SpecialIssue:TimeintheBrain
Review
Roles
of
Brain
Criticality
and
Multiscale
Oscillations
in
Temporal
Predictions
for
Sensorimotor
Processing
Satu
Palva
1,* and
J.
Matias
Palva
1,*
Sensorimotorpredictionsareessentialforadaptivebehavior.Innatural envi-ronments,eventsthatdemand sensorimotorpredictionsunfoldacross many timescales,andcorrespondingtemporalpredictions(eitherexplicitorimplicit) shouldthereforeemerge in braindynamics. Neuronal oscillations are scale-specificprocessesfoundinseveralfrequencybands.Theyunderlieperiodicity insensorimotor processing andcan represent temporal predictionsviatheir phasedynamics.Theseprocessesbuilduponendogenousneuralrhythmicity and adapt in response to exogenous timing demands. While much of the researchonperiodicityinneuralprocessinghasfocusedonsubsecond oscil-lations,thesefast-scalerhythmsareinfactparalleledbycritical-like,scale-free dynamicsandfluctuationsofbrainactivityatvarioustimescales,rangingfrom seconds to hundreds of seconds. In this review, we put forth a framework positing that critical brain dynamics are essential for the role of neuronal oscillations in timing and that cross-frequency coupling flexibly organizes neuronalprocessingacrossmultiplefrequencies.
Critical-likeMultiscale BrainOscillationsforProcessinga Critical-like MultiscaleEnvironment?
Perceptionandactioninnaturalenvironmentsnecessitateinterplayamongrepresentationsof pastevents,thecurrentstate,as wellaspredictionsofboth futureeventsandthe conse-quencesofourownactionsacrossacontinuumofvastlydifferenttimescales.Notably,natural visual[1]andauditory[2]scenesarecharacteristicallyscale-freeandarrhythmicsignalswith power-lawscalingbehavior(Box1andFigure1A–C).Suchlackofscalingorperiodicitiesis perhapsnottoosurprising,consideringthatmanyaspectsofourenvironmentare ongoing processes involving nonlinear interactions among many agents, or are products of such processes,andtherebyencompasscomplexor‘critical’spatiotemporaldynamics[3].Critical dynamicsappearinsystemspoisedatatransitionbetweentwophases,andsuchsystemsare characterizedbystochasticfractal-likearchitectures,power-lawcorrelations,andrapid inter-mittentstatetransitions[3,4].However,inmanyreal-worldsettingsscale-freeenvironmental contextsareaccompaniedbydistinctscale-specificphenomena(i.e.,periodicorquasiperiodic signals;Box1).Suchscale-specificcomponentsarisebothfromartificialsourcesandfrom biologicalsystems,forinstancethroughphenomenawithrhythmiccomponents,suchasgait andproductionofspeechandmusic(Figure1C–E).
What are the neuralprocesses that facilitateeffective perception,cognition,and action in environmentsthatconsistofbothscale-freeandscale-specifictemporalstructures?Therange ofbehaviorallyrelevantenvironmentaltemporalscales iswellparalleledbythe spectrumof
Highlights
Neuronaloscillationsinongoingbrain activityadapttobehaviorallyrelevant sensorimotor rhythmicities byphase predictionandentrainment. Neuronal oscillations also endogen-ouslyimposerhythmicityon percep-tual, attentional, and action-generationprocesses.
Theseoscillations arespectrally dis-tributed and functionallyspecialized, andthus theydependon cross-fre-quency coupling for functional integration.
Infraslow(0.01–0.1Hz)andslow(0.1– 1Hz)EEGandBOLDfluctuationsare phasecoupledwith >1Hz neuronal oscillationsandslowbehavioral perfor-mancefluctuations.
Neuronaloscillationsarelikelyto oper-ateinacritical-likestatewithsignificant interindividual variability, which has fundamentalimplicationsforthe cap-abilityofindividualsubjects’neuronal networkstosynchronize,respondto phase-resettingstimuli,andentrainto exogenousoscillation.
1
NeuroscienceCenter,Helsinki InstituteofLifeScience,Universityof Helsinki,Haartmaninkatu3,00029 Helsinki,Finland
*Correspondence:
[email protected](S.Palva)and
[email protected](J.M.Palva). 729
neuronalactivities, spanning from frequencies as low as 0.01Hz up to 200Hz. Neuronal oscillationsarescale-specificrhythmicorperiodicexcitabilityfluctuationsinneuronal popu-lationsthatalsomodulatepsychophysicalperformance(Figure1F,G).Theyimposean oscilla-tion-phase-dependent bias on neuronal firing [5], so that firing is facilitated in the high-excitabilityphaseandsuppressedinthelow-excitabilityphase.Oscillationphaseprogression intrinsically contains prospective temporal information about the moments when neuronal processingwillbeenhanced.Henceoscillations,bydefinition,implyamechanismfor repre-sentingtemporalpredictions.Itappears,indeed,thatthe brainuses oscillationsto sample sensoryinformationandmotoractionsrhythmically,andthatitadaptivelyexploitstheoscillation phasetooptimizeprocessinginenvironmentallyappropriatemoments.
In this review, we aim to bridge three lines of research relevant to this theme. First, we recapitulatethe often-suggestedroleof neuronaloscillationsinproviding endogenous and adaptiveclockingformomentarilyenhancingneuronalprocessing.Second,weproposethat distinctformsofcross-frequencycouplingplayfunctionalrolesinorganizingthecooperationof neuronalprocessesmanifestingindistincttimescalesorfrequencybands.Third,wesuggest thatcritical-likedynamics,asimpliedintheslowfluctuationsoftheseoscillations,areessential forenablingtheirfunctionalrolesbyendowingthemwiththreecrucialproperties:rapidphase resetting,entrainability,andsynchronizability.
AdaptationofNeuronalOscillations toExternal RhythmicityviaEntrainment Efficientneuronalprocessingrequiresadaptiveoptimizationtomatchtheexpectedtemporal structuresinsensoryinformationatvarioustimescales.Inparadigmsinvolvingperiodicstimulus presentation,thistemporalstructureissalientand,indeed,suchperiodicstimulioftenentrain neuronaloscillationsatthecorrespondingfrequency.Forexample,auditorystimulipresented atdeltafrequencies(1–4Hz)entraindeltaoscillationsandleadtoimprovedstimulusdetection probabilities compared to nonperiodic stimulus presentation [6–10]. Moreover, if there is temporalvariabilityintheacousticsequences,thephase relationshipofdeltaoscillationsin theauditorycortexandthestimulussequencepredictsthedetectionofnear-thresholdstimuli
Box1.Arrhythmic,Quasiperiodic,andPeriodicSignalsCharacterizeNaturalEnvironmentsandBrain
Activity
Thespatiotemporalstructureofenvironmentalinformationisstatisticallyfractal-likeandtypicallylackscharacteristic scales(i.e.,itis‘scale-free’).Forexample,pictures[1]andaudiorecordings[2]ofnatural(includingurban)environments (Figure1A)have1/fa
-noiselikepowerspectrawithroughlyalog–loglineardecayofpowerasafunctionoffrequency (Figure1B)andalackofpeaksbothinsingletrialsandindataaveragedacrossmanytrials.However,especiallyin human-madecontextsorinthepresenceofotheranimals/humans,itiseasilyconceivabletohavebehaviorallyrelevant informationthathasacharacteristicscale(i.e.,thatisatleasttransientlyoscillatory;Figure1C).Processesthatproduce recurrentpatternswith an irregularperiod are termed‘quasiperiodic’oscillations. Insingletrials, quasiperiodic processesmayexhibitpower-spectralpeaks(Figure1D),butwithadequatevariabilityorsummationofsignalsfrom manyprocessesexpressingdistinctperiodicitiesindifferenttrials,theaveragedpowerspectrummaybeexperimentally indistinguishablefromthespectraofgenuinelyarrhythmicsignals(Figure1B).Inthecaseofoscillationsthathavestable periodicityacrossmanytrials,boththesingletrialandaveragedpowerspectrahavepeaks.Thefundamentaldifference betweenarrhythmicandquasiperiodicprocessesisthatforquasiperiodic,exactlylikeforperiodicprocesses,phaseisa functionallysignificantvariablewhereasforarrhythmicprocesses,phaseisirrelevantandpoorlydefinedbecauseno periodicitiesexist.
Humanpsychophysicalperformanceisalsocharacterizedbyscale-freedynamics.ConcurrentEEGrecordingsreveal infraslow(0.01–0.1Hz)neuronalfluctuations ofwhichthephaseiscorrelatedwiththefluctuationsofbehavioral performance[101].ThisimpliesthatinfraslowEEGfluctuationsarequasiperiodicoscillationsbecausetheiraveraged powerspectrumdoesnotexhibitsalientpeaks(Figure1G)suchastheoneobservedat10Hzforalphaoscillations, evenifpeaksmaybeobservedinshorterdatasegments[120].InfraslowscalpEEGfluctuationsarecoupledwithfMRI resting-statenetworks[106]andcurrentlyrepresenttheslowestprocesswherephasebiasesbehavior.
(E)
Time
Power
Power
Single trials Averaged data
Frequency Power Frequency (Periodic) oscillaƟons Quasiperiodic oscillaƟons Arrhythmic signals/ ‘1/f noise’ (A) (C) (B) (D) Po w er (V 2) 10-10 10-8 10-6 Frequency (Hz) 0.01 0.1 1 10 50 (F) (G) Hit Miss EEG(0.01-0.1 Hz) EEG(0-200 Hz) EEGPhase 50 s 100 μV Figure1.
(Figurelegendcontinuedonthebottomofthenextpage.)
Arrhythmic,Quasiperiodic,andPeriodicSignalsintheEnvironmentandintheBrain.(A)Natural
imagesandaudiorecordingstypicallyexhibitpower-lawstatistics.Thesignaltraceisasimulated1/fnoisethat(B)inboth singlerealizations(left)andinaverageddata(right)yields1/fpowerspectra.(C)Signalsemergingfrombothnatural environmentsandinonesshapedbyhumansoftencontainperiodicities.Intheimageshownhere,periodicitiescouldbe related,forinstance,topolicecarsirens,trafficlights,andringtones,aswellashumangait,speech,andmusic.Mixturesof transientperiodicitiesfrommultiplesourcesorquasiperiodicoscillatorswithalargevariabilityinoscillationperiodsgiverise topowerspectra(D)thatexhibitpeaksatthecorrespondingfrequencies(greenandbluebars)insingletrials,eventhough inaverageddatathespectramaybevoidofpeaks.(E)Onlyoscillationswithastableperiodretainpeaksinaveragedpower spectra.(F)Humanpsychophysicalperformanceisoftencharacterizedbyscale-freedynamics.Inasomatosensory thresholdstimulusdetectiontask,forinstance,detected(blue)andundetected(red)stimuliareclusteredwithpower-law, long-range,temporalcorrelations[101,103](here,dataarefromarepresentativesubject).Further,scalpEEGrecordings revealinfraslow(0.01–0.1Hz)neuronalfluctuations(black)ofwhichthephase(green)ispredictiveofbehavioraltimingin 10–100stimescales.Infraslowfluctuations(black)areobtainedbybandpassfilteringtherawEEGsignal(0–200Hz,grey)
inamannerdependentonthedeltaamplitude[6](Figure2A,B).Thus,successfulentrainment ofneuronaloscillationsisparalleledbyabehavioraladvantage.
Spoken language is of special importance in human social systems. Unlike periodically presentedauditorystimuli,thespeechmodulationspectrumspansawiderangeoffrequencies between 0.25Hz and 30Hz [11]. Correspondingly, speech stimuli entrain brain rhythms similarly in multiple frequency bands [12–15]. Interestingly, however, speech modulation exhibitsamodeataroundthesamefrequencyof5Hzformanylanguages.Arecentstudy using electricalbrain stimulation with speech envelopesshowed that enhancing neuronal entrainmentleadsto improvedspeechcomprehensionwiththeeffectpeakingat5Hz[16]. Entrainment isnotlimited thoughto auditory stimuli. Natural-likevisualstimuli [17,18] also entrain oscillations in multiple frequencies. In the audiovisual domain, concurrent phase
between0.01to0.1Hz.(G)Grand-averageEEGpowerspectrum(black)doesnotindicatesalientslowperiodicity.Grey linesaresingle-subjectpowerspectraaveragedacross30minofdata.Photosin(A)and(C)courtesyofhttp://www. maxpixel.net.(F)and(G)adaptedfrom[101].
LH 0.4 0.3 0.2 0.1 2 Am plitu de (p T/m ) 6 4 Frequency (Hz) 0 (A) Frequency (Hz ) 30 20 10 −300 −100 0 Latency (ms) −200 (C) 0.04 0 Hit rate −0.04 −0.4 0.4 −0.4 0.4 Phase Phase High A Low A −5/6π 5/6π Phase Phase −5/6π 5/6π Dec. t hr esh. Con fidence (B) 1 0.2 (D) 70 30 64 32 16 8 4 2 1 0.1 0.01 Frequency (Hz )
Perceptual AƩenƟonal CogniƟve Motor
(E)
Figure2.BothEntrainedand
Intrin-sically Periodic Sampling Support
Perception and Attentional
Func-tions. (A) Temporally variable auditory
stimulussequenceofanaveragerhythm of2Hzentrains2-Hzdeltaoscillationsin the auditorycortex. (B)Neuronal 2-Hz oscillations express temporal expecta-tionssothat thehitrate isdependent ontheoscillationphase,anditismore soathigh(left)oscillationamplitude,and muchlesssoatlow(right)amplitudes. Adaptedfrom[6].(C)Prestimulusphase lockingofalpha oscillationsina visual detectiontaskthatmanipulated percep-tualexpectationsandattention(yellow– redcolorsindicatep<0.001).(D)Left: occipital alpha phase predicted the weighting of expectations on decision threshold(thresholdforreportingstimulus presence; blue, ‘expect stimulus pre-sence’;red,‘expect stimulusabsence’; decision threshold [Dec. thresh.]; blue 25%,andred75%).Right:alphaphase alsoreflectedtheinfluenceof expecta-tionsonconfidence(green,‘congruent’ [stimuluspresent/absentwhenexpecting presence/absence] red, ‘incongruent’ (viceversa)).Adaptedfrom[26].(E) Illus-trationofthefrequenciesobservedin stu-dies finding a correlation between behavioral/action-relatedmeasures (hor-izontalaxis)and thephaseof sponta-neous (black dots) or entrained (red dots)neuronaloscillations.WM,working memory; DM, decision making; RSN, resting-statenetwork;Ctx, cortex.This collection of studies is tabulated in TableS1inthesupplementalinformation onlineandincludesasubsetofstudies earlierreportedin[19].
entrainmentofdelta-(1–4Hz)andtheta-frequency(5–8Hz)bandoscillationsintheauditory cortexhasbeenproposedtoprovideabasisforcontinuoustrackingofnaturalisticaudiovisual streamsandintegrationofauditoryandvisualinformation[10].Thephasedynamicsofneuronal oscillationsthusappearbothhighlyadaptivetotemporalpredictabilityintheenvironmentand activelyutilizedinthetimingofneuronalprocessingtooptimizetaskperformance.
PeriodicPerceptualandAttentionalSamplingofSensory Information
Apartfrom oscillatoryentrainment byexternalstimuli,aconsiderablebodyof researchhas revealedhowperceptionisalsoendogenouslydependentonprestimulusneuronaloscillations
[19],sothatsensoryprocessingisfacilitatedwhenthestimuluspresentationevokesactivityin the high-excitability phase of neuronal oscillations [5,20]. Behavioral data, as well as electrophysiologicalevidenceobtainedusingelectroencephalography(EEG)and magnetoen-cephalography(MEG),suggestthatvisualperceptionisintrinsicallysampledrhythmicallyinthe alpha-frequencyband(andpossiblyotherbandsaswell).Primaryevidenceforthiscomesfrom findings showing that the prestimulus phase [21–25] of alpha oscillations modulates the detectionanddiscriminationofvisualstimuli.Arecentstudydissectingtheputativefunctional rolesoftheprestimulus phaseeffectsexaminedhowtheprestimulusoccipitalalphaphase relatestoperceptualdecisions.Thestudyshowedthattheprestimulusoccipitalalphaphase predicts,specifically,thebiasingofthedecisionthresholdbyexpectationsandtheinfluenceof confidence in a manner independent of attentional modulation [26] (Figure2C,D). Further evidencefordiscretizedorrhythmicalpha-bandperceptionisprovidedbyfindingsthat,within alphacycles,visualobjectinformationisfused[27–29],suggestingthatalphacyclesreflectthe smallest temporal units of subjective visual perception. Most studies on the topic have observedsuchintrinsicperceptualsamplingtotakeplaceat10Hz(Figure2E).Interestingly, attentionalandothertop–downtemporalpredictionsalsoappeartoimposerhythmicityonthe samplingofsensoryinformation[21,26,30–32].Forattendedsampling,however,perceptionis oftenpredicted bylow alpha- ortheta-bandphases (Figure2E).Thus, attention-mediated sampling appears to operate at lower frequencies than those associatedwith perceptual sampling.Moreover,iftwosensorystimuliareobservedconcurrently,perceptionissampledat deltafrequencies[6,32–34],as ifacentraltheta-frequencyattentionalsamplerwouldfocus cycle-by-cycle on alternating object locations [19,32]. The alpha-vs.-theta dichotomy of perceptual-vs.-attentional sampling periodicity is further underscored by the associated scalp-EEGtopographiesoftheseeffects,whichareoccipitalforalphaandfrontalfortheta, respectively[19].Emphasizingtheimportanceofexperimentalnuances,inanattentive tem-poralcueingtask,perceptualaccuracywasstilldeterminedbythephaseofposterioralpha ratherthan thatoffrontaltheta[35].Importantly,thisstudyalsoshowedthat thepretarget stimulus alpha phase was influenced by the preceding cue, which implies that accurate temporalpredictionsmaybeimplementedbytop–downcontrolofthetimingofvisualcortex alphaoscillations[35].Putatively,suchtop–downcontrolcouldbeachievedthrough frontovi-sualalpha-bandphasesynchronizationthatlinkfrontalandvisualcorticesduringvisuospatial attention[36].Thesefrequencydifferencesamongperceptualandattentionalsampling sug-gestthatdelta,theta,andalphaoscillationsformtemporalhierarchiesmatchingthehierarchies ofcognitiveoperationssothathigherlevelfunctionsareassociatedwithslowerfrequencies.
Modulationofperceptionbyintrinsicbrainrhythmsinsensorymodalitiesotherthanvisionhas beenlessstudied.Inthesomatosensorymodality,bothalphaandbetaoscillationshavebeen associatedwithmodulationsofdetectionprobability[37,38].Alsointheauditorydomain,target detectionprobabilityhasbeenshowntodependonboththeta-andalpha-bandprestimulus phases[39,40].Thesestudiesthussuggestthatendogenoustemporalintegrationandparsing
ofsensoryinformationcouldhaveasharedmechanisticbasisacrosssensorymodalities,but withdistinctionsinentrainabilityandtheroleoftheprestimulusphase[41].
Brainstimulationstudiesprovideacomplementarylineofevidenceforassessingthe relation-shipofperceptionandtheprestimulusphaseofneuronaloscillations.Single-pulsetranscranial magneticstimulation(TMS)appliedoverthehumanvisualcortexinducesillusoryperceptions. Similarlytoperceptionsinducedbyvisualstimuli,TMS-inducedperceptionsaremostreliably triggeredinagivenphaseofprestimulusalphaoscillations[42,43].Providingcausalevidence for the relationship between alpha phase and perception in the auditory modality, alpha-frequency-modulated transcranial direct current stimulation (tDCS) biases the stimulus-detectionthresholdina phase-dependentmanner[44].Hence,inboth auditoryandvisual modalities,brainstimulationstudiessupporttheideaofalpha-frequencyperceptualsampling, but,toourknowledge,therearenobrainstimulationstudiestestingthecausalityofthisphase relationshipbyspecificallymanipulatingthephaseofprestimulusneuronaloscillations. SensorimotorCoordinationbyBrainRhythmicity
Innaturalenvironments,action andperceptionare invariablyintertwined,andperceptionis always‘active’inthesenseofinvolvingcognitiveandmotorcontrolofsensorysampling.In primates, the sampling of visual information is dictated by eye movements, especiallyby saccadesand microsaccades that typically take place at a rate of one to three discrete movementspersecond[45].Saccadessample visualinformationaccordingto bothvisual scenerequirementsandtask attentionaldemands [46,47].Theapparentdelta-band rhyth-micityofsaccadeshasbeensuggestedtoarisesothateachsaccadeisinitiatedattheendofa refractoryperiod,whentheprocessingoftheprevioussaccadeisfinished[48].Nonetheless, saccadeshavebeenshowntobephase-lockedtoprestimulusalphaoscillationsinthevisual andmedial temporal corticesduring memorization of natural images, withthe strengthof phase-lockingpredicting memory performance[49].Importantly, as inintrinsic perceptual/ attentionalsampling,thephaseofpresaccadethetaoscillationspersepredictstheperceptual magnitudeofperisaccadicmislocalization[50],andthephaseofalphaoscillations(saccadic reactiontimes)[22].Saccadesarefollowedbypreciselytimedsynchronousdischarges[51]
andpronouncedchangesinoscillatoryactivityinthