Physiological Repo s. 2024;12:e70079.
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h ps://doi.o g/10.14814/phy2.70079
wileyonlinelib a y.com/jou nal/phy2
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INTRODUCTION
Oscilla ions a e undamen al o ganizing phenomena in
biological sys ems, p o iding unc ional ad an ages like
p edic i e capabili ies, ene gy- e iciency, and enhanced
communica ion h ough noise esilience and desensi-
iza ion p e en ion (Glass, 2001; Rapp, 1987; Xiong &
Ga inkel,2023). In he human body, nume ous physiolog-
ical p ocesses, including hea bea , espi a ion, and neu al
ac i i y, exhibi oscilla o y beha io and o en in e ac wi h
Recei ed: 23 July 2024
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Re ised: 9 Sep embe 2024
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Accep ed: 24 Sep embe 2024
DOI: 10.14814/phy2.70079
ORIGINAL ARTICLE
Sys emic neu ophysiological en ainmen o beha io ally
ele an hy hmic s imuli
ManuelMuñoz- Ca acuel1,2
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VanesaMuñoz1
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F ancisco J.Ruiz- Ma ínez1
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An onio J.VázquezMo ejón2,3
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Ca los M.Gómez1
This is an open access a icle unde he e ms o he C ea i e Commons A ibu ion License, which pe mi s use, dis ibu ion and ep oduc ion in any medium, p o ided
he o iginal wo k is p ope ly ci ed.
© 2024 The Au ho (s). Physiological Repo s published by Wiley Pe iodicals LLC on behal o The Physiological Socie y and he Ame ican Physiological Socie y.
1Depa men o Expe imen al
Psychology, Uni e si y o Se ille,
Se illa, Spain
2Men al Heal h Uni , Hospi al
Uni e si a io Vi gen del Rocio, Se ille,
Spain
3Depa men o Pe sonali y, E alua ion
and Psychological T ea men s,
Uni e si y o Se ille, Se illa, Spain
Co espondence
Manuel Muñoz- Ca acuel, Human
Psychobiology Labo a o y,
Expe imen al Psychology Depa men ,
Uni e si y o Se ille, Calle Camilo José
Cela, S/N, Se illa 41018, Spain.
Email: [email p o ec ed]
Funding in o ma ion
MEC | Agencia Es a al de In es igación
(AEI), G an /Awa d Numbe :
PID2022- 139151OB- I00; Conseje ía
de Economía, Inno ación, Ciencia y
Empleo, Jun a de Andalucía (Minis y
o Economy, Inno a ion, Science
and Employmen , Go e nmen o
Andalucia), G an /Awa d Numbe :
P20_00537
Abs ac
Physiological oscilla ions, such as hose in ol ed in b ain ac i i y, hea bea , and
espi a ion, display inhe en hy hmici y ac oss a ious imescales. Howe e ,
adap i e beha io a ises om he in e ac ion be ween hese in insic hy hms
and ex e nal en i onmen al cues. In his s udy, we used mul imodal neu ophysi-
ological eco dings, simul aneously cap u ing signals om he cen al and au-
onomic ne ous sys ems (CNS and ANS), o explo e he dynamics o b ain and
body hy hms in esponse o hy hmic audi o y s imula ion ac oss h ee condi-
ions: baseline (no audi o y s imula ion), passi e audi o y p ocessing, and ac i e
audi o y p ocessing (disc imina ion ask). Ou indings demons a e ha ac i e
engagemen wi h audi o y s imula ion synch onizes bo h CNS and ANS hy hms
wi h he ex e nal hy hm, unlike passi e and baseline condi ions, as e idenced by
powe spec al densi y (PSD) and cohe ence analyses. Impo an ly, phase angle
analysis e ealed a consis en alignmen ac oss pa icipan s be ween hei physi-
ological oscilla o y phases a s imulus o esponse onse s. This alignmen was
associa ed wi h eac ion imes, sugges ing ha ce ain phases o physiological
oscilla ions a e spon aneously p io i ized ac oss indi iduals due o hei adap-
i e ole in senso imo o beha io . These esul s highligh he in ica e in e play
be ween CNS and ANS hy hms in op imizing senso imo o esponses o en i-
onmen al demands, sugges ing a po en ial mechanism o embodied p edic i e
p ocessing.
KEYWORDS
embodimen , en ainmen , oscilla ions, p edic i e p ocessing, hy hm
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MUÑOZ- CARACUEL e al.
ex e nal en i onmen al hy hms, as seen in music, dance,
language, spo s, and o he human ac i i ies ha equi e
adap i e esponses o hy hmic pa e ns (Cha alambous &
Djebba a,2023; G een ield e al.,2021; Ko z e al.,2018).
Physiological oscilla ions a y ac oss imescales, bu
adap i e beha io al con ol may ely on hei empo al
alignmen wi h ex e nal inpu s, acili a ing e icien e-
sponses o en i onmen al demands (Penzel e  al., 2017;
Pezzulo e  al., 2015). In his con ex , when physiological
oscilla ions synch onize in phase and/o equency wi h
an ex e nal oscilla o y sou ce, hey a e said o be en ained
o ha hy hm (Laka os e al.,2019; Piko sky e al.,2001).
En ainmen has been p oposed o unde lie cogni i e p o-
cesses like selec i e a en ion by aligning neu al phases o
high exci abili y wi h pe iodic ea u es o ele an ex e nal
e en s, he eby enhancing neu al gain o a ended senso y
inpu s (Hen y e al.,2014; Laka os e al.,2008, 2013, 2019;
Oblese & Kayse ,2019; Sch oede & Laka os,2009; Zion
Golumbic e al.,2013). Neu al en ainmen has also been
shown o imp o e beha io al pe o mance, educing eac-
ion imes, and inc easing accu acy in audi o y senso imo-
o asks (Noza adan e al.,2016; S e anics e al.,2010).
Howe e , less in known abou au onomic en ain-
men . While hea a e has shown inconsis en e idence
o en ainmen wi h hy hmic ex e nal s imuli (Mü ze
e al.,2020), espi a ion has been shown o en ain o audi-
o y ex e nal s imuli ha in ol e mo o esponses, hough
no du ing passi e lis ening o wi h complex hy hms
(Haas e al.,1986; Wilke e al.,1975). Respi a ion is ec-
ognized as an ac i e senso y selec ion p ocess modula ing
neu al synch oniza ion ac oss widesp ead co ical egions
o enhance in o ma ion p ocessing and cogni i e pe o -
mance (B ændhol e al.,2023; He e o e al.,2018; Kluge
e al., 2021; Ma ic e  al.,2020; Pe l e  al., 2019; Zelano
e  al., 2016). Mo eo e , he b ea hing phase has been
shown o sys ema ically in luence cogni i e pe o mance
(Pe l e al.,2019; Zelano e al.,2016), highligh ing he bi-
di ec ional communica ion be ween body–b ain signals
aimed a adap ing o ask- speci ic demands (C iscuolo
e al.,2022).
Addi ionally, om he p edic i e p ocessing pe spec i e
(Cla k,2013; F is on,2010; Hohwy,2013), he in e play be-
ween bodily and neu al hy hms in esponse o ex e nal
senso y inpu s se es a p edic i e iming unc ion. I aligns
in o ma ion sampling wi h bodily hy hms o minimize
disc epancies be ween expec ed (p e e ed) and sensed
body s a es h ough ecu en message passing among di -
e en hie a chical le els. This p ese es o ganism in eg-
i y and suppo s adap i e goal- di ec ed beha io (Allen &
F is on,2018; Engel e al.,2001; Owens e al.,2018; Pa
e al.,2022; Pezzulo e al.,2015; Se h & F is on,2016).
Despi e e idence o neu al en ainmen wi h pe iodic ex-
e nal e en s and he ole o espi a ion in b ain unc ion,
he in ica e in e play be ween b ain–body hy hms and ex-
e nal inpu s emains poo ly unde s ood. Ac i e engagemen
wi h senso y s imuli is belie ed o op imize bodily s a es o
highe - le el in o ma ion p ocessing (C iscuolo e al.,2022;
Pa iainen e al.,2022), bu in eg a ed expe imen al indings
using mul imodal neu ophysiological measu es a e sca ce.
Mo eo e , mos s udies explo ing bodily en ainmen o ex-
e nal s imuli ha e p edominan ly u ilized simple epe i i e
s imuli o assess empo al p edic i e abili ies. Inco po a ing
expe imen al designs ha accoun o he inhe en complex-
i ies o disc imina o y senso imo o asks could signi ican ly
con ibu e o unde s anding he mul i ace ed dynamics go -
e ning b ain–body in e ac ions wi h hy hmic cues.
This s udy aims o shed new ligh on he complex phys-
iological dynamics o b ain and body hy hms in esponse
o hy hmic audi o y s imula ion, and o elucida e hei
beha io al implica ions by di e en ia ing be ween pas-
si e and ac i e senso y encoding p ocesses. Simul aneous
eco dings o he cen al and au onomic ne ous sys ems
(CNS and ANS) we e used du ing an expe imen al pa a-
digm ea u ing empo ally in a ian hy hmic s imula ion.
Dis inc audi o y sequences we e p esen ed o dynami-
cally modula e a en ional demands on a ial- by- ial basis
du ing a cueing ask, os e ing adap i e speed- accu acy
esponses ha be e e lec dynamical senso imo o con-
ex s. We hypo hesize ha ac i e engagemen wi h hy h-
mic audi o y s imuli will lead o g ea e en ainmen
o CNS and ANS physiological hy hms o he ex e nal
hy hm, compa ed o passi e lis ening and baseline condi-
ions. Fu he mo e, du ing he ac i e condi ion, we expec
highe accu acy and educed eac ion imes o he mo e
p e alen phases o bodily oscilla ions a he a ge onse .
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MATERIALS AND METHODS
We employed a sys emic app oach o examine a ious b ain
and body neu ophysiological measu es in esponse o hy h-
mic audi o y s imula ion unde di e en a en ional condi-
ions. Ou analysis included powe spec al analysis and
phase cohe ence o compa e he alignmen o physiological
oscilla ions wi h he ex e nal hy hm ac oss condi ions. In
addi ion, we used ci cula s a is ics and linea mixed models
o explo e de ailed pa e ns and hei beha io al implica ions.
2.1
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Pa icipan s
A sample o 32 heal hy pa icipan s (12 males and 20 e-
males, 29 igh - handed and 3 le - handed) aged be ween
19 and 36 yea s old (mean = 28.31 ± 3.87 SD) we e e-
co ded. Two pa icipan s we e excluded om he analy-
sis due o echnical issues du ing he EEG eco ding. The
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MUÑOZ- CARACUEL e al.
s udy was app o ed by he Bioe hical Commi ee o he
Jun a de Andalucía. Pa icipan s did no epo any neu o-
logical disease o audi o y impai men . The expe imen s
we e conduc ed wi h he in o med and w i en consen o
each pa icipan , ollowing he Helsinki P o ocol.
2.2
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Signal acquisi ion
A mul imodal signal acquisi ion was pe o med,
co e ing di e en measu es o he cen al and
pe iphe al ne ous sys em a he same ime (Figu e1a).
Elec oencephalog aphy (EEG) and in ace eb al
Func ional Nea In a ed Spec oscopy ( NIRS- i) e-
co ded he b ain elec ical ac i i y and oxygena ion
changes, espec i ely. Elec ode mal ac i i y (EDA),
Elec oca diog am (ECG), Ple hysmog aphy o he
Pe iphe al Pulse (PPG), Respi a o y e o (RSP), and
ex ace eb al Func ional Nea In a ed Spec oscopy
( NIRS- e) we e eco ded o assess physiological pe iphe al
signals con olled by Au onomic Ne ous Sys em (ANS)
ac i i y.
FIGURE 1 (a) Mul imodal signal acquisi ion ep esen a ion (c ea ed in BioRe nde . com). (b) P obe layou o NIRS op odes and EEG
elec odes. Red ci cles indica e NIRS sou ces, blue ci cles NIRS de ec o s, and g een ci cles EEG channels. Yellow linea segmen s be ween
sou ces and de ec o s indica e NIRS s anda d sepa a ion channels. O e lapping small blue ci cles indica e he loca ion o NIRS sho
sepa a ion channels. (c) G aphical ep esen a ion o he expe imen al design. Th ee condi ions: Baseline, Passi e (audi o y s imuli + mu ed
ilm) and Ac i e (audi o y s imuli + p essing ei he he up o down a ow o he keyboa d). (d) Audi o y s imula ion exempli ica ion o
he passi e and ac i e condi ion, espec i ely. S1 and S4 indica e he i s (cue) and ou h s imulus ( a ge ), espec i ely, o each audi o y
sequence. Linea audi o y sequences we e occasionally b oken (20% incong uen ials) in he las s imulus (S4). In he ac i e condi ion, he
expec ed esponses a e he a i al o S4 a e indica ed by black a ows, esul ing om he compa ison o he audi o y equency o S4 wi h
ha o he p eceding s imulus (S3) wi hin each ial.
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2.2.1
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EEG
The EEG was eco ded using ac i e elec odes (Ac iCAP)
om 13 scalp si es mainly dis ibu ed along he midline
(Figu e1b) wi h a B ain Vision V- Amp DC ampli ie
(B ain P oduc s, Munich, Ge many). The elec ode im-
pedance was kep below 20 kΩ. The le mas oid was used
as he e e ence. DC ampli ica ion gain was 20,000, and
he sampling a e was 1000 Hz. Da a acquisi ion was done
using B ainVision Reco de 1.20 (B ain P oduc s).
2.2.2
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NIRS
The NIRS signal was eco ded using a NIRScou XP de ice
(NIRx Medical Technologies, Glen Head, NY, USA) wi h
16 LED sou ces and 30 de ec o s (14 s anda d sepa a ion
de ec o s +16 sho sepa a ion de ec o s) placed along he
on o- empo al a eas o bo h sides o he scalp (Figu e1b),
ob aining a o al o 58 channels (42 s anda d sepa a ion chan-
nels, 16 sho sepa a ion channels). In ace eb al (co ical)
oxygena ion changes ( NIRS- i) we e cap u ed by he s and-
a d sepa a ion channels, whe eas ex ace eb al oxygena ion
changes ( NIRS- e) on he scalp su ace we e cap u ed by
he sho sepa a ion channels. The sou ce- de ec o dis ance
was 3 and 0.8 cm o he s anda d and he sho sepa a ion
channels, espec i ely. The sampling a e was 3.91 Hz. Da a
acquisi ion was accomplished wi h NIRS a .14.2 so wa e.
2.2.3
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Pe iphe al signals
The au onomic pe iphe al signals we e eco ded using
an MP160 (BIOPAC Sys ems, Gole a, CA, USA) wi h ou
ampli ie modules (PPG- 100C, EDA- 100C, ECG- 100C,
and RSP- 100C), o ob ain he PPG, EDA, ECG, and RSP
signals, espec i ely.
The pe iphe al pulse was eco ded h ough a ple hys-
mog aph (TSD200 ansduce wi h a wa eleng h o
860 nm ± 60 nm- in a ed ligh ) placed on he index inge
o he le hand. EDA was ob ained using bipola Ag- AgCl
inge elec odes placed on he ing and middle inge s o
he le hand. ECG was acqui ed using h ee Ag- AgCl lead
elec odes (posi i e, nega i e, and g ound, TSD203 ans-
duce ) placed on he le w is , igh w is , and igh ankle,
espec i ely. RSP was eco ded h ough a ansduce band
placed on he ches (TSD201 ansduce ), which allows
measu emen o changes in ho acic ci cum e ence ha
occu wi h b ea hs. The ampli ica ion gain was se as 100
in PPG, 5 in EDA, 1000 in ECG, and 10 in he RSP ampli-
ie s. The sampling equency was 1000 Hz. Da a acquisi-
ion was pe o med using AcqKnowledge .5.0.1 so wa e
(BIOPAC Sys ems).
2.3
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Expe imen al pa adigm
Pa icipan s we e com o ably sea ed in a quie oom
in on o a able equipped wi h a sc een and a com-
pu e keyboa d. The expe imen was di ided in h ee
consecu i e s ages o condi ions: baseline, passi e and
ac i e condi ion (Figu e1c). Du ing he baseline condi-
ion, pa icipan s we e asked o s ay as quie as possible
o 3 min, looking a a ixed c oss in he middle o he
sc een wi hou any o he equi emen . In he passi e
and he ac i e condi ions, he same audi o y s imula ion
was used (Figu e1d). The s imuli consis ed o ials o
ou consecu i e pu e audi o y ones, p esen ed in an
ascending o descending equency pa e n (50%–50%,
andomly coun e balanced wi hin pa icipan s) ha
was occasionally b oken (incong uen ial) in he las
one o he ial (S4). The audi o y equencies used o
he ones we e 600, 900, 1200, and 1500 Hz. Each one
las ed 200 ms (wi h a 20 ms ise- all ime) and he in e -
s imulus in e al (ISI) was 100 ms, esul ing in 1100 ms
o audi o y s imula ion, which was ollowed by 1900 ms
o silence, comp ising a o al o 3000 ms (0.33 Hz) wi hin
each ial. Each condi ion consis ed o 240 ials, wi h
an 80%–20% a io be ween cong uen and incong uen
ials, espec i ely. The expe imen was p og ammed
using PsychoPy .2021.1.2 ee so wa e package
(Pei ce,2009). The audi o y s imuli we e edi ed using
Audaci y ee so wa e package, .3.0.0.
Bo h he passi e and ac i e condi ions las ed 12 min and
sha ed he same audi o y pa adigm, bu he ask ins uc ions
in each condi ion we e di e en . In he passi e condi ion, he
pa icipan s we e asked o wa ch a mu ed ilm and igno e he
audi o y ones, whe eas in he ac i e condi ion he pa ici-
pan s we e asked o pay a en ion o he ones and o espond
in each o he ials by p essing he up o down a ow bu on
on he keyboa d, depending on whe he he ou h audi o y
one o he sequence (S4, a ge s imulus) had a lowe o
highe audi o y equency han he p e ious one (S3), ying
o combine accu acy and speed in hei esponses (wi hin a
1900 ms ime window be o e he nex ial).
The ac i e ask was di ided in o ou blocks o 3 min
(60 ials), sepa a ed by a sho b eak, o p e en he
e ec s o a igue. The e was a aining pe iod wi h an
explana o y ideo, ollowed by a es ial wi h isual
eedback. The o de o condi ions, i s passi e and hen
ac i e condi ion, was in en ionally kep cons an ac oss
pa icipan s o a oid possible in e e ence o condi ion-
ing ac o s om he ac i e o he passi e condi ion in
he audi o y p ocessing sys em. Audi o y s imuli we e
p esen ed h ough wo speake s (Dell, model A215),
posi ioned on ei he side o he pa icipan 's head,
a a com o able olume o 60 dB (measu ed wi h a
Velleman- DVM1326 le el sound- me e ).
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MUÑOZ- CARACUEL e al.
2.4
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Signal p epa a ion and
p ep ocessing
2.4.1
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EEG
EEG eco dings we e impo ed in o EEGLAB .14.1.1
and MATLAB 2022a so wa e. To co ec he EEG o
eye blinking, ocula mo emen s and muscle a i ac s, an
Independen Componen Analysis (ICA) was compu ed.
These componen s we e emo ed, and he EEG signal was
pos e io ly econs uc ed (mean = 3.5 ± 0.93 SD, ange
2–7). The signal ampli udes in he elec odes Fp2, Fz9,
and Fz10 we e used o de ec eye blink a i ac s and ocula
mo emen s, while he 6 channels in he midline (AFz, Fz,
FCz, Cz, CPz, and Pz) we e he ones accoun ed o subse-
quen analysis.
2.4.2
|
NIRS
NIRS aw da a we e impo ed in o HOMER2 .2.8 ool-
box (Huppe e al.,2009) and MATLAB 2022a so wa e.
Fo he educ ion o he signal mo ion a i ac s, he unc-
ion hm Mo ionCo ec ionWa ele was applied, wi h an
in e qua ile ange o 1.5. This unc ion has p o en o
be qui e obus in dec easing mo ion a i ac s h ough
wa ele decomposi ion (B igadoi e  al., 2014; Coope
e al., 2012; Mola i & Dumon , 2012). Addi ionally, he
enPCAFil e unc ion was applied, wi h one p incipal
componen being emo ed om he da a (mean explained
a iance o he emo ed i s componen : 37% ±14%). The
unc ion hm SSR was also applied, which eg esses he
ac i i y in he sho sepa a ion channels om he ac i i y
o he s anda d channels o elimina e he con ibu ion o
he ex ace eb al signal om he in ace eb al (co ical)
signal. The oxyhemoglobin (HbO) concen a ion changes
we e ob ained employing he modi ied Bee –Lambe
law wi h a di e en ial pa ial pa hleng h ac o o 6.0 o
760 nm and 5.0 o he 850 nm wa eleng h (F. Scholkmann
e al.,2010). 3D b ain images o he NIRS channels lay-
ou we e ex ac ed wi h he A lasViewe oolbox (Huppe
e al.,2009).
2.4.3
|
Pe iphe al signals
Pe iphe al signals we e impo ed in o AcqKnowledge
.5.0.1 (BIOPAC Sys ems), Ledalab (Benedek &
Kae nbach,2010) and MATLAB 2022a so wa e. Signals
we e isually inspec ed o check ha hey we e mos ly
ee o mo emen a i ac s. The skin conduc ance e-
sponse (SCR) om he EDA signal was ex ac ed wi h
he Ledalab so wa e (Benedek & Kae nbach, 2010)
using he Con inuous Decomposi ion Analysis (CDA)
unc ion. HR was ex ac ed wi h he AcqKnowledge
so wa e om he R peaks de ec ion o he ECG sig-
nal. The ECG and PPG signals we e used o calcula e
he Pulse T ansi Time (PTT), a p oxy measu e o he
a e ial p essu e ha can be calcula ed om he ime
in e al be ween he R- wa e and he peak o he pulse
wa e a he inge . RSP and PPG aw signals we e di-
ec ly ex ac ed om AcqKnowledge. Respi a o y a e
(RSP a e) was ex ac ed om he RSP peak de ec ion in
AcqKnowledge.
All da a, excep o NIRS, we e downsampled o
100 Hz and de ended by sub ac ing a bes - i hi d
o de polynomial. The HR and PTT achog ams we e
addi ionally smoo hed using he smoo h. m unc ion o
MATLAB wi h he ‘loess’ me hod, applying a smoo h-
ing pa ame e o 0.01. Da a om he passi e condi ion
we e a i icially segmen ed in o ou blocks, aligning
hem wi h he s uc u e o he ac i e condi ion o com-
pa a i e analysis. Consequen ly, a o al o nine blocks
(comp ising one baseline, ou passi e and ou ac i e
condi ions), each consis ing o 3 min o con inuous
da a, we e ex ac ed o each signal and pa icipan o
subsequen da a analysis.
