Supplementary materials

1) Pilot study 1: VO2peak determination

The effects of exercise on cognition vary with the intensity of exercise, which is

also participant to change among participants. To maintain a moderate-intensity for

each participant, moderate-intensity exercise was defined as 50% of a subjects’ peak

oxygen intake (VO2peak) based on classification of physical activity intensity of the

American College of Sports Medicine (ACSM, 2014).

To determine VO2peak, all participants underwent a graded exercise testing on a

recumbent ergometer (Strength-ergo 240, Mitsubishi Electric Corporation, Tokyo,

Japan). After a warm-up exercise of 3 minutes at 30W, the work rate increases by

20W (female: 15W) per minute in a constant and continuous manner to exhaustion.

The pedaling rate was kept at 60 rpm.

We measured heart rate (HR) and the participant’s rating of perceived exertion

(RPE) every minute (Borg, 1982). Ventilation parameters, oxygen intake (VO2) and

carbon dioxide output (VCO2) were measured breath-by-breath by using a gas

analyzer (Aeromonitor AE280S, Minato Medical Science, Osaka, Japan). The

respiratory exchange ratio (R) was calculated as a VO2 / VCO2 ratio. VO2peak was

determined once two of the following criteria were satisfied: R exceeding 1.05,

achievement of age-predicted peak HR (HR peak), and a RPE of 19 or 20. VO2peak and

other respiratory and metabolic parameters at VO2peak are shown in Table 1.

Table 1. Participant characteristics.

R RPE HR (bpm) Workload (W)VO2peak

(ml・kg・min -1)

50% VO2peak 0.9 ± 0.1 12.1 ± 2.8 115.6 ± 5.6 84.3 ± 23.5

VO2peak 1.1 ± 0.1 19.4 ± 1.1 168.2 ± 8.3 185.4 ± 47.1 38.8 ± 6.4

Inter-subject mean values of respiratory exchange ratio (R, VO2 / VCO2), rating of perceived exertion

(RPE), heart rate (HR), workload (W), and peak oxygen intake (VO2peak) recorded at the end of the

exercise at an intensity of 50% VO2peak and an intensity of VO2peak.

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To determine exertion needed achieve 50% of VO2peak, it is plotted VO2 against

the output power of the strength ergometer to VO2peak (Wasserman et al., 1973). It is

linearly regressed the measured points using the least square method, and 50% VO2peak

was estimated from delta VO2peak (VO2peak - VO2 at the resting period) for each subject.

2) Pilot study 2: Assessment of non-cortical physiological changes induced by

exercise.

fNIRS is a powerful equipment for investigating the effects of exercise on

cognitive function. In most studies, cognitive tasks were performed at few minutes

after the end of the exercise. However, simply performing cognitive tasks at any one

time may not be appropriate for fNIRS measurement because its measurements are

susceptible to exercise-induced physiological signals as well as cerebral

hemodynamics (Katura et al., 2006). Especially, increases in skin blood flow by an

acute of exercise significantly effect on fNIRS measurement (Davis, 2006). Moreover,

mild hypoxic condition reduced hemodynamic responses to electrical stimulation in

the forepaw, but EEG responses remained unchanged compared with the normoxic

condition (Sumiyoshi et al., 2012). Thus, decreasing percutaneous arterial oxygen

saturation (SpO2) and regional cerebral tissue oxygenation (Cerebral rSO2) may affect

fNIRS measurement.

Five young adults (mean age 20.2 ± 2.8 years (range 18 to 25 years); 3 female)

participated in this pilot study. Some physiological parameters including the middle

cerebral artery mean blood velocity (MCA Vmean), skin blood flow (SBF), respiration

properties (oxygen intake: VO2; carbon dioxide output: ETCO2), heart rate (HR),

percutaneous arterial oxygen saturation (SpO2) and regional cerebral tissue

oxygenation (cerebral rSO2) were monitored before, during, and after 15 minutes of

moderate intensity exercise under hypoxia (FIO2 = 0.135) to set the appropriate

experimental protocol. At the beginning of the experiment, the participants were

exposed to the hypoxic or normoxic gas for 10 min while sitting on the cycle

ergometer. The timing of the post cognitive task as well as brain monitoring with

fNIRS should be determined when those parameters returned to the basal level and

stabilized to eliminate any contaminations.

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MCA Vmean was sampled every minute by a transcranial Doppler ultrasonography

(WAKI 1-TC, Atys Medical, France). A 2-MHz Doppler probe was placed over the

right temporal window (Zhang et al., 2002). The probe was fixed with an adjustable

headband and adhesive ultrasonic gel. The MCA Vmean at the resting period was set at

100% and the relative percentage change of MCA Vmean was sampled every minute.

SBF was monitored with a laser-Doppler probe (FLO-C1, Omegawave, Japan)

attached at Fpz of the international 10-20 system. The SBF at the resting period was

set at 100% and the relative percentage change of SBF was sampled every minute. HR

and ETCO2 were continuously monitored as aforementioned and the average HR and

VCO2 were calculated every minute. SpO2 was monitored every minute by a pulse

oximeter (OLV-3100, Nihon Kohden, Japan) placed on the left earlobe. The cerebral

rSO2 was monitored every minute by a NIRS system (BOM-L1 TRW, Omegawave,

Japan). A probe holder was attached at the left side of the forehead, as previously

studies (Ando et al., 2013, 2010; Komiyama et al., 2015). The effect of exercise,

compared to the resting period (of the 3 minutes before exercise), was evaluated using

a t-test with Dunnett correction. Statistical significance was set at p < 0.05.

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Fig. 1. Illustrations of the physiological parameters at rest, during, and after an acute exercise

bout. Inter-subject mean of physiological parameters at each time point are plotted. Error bars indicate

with standard deviations. Time points with significant exercise effects compared to the signal intensity

at the onset are indicated with asterisks (p<0.05, Dunnet’s test). (A) MCA Vmean: middle cerebral artery

mean blood velocity; SBF: skin blood flow; HR: Heat rate; ETCO2: end-tidal carbon dioxide (B) SpO2:

percutaneous arterial oxygen saturation; cerebral rSO2: cerebral oxygen saturation output at rest,

during, and after the 10 minutes of exercise at 50% of peak oxygen intake under hypoxia (FIO2 = 0.13).

MCA Vmean and ETCO2 didn’t alter by exercise. Significant increase of SBF was

observed from 9 minutes after the onset of exercise to 2 minutes after the end of

exercise. HR increased rapidly, reaching a significant level at 1 minute after the onset

of exercise, and decreased to an insignificant level 3 minute after the end of the

exercise. Significant decrease of SpO2 was observed from 2 minutes after the onset of

exercise to 2 minutes after the end of exercise. Cerebral rSO2 significantly decreased

for 6 minutes from 2 minutes after the onset of exercise. The exercise-induced

physiological parameters returned to the baseline level and stabilized at an average of

3 minutes after the end of exercise (Fig. 1).

In the present study, 10 minutes of moderate intensity exercise significantly

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impacted on non-cortical physiological parameters including skin blood flow, MCA

Vmean, respiratory profiles, and HR as our previous studies confirmed with different

intensity and mode of exercise (Timinkul et al., 2008; Yanagisawa et al., 2010).

Furthermore, moderate intensity exercise under hypoxia also impacted SpO2 and

cerebral rSO2. However, these effects were diminished within 3 minutes after the

exercise. In addition, it takes more than 3 minutes to instrument the fNIRS probe on

subject’s head after the exercise. Therefore, the cortical responses measured at 15

minutes after the end of exercise under hypoxia in the current study could be free

from contaminated physiological signals, just similar as previously study in normoxia

(Yanagisawa et al., 2010).

2) Pilot study 3: Assessment of cortical activation induced by normobaric

hypoxia.

Unlike other neuroimaging methods, fNIRS is compact, portable, and can be

easily installed in a gym (Timinkul et al., 2008). These features allow strict control of

exercise intensity well as that of hypoxic condition, and subsequent on-site

neuroimaging allows precise control of the interval between exercise and

neuroimaging experiments. In addition, fNIRS allows participants to perform tasks in

a natural and comfortable environment without being confined to a small, restricted

space, keeping possible outside influences on cognitive tasks minimal. On the other

hand, changes in cerebral hypoxia may affect the near-infrared signal independent of

changes in cerebral oxygenation, despite neural activity did not change (Sumiyoshi et

al., 2012). In this pilot study, we examine the hypoxic effect on cognition and cortical

hemodynamic change.

Ten right-handed young participants performed color-word Stroop task (CWST)

under normoxic (NO) or hypoxic conditions (HY) with the order being

counterbalanced across participants. In the HY condition, participants breathed the

hypoxic gas, which was mixture of 13.5% O2 and 0.03% CO2 in nitrogen N2 (FIO2 =

0.135; approximately 3,500m equivalent altitude), through a mask that was connected

to Douglas bags. In the NO condition, participants breathed the ambient air at sea

level (normoxic gas), through a mask that was connected Douglas bags. Expired air

was directly exhausted outside the mask so that the participants did not re-breathe the

expired air. The participants were exposed to the hypoxic or normoxic gas from 10

min before the pre-Stroop session. Cortical hemodynamic changes in regions of

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interest (ROIs) were monitored with an fNIRS while participants performed the

CWST. Reaction time were subjected to repeated measures two-way ANOVA with

trial (incongruent/neutral) and condition (NO/HY) as within-subject factors to

examine whether the general tendencies for the Stroop task could be reproduced in all

conditions. The reaction time of Stroop interference (incongruent – neutral) under HY

condition compared to NO condition, was evaluated using a t-test. The (incongruent –

neutral) contrasts of oxy-Hb signals were analyzed with paired t-test to compare with

NO and HY conditions. Statistical significance was set at p < 0.05.

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Fig. 2. Color-word Stroop task in reaction time (A) and Stroop interference for oxy-Hb signal (B) in

normoxic and hypoxic (FIO2 = 0.135) conditions. The prefrontal activation of the Stroop interference

was smaller at left DLPFC under hypoxic condition compared with normoxic condition, despite the

CWST performance did not change. For box-and-whisker plots, the tops and bottoms of the boxes are

third and first quartiles, respectively. The upper and lower ends of whiskers represent the highest

data points within 1.5 interquartile ranges of the upper quartiles and the lowest data points within

1.5 interquartile ranges of the lower quartiles, respectively. The bands inside the boxes indicate

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medians. The x’s show averages of reaction time and oxy-Hb signals.

Reaction time were subjected to a repeated measure two-way ANOVA with trial

(incongruent/neutral) and condition (NO/HY) being within-subject factors. The

ANOVA exhibited significant main effects of trial on reaction time (F(1,9) = 30.231, p

< 0.001). This analysis was limited to the main effect of the trial because the purpose

of the ANOVA was to examine the occurrence of the Stroop effect. These results

verified that Stroop interference could be generally observed in all the sessions used

in this experiment. Thus, to clarify the effect of hypoxic condition on a specifically

defined cognitive process, we focused on the analyses of Stroop interference

(incongruent - neutral). The reaction time of Stroop interference was no significantly

difference between NO and HY conditions (Fig. 2A).

Next, we assessed the effects of hypoxic condition on the prefrontal activation

focusing on Stroop interference. The difference of (incongruent – neutral) contrasts

between NO and HY conditions for each ROIs was analyzed with a t-test. Although

prefrontal activation of Stroop interference was not significantly different between

NO and HY conditions at all ROIs, the activations of all ROIs tended to decrease

under HY condition compared with under NO condition. This result was similar to

previous animal fMRI study reporting that the mild hypoxic condition reduced

hemodynamic responses to electrical stimulation in the forepaw, but EEG responses

remained unchanged compared with normoxic condition (Sumiyoshi et al., 2012).

Therefore, the Oxy-Hb response with CWST may be small in hypoxia despite the

neural activation of Stroop interference is occurred.

3) fNIRS data of experimental 2

The (incongruent – neutral) contrasts were analyzed with repeated measures of

two-way ANOVA including exercise (EX/CTL) and session (pre/post) as within-

subject factors. The ANOVA performed on each of the six ROIs revealed significant

interaction in the l-DLPFC (F(1, 14) = 10.708, p < 0.05, Bonferroni-corrected)(Fig.

4).

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Fig. 3. Time lines of changes of oxy-Hb and deoxy-Hb during the Stroop task for all ROIs in pre-

session (A) and in the post-session (B, C).

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Fig. 4. Oxy-Hb signal changes for Stroop interference in all 6 ROIs for Ex and Con sessions.

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4) Short Report for McNemar Test (Modified after Siegel, Sidney & Castellan,

1988).

Origin: first introduced by McNemar, Quinn in 1947

General use of this test: to test the difference between two associated*

proportions/frequencies in 2 x 2 dichotomous variables (can be repeated / matched

measure) where an ordinary chi-square test is inappropriate due to the violation of

assumptions of independence.

*This test can be also used for independent observations.

Advantages of using this method in the currently submitted article:

1) Because a large variability within participants and restricted range-like

problem after the treatments due to the nature of the manipulation made the

association ambiguous, it was not appropriate and robust to use parametric

tests such as Pearson r for the data.

2) In addition, observations of not strong interest (ones with no changes between

pre and post treatment) appeared to distort the results for ordinary

nonparametric tests such as Spearman rank-order correlation and Kendall’s

tau.

3) As a result, McNemar test was employed because directionality of the

variability in the data was consistent despite the problems discussed above,

and McNemar test was able to eliminate influences from uninterested

observations (diagonal cells A & D in Table 1 below).

Data format:

Table 1. Data format for McNemar test

Pre

1 0 Total

1 A B A+B

Post 0 C D C+D

total A+C B+D

A+B+C+

D

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Notes: frequency of positive (1) and change (0) infection pre and post the medication

Assumptions for McNemar test:

1) Variables should be dichotomous where diagonal (A & D) represent

unchanged observations.

2) It is commonly used for repeated / matched measures*.

*This test can be also used for independent observations.

3) Sample size: summed frequency at diagonal A + D should be larger than

10.

Hypothesis to be tested:

H0: p (pre = 0, post = 1) = p (pre = 1, post = 0)

H1: p (pre = 0, post = 1) p (pre = 1, post = 0)

Formula for ZMcNemar and 2McNemar:

ZMcNemar = FrecCFreqBFrecCFreqB

1||

, df = (rows - 1) (columns - 1) = 1

2McNemar = FreqCFreqB

FrecCFreqB

2)1|(|

, df = (rows - 1) (columns - 1) = 1

Problem and justification to use this method: Since this test compares p(pre = 1,

post = 0) and p(pre = 0, post = 1), it ignores the effects of two other probabilities p(pre

= 1, post = 1) =and p(pre = 0, post = 0). As shown below, data from Table 5 and Table

6 indicates the same significance level, but interpretation of result would be different.

As opposed to the data from Table 4, the data 2’s effect is actually larger. In other

words, ratio of decreasing positive infection is larger compared with remaining cells.

However, in the current study, our purpose was to find the directionality of change

after the treatment ignoring the observations that did not change between pre and post

treatments, this test fitted our aim.

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