modulation of visual gamma oscillation by excitatory drive ...al., 2017; nelson and valakh, 2015;...

1

Modulation of visual gamma oscillation by excitatory drive and the

excitation/inhibitionbalanceinthevisualcortex.

Orekhova EV1,2, Sysoeva OV2, Schneiderman JF3,4, Lundström S1, Galuta IA5, Goiaeva DE5,

ProkofyevAO2,RiazB3,4,KeelerC3,HadjikhaniN1,6,GillbergC1,StroganovaTA2,5

1.UniversityofGothenburg,GillbergNeuropsychiatryCentre(GNC),Gothenburg,Sweden;

2. Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and

Education;

3.MedTechWest,Gothenburg,Sweden;

4.UniversityofGothenburg,InstNeurosci&Physiol,Gothenburg,Sweden;

5. Autism Research Laboratory, Moscow State University of Psychology and Education, Moscow,

Russia;

6.HarvardMedicalSchool,MGH/MIT/HSTMartinosCenterforBiomedicalImaging,Charlestown,MA

USA

Correspondingauthor:

ElenaVOrekhova,

GillbergNeuropsychiatryCentre

UniversityofGothenburg

Kungsgatan12,

SE41119Göteborg,Sweden

Tel.+46735912392

[email protected],[email protected]

was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (whichthis version posted September 13, 2017. ; https://doi.org/10.1101/188326doi: bioRxiv preprint

https://doi.org/10.1101/188326

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Abstract

Cortical gamma oscillations are generated in circuits that include excitatory (E) and

inhibitory (I) neurons. Prominent MEG/EEG gamma oscillations in visual cortex can be

inducedbystaticormovinghigh-contrastedgesstimuli. Inapreviousstudyinchildren,we

observed that increasing velocity of visual motion substantially accelerated gamma

oscillations, and led to the suppression of gamma response magnitude. These velocity-

related modulations might reflect the balance between neural excitation induced by

increasingexcitatorydrive,andefficacyofinhibition.

Here, we searched for functional correlates of visual gamma modulations and

assessedtheirdevelopmentin75typicallydevelopingindividualsaged7-40years.Gamma

oscillationsweremeasuredwithMEGinresponsetohigh-contrastannulargratingsdrifting

at1.2,3.6,or6.0°/s.Inadults,wealsorecordedpupillaryconstrictionasanindirectmeasure

ofexcitatorydrive.

Pupil constriction increased with increasing velocity, thus suggesting increased

excitatorydrivetothecortex.Despitedrasticdevelopmentalchanges ingammafrequency

andresponsestrength,themagnitudeofthevelocity-relatedgammamodulations–ashift

tohigherfrequencyandamplitudesuppression–remainedremarkablystable.Basedonthe

previous simulation studies, we hypothesized that gamma suppression might result from

excessivelystrongexcitatorydrivecausedbyincreasingmotionvelocityandreflectsatrade-

offbetweenoverexcitedexcitatoryandinhibitorycircuitry.Inchildren,thestrongergamma

suppressioncorrelatedwithhigherIQ,suggesting importanceofanoptimalE/Ibalancefor

cognitivefunctioning.

The velocity-related changes in gamma response may appear useful to assess E/I

balanceinthevisualcortex.

Keywords: MEG, gamma oscillations, excitation/inhibition balance, visual motion,

development,IQ


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Highlights

• Speedingvisualmotionincreasesfrequencyandsuppressespowerofgamma.

• Gammafrequencydecreasesfrom7yearson,fasterinchildrenthaninadults.

• Magnitudeofvelocity-relatedmodulationremainsstableacross7-40years.

• GammasuppressioncorrelateswithgammafrequencyandIQinchildren.

• Gammasuppressionmayreflectefficiencyofexcitation/inhibitionbalance.


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1.Introduction

Animalstudiessuggestthataprecisebalancebetweenexcitationandinhibition(E/Ibalance)

inneural networksorchestratesneural activity in spaceand time, and that thisbalance is

importantforcorticalfunctioning(Dorrn,etal.,2010;IsaacsonandScanziani,2011;Xue,et

al.,2014).Thefine-tuningoftheE/Ibalancetakesplaceduringdevelopment(Dorrn,etal.,

2010; Froemke, 2015) and is mediated by maturational changes in γ-aminobutyric acid

(GABA)- and glutamate-mediated neurotransmission (Carmignoto and Vicini, 1992; Le

Magueresse and Monyer, 2013; Piekarski, et al., 2017). Pathological alternations of E/I

balancecharacterizeepilepsy(MaheshwariandNoebels,2014;MannandMody,2008;Wu,

et al., 2015) and, according to animal models of neuro-developmental disorders, may

represent a key neurophysiological mechanism underlying abnormal perceptual and

cognitive processes in neurodevelopmental disorders (LeBlanc and Fagiolini, 2011; Lee, et

al.,2017;NelsonandValakh,2015;RubensteinandMerzenich,2003).

DespitetheurgentneedforbiomarkersoftheE/Ibalanceinthehumanbrain(Ecker,

et al., 2013; Levin and Nelson, 2015), valid non-invasive measurements are still lacking.

Recently,thestimulus-inducedhigh-frequencygammaoscillationsinbrainactivitymeasured

withEEG/MEGhaveattractedconsiderableattentionastheputativenoninvasiveindicators

ofanalteredE/Ibalance incorticalnetworks (LevinandNelson,2015;NelsonandValakh,

2015). Gamma oscillations (30-100 Hz) are generated by populations of interconnected

excitatoryandinhibitoryneuronsinresponsetosufficientlystrongexcitatorydrive(Buhl,et

al., 1998; Munk, et al., 1996). These oscillations critically depend on fast-spiking (FS)

parvalbumin-sensitive (PV+) interneurons thatarecapableofquickly synchronizingactivity

of the principle cells by inhibiting them through a negative feedback loop (Takada, et al.,

2014). Because gamma synchronization depends on the excitatory state of both E and I

circuitry,thestrengthofthegammaresponsetoexternal input is intimatelyrelatedtothe

E/Ibalance inneuralnetworks. Increasing intensityoftheexcitatory inputusually induces

an increasingly strong gamma response, and the gamma oscillations are particularly

powerfulwhenexcitedEcircuitryisnotproperlybalancedbyinhibition(i.e.incaseofhigh

E/Iratio)(seee.g.(Yizhar,etal.,2011).

Therehasbeenanumberofstudiesattemptingtoassessgammamodulationbythe

intensityof sensory stimulationof differentmodalities (Rossiter, et al., 2013; Schadow, et

al., 2007a; Schadow, et al., 2007b). Visual gamma oscillations recorded by MEG

(Hoogenboom, et al., 2006) have beenmost thoroughly investigated because their strong


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heritability (vanPelt, et al., 2012)andhighly reliability (Muthukumaraswamy,et al., 2010;

Tan,etal.,2016)highlighttheirpotentialvalueasbiomarkers.Thevisualgammaoscillations

are thought tobe generated through the interactionof principle excitatory cells and fast-

spiking inhibitoryneurons (socalledprincipal-inhibitory-neuron-gammaorPING) (Vinck,et

al., 2013). It has been demonstrated that visually inducedMEG gamma activity increases

withincreasingintensityofthestimulationmanipulatedbyluminancecontrast(Hadjipapas,

et al., 2015; Hall, et al., 2005; Perry, 2015; Perry, et al., 2015), coherentmotion strength

(Peiker,etal.,2015;Siegel,etal.,2007),eccentricity(vanPeltandFries,2013),ortransition

fromstationarytomovingstimulus(MuthukumaraswamyandSingh,2013;Swettenham,et

al.,2009).

Incontrasttopreviouslyreportedresults,ourrecentMEGstudyinchildrenrevealed

a strong suppressive effect of increasing stimulus velocity on the gamma response power

thatwasparalleledbyaprominent increased ingammapeak frequency (Orekhova,et al.,

2015). These unexpected findings imply that highly powerful visual input might cause

gammapowersuppressioninsteadofenhancement.

Although unexpected, the velocity-related gamma suppression phenomenon

resembles that which has been observed in some invasive studies in primates that used

visualstimuliofhighluminancecontrast(Hadjipapas,etal.,2015;Jia,etal.,2013;Roberts,

et al., 2013). These studies demonstrate a non-linear bell-shaped dependency of visual

gammapoweronluminancecontrast.

The transition from gamma power enhancement to suppression as a function of

input drive can be explained by a computational model of PING gamma oscillations

suggested by Borgers and Kopell (Borgers and Kopell, 2005). The transition from low to

modest levels of excitatory input to the Inhibitory-cells (I-cells) increases the number of

recruited neurons, thereby facilitating gamma oscillations at the populational level, and

eliciting an increase in gammapower.However, a too strongexcitatorydrivemaydisrupt

synchronization between I cells and/or E-I synchronization and lead to a reduction in the

powerofgammaoscillations.Abell-shapeddependenceofgammapowerasa functionof

the strength of excitatory drive is thus expected: an initial increase up to some critical

intensityisfollowedbysuppressionatyethigherintensities.

InthecontextoftheBorgersandKopell’smodel,ourfindingsofgammasuppression

suggest that stimuli moving with a sufficiently high speed may result in a failure of the

excessivelyactivatedI-cellstosynchronizenetworkactivity,andconsequentlycorrespondto

adescendingbranchofthebell-shapedcurve.Therefore,thegammasuppressiondrivenby


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an increase in stimulus velocity seems to reflect adistinct neural phenomenon, which

highlights thenonlinear relationshipsbetweenexternaldrive,E/I ratio,andgammapower

modulation in the human brain. The model predicts that the stronger and more rapid

suppressionofgammaoscillationpoweratthedescendingbranchofthebell-shapedcurve

occurswhenexcitationof theE-cells iseffectively reducedby inhibitory synaptic currents,

i.e.atarelativelylowerE/Iratio.Ontheotherhand,weakerandslowlydevelopinggamma

suppressionisexpectedincaseofhigherE/Iratio.Thus,modulationofgammaoscillations

byanincreaseinthestimulusvelocitycouldpavethewaytocharacterizationofE/Ibalance

in normal and disturbed visual networks and, if confirmed in systematic studies of typical

individualsandindividualswithneurodevelopmentalconditionsmayhaveimportantclinical

implications.

In this study we aimed to characterize the incidence, properties and individual

variabilityofgammasuppression ina largepopulationofneuro-typicalchildrenandadults

throughabroadagerange(6–40years),therebyclarifyingwhetherthegammasuppression

isage-specificoramoregeneralphenomenon.Inordertomodulatetheexcitatorydriveto

the visual cortex, we used the ‘visual motion’ paradigm that revealed the paradoxical

suppression of gamma power in our previous studies in children (Orekhova, et al., 2015;

Stroganova,etal.,2015).WeexpectedthatmaturationofGABA-,andglutamate-mediated

neurotransmissionwouldchange the frequencyandpowerofgamma responseandaffect

theirmodulationsasafunctionofexcitatorydrivetothecortex.

Toconfirmthatanincreaseinstimulusvelocityelicitshigherexcitatoryinputtothe

visualcortex,wemeasuredpupillaryreactioninasubsampleofourparticipants.Atransient

constrictionof thepupil canbeprovokedbyvisualmotionwithout increment in light flux,

reflectinganincreaseintheneuronalactivitywithinthevisualcorticalareas(Barbur,etal.,

1998;SahraieandBarbur,1997;Wilhelm,etal., 2002). Therefore, if fastermotionof the

annular grating does intensify excitatory drive to the visual cortex, it is likely to be

accompaniedbygreaterconstrictionofthepupil.

An important prediction derived from Borgers and Kopell’s model (Borgers and

Kopell,2005;Cannon,etal.,2014)isalinkbetweenamagnitudeofgammasuppressionand

theefficacyofneural inhibition in thevisual cortex.Here,weaddressed thisprediction in

two ways. First, because relatively high excitability of inhibitory neurons should facilitate

gammasuppressionandaccelerategammaoscillations(FerandoandMody,2015;Mannand

Mody,2010),weexpectedthatthemagnitudeofgammasuppressionwouldcorrelatewith

individualvariations ingamma frequency.Second,becausemoreefficient inhibition in the


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visualcortexwasshowntoberelatedtobettervisualperceptualskillsandhigherIQ(Cook,

et al., 2016;Melnick, et al., 2013),weexpected thatmagnitudeof suppressionof gamma

oscillations inresponseto increasingvelocityofvisualmotionwillcorrelatepositivelywith

IQ.

2.Materialandmethods

2.1.Participants

2.1.1.Children

Fifty typically developing boys between6 to 15 years of agewere recruited for the study

from local schools in Moscow. The inclusion criterion was absence of neurological or

psychiatric diagnoses. In all but one child (his parents refused the additional visit for IQ

assessment)theIQwasassessedwiththeKaufmanAssessmentBatteryforChildrenKABCII

(KaufmanandKaufman,2004).

Two control childrenwere later excluded from theMEGdata analysis.Oneof the

twohadexcessivemyogenicartifactsduringMEGrecordingandanotheronefailedtofollow

theinstructiontorespondwithabuttonpress.Ofthe48boysleft,twowerelaterexcluded

fromthegroupanalysisduetothereasonsdescribedintheResultssection.Thisresultedin

afinalsamplesizeof46children(agerange6.7–15.5years,mean11.0years,SD=2.2).

TheinvestigationwasapprovedbytheEthicalCommitteeoftheMoscowUniversity

ofPsychologyandEducation.Allchildrenprovidedtheirverbalassenttoparticipate inthe

studyandwere informedabout their right towithdrawfromthestudyatany timeduring

the testing.Written informed consentwas also obtained from a parent/guardian of each

child.

2.1.2.Adults

Theinitialadultsampleincludedtwentysevenadultsubjects.Twentytwoadultparticipants

were recruited in Gothenburg. The inclusion criterion was an absence of reported

neurologicalorpsychiatricconditions. Onemalesubject (20years-old)was laterexcluded

fromtheMEGgroupanalysisbecauseofapost-hocdiscoveryofanunreportedepilepsy.His

results are described separately. The age of the other 26 adult participants (3 females)

rangedfrom19.2to40.1years (mean27.9years,SD=6.3). In20of26theparticipants (all

males)theIQhasbeenassessedusingWechslerAdultIntelligenceScale,4thEdition(WAIS-

IV)(Wechsler,2010).


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The study was approved by the Gothenburg Regional Ethical Review Board

(Regionala etikprövningsnämnden i Göteborg). All participants gave written informed

consentaftertheexperimentalprocedureshadbeenfullyexplained.

2.2.Experimentaltask

Weappliedthesameexperimentalparadigmasthatusedinourpreviousstudiesinchildren

(Orekhova, et al., 2015; Stroganova, et al., 2015). The subjects sat in a dimmagnetically

shielded room, with their head resting against the back of and positioned as closely as

possibletothetopof,thehelmet-shapedsurfaceoftheheliumdewar.Forchildren,weused

aPT-D7700E-KDLPprojectorthatpresentedimageswith1280х1024screenresolutionand

60Hzrefreshrate.Foradults,thestimuliwerepresentedusingFL35LEDDPLprojectorwith

1920х1080screenresolutionand120Hzrefreshrate. Thestimuliconsistedofblackand

whiteannulargratingswithaspatialfrequencyof1.66circlesperdegreeofvisualangleand

outer diameter of 18 degrees of visual angle (Fig. 1A, insert). Theywere generated using

Presentation software (Neurobehavioral Systems Inc., USA). The grating appeared in the

centerof the screenoverblackbackgroundanddrifted to the central point atoneof the

threevelocities:1.2°/s;3.6°/s;6.0°/s, referredtobelowas ‘slow’, ‘medium’and ‘fast’.The

frequenciesofblacktowhitetransitionsatagivenposition inthevisual fieldproducedby

thesevelocitieswere4,12,and20Hz,respectively.

Eachtrialbeganwiththepresentationofawhitefixationcrossinthecentrumofthe

displayoverablackbackgroundfor1200ms,thatwasfollowedbyamovinggrating.After

movingforarandomintervalvaryingbetween1200and3000ms,thestimulusstoppedand

remainedon the screen for the lengthof the ‘responseperiod’. The responseperiodwas

individually adjusted in children (see (Orekhova, et al., 2015) for details) andwas fixed at

1000msinadults. Tokeepparticipantsalert,weinstructedthemtodetectterminationof

the motion and to respond by pressing a button as soon as the motion stopped. If no

responseoccurredwithintheresponseperiodthegratingwassubstitutedbyadiscouraging

message “too late!” (in Russian/Swedish for participants in Moscow/Gothenburg) that

remainedonthescreenfor2000ms,afterwhichanewtrialbegun.Theparticipantswere

instructedtoconstantlymaintaintheirgazeatthefixationcrossintheintervalsbetweenthe

stimuli.

Individual omission and commission (responses that occurred during motion or

earlier than 150 ms following termination of motion) errors were measured in all


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participantsexcepttwochildrenforwhomthebehavioralresponseswerenotrecordeddue

toequipmenterror.TheerrortrialswereexcludedfromtheMEGanalysis.

The experiment included three blocks of trials; stimuli of different velocitieswere

presentedinallthreeblocksinarandomorder.Theparticipantsrespondedwitheitherthe

rightorlefthand(50%trialseach)inasequencethatwascounterbalancedbetweenblocks

andparticipants. Each stimulus velocitywaspresented30 timeswithin eachexperimental

block resulting in 90 trials per stimulus velocity. In order to minimize visual and mental

fatigue, short (3-6 s) animated cartoon characters were presented between every 2-5

stimuli.Thesubjects’headpositionwascontinuouslymonitoredandadjustedtoacommon

positionusingMaxFilter™(v2.2).

2.3.Pupillometry

Pupil size was measured in 19 of 27 adult participants using an EyeLink 1000 binocular

tracker.Wecalculated theamplitudeof themaximalpupil constriction (i.e.,minimalpupil

size) during the 0 to 1200ms stimulus interval as a percentage of the average pupil size

duringthe-100to0mspre-stimulusinterval:

%relativeconstriction=(Apre-mean-Apost-min)/Apre-mean*100

These values were averaged separately for slow, medium and fast conditions. Trials

contaminated by blinks in the window of interest (-100 to 1200 ms), those preceded by

cartoonsanderrortrialswereexcludedfromanalysis.

2.4.MEGrecording

In children, neuro-magnetic brain activity was recorded at theMoscowMEG Centre (the

MoscowStateUniversityofPsychologyandEducation)usinga306-channeldetectorarray

(Vectorview;Neuromag,Helsinki,Finland)positionedinamagneticallyshieldedroom(AK3b;

Vacuumschmelze,Hanau,Germany). In adults,MEGwas recorded at theNatMEGCentre

(The Swedish National Facility for Magnetoencephalography, KarolinskaInstitutet,

Stockholm) using a similar 306-channel system (ElektaNeuromag TRIUX) located in 2-layer

magneticallyshieldedroom(VacuumschmelzeGmbH).Bothsystemscomprise102identical

triple sensor elements. Each sensor element consists of three superconducting quantum

interferencedevices, twowithorthogonalplanar gradiometerpickup coils andonewitha

magnetometerpickupcoilconfiguration.Fourelectrooculogram(EOG)electrodeswereused


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torecordhorizontalandverticaleyemovements.EOGelectrodeswereplacedattheouter

cantioftheeyesandaboveandbelowthelefteye. Tomonitortheheartbeats inchildren

oneECGelectrodewasplacedatthemanubriumsterniandtheotheratthemid-axillaryline

(V6ECGlead). InadultstheECGelectrodeswereplacedat leftandrightsidesofthechest

underthecollarbone.MEG,EOG,andECGsignalswererecordedwithaband-passfilterof

0.03–330Hz inMoscowand0.1-330Hz inStockholm,digitizedat1000Hz,andstored for

off-lineanalysis.

2.5.MEGdatapreprocessing

The data was first de-noised using the Temporal Signal-Space Separation (tSSS) method

(Taulu and Hari, 2009) implemented in MaxFilter™ (v2.2) with parameters: ‘-st’=4 and ‘-

corr’=0.90. Forall threeexperimentalblocks, theheadoriginpositionwasadjusted to the

initial headorigin position in the block #2. For further pre-processingweused theMNE-

pythontoolbox(Gramfort,etal.,2013)andcustomPythonandMatlabscripts.

The de-noised data was filtered between 1 and 145 Hz. To remove biological

artifacts (blinks, heart beats, and in some cases myogenic activity), we then applied

independentcomponentanalysis(ICA).TheMEGperiodswithtoohigh(4000e-13fT/cmfor

gradiometersand4e-12fT formagnetometersinadults,4000e-13fT/cmforgradiometers

and8e-12fT formagnetometers inchildren)or tooflat (0.1e-12fT/cmforgradiometers

and1e-13fTformagnetometers)amplitudeswereexcludedfromtheanalysis.Thenumber

of independent components was set at dimensionality of the raw ‘SSS’ed’ data (usually

around70).WefurtherusedanautomatedMNE-pythonproceduretodetectartificialEOG

andECGcomponents,whichwecomplementedwithvisualinspectionoftheICAresults.The

numberofrejectedartifactcomponentswasnormally1forverticaleyemovements,0-3for

cardiacand0-6formyogenicartifacts.

Theartifact-correcteddatawasepochedfrom-1.0to1.2srelativetothestimulus

onset. The epochs containing strongmuscle artifacts were excluded by thresholding the

mean absolute value of the high-frequency (>70 Hz) signal. The threshold was set at 3

standard deviations from the power of the 70-Hz high-passed signal averaged across

channels. The epochs left were visually inspected for the presence of undetected high-

amplitudeburstsofmyogenicactivityandtheepochscontaminatedbysuchartifactswere

manuallymarkedandexcludedfromtheanalysis.Thenumberofartifact-freeepochswith

correctbutton-pressresponsesforthe‘slow’,‘medium’and‘fast’conditionswasonaverage

70.5,70.8and70.5inchildrenand79.7,79.3and78inadults.


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2.6.Time-frequencyanalysisatthesensorlevel

The time-frequencyanalysisat the sensor levelhasbeenperformed inallparticipants. In

this study only planar gradiometers were analyzed. For spectral estimation we used

multitaper analysiswith discrete prolate spheroidal sequences (DPSS). We estimated the

spectrainoverlappingwindowsof400ms(shiftedby50ms)atfrequenciesfrom2.5to120

Hz.Thefrequencystepwas2.5Hz,thenumberofcycleswassetatfrequency/2.5andthe

bandwidth was 4, which resulted in spectral smoothing ±5 Hz around each center

frequency.Todecreasethecontributionofphase-lockedactivityrelatedtotheappearance

of the stimulus on the screen, as well as photic driving that could be induced by the

temporal frequency of the stimulation and screen refresh rate (or their interaction), we

subtractedaveragedevokedresponsesfromeachsingledataepochusing‘subtract_evoked’

MNEfunction.

The relative gamma response (indB)wasestimated in400-1000mspost-stimulus

periodrelativetothe-700to-200msperiodofpre-stimulusbaseline(centralpointsofthe

400ms spectral window) using the ‘tfr_multitaper’MNE-python function. The individual

gammaresponse frequencyandpowerwereassessedat thegradiometersensorpairwith

themaximal average response. To identify the location of the ‘maximal response sensor

pair’ thepowerchangeswereaveragedoverthetwogradiometersofeachsensorstriplet,

overthe400-1000mspost-stimulusperiodandoverfrequenciesofthegammarange(50-

110 Hz in children and 35-100 Hz in adults). Because our previous study showed that

detectablegammaresponseonthegrouplevelisobservedatposteriorgradiometersensors

(withthemaximumatthe 'MEG2112/3'pairofgradiometers), the locationofthemaximal

increase in gamma power was defined at one of the selected posterior locations

('MEG1932/3', 'MEG1922/3', 'MEG2042/3', 'MEG2032/3', 'MEG2112/3', 'MEG2122/3',

'MEG2342/3'and'MEG2332/3'),separatelyforeachcondition.

Toassessindividualgammapeakfrequencywefirstdefinedthefrequencybinwith

themaximalpowerofthegammaresponse.Becausethespectraofthegammaresponses

were not always perfectly Gaussian, we adjusted the peak frequency by calculating a

weighted average over whose frequency bins (subject/condition-specific band) where the

post-stimulus/baselineratioexceeded2/3oftheabsolutemaximum.Theweightedgamma

peak power was calculated in dB, as 10*log10(post-stimulus/baseline), where post-

stimulus/baselineisanaverageratiooverthesefrequencybins.


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Toassessthereliabilityofstimulus-relatedincreaseingammapowerineachsubject

and for each velocity condition we compared the single epochs’ average pre- and post-

stimuluspoweratthefrequencybinofthemaximalgammaresponseusingMann–Whitney

Utest.

2.7.Time-frequencyanalysisatthesourcelevel

ThestructuralMRIs(1mm3T1-weighted)wereavailablefor23of28adultparticipants.For

these participants we performed source analysis using FieldTrip open source software

(http://www.ru.nl/neuroimaging/fieldtrip/).Wesought tocheck for the similaritybetween

gamma response parametersmeasured at the source and the sensor levels, aswell as to

assesspositionofthemostsignificantgammaincrease(‘maximallyinducedvoxel’)ineachof

thevelocityconditions.Priortoanalysiseachsubject’sbrainhasbeenmorphedtotheMNI

templatebrainusing linearnormalizationand0.6mmgrid.Toperformsource localization

we then adapted the two-step approach. As the first step time-frequency analysis was

performedontheartifact-freeepochs(-0.9to-0.1pre-stimulusand0.4to1.2post-stimulus)

using multitaper method. The analysis window was centered at the sensor-defined

subject/conditionspecific frequency±25Hz.TheDICS inverse-solutionalgorithm(Gross,et

al., 2001) was used to derive the common source spatial filters. Subsequently, bootstrap

resampling source statisticswasperformed (with10000MonteCarlo repetitions) toverify

for each participant and condition presence of a significant (p<0.05) brain cluster of

poststimulus increase of gamma power in the visual cortex (L/R cuneus, lingual, occipital

superior, middle occipital, inferior occipital, or calcarine areas according to the AAL atlas

(Tzourio-Mazoyer,etal.,2002)).

At the secondstep the signalwas filtered in30-120Hz range, the ‘virtual sensors'

time serieswere extracted for each participant and velocity condition from each of 6855

brain voxels, and the time-frequency analysis (multitapermethod, ±5Hz smoothing, ~1Hz

frequency resolution) has been performed. The voxel with the most significant post-

stimulus increase of 45-90 Hz power in visual cortex was found. The gamma response

parameterswere defined for the average of this and the 25 closest voxels. To assess the

source-derivedgammaresponsefrequencyandamplitudewecalculatedpost-/pre-stimulus

ratioforeachfrequencyinthebroadgammarange(30-120Hz)andthenassessedweighted

subject/conditionspecificfrequencyandamplitudeusingtheapproachappliedinthesensor

space.


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2.8.Statisticalanalysis

StatisticalanalysiswasperformedusingSTATISTICA-13software(DellInc,2015).Totestfor

the effect of the velocity condition (hereafter Velocity) on MEG parameters and pupil

contractionamplitude,weused the repeatedmeasuresanalysisof variance (ANOVA).The

Greenhouse-Geisser correction for violation of sphericity has been applied when

appropriate.Theoriginaldegreesoffreedomandcorrectedp-valuesarereportedtogether

withepsilon(ε).Partialeta-squared(η2)wascalculatedtoestimateeffectsizes.ANOVAwas

alsousedtoassess theeffectofagegroup. Spearmancoefficientswereusedtocalculate

correlationsbetweenvariables.DetailsaboutthestatisticalanalysisaregivenintheResults

section.

3.Results

3.1.Pupilconstriction

TherepeatedmeasuresANOVArevealedahighlyreliableeffectoftheVelocity(F(2,36)=16.9,

ε =0.92, p<0.0001; η2=0.48). The magnitude of pupil constriction increased with

increasingmotionvelocityoftheannulargrating(Fig.1).

Figure 1. Pupillary constrictionresponse to the annulargratings drifting towards thecenter of the display at threedifferentvelocities (‘Slow’ -1.2°/s, ‘Medium’ - 3.6 °/s; and‘Fast’ - 6.0 °/s). Vertical barsdenote 0.95 confidenceintervals. The insert shows thestimulus display; the arrowsindicatemotiondirection.

3.2. Incidenceof gamma responses to visualmotion in the sensor space in childrenand

adults.

Groupaveragetime-frequencyplotsinchildrenandadultsareshowninfigure2.Theseplots

arebasedon46childrenand26adults, (2 childrenand1adultparticipantwereexcluded

fromthegroupanalysisduetothereasonsdescribedbelow).

Slow Medium Fast Velocity

50

52

54

56

58

60

Pupi

l con

stric

tion

(a.u

.)

p<0.001

p<0.001


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Figure 2. Grand average time-

frequency plots for the three

velocity conditions in children and

adults.

Table 1 shows the number of

subjects who demonstrated

stimulation-related increases in the

gamma power at p<0.05 and

p<0.0001probabilitylevels.

Table1. Incidence,mean frequencyandmeanpowerofmotion-relatedgammaresponsesdetectedatdifferentp-levels(Mann-WhitneyUtest)inchildrenandadults.

P<0.05 P<0.0001

Children,N=46Stimulusvelocity

Frequency(Hz)

Power(dB)

Nand%sample

Frequency(Hz)

Power(dB)

Nand%sample

Slow 66.1(6.1)* 3.1(2.0) 45,98% 65.2(5.3) 3.6(1.8) 37,80%Medium 73.2(8.5) 2.2(1.8) 43,94% 72.3(8.0) 2.8(1.8) 31,67%Fast 82.2(10.8) 1.3(1.0) 37,80% 79.6(7.4) 1.9(1.1) 21,46%

Adults,N=26Stimulusvelocity

Frequency(Hz)

Power(dB)

Nand%sample

Frequency(Hz)

Power(dB)

Nand%sample

Slow 55.7(5.7) 3.8(1.4) 26,100% 55.7(5.7) 3.8(1.4) 26,100%Medium 65.1(6.0) 2.6(1.3) 26,100% 65.1(6.0) 2.6(1.3) 26,100%Fast 70.0(8.5) 1.3(0.7) 26,100% 69.5(5.4) 1.5(0.7) 19,73%*Standarddeviationsaregiveninparentheses.

Undertheslowandmediumvelocityconditions,gammaresponsepeakswerehighly

reliable(responsevs.baseline:p<0.0001)inalltheadultsandinthemajorityofthechildren.

Forthethirdvelocity,thehighlyreliablepeaksweredetected inthemajorityoftheadults

andinabouthalfofthechildren.

3.3.Comparisonofthesource-andsensor-derivedgammapowerandamplitude.


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Thesourceanalysiswasperformedin23of26adultsforwhomthestructuralMRIdatawere

available.TheDICSbeamformeranalysisshowedpresenceofasignificantclusterofgamma

increase in visual cortex in response to the ‘slow’ and ‘medium’ Velocity in all these

participants. Inresponsetothe‘fast’Velocity,asignificantclusterwaspresentin19of23

participants. In all three conditions, themost frequent locationof the ‘maximally induced

voxel’wasinthecalcarinesulcus(in17/23subjectsinthe‘slow’condition,in16/23subjects

in the ‘medium’ condition and in 11/18 subjects in the ‘fast’ condition, see also the

Supplementarytable1fortheMNIcoordinatesofgammaresponseforeachsubject).Figure

3 illustrates sourcedistributionof thegamma responseobtainedusingDICSbeamforming

technique.Nosignificantshiftofthe‘maximallyinducedvoxel’ineitherx,yorzcoordinate

hasbeenobservedbetweenthethreevelocitycondition(rmANOVA:allp’s>0.4).Themean

intra-individual distance between ‘maximally induced voxels’was 5.4mm (sd=6.2) for the

‘slow’vs‘medium’conditions,10mm(sd=7.6)forthe‘medium’vs‘fast’conditions,and9.3

mm (sd=8.5) for the ‘slow’ vs ‘fast’ conditions. The group average [x y z] position of the

‘maximally induced voxel’ in the MNI coordinates was [0.0 -93.7 -0.7] for the ‘slow’

condition,[-0.6-94.0-0.7]forthemediumconditionand[2-94.70.0]forthefastcondition.

Considering6mmspatial resolutionof thegridthemeanpositionofthegammaresponse

maximum corresponded to the sameMNI template voxel in the left calcarine gyrus in all

threeconditions.

Figure 3. Source localization of the gamma response in the three velocity conditions in

adults:thegrandaverage.

Slow Medium Fast

(pos

tstim

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Table 2. Spearman correlations between source- and sensor-derived gamma response

frequency (F) and power (P) parametersmeasured in ‘slow’, ‘medium’ and ‘fast’ velocity

conditions.

SourceFslow SourceFmedium SourceFfast SourcePslow SourcePmedium SourcePfast

SensorFslow 0.96(N=23*)

SensorFmedium 0.93(N=23)

SensorFfast 0.83(N=18)

SensorPslow 0.79(N=23)

SensorPmedium 0.91(N=23)

SensorPfast 0.91(N=23)

*Onlysubjectswithreliablegammaincreaseatbothsensorandsourcelevelswereincluded

intothefrequencycorrelationanalysis.

The high correlations between source- and sensor-derived gamma response

parameters (Tab. 2) suggest close correspondence between sensor- and source-derived

results. Moreover, source-space analysis showed that in all velocity conditions themajor

sourceofgammaoscillationswassituated in theoverlappingregionsof theprimaryvisual

cortex.ConsideringthatthestructuralMRIdatawerenotavailableforalloftheparticipants

wefurtherrestrictedanalysistothesensorspacedata.

3.4.Velocity-relatedmodulationsofgammaoscillations,theirvariabilityandoutliers

3.4.1.Power

Hereandinallfurtherstatisticalanalysesthatexaminedtheeffectofvelocityonthegamma

responsepoweratthegrouplevel,weincludedonlythoseparticipantswhodemonstrated

the reliable (p<0.0001) gamma peaks under the ‘slow’ condition. This strict criterion was

applied because themagnitude of the velocity-related power suppression can be reliably

estimated only when a reliable gamma response under the slow velocity condition is

present.Thiscriteriondidnotaffectthesizeoftheadultsample,butreducedthenumberof

children in the analysis (adults: 26, children: 37). The children excluded from the gamma

power modulation analysis (n=9; mean age=9.4 years; range: 6.7 to 12.6 years) were

significantlyyounger(M-Wtest:z=2.33;p=0.02)thantherestofthechildreninthesample

(n=37;meanage=11.4;range:7.7to15.5years).

Themajorityofparticipants,regardlessofage,demonstratedgradualattenuationof

gamma responsewith increasing stimulus velocity from ‘slow’ to ‘medium’ and further to

‘fast’ (Fig. 4A). Two examples of typical gamma spectra are shown in Figure 5A. In a few

subjects,thetransitionfrom‘slow’to‘medium’conditionelicitedanincrease(ornochange)


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ingammapower,succeededbyitsdecreaseunderthe‘fast’condition(Fig.5B). Oneadult

andtwochildrenshowedvirtuallynochangeingammaresponsepower,evenatthehighest

velocity (Fig.5C,seealsoFig.4A,dashed lines).Visual inspectionof theMEGrecordof the

adultsubject(20yearsold)withtheatypicallackofgammasuppressionhasshownpresence

of the spike-and-wave complexes over the posterior brain regions. Post-hoc examination

revealed that he had an unreported diagnosis of epilepsy. The post-hoc inspection of the

MRIofoneofthetwochildrenlackingthegammasuppression(13years-old;hisspectraare

picturedinFig.5C,upperpanel)byaclinicalneuro-radiologistrevealedpresenceofArnold-

Chiari malformation. The other child who did not suppress the gamma response with

increasingmotionvelocity(7yearsold)hasnothadaformalmedicaldiagnosis;however,he

sufferedfromminormotorticksandfrequentnightmaresaccordingtotheparentalreport.

Thetwochildrenandoneadultwhohadpost-hocrevealedmedicalconditionsand

displayed atypical lack of gamma suppression were excluded from all the further group

analyses.

Figure 4. Velocity-related changes in

gamma response power and frequency:

individual subjects plots. A. Gamma

response power. Only children with

reliable(p<0.0001)gammaresponseunder

the ‘slow’ velocity condition are included.

B.Gamma frequency.Thedataare shown

only for reliable gamma peaks. The thick

dashedlinescorrespondtosubjectslacking

thepowersuppression.

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Figure 5. Individual spectra of visual gamma responses and their modulation by motionvelocityinchildrenandadults:typical(A)andinfrequent/atypical(BandC)gammaresponsemodulations.

InchildrentherepeatedmeasuresANOVArevealedaprofoundreductionofgamma

response powerwith transition from the ‘slow’ to the ‘medium’ and further to the ‘fast’

condition(F(2,72)=93.0,ε=0.8, p<10e-6;η2=0.73;Fig.6A).

40 50 60 70 80 90 100-1

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Figure6.Changesingammaresponsepower(A)andfrequency(B)asafunctionofmotionvelocityinchildrenandadults.Verticalbarsdenote95%confidenceintervals.

In adults the repeatedmeasures ANOVA also showed highly reliable reduction of

gammaresponsepowerwithchangingvelocityfrom‘slow’to‘medium’andfurtherto‘fast’

condition(F(2,50)=83.1,ε=0.83, p<10e-6;η2=0.77;Fig.6A).

3.3.2.Frequency

Aviableestimateof thegammapeakfrequency ispossibleonlywhenthepeak is reliable.

We estimated the frequency of an individual gamma response peak only if the stimulus-

inducedpowerenhancementwassignificantatthep<0.0001 level. Fortheanalysisofthe

velocity effect on gamma frequency (‘frequency ANOVA’), we included those participants

whohadthereliablepeaksunderallthreeexperimentalconditions(21children,19adults).

In children we observed a robust effect of visual motion velocity on gamma

frequency (F(2,40)=173.6, ε=0.8, p<10e-6;η2=0.9). The gamma peak frequency increased

from the ‘slow’ through the ‘medium’ to the ‘fast’ condition (Fig. 6B); the difference

betweenthe‘slow’andthe‘fast’conditionswas15.3Hzonaverage.Therobustincreasein

gammafrequencywasalsoobservedinadults(F(2,36)=145.9,ε=0.8, p<10e-6;η2=0.89)where

itwas14.6Hzonaverage.

0.5

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3.4.Effectofageongammaoscillations.

3.4.1.Power

Themagnitudeofthestimulus-inducedgammaresponsesignificantlyincreasedwithagein

children between 6 and 15 years, but did not change in adults between 19 and 40 years

(Tab.3).Infigure7Awepresenttheage-relatedchangesinpowerofgammaresponsefor

the‘slow’condition.

Table3.Spearmancorrelationsbetweenmagnitudeofthestimulus-inducedgammaresponse(indB)andageinchildrenandadults.Stimulusvelocity Children(N=46) Adults(N=26)Slow 0.43** -0.07Medium 0.43** -0.13Fast 0.37* -0.30*p<0.05,**p<0.01

Figure 7. Developmental changes of gamma response power (A) and frequency (B) inchildrenandadults:slowvelocitycondition.AsterisksdenotethesignificanceofSpearmancorrelations:*p<0.01;**p<0.0001

To analyze possible differences in velocity-related changes of gamma response

magnitudeinchildrenandadults,weperformedanANOVAwiththemainfactorAge-Group

(adults, children) and repeated measures factor Velocity (3 conditions). This revealed a

highlysignificanteffectofVelocity (F(2, 122)=172.8,ε=0.87, p<1e-6; η2=0.74),butnoeffect

fortheAge-GrouportheAge-Group*Velocityinteraction(p’s>0.6).

3.4.2.Frequency

5 10 15 20 25 30 35 40 45Age (years)

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A B

R=0.43* R=-0.63**

R=0.07

R=-0.71**


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Inboththechildandtheadultsamples,thegammapeakfrequencydecreasedwithage(Tab

3). In figure 7Bwepresent the agedependent changes in thepeak frequencyof gamma

response inducedby ‘slowly’ drifting gratings.Notably, thedrop in gamma frequencywas

morerapidduringchildhood(1.712Hzperyear)thanduringadulthood(0.644Hzperyear).

The homogeneity of slopes analysis revealed significant difference between gamma

frequency age-related slopes in children and adults for the ‘slow’ (F(1, 59)=7.8, p<0.005,

η2=0.13) and the ‘fast’ (F(1, 36)=7.5, p<0.01,η2=0.17) velocity conditions, but did not reach

significancelevelforthe‘medium’condition(F(1,53)=1.3,p>0.1,η2=0.03).Inspectionoffigure

7B suggests that the decrease of gamma frequency in adults takes placemainly after 25

yearsofage,andmightreflectaging-relatedprocesses.

Table4.Spearmancorrelationsbetweengammaresponsefrequencyandageinchildrenandadults.Stimulusvelocity Children AdultsSlow -0.63***(N=37) -0.71***(N=26)Medium -0.39*(N=31) -0.74***(N=26)Fast -0.54*(N=21) -0.42(N=19)*p<0.05,***p<0.0001

Toanalyzepossibledifferences in velocity-related changesof gamma frequency in

childrenandadultsweperformedanANOVAwithfactorsAge-Group(adults,children)and

Velocity (3 conditions). The ANOVA revealed highly significant effects of both Age-Group

(F(1,38)=27.1,p<0.0001;η2=0.42)andVelocity(F(2,76)=317.2,ε=0.81,p<1e-6;η2=0.89),butno

Age-Group*Velocity interaction (p=0.4), suggesting developmental stability of velocity-

relatedincreaseingammafrequency.

3.5.Gammapowersuppressionandgammafrequency

Based on the animal data and computational model reviewed in the introduction, we

anticipated that subjectswith relatively higher gamma frequency and/or greater velocity-

related increase of gamma frequency (i.e. those who might have greater excitation of I

neurons or greater velocity-related increase in their excitation) would demonstrate 1)

greateroverallmagnitudeofgammapowersuppressionwithtransitionfrom‘slow’to‘fast’

velocity and/or 2)more ‘rapid’ gamma power suppression that would occur already at a

milderlevelofexcitatorydrive(withtransitionfrom‘slow’to‘medium’velocity).

Toassessgammapowersuppression,weintroducedtwoparameters.Thefirstone-

gamma Suppression Magnitude (gSM) characterizes degree of gamma power response


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suppressionwithtransitionfrom‘slow’to‘fast’condition,normalizedbythe‘slow’gamma

power:

gSM=(POWslow-POWfast)/POWslow.

Thenormalizationeliminatesinter-individualdifferencesinmagnitudeofgammaoscillations

thatmightbeexplainedbyage,anatomicalfactors,etc.Note,thatthepositivegSMvalues

characterizegammasuppression.

Thesecondmeasure-gammaSuppressionSlope(gSS)-wascalculatedas

gSS=(POWslow-POWmedium)/(POWslow-POWfast).

and characterizes how fast with increasing velocity the gamma suppression occurs.

Considering that the denominator was positive in all the participants included in the

analysis,thegSSwaspositiveifthesuppressiontookplacealreadyatthe‘medium’velocity.

Itwasnegativeinthecaseofinitialpowerincreaseinthe‘medium’velocityconditionwith

thefollowingdecreaseinthe‘fast’condition(e.g.Fig.5B).

ThePOWslow,POWmediumandPOWfastwerecalculatesasanabsolutedifference

betweenmean post-stimulus and baseline power in the respective condition and for the

subject/condition-specific band (see Methods). The higher gSM value indicates stronger

gammasuppressionwithincreasingexcitatorydrive.ThehighergSSvaluesignifiesthatthe

relatively greater part of the gamma suppression occurs already with the moderate

excitatorydrive(i.e.atthe‘medium’velocity).

When all subjects with the reliable gamma response in the ‘slow’ condition

(p<0.0001) were included into analysis, in children gSS correlated positively with gamma

frequency in the ‘fast’ (R=0.6, p<0.01) and ‘medium’ (R-0.36, p=0.05) conditions (Tab. 5.)

These correlations could not be accounted for by age, because gSS did not significantly

changewithage(R(29)=-0.3,p>0.1).Thismeansthatchildrenwhohadhigherfrequencyof

gammaoscillationsathighandmediumvelocity,suppressedgammaresponsemorerapidly,

i.e.alreadyatthetransitionfromthe‘slow’tothe‘medium’velocity.Unlikethatinchildren,

nocorrelationswithgammaresponsefrequencywerefoundinadults(Tab.5).

We further tested whether the velocity-related changes in gamma response

amplitude correlated with changes in gamma frequency. In adults, the slow-to-medium


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change in frequency correlated positivelywith both gSS (R= 0.44,N=26, p<0.05) and gSM

(R=0.43,N=26, p<0.05), while the slow-to-fast frequency growth tended to correlate with

gSM(R=0.43,N=19,p=0.07)

Table 5. Spearman correlations between gamma suppression parameters (gSM, gSS) andgammaresponsefrequencymeasuredinthethreevelocityconditions. Children Fslow,

N=37#Fmedium,N=30

Ffast,N=21

Fmedium-Fslow,N=30

Ffast-Fslow,N=21

gSM 0.11 0.05 -0.09 0.12 -0.17

gSS 0.25 0.36 0.60** 0.19 0.26 Adults Fslow,

N=26Fmedium,N=26

Ffast,N=19

Fmedium-Fslow,N=26

Ffast-Fslow,N=19

gSM -0.34 -0.07 -0.29 0.44* 0.43gSS -0.25 0.04 -0.14 0.43* 0.22*p<0.05,**p<0.01.# Note that the number of subjects included in each analysis depends on the presence of a reliable

gamma peak in the corresponding velocity condition.

3.6.GammapowersuppressionandIQ

In children, theKABC-IIMental ProcessingComposite (MPC) IQ score correlatedpositively

withbothgSMandgSS (N=36;gSM:R=0.33,p<0.05,gSS:R=0.54,p<0.001;Tab.6).ThegSS

also correlated positively with KABC-II Simultaneous IQ Score (R=0.51, p<0.01). These

correlationssuggestthatchildrenwithhigher IQshowgreaterandmorerapidsuppression

ofgammaresponsewithincreasingexcitatorydrive.

Pattern of correlations between MPC IQ and gamma response magnitude in separate

velocity conditions suggests that the correlation with gSM and gSS are mainly driven by

gamma response power in the ‘medium’ and ‘fast’ conditions ( ‘slow’: R(36)=-0.07, n.s.,

‘medium’:R(36)=-0.31(p=0.06),‘fast’:R(36)=-0.29,p=0.09).


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Table6.Spearmancorrelationsbetweengammasuppressionparameters(gSM,gSS)andIQscores. Children,KABCII,

N=36Adults,WAISIVN=17

Sequential/Short-TermMemory’

Simultaneous/VisualProcessing

MentalProcessingComposite

TotalIQ

gSM 0.13 0.28 0.33* 0.1

gSS 0.15 0.51** 0.54*** 0.2

*p<0.05,**p<0.01,***p<0.001

Figure 8. The relationship between gamma suppression parameters and IQ in children.AsterisksdenotethesignificanceofSpearmancorrelations:*p<0.01;**p<0.0001

No correlations between gamma suppression parameters and IQ reached

significancelevelinadults.

4.Discussion

Thecurrentstudyexaminedtheparadoxicalsuppressionofvisualgammaoscillationscaused

byincreasingvelocityofdriftingannulargratings.Inalargesampleofchildren,wereplicated

ourpreviousfindings(Orekhova,etal.,2015)indicatinganegativecorrelationofthevisual

motion velocity with the magnitude of gamma power enhancement and its positive

correlationwith the frequency of gamma oscillations We also found the same effects in

adults. Thepupillometry findings suggest that theeffectof velocityongammaoscillations

may be at least partly mediated by changes in excitatory drive. We further investigated

developmentalstabilityofthisphenomenonanditsfunctionalcorrelates.Despiteprofound

90 100 110 120 130 140 150

MPC IQ

0,2

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developmentaltransformationinabsolutevaluesofgammaresponsefrequencyandpower,

the magnitude of velocity-related gamma power and frequency modulations remained

stable from middle childhood to adulthood. Based on previous modeling studies, we

hypothesizedthatthesuppressionofgammapowerwithincreasinglystrongexcitatorydrive

reflects the capacity of inhibitory neurons to counteract excitation in the visual networks

(i.e., the E/I ratio). Since the E/I ratio depends, amongst other factors, on the tonic

excitability of inhibitory neurons, which in turn strongly affects the frequency of gamma

oscillations, we expected that the suppression of gamma power would correlate with

gamma frequency parameters. Following the prediction from the model of (Borgers and

Kopell, 2005),weanticipated that thegreater suppressionof gammapowerwould reflect

lower E/I ratio that might be beneficial for an individual subject’s level of cognitive

functioning(Cook,etal.,2016;Melnick,etal.,2013). Thesepredictionswereconfirmedin

children,butonlypartially inadults,suggestingthatE/Ibalancemaybebasedondifferent

mechanismsduringchildhoodandadulthood.

4.1.Gammaparametersinthesourceandsensorspace.

Comparison of the source and sensor analysis results shows close correspondence of the

gammaparametersmeasuredinthesourceandsensorspace. Thisresultagreeswellwith

thefindingsofTanetal(Tan,etal.,2016)whoreportedhighreliabilityofbothsource-and

sensor-derivedparametersofvisualgammaresponseandtheirclosecorrespondence.

Importantly, in all three velocity conditions, the individual maxima of gamma

responses were most frequently located in the primary visual cortex (calcarine gyri, see

SupplementaryMaterials) and we did not find any evidence for a systematic shift of the

maximumwithchangesinmotionvelocity.Thesefindingssuggestthatgenerationofgamma

activityinallthreevelocityconditionswasbasedonactivityofthesameneuralcircuits.

4.2.Developmentalchangesinstrengthandfrequencyofvisualgammaresponse

Visual stimulationwithmovingannulargratings resulted inprominent increaseof induced

gamma power under conditions of ‘slow’, ‘medium’ and ‘fast’ stimulus velocities in both

childandadultsamples(Fig.2).Inchildren,thestrengthofthegammaresponseincreased

between6and15yearsofage,whileinadultsitremainedstablebetween19and40years

of age (Tab.2, Fig. 7A). This developmental growth in the power of induced gamma

oscillationswasaccompaniedbyadropintheirfrequencythatcontinuesduringadulthood

butatamuchslowerpace(Tab.4,Fig.7B).


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Adevelopmental increase in the strengthof visual gamma response is compatible

with previous reports of amaturational rise in auditory gamma oscillations evidenced by

increaseinamplitudeand/orinter-trialcoherencyof40-Hzauditorysteady-stateresponses

(ASSR)(Cho,etal.,2015;Edgar,etal.,2016).Choetal(Cho,etal.,2015)observedthatthe

amplitude of the ASSRs increased in children between 8 and 16 years of age, but then

droppedbetween15-16and20-22years.Althoughwehadveryfewsubjectswithinthe15–

22yearagerange,(whichprecludesstatisticalanalysisofthelattereffect),thepresenceof

veryhighgammaresponsevalues in theolderchildren inoursampleandtheirabsence in

adults(Fig.7A)suggeststhatthereductionofgammaamplitudebetweenadolescenceand

earlyadulthoodmaybeacommonfeatureofboththeauditoryandvisualcortices.

Severalfactorsmayunderliedevelopmentalchangesingammaresponsepower.The

maincontributormightbethematurationofinhibitoryGABAergicneurotransmissionthatin

primatesisprotractedintoearlyadulthood(LeMagueresseandMonyer,2013).Changesin

subunit composition of GABAR (GABA-Receptor) (Hashimoto, et al., 2009), as well as

increasing ‘on-demand’GABA release (Pinto, et al., 2010) produce a developmental shifts

toward stronger, more precise inhibitory currents, thereby resulting in highly coherent

activityofprincipalcellsintheuppergammafrequencyband(Doischer,etal.,2008).

Developmentalslowingofthevisualgammaoscillationsobservedinourstudyis in

linewithresultsofGaetzandcolleagues(Gaetz,etal.,2012)whoreportedadecreaseinthe

frequencyofgammaoscillationsinducedbystaticvisualgratingsinsubjectsaged8-45years.

However,thesmallnumberofchildren(12subjects)andmuchlargernumberofadults(47

subjects)includedintheirsampledidnotallowGaetzetal.toperformaseparateanalysisof

developmental trajectories in thematuringand theagingbrain.Our study shows that the

rate- and most probably the mechanism - of the developmental decrease in gamma

frequencydifferinchildrenandadults.

The frequency of the induced gamma oscillations predominantly depends on the

excitatorystateofFSPV+ inhibitoryneuronswithhigher frequency related to theirhigher

tonic excitability (Anver, et al., 2011; Ferando andMody, 2015; Mann andMody, 2010).

ThereisconvincingevidenceofadevelopmentalshifttowardslowerNMDA-mediatedtonic

excitationoftheFSPV+cells,aswellasofadevelopmentalreductionofthenumberofFS

PV+ cells exhibiting excitatory NMDA-mediated currents (Wang and Gao, 2009). On the

otherhand,tonicinhibitoryregulationoftheFSPV+interneuronsgraduallyincreasesduring

postnatal development (Le Magueresse and Monyer, 2013). These maturational

transformations leading to subtler tonicexcitabilityof inhibitoryneuronsmaywellexplain


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thehighlypronouncedandrapiddecreaseinthefrequencyofvisualgammaoscillationsthat

weobservedbetween childhoodandadolescence.Ofnote, theexpressionof theNMDAR

units (NR1, NR2a, NR2b) proceeds to decrease even in themature brain - from young to

middle-agedadulthood(Adams,etal.,2008). Thesechanges inNMDAtransmissionmight

explainthemoregradualdecreaseofvisualgammafrequencybetween19and40yearsof

ageinourstudy.

4.3.Modulationofgammaoscillationsbyexcitatorydrive.

Increasing velocity of visual motion from 1.2 to 6.0°/s (which also shifts the temporal

frequency of the stimulation from 4 to 20 Hz) was associated with significantly stronger

relativepupillaryconstriction(Fig.1).Ithasbeenpreviouslyfoundthatpupilconstrictioncan

be induced by changing attributes of a visual stimulation (e.g. spatial structure, color or

motion coherence) without any changes in light flux (Barbur, et al., 1998; Sahraie and

Barbur, 1997; Wilhelm, et al., 2002). Thesepupillary reactions are thought to reflect the

activationofcorticalareasinvolvedinprocessingofrespectivefeaturesofthevisualstimuli

(Barbur,etal.,1998). Inourparadigm, the increase inpupil constriction fromslow to fast

motion velocity is likely to reflect increasing activation of the motion-processing cortical

areas (includingV1,V5andbeyond)that inturncausestransientweakeningof thecentral

sympathetic inhibition of the parasympathetic Edinger-Westphal nucleus of the midbrain

(Wilhelm, et al., 2002). Thus, the pupillometry findings support our interpretation of the

effectofthemotionvelocityongammaresponseasbeingadirectconsequenceoftherising

excitatorydrivetothevisualcortex.Thelatterisfurthersupportedbytheobservationthat

thegratingsmovingatfastvelocitywereoftenperceivedbytheparticipantsasmostintense

andevenunpleasant.

Ourresultssuggestthatthefrequencyandpowerof inducedgammaoscillations

stronglydependonthestrengthoftheexcitatorydrive.Theincreasingexcitatorydrivewith

increasingmotionvelocityfrom‘slow’to‘fast’leadtoaccelerationofgammaoscillationsin

all participants with reliably detectable gamma peaks (Fig. 4B). The mean increase in

frequencywasalmostthesameinchildren(15.3Hz)andadults(14.6Hz).

Experimental studies in animals (Mann andMody, 2010), as well as simulation

studies(Moca,etal.,2014),suggestthatexcitationofinhibitoryFSinterneuronsisthemain

factor affecting the frequency of gamma oscillations. The recent DCMmodeling study by

Shawandcolleagues(Shaw,etal.,2017)hasshownthatreductionofgammafrequencyby

GABA reuptake inhibitor tiagabine ispartlymediatedby itseffecton the timeconstantof


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theinhibitoryinterneurons.Therefore,theincreaseingammafrequencywithspeedingthe

visualmotionmightbeatleastpartlymediatedbyincreasingexcitationofFSinterneuronsin

responsetoincreasingexcitatorydrive.

The gamma response power decreased with increasing motion velocity in all

participants who had reliable gamma responses under the ‘slow’ condition, with the

exceptionofthreesubjects(oneadultandtwochildren;Fig.4A,dashedlines),allofwhom

hadneurologicalconditionsrevealedpost-hoc(epilepsy,Arnold-Chiarimalformation,minor

motorticksandfrequentnightmares).Inthemajorityoftheparticipants,regardlessoftheir

age, the gamma response decreased already in the transition from ‘slow’ to ‘medium’

velocity (see two examples in Fig. 5A), although in a few subjects the ‘medium’ velocity

augmentedthegammaresponse,whichstarted toattenuateonlyunder the ‘fast’velocity

condition(Fig.5B).

The robustness of the gamma suppression with increasing velocity of visual

motionaswellasitsoverallsimilarityinchildrenandadultssuggestthatthisphenomenonis

not age-specific and reflects a verybasicmechanismunderlying thegenerationof gamma

oscillations in the visual cortex. Concurrently, as we mentioned in the introduction, the

gamma suppression phenomenon is at odds with the results of other MEG studies that

consistentlyreportedincreasesingammaresponsepowerwithincreasingexcitatorydriveto

thevisualcortex(Hadjipapas,etal.,2015;Hall,etal.,2005;MuthukumaraswamyandSingh,

2013;Peiker,etal.,2015;Perry,etal.,2015;Siegel,etal.,2007;Swettenham,etal.,2009).

Theoppositebehaviorofgammaoscillationsinourandotherstudiesfitswellthe

predictions from the Borgers and Kopell model (Borgers and Kopell, 2005) and may be

explained by an inverted U-shaped relationship between strength of excitatory drive and

power of gamma oscillations (Fig. 9). The reliable enhancement of gamma response

observed in MEG studies that manipulated excitatory drive with luminance contrast

(Hadjipapas, et al., 2015; Hall, et al., 2005; Perry, et al., 2015), eccentricity (van Pelt and

Fries, 2013), motion coherence (Peiker, et al., 2015; Siegel, et al., 2007), or compared

stationaryandslowlymovingstimuli(MuthukumaraswamyandSingh,2013;Swettenham,et

al.,2009)mightbeexplainedbyarelativelyweakexcitationproducedbysuchmanipulations

attheascendingportionofthebell-shapedcurve(Fig.9A).


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Figure 9. Ahypothetical relationship between power of gamma response and strength ofexcitatorydrive.A.Bell-shapeddependencyofgammapoweronthestrengthofexcitatorydrive. The previous reports on MEG gamma enhancement with increasing luminancecontrast,motioncoherence,decreasingeccentricity,ortransitionfromstationarytoslowlydriftinggratingsarecompatiblewiththeeffectsofmodeststimulusintensityattheupwardbranch of the bell-shaped curve. In our studies suppression of the gamma responsewithincreasingstimulusvelocityhasbeeninducedbystrongexcitatorydrivewhoseintensitywascloseto(slowvelocity)orwellabove(fastvelocity)thesuppressiontransitionpoint.B.Theschematic representation of the effects of optimal (blue) and elevated (orange, red) E/Iratioson thegammapowermodulation.The increasedE/I ratio shifts the transitionpointtowardshigherexcitatorydriveandresultsinreducedorabsentgammapowersuppression.

Atacertainhighleveloftheexternalexcitatorydrive(the‘suppressiontransition

point’),whentheFScellsaresufficientlyexcited,theylosetheircapacitytosupportgamma

oscillations, since they either walk out of phase with pyramidal cells or desynchronize

(BorgersandKopell,2005;BorgersandWalker,2013;Cannon,etal.,2014).Itisconceivable

thatinthemajorityofoursubjects(withtheexceptionofafewcasespicturedinFig.5Band

5C), the excitatory drive produced by the ‘slow’ motion was close to or above the

‘suppressiontransitionpoint’andthatthefurtherincreaseofvelocityuptothemediumand

high values put the visual networks in the high excitatory state corresponding to the

descendingportionofthebell-shapedcurve,thusresultinginrelativesuppressionofgamma

responsepower.

Remarkably, the model by Borgers and Kopell explains while the PING gamma

oscillations arenot supportedbeyond the30-90Hz frequency range (Kopell, et al., 2010).

Therefore,thegammaamplitudereductioninthe‘fast’velocityconditioniswellinlinewith

Gam

ma

pow

er

Excitatory drive

A

B

Balanced E/I

E>>IE>I

Gam

ma

pow

er

Excitatory drive

Orekhova et al, 2015current studySuppressionSuppression

transition transition pointpoint

Hall et al, 2005Siegel et al, 2007Swettenham et al, 2009Muthukumaraswamy,Singh, 2013Hadjipapas et al, 2015Peiker et al, 2015Perry et al, 2015


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30

accelerationofthevisualgammaoscillationsthatapproachtheupperborderofthisrangein

responsetothe‘fast’velocity(especiallyinchildren,seeTab.2).

4.4.GammasuppressionanditsrelationtotheE/Ibalanceinvisualnetworks

TheBorgersandKopell’smodelstatesthatthegammasuppressionwithincreasinglystrong

excitatorydriveoccursmoreeasilywhenexcitatorypostsynapticcurrentsinpyramidalcells

arewell compensated by inhibitory ones (Borgers and Kopell, 2005). Therefore, onemay

expect that at the same level of a sufficiently high external excitatory drive, the gamma

powersuppressionwillberelativelystrongerwhenthereisstrongerexcitationofinhibitory

neurons and/or weaker excitation of excitatory pyramidal cells (Fig. 9B). Since a greater

tonic excitation of the inhibitory neurons also accelerates gamma oscillations (Mann and

Mody, 2010), one can expect that individual variability in gamma suppression parameters

and gamma frequency are partially controlled by the same neuralmechanism. Therefore,

individualswithhighergammapeak frequency shouldbe likely todisplaygreatervelocity-

related gamma power suppression, which also has a steeper slope, i.e. gamma power

suppression peaks at a relatively lower excitatory drive. By the same token, both the

magnitudeandtheslopeofgammasuppressionshouldbereducedincaseofanelevatedE/I

ratio driven by either excessive excitation of excitatory circuitry or by relatively weak

inhibition. The predicted link between gamma suppression parameters, gamma frequency

andE/Ibalancewassupportedbyourdatainseveralways.

First, we found the predicted positive correlations between gamma suppression

parameters and peak gamma frequency (in children) or changes in frequency (in adults)

(Table4).Thedissimilarpatternof thecorrelationsmay reflect lowpowerof theanalysis,

butmayalsoreflectdifferentmechanismsoftheE/Ibalanceinthedevelopingandmature

brain.

Secondly, consistentwith results of theprevious behavioral (Melnick, et al., 2013)

andprotonmagnetic resonance spectroscopy (H1-MRS) (Cook,etal., 2016) studies linking

thestrengthofneuralinhibitioninthevisualcortexwithcognitivefunctions,wefoundthat

thestrongerand fastergammaresponsesuppressioncorrelatedwithhigher IQ inchildren

(Tab.6,Fig.8).ThiscorrelationsuggeststhatlowerE/Iratiointhevisualcortexinassociated

with better cognitive functioning in neurotypical children. It is however unclear if the E/I

balance in the visual cortex is important by itself or as a reflection of thewidespread E/I

level in the brain. Absence of significant correlations between general IQ and gamma


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31

suppressioninadultssuggeststhatanoptimalE/Ibalanceinthevisualcortexmightbeless

importantforthegeneralcognitivefunctioningofthematurebrain.

Thirdly, the deviations from the typical gamma suppression values seemed to be

associated with abnormally elevated E/I ratio in the visual cortex. Three out of 66 (39

childrenand27adults)participantswithhighlyreliablegammapeaks inthe ‘slow’velocity

conditionfailedtosuppressgammaoscillationsevenatthehighestvelocity/excitatorydrive

(see Fig. 4C for the spectra of two of these subjects), and their post-hoc examination

revealed neurological conditions that might be associated with cortical hyper-excitability

(epilepsy, Arnold-Chiari malformation and a case with minor motor tics and frequent

nightmares).Althoughmoresystematic investigation iswarranted, theseoutliers’dataare

generally in line with the prediction that the low or absent gamma suppression at the

descending branch of the bell-shaped curve is indicative of global imbalance in excitation

withinthevisualcorticalnetworks.

Ofnote,theunbalancedexcitationinvisualcortexofsubjectswithneuropsychiatric

disordersmight be reflected not only in an absent/reduced gammapower suppression at

thedescendingbranchofthebell-shapedcurve,butalsoasanabnormalgammapowergain

at its ascending branch. Indeed, Peiker et al (Peiker, et al., 2015) have found abnormally

strongmodulationof gammapowerbygradually increasing coherenceof visualmotion in

peoplewithautismspectrumdisorders. Investigationofgammaresponsemodulationasa

functionofexcitatorydriveatboththeascendinganddescendingbranchesofthebell-curve

wouldaddimportantknowledgeaboutE/Ibalanceinnormalandatypicalbrain.

4.5.Gammamodulationsandage

Highly reliable suppression of gamma power and increase of gamma frequency with

increasingvelocityofvisualmotionhasbeenobserved inbothchildrenandadults (Fig.6).

ThedevelopmentalstabilityofthegammamodulationssuggeststhattheglobalE/Ibalance

underincreasingexcitatorydriveisefficientlyregulatedinthehealthybrainthroughoutthe

broadagerangeofsubjectsincludedinthestudy.Moreover,giventhesubstantiallyhigher

gammafrequencyinchildrenascomparedtoadults(Tab.1,Fig.7B),theadjustmentofthe

globalE/Ibalanceintheimmaturebrainseemstobeachievedatahigherlevelofexcitability

ofbothEandIneurons.Thedevelopmentalstabilityofthegammaresponsemodulationsis

in linewithan invitroobservationthatthenumberof inhibitorysynapsesontheneuronal

branches scale together with the number of excitatory synapses during development,

resultinginarelativelystableE/Iratioacrossseveraldevelopmentalstages(Liu,2004).


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32

However, a few words of caution are warranted regarding the lack of a reliable

developmental trend in gammamodulationparameters. According to animal studies, the

maturation of neural processes underlying the E/I balance in auditory and visual cortices

takesplaceduringearlysensitiveperiodsofbraindevelopment(Froemke,2015).Allofour

participantswereolderthan6yearsofage,and it isnotunlikelythat infantsandtoddlers

would present gamma modulation parameters that are still age-dependent due to a

continuingdevelopmentalfine-turningoftheE/Ibalanceinthevisualcortex.Thiscouldalso

be true for theyoungest children fromour sample. These childrenweremore frequently

excluded from the gamma suppression analysis due to a low signal to noise ratio of the

gammaresponse.Whileonlyincludingcaseswithhighlyreliablegammaresponsesinterms

ofsignal-to-noiseratioallowedustoreducetheimpactofnoiseonthegammasuppression

parameters,italsoloweredtheproportionofyoungestparticipantsinourchildsample.

5.Conclusions

AlthoughalinkbetweengammaoscillationsandtheE/Ibalanceinneuralnetworksishardly

disputable,thehopethatthefrequencyorpowerofhumangammaresponsemayserveasa

single non-invasive indicator of the E/I balance is perhaps overly simplistic. Here, we

investigated thephenomenonofgammapowersuppressioncausedbygradual increase in

stimulus velocity in children and adults and provided evidence for its potential role as a

putativebiomarkerof theE/Ibalance in thevisual cortex.Our study resulted in twomain

novelfindings.

First, we found a substantial developmental slowing of visual gamma oscillations

fromalready7yearsofage,andthatthereforecannotbeexplainedby‘aging’(Gaetz,etal.,

2012).Thisfindingchallengestheprevailingview,whichpredictsthatmaturationalchanges

in neurotransmission should improve precision with which populational neural activity is

synchronizedand,asaresult, increasebothgammasynchronizationandgammafrequency

(Uhlhaas,etal.,2010).Giventheanimaldata(MannandMody,2010;WangandGao,2009),

thelikelymechanismfordevelopmentalslowingofgammaoscillationsisthedevelopmental

decrease in tonic excitability of the PV+ FS inhibitory neurons in the visual cortex.

Functionally, given the role of inhibitory transmission in neural plasticity, faster juvenile

visual gamma oscillationsmay be beneficial for perceptual learning, which proceedswith

higherintensityinyoungerascomparedtoolderbrains.

Thesecondnovelfindingisthedevelopmentalstabilityofthesuppressiveeffectof

anincreasingvelocityofvisualmotiononthestrengthofgammaresponsecoupledwithan


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33

acceleration of gamma oscillations. Although the suppressive effect of excessively strong

excitatory drive on MEG gamma power is well in line with the theoretical predictions

(Borgers and Kopell, 2005; Cannon, et al., 2014), to our knowledge it has not been

demonstratedbefore,withtheexceptionofourearlierstudiesinchildren(Orekhova,etal.,

2015; Stroganova, et al., 2015). Because gamma oscillations facilitate information

transmission (Vinck,etal., 2013), theireffective suppressionwitha ‘too strong’excitatory

drivemayhaveaprotectivefunctionviareducingthenumberofcorticalassemblesengaged

ininformationflowandpreventingcorticalhyper-excitationinresponsetosensorystimuli.

Given that the slope of gamma suppression correlatedwith the frequency of the

gammaresponseinthedevelopingbrain,wesuggestthattheseparametersaresensitiveto

partly inter-relatedneural processes.However,while the frequencyof gammaoscillations

predominantly depends on the tonic excitability of inhibitory neurons (Mann and Mody,

2010), efficient gamma suppression may reflect a global balance between

mutuallycounteractingexcitatoryandinhibitorycircuits.Thelinkbetweenthemagnitudeof

gamma suppression and IQ values in children points to the importance of an optimal E/I

balanceforcognitiveabilities.Aremarkabledevelopmentalstabilityofgammasuppression

in healthy brain and its atypical absence in a small proportion of our participants with

neurological abnormalities allow us to speculate that a gamma suppression deficit may

characterizeadisturbedE/Ibalanceinthehyper-excitablevisualcortexinindividualsovera

wideagerange.Duetothelackofdirectneurophysiologicalevidenceinthecontextofthe

same ‘visual motion’ experimental paradigm, this interpretation should be treated with

caution. However, it allows us to make several testable predictions for future research

regardingfunctionalconsequencesofindividualvariabilityingammasuppression.

Weexpectthatagammasuppressiondeficitatthehighlevelsofexcitatorydrivewill

characterize:patientswithseizureonsetzoneslocatedintheoccipitalcortex,neuro-typical

individuals with visual hypersensitivity, and patients from clinical groups characterized by

sensory modulation problems. Furthermore, a deficit in gamma suppression is likely to

correlatewith other non-invasive indexes of unbalanced excitation/inhibition in the visual

cortexprovidedby,e.g.,theH-MRSandvisualperceptualtasks.

In conclusion, we suggest that velocity-related changes in gamma oscillations

characterizeindividualvariationinE/Ibalanceandefficacyofneuralinhibitioninthevisual

cortex and may provide a valuable inhibition-based biomarker for patients with

neuropsychiatricdisorders.


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34

Funding

This work is financed by Torsten Söderberg Foundation (M240/13to CG) andthe Russian

ScientificFoundation(14-35-00060toTS).AuthorsJFSandBRaresupportedbytheKnutand

AliceWallenberg Foundation (grant 2014.0102), the SwedishResearchCouncil (grant 621-

2012-3673), and the Swedish Childhood Cancer Foundation (grant MT2014-0007). The

authorNHwassupportedbytheLifeWatchFoundation.TheauthorTSwassupportedbythe

CharityFoundation"WayOut".

Acknowledgements

Weheartilythankallparticipantsandchildrenfamiliesfortheirparticipationinthisstudy.

WearegratefultoMariaS.DavletshinaandAnnaV.Butorinaforhelpwithdatacollection.

References

Adams, M.M., Shi, L., Linville, M.C., Forbes, M.E., Long, A.B., Bennett, C., Newton, I.G.,Carter, C.S., Sonntag, W.E., Riddle, D.R., Brunso-Bechtold, J.K. (2008) CaloricrestrictionandageaffectsynapticproteinsinhippocampalCA3andspatiallearningability.ExperimentalNeurology,211:141-149.

Anver, H.,Ward, P.D.,Magony, A., Vreugdenhil,M. (2011) NMDA Receptor HypofunctionPhase Couples Independent gamma-Oscillations in the Rat Visual Cortex.Neuropsychopharmacology,36:519-528.

Barbur,J.L.,Wolf,J.,Lennie,P.(1998)Visualprocessinglevelsrevealedbyresponselatenciesto changes in different visual attributes. Proceedings of the Royal Society B-BiologicalSciences,265:2321-2325.

Borgers,C.,Kopell,N.(2005)Effectsofnoisydriveonrhythmsinnetworksofexcitatoryandinhibitoryneurons.NeuralComputation,17:557-608.

Borgers,C.,Walker,B.(2013)Togglingbetweengamma-frequencyactivityandsuppressionofcellassemblies.FrontiersinComputationalNeuroscience.

Buhl, E.H., Tamas, G., Fisahn, A. (1998) Cholinergic activation and tonic excitation inducepersistentgammaoscillationsinmousesomatosensorycortexinvitro.JPhysiol,513(Pt1):117-26.

Cannon,J.,McCarthy,M.M.,Lee,S.,Lee,J.,Borgers,C.,Whittington,M.A.,Kopell,N.(2014)Neurosystems:brainrhythmsandcognitiveprocessing.EurJNeurosci,39:705-719.

Carmignoto,G.,Vicini,S.(1992)Activity-DependentDecreaseinNmdaReceptorResponsesduringDevelopmentoftheVisual-Cortex.Science,258:1007-1011.

Cho, R.Y.,Walker, C.P., Polizzotto, N.R.,Wozny, T.A., Fissell, C., Chen, C.M.A., Lewis, D.A.(2015)DevelopmentofSensoryGammaOscillationsandCross-FrequencyCouplingfromChildhoodtoEarlyAdulthood.CerebCortex,25:1509-1518.

Cook,E.,Hammett,S.T.,Larsson, J. (2016)GABApredictsvisual intelligence.NeurosciLett,632:50-4.

Doischer, D., Hosp, J.A., Yanagawa, Y., Obata, K., Jonas, P., Vida, I., Bartos, M. (2008)PostnatalDifferentiationofBasketCellsfromSlowtoFastSignalingDevices.JournalofNeuroscience,28:12956-12968.

Dorrn, A.L., Yuan, K., Barker, A.J., Schreiner, C.E., Froemke, R.C. (2010) Developmentalsensoryexperiencebalancescorticalexcitationandinhibition.Nature,465:932-937.


https://doi.org/10.1101/188326

35

Ecker, C., Spooren,W.,Murphy,D.G.M. (2013) Translational approaches to the biology ofAutism:falsedawnoranewera?MolPsychiatr,18:435-442.

Edgar,J.C.,Fisk,C.L.,Liu,S.,Pandey,J.,Herrington,J.D.,Schultz,R.T.,Roberts,T.P.L.(2016)Translating Adult Electrophysiology Findings to Younger Patient Populations:DifficultyMeasuring40-HzAuditorySteady-StateResponsesinTypicallyDevelopingChildrenandChildrenwithAutismSpectrumDisorder.DevelopmentalNeuroscience,38:1-14.

Ferando, I., Mody, I. (2015) In vitro gamma oscillations following partial and completeablation of delta subunit-containing GABA(A) receptors from parvalbumininterneurons.Neuropharmacology,88:91-98.

Froemke, R.C. (2015) Plasticity of Cortical Excitatory-Inhibitory Balance. Annual Review ofNeuroscience,Vol38,38:195-219.

Gaetz, W., Roberts, T.P.L., Singh, K.D., Muthukumaraswamy, S.D. (2012) Functional andstructural correlates of the aging brain: Relating visual cortex (V1) gamma bandresponsestoage-relatedstructuralchange.HumanBrainMapping,33:2035-2046.

Gramfort,A., Luessi,M., Larson, E., Engemann,D.A., Strohmeier,D.,Brodbeck,C.,Goj,R.,Jas,M.,Brooks,T.,Parkkonen,L.,Hamalainen,M.(2013)MEGandEEGdataanalysiswithMNE-Python.FrontNeurosci,7:267.

Gross, J., Kujala, J., Hamalainen, M., Timmermann, L., Schnitzler, A., Salmelin, R. (2001)Dynamic imaging of coherent sources: Studying neural interactions in the humanbrain.PNatlAcadSciUSA,98:694-699.

Hadjipapas,A.,Lowet,E.,Roberts,M.J.,Peter,A.,DeWeerd,P.(2015)Parametricvariationof gamma frequency and powerwith luminance contrast: A comparative study ofhumanMEGandmonkeyLFPandspikeresponses.Neuroimage,112:327-340.

Hall,S.D.,Holliday,I.E.,Hillebrand,A.,Singh,K.D.,Furlong,P.L.,Hadjipapas,A.,Barnes,G.R.(2005)Themissing link:analogoushumanandprimatecorticalgammaoscillations.Neuroimage,26:13-7.

Hashimoto,T.,Nguyen,Q.L.,Rotaru,D.,Keenan,T.,Arion,D.,Beneyto,M.,Gonzalez-Burgos,G., Lewis,D.A. (2009) ProtractedDevelopmental Trajectories ofGABA(A)Receptoralpha1andalpha2SubunitExpressioninPrimatePrefrontalCortex.BiolPsychiatry,65:1015-1023.

Hoogenboom,N., Schoffelen, J.M.,Oostenveld, R., Parkes, L.M., Fries, P. (2006) Localizinghuman visual gamma-band activity in frequency, time and space. Neuroimage,29:764-773.

Isaacson,J.S.,Scanziani,M.(2011)HowInhibitionShapesCorticalActivity.Neuron,72:231-243.

Jia,X.X.,Xing,D.J.,Kohn,A.(2013)NoConsistentRelationshipbetweenGammaPowerandPeakFrequencyinMacaquePrimaryVisualCortex.JournalofNeuroscience,33:17-U421.

Kaufman,A.S.,Kaufman,N.L.(2004)KABC-II:KaufmanAssessmentBatteryforChildren.2nded.CirclePines,MN:AGSPub.

Kopell, N., Börgers, C., Pervouchine, D., Malerba, P., Tort, A. (2010) Gamma and ThetaRhythms inBiophysicalModelsofHippocampalCircuits. In:Cutsuridis,V.,Graham,B.P., Cobb, S., Vida, I., editors. Hippocampal Microcircuits: A ComputationalModeller'sResourceBook.:Springerp423-457.

Le Magueresse, C., Monyer, H. (2013) GABAergic Interneurons Shape the FunctionalMaturationoftheCortex.Neuron,77:388-405.

LeBlanc,J.J.,Fagiolini,M.(2011)Autism:A"CriticalPeriod"Disorder?NeuralPlasticity.Lee, E., Lee, J., Kim, E. (2017) Excitation/Inhibition Imbalance in AnimalModels of Autism

SpectrumDisorders.BiolPsychiatry,81:838-847.


https://doi.org/10.1101/188326

36

Levin,A.R.,Nelson,C.A. (2015) Inhibition-BasedBiomarkers forAutismSpectrumDisorder.Neurotherapeutics,12:546-52.

Liu,G.(2004)Localstructuralbalanceandfunctionalinteractionofexcitatoryandinhibitorysynapsesinhippocampaldendrites.NatureNeuroscience,7:373-9.

Maheshwari,A.,Noebels,J.L.(2014)Monogenicmodelsofabsenceepilepsy:windowsintothe complex balance between inhibition and excitation in thalamocorticalmicrocircuits.GeneticsofEpilepsy,213:223-252.

Mann,E.O.,Mody, I. (2008)Themultifacetedroleof inhibition inepilepsy: seizure-genesisthrough excessive GABAergic inhibition in autosomal dominant nocturnal frontallobeepilepsy.CurrentOpinioninNeurology,21:155-160.

Mann, E.O.,Mody, I. (2010)Controlofhippocampal gammaoscillation frequencyby tonicinhibitionandexcitationofinterneurons.NatureNeuroscience,13:205-U90.

Melnick,M.D.,Harrison,B.R.,Park,S.,Bennetto,L.,Tadin,D.(2013)Astronginteractivelinkbetweensensorydiscriminationsandintelligence.CurrBiol,23:1013-7.

Moca, V.V., Nikolic, D., Singer, W., Muresan, R.C. (2014) Membrane resonance enablesstableandrobustgammaoscillations.CerebCortex,24:119-42.

Munk, M.H., Roelfsema, P.R., Konig, P., Engel, A.K., Singer, W. (1996) Role of reticularactivationinthemodulationofintracorticalsynchronization.Science,272:271-4.

Muthukumaraswamy, S.D., Singh, K.D. (2013) Visual gamma oscillations: The effects ofstimulus type, visual field coverage and stimulus motion on MEG and EEGrecordings.Neuroimage,69:223-230.

Muthukumaraswamy, S.D., Singh,K.D., Swettenham, J.B., Jones,D.K. (2010)Visual gammaoscillations and evoked responses: Variability, repeatability and structural MRIcorrelates.Neuroimage,49:3349-3357.

Nelson, S.B., Valakh, V. (2015) Excitatory/Inhibitory Balance and Circuit Homeostasis inAutismSpectrumDisorders.Neuron,87:684-698.

Orekhova,E.V.,Butorina,A.V., Sysoeva,O.V.,Prokofyev,A.O.,Nikolaeva,A.Y., Stroganova,T.A.(2015)Frequencyofgammaoscillationsinhumansismodulatedbyvelocityofvisualmotion.JNeurophysiol,114:244-255.

Peiker, I., Schneider, T.R., Milne, E., Schottle, D., Vogeley, K., Munchau, A., Schunke, O.,Siegel, M., Engel, A.K., David, N. (2015) Stronger Neural Modulation by VisualMotionIntensityinAutismSpectrumDisorders.PLoSOne,10:e0132531.

Perry,G. (2015)Theeffectsofcross-orientationmaskingon thevisualgammaresponse inhumans.EurJNeurosci,41:1484-1495.

Perry, G., Randle, J.M., Koelewijn, L., Routley, B.C., Singh, K.D. (2015) Linear Tuning ofGamma Amplitude and Frequency to Luminance Contrast: Evidence from aContinuousMappingParadigm.PLoSOne,10.

Piekarski, D.J., Johnson, C.M., Boivin, J.R., Thomas, A.W., Lin,W.C., Delevich, K., E, M.G.,Wilbrecht,L.(2017)Doespubertymarkatransitioninsensitiveperiodsforplasticityintheassociativeneocortex?BrainRes,1654:123-144.

Pinto, J.G.A., Hornby, K.R., Jones, D.G., Murphy, K.M. (2010) Developmental changes inGABAergic mechanisms in human visual cortex across the lifespan. Frontiers inCellularNeuroscience.

Roberts, M.J., Lowet, E., Brunet, N.M., Ter Wal, M., Tiesinga, P., Fries, P., De Weerd, P.(2013) Robust Gamma Coherence between Macaque V1 and V2 by DynamicFrequencyMatching.Neuron,78:523-536.

Rossiter, H.E.,Worthen, S.F.,Witton, C., Hall, S.D., Furlong, P.L. (2013)Gammaoscillatoryamplitudeencodesstimulus intensity inprimarysomatosensorycortex.FrontHumNeurosci,7.

Rubenstein, J.L.R., Merzenich, M.M. (2003) Model of autism: increased ratio ofexcitation/inhibitioninkeyneuralsystems.GenesBrainandBehavior,2:255-267.


https://doi.org/10.1101/188326

37

Sahraie, A., Barbur, J.L. (1997) Pupil response triggered by the onset of coherentmotion.GraefesArchClinExpOphthalmol,235:494-500.

Schadow, J., Lenz, D., Thaerig, S., Busch, N.A., Frund, I., Herrmann, C.S. (2007a) Stimulusintensity affects early sensory processing: Sound intensity modulates auditoryevoked gamma-band activity in human EEG. International Journal ofPsychophysiology,65:152-161.

Schadow,J.,Lenz,D.,Thaerig,S.,Busch,N.A.,Frund,I.,Rieger,J.W.,Herrmann,C.S.(2007b)Stimulus intensity affects early sensory processing: Visual contrast modulatesevoked gamma-band activity in human EEG. International Journal ofPsychophysiology,66:28-36.

Shaw, A.D., Moran, R.J., Muthukumaraswamy, S.D., Brealy, J., Linden, D.E., Friston, K.J.,Singh, K.D. (2017) Neurophysiologically-informed markers of individual variabilityandpharmacologicalmanipulationofhumancorticalgamma.Neuroimage,161:19-31.

Siegel,M.,Donner,T.H.,Oostenveld,R.,Fries,P.,Engel,A.K.(2007)High-frequencyactivityin human visual cortex is modulated by visual motion strength. Cereb Cortex,17:732-741.

Stroganova, T.A., Butorina, A.V., Sysoeva, O.V., Prokofyev, A.O., Nikolaeva, A.Y., Tsetlin,M.M.,Orekhova,E.V.(2015)Alteredmodulationofgammaoscillationfrequencybyspeed of visual motion in children with autism spectrum disorders. J NeurodevDisord.p21.

Swettenham, J.B., Muthukumaraswamy, S.D., Singh, K.D. (2009) Spectral Properties ofInduced and Evoked GammaOscillations in Human Early Visual Cortex toMovingandStationaryStimuli.JNeurophysiol,102:1241-1253.

Takada, N., Pi, H.J., Sousa, V.H., Waters, J., Fishell, G., Kepecs, A., Osten, P. (2014) Adevelopmentalcell-typeswitchincorticalinterneuronsleadstoaselectivedefectincorticaloscillations.NatureCommunications.

Tan, H.R.M., Gross, J., Uhlhaas, P.J. (2016) MEG sensor and source measures of visuallyinducedgamma-bandoscillationsarehighlyreliable.Neuroimage,137:34-44.

Taulu, S., Hari, R. (2009) Removal of magnetoencephalographic artifacts with temporalsignal-spaceseparation:demonstrationwithsingle-trialauditory-evokedresponses.HumBrainMapp,30:1524-34.

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N.,Mazoyer,B.,Joliot,M.(2002)Automatedanatomical labelingofactivations inSPMusing a macroscopic anatomical parcellation of theMNIMRI single-subject brain.Neuroimage,15:273-289.

Uhlhaas,P.J.,Roux,F.,Rodriguez,E.,Rotarska-Jagiela,A.,Singer,W.(2010)Neuralsynchronyandthedevelopmentofcorticalnetworks.TrendsCognSci,14:72-80.

van Pelt, S., Boomsma, D.I., Fries, P. (2012)Magnetoencephalography in Twins Reveals aStrongGenetic Determination of the Peak Frequency of Visually InducedGamma-BandSynchronization.JournalofNeuroscience,32:3388-3392.

van Pelt, S., Fries, P. (2013) Visual stimulus eccentricity affects human gamma peakfrequency.Neuroimage,78:439-447.

Vinck,M.,Womelsdorf, T., Fries, P. (2013) Gamma-band synchronization and informationtransmission.In:Quiroga,R.Q.,Panzeri,S.,editors.PrinciplesofNeuralCoding:CRCPress.p449-469.

Wang, H.X., Gao, W.J. (2009) Cell Type-Specific Development of NMDA Receptors in theInterneuronsofRatPrefrontalCortex.Neuropsychopharmacology,34:2028-2040.

Wechsler, D. (2010) WAIS IV: Wechsler Adult Intelligence Scale – Fourth Edition. Svenskversion.NCSPearsonInc..


https://doi.org/10.1101/188326

38

Wilhelm,B.J.,Wilhelm,H.,Moro,S.,Barbur,J.L.(2002)Pupilresponsecomponents:studiesinpatientswithParinaud'ssyndrome.Brain,125:2296-2307.

Wu, Y., Liu, D., Song, Z. (2015) Neuronal Networks and Energy Bursts in Epilepsy.Neuroscience,287:175-186.

Xue, M.S., Atallah, B.V., Scanziani, M. (2014) Equalizing excitation-inhibition ratios acrossvisualcorticalneurons.Nature,511:596-600.

Yizhar, O., Fenno, L.E., Prigge, M., Schneider, F., Davidson, T.J., O'Shea, D.J., Sohal, V.S.,Goshen, I., Finkelstein, J., Paz, J.T., Stehfest, K., Fudim, R., Ramakrishnan, C.,Huguenard, J.R., Hegemann, P., Deisseroth, K. (2011) Neocorticalexcitation/inhibition balance in information processing and social dysfunction.Nature,477:171-178.


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modulation of visual gamma oscillation by excitatory drive ...al., 2017; nelson and valakh, 2015;...

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