2.5
|
Powe spec al densi y
Powe spec al analysis o all di e en signals, pa -
icipan s, blocks and channels (in he case o EEG and
NIRS) was pe o med using a cus om MATLAB sc ip .
Fi s , he da a ma ices we e ze o- padded by a ac o o
10 o inc ease he equency esolu ion o he spec al
analysis. Following ze o- padding, he FFT was applied
o he da a. F om he FFT esul , he one- sided absolu e
alues we e ob ained, since he FFT yields a complex e-
sul wi h bo h posi i e and nega i e equencies. These
absolu e alues we e hen squa ed and no malized by
he sampling equency and numbe o da a poin s o
ob ain he powe spec al densi y (PSD). Finally, he
esul ing powe was doubled, excep o he DC and
Nyquis equencies.
To compa e condi ions wi h absolu e powe di e -
ences and ocus on ela i e powe changes, he PSD
esul s we e no malized di iding he alue a each e-
quency poin by he o al powe . The PSD esul s o he
ou da a blocks in bo h passi e and ac i e condi ions
we e a e aged, esul ing in a single PSD signal pe con-
di ion and pa icipan , co e ing equencies om 0 o he
Nyquis equency (50 Hz). Finally, o cap u e he spec al
powe a he equency o in e es (audi o y s imula ion a
0.33 Hz) and compa e i be ween condi ions, he in eg al
o he PSD closely a ound his equency (0.33 ± 0.005 Hz)
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MUÑOZ- CARACUEL e al.
was calcula ed o each condi ion and pa icipan and ex-
po ed o s a is ical analysis.
2.6
|
Phase cohe ence
Phase Cohe ence analysis be ween all eco ded signals and
he audi o y s imula ion o each pa icipan , block, and
channel (in he case o EEG and NIRS) was pe o med using
a cus om MATLAB sc ip , enabling he measu emen o he
consis ency o phase di e ences o e ime. Fi s , all da a ma-
ices we e esampled o a common equency a e o 20 Hz.
Then, a ze o- phase 8 h o de bandpass IIR il e be ween
0.2–0.5 Hz was used. The eason o using his equency
ange was o cap u e he equency o in e es (audi o y
s imula ion a 0.33 Hz) a he same ime ha a po en ial con-
ounde such as he espi a o y equency band (commonly
epo ed a ound 0.15–0.5 Hz) con aining i . The commonly
used lowe limi o 0.15 Hz o he espi a o y equency
band was no used because i exceeded hal he equency
o in e es (0.33/2 = 0.165 Hz) and could in oduce some
ha monics ha could al e he phase analysis. To enable
he compu a ion o phase cohe ence be ween physiological
signals and s imuli, a cosine wa e a 0.33 Hz was gene a ed
om he onse o audi o y s imula ion in each block. This
cosine wa e was hen included as an addi ional signal o
cohe ence analysis, ep esen ing he audi o y s imula ion.
The con inuous analy ic signal o all da a signals was ob-
ained h ough he Hilbe ans o ma ion, and he phase
angle was compu ed om hese signals. Then, he phase
di e ence be ween each pai o da a signals we e ob ained,
con e ed in o complex space h ough he Eule 's o mula
(eⁱx = cos x + i sin x), a e aged, and ans o med o absolu e
alues o inally ob ain he phase cohe ence be ween all pai
o signals, anging i s alue om 0 (no cohe ence) o 1 (max-
imum cohe ence). Finally, he cohe ence esul s om he
ou da a blocks in bo h passi e and ac i e condi ions we e
independen ly a e aged o ob ain a single alue o each
condi ion, acili a ing a di ec compa ison among he base-
line, passi e, and ac i e condi ions. Likewise, cohe ence e-
sul s om all EEG, NIRS- i and NIRS- e channels we e also
a e aged o each da a signal sepa a ely.
2.7
|
Phase angle
As an addi ional analysis o explo e he speci ic con ibu-
ion o dis inc phases o he eco ded signals o he phase
cohe ence esul s in he ac i e condi ion, he phase angles
a he a i al o e e y i s s imulus (S1, cue s imulus),
ou h s imulus (S4, a ge s imulus), and esponse (R),
as he d i e s o in e es , we e ex ac ed om he Hilbe
ans o m o he p ep ocessed signals o each pa icipan ,
con e ed o complex space h ough he Eule 's o mula
and a e aged ac oss ials. The esul an mean alue o he
phase angles o each signal, pa icipan and ype o d i e
(S1, S4, and R) was hen expo ed o subsequen s a is-
ical analysis. In he case o EEG, he FCz elec ode was
he one accoun ed o phase angle analysis as a cen al,
equidis an , and ela i ely a i ac - ee e e ence poin . In
he case o NIRS- i, he channel be ween sou ce numbe
14 and de ec o numbe 13 (Figu e1b), on he igh su-
pe io empo al gy us, was he one conside ed due o he
audi o y na u e o he expe imen , whe eas in he case o
NIRS- e, he sho sepa a ion channel adjacen o sou ce
numbe 14 (Figu e1b), on he igh empo al egion, was
he one expo ed o phase angle s a is ical analysis.
Complemen a ily, o analyze po en ial linea - ci cula e-
la ionships be ween eac ion imes and phase angles, hese
measu es we e ex ac ed o each ial, ype o ial (con-
g uen o incong uen S4), pa icipan and eco ded signal.
Subsequen ly, sine and cosine ans o ma ions we e applied
o he ci cula a iable (phase angles), which allows o cap-
u e he pe iodic na u e o he ci cula a iable o s a is ical
analysis. The ela ionship o hi s and e o s wi h phase an-
gles was no inally analyzed due o he ma ginal exis ence
o e o s in he sample, as desc ibed in he esul s sec ion.
