AC1–AC3, rest 1–2 RE1–RE2) for each condition and calcu-

lated the mean of the fNIRS signal for each of these segments. For EMG data the signals were averaged according to the same time segments. (Fig. 2). Statistical analyses for experiment 1 included a repeated measurement ANOVA for the fNIRS and EMG data with three factors (side: left and right hemisphere; condition: verbal fluency and control condition; time: six time segments). For experiment 2, the same ANOVAs were calculated with the fourth factor output (pronouncing and writing). Brain activity during block design tasks is considered as an increase during the activation phase and a decrease during the resting phase. Thus, for all ANOVAs, verbal fluency related activity could be characterized by a significant condition⫻time interaction emerging from higher activity (increase of EMG, O2Hb, and decrease of HHb) during verbal fluency activation in contrast to the control condition. In regard to the aim of this manuscript, we focused our analyses on condition⫻time interaction effects with the described pattern, in combination with effects of the factor output in experiment 2. All significant condition⫻time interactions were further analysed by post hoc t-tests between the activity during fluency in contrast to the control condition. For this contrast we used the activation phase AC3 controlled for the baseline phase BL (AC3 minus BL). Fig 3 shows the corresponding t-maps for the whole probe set. We abstained from including further t-tests (AC1 or AC2 against BL) as our data showed activity increases during the activation phase resulting in the maximal activity during AC3. This is affirmed by the supplemental Table 1 that shows significant t-tests for the AC1 and AC2 contrasts, the fewest for the AC1 contrast, more for the AC2 contrast, and the most for the AC3 contrast; channels without significant t-tests are mainly related to HHb—the chromophore often associated with a lower statistical power as elicited by fNIRS measurements—and to the outer channels of the cluster of activated channels. For reasons of clarity we omitted the presentation of the statistical values of the post hoc t-tests. We ignored condition⫻time⫻side interaction effects as they were outside the main issue of the investigation; in addition, we found laterality effects— higher amplitudes for fluency on the left in contrast to the right hemisphere for experiment 1— only with minor statistical significances. We calculated Pearson correlations between fNIRS and EMG signals for the activation phases AC1–AC3 for all channels of the left and right probe set to evaluate the association of both measures at the group level (Table 2). We inspected the fluency and control conditions, O2Hb and HHb, cluster of fluency related chan-

M. Schecklmann et al. / Neuroscience 171 (2010) 434 – 442

437

Fig. 2. Illustration of raw data analysis and extract of the fluency task protocol (baseline BL, activation phases AC1, AC2, and AC3, and rest phase RE1 and RE2). The depicted electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) raw data and the averaged fNIRS waveform are representative and represent no existing signal. Raw data show increased activity during the fluency and lower activity during the control condition. The average fNIRS waveform characterizes fluency related activity, that is an increase during the task period and a decrease thereafter.

nels (as indicated by significant condition⫻time interactions) and cluster of not fluency related channels, and for experiment 2 the output conditions separately, that is we calculated 132 correlations (22 channels⫻2 probe sets⫻3 AC phases) for each of these conditions. As indicated in Table 2, we presented the range and the mean of the correlations for these conditions, the P-value of the Kolmogorov–Smirnov-test (K–S-test) for normal distribution, and the

proportion of channels with significant correlations at an uncorrected level of significance. The mean value in interaction with the K–S-test would indicate if the correlations were unimodal distributed at zero, that is the correlations of fNIRS and EMG could be interpreted as randomly distributed. For data analyses and statistics, we used Vision Analyzer (Brain Products GmbH, Germany), MatLab (The MathWorks Inc., USA), and SPSS (SPSS Inc., USA).

