13 January 2014 Cerebral hemodynamic and oxygenation changes induced by inner and heard speech: a study combining functional near-infrared spectroscopy and capnography
Author Affiliations +
J. of Biomedical Optics, 19(1), 017002 (2014). doi:10.1117/1.JBO.19.1.017002
The aim of this study was to investigate the effects of inner and heard speech on cerebral hemodynamics and oxygenation in the anterior prefrontal cortex (PFC) using functional near-infrared spectroscopy and to test whether potential effects were caused by alterations in the arterial carbon dioxide pressure (PaCO2). Twenty-nine healthy adult volunteers performed six different tasks of inner and heard speech according to a randomized crossover design. During the tasks, we generally found a decrease in PaCO2 (only for inner speech), tissue oxygen saturation (StO2), oxyhemoglobin ([O2 Hb]), total hemoglobin ([tHb]) concentration and an increase in deoxyhemoglobin concentration ([HHb]). Furthermore, we found significant relations between changes in [O2 Hb], [HHb], [tHb], or StO2 and the participants’ age, the baseline PETCO2, or certain speech tasks. We conclude that changes in breathing during the tasks led to lower PaCO2 (hypocapnia) for inner speech. During heard speech, no significant changes in PaCO 2 occurred, but the decreases in StO2, [O2 Hb], and [tHb] suggest that changes in PaCO2 were also involved here. Different verse types (hexameter and alliteration) led to different changes in [tHb], implying different brain activations. In conclusion, StO2, [O2 Hb], [HHb], and [tHb] are affected by interplay of both PaCO2 reactivity and functional brain activity.
Scholkmann, Klein, Gerber, Wolf, and Wolf: Cerebral hemodynamic and oxygenation changes induced by inner and heard speech: a study combining functional near-infrared spectroscopy and capnography



Previous studies of guided rhythmic speech exercises in the context of arts speech therapy (AST) on human physiology showed that AST tasks, i.e., recitations, are associated with characteristic changes in variations in the heart rate1,2 and cardiorespiratory interaction. In a next step, effects on cerebral and systemic changes of hemodynamics and oxygenation were investigated by our research group using functional near-infrared spectroscopy (fNIRS).3,4 A decrease in cerebral hemodynamics and oxygenation was found to occur during speech exercises, which was hypothesized to be a result of a decrease in the partial pressure of carbon dioxide (CO2) in the arterial blood (PaCO2) during speaking. This hypothesis was confirmed in a subsequent study combining fNIRS and capnography,5 where we observed changes in end-tidal CO2 (PETCO2), a reliable and accurate estimate of PaCO2,67.8 during all speech tasks and even during the control task (mental arithmetic). This led us to conclude that the changes in hemodynamics and oxygenation are a combination of two factors: (1) a hypercapnia mediated by changes in breathing (hyperventilation) during the tasks and (2) changes in brain activity (neurovascular coupling), where previously the first factor was probably stronger than the second one.

In a following study,9 we demonstrated that even inner speech, i.e., speech not spoken aloud, leads to significant changes in PETCO2 as well as cerebral hemodynamics and oxygenation. Table 1 gives an overview of previous study results.

Table 1

Listing of study details.

ReferenceExperimental protocolResults
1N=15, age: 44±10  years, task: rhythmic speech, rest. Measured parameters: HRR, HRVSpeech-induced changes in HRR, HRV, and cardiorespiratory interaction
2N=20, age: 43±6.6  years, tasks: hexameter recitation (H), controlled breathing (C), and spontaneous breathing (S). Measured parameters: HRVStrength of cardiorespiratory synchronization: H>C>S
4N=7, age: 29 to 49 years, task: hexameter recitation (H). Measured parameters at PFC and calf mucle: StO2, [O2Hb], [HHb], [tHb]PFC (during H): StO2↓, [O2Hb]↓, [tHb]↓ CM (during H): [tHb]↓, [HHb]↓
3N=17, age: 35.6±12.7  years, tasks: recitation of hexameter (H), alliteration (A), or prose (P). Measured parameters at PFC and calf mucle: StO2, [O2Hb], [HHb], [tHb]PFC (during all tasks): StO2↓, [O2Hb]↓ CM (during all tasks): [O2Hb]↑, [tHb]↑
5N=24, age: 22±6.4  years, tasks: mental arithmetic (M), recitation of hexameter (H), alliteration (A), or prose (P). Measured parameters: (i) PFC: StO2, [O2Hb], [HHb], [tHb]; (ii) PETCO2During all tasks: PETCO2↓ PFC (during H, A, P): StO2↓, [O2Hb]↓, [HHb]↑, [tHb]↓ PFC (during M): StO2↑, [HHb]↑
9N=7, age: 34.9±9.3  years, tasks: inner mental arithmetic (iM), inner recitation of hexameter (iH), or prose (iP). Measured parameter: (i) PFC: StO2, [O2Hb], [HHb], [tHb]; (ii) PETCO2During all tasks: PETCO2↓ PFC (during iH, iP): StO2↓, [O2Hb]↓ PFC (during M): StO2↓

Note: HRR, heart rate rhythmicity; HRV, heart rate variability; CM, calf muscle; PFC, prefrontal cortex.

These results triggered the question of whether simply hearing a recitation also causes changes in PaCO2 and, thus, in cerebral hemodynamics and oxygenation. Since this question had not been addressed yet, but is of importance in functional studies involving speech, the aim of the present study was to investigate the effect of different types of (1) inner speech and (2) heard speech on PETCO2 dynamics as well as cerebral hemodynamics and oxygenation measured using fNIRS and capnography.


Materials and Methods


Subjects, Experimental Protocol, and Instrumentation

Twenty-nine healthy subjects (14 men, 15 women, mean age: 47.0±12.8years) participated in this study, which was carried out as a controlled and randomized crossover trial. Study participants were German/Swiss German native speakers who had no previous knowledge of AST and were asked not to smoke, eat, or consume any stimulants (such as caffeine or other ingredients in energy drinks) for at least 2 h before the start of the measurements. Approval for the study was obtained from the Ethical Committee of the Canton of Zurich: the design of the study was in accordance with the Declaration of Helsinki and informed consent was obtained from the subjects prior to each measurement.

Three task modalities were applied: (1) inner recitation of the text (inner speech, IS), and listening to the text (heard speech, HS) while (2) the text was recited by a person (HSP), or (3) a recorded recitation was played (HSR). Two different types of text were used, i.e., one with hexameter and one with alliterative verses. Thus, six different tasks were investigated: inner speech of a (1) hexameter (IS-H) and (2) alliterative (IS-A) text, listening to a live recited (3) hexameter (HSP-H) and (4) alliterative (HSP-A) text, and listening to a recorded (5) hexameter (HSR-H) and (6) alliterative (HSR-A) recitation.

The two text types have the following characteristics: alliteration is a form of rhythmic speech with repetition of a particular initial sound in the first syllables of a series of words or phrases; the hexameter is a metrical line of verse consisting of six feet. These two verse types induce a different flow of speech and may induce different patterns of brain activity. Alliteration and hexameter recitation were investigated in this study since they are frequently used in the context of AST. Recitation of a person was applied because it closely resembles the situation in AST; recorded recitation was additionally employed because it ensures the same recitation for every participant.

Each measurement lasted 43 min [8 min baseline (interval 1), 5 min task (interval 2), 5 min recovery (interval 3), 5 min task (interval 4), and 20 min recovery (intervals 5 to 8)]. To avoid potential carry-over effects, each task was performed on a separate day. During the IS and HSP tasks, the subjects sat opposite a person who recited the respective text verse-by-verse. During the HSR tasks, the subjects sat opposite two speakers (LS11, Logitech Inc., Fremont, USA) and the prerecorded recitation (performed by the same person who recited during the IS and HSP tasks) of the texts was played. The recitations were recorded using an electret condenser microphone (ECIMF8, Sony, Japan) and the open-source software Audacity ( http://audacity.sourceforge.net). In order to improve sound quality, the recorded recitations were denoised, normalized, and equalized by Audacity. The recorded recitation was played at such a volume that at the position of the subject (directly in front of his face), the sound level was 65 dBA on average, which was measured using a sound meter (320, Voltcraft, Hirschau, Germany). Also during the HSP condition, the sound level was 65 dBA on average.

As in the previous study,5 the following physiological parameters were measured: (1) StO2 and absolute concentrations of [O2Hb], [HHb], and [tHb] (device: ISS OxiplexTS frequency-domain NIR spectrometer, sampling rate: 50 Hz) and (2) PETCO2 (devices: Nellcor N1000 and Datex NORMOCAP capnograph).

The OxiplexTS optodes were placed on the left and right side of the forehead over the prefrontal cortex (PFC) at Fp1-F3/Fp2-F4 according to the international 10–20 system.10 The PFC was chosen because it constitutes a region of the brain that has been demonstrated to be activated in language processing,11 speech production,12 semantic processing, and phonological/lexical processing,13,14 and also easily allows for fNIRS measurements with a high signal-to-noise ratio due to the absence of absorbing hair in this region of the head.

The PETCO2 probe was positioned directly below the right nostril of the subject. A visualization of the placement of the NIRS optode, the CO2 sensor, and a schematic representation of the NIRS optode can be found in Fig. 1. For a detailed description of the ISS OxiplexTS frequency-domain NIR spectrometer specifications, please refer to Ref. 5. Absolute values of StO2, [O2Hb], [HHb], and [tHb] were calculated by the OxiplexTS software using the frequency-domain multidistance (FDMD) method, which enables sensitivity to the extracerebral tissue to be reduced.15,16 This methodological approach is superior to the traditional modified Beer-Lambert method in excluding extracranial hemodynamic changes.17 The FDMD method calculates the following slopes: (1) the amplitude slope (SAC), i.e., ln(d2UAC) versus d, where d is the distance and UAC is the amplitude of the light intensity, and (2) the phase slope (Sφ), i.e., φ versus d, where φ is the phase shift of the light. From SAC and Sφ the absorption coefficient was determined using the equation

with v the speed of light in the tissue and ω the angular modulation frequency of the source intensity. Based on the μa at 690 and 830 nm and using the molar extinction coefficients for O2Hb and HHb, the concentrations of O2Hb and HHb were calculated in a subsequent step (for the equations, please refer to Ref. 5).

Fig. 1

(a) Schematic representation of the near-infrared spectroscopy (NIRS) optode with respective source detector distances. (b) The placement of NIRS optodes at the Fp1-F3 and Fp2-F4 positions according to the international 10-20-system and the CO2 sensor in the nostril.



Signal Processing and Statistical Analysis

Signal processing was performed using MATLAB® (version 2008b, The Mathworks, Natick, MA). Movement artifacts in StO2, [O2Hb], [HHb], and [tHb] were removed using the method presented in Ref. 18 after successful application in previous studies3,5,1920. and positively evaluated in a recent analysis comparing different movement artifact correction techniques for fNIRS.26 Care was taken to ensure that no artificial new trends were introduced to the signals while applying the algorithm.

The PETCO2 values were calculated directly from the capnography waveform signal by detecting the peaks of each breath cycle using a method recently developed in-house27 and by interpolating the peaks by a piecewise cubic interpolation to form a continuous signal.

Each time series was segmented into intervals with a length of 3 min each, covering the time intervals 4 to 7, 9 to 12, 14 to 17, 19 to 22, 24 to 27, 29 to 32, 34 to 38, and 39 to 42 min. For further analysis, all signals were downsampled to 5 Hz and the fNIRS-derived signals were low-pass filtered using a moving average filter (window length: 10 s). For all signals, median values of each interval were calculated and normalized by subtracting the median value of the first interval. Statistical analysis was performed using SPSS software (version 20.0, IBM Corp., Armonk, NY). It was tested whether (1) the interval median values have a distribution with a zero median (Wilcoxon test), (2) the changes in the left PFC are different from those in the right PFC (Wilcoxon paired test), (3) the six types of tasks cause significantly different changes (Kruskal-Wallis test), and (4) the two types of recitation texts (hexameter and alliteration) are associated with different changes in the signals (Mann-Whitney-U test). For all tests we calculated the raw p-values and also applied the Benjamini-Hochberg correction28 to account for the multiple comparison situation.

Additionally, data of the present and two previous own studies with equal design5,9 were combined. To test for significant relations between changes in cerebral hemodynamics and oxygenation and the different speech tasks, linear regression analyses were calculated with a stepwise procedure for each StO2, [O2Hb], [HHb], and [tHb] as the dependent variable and age, gender, body mass index, side of measurement (right or left PFC), baseline PETCO2, and the various speech tasks as the covariates.



Figure 2 reports the changes in StO2, [O2Hb], [HHb], and [tHb] at the right and left PFC for the six different tasks. Figure 3(a) shows the changes in PETCO2 for all tasks. A change induced by a task was considered significant when a significant, i.e., p<0.05, change occurred in interval 2 or interval 4 or in both.

Fig. 2

Changes in StO2, [O2Hb], [HHb], and [tHb] over the course of the experiments. Each interval refers to a time span of 3 min. The shaded areas indicate the periods when the tasks were performed. All data are shown as median values (black circles)±standard error of the median (SEmedian=MAD/n0.5, where MAD is the median absolute deviation and n is the number of measurements). Significant changes are marked with an asterisk: p<0.05 (*). Corrected significant p-values (according to the Benjamini-Hochberg correction) are marked with two vertically aligned asterisks.


Fig. 3

(a) Changes in PETCO2 over the course of the experiments. Each interval refers to a time span of 3 min. The shaded areas indicate the periods when the tasks were performed. All data are shown as median values (black circles)±standard error of the median (SEmedian=MAD/n0.5, where MAD is the median absolute deviation and n is the number of measurements). Significant changes are marked with an asterisk: p<0.05 (*). Corrected significant p-values (according to the Benjamini-Hochberg correction) are marked with two vertically aligned asterisks. During the task, significant changes were only found for inner speech. (b) Scatter plots showing the relationships between changes in PETCO2 and [tHb] (from right prefrontal cortex: black circles; from left prefrontal cortex: black dots) depending on the specific task conditions.


Qualitative summary of the results are as follows (for significances, see Figs. 2 and 3): (1) During the tasks (i.e., intervals 2 and/or 4): PETCO2 decreased significantly only during IS-H and IS-A. StO2, [O2Hb], and [tHb] showed generally a decrease and [HHb] an increase at both PFCs. (2) During the recovery period (i.e., intervals 5 to 8): StO2, [O2Hb], and [tHb] showed generally an increase and [HHb] a decrease at both PFCs.

The changes (during the task and during the recovery period) in StO2 and [HHb] were statistically significantly different between the right and left PFC (Benjamini-Hochberg corrected) for the HSR-A task. StO2 generally decreased (and [HHb] increased) more strongly at the right PFC compared to the left PFC, reaching statistically significant changes for the HSR-A task.

The test of different dynamic behaviors of the signals over the right and left PFC when combining all tasks performed in the present study showed statistically significant differences. The comparison of the changes with respect to the two different speech types (hexameter versus alliteration) showed that for the left PFC, statistically significant differences in changes occurred for [tHb] during and after the task, and for [O2Hb], after the task. The right PFC showed no differences; also PETCO2 was not different for the two types of tasks.

The test of differences in changes for all tasks and signals revealed only the PETCO2 changes in the recovery period were statistically different when comparing HSP-A and HSR-A. When comparing hexameter and alliteration tasks, the changes in [tHb] at the left PFC during tasks and after them were statistically different.

Linear regression analyses revealed several significant relations between changes in [O2Hb], [HHb], [tHb], or StO2 and the participants’ age, the baseline PETCO2, or certain speech tasks (see Table 2).

Table 2

Linear regression analyses to test for significant relations between changes in cerebral hemodynamics and oxygenation and the different speech tasks.a

Model no.12345678
Dependent variableΔStO2ΔStO2Δ[O2Hb]Δ[O2Hb]Δ[HHb]Δ[HHb]Δ[tHb]Δ[tHb]
Time intervalb47474747
Independent variablesc
Body mass index−0.052*
Baseline PETCO20.033**0.038*−0.014**
Inner speech of hexameter0.224*
Listening to person reciting hexameter0.905*0.578**0.627**
Listening to record of hexameter0.364*0.627**
Mental arithmetic1.189*0.888***−0.297**
Inner speech of alliteration0.897*
Listening to record of alliteration1.624**
Adjusted R20.0240.0090.0990.0450.0480.0370.0740.037


Nonstandardized B coefficients are shown. Significant coefficients are marked as follows: * p<0.05, ** p<0.01, *** p<0.001. Recitation of prose was considered the basic task.


Interval 1 represents the baseline value. During interval 4, the task is performed for the second time, and intervals 5 to 7 (or 8, depending from which of the three studies the data are from) represent recovery time.


Only those variables are listed that were included in at least one of the models.


Male=0, female=1.


Right PFC=0, left PFC=1.

The participants’ age influenced changes in cerebral hemodynamics and oxygenation. With increasing age, the decreases in StO2, [O2Hb], and [tHb] during the tasks (interval 4) were less pronounced. This significant relation largely vanished when only participants below the age of 50 were included in the analyses (not shown). We also found that compared to younger subjects, older subjects had a lower [O2Hb] and [HHb] value in the baseline period (interval 1).

The recitations of hexameter or alliteration verses aloud had no significant relations with any of the dependent variables, in contrast to mental arithmetic, inner speech, or listening to hexameter or alliteration verses. Hexameter verses affected changes during the tasks (interval 4), while alliteration verses only affected changes during the recovery phase (interval 7).

Figure 4, illustrated by the inner recitation tasks, shows that the changes in PETCO2 appear quite early after the start of the tasks.

Fig. 4

Block average (median of all subjects and two tasks) of the PETCO2 changes for inner recitation of hexameter and alliteration. Shown are the median values (thick blue line), standard error of the median (light blue error bars), and a trend (smoothing of the median values, span: 30 s, thick red line).





Measured Changes in End-Tidal CO2, Cerebral Hemodynamics, and Oxygenation

In order to explain the observed cerebral hemodynamic and oxygenation changes obtained from the studies involving speech tasks, two major physiological processes should be considered:5 neurovascular coupling (NC) and CO2 reactivity (CO2R).

NC occurs due to increased neuronal activity leading to an increase in the cerebral metabolic rate of O2, resulting in an increase in cerebral blood flow (CBF) and thus volume (CBV).29,30 This effect causes the following characteristic changes of the fNIRS signals: increase in [O2Hb], decrease in [HHb], and increase in [tHb] and StO2.

CO2R describes the effect of changes in CBF and CBV in response to changes in PaCO2, mediated by a cerebral vasoconstriction or vasodilation. Changes in PaCO2 have a strong and robust effect on cerebral hemodynamics, i.e., an increase in the frequency and/or breathing volume (hyperventilation) causes a decrease in PaCO2 (hypocapnia), which leads to a reduction in CBF by cerebral vasoconstriction.31,32 This effect results in the following characteristic changes of the fNIRS signals: decrease in [O2Hb], increase in [HHb], and decrease in [tHb] and StO2.

Regarding the results obtained from the present study, the questions arises whether one of the two effects mentioned was prevailing or whether they were caused by a combination of both. The decrease generally found in StO2, [O2Hb], and [tHb] as well as the increase in [HHb] during all six interventions may at first glance be interpreted as if the NC was overpowered by the CO2R, i.e., a hyperventilation induced hypocapnia, which causes a cerebral vasoconstriction as the main effect. However, our results show that the relationships between changes in PETCO2 and hemodynamics and oxygenation are not linear [see Fig. 3(b)]. If the hypocapnia was the sole relevant effect, we would expect linear relationships (at least in the first approximation) as demonstrated by other investigations.33 But in the present study, a task evoking a significantly higher change in PETCO2 compared to another task is not accompanied by a larger change in tHb [see Fig. 3(b)]. In addition, the response of the cerebral blood vessels to changes in PETCO2 is known to be robust and much stronger than other physiological parameters, such as oxygenation or blood pressure. Thus, even a small change in PETCO2, which does not reach significance in our results, may have a relevant and significant effect on cerebral hemodynamics.34 For both reasons, the observed effects in hemodynamics and oxygenation cannot solely be explained by a hypercapnia. Thus, we consider a combination of NC and CO2R. The NC characteristics are task-dependent, i.e., they counteract the CO2R to different degrees leading to an apparent nonlinearity between PETCO2 and hemodynamics/oxygenation. As already indicated in Ref. 9, it is reasonable that the different speech tasks are associated with different characteristics of brain activity. It is, for example, known that mainly stress3536.37 and specific types of cognitive processes (particularly memory retrieval and multitasking)38 are modulating factors for the activity of the PFC. Thus, we deem it likely that two overlapping and counterbalancing effects (NC and CO2R) are causal in our study, where the strength of each effect appears to be task-dependent. For inner speech and heard speech (person) of a hexameter, the CO2R was quite different, yet the changes in StO2 were the same. This indicates that the NC during inner speech was stronger and counteracted the CO2R stronger, which therefore resulted in similar changes in StO2. We also would expect that inner speech, which includes hearing and reciting the verses, necessitates a more pronounced effort than simply hearing the verses and, consequently, leads to larger brain activation. This is in line with the results of the measurements, and thus, it appears to be reasonable to assume that the effects elicited by speech cannot be explained by the CO2R alone.

For a visualization of the interplay between NC and CO2R during a speech task, please refer to Fig. 4 in Ref. 5. The observation from this figure that PETCO2 changes quite early after the start of the tasks indicates that changes in PETCO2 can also be elicited during relatively short task intervals—a fact that might be relevant for all experimental protocols involving speech task in general.

The increase of StO2 and [O2Hb] in the postbaseline period after the tasks (i.e., intervals 5 to 8) (see Fig. 2) cannot simply be explained by a change in regional CBF (rCBF) due to relaxation and entering a state of consciousness on the transition to sleep since a reduction of rCBF in the PFC was found during light sleep,39 which would lead to a decrease in StO2 and [O2Hb].

The finding that hexameter verses influenced changes during the tasks (interval 4) and alliteration verses only influenced changes during the recovery phase (interval 7) (see Fig. 2) suggests that the different flow of speech of these verses might induce different patterns in brain activity and different dynamics of the interplay between NC and CO2R.

The results obtained by the present study are in general agreement with a previous fNIRS speech study. We demonstrated that inner speech (recitation of hexameter and prose) induces a CO2R associated with characteristic changes in cerebral hemodynamics and oxygenation.9 The nonlinearity between PETCO2 changes and changes in the fNIRS parameters was also observed and attributed to a task-dependent intensity of the brain activity and thus NC. In an fNIRS study involving reading aloud as a task, Fallgatter et al.40 observed a decrease in [O2Hb] and an increase in [HHb], which is in line with our observation. We are not aware of functional magnetic resonance imaging (fMRI) studies with a similar study design as in our study. Studies investigating language processing and speech production, e.g., Refs. 11 and 12, reported changes in the blood-oxygen-level-dependent (BOLD) signal but not if these changes reflect an actual increase or decrease in hemodynamics/oxygenation. In general, the comparison of fMRI and fNIRS results is also not straight forward since the CO2R depends on the specific type of tissue compartment and characteristic (arterial, venous, arterio-venous, small versus large vessel radius),41 and since fMRI and fNIRI are sensitive to different tissue structures: fNIRS is sensitive to the microvasculature comprising arterioles, venules, and capillaries, i.e., small vessels,42 whereas fMRI is more sensitive to larger vessels43 and especially, large draining veins.44 Because the CO2R is especially high in arterioles and venules,41 fNIRS seems generally to be more sensitive to task-evoked CO2 changes compared to fMRI.

A novel finding of the present study is that even simply hearing speech causes a weak and often not significant change in PETCO2, partly interfering with NC and thus leading to changes in fNIRS signals that were not expected when not considering CO2R and only NC as the cause of the observed changes.

The generally stronger decrease in StO2 and increase in [HHb] observed at the right PFC compared to the left PFC during the speech tasks may indicate that the task-related NC in the left PFC is stronger than in the right PFC, counterbalancing the CO2R effect. This conclusion is in agreement with fMRI findings that overt and inner speech cause a left-hemispheric dominance of activity in the PFC.45 A higher activity in the left part of the cortex was also observed by fNIRS.46 A dichotic listening test revealed hemispheric lateralization of speech sound perception, where the largest [O2Hb] increase and [HHb] decrease was found in left superior temporal gyrus. Furthermore, Ref. 47 found in an fMRI study involving a word generation task a maximum brain activity in the left hemisphere, mainly in the frontal lobe (Broca’s area, premotor cortex, and dorsolateral PFC).

The large intersubject variability of the analyzed signals in our study is in agreement with findings in numerous other fNIRS studies (e.g., Refs. 4849.50.51). However, on the group level, fNIRS studies provide reproducible results.52

When interpreting our results it would also be worth considering systemic changes that might have interfered with the two main effects (NC and CO2R): (1) changes in the activity state of the autonomic nervous system (ANS), which have an influence on cerebral hemodynamics and oxygenation,5354.55 and (2) changes in mean blood pressure (MBP).56,57 However, our fNIRS signals are reasonably immune regarding superficial MBP changes (due to the FDMD method used),15 and the cerebral autoregulation in healthy adult subjects is expected to reduce the effect of systemic MBP changes on cerebral hemodynamics. However, due to the transient changes in PETCO2 during the tasks (see Fig. 4), cerebral autoregulation might not remain unaffected, and thus the fNIRS signals might also contain a component originating from MBP changes. Further studies should investigate this aspect more closely.

In addition to the results of the present study, when analyzing our last three studies (i.e., the present study and the studies reported in Refs. 5 and 9) combined using regression models, we found significant relations between the participants’ age as well as baseline (i.e., interval 1) PETCO2 and the changes in [O2Hb], [HHb], and [tHb] during and after the tasks. It is known that elderly subjects have lower resting-state cerebral [O2Hb], [HHb], and [tHb] (Ref. 58) and show less activation in the motor or PFCs in motor or verbal fluency tasks, respectively.5960.61.62 Age-related diminished changes in cerebral hemodynamics and oxygenation ([O2Hb] and [HHb]) in response to hypoxia (breath holding) found by Ref. 63 were in agreement with our results since the observed hypocapnia in our study is associated with a mild cerebral hypoxia. An age-related decrease in resting-state CBF was also shown in studies using arterial spin labeling,64 positron emission tomography,6566.67.68 and single photon emission computed tomography.69

Regarding the observed significant relation between the baseline PETCO2 values and the changes in cerebral hemodynamics and oxygenation during and after the tasks, similar effects were found in other studies. For example, Blockley et al.70 showed that the amplitude of the fMRI BOLD response depends on the baseline physiological state (hematocrit, oxygen extraction fraction, and CBV) of the subject. Other studies demonstrated that the magnitude of the BOLD response71,72 and the strength of neural activity73,74 depend on the PaCO2 level.

Regarding possible confounding factors in our study design, two factors should be discussed: (1) possible different characteristics and (2) different personal perception of the texts. We controlled for the first factor by choosing texts with similar substance and emotional content. Concerning the second factor, we reduced its influence by measuring a large number of subjects and thus compensating for personal differences.


Implications for Further Research on the Topic

The results obtained in the present study and the combined analysis of our last three studies with regression models allow four implications to be drawn for further research on the topic: (1) The impact of changes in PaCO2 should be considered in the interpretation of fNIRS studies involving speech tasks, including audible, inner, and even heard speech. (2) The use of capnography in combination with fNIRS is recommended. (3) Signal processing techniques should be developed and applied to distinguish between CO2R-related and NC-related changes in fNIRS signals. (4) In order to investigate the influence of the ANS state on the fNIRS signals, the measurement and analysis of skin conductance and heart rate variability changes during speech tasks might also be important for a proper interpretation of fNIRS signals. In addition, measurements of MBP changes in future studies would contribute to interpreting the fNIRS results.

In conclusion, we found that changes in brain activation and breathing during different speech tasks affected cerebral hemodynamics and oxygenation. We showed that inner speech causes changes in PaCO2, which have an impact on cerebral hemodynamics and oxygenation. A new finding is that even during heard speech, a CO2R takes place, leading to characteristic changes in cerebral hemodynamics and oxygenation. In the left PFC, we found a significant difference between the alliteration and hexameter verses. Our analysis also showed that hexameter verses influenced changes during the tasks, while alliteration verses influenced changes only during the recovery phase, indicating that the two different types of verses seem to evoke different physiological reactions. Furthermore, we found significant relations between changes in [O2Hb], [HHb], [tHb], or StO2 and the participants’ age, the baseline PETCO2, or certain speech tasks.

To the best of our knowledge, the present study is the first to investigate the impact of PaCO2 changes on cerebral hemodynamics and oxygenation during speech listening tasks measured with fNIRS and capnography simultaneously. We highlight that the measurement of PETCO2 during functional speech tasks appears to be an important parameter for reliable and correct interpretation when using fNIRS. Thus, we recommend that PETCO2 changes be measured in future fNIRS and possibly also fMRI neuroscientific studies involving speech tasks.


The authors like to express their gratitude to all subjects who participated in this study and the arts speech therapist Andrea Klapproth for valuable advice regarding the speech tasks. For proofreading of the manuscript we thank Rachel Scholkmann.


1. H. Bettermannet al., “Effects of speech therapy with poetry on heart rate rhythmicity and cardiorespiratory coordination,” Int. J. Cardiol. 84(1), 77–88 (2002).IJCDD50167-5273 http://dx.doi.org/10.1016/S0167-5273(02)00137-7 Google Scholar

2. D. Cysarzet al., “Oscillations of heart rate and respiration synchronize during poetry recitation,” Am. J. Physiol. Heart Circ. Physiol. 287(2), H579–H587 (2004).0363-6135 http://dx.doi.org/10.1152/ajpheart.01131.2003 Google Scholar

3. U. Wolfet al., “Changes in hemodynamics and tissue oxygenation saturation in the brain and skeletal muscle induced by speech therapy: a near-infrared spectroscopy study,” ScientificWorldJournal 11, 1206–1215 (2011). http://dx.doi.org/10.1100/tsw.2011.116 Google Scholar

4. M. WolfD. von BoninU. Wolf, “Speech therapy changes blood circulation and oxygenation in the brain and muscle: a near-infrared spectrophotometry study,” Adv. Exp. Med. Biol. 701, 21–25 (2011).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-1-4419-7756-4 Google Scholar

5. F. Scholkmannet al., “End-tidal CO2: an important parameter for a correct interpretation in functional brain studies using speech tasks,” Neuroimage 66, 71–79 (2013).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.10.025 Google Scholar

6. P. A. Robbinset al., “A comparison of indirect methods for continuous estimation of arterial PCO2 in men,” J. Appl. Physiol. 68(4), 1727–1731 (1990).JAPYAA0021-8987 Google Scholar

7. S. R. Dageret al., “Proton magnetic resonance spectroscopy investigation of hyperventilation in subjects with panic disorder and comparison subjects,” Am. J. Psychiatry 152(5), 666–672 (1995).AJPSAO0002-953X Google Scholar

8. W. L. Younget al., “Cerebral blood flow reactivity to changes in carbon dioxide calculated using end-tidal versus arterial tensions,” J. Cereb. Blood Flow Metab. 11(6), 1031–1035 (1991).JCBMDN0271-678X http://dx.doi.org/10.1038/jcbfm.1991.171 Google Scholar

9. F. ScholkmannM. WolfU. Wolf, “The effect of inner speech on arterial CO2, cerebral hemodynamics and oxygenation: a functional NIRS study,” Adv. Exp. Med. Biol. 789, 81–87 (2013).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-1-4614-7411-1_12 Google Scholar

10. H. Jaspers, “The ten-twenty electrode system of the International Federation,” Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1958).ECNEAZ0013-4694 Google Scholar

11. N. L. Voetset al., “Distinct right frontal lobe activation in language processing following left hemisphere injury,” Brain 129(3), 754–766 (2006).BRAIAK0006-8950 http://dx.doi.org/10.1093/brain/awh679 Google Scholar

12. R. L. BucknerM. E. RaichleS. E. Petersen, “Dissociation of human prefrontal cortical areas across different speech production tasks and gender groups,” J. Neurophysiol. 74(5), 2163–2173 (1995).JONEA40022-3077 Google Scholar

13. R. A. Poldracket al., “Functional specialization for semantic and phonological processing in the left inferior prefrontal cortex,” Neuroimage 10(1), 15–35 (1999).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.1999.0441 Google Scholar

14. J. T. DevlinP. M. MatthewsM. F. Rushworth, “Semantic processing in the left inferior prefrontal cortex: a combined functional magnetic resonance imaging and transcranial magnetic stimulation study,” J. Cogn. Neurosci. 15(1), 71–84 (2003).JCONEO Google Scholar

15. M. A. Franceschiniet al., “Influence of a superficial layer in the quantitative spectroscopic study of strongly scattering media,” Appl. Opt. 37(31), 7447–7458 (1998).APOPAI0003-6935 http://dx.doi.org/10.1364/AO.37.007447 Google Scholar

16. J. Choiet al., “Noninvasive determination of the optical properties of adult brain: near-infrared spectroscopy approach,” J. Biomed. Opt. 9(1), 221–229 (2004).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.1628242 Google Scholar

17. D. Canovaet al., “Inconsistent detection of changes in cerebral blood volume by near infrared spectroscopy in standard clinical tests,” J. Appl. Physiol. 110(6), 1646–1655 (2011).JAPYAA0021-8987 http://dx.doi.org/10.1152/japplphysiol.00003.2011 Google Scholar

18. F. Scholkmannet al., “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).PMEAE30967-3334 http://dx.doi.org/10.1088/0967-3334/31/5/004 Google Scholar

19. L. Dommeret al., “Between-brain coherence during joint n-back task performance: a two-person functional near-infrared spectroscopy study,” Behav. Brain Res. 234(2), 212–222 (2012).BBREDI0166-4328 http://dx.doi.org/10.1016/j.bbr.2012.06.024 Google Scholar

20. L. Holperet al., “Trial-to-trial variability differentiates motor imagery during observation between low versus high responders: a functional near-infrared spectroscopy study,” Behav. Brain Res. 229(1), 29–40 (2012).BBREDI0166-4328 http://dx.doi.org/10.1016/j.bbr.2011.12.038 Google Scholar

21. L. Holperet al., “Extension of mental preparation positively affects motor imagery: a functional near-infrared spectroscopy study,” Cortex 48(5), 593–603 (2012).0010-9452 http://dx.doi.org/10.1016/j.cortex.2011.02.001 Google Scholar

22. L. HolperF. ScholkmannM. Wolf, “Between-brain connectivity during imitation measured by fNIRS,” NeuroImage 63(1), 212–222 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.06.028 Google Scholar

23. N. Kobashiet al., “Enhancement of motor imagery-related cortical activation during first-person observation measured by functional near-infrared spectroscopy,” Eur. J. Neurosci. 35(9), 1513–1521 (2012).EJONEI0953-816X http://dx.doi.org/10.1111/ejn.2012.35.issue-9 Google Scholar

24. S. Spichtiget al., “Assessment of potential shortterm effects of intermittent UMTS electromagnetic fields on blood circulation in an exploratory study, using near-infrared imaging,” Adv. Exp. Biol. Med. 737, 83–88 (2012).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-1-4614-1566-4 Google Scholar

25. S. Spichtiget al., “Assessment of intermittent UMTS electromagnetic field effects on blood circulation in the human auditory region using a near-infrared system,” Bioelectromagnetics 33(1), 40–45 (2012).BLCTDO0197-8462 http://dx.doi.org/10.1002/bem.v33.1 Google Scholar

26. R. J. Cooperet al., “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).FCNEEG0887-3658 http://dx.doi.org/10.3389/fnins.2012.00147 Google Scholar

27. F. ScholkmannJ. BossM. Wolf, “An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals,” Algorithms 5(4), 588–603 (2012).ALGOCH1999-4893 http://dx.doi.org/10.3390/a5040588 Google Scholar

28. Y. BenjaminiY. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” J. R. Stat. Soc. Series B Methodol. 57(1), 289–300 (1995).JSTBAJ0035-9246 http://dx.doi.org/10.2307/2346101 Google Scholar

29. R. B. Buxton, “Dynamic models of BOLD contrast,” Neuroimage 62(2), 953–961 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.01.012 Google Scholar

30. M. Wolfet al., “Different time evolution of oxyhemoglobin and deoxyhemoglobin concentration changes in the visual and motor cortices during functional stimulation: a near-infrared spectroscopy study,” Neuroimage 16(3), 704–712 (2002).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2002.1128 Google Scholar

31. T. D. Poeppelet al., “Cerebral haemodynamics during hypo- and hypercapnia: determination with simultaneous 15O-butanol-PET and transcranial Doppler sonography,” Nuklearmedizin 46(3), 93–100 (2007).NMIMAX0029-5566 http://dx.doi.org/10.1160/nukmed-0046 Google Scholar

32. K. Szaboet al., “Hypocapnia induced vasoconstriction significantly inhibits the neurovascular coupling in humans,” J. Neurol. Sci. 309(1–2), 58–62 (2011).JNSCAG0022-510X http://dx.doi.org/10.1016/j.jns.2011.07.026 Google Scholar

33. R. L. Grubbet al., “The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time,” Stroke 5(5), 630–639 (1974).SJCCA70039-2499 http://dx.doi.org/10.1161/01.STR.5.5.630 Google Scholar

34. R. F. SchmidtG. ThewsF. Lang, Physiologie des Menschen, Springer, Heidelberg (2000). Google Scholar

35. R. M. SullivanA. Gratton, “Prefrontal cortical regulation of hypothalamic-pituitary-adrenal function in the rat and implications for psychopathology: side matters,” Psychoneuroendocrinology 27(1–2), 99–114 (2002).PSYCDE0306-4530 http://dx.doi.org/10.1016/S0306-4530(01)00038-5 Google Scholar

36. D. DiorioV. ViauM. J. Meaney, “The role of the medial prefrontal cortex (cingulate gyrus) in the regulation of hypothalamic-pituitary-adrenal responses to stress,” J. Neurosci. 13(9), 3839–3847 (1993).JNRSDS0270-6474 Google Scholar

37. R. M. BuijsC. G. Van Eden, “The integration of stress by the hypothalamus, amygdala and prefrontal cortex: balance between the autonomic nervous system and the neuroendocrine system,” Prog. Brain Res. 126, 117–132 (2000).PBRRA40079-6123 http://dx.doi.org/10.1016/S0079-6123(00)26011-1 Google Scholar

38. E. KoechlinA. Hyafil, “Anterior prefrontal function and the limits of human decision-making,” Science 318(5850), 594–598 (2007).SCIEAS0036-8075 http://dx.doi.org/10.1126/science.1142995 Google Scholar

39. A. R. Braunet al., “Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study,” Brain 120(7), 1173–1197 (1997).BRAIAK0006-8950 http://dx.doi.org/10.1093/brain/120.7.1173 Google Scholar

40. A. J. FallgatterT. J. MüllerW. K. Strik, “Prefrontal hypooxygenation during language processing assessed with near-infrared spectroscopy,” Neuropsychobiology 37(4), 215–218 (1998).NPBYAL0302-282X http://dx.doi.org/10.1159/000026506 Google Scholar

41. S. K. PiechnikP. A. ChiarelliP. Jezzard, “Modelling vascular reactivity to investigate the basis of the relationship between cerebral blood volume and flow under CO2 manipulation,” Neuroimage 39(1), 107–118 (2008).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2007.08.022 Google Scholar

42. H. Liuet al., “Influence of blood vessels on the measurement of hemoglobin oxygenation as determined by time-resolved reflectance spectroscopy,” Med. Phys. 22(8), 1209–1217 (1995).MPHYA60094-2405 http://dx.doi.org/10.1118/1.597520 Google Scholar

43. J. L. Boxermanet al., “The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo,” Magn. Reson. Med. 34(1), 4–10 (1995).MRMEEN0740-3194 http://dx.doi.org/10.1002/(ISSN)1522-2594 Google Scholar

44. S.-G. KimT. JinM. Fukuda, “Spatial resolution of fMRI techniques,” in fMRI, S. UlmerO. Jansen, Eds., pp. 15–21, Springer, Berlin, Heidelberg (2010). Google Scholar

45. M. V. Baciuet al., “fMRI assessment of hemispheric language dominance using a simple inner speech paradigm,” NMR Biomed. 12(5), 293–298 (1999).NMRBEF0952-3480 http://dx.doi.org/10.1002/(ISSN)1099-1492 Google Scholar

46. H. SatoT. TakeuchiK. L. Sakai, “Temporal cortex activation during speech recognition: an optical topography study,” Cognition 73(3), B55–66 (1999).CGTNAU0010-0277 http://dx.doi.org/10.1016/S0010-0277(99)00060-8 Google Scholar

47. C. A. Cuenodet al., “Functional MRI during word generation, using conventional equipment: a potential tool for language localization in the clinical environment,” Neurology 45(10), 1821–1827 (1995).NEURAI0028-3878 http://dx.doi.org/10.1212/WNL.45.10.1821 Google Scholar

48. G. K. AguirreE. ZarahnM. D’Esposito, “The variability of human BOLD hemodynamic responses,” Neuroimage 8(4), 360–369 (1998).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.1998.0369 Google Scholar

49. F. Miezinet al., “Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing,” Neuroimage 11(6 Pt 1), 735–759 (2000).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2000.0568 Google Scholar

50. M. E. Shawet al., “Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics,” Neuroimage 19(3), 988–1001 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/S1053-8119(03)00116-2 Google Scholar

51. T. Whiteet al., “Anatomic and functional variability: the effects of filter size in group fMRI data analysis,” NeuroImage 13(4), 577–588 (2001).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2000.0716 Google Scholar

52. M. M. Plichtaet al., “Event-related functional near-infrared spectroscopy (fNIRS),” Hum. Brain Mapp. 28(8), 733–741 (2007).HBRME71065-9471 http://dx.doi.org/10.1002/(ISSN)1097-0193 Google Scholar

53. S. Purkayasthaet al., “α1-Adrenergic receptor control of the cerebral vasculature in humans at rest and during exercise,” Exp. Physiol. 98(2), 451–461 (2013).EXPHEZ0958-0670 http://dx.doi.org/10.1113/expphysiol.2012.066118 Google Scholar

54. K. C. Peebleset al., “Sympathetic regulation of the human cerebrovascular response to carbon dioxide,” J. Appl. Physiol. 113(5), 700–706 (2012).JAPYAA0021-8987 http://dx.doi.org/10.1152/japplphysiol.00614.2012 Google Scholar

55. P. Sándor, “Nervous control of the cerebrovascular system: doubts and facts,” Neurochem. Int. 35(3), 237–259 (1999).NEUIDS0197-0186 http://dx.doi.org/10.1016/S0197-0186(99)00067-4 Google Scholar

56. I. Tachtsidiset al., “False positives in functional near-infrared topography,” Adv. Exp. Med. Biol. 645, 307–314 (2009).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-0-387-85998-9_46 Google Scholar

57. I. Tachtsidiset al., “Measurement of frontal lobe functional activation and related systemic effects: a near-infrared spectroscopy investigation,” Adv. Exp. Med. Biol. 614, 397–403 (2008).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-0-387-74911-2_44 Google Scholar

58. B. Hallacogluet al., “Absolute measurement of cerebral optical coefficients, hemoglobin concentration and oxygen saturation in old and young adults with near-infrared spectroscopy,” J. Biomed. Opt. 17(8), 081406 (2012).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.17.8.081406 Google Scholar

59. U. Wolfet al., “Correlation of functional and resting state connectivity of cerebral oxy-, deoxy-, and total hemoglobin concentration changes measured by near-infrared spectrophotometry,” J. Biomed. Opt. 16(8), 087013 (2011).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3615249 Google Scholar

60. M. J. Herrmannet al., “Cerebral oxygenation changes in the prefrontal cortex: effects of age and gender,” Neurobiol. Aging 27(6), 888–894 (2006).NEAGDO0197-4580 http://dx.doi.org/10.1016/j.neurobiolaging.2005.04.013 Google Scholar

61. K. Kahlaouiet al., “Contribution of NIRS to the study of prefrontal cortex for verbal fluency in aging,” Brain Lang. 121(2), 164–173 (2012).0093-934X http://dx.doi.org/10.1016/j.bandl.2011.11.002 Google Scholar

62. D. J. Mehagnoul-Schipperet al., “Simultaneous measurements of cerebral oxygenation changes during brain activation by near-infrared spectroscopy and functional magnetic resonance imaging in healthy young and elderly subjects,” Hum. Brain Mapp. 16(1), 14–23 (2002).HBRME71065-9471 http://dx.doi.org/10.1002/(ISSN)1097-0193 Google Scholar

63. L. P. Safonovaet al., “Age-correlated changes in cerebral hemodynamics assessed by near-infrared spectroscopy,” Arch. Gerontol. Geriatr. 39(3), 207–225 (2004).AGGEDL0167-4943 http://dx.doi.org/10.1016/j.archger.2004.03.007 Google Scholar

64. L. M. Parkeset al., “Normal cerebral perfusion measurements using arterial spin labeling: reproducibility, stability, and age and gender effects,” Magn. Reson. Med. 51(4), 736–743 (2004).MRMEEN0740-3194 http://dx.doi.org/10.1002/(ISSN)1522-2594 Google Scholar

65. A. J. Martinet al., “Decreases in regional cerebral blood flow with normal aging,” J. Cereb. Blood Flow Metab. 11(4), 684–689 (1991).JCBMDN0271-678X http://dx.doi.org/10.1038/jcbfm.1991.121 Google Scholar

66. M. Bentourkiaet al., “Comparison of regional cerebral blood flow and glucose metabolism in the normal brain: effect of aging,” J. Neurol. Sci. 181(1–2), 19–28 (2000).JNSCAG0022-510X http://dx.doi.org/10.1016/S0022-510X(00)00396-8 Google Scholar

67. K. L. Leenderset al., “Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age,” Brain 113(1), 27–47 (1990).BRAIAK0006-8950 http://dx.doi.org/10.1093/brain/113.1.27 Google Scholar

68. G. Marchalet al., “Regional cerebral oxygen consumption, blood flow, and blood volume in healthy human aging,” Arch. Neurol. 49(10), 1013–1020 (1992).ARNEAS0003-9942 http://dx.doi.org/10.1001/archneur.1992.00530340029014 Google Scholar

69. H. Matsudaet al., “Correction for partial-volume effects on brain perfusion SPECT in healthy men,” J. Nucl. Med. 44(8), 1243–1252 (2003).JNMEAQ0161-5505 Google Scholar

70. N. P. Blockleyet al., “A review of calibrated blood oxygenation level-dependent (BOLD) methods for the measurement of task-induced changes in brain oxygen metabolism,” NMR Biomed. 26(8), 987–1003 (2012).NMRBEF0952-3480 http://dx.doi.org/10.1002/nbm.2847 Google Scholar

71. F. Xuet al., “The influence of carbon dioxide on brain activity and metabolism in conscious humans,” J. Cereb. Blood Flow Metab. 31(1), 58–67 (2010).JCBMDN0271-678X http://dx.doi.org/10.1038/jcbfm.2010.153 Google Scholar

72. C. J. Gauthieret al., “Elimination of visually evoked BOLD responses during carbogen inhalation: implications for calibrated MRI,” Neuroimage 54(2), 1001–1011 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2010.09.059 Google Scholar

73. A. C. Zappeet al., “The influence of moderate hypercapnia on neural activity in the anesthetized nonhuman primate,” Cereb. Cortex 18(11), 2666–2673 (2008).53OPAV1047-3211 http://dx.doi.org/10.1093/cercor/bhn023 Google Scholar

74. E. L. Hallet al., “The effect of hypercapnia on resting and stimulus induced MEG signals,” Neuroimage 58(4), 1034–1043 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.06.073 Google Scholar

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© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Felix Scholkmann, Sabine D. Klein, Ursina Gerber, Martin Wolf, Ursula Wolf, "Cerebral hemodynamic and oxygenation changes induced by inner and heard speech: a study combining functional near-infrared spectroscopy and capnography," Journal of Biomedical Optics 19(1), 017002 (13 January 2014). https://doi.org/10.1117/1.JBO.19.1.017002


Functional magnetic resonance imaging

Near infrared spectroscopy


Signal processing

Carbon monoxide

Near infrared

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