2.8
|
S a is ical analysis
S a is ical analyses we e ca ied ou using SPSS 29.0.1 and
R 4.3.2 (R Co e Team, 2023) so wa e packages. We i s
es ed he no mali y o da a dis ibu ion using a Shapi o–
Wilk es and, when he no mali y assump ion was iola ed,
we applied app op ia e nonpa ame ic es s. To assess s a-
is ical di e ences be ween he p ocessed signals, epea ed-
measu es analyses o a iance (ANOVA) we e pe o med
wi h condi ion as a ac o wi h h ee le els: baseline, passi e
and ac i e. G eenhouse–Geisse co ec ion was used when
sphe ici y was iola ed. Fo EEG, NIRS- i and NIRS- e, he
channels we e included as a ac o , in addi ion o condi ion.
Pos - hoc con as s ( wo- ailed pai ed - es ) we e ca ied
ou when ANOVA di e ences eached signi ican esul s
(p < 0.05). To assess he ci cula uni o mi y be ween phase
angles o pa icipan s o each ype o d i e (S1, S4, and R)
and p ocessed signal, he Rayleigh's es (Jammalamadaka
& SenGup a,2001) was pe o med using he ci cula pack-
age implemen ed in R. A linea mixed- e ec s (LME) model
was u ilized, using he lme unc ion in he lme Tes and
lme 4 packages implemen ed in R, o examine he associa-
ion be ween eac ion imes and sine/cosine- ans o med
phase angles, accoun ing o po en ial pa icipan - speci ic
a iabili y. The model included eac ion ime as he de-
penden a iable, wi h sine and cosine- ans o med phase
angles and ype o ial (cong uen /incong uen ) as ixed
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MUÑOZ- CARACUEL e al.
e ec s. Addi ionally, a andom in e cep was inco po a ed
o each pa icipan o accoun o indi idual di e ences
in eac ion ime. In all cases, he p alues we e adjus ed
o mul iple compa isons when necessa y by applying he
False Disco e y Ra e (FDR) co ec ion using he Benjamini-
Hochbe g p ocedu e.
3
|
RESULTS
3.1
|
Beha io al ask
In he ac i e condi ion ask, he a e age hi a e was 236
ou o 240 (98%) ± 5.54 SD, wi h hal o he sample com-
mi ing ei he jus one e o o none. The e o e, u he
analysis ega ding accu acy pe o mance ela ionship
wi h phase angle could no be conduc ed. The mean eac-
ion ime was 692 ms (667 ms o cong uen ials; 764 ms
o incong uen ials) ± 202 ms SD. Di e ences in eac-
ion ime be ween ype o ials eached signi ican esul s
[ (1, 31) = 7.27; p < 0.001].
3.2
|
Powe spec al densi y
The PSD o he di e en da a signals a ound he equency
o in e es (0.33 Hz) is shown in Figu e2a. The epea ed-
measu es ANOVA ound signi ican s a is ical di e ences
be ween condi ions o all he eco ded signals (Table1).
The addi ional epea ed- measu es ANOVA, inco po-
a ing channels as a ac o , e ealed a signi ican main e -
ec o channels on he PSD a ound 0.33 Hz o NIRS- e [F
(6.029, 186.902) = 5.813; p < 0.001; η2 = 0.158], NIRS- i [F
(6.644, 205.964) = 7.462; p < 0.001; η2 = 0.194], and EEG [F
(2.977, 92.291) = 5.415; p = 0.002; η2 = 0.149]. These esul s
indica e ha PSD a ies ac oss di e en NIRS- i, NIRS- e,
and EEG channels. A signi ican in e ac ion be ween con-
di ion and channels we e also ound in he case o EEG [F
(3.124, 96.843) = 3.614; p = 0.015; η2 = 0.104]. Howe e , his
in e ac ion and he e ec o channels in EEG and NIRS- e
will no be u he examined in his s udy, as ou ocus is
on NIRS- i o localiza ion opog aphy due o i s supe io
spa ial esolu ion. A co ical opog aphical isualiza ion
o he PSD o NIRS- i esul s a ound 0.33 Hz is depic ed in
Figu e3a, which illus a es pai ed - es s (FDR co ec ed
o 126 compa isons: 3 condi ions × 42 channels) be ween
condi ions ac oss all NIRS- i channels.
3.3
|
Phase cohe ence
The cohe ence o he di e en da a signals wi h he au-
di o y s imula ion signal (0.33 Hz) is shown in Figu e2b.
The epea ed- measu es ANOVA showed a main e ec o
condi ion on phase cohe ence wi h he audi o y s imula-
ion signal (0.33 Hz) o all he eco ded signals (Table2).
Addi ional epea ed- measu es ANOVA wi h chan-
nels as an added ac o ound a main e ec o channels
on phase cohe ence wi h he audi o y s imula ion sig-
nal (0.33 Hz) o NIRS- i [F (13.698, 397.236) = 1.895;
p < 0.001; η2 = 0.061] and EEG [F (2.800, 81.214) = 6.923;
p < 0.001; η2 = 0.193]. Signi ican in e ac ions be ween
condi ions and channels we e also ound in NIRS- i [F
(16.654, 482.971) = 2.070; p = 0.008; η2 = 0.067] and EEG
[F (4.125, 120.204) = 4.429; p = 0.002; η2 = 0.132]. These
esul s indica e ha cohe ence wi h he audi o y s im-
ula ion signal (0.33 Hz) a ies ac oss NIRS- i and EEG
channels, bu only unde speci ic condi ions. Figu e3b
displays pai ed - es s (FDR co ec ed o 126 compa i-
sons: 3 condi ions × 42 channels) compa ing condi ions
o he cohe ence be ween he HbO signal o all NIRS- i
channels and he audi o y s imula ion signal a 0.33 Hz,
p o iding a opog aphical isualiza ion o he cohe ence
esul s on he co ical su ace.
3.4
|
Phase angle
The ci cula ep esen a ions o he phase angles in he
ac i e condi ion o each p ocessed signal and ype o
d i e (S1, S4, and R) a e shown in Figu e4. Rayleigh's
es e ealed signi ican unimodal clus e ing among pa -
icipan s (non- uni o m ci cula dis ibu ion) a ound a
speci ic mean angle di ec ion (θ) o he signals speci ied
in Table3 (FDR co ec ion applied o 24 compa isons: 3
ypes o d i e s × 8 signals). These esul s indica e ha
o ce ain signals, speci ic phase angles we e consis en ly
p io i ized ac oss pa icipan s a he onse s o s imuli and
esponses.
A mixed- e ec s model was applied o all eco ded sig-
nals. FDR co ec ion was applied o 48 compa isons, e-
sul ing om 3 ypes o d i e s × 8 signals × 2 ans o med
phase angles (sine and cosine). Type o ial showed a
clea associa ion wi h eac ion ime, wi h longe eac ion
imes o incong uen ials ( = 17.489, p (FDR) < 0.001).
Signi ican associa ions we e also iden i ied be ween
eac ion ime and he sine- ans o med phase angle o
he esponse (R) in RSP ( = 2.864, p (FDR) = 0.038; sho e
RT when close o 270°), he cosine- ans o med phase
angle o S1 in HR ( = −2.654, p (FDR) = 0.048; sho e RT
when close o 0°), he sine- ans o med phase angle o
S4 in HR ( = −3.744, p (FDR) = 0.003; sho e RT when
close o 90°), he cosine- ans o med phase angle o S1
in EEG ( = 3.042, p (FDR) = 0.028; sho e RT when close
o 180°), he sine and cosine ans o med phase angle
o S4 in EEG ( = 2.620, 4.716; p (FDR) = 0.040, <0.001,
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8 o 16
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MUÑOZ- CARACUEL e al.
espec i ely; sho e RT when close o 180° and 270°,
espec i ely), he cosine- ans o med phase angle o
he esponse in EEG ( = 2.643, p (FDR) = 0.040; sho e
RT when close o 180°) and he sine- ans o med
phase angle o S1 in NIRS- i ( = −2.663, p (FDR) = 0.040;
sho e RT when close o 90°). Random e ec s o pa -
icipan indica ed signi ican a iabili y in he in e cep
o eac ion ime (pa icipan : Va = 11,619, SD = 108, in
milliseconds).
O e all, hese esul s indica e ha longe eac ion
imes we e obse ed when he RSP phase angle was nea
90° a esponse onse , app oxima ing he mean phase
angle o 63° ac oss pa icipan s. Simila ly, longe eac ion
imes occu ed when he NIRS- i phase angle was nea
270° a S1 onse , close o he g oup mean o 301°. In con-
as , sho e eac ion imes we e associa ed wi h an EEG
phase angle a ound 180° a S4 onse , which app oxima es
he g oup mean phase angle o 164°. Addi ionally, sho e
eac ion imes we e no ed when he HR phase angle was
nea 90° a S4 onse , al hough his did no align wi h he
mean phase angle o 347° ac oss pa icipan s.
Figu e5 displays he a e age RT compu ed o all ials
and pa icipan s a e e y phase angle (360°). Only signals
ha show signi ican associa ions be ween eac ion ime
and sine/cosine- ans o med phase angles o S1, S4, o R
a e displayed.
4
|
DISCUSSION
This s udy aimed o shed ligh on he complex dynamics
o b ain and body hy hms in esponse o hy hmic au-
di o y s imula ion and hei beha io al implica ions, dis-
inguishing be ween passi e and ac i e senso y encoding
FIGURE 2 (a) PSD o he h ee expe imen al condi ions (baseline, passi e, and ac i e) and he di e en da a signals. The uppe
bounded line ep esen s he accoun ed equency window o s a is ical analysis. (b) Violin plo s o he phase cohe ence be ween he
di e en da a signals and he audi o y s imula ion a 0.33 Hz o he h ee expe imen al condi ions. Fo EEG, NIRS- i, and NIRS- e, he
plo ed PSD and phase cohe ence was he a e age o all channels. Ve ical whi e lines inside he iolins indica e he in e qua ile ange,
whe eas he whi e poin indica es he median alue. Each colo ed poin inside he iolins ep esen s a pa icipan (n = 30). Signi ican
di e ences be ween condi ions a e speci ied wi h an as e isk o p (FDR) < 0.05 and wo as e isks o p (FDR) < 0.01. A, ac i e condi ion; B,
baseline condi ion; EEG, elec oencephalog aphic ac i i y; NIRS- e, ex ace eb al HbO; NIRS- i, in ace eb al/co ical HbO; HR, hea a e;
P, passi e condi ion; PPG, pe iphe al pulse; PTT, pulse ansi ime; RSP, espi a o y e o ; SCL, skin conduc ane le el.
2051817x, 2024, 19, Downloaded om h ps://physoc.onlinelib a y.wiley.com/doi/10.14814/phy2.70079 by Readcube (Lab i a Inc.), Wiley Online Lib a y on [21/10/2024]. See he Te ms and Condi ions (h ps://onlinelib a y.wiley.com/ e ms-and-condi ions) on Wiley Online Lib a y o ules o use; OA a icles a e go e ned by he applicable C ea i e Commons License
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9 o 16
MUÑOZ- CARACUEL e al.
p ocesses. Ou indings indica e ha ac i e engagemen
wi h hy hmic audi o y s imuli holis ically en ains a i-
ous bodily oscilla ions o he ex e nal hy hm, o e ing
insigh s in o he in ica e in e play be ween bodily and
en i onmen al hy hms and hei implica ions o senso-
imo o pe o mance.
4.1
|
Powe spec al densi y
The PSD esul s e ealed a p onounced peak a 0.33 Hz
ac oss all cen al and au onomic ne ous sys em signals
eco ded du ing he ac i e condi ion, co esponding o he
equency o audi o y s imula ion. S a is ical analysis con-
i med signi ican di e ences in PSD be ween he ac i e
condi ion and bo h passi e and baseline condi ions, ex-
cep o PPG, which did no exhibi signi ican di e ences
be ween he ac i e and passi e condi ions. These esul s
align wi h exis ing li e a u e on neu al en ainmen as
a mechanism o selec i e a en ion (Laka os e al.,2008,
2019; Oblese & Kayse , 2019), ex ending his phenom-
enon o co ical oxygena ion le els and au onomic e-
sponses du ing a en ional engagemen in senso imo o
cogni i e asks.
Addi ionally, he PSD o he EEG a 0.33 Hz was
highe in he passi e condi ion compa ed o he base-
line condi ion, indica ing neu al en ainmen o senso y
s imula ion e en in he absence o conscious a en ional
e o , consis en wi h p e ious epo s (C iscuolo,
Schwa ze, Hen y, e  al., 2023; C iscuolo, Schwa ze,
P ado, e al.,2023). Howe e , no en ainmen was ob-
se ed o co ical oxygena ion le els du ing he passi e
condi ion compa ed o he baseline condi ion, sugges -
ing ha passi e en ainmen may equi e lowe me a-
bolic b ain esou ces.
Conce ning he co ical opog aphy o he PSD e-
sul s a he equency o audi o y s imula ion, measu ed
h ough NIRS- i, he indings demons a ed a g ea e am-
pli ude o co ical HbO a 0.33 Hz in he ac i e condi ion
compa ed o bo h passi e and baseline condi ions, pa ic-
ula ly in he in e io on al gy us (IFG), supe io empo-
al gy us (STG), and in e io pa ie al lobule (IPL), which
a e co ical egions associa ed wi h keeping he me e in
musical con ex s (Vuus e al.,2006).
These PSD esul s sugges a gene alized en ainmen
ac oss a ious bodily oscilla ions as a means o ac i ely
engaging wi h hy hmic audi o y s imula ion, while he
en ainmen o co ical oxygena ion le els exhibi s a lo-
calized e ec in b ain egions specialized o audi o y
p ocessing.
4.2
|
Phase cohe ence
Consis en wi h he PSD esul s, he cohe ence analysis
p o ided addi ional e idence o inc eased en ainmen
o audi o y s imula ion o all eco ded signals du ing
he ac i e condi ion, in compa ison o bo h passi e and
baseline condi ions. In his case, alongside EEG, SCR also
demons a ed heigh ened synch oniza ion wi h incoming
audi o y inpu s du ing he passi e condi ion compa ed o
he baseline. In e es ingly, no signi ican di e ences we e
obse ed in SCR cohe ence wi h audi o y inpu s be ween
he ac i e and passi e condi ions. Gi en ha phasic EEG-
EDA esponses ha e been shown o ep esen salience-
dependen eac ions o incoming s imuli as pa o he
o ien ing o de ensi e e lex, i espec i e o a en ional
engagemen (Muñoz- Ca acuel e al.,2021; Sokolo ,1990),
hese indings could be in e p e ed as indica i e o a sub-
le o m o unde lying communica ion be ween he CNS
and ANS occu ing in he con ex o hy hmic s imula ion
wi h a iable sequence p esen a ion.
The co ical opog aphy o cohe ence esul s wi h au-
di o y s imula ion, in con as o PSD co ical opog aphy,
e ealed g ea e cohe ence o HbO le els wi h he audi-
o y s imula ion ac oss he en i e eco ded co ical su ace
du ing he ac i e condi ion, a he han a mo e specialized
TABLE 1 S a is ical esul s om he PSD analysis a ound he
equency o in e es (0.33 Hz).
Signal d F p η2
SCR 1.236, 38.309 7.418 0.006 0.193
HR 1.400, 43.403 10.068 0.001 0.245
PPG 1.403, 43.487 3.586 0.051 0.104
RSP 1.279, 39.639 11.636 < 0.001 0.273
PTT 1.023, 31.710 7.659 0.009 0.198
NIRS- e 1.426, 44.195 7.601 0.004 0.197
NIRS- i 1.362, 42.234 14.172 < 0.001 0.314
EEG 1.088, 33.741 39.592 < 0.001 0.561
No e: Pos hoc compa ison analysis (FDR co ec ed o 24 compa isons: 3
condi ions × 8 signals) e ealed ha he PSD o SCR, HR, RSP, EEG, NIRS- i,
NIRS- e, and PTT a ound 0.33 Hz we e highe o he ac i e as compa ed
wi h he passi e condi ion [ (1, 29) = 2.546, 3.289, 3.149, 6.544, 3.509, 3.469,
and 2.645, espec i ely; p (FDR) = 0.016, 0.003, 0.004, <0.001, 0.001, 0.002, and
0.013, espec i ely], as well as o he ac i e as compa ed wi h he baseline
condi ion [ (1, 29) = 3.093, 3.430, 3.872, 6.759, 4.308, 2.422, and 2.890,
espec i ely; p (FDR) = 0.004, 0.002, <0.001, <0.001, <0.001, 0.021, and 0.007,
espec i ely]. The PSD o PPG was g ea e o he ac i e as compa ed wi h
he baseline condi ion [ (1, 29) = 2.056; p (FDR) = 0.048]. The PSD o EEG and
PTT was also highe o he passi e as compa ed wi h he baseline condi ion
[ (1, 29) = 3.333, 2.218, espec i ely; p (FDR) = 0.002, 0.034, espec i ely].
Al oge he , hese esul s indica e a signi ican ampli ude peak a 0.33 Hz
ac oss all eco ded signals du ing he ac i e condi ion.
Abb e ia ions: d , deg ees o eedom; EEG, elec oencephalog aphic
ac i i y; F, F- s a is ic; NIRS- e, Ex ace eb al HbO; NIRS- i, In ace eb al/
co ical HbO; HR, hea a e; p, p- alue; PPG, pe iphe al pulse; PTT, pulse
ansi ime; RSP, espi a o y e o ; SCR, skin conduc ance esponse; η2, e a
squa ed (e ec size).
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