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phonological fluency: pronouncing

semantical paced fluency: pronouncing

semantical paced fluency: writing

O2Hb

-6

t-values

O2Hb (mmol x mm) in channel 3

6

HHb

EMG (μV)

Fig. 3. Top: fluency related brain activation indicated by t-maps (baseline corrected fluency task against control task during activation phase 3 according to post hoc tests); below: fNIRS and EMG signals (mean⫾standard deviation) according to the time segments of analysis (baseline BL, activation phases AC1, AC2, and AC3, and rest phase RE1 and RE2) for oxygenated haemoglobin (O2Hb) exemplary in one of the fluency related channels on the left side and for electromyography (EMG) on the right side (red line: phonological fluency—free pronouncing, black: semantical fluency—paced pronouncing, blue: semantical fluency—paced writing; continuous line: fluency condition, dashed line: control condition); we abstained from showing all channels and deoxygenated haemoglobin for reasons of clarity.

M. Schecklmann et al. / Neuroscience 171 (2010) 434 – 442

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Table 1. F-statistics of interaction effects the analyses of variance indicating fluency related activity (df⫽5,75) Channel

Experiment 1

Experiment 2

Condition⫻time

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Emg

Condition⫻time

Condition⫻time⫻output

O2Hb

HHb

O2Hb

HHb

O2Hb

F⫽27.0; P⬍0.001 F⫽43.3; P⬍0.001 F⫽52.7; P⬍0.001 F⫽31.4; P⬍0.001 F⫽4.7; P⬍0.001 F⫽34.3; P⬍0.001 F⫽54.6; P⬍0.001 F⫽44.0; P⬍0.001 F⫽7.3; P⬍0.001 F⫽8.8; P⬍0.001 F⫽28.6; P⬍0.001 F⫽18.9; P⬍0.001 F⫽6.1; P⬍0.001 # F⫽2.9; P⫽0.018 F⫽5.2; P⬍0.001 # F⫽4.2; P⫽0.002 # # # # F⫽12.0; P⬍0.001

F⫽7.7; P⬍0.001 F⫽10.4; P⬍0.001 F⫽18; P⬍0.001 F⫽22.5; P⬍0.001 # F⫽14.9; P⬍0.001 F⫽15.8; P⬍0.001 F⫽18.4; P⬍0.001 F⫽2.9; P⫽0.018 # F⫽8.9; P⬍0.001 F⫽6.6; P⬍0.001 # # # # # F⫽3.1; P⫽0.014* # # # #

F⫽14.6; P⬍0.001 F⫽31.5; P⬍0.001 F⫽51.1; P⬍0.001 F⫽21.2; P⬍0.001 # F⫽13.7; P⬍0.001 F⫽35.7; P⬍0.001 F⫽22.7; P⬍0.001 # # F⫽5.6; P⬍0.001 F⫽6.8; P⬍0.001 # # # # # # # # # # F⫽2.2; P⫽0.061

F⫽8.0; P⬍0.001 F⫽12.4; P⬍0.001 F⫽14; P⬍0.001 F⫽17.1; P⬍0.001 # F⫽9.9; P⬍0.001 F⫽8.8; P⬍0.001 F⫽15.8; P⬍0.001 # # F⫽9.0; P⬍0.001 F⫽5.6; P⬍0.001 # # # # # # # # # #

F⫽0.1; F⫽1.0; F⫽1.1; F⫽0.1; # F⫽0.5; F⫽0.3; F⫽0.1; # # F⫽0.3; F⫽0.3; # # # # # # # # # # F⫽9.3;

HHb P⫽0.988 P⫽0.447 P⫽0.354 P⫽0.978 P⫽0.812 P⫽0.911 P⫽0.992

P⫽0.885 P⫽0.936

F⫽1.7; F⫽3.7; F⫽2.6; F⫽1.5; # F⫽1.5; F⫽2.6; F⫽0.7; # # F⫽0.8; F⫽2.0; # # # # # # # # # #

P⫽0.154 P⫽0.005 P⫽0.031 P⫽0.202 P⫽0.217 P⫽0.033 P⫽0.602

P⫽0.579 P⫽0.086

P⬍0.001

# No significant interaction or no fluency related activity respectively. * Post hoc t-test was not significant.

RESULTS Experiment 1 The number of produced words did not differ between the fluency and control condition (nouns: 7.73⫾1.40; weekdays: 7.73⫾1.41). EMG data showed a significant condition⫻time interaction, that is EMG activity for the fluency condition was increased during the activation phases 1–3 (AC1–AC3) in contrast to the baseline (BL) and rest phases (RE1, RE2) and in contrast to AC1–AC3 during the control condition (Table 1, Fig. 3). fNIRS showed similar fluency related significant condition⫻time interactions for channels over inferior parts of the probe sets (O2Hb: 16 channels and HHb: 10 channels) (Table 1, Fig. 3). P-values of all significant channels (except for one channel for O2Hb and HHb each) were below a Bonferroni corrected significance level. Table 2 shows indices for the correlation analyses between EMG and fNIRS signals (range, mean of the correlations, K–S-test for normal distribution, and proportion of channels with significant correlations). Correlations for all channels, for both probe sets, cluster of fluency related and not related channels, and for AC1–AC3 indicated a normal distributed range between medium (0.3⬍r⬍0.5) to high (r⬎0.5) negative and positive correlations with mean values around zero with no systematic differences for the chromophores or the inspected cluster of channels. The proportion of significant correlations was not higher than 5% (supplemental Fig. 1).

Experiment 2 For experiment 2 the answers were paced and resulted in eight pronounced or written words per block. EMG data showed a condition⫻time interaction effect with a statistical trend and a significant condition⫻time⫻output interaction effect (Table 1, Fig. 3); for the pronouncing condition, EMG activity was increased during the fluency activation phases in contrast to BL and RE phases and in contrast to the control condition; for the writing condition EMG activity was reduced during the fluency and the control activation phases in contrast to BL and RE phases. That means that the temporalis muscle showed typical fluency related activity for pronouncing, but not for writing. fNIRS again showed fluency related significant condition⫻time interactions for channels over inferior parts of the probe sets (O2Hb and HHb: nine channels each) (Table 1, Fig. 3). P-values of all significant channels were below a Bonferroni corrected significance level. The condition⫻time⫻output interaction effects were not significant except for three channels for HHb (and one with a statistical trend). These interaction effects could be explained by more positive HHb values for the activation phases during the control condition during writing in contrast to pronouncing. For the fluency activation phases no significant differences could be detected, that is both output conditions showed comparable fluency related fNIRS activity. As shown in Table 2, correlations between EMG and fNIRS signals indicated a normal distributed

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M. Schecklmann et al. / Neuroscience 171 (2010) 434 – 442

Table 2. Statistics of the positive correlations* between fNIRS and EMG (means of activation phases) Fluency related channels

Phonological fluency: free pronouncing O2Hb Fluency Control HHb Fluency Control Semantical fluency: paced pronouncing O2Hb Fluency Control HHb Fluency Control Semantical fluency: paced writing O2Hb Fluency Control HHb Fluency Control

Not fluency related channels

Min

Max

Mean

ND**

%***

Min

Max

Mean

⫺0.50 ⫺0.58

0.45 0.42

⫺0.04 ⫺0.05

0.71 0.89

⫺0.36 ⫺0.37

0.45 0.46

0.05 ⫺0.03

⫺0.50 ⫺0.47

0.70 0.36

⫺0.62 ⫺0.18

ND**

%***

0 0

⫺0.25 ⫺0.32

0.36 0.29

0.03 ⫺0.09

0.73 0.68

0 0

0.93 0.33

0 0

⫺0.44 ⫺0.41

0.66 0.55

0.12 0.04

0.98 0.68

0 0

0.02 ⫺0.10

0.65 0.79

9 0

⫺0.60 ⫺0.40

0.56 0.53

⫺0.01 0.08

⬎0.99 0.47

4 4

0.11 0.66

⫺0.23 0.28

0.93 0.96

11 0

⫺0.54 ⫺0.56

0.33 0.54

⫺0.08 0.01

0.91 0.24

3 3

⫺0.25 ⫺0.54

0.41 0.47

0.13 ⫺0.11

0.35 0.26

0 0

⫺0.18 ⫺0.55

0.50 0.50

0.10 ⫺0.12

0.94 0.79

1 1

⫺0.43 ⫺0.60

0.24 0.84

⫺0.09 0.18

0.87 0.27

0 4

⫺0.49 ⫺0.77

0.57 0.55

0.08 0.03

0.88 0.81

0 4

* i.e. positive associations between fNIRS and EMG (the higher fNIRS activity (the higher O2Hb or the lower HHb), the higher EMG activity). ** P-values of the Kolmogorov–Smirnov-Test for normal distribution. *** Proportion of channels with significant correlations (5% or 0.05 represents the statistical threshold).

range between medium to high negative and positive correlations with mean values around zero with no systematic differences for the chromophores or the inspected cluster of channels. The mean values of HHb in the fluency related channels for the fluency and control conditions were higher than |0.2| for verbal output. The proportion of significant correlations was not higher than 5% except for O2Hb and HHb for the verbal output condition of the fluency conditions in the fluency related channels. The reported two high mean values of correlation coefficients and the two exceedings of the proportion of significant channels over 5% provide no systematic pattern, that is we interpret the correlation findings as no systematic association of fNIRS and EMG data (supplemental Fig. 1).

DISCUSSION In the first experiment, we measured activity of the temporalis muscle by means of EMG, and (brain) oxygenation changes over fronto-temporal regions by means of fNIRS during a phonological fluency task. In the second experiment, subjects completed a semantical fluency task with paced verbal or manual output to control for differences in output between conditions and to control the influence of pronouncing in contrast to writing. Performance of the current sample in experiment 1 was comparable to behavioural data obtained in previously investigated samples at our laboratory (Herrmann et al., 2005; Ehlis et al., 2007; Schecklmann et al., 2007, 2008a,b). In contrast to previous studies reporting a higher word production for control conditions compared to word

fluency conditions especially in psychiatric samples (Schecklmann et al., 2007, 2008b), word production was similar for fluency and control conditions in the current sample. Therefore, we can conclude that there was no bias between the fluency and control condition in regard to the amount of verbal output (number of produced words), neither for experiment 1 nor for experiment 2 during which answers were paced by the investigator. As expected, for fNIRS data we found condition⫻time interactions in inferior channels of the probe sets for both experiments independent of the output condition indicating higher O2Hb increases or HHb decreases during fluency in contrast to the control condition over fronto-temporal brain areas. This activation pattern is in line with previous fNIRS findings of our laboratory and other work groups (Matsuo et al., 2000, 2002, 2004, 2005; Herrmann et al., 2003, 2004, 2005, 2006; Suto et al., 2004; Kameyama et al., 2006; Ehlis et al., 2007; Schecklmann et al., 2007, 2008a,b). Phonological and semantical fluency show similar oxygenation patterns as assessed by fNIRS (Ehlis et al., 2007; Schecklmann et al., 2007, 2008b). The slightly more ventral location of activity during semantical fluency compared to phonological fluency as measured with fMRI (Costafreda et al., 2006) is hardly detectable by fNIRS due to the method’s low spatial resolution. Writing per se might cause activation in fronto-temporal areas due to cognitive processing (Harrington et al., 2006; James and Gauthier, 2006) or due to tilting of the head. However, we used a control condition with a similar motor output. Pacing could also directly lead to changes in NIRS signals, particularly in

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brain areas processing executive control. Executive control or response inhibition is supposed to be located in inferior parts of the prefrontal cortex (Aron et al., 2004), that is the region beneath our probe set. However, for experiment 2 our control condition was associated with the same pacing component. Thus, the reported oxygenation pattern is most probably related to the fluency component of the task and not to the writing or pacing component. However, for experiment 1 the control condition in contrast to the fluency condition is associated with the external stimuli of the experimenter’s pacing comments and accordingly with an inhibitory component. Among other things, we conducted experiment 2 to control for these biases. In line with this consideration, experiment 2 showed comparable temporal and spatial patterns of fNIRS activity (increase of fNIRS amplitudes during fluency in contrast to the baseline and the control condition in inferior channels of the probe set). However, experiment 1 seemed to be associated with higher amplitudes and a more extended region of fluency related activity. As fNIRS activity (temporal and spatial pattern) was comparable between conditions in both experiments (pronouncing vs. writing, phonological vs. semantical fluency, and free vs. paced answers), the conclusions of experiment 2 can be transferred to other designs of the verbal fluency task as implemented in experiment 1 or in already published manuscripts. Regarding the main objective of our study, we found three indications supporting the assumption that fNIRS actually measures brain activity (as opposed to the muscle). First, subjects showed increased fNIRS activity (O2Hb and HHb) during verbal fluency in contrast to the control condition for the writing condition without a concomitant increase in temporalis muscle activity. Second, fNIRS activity during writing was not significantly different from fNIRS activity during pronouncing (overt speech) in experiment 2 (in fact, it was numerically even higher). Third, correlations did not indicate a systematic association of fNIRS and EMG signals. One of our findings might be considered critical for the assumption that fNIRS measures brain activity. In contrast to our predictions, EMG activity was increased during the fluency in contrast to the control condition for verbal output in experiment 1 and 2, and decreased for manual output during experiment 2. During fluency conditions with verbal output it is a very common observation that subjects elongate the requested initial letters, tense their muscle, or use filler words before they come up with a new word which could cause muscle/EMG activity unrelated to the actual production of target words. This might in turn account for the increased activity of the temporalis muscle during the fluency condition compared to the control condition with verbal output reported above. The effort involved in performing the fluency task might result in the cognitive effort (fNIRS activity) and concomitantly in more somatic effort with exerted muscle contraction. In a methodical point of view the effort might act as a third variable that could explain the association of fNIRS and EMG activity (higher activity during the fluency in contrast to the control condi-

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tion). In our opinion, this contrast represents a correlational, but not a causal association. Interestingly, this observed contrast could not be overcome by pacing the answers of the subjects as it was done in experiment 2. Thus, the control condition “weekday reciting” does not seem to be adequate to control for differences in temporalis muscle activity. Thus, future investigations of the issue of influence of head muscle should try to hold the muscle activity comparable between fluency and control conditions. More detailed information in regard to this issue may be could reveal other measures of extracerebral tissue, for example measurement of blood flow in scalp and tissue. However, one of the most important future issues would be if head muscle activity is causally related to fNIRS activity by experimental designs that systematically manipulate muscle activity and observe the effects of these manipulations on the fNIRS signal. In a further step it would be necessary to design mathematical tools that reveal the possibility to filter out the pure fNIRS signal caused by muscle activity, for example the EMG signal as predictor in a regression analysis with the fNIRS signal as dependent variable. We abstained from doing such regression analyses as our design was not conceptualized for this approach which would result in a suboptimal EMG regressor. Additionally, as fNIRS is a preferred tool in research on brain disorders, the influence of muscle activity on group differences between patient samples and controls should be investigated. An open question would be whether or not this conclusion is also tenable for other muscles, for example the frontalis muscle at the forehead which could be considered to mask dorso-lateral prefrontal activity. Another open question is related to the influence of general head muscle activity such as neck movements, strong biting, and blinking on fNIRS signals. For example (Suto et al., 2004) commented that such body motions cause artefacts. However, so far, no quantitative data are available and no rules for detection and handling of artefacts due to (these) body movements have been suggested. Systematic investigations provoking artefacts (Izzetoglu et al., 2005) would add knowledge to the postulated low susceptibility of NIRS to movement artefacts and to procedures to further reduce them. In conclusion, we found good arguments that fNIRS over fronto-temporal areas actually measures brain activity. Thus, declarations of good feasibility of language tasks with fNIRS are justified even for brain regions lying beneath muscles associated with language production. Acknowledgments—The authors would like to thank Hitachi Medical Corporation for the ETG-4000 equipment and the skilled technical and methodical support. The study was supported by the Deutsche Forschungsgemeinschaft, KFO 125-1.

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APPENDIX Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroscience.2010.08.072.

(Accepted 30 August 2010) (Available online 19 September 2010)