1
Additiveeffectofcontrastandvelocityprovestheroleofstrong1
excitatorydriveinsuppressionofvisualgammaresponse.2
3
OrekhovaE.V.1,2,3,ProkofyevA.O.1,NikolaevaA.Yu.1,SchneidermanJ.F.2,3,StroganovaT.A.14
51.MoscowStateUniversityofPsychologyandEducation,CenterforNeurocognitiveResearch(MEG6
Center),Moscow,Russia.72.UniversityofGothenburg,InstituteofNeuroscience&Physiology,DepartmentofClinical8
Neurophysiology93.ChalmersUniversityofTechnologyandMedTechWest,Gothenburg,Sweden10
11
12
13
14
15
Correspondingauthor16
ElenaVOrekhova.MoscowStateUniversityofPsychologyandEducation(MSUPE),Centerfor17
NeurocognitiveResearch(MEGCenter),Shelepihinskajaembankment2a,123290Moscow,18
Russia19
E-mailaddresses:[email protected]
21
22
Acknowledgments23ThisworkwassupportedbytheMoscowStateUniversityofPsychologyandEducation;the24
CharityFoundation"WayOut";SwedishChildhoodCancer(#MT2014-0007); KnutandAlice25
WallenbergFoundation(#2014.0102)and SwedishResearchCouncil(#2017-0068). Weare26
gratefultoallvolunteerparticipants. 27
28
29
Keywords:30
MEG,gammaoscillations,visualmotion,contrast,excitation-inhibitionbalance 31
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2
Abstract1
2Visualgammaoscillationsaregeneratedthroughinteractionsofexcitatoryandinhibitory3neuronsandarestronglymodulatedbysensoryinput.Amoderateincreaseinexcitatory4drive to the visual cortex via increasing contrast or motion velocity of drifting gratings5results in strengthening of the gamma response (GR). However, increasing the velocity6beyond some ‘transition point’ leads to the suppression of the GR. There are two7theoreticalmodels thatcanexplainsuchsuppression.The ‘excitatorydrive’model infers8that,athighdriftingrates,GRsuppression iscausedbyexcessiveexcitationof inhibitory9neurons. Since contrast and velocity have an additive effect on excitatory drive, this10modelpredictsthattheGR‘transitionpoint’forlow-contrastgratingswouldbereachedat11ahighervelocity,ascomparedtohigh-contrastgratings.Thealternative ’velocitytuning’12modelimpliesthattheGRismaximalwhenthedriftingrateofthegratingcorrespondsto13the preferable velocity of the motion-sensitive V1 neurons. This model predicts that14loweringcontrasteitherwillnotaffectthetransitionpointorwillshiftittoalowerdrifting15rate.Wetestedthesemodelswithmagnetoencephalography-basedrecordingsoftheGR16during presentation of low (50%) and high (100%) contrast gratings drifting at four17velocities. We found that lowering contrast led to a highly reliable shift of the GR18suppression transition point to higher velocities, thus supporting the excitatory drive19model.Noeffectsofcontrastorvelocitywerefoundforthealpha-betaresponsepower.20Theresultshaveimportant implicationsfortheunderstandingoftheneuralmechanisms21underlyinggammaoscillationsandthedevelopmentofgamma-basedbiomarkersofbrain22disorders.23
24
25
Introduction26
Gamma-bandoscillationsarisefromapreciseinterplaybetweenexcitation(E)andinhibition(I)27(BuzsakiandWang,2012;Vincketal.,2013)andtheirparametersareaffectedbyexternaland28
internalfactorsthatcanshifttheE-Ibalance(Chenetal.,2017;Hadjipapasetal.,2015;Jiaet29
al.,2013;Salelkaretal.,2018;Sumneretal.,2018).Theseoscillationsaremostprominent in30
theprimaryvisualcortex(V1)andcanbe induced inbothhumansandanimalsbyarangeof31
visual stimuli. Human visual gamma oscillations recorded with Magnetoencephalography32
(MEG) have high within-subject reproducibility (Hoogenboom et al., 2006;33
Muthukumaraswamy et al., 2010; Tan et al., 2016) and are strongly genetically determined34
(van Pelt et al., 2012). This makes the properties of gamma oscillations potentially useful35
signatures of theconstitutionally up- and down-regulatedE-I balance in the human visual36
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3
cortex. Various attempts have been made to relate the individual values of power and1
frequencyof thevisual inducedgamma response (GR) to indirectmeasuresof theexcitatory2
state of visual circuitry, such as concentration of GABA and glutamate neurotransmitters3
(Cousijn et al., 2014; Edden et al., 2009), GABAa receptor density (Kujala et al., 2015), or4
performance in a visual orientation discrimination task (Edden et al., 2009), but the link5
betweengammaactivityandtheexcitatoryandinhibitoryprocessesinthevisualcortexisstill6
farfromclear.7Therelationshipbetweentheexcitatorystateofthevisualcortexandthepropertiesof8
gammaoscillationscanbeclarifiedthroughmanipulationsoftheintensityofexcitatoryinput.9
Interestingly, the intensity of visual stimulation has a different impact on the frequency and10
powerofinducedgammaoscillationsinbothmonkeysandhumans.Thefrequencyofgamma11
oscillations nearly linearly increases with increasing visual input strength (Hadjipapas et al.,12
2015; Jia et al., 2013;Murty et al., 2018; Orekhova et al., 2015; Perry et al., 2015; Ray and13
Maunsell,2010;vanPeltetal.,2018)whilegammapowerundergoesnonlinearchanges(Jiaet14
al.,2013;Orekhovaetal.,2018b;Salelkaretal.,2018).15
Localfieldpotential(LFP)studiesofthemonkeyprimaryvisualcortexhaveshownthat16
theincreaseingammafrequencycausedbyincreaseineitherluminancecontrast(Hadjipapas17
etal.,2015;HenrieandShapley,2005)ortemporal frequencyofmovinggratings (Salelkaret18
al.,2018)wasparalleledbya rise inneuronal spiking rate.This suggests that the increaseof19
gamma frequency is associated with growing neural excitation in V1, and particularly with20excitationofinhibitorycircuitrythathavebeenshowntoplaythemajorroleinregulatingthe21
frequency of gamma oscillations (Anver et al., 2011; Ferando and Mody, 2013; Mann and22
Mody,2010).23
Thesamechangesinthestimulationpropertiesthatinducealinearincreaseingamma24
frequency leadtononlinear ---oftenbell-shaped---changes ingammapower inmonkeyLFP25
(Hadjipapasetal.,2015;Jiaetal.,2011;Jiaetal.,2013;Lowetetal.,2015;Salelkaretal.,2018).26
Ina recentMEGstudy (Orekhovaetal., 2018b),weappliedhigh-contrast gratingsdriftingat27
different rates (motion velocities) and found the same bell-shaped changes in the gamma28
responsepowerasthosedescribedintheLFPstudiesonmonkeys.Themajorityofourhealthy29
subjects increasedvisualGRpowerfromthestatictotheslowlymoving(1.2°/s)stimulusand30
then suppressed the response at higher velocities of 3.6 °/s and 6.0 °/s. These findings are31
consistentwiththoseofvanPeltandco-authors(vanPeltetal.,2018),whofoundacontinuous32
growthinthestrengthofvisualgammaresponsewhilechanginggratingvelocitiesfrom0°/sto330.33°/s and further to 0.66 °/s. Given that the fastest velocity used in their study was still34
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4
below 1.2°/s, the combined results from both studies are consistent with our previous1
observation that the effect of stimulus velocity on induced gamma power changed from a2
facilitative to a suppressive one at a stimulus speed of about 1.2°/s (‘suppression transition3
velocity’)(Orekhovaetal.,2018b).4
Seeminglyparadoxically,thenonlinearvelocity-relatedgammapowerchangesmaybe5
at least partly explained by rising neural excitation --- in particular, the tonic excitation of I-6
neurons. According to modeling studies, the excitation of I-neurons beyond some critical7‘suppressiontransition’pointresults intheir lossofsynchronywitheachotherandexcitatory8
pyramidal cells that, in turn, leads to suppressionof gammaoscillations (BorgersandKopell,9
2005; Cannon et al., 2014; Kopell et al., 2010). Thus, the gamma suppression at high10
velocities/temporal-frequencies of visualmotionmight be explained by this ‘excitatory drive11
hypothesis’.12
Iftrue,thelinkbetweenstronginhibitionandgammasuppressionmayhaveimportant13
implications for studying the E and I processes in the healthy and diseased human brain. In14
particular,individualvariationsingammasuppressionmayreflectthecapacityofIcircuitryto15
suppressexcessiveexcitationinthenetwork,thuspreventingsensoryoverload.However,the16
question remained to be answered –whether the observed nonlinear behavior of theMEG17
gammaoscillations is related tochanges in the strengthofexcitatorydriveor toaparticular18
property of the visual stimulation. Indeed, rather than being a consequence of the strong19
excitatorydrive to the visual cortex, the suppressionof gamma responsepower could result20from tuningof theV1neurons to theparticular velocity/temporal frequency.Animal studies21
showthat,whilecellsintheLGNrespondtovisualstimuliofratherhightemporalfrequencies,22
theneurons in theprimaryvisual cortex showband-passproperties (Priebeetal.,2006;Van23
Hooseretal.,2013).Onethereforecannotexcludethatapresenceofan'optimalvelocity'that24
induces themaximal visualGRpower simply reflects thenumberofV1neurons ‘tuned’ toa25
respectivevelocity/temporalfrequency.26
Herein,andinordertotestthesetwoalternativeexplanations,wemodulatedtheGR27
bychangingthevelocityofdriftinggratings(stationaryandmovingat1.2,3.6and6.0°/sthat28
correspondedtotemporalfrequenciesof0,2,6and10Hz)fortwoluminancecontrastvalues:29
100%(highcontrast)and50%(lowcontrast).30
Intermsoftheexcitatorydrivemodel,concomitant increasesincontrastandvelocity31
of the stimulation should have an additive effect. If the transition from enhancement to32
suppressionoftheGRrequiresacertainlevelofexcitatorydrive,thenthislevelwillbereached33at a relatively higher motion velocity in case of lower contrast. The excitatory drive model34
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5
therefore predicts that the ‘gamma suppression transition velocity’ would be higher at the1
50%,ascomparedtothe100%,contrast.2
The velocity-tuning model predicts a different outcome from that of the excitatory3
drive model. In this case, lowering contrast from 100% to 50% either will not affect the4
'transitionpoint’or shift it to lowervelocity.The shift toa lowervelocity in this casecanbe5
expected,becauseadecreaseinthestimuluscontrastshiftsthetuningofthemotion-sensitive6
V1neurons to lower temporal frequencies (Alitto andUsrey, 2004; Livingstone andConway,72007;Priebeetal.,2006).8
Similar to gamma, the oscillations within the alpha-beta range are also thought to9
reflect the level of cortical activation (Romei et al., 2008; Yuan et al., 2010). Whereas10
suppressionofthealpha-betaoscillationshasrepeatedlybeenshowntoaccompanyanactive11
state of cortical networks related to processing of incoming sensory information, the ‘high-12
alpha’statesareassociatedwithcorticalidlingoractiveinhibitionoftask-irrelevantprocesses13
thatmightotherwiseinterferewithtaskperformance(PalvaandPalva,2011;Pfurtschellerand14
daSilva,1999).Usingthevisualmotionparadigmsimilartothatusedinourstudy,Scheeringa15
et al have shown that the power of alpha-beta and gamma oscillations was inversely16
proportionaltochangesinthebloodoxygenlevel-dependent(BOLD)signalinthevisualcortex,17
althoughatdifferentcorticaldepths(Scheeringaetal.,2016).However,thepowersofalpha-18
beta and gamma oscillations are not interchangeable indexes of cortical activation. In19
particular, changes in the velocity of visual motion affected alpha and gamma oscillations20differently in theLFP inmonkeys (Salelkaretal.,2018).There isevidence thatvisualgamma21
mainlyreflectsfeed-forwardprocesses(Robertsetal.,2013;vanKerkoerleetal.,2014),while22
alphaandbetapowerinthevisualcortexarestronglymodulatedbytop-downinfluencesfrom23
upstreamcorticalareas(Liuetal.,2016;SnyderandFoxe,2010).Therefore,weexpectedthat24
the bell-shaped pattern of power changes would be specific for theMEG gamma band and25
would not be mirrored by opposing changes in alpha-beta activity (i.e. gamma response26
suppression–alphafacilitation).27
Overall, our current MEG study pursued these two main goals. Firstly, we tested28
whether the strength of excitatory drive is the main factor contributing to the initially29
facilitative, and then suppressive, effects of velocity on the visual gamma response power.30
Secondly, by comparing the effects of increasing excitatory drive on gamma and alpha31
oscillations,wesoughttounraveltheuniqueinformationregardingactivationofthevisualthat32
isconveyedbyeachofthethesetwofrequencybands.3334
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6
1
Methods2
3
Participants.4
Seventeen neurologically healthy subjects (age 18-39, mean=27.2, sd=6.1; 7 males) were5
recruitedtothestudy.Allparticipantshadnormalorcorrectedtonormalvision.Theinformed6consent form was obtained from all the participants. The study has been approved by the7
ethicalcommitteeofMSUPE.8
9
Experimentaltask.10
ThevisualstimuliweregeneratedusingPresentationsoftware(NeurobehavioralSystemsInc.,11
USA). We used a PT-D7700E-K DLP projector to present images with a 1280 x 1024 screen12
resolutionanda60Hzrefreshrate.Theexperimentalparadigmisschematicallypresentedin13
figure 1. The stimuli were gray-scale sinusoidally modulated annular gratings presented at14
~100% or 50% of Michelson contrast. Luminance of the display, measured from the eye15
position, was 46 Lux during presentation of both 50% and 100% contrast stimuli and 2 Lux16
duringinter-stimulusintervals.Thegratingshadaspatialfrequencyof1.66cyclesperdegreeof17
visualangleandcovered18×18degreesofvisualangle. Theyappearedinthecenterofthe18
screenoverablackbackgroundanddriftedtothecenterwithoneoffourvelocities:0,1.2,3.6,19or6.0°/s, (0,2,6and10Hztemporal frequency)referredtoas ‘static’, ‘slow’, ‘medium’,and20
‘fast’.Eachtrialbeganwith1200mspresentationofafixationcrossinthecenterofthedisplay21
over a black background that was followed by the presentation of the grating that either22
remainedstaticordriftedwithoneofthethreevelocities.Afterarandomlyselectedperiodof23
1200–3000ms,themovementstoppedorthestaticstimulusdisappeared.Tokeepparticipants24
alert,weaskedthemtorespondtothechangeinthestimulationflow(stopofthemotionor25
disappearance of the static stimulus) with a button press. If no response occurred within 126
second,adiscouragingmessage,“toolate!”appearedandremainedonthescreenfor2000ms,27
afterwhich a new trial began. The static stimulus and those drifting at different rateswere28
intermixed and appeared in a random order within each of three experimental blocks. The29
participants responded with either the right or the left hand in a sequence that was30
counterbalancedbetweenblocksandparticipants.Eachofthestimulitypeswaspresented3031
timeswithineachexperimentalblock. Inordertominimizevisualfatigueandboredom,short32(3–6 s) animated cartoon characters were presented between every 2–5 stimuli. The high33
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7
(100%)andlow(50%)contrastgratingswerepresentedindifferentexperimentalsessionsand1
theorderofthesesessionswascounterbalancedbetweensubjects.2
3
4
Fig. 1.Experimentaldesign.Eachtrialbeganwiththepresentationofafixationcross that5wasfollowedbyanannulargratingthatremainedstatic(0°/s)ordriftedinwardfor1.2-3s6at one of the three velocities: 1.2, 3.6, 6.0°/s. The 100% and 50% contrast gratingswere7presented in separate sessions. Participants responded to the change in the stimulation8flow (disappearance of the static grating or termination of motion) with a button press.9Shortanimatedcartooncharacterswerepresentedrandomlybetweenevery2-5stimulito10maintainvigilanceandreducevisualfatigue.11
12
Datarecording.13
Neuromagnetic brain activity was recorded using a 306-channel detector array (Vectorview;14
Neuromag,Helsinki,Finland).Thesubjects’headpositionswerecontinuouslymonitoredduring15MEGrecordings.Fourelectro-oculogram(EOG)electrodeswereusedtorecordhorizontaland16
verticaleyemovements.EOGelectrodeswereplacedattheoutercantioftheeyesandabove17
andbelowthelefteye.Tomonitortheheartbeats,oneelectrocardiogram(ECG)electrodewas18
placedatthemanubriumsterniandtheotheroneatthemid-axillaryline(V6ECGlead).MEG,19
+
time
baseline1.2 s
drifting or static grating
1.2-3 s
+
+
rest 3-6 s
+
time
baseline1.2 s
+
+
rest 3-6 s
100% contrast
50% contrast
drifting or static grating
1.2-3 s
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8
EOG,andECGsignalswererecordedwithaband-passfilterof0.03–330Hz,digitizedat10001
Hz,andstoredforoff-lineanalysis.2
3
MEGdatapreprocessing.4
Thedatawasfirstde-noisedusingtheTemporalSignal-SpaceSeparation(tSSS)method(Taulu5
andHari,2009)implementedinMaxFilter™(v2.2)withparameters:‘-st’=4and‘-corr’=0.90.For6
all three experimental blocks, the head origin positionwas adjusted to a common standard7position.Forfurtherpre-processing,weusedtheMNE-pythontoolbox(Gramfortetal.,2013)8
aswellascustomPythonandMATLAB®(TheMathWorks,Natick,MA)scripts.9
Thede-noiseddatawasfilteredbetween1and200Hzanddownsampledto500Hz.10
To remove biological artifacts (blinks, heart beats, and in some casesmyogenic activity),we11
then applied independent component analysis (ICA). TheMEG periods with too high (4e-1012
fT/cm for gradiometers and 4e-12 fT for magnetometers) or too low (1e-13 fT/cm for13
gradiometers and1e-13 fT formagnetometers) amplitudeswereexcluded from theanalysis.14
The number of independent components was set to the dimensionality of the raw ‘SSS-15
processed’data(usuallyaround70).WefurtherusedanautomatedMNE-pythonprocedureto16
detectEOGandECGcomponents,whichwecomplementedwithvisual inspectionof the ICA17
results. The number of rejected artifact components was usually 1-2 for vertical eye18
movements,0-3forcardiac,and0-6formyogenicartifacts.19
The ICA-correcteddatawas then filteredwithadiscreteFourier transform filter to20remove power-line noise (50 and 100 Hz) and epoched from −1 to 1.2 sec relative to the21
stimulusonset.Wethenperformedtime-frequencymultitaperanalysis(2.5Hzstep;numberof22
cycles = frequency/2.5) and excluded epochs contaminated by strong muscle artifacts via23
thresholding the high-frequency (70 - 122.5 Hz) power. For each epoch, the 70 - 122.5 Hz24
powerwas averaged over sensors and time points and the thresholdwas set at 3 standard25
deviationsofthisvalue.Theepochsleftwerevisuallyinspectedforthepresenceofundetected26
high-amplitude bursts of myogenic activity and those contaminated by such artifacts were27
manually marked and excluded from the analysis. After rejection of artifacts the average28
number of epochs for the 100% contrast condition was 79, 78, 78, and 79 for the ‘static’,29
‘slow’,‘medium’and‘fast’conditions,respectively.Forthe50%contrast,therespectivevalues30
were79,80,80,and82.31
32
StructuralMRI33
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9
Structural brain MRIs (1 mm3 T1-weighted) were obtained for all participants and used for1
sourcereconstruction.2
3
Time-frequencyanalysisoftheMEGdata4
The following steps of the data analyses were performed using Fieldtrip Toolbox functions5
(http://fieldtrip.fcdonders.nl; (Oostenveld et al., 2011)) and custom scripts developedwithin6
MATLAB.7Wesubtractedtheaveragedevokedresponses fromeachdataepoch.Thisdecreases8
thecontributionofphase-lockedactivitythatcouldberelatedtotheappearanceofthevisual9
stimulusonthescreenandtheeffectofphoticdrivingrelatedtothetemporalfrequencyofthe10
stimulation,screenrefreshrate,ortheirinteraction.11
The lead field was calculated using individual 'single shell' headmodels and cubic 612
mm-spaced grids linearly warped to theMNI-atlas-based template grid. The time-frequency13
analysisoftheMEGdatawasthenperformedinthefollowingtwosteps.14
First,we tested for the presence of reliable clusters of gamma facilitation and alpha15
suppressionat the source levelusing theDICS inverse-solutionalgorithm (Grosset al., 2001)16
andthecluster-basedpermutationtest(NicholsandHolmes,2002).Thefrequencyofinterest17
wasdefinedindividuallyforeachsubjectandconditionbasedonthesensordata(onlythedata18
from gradiometers were used at this stage). For the gamma range, we performed time-19
frequencydecompositionofpre-stimulus(‘pre’:-0.9to0s)andpost-stimulus(‘post’:0.3to1,220s)MEGsignalsusing8discreteprolatespheroidalsequences(DPSS)taperswith±5Hzspectral21
smoothing.Forthelowfrequencyrange,weused2DPSStapersand±2Hzspectralsmoothing.22
Wethencalculatedtheaverage(post-pre)/preratioandfoundthe4posteriorsensorswiththe23
maximalpost-stimulusincreaseingammapower(45-90Hz)andthosewiththemaximalpost-24
stimuluspowerdecreaseinthelow-frequencyband(7-15Hz).Byaveragingthose4channels,25
we have found the frequencies corresponding to themaximal post-stimulus power increase26
(forgamma)ordecrease(forlowfrequencyrange).Thefrequencyrangesofinterestwerethen27
established within 35-110 Hz and 5-20 Hz limits, where the ratios exceeded 2/3 of the28
respective peak value. The center of gravity of the power over these frequency ranges was29
used as the peak frequency of the gamma and the slow responses, respectively. The time-30
frequency decomposition was then repeated while centered at these peak frequencies.31
Common source analysis filters were derived for combined pre- and post-stimulus intervals32
usingDICSbeamformingwitha5%lambdaparameterandfixeddipoleorientation(i.e,onlythe33largest of the three dipole directions per spatial filterwas kept). The filterwas then applied34
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10
separately to the pre- and post-stimulus signals. Subsequently, we calculated univariate1
probabilities of pre- topost-stimulusdifferences in single trial power for each voxel using T-2
statistics. We then performed bootstrap resampling (with 10 000 Monte Carlo repetitions)3
between ‘pre’ and ‘post’ timewindows todetermine individual participants'maximal source4
statisticsbasedonthesumofT-valuesineachcluster.5
Second,inordertoanalyzetheresponseparametersatthesourcemaximum,weused6
linearly constrained minimum variance (LCMV) beamformers (VanVeen et al., 1997). The7spatialfilterswerecomputedbasedonthecovariancematrixobtainedfromthewholeepoch8
and for the three experimental conditions with a lambda parameter of 5%. Prior to9
beamforming,theMEGsignalwasband-passedbetween30and120Hzforthegamma-range10
andlow-passedbelow40Hzforthelowfrequencyrange.The‘virtualsensors'timeserieswere11
extracted for each brain voxel and time-frequency analysis with DPSS multitapers (~1 Hz12
frequencyresolution)wasperformedonthevirtual sensorsignals.For thegammarange,we13
used±5Hzsmoothing.Forthelow-frequencyrange,thisparameterwas±2Hz.The‘maximally14
induced’gammavoxelwasdefinedasthevoxelwiththehighestrelativepost-stimulusincrease15
of45-90Hzpower inthe ‘slow’motionvelocityconditionwithinthevisualcorticalareas(i.e.16
L/R cuneus, lingual, occipital superior,middle occipital, inferior occipital, and calcarine areas17
according to theAALatlas (Tzourio-Mazoyeretal., 2002)). The ‘slow’ conditionwas selected18
because reliability of the gamma response was highest in this condition. The ‘maximally19
suppressed’voxelinthelow-frequencyrangewasthendefinedasthevoxelwiththestrongest20relativesuppressionof7-15Hzpower.Theweightedpeakparametersforthegammaandthe21
low-frequency activity were calculated for the average spectra of the virtual sensors in 2522
voxels closest to, and including, the ‘maximally induced’ and ‘maximally suppressed’ voxels,23
respectively,usingtheapproachdescribedforthesensorlevelanalysis.Foreachindividualand24
condition,wealsoassessedthereliabilityofthepre-topost-stimuluspowerchangesatthese25
25 averaged voxels. The change was considered reliable if its absolute peak value was26
significantwithp<0.0001(Wilcoxonsignedranktest).Coordinatesofthevoxelsdemonstrating27
greatestpowerchangesweredefinedinMNIcoordinates(SupplementaryTables).28
29
30
Results31
32
WemeasuredMEGbrain responses induced by concentric visual grating drifting inwardly at33
fourvelocities(0,1.2,3.6,6.0°/s)andtwocontrastlevels(50%and100%).Belowwepresent34
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11
theresultsseparatelyforthegammaandthealpha-betaranges.1
2
Gammafrequencyrange3
Localizationandreliability4
Figure2showsgrandaveragesourcelocalizationoftheinducedgammaresponses.5
6
7Fig. 2. Grand average statistical maps of the cortical GRs to drifting visual8gratings.Themapsaregivenfortheweightedpeakgammapower,separatelyfor9the two luminance contrasts and fourmotion velocities. Positive sign of the T-10statistics corresponds to stimulation-related increase in gamma power. Note11different scales for the50%and100%contrastsand that themagnitudeof the12gammaresponsewasaffectedbybothcontrastandvelocity.13
14
For the 100% contrast stimuli, significant (p<0.05) activation clusterswithmaxima in15
visualcorticalareaswerefoundinallparticipantsandmotionvelocityconditions.Forthe50%16
contrast, the significant activation clusters were absent in one participant in all velocity17
conditions, in two participants in the static condition, and in one participant in the 6.0°/s18
condition.19
Thegrandaverageandindividualgammaresponsespectraatthe‘maximallyinduced’20
groupofvoxelsareshown in figures3and4.TheparticipantsA007,A008,andA016hadno21reliable increase in gammapower (p>0.0001, seeMethods for details) at 50% contrast in at22
-10
10
0
100%
con
tras
t
0°/s 1.2°/s 3.6°/s 6.0°/s
50%
con
tras
t
-8
8
0
T-statistic
T-statistic
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12
least one of the velocity conditions (A007: all velocities; A008 and A016: 0°/s). In all other1
participants,theincreasesingammapowerattheselectedvoxelsweresignificantaccordingto2
ourcriteriainallconditions.3
Thepeaksthatwereunreliableaccordingtoatleastoneofthecriteria(i.e.eitherthe4
absence of an activated cluster or low probability (p>0.0001) of gamma increase at the5
selectedvoxels)werefurtherexcludedfromanalysesofthepeakfrequencyandthepositionof6
themaximallyinducedvoxel.Notethatexclusionoftheunreliablegammapeaksaffectedthe7degreesoffreedominthecorrespondingANOVAspresentedbelow.8
9
10
11
12
1314
Fig. 3. Grand average spectra of cortical GRs to the gratings of 100% (left15panel)and50%(rightpanel)contrastsdriftingatfourmotionvelocities.Here16andhereafter,theGRpoweriscalculatedas(post-pre)/preratio,where‘pre’17and‘post’arethespectralpowervaluesinthe-0.9to0and0.3to1.2stime18windowsrelativetothestimulusonset.19
20
21
30 50 70 90 0
1
2
3
30 50 70 90
0°/s1.2°/s3.6°/s6.0°/s
Frequency [Hz] Frequency [Hz]
Powe
r cha
nge
[a.u
.]
100% contrast 50% contrast
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13
1
23
Fig.4.IndividualspectraofcorticalGRs.TheplotsarethesameasinFig.3,seeMethodsfor4furtherdetails.5
6
7
In themajority of cases, the voxel with themaximal increase in gamma power was8
locatedinthecalcarinesulcus(seetheSupplementaryTable1Aand1Bforcoordinatesofthe9‘maximally induced’ voxel). There was a significant effect of Velocity for the ‘z’ coordinates10
(F(3,39)=4.7, e=0.84, p<0.05). In the ‘fast’ condition, the voxel with the maximal increase in11
100% 50%
Frequency [Hz]
30 50 70 900246
Powe
r cha
nge
[a.u
.] case A001
30 50 70 90
0°/s1.2°/s3.6°/s6.0°/s
30 50 70 900246
case A002
30 50 70 90Powe
r cha
nge
30 50 70 9002468
case A003
30 50 70 90Powe
r cha
nge
30 50 70 900
2
4case A004
30 50 70 90Powe
r cha
nge
30 50 70 900
1
2case A005
30 50 70 90
Powe
r cha
nge
30 50 70 900123
case A006
30 50 70 90
Powe
r cha
nge
30 50 70 900
1
2case A013
30 50 70 90
30 50 70 900246
case A014
30 50 70 90
30 50 70 90024
case A015
30 50 70 90
30 50 70 90
0
1 case A016
30 50 70 90
30 50 70 900
2
4case A017
30 50 70 90
30 50 70 90
0
1 case A007
30 50 70 90
30 50 70 900123
case A008
30 50 70 90
30 50 70 900
2
4case A009
30 50 70 90
30 50 70 900
2
case A010
30 50 70 90
30 50 70 900
2
4case A011
30 50 70 90
30 50 70 900246 case A012
30 50 70 90Frequency [Hz]
4
100% 50% 100% 50%
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gammapowerwaslocatedonaverage2.6mmmoresuperiorrelativetotheother3conditions1
(Zcoordinateforthestatic:0.61cm,slow:0.54cm,medium:0.52cm,fast:0.82cm).Therewas2
also Velocity*Contrast interaction for the ‘y’ coordinate (F(3,39)=3.6, e=0.59, p<0.05), that is3
explainedbyarelativelymoreposteriorsource locationof thegammamaximuminthe ‘fast’4
low-contrastconditionthan inthe ‘fast’high-contrastcondition(-9.5vs-9.2cm).Considering5
thesmallcondition-relateddifferencesinthepositionofthevoxelwiththemaximalincreasein6
gammapower(<0.6cm,whichwasthesizeofthevoxelusedforthesourceanalysis),all the7stimuliactivatedlargelyoverlappingpartsoftheprimaryvisualcortex.8
9
Effectsofcontrastandvelocity10
Frequency. Figure5Ashowsgroupmeangammapeakfrequencyvalues for thetwocontrast11
andfourvelocityconditions.Gammafrequencywasstronglyaffectedbymotionvelocityofthe12
grating(F(3,39)=78.2,e=0.62p<1e-6)and,toalesserextent,byitscontrast(F(1,13)=14.3,p<0.01).13
Inspection of figure 5A shows that increase in velocity resulted in substantial increase of14
gammafrequency(~12-17Hz).Forthefull-contrastgratingthefrequencyincreasedfrom51.615
Hzincaseofthestaticto68.6Hzincaseofthefastmovingstimulus.Forthe50%contrastthe16
respectivevalueswere51.1and63.0Hz. Increasingcontrast,on theotherhand, led to5Hz17
increaseinfrequencyofgammaresponsetomovingstimuli.18
19
2021
Fig.5. Grandaveragepeak frequency (A) andpower (B)ofcorticalGRsasa22functionofContrastandmotionVelocity.Verticalbarsdenote0.95confidence23
intervals.24
25
Power. Figure 5B shows group mean GR peak power values for the two contrast and four26
velocity conditions. Therewas highly significant effect of Contrast (F(1,16)=66.2,p<1e-6) onGR27
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15
peak power, which is explained by a generally higher gamma power in case of high, as1
comparedtolowcontrast.ThesignificanteffectofVelocity(F(3,48)=9.3,e=0.46,p<0.01)isdueto2
aninitialincreaseinGRpowerfromthestatictoslowcondition(F(1,16)=43.1,p<1e-5)followed3
by a decrease from the medium to the fast velocity condition (F(1,16)=14.2, p<1e-4).4
Importantly, there was highly significant Contrast*Velocity interaction effect (F(3,48)=12.4,5
e=0.78, p<1e-4). The contrast had a stronger effect on GR power for the static and slowly6
moving stimuli, as compared to the faster (medium and fast) velocities. As a result, the7maximumoftheinvertedbell-shapedpowervs.motionvelocitycurvemovedtohighervelocity8
inthelowcontrastcondition,ascomparedtothefullcontrastone.9
Generally, the results confirmed our previous finding of an inverted bell-shaped10
dependencyofGRpoweronthevelocityofvisualmotionandextendedthemtotwocontrast11
conditions. The significant Contrast*Velocity interaction showed that the form of this bell-12
shapedcurvewasmodulatedbytheluminancecontrastinsuchawaythatthetransitiontoGR13
suppressionatlowcontrastoccurredatahighervelocity,aspredictedbytheexcitatorydrive14
model.15
16
Suppression transition velocity.We further sought to investigatewhether the shift to higher17
velocities intheGRsuppressiontransitionpointunder lowcontrast isarobustphenomenon,18
which can be detected at the individual subject level. For each subject, we calculated the19
‘suppressiontransitionvelocity’(STVel)asthecentreofgravityvisualmotionvelocity:2021
STVel=(Pow0*0+Pow1.2*1.2+Pow3.6*3.6+Pow6.0*6.0)/(Pow0+Pow1.2+Pow3.6+Pow6.0),22
23
where‘Pow’istheweightedGRpeakpowerintherespectivevelocitycondition(i.e.0,1.2,3.624
or 6.0 °/s). Although the STVel may not precisely correspond to the velocity condition for25
which the GR was at maximum, it reflects the distribution of power between velocity26
conditions.Moreimportantly,itallowsustocharacterizecontrast-dependentshiftsinamore27
rigorousmannerthanrelyingexclusivelyonthediscretevaluesofmotionvelocitiesforwhich28
theGRwasmaximal.DecreasingluminancecontrastresultedinareliableincreaseinSTVel,and29
thustheGRsuppressiontransitionpoint,inall17participants(F(1,16)=202.0;p<1e-6;Fig.5A).30
Lowering contrast led to slowing of gamma oscillations (Fig. 4A); we therefore also31
tested if, irrespectiveof contrast, thepointof transition toGR suppression corresponds toa32
certaingamma frequency.Todo this,weapproximated the suppression transition frequency33(STFreq,i.e.thegammapeakfrequencythatcorrespondstothemaximalgammaresponse)as34
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16
thecenterofgravityoftheGRpowerforeachsubject(i.e.,inthesamewayaswedidforthe1
STVel):2
3STFreq=(Pow0*Freq0+Pow1.2*Freq1.2+Pow3.6*Freq3.6+Pow6.0*Freq6.0)/(Pow0+Pow1.2+Pow3.6+Pow6.0),45
where‘Freq’and‘Pow’arethepeakgammaresponsefrequencyandpowerintherespective6
velocityconditions.7
Thesuppressiontransitionfrequencywasslightly,butsignificantly, lower forthe50%8
thanforthe100%contrast(60.5Hzvs63.1Hz;F(1,13)=10.2,p<0.01).9
10
11Fig.6.Effectofcontrastonthegammasuppressiontransitionpoint:individualvariability.A.12The centre of gravity of visual motion velocity that approximates the GR suppression13transitionpoint (‘suppression transition velocity’ - STVel). In every single subject, the STVel14increasedwithdecreasingcontrast. B.Thecentreofgravityofgammapeakfrequencythat15approximates the peak frequency of the GR at the gamma suppression transition point16(‘suppressiontransitionfrequency’-STFreq).TheSTFreqslightly,butsignificantly,decreased17withdecreasingthecontrast.18
19
Rank-orderconsistencyofgammaparameters20
Considering the drastic stimulation-related changes in gamma parameters, their intra-21
individual stability across experimental conditions (i.e. ‘rank-order consistency’) remained an22
open question. In order to investigate if the power and frequency of GR measured in one23
experimentalconditionpredictsthesubject’srankpositioninanotherexperimentalcondition,24
we calculated two types of correlations: 1) between contrasts for the respective velocity25
conditionsand2)betweenvelocities,separatelyforeachofthecontrasts.Sincefrequency,but26
Suppression transition velocity
Contrast100% 50%
1
2
3
4
Mot
ion
velo
city
[°/s
]
Suppression transition frequency
Contrast
80
100% 50%40
50
60
70
Peak
freq
uenc
y [H
z]
A B
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17
notpower,ofthevisualgammaoscillations isstronglyaffectedbyageinadults (Gaetzetal.,1
2012; Orekhova et al., 2018b), we calculated partial correlations taking age as a nuisance2
variablewithfrequency.3
The correlations between contrast conditions were generally very high for gamma4
frequency (all R’s>0.89; all p’s <0.0001),with the noticeable exception of the static stimulus5
(R=0.36,n.s.).Forpower,allthebetween-contrastcorrelationsweremodestlytohighlyreliable6
(Fig.7A).ThecorrelationbetweenSTVelvaluesmeasuredat the50%and100%contrastwas7high (Fig. 6B, STVel: R(17)=0.91, p<1e-6), suggesting that thismeasure reliably characterises a8
subject’srankpositioninthegroup,irrespectiveofcontrastchanges.9
The correlations between velocity conditions were also generally high for frequency10
(SupplementaryTable3),butvaried incaseofpower.Thepowerof theGRelicitedby static11
stimulididnotpredictthepoweroftheGRelicitedbygratingsmovingwith‘medium’of‘fast’12
velocities (Fig.7B).Theresultswereverysimilar forthetwocontrasts (SupplementaryFigure13
1).14
Tosumup,rank-orderconsistencyforgammafrequencywasgenerallyhighacrossall15
conditionsexceptforstationarystimuli.Forgammapower,arank-orderconsistencybetween16
stationaryandmedium-to-fastmovinggratingswaslacking.17
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18
12Fig. 7.Rank-orderconsistencyof theGRpoweracrossconditions. A.CorrelationsbetweenGRpower3measuredat50%and100%contrasts.B.CorrelationsbetweenGRpowervaluesmeasuredindifferent4velocity conditionsat the100%contrast (seeSupplementary Figure1 for a similar figure for the50%5contraststimuli).BluedotsinAandBdenoteindividualgammaresponsepowervalues.C.Correlations6betweenSTVelvaluesatthetwocontrasts.Greensquaresdenote individualSTVelvalues. The linear7regressionisshowninred.Dashedlinesinallplotscorrespondtotheaxisofsymmetry.8
1 3 5100%, 0°/s
0,0
0,5
1,0
50%
, 0°/s
R=0.7**0 2 4 6
100%, 1.2°/s
1
2
3
4
50%
, 1.2
°/s R=0.64**
0 2 4 6 8100%, 3.6°/s
2
4
6
50%
, 3.6
°/s
R=0.87***0 2 4 6
100%, 6.0°/s
1234
50%
, 6.0
°/s
R=0.96***
R=0.51*0 2 4
0°/s
2
4
6
6.0°
/s
0 2 4 0°/s
2
4
6
83.
6°/s
0 2 4 0°/s
2
4
6
1.2
°/s
0 2 4 6 1.2°/s
2
4
6
8
3.6
°/s
0 2 4 6 1.2°/s
2
4
6 6
.0°/
s
0 2 4 6 8 3.6°/s
2
4
6
6.0
°/s
R=0.51*
R=0.62*
R=0.97***
R=0.0 R=-0.09
R=0.51*
1 2 3 4 5STVel 100%-contrast
2
3
4
5
STVe
l 50%
-con
trast
A
B
C
R=0.91***
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19
1
2
Alpha-betafrequencyrange3
Localizationandreliability4
Figure8showsgrandaveragesourcelocalizationofthealpha-betaresponsetothemoving5
visualgratings.6
7
89
Fig. 8. Grand average power changes in the alpha-beta range. A. Statisticalmaps of the10stimulation-related changes in the two luminance contrasts and four motion velocity11conditions. Blue color corresponds to suppression of the alpha-beta power. B. Spectral12changesataselectionof‘maximallysuppressed’voxels.Dashedlinesshowabsolutepower13spectra(righty-axis,arbitraryunits)correspondingtotheperiodsofvisualstimulationwith14movinggratings.Thedot-dashedlinesofthesamecolorsshowthepowerspectraforthe15respectivepre-stimulusintervals(alsorighty-axis).Solidlinesshowrespectivestimulation-16relatedchangesinpower([post-pre]/preratio,lefty-axis,arbitraryunits).Neithercontrast17norvelocityhadsignificanteffectsonthemagnitudeofthealpha-betasuppression.18
19
20
Clustersofsignificant(p<0.05)alpha-betapowersuppressionwerefoundinnearlyall21
subjectsandconditions.Forthe100%contraststimuli,theclusterswereabsentinonesubject22
inallvelocityconditionsandinonsubjectinthestaticcondition.Thesuppressionmeasuredat23
the selection of the ‘maximally suppressed’ voxels was significant in all motion velocity24
-6
0
100%
con
tras
t
0°/s 1.2°/s 3.6°/s 6.0°/s
50%
con
tras
t
6T-statistic
5 10 15 20-0.7
-0.5
-0.3
-0.1
1
3
5
710-12100% contrast
Powe
r cha
nge
[a.u
.]
Frequency [Hz]
LCMV power [a.u.]
0°/s1.2°/s3.6°/s6.0°/s
5 10 15 20-0.7
-0.5
-0.3
-0.1
1
3
5
710-1250% contrast
Powe
r cha
nge
[a.u
.]
Frequency [Hz]
LCMV power [a.u.]
A B
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conditionsat100%contrast. Forthe50%contraststimuli,asignificantclusterforalpha-beta1
suppressionwasabsentinonlyonesubjectandonlyinresponsetothestaticstimulus.Inyet2
another subject, the alpha-beta suppression measured at the voxels’ selection was not3
significant (p>0.0001) for the 1.2 °/s condition. The grand average spectra in the alpha-beta4
rangeforthemaximallysuppressedvoxelsareshowninfigure8.5
ANOVAwithfactorsContrastandVelocityrevealedneithersignificantmaineffectsof6
ContrastandVelocitynorContrast*Velocityinteractionforthe‘x’,‘y’,or‘z’coordinatesofthe7‘maximallysuppressed’voxel(allp’s>0.15).8
WethenusedANOVAwithfactorsBand,Contrast,andVelocitytocomparepositions9
of thevoxelswithmaximalalpha-betasuppressionand thosewithmaximalgamma increase.10
The effect of Band was highly significant for the absolute value of the ‘x’ coordinate11
(F(1,16)=81.9,p<1e-6).Themaximallysuppressedalphavoxelwaspositionedsubstantiallymore12
laterally(onaverage25mmfromthemidline)thanthatofthemaximallyinducedgammavoxel13
(on average 8mm from themidline). As can be seen from Supplementary Table 2(A,B), the14
maximally suppressed alpha voxel wasmore frequently located in the lateral surface of the15
occipitallobethanincalcarinesulcusorcuneusregion.NoBandrelateddifferencesintheyor16
zcoordinateswerefound.17
18
Effectsofcontrastandvelocity19
Tocheckifthemagnitudeorpeakfrequencyofthealpha-betasuppressionresponsedepends20onthepropertiesofthestimulation,weperformedANOVAwithfactorsContrastandVelocity.21
22
Power.TherewasnosignificanteffectsofContrastorVelocityortheirinteractioneffectforthe23
alpha-betasuppressionmagnitude(allp’s>0.5).24
25
Frequency. The peak frequency of the alpha-beta suppression sightly, but significantly,26
increasedwithincreasingmotionvelocity(Velocity:F(3,36)=7.1,e=0.57,p<0.01;11.8Hzfor0°/s,27
11.9Hzfor1.2°/s,12.0Hzfor3.6°/s,12.2Hzfor6.0°/s).28
29
30
Discussion31
32Wetestedforanadditiveeffectofcontrastandvelocityofdriftingvisualgratingsonthepower33
of induced MEG gamma oscillations. For both full-contrast and low-contrast gratings, the34
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21
power of gamma response initially increased with increasing motion velocity and was then1
suppressedatyethighervelocities.Mostimportantly,wefoundthatloweringcontrastledtoa2
highlyreliableshiftinthegammasuppressiontransitionpointtohighervelocities.Thisfinding3
provides an experimental proof for the crucial role of intensive excitatory input in GR4
suppression. The systematic relationship between the intensity of the visual input and the5
strengthoftheneuralresponsewasclearforgammaoscillationsandabsentinthealpha-beta6
frequencyrange.78
Theroleofexcitatorydriveininducedgammaresponse9
In linewith theprevious studies (Jiaetal.,2013;Perryetal.,2015;RayandMaunsell,2010;10
Roberts et al., 2013; van Pelt et al., 2018), we found that the peak frequency of the visual11
gamma response increased with increasing luminance contrast (Fig. 4A). As an increase in12
luminancecontrastcausesariseofneuronalspikingrate(Bartoloetal.,2011;Hadjipapasetal.,13
2015; Ray and Maunsell, 2010), contrast can be considered as a proxy for excitatory drive14
(Lowet et al., 2015). Therefore, contrast-dependent changes in gamma frequency and15
amplitudecanbeattributedtochangesinexcitatoryinputtothevisualcortex.16
Also in accordance with our previous results—and those of others—17
(Muthukumaraswamy and Singh, 2013; Orekhova et al., 2015; Orekhova et al., 2018b;18
Swettenhametal.,2009;vanPeltetal.,2018),wefoundthatthepeakfrequencyoftheMEG19
GR prominently increased (17 Hz on average for the 100% contrast) with increasingmotion20velocityfrom0to6°/s,whichcorrespondsto0-10Hzoftemporalfrequencyinourstudy(Fig21
4A). No saturation effect has been observed for the gamma frequency even at the highest22
speeds of stimulation.Most probably, such noticeable acceleration of gamma oscillations at23
the high stimulus velocity reflects a high excitatory state of the visual cortex caused by fast24
visualmotion. This assumption is supportedby studiesonmonkeys showing that increasing25
thetemporalfrequencyofdriftinggratingsfrom0Hzuptoapproximately10Hzleadstoarise26
inneuronalspikingandanincreaseingammafrequencyinV1(Salelkaretal.,2018).Fastvisual27
motion might engage more attention, which may also affect the excitatory state of V128
(ReynoldsandChelazzi,2004)and thusmodulate thegammafrequencyduringvisualmotion29
stimulation(vanPeltetal.,2018).Theeffectofattentionaloneis,however,unlikelytoexplain30
theobserved12-17Hzincreaseingammafrequencywithtransitionfromstatictofast-moving31
stimuli presented herein. Indeed, studies inmonkeys have shown that attention has aweak32
effectongammafrequency(approximately3Hz)(Bosmanetal.,2012).Therefore,changesin33gamma frequencywith increasingmotion velocity aremost likely accounted for by a rise in34
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22
bottom-upexcitatoryinputtothevisualcortex.1
Themostimportantfindingofthecurrentstudyistheeffectofluminancecontraston2
thebell-shapedinput-outputrelationshipsbetweenvisualmotionvelocityandthemagnitude3
of induced GR . Although at both 50% and 100% contrast, the initially facilitative effect of4
increasing velocity on gamma response strength was followed by a suppressive one, the5
maximumofthisbell-shapedcurveatthe50%contrastshiftedtohighervelocity,ascompared6
tothe100%contrast(Fig.3,5).Inspectionoftheindividualdatahasshownthatwithlowering7contrast, the approximated velocity of the gamma suppression transition increased in every8
singlesubject(Fig.6A,7C).Thisfindingsuggeststhatacertain levelofexcitation isnecessary9
for the gamma suppression to occur, which in case of the low contrast is achieved at a10
relativelyhighvelocity.Thus,ourexperimental resultsstronglysupport the“excitatorydrive”11
model thatpredictsanadditiveeffectof increasingcontrastandvelocity/temporal-frequency12
onthegammasuppressiontransitionpoint.13
The shift of the GR suppression transition point to higher velocities with lowering14
luminancecontrastcontradictsthe‘velocitytuning’model.Animalstudieshaveshownthatat15
lowercontrast, themajorityof responsiveneurons inV1 (AlittoandUsrey,2004;Livingstone16
and Conway, 2007; Priebe et al., 2006), LGN (Alitto and Usrey, 2004), and even the retina17
(ShapleyandVictor,1978,1981)are turned to lower temporal frequencies.Therefore, if the18
maximalGRwouldcorrespondtoan‘optimal’velocity,thesuppressiontransitionpointwould19
rathershifttoalowerdriftingratewhencontrastisreduced.20Another and more general argument against the ‘velocity tuning’ hypothesis is the21
mismatchinvelocitytuningoftheV1multiunitactivityandthatoftheV1gammaresponse.A22
recent study in nonhuman primates have shown that the gamma response power starts to23
suppressat lower temporal frequencies than thoseelicitingmaximal spiking rate inmultiunit24
activity. Specifically, at the temporal frequencies of visual motion corresponding to our25
‘medium’and‘fast’velocities,thegammaresponsealreadypasseditsmaximumandstartedto26
decrease,whileneuralspikingcontinuedtoincreaseorsaturated(seeFig.4in(Salelkaretal.,27
2018)).28
Althoughourfindingsfavortheexcitatorydrivemodel,apartfromtheexcitatorydrive,29
theremaybeother factors that also affect gammaparameters at the suppression transition30
point. The low-contrast stimuli, as comparedwith the high-contrast ones, generally induced31
lower-amplitudegammaresponsesandtriggeredthetransitiontothegammasuppressionat32
lower amplitude and frequencyof theGR (Fig. 5A, 6B). Lower contrast stimuli are known to33activate fewer neurons and to produce lower spiking rates in neural circuitry involved in34
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23
gamma synchronization (Henrie and Shapley, 2005). This explains the lower power and1
frequency of the gamma response at the suppression transition point in case of the lower2
contrast.3
4
Stateandtraitdependencyofvisualgammaparameters.5
Previous studies show that parameters of visual gamma oscillations are strongly genetically6
determined (van Pelt et al., 2012) and highly reproducible when measured across time7
(Hoogenboom et al., 2006; Muthukumaraswamy et al., 2010). As such, they represent8
constitutional traits that aredeterminedbyneurophysiological andneuro-anatomical factors9
(Schwarzkopfetal.,2012;vanPeltetal.,2018).10
While being individually stable in a single experimental condition, the gamma11
parameters are strongly affected by the properties of the visual stimulation. This raises the12
question of whether gamma measured in response to different visual stimuli --- e.g. those13
having different contrasts or moving with different speeds --- reflect the same or different14
neurophysiologicaltraits.15Previous studies that sought to find a link between visual gamma oscillations and16
processesofneuralexcitationandinhibitioninthehumanbrain(Cousijnetal.,2014;Eddenet17
al.,2009;Kujalaetal.,2015)measuredparametersoftheinducedgammaresponseinasingle18
experimental condition. Such an approach implicitly assumes that the features of gamma19
response do not substantially vary with stimulus properties in term of the rank-order20
consistencyoftheindividualvalues.Recently,vanPeltetal(vanPeltetal.,2018)reportedhigh21
between-condition correlations for gamma peak frequency and power measured at two22
contrasts (50%and100%)andat three velocities (0, 0.33°/s, 0.66°/s) that support this view.23
Importantly,thegammaresponsepowerinthatstudymonotonouslyincreasedwithincreasing24
velocity.ThismeansthatallofthestimulationvelocitieschosenbyvanPeltetalwerebelow25
the‘gammasuppressiontransitionvelocity’andcorrespondedtotheascendingbranchofthe26
bell–shapedcurvecharacterizingtherelationbetweenvelocityandgammapower.27
When we used a broader range of velocities, we found no reliable correlations28between the amplitudes of gamma responses caused by static (0°/s) and rapidly moving29
(3.2°/s,6.0°/s)stimuli(Fig.7B).Thus,theintra-individualstabilityofgammapoweracrossthese30
conditionswaslacking,suggestingthatthestrengthoftheGRattheascendinganddescending31
branches of the bell-curve is mediated by distinct neural processes. These findings clearly32
indicate that the analysis of visually-induced changes in gamma oscillations should extend33
beyondtheclassicalgammafrequencyandpowermetricsinsingleexperimentalconditions.34
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24
We found a remarkably strong and highly reliable correlation between gamma1
suppressiontransitionvelocitiesatthe100%and50%contrasts(SpearmanR(17)=0.91),pointing2
to the high within-subject reliability of individual STVel values across different experimental3
conditions. As we will argue in the following discussion, the STVel is a physiologically4
meaningfulmeasure thatmayhelp to characterize thecapacityof thenetwork to regulate a5
balancebetweenexcitationandinhibition.6
7
Magnitudeofthealpha-betasuppressionisnotsensitivetothestrengthofvisualinput.8
Althoughstationaryanddriftingvisualgratingsleadalsotoastrongandreliablesuppressionof9
theMEGpower in the alpha-beta rangeover theposterior cortical areas (Fig.3), the visually10
inducedalpha-betaresponsewasclearlydifferentfromthatinthegammarange.First,despite11
apparentoverlap(Fig.2,8),thespatial localizationofthealpha-betasuppressionandgamma12
facilitationsignificantlydiffered.Whereasthemaximumofthegammaresponsewaslocalized13
near the calcarine sulcus, the alpha-beta suppression occurred in more lateral areas of the14
occipital lobes (see also Supplementary Table 1 and 2 for localization of themaximal effect15
voxels).Thisfindingisinlinewithresultsofpreviousstudiesthatshowmorelateralpositions16
of the sources of alpha-beta suppression relative to those of gamma enhancement17
(Hoogenboometal.,2006;Koelewijnetal.,2011;Muthukumaraswamyetal.,2016).Second,18
and more importantly, in sharp contrast with the gamma response, neither contrast nor19
velocity affected power suppression in the alpha-beta frequency range. This lack of a20relationship between the intensity of visual stimulation and the degree of the alpha-beta21
suppression questions the commonly held notion that the magnitude of visual alpha22
suppression indexestheexcitatorystateof thevisualcortex.Ontheotherhand,our findings23
donot contradict thenumerous results associating relativedecreasesand increases in visual24
alpha-bandactivitywithattention-relatedinfluencesmodulatingexcitabilityofvisualcortex(de25
Pestersetal.,2016;Jensenetal.,2014;Klimesch,2012;Stormeretal.,2016).26
Of note, the weighted peak frequency of the alpha-beta suppression slightly, but27
significantly, increased with increasing velocity of visual motion, most probably because of28
strongerbetasuppressionassociatedwithhighervelocity(Fig.8B). Itwouldbeinterestingto29
investigate in more detail how activity in the beta and low gamma ranges is affected by30
increasingvelocityofthevisualmotion,butthistopicisbeyondthescopeofthepresentstudy.31
32
GammasuppressiontransitionpointandE-Ibalanceinthevisualcortex.33
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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25
Overall, our findings indicate that the strengthof excitatorydrive, but not velocity tuningof1
visualneurons, is themain factor contributing toan initially facilitativeand thensuppressive2
effectofvisualmotionvelocityonthegammaresponsepower.Thisfactisfullyconsistentwith3
predictionsofcomputationalmodelingstudies(BorgersandKopell,2005;BorgersandWalker,4
2013;Cannonetal.,2014;Kingetal.,2013;Kopelletal.,2010),and, takenonestepfurther,5
favors the idea that the gamma suppression transition point could serve as a non-invasive6
biomarkeroftheefficiencyoftheE/Ibalanceregulationinthevisualcortex.7Itiscommonlyacceptedthattheactivestatesofthebrainrelatedtotheprocessingof8
sensory information are characterized by a ‘desynchronized’ pattern of electromagnetic9
activity, i.e. by a relative predominance of high-frequency (gamma) oscillations and relative10
suppression of those of lower frequencies (alpha, beta). Indeed, we have found that a11
moderate rise in sensory input drive produced an increase of gammapower,most probably12
due toaprogressivelygreater recruitmentof visualneurons intogammasynchronization.At13
first glance, the suppressionof thegamma responseata yethigher levelof inputdrivemay14
appearcounterintuitive. Ithasbeen,howeverpredictedthatgammamaydesynchronizeata15
high level of excitatory drive due to an overexcited state of inhibitory neurons (Borgers and16
Kopell,2005;Kingetal.,2013).Ithasbeenarguedthattheasynchronousactivityofthehighly17
excitedinhibitoryneuronsisparticularlyeffectiveindown-regulatingactivity intheexcitatory18
principlecells,andthereforehasanimportantroleinhomeostaticregulationoftheneuralE/I19
balance(BorgersandKopell,2005).Incaseofstrongexcitabilityoftheprincipalcells,stronger20inhibitory influences would be required to achieve a breakdown of gamma synchrony. This21
meansthattheconstitutivefactorsthatincreaseordecreasetheexcitabilityoftheE-orI-cells22
mayresultinshiftingthegammasuppressionpointtohigherorlowerintensitiesofexcitatory23
drive.24
Specifically,thetransitiontosuppressionofthegammaresponseatarelativelyhigher25
level of excitatory drive and/or the shallower slope of this suppression would reflect less26
effectiveinhibition.Inindirectsupportofthisassumption,wehaverecentlydescribedalackof27
velocity-relatedgammasuppressioninasubjectwithepilepsyandoccipitalspikes(Orekhovaet28
al.,2018b),whomighthaveanelevatedE-Iratiointhevisualcortex.Thecausallinkbetween29
the E-I ratio and the gamma suppression transition point could, in the future, be proved30
directly using pharmacological or other (e.g. tDCS, TMS) approaches tomanipulating the E-I31
balance.32
Gamma synchronization facilitates neural communication and increases the effective33outputofpresynaptic(Bastosetal.,2015;Fries,2005,2009,2015;Nietal.,2016;Rigoulotet34
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26
al., 2017; Vinck et al., 2013). Therefore, synchronization of V1 neural activity in the gamma1
range is thought toenhancetheeffectiveoutput fromtheprimaryvisualcortex toupstream2
cortical areas, thus affecting visual perception. Aweak suppression of gammaoscillations at3
highstimulationintensitiesmaythenresultinastrongerimpactofthesensorystimulationon4
theneural network, and lead to sensoryoverload. Indeed, in a recent study,wehave found5
that a weaker suppression of gamma response at fast motion velocities (of high-contrast6
gratings) correlated with enhanced sensory sensitivity, especially in the visual domain7(Orekhovaetal.,2018a).8
Futurestudiesinclinicalpopulationscharacterizedbyelevatedcorticalexcitability(e.g.9
epilepsy,migraine, some forms of ASD) would help to assess the potential value of gamma10
suppressionasabiomarkerforimpairedregulationoftheE/Ibalance.11
12
13
CONCLUSIONS14
15
To summarize, our current findings provide strong support for the hypothesis linking16
magnitude of the visual gamma response caused by drifting gratings to the strength of17
excitatory drive. At the same time, our results indicate that ‘velocity turning’ of V1 neurons18
doesnotplayaprimaryroleinregulatingthemagnitudeofthegammaresponse.19
In both low- and high-contrast conditions the power of visual gamma oscillations20
demonstrated bell-shaped dependency on velocity of visual motion: it first increased with21
increasingvelocityandthendecreaseswithfurtherincreaseinthedriftingrate.Aspredicted22
by the ‘excitatorydrivemodel’, reducing intensityof thevisual inputby loweringcontrastof23the grating shifted the maximum of the bell-shaped curve --- the ‘suppression transition24
velocity’’ --- to higher velocity values. Unlike induced gamma oscillations, the visual alpha25
responsewasnotsensitivetothechangesinthegratings'velocityorcontrast.26
Our present findings, which support the causative role of strong excitatory drive in27
visual gamma response suppression, have important theoretical implications. The28
computationalmodelingstudiesandourpreviousexperimentalresults implythatsuchcausal29
relationships may reflect the capacity of inhibitory neurons to down-regulate growing30
excitation and to maintain the E-I balance. Therefore, the shape of the gamma response31
modulationcurveand, inparticular,the‘suppressiontransitionvelocity’parameterevaluated32
inthepresentstudy,mayprovideimportantinformationaboutregulationoftheE-Ibalance.33
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27
Weanticipatethatsuchacombinatoryindexas‘suppressiontransitionvelocity’willbe1
abettermeasureoftheE-Idysfunctioninbraindisordersthanvisualgammaparametersthat2
areextractedinasingleexperimentalcondition.Apartfromhavingtheoreticalrelevance,this3
index evades several caveats of using peak gamma amplitude and frequency. Firstly, it is4
inherently relational, and is therefore less sensitive to inter-individual differences in cortical5
anatomy and SNR. Secondly it allows to avoid ambiguity related to single-condition6
assessmentsofgammaamplitudes,whichdonotalwayscorrelatewitheachother(e.g.,when7measured with static vs. ‘fast movement’ conditions); Thirdly, it's individual values have8
remarkablerank-orderconsistencywhenmeasuredatdifferentcontrasts,andthereforeshould9
have high test–reteststability. Altogether, these considerations suggest that the ‘gamma10
suppressiontransitionvelocity’hasatranslationalpotentialasan indexoftheE-Ibalancefor11
informingclinicalpracticeandtrials.12
13
14
15
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28
1
Referenses2
3
Alitto, H.J., Usrey,W.M., 2004. Influence of contrast on orientation and temporal frequency4
tuninginferretprimaryvisualcortex.JournalofNeurophysiology91,2797-2808.5
Anver,H.,Ward,P.D.,Magony,A.,Vreugdenhil,M.,2011.NMDAReceptorHypofunctionPhase6
Couples Independent gamma-Oscillations in the Rat Visual Cortex.7
Neuropsychopharmacology36,519-528.8
Bartolo, M.J., Gieselmann,M.A., Vuksanovic, V., Hunter, D., Sun, L., Chen, X., Delicato, L.S.,9
Thiele, A., 2011. Stimulus-induced dissociation of neuronal firing rates and local field10potential gamma power and its relationship to the resonance blood oxygen level-11
dependentsignalinmacaqueprimaryvisualcortex.EuropeanJournalofNeuroscience34,12
1857-1870.13
Bastos,A.M.,Vezoli,J.,Bosman,C.A.,Schoffelen,J.M.,Oostenveld,R.,Dowdall,J.R.,DeWeerd,14
P.,Kennedy,H.,Fries,P.,2015.VisualAreasExertFeedforwardandFeedback Influences15
throughDistinctFrequencyChannels.Neuron85,390-401.16
Borgers,C., Kopell,N., 2005. Effectsofnoisydriveon rhythms innetworksofexcitatoryand17
inhibitoryneurons.NeuralComputation17,557-608.18
Borgers,C.,Walker,B.,2013.Togglingbetweengamma-frequencyactivityandsuppressionof19
cellassemblies.FrontComputNeurosc.20
Bosman, C.A., Schoffelen, J.M., Brunet, N., Oostenveld, R., Bastos, A.M., Womelsdorf, T.,21
Rubehn, B., Stieglitz, T., De Weerd, P., Fries, P., 2012. Attentional Stimulus Selection22
throughSelectiveSynchronizationbetweenMonkeyVisualAreas.Neuron75,875-888.23Buzsaki, G., Wang, X.J., 2012. Mechanisms of Gamma Oscillations. Annual Review of24
Neuroscience,Vol3535,203-225.25
Cannon, J.,McCarthy,M.M., Lee, S., Lee, J., Borgers, C.,Whittington,M.A., Kopell,N., 2014.26
Neurosystems:brainrhythmsandcognitiveprocessing.EuropeanJournalofNeuroscience27
39,705-719.28
Chen, G., Zhang, Y., Li, X., Zhao, X., Ye, Q., Lin, Y., Tao, H.W., Rasch, M.J., Zhang, X., 2017.29
DistinctInhibitoryCircuitsOrchestrateCorticalbetaandgammaBandOscillations.Neuron30
96,1403-1418e1406.31
Cousijn, H., Haegens, S., Wallis, G., Near, J., Stokes, M.G., Harrison, P.J., Nobre, A.C., 2014.32
Resting GABA and glutamate concentrations do not predict visual gamma frequency or33
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
29
amplitude. Proceedings of the National Academy of Sciences of the United States of1
America111,9301-9306.2
dePesters,A.,Coon,W.G.,Brunner,P.,Gunduz,A.,Ritaccio,A.L.,Brunet,N.M.,deWeerd,P.,3
Roberts,M.J.,Oostenveld,R.,Fries,P.,Schalk,G.,2016.Alphapowerindexestask-related4
networksonlargeandsmallscales:AmultimodalECoGstudyinhumansandanon-human5
primate.Neuroimage134,122-131.6
Edden, R.A., Muthukumaraswamy, S.D., Freeman, T.C., Singh, K.D., 2009. Orientation7discrimination performance is predicted by GABA concentration and gamma oscillation8
frequencyinhumanprimaryvisualcortex.JNeurosci29,15721-15726.9
Ferando,I.,Mody,I.,2013.Alteredgammaoscillationsduringpregnancythroughlossofdelta10
subunit-containing GABA(A) receptors on parvalbumin interneurons. Frontiers in Neural11
Circuits,p.144.12
Fries, P., 2005. A mechanism for cognitive dynamics: neuronal communication through13
neuronalcoherence.TrendsinCognitiveSciences9,474-480.14
Fries, P., 2009. Neuronal gamma-band synchronization as a fundamental process in cortical15
computation.AnnualReviewofNeuroscience,Vol3532,209-224.16
Fries, P., 2015. Rhythms for Cognition: Communication through Coherence.Neuron 88, 220-17
235.18
Gaetz, W., Roberts, T.P.L., Singh, K.D., Muthukumaraswamy, S.D., 2012. Functional and19
structuralcorrelatesoftheagingbrain:Relatingvisualcortex(V1)gammabandresponses20toage-relatedstructuralchange.HumanBrainMapping33,2035-2046.21
Gramfort,A.,Luessi,M.,Larson,E.,Engemann,D.A.,Strohmeier,D.,Brodbeck,C.,Goj,R.,Jas,22
M., Brooks, T., Parkkonen, L., Hamalainen, M., 2013. MEG and EEG data analysis with23
MNE-Python.FrontNeurosci.24
Gross,J.,Kujala,J.,Hamalainen,M.,Timmermann,L.,Schnitzler,A.,Salmelin,R.,2001.Dynamic25
imagingofcoherentsources:Studyingneuralinteractionsinthehumanbrain.Proceedings26
oftheNationalAcademyofSciencesoftheUnitedStatesofAmerica98,694-699.27
Hadjipapas,A.,Lowet,E.,Roberts,M.J.,Peter,A.,DeWeerd,P.,2015.Parametricvariationof28
gamma frequency and power with luminance contrast: A comparative study of human29
MEGandmonkeyLFPandspikeresponses.Neuroimage112,327-340.30
Henrie, J.A., Shapley,R.,2005. LFPpower spectra inV1cortex: thegradedeffectof stimulus31
contrast.JNeurophysiol94,479-490.32
Hoogenboom, N., Schoffelen, J.M., Oostenveld, R., Parkes, L.M., Fries, P., 2006. Localizing33humanvisualgamma-bandactivityinfrequency,timeandspace.Neuroimage29,764-773.34
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
30
Jensen, O., Gips, B., Bergmann, T.O., Bonnefond, M., 2014. Temporal coding organized by1
coupled alpha and gamma oscillations prioritize visual processing. Trends Neurosci 37,2
357-369.3
Jia, X.X., Smith,M.A., Kohn, A., 2011. Stimulus Selectivity and Spatial Coherence of Gamma4
ComponentsoftheLocalFieldPotential.JNeurosci31,9390-9403.5
Jia, X.X., Xing, D.J., Kohn, A., 2013. No Consistent Relationship between Gamma Power and6
PeakFrequencyinMacaquePrimaryVisualCortex.JNeurosci33,17-U421.7King,P.D.,Zylberberg,J.,DeWeese,M.R.,2013.Inhibitoryinterneuronsdecorrelateexcitatory8
cellstodrivesparsecodeformationinaspikingmodelofV1.JNeurosci33,5475-5485.9
Klimesch, W., 2012. alpha-band oscillations, attention, and controlled access to stored10
information.TrendsCognSci16,606-617.11
Koelewijn, L., Dumont, J.R.,Muthukumaraswamy, S.D., Rich, A.N., Singh, K.D., 2011. Induced12
andevokedneuralcorrelatesoforientationselectivityinhumanvisualcortex.Neuroimage13
54,2983-2993.14
Kopell,N.,Börgers,C.,Pervouchine,D.,Malerba,P.,Tort,A.,2010.GammaandThetaRhythms15
in Biophysical Models of Hippocampal Circuits. Springer Series in Computational16
NeuroscienceSpringerScience+BusinessMedia.17
Kujala,J.,Jung,J.,Bouvard,S.,Lecaignard,F.,Lothe,A.,Bouet,R.,Ciumas,C.,Ryvlin,P.,Jerbi,18
K.,2015.GammaoscillationsinV1arecorrelatedwithGABA(A)receptordensity:Amulti-19
modalMEGandFlumazenil-PETstudy.SciRep5,16347.20Liu,Y.,Bengson,J.,Huang,H.,Mangun,G.R.,Ding,M.,2016.Top-downModulationofNeural21
Activity in Anticipatory Visual Attention: ControlMechanisms Revealed by Simultaneous22
EEG-fMRI.CerebCortex26,517-529.23
Livingstone, M.S., Conway, B.R., 2007. Contrast affects speed tuning, space-time slant, and24
receptive-fieldorganizationofsimplecellsinmacaqueV1.JNeurophysiol97,849-857.25
Lowet,E.,Roberts,M.,Hadjipapas,A.,Peter,A.,vanderEerden,J.,DeWeerd,P.,2015.Input-26
Dependent Frequency Modulation of Cortical Gamma Oscillations Shapes Spatial27
SynchronizationandEnablesPhaseCoding.PlosComputationalBiology11.28
Mann, E.O., Mody, I., 2010. Control of hippocampal gamma oscillation frequency by tonic29
inhibitionandexcitationofinterneurons.NatureNeuroscience13,205-U290.30
Murty,D.,Shirhatti,V.,Ravishankar,P.,Ray,S.,2018.LargeVisualStimuliInduceTwoDistinct31
GammaOscillationsinPrimateVisualCortex.JNeurosci38,2730-2744.32
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
31
Muthukumaraswamy,S.D.,Routley,B.,Droog,W.,Singh,K.D.,Hamandi,K.,2016.Theeffects1
ofAMPAblockadeonthespectralprofileofhumanearlyvisualcortexrecordingsstudied2
withnon-invasiveMEG.Cortex81,266-275.3
Muthukumaraswamy,S.D.,Singh,K.D.,2013.Visualgammaoscillations:Theeffectsofstimulus4
type,visualfieldcoverageandstimulusmotiononMEGandEEGrecordings.Neuroimage5
69,223-230.6
Muthukumaraswamy, S.D., Singh, K.D., Swettenham, J.B., Jones, D.K., 2010. Visual gamma7oscillationsandevokedresponses:Variability,repeatabilityandstructuralMRIcorrelates.8
Neuroimage49,3349-3357.9
Ni,J.G.,Wunderle,T.,Lewis,C.M.,Desimone,R.,Diester, I.,Fries,P.,2016.Gamma-Rhythmic10
GainModulation.Neuron92,240-251.11
Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional12
neuroimaging:Aprimerwithexamples.HumanBrainMapping15,1-25.13
Oostenveld,R.,Fries,P.,Maris,E.,Schoffelen,J.M.,2011.FieldTrip:Opensourcesoftwarefor14
advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell15
Neurosci.16
Orekhova,E.V.,Butorina,A.V.,Sysoeva,O.V.,Prokofyev,A.O.,Nikolaeva,A.Y.,Stroganova,T.A.,17
2015. Frequency of gamma oscillations in humans is modulated by velocity of visual18
motion.JournalofNeurophysiology114,244-255.19
Orekhova, E.V., Stroganova, T.A., Schneiderman, J.F., Lundström, S., Riaz, B., Sarovoc, D.,20Sysoeva,O.V.,Brant,G.,Gillberg,C.,Hadjikhani,N.,2018a.Neuralgaincontrolmeasured21
through cortical gamma oscillations is associated with sensory sensitivity. Hum Brain22
Mapp,inpress.23
Orekhova, E.V., Sysoeva, O.V., Schneiderman, J.F., Lundstrom, S., Galuta, I.A., Goiaeva, D.E.,24
Prokofyev,A.O., Riaz, B., Keeler, C.,Hadjikhani,N.,Gillberg, C., Stroganova, T.A., 2018b.25
Input-dependentmodulationofMEGgammaoscillationsreflectsgaincontrolinthevisual26
cortex.SciRep8,8451.27
Palva,S.,Palva, J.M.,2011.Functional rolesofalpha-bandphasesynchronization in localand28
large-scalecorticalnetworks.FrontPsychol2,204.29
Perry,G.,Randle,J.M.,Koelewijn,L.,Routley,B.C.,Singh,K.D.,2015.LinearTuningofGamma30
Amplitude and Frequency to LuminanceContrast: Evidence froma ContinuousMapping31
Paradigm.PLoSOne.32
Pfurtscheller, G., da Silva, F.H.L., 1999. Event-related EEG/MEG synchronization and33desynchronization:basicprinciples.ClinicalNeurophysiology110,1842-1857.34
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
32
Priebe, N.J., Lisberger, S.G., Movshon, J.A., 2006. Tuning for spatiotemporal frequency and1
speedindirectionallyselectiveneuronsofmacaquestriatecortex.JournalofNeuroscience2
26,2941-2950.3
Ray, S., Maunsell, J.H., 2010. Differences in gamma frequencies across visual cortex restrict4
theirpossibleuseincomputation.Neuron67,885-896.5
Reynolds,J.H.,Chelazzi,L.,2004.Attentionalmodulationofvisualprocessing.AnnualReviewof6
Neuroscience27,611-647.7Rigoulot, S., Knoth, I.S., Lafontaine,M.P., Vannasing, P.,Major, P., Jacquemont, S.,Michaud,8
J.L.,Jerbi,K.,Lippe,S.,2017.AlteredvisualrepetitionsuppressioninFragileXSyndrome:9
NewevidencefromERPsandoscillatoryactivity.IntJDevNeurosci59,52-59.10
Roberts,M.J.,Lowet,E.,Brunet,N.M.,TerWal,M.,Tiesinga,P.,Fries,P.,DeWeerd,P.,2013.11
RobustGammaCoherencebetweenMacaqueV1andV2byDynamicFrequencyMatching.12
Neuron78,523-536.13
Romei,V.,Brodbeck,V.,Michel,C.,Amedi,A.,Pascual-Leone,A.,Thut,G.,2008.Spontaneous14
fluctuationsinposterioralpha-bandEEGactivityreflectvariabilityinexcitabilityofhuman15
visualareas.CerebCortex18,2010-2018.16
Salelkar, S., Somasekhar, G.M., Ray, S., 2018. Distinct frequency bands in the local field17
potentialaredifferentlytunedtostimulusdriftrate.JNeurophysiol.18
Scheeringa,R.,Koopmans,P.J.,vanMourik,T.,Jensen,O.,Norris,D.G.,2016.Therelationship19
betweenoscillatoryEEGactivityandthelaminar-specificBOLDsignal.Proceedingsofthe20NationalAcademyofSciencesoftheUnitedStatesofAmerica113,6761-6766.21
Schwarzkopf, D.S., Robertson, D.J., Song, C., Barnes, G.R., Rees, G., 2012. The frequency of22
visually induced gamma-band oscillations depends on the size of early human visual23
cortex.JNeurosci32,1507-1512.24
Shapley,R.M.,Victor,J.D.,1978.Theeffectofcontrastonthetransferpropertiesofcatretinal25
ganglioncells.JPhysiol285,275-298.26
Shapley, R.M., Victor, J.D., 1981. How the contrast gain control modifies the frequency27
responsesofcatretinalganglioncells.JPhysiol318,161-179.28
Snyder,A.C.,Foxe,J.J.,2010.Anticipatoryattentionalsuppressionofvisualfeaturesindexedby29
oscillatoryalpha-bandpowerincreases:ahigh-densityelectricalmappingstudy.JNeurosci30
30,4024-4032.31
Stormer,V.,Feng,W.,Martinez,A.,McDonald,J.,Hillyard,S.,2016.Salient,IrrelevantSounds32
ReflexivelyInduceAlphaRhythmDesynchronizationinParallelwithSlowPotentialShiftsin33VisualCortex.JCognNeurosci28,433-445.34
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
33
Sumner,R.L.,McMillan,R.L.,Shaw,A.D.,Singh,K.D.,Sundram,F.,Muthukumaraswamy,S.D.,1
2018. Peak visual gamma frequency is modified across the healthy menstrual cycle.2
HumanBrainMapping39,3187-3202.3
Swettenham,J.B.,Muthukumaraswamy,S.D.,Singh,K.D.,2009.SpectralPropertiesofInduced4
and EvokedGammaOscillations inHumanEarlyVisual Cortex toMoving and Stationary5
Stimuli.JournalofNeurophysiology102,1241-1253.6
Tan,H.R.M.,Gross,J.,Uhlhaas,P.J.,2016.MEGsensorandsourcemeasuresofvisuallyinduced7gamma-bandoscillationsarehighlyreliable.Neuroimage137,34-44.8
Taulu,S.,Hari,R.,2009.Removalofmagnetoencephalographicartifactswithtemporalsignal-9
space separation: demonstrationwith single-trial auditory-evoked responses.HumBrain10
Mapp30,1524-1534.11
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N.,12
Mazoyer,B.,Joliot,M.,2002.AutomatedanatomicallabelingofactivationsinSPMusinga13
macroscopicanatomicalparcellationoftheMNIMRIsingle-subjectbrain.Neuroimage15,14
273-289.15
Van Hooser, S.D., Roy, A., Rhodes, H.J., Culp, J.H., Fitzpatrick, D., 2013. Transformation of16
Receptive Field Properties from LateralGeniculateNucleus to Superficial V1 in the Tree17
Shrew.JournalofNeuroscience33,11494-11505.18
van Kerkoerle, T., Self, M.W., Dagnino, B., Gariel-Mathis, M.A., Poort, J., van der Togt, C.,19
Roelfsema, P.R., 2014. Alpha and gamma oscillations characterize feedback and20feedforward processing in monkey visual cortex. Proc Natl Acad Sci U S A 111, 14332-21
14341.22
vanPelt,S.,Boomsma,D.I.,Fries,P.,2012.MagnetoencephalographyinTwinsRevealsaStrong23
Genetic Determination of the Peak Frequency of Visually Induced Gamma-Band24
Synchronization.JournalofNeuroscience32,3388-3392.25
van Pelt, S., Shumskaya, E., Fries, P., 2018. Cortical volume and sex influence visual gamma.26
Neuroimage178,702-712.27
VanVeen, B.D., vanDrongelen, W., Yuchtman, M., Suzuki, A., 1997. Localization of brain28
electrical activity via linearly constrained minimum variance spatial filtering. Ieee29
TransactionsonBiomedicalEngineering44,867-880.30
Vinck, M., Womelsdorf, T., Fries, P., 2013. Gamma-band synchronization and information31
transmission. In:Quiroga,R.Q.,Panzeri, S. (Eds.),PrinciplesofNeuralCoding.CRCPress,32
pp.449-469.33
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint
34
Yuan,H.,Liu,T.,Szarkowski,R.,Rios,C.,Ashe, J.,He,B.,2010.Negativecovariationbetween1
task-related responses in alpha/beta-band activity and BOLD in human sensorimotor2
cortex:anEEGandfMRIstudyofmotorimageryandmovements.Neuroimage49,2596-3
2606.4
5
.CC-BY-ND 4.0 International licenseavailable under awas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted December 28, 2018. ; https://doi.org/10.1101/497214doi: bioRxiv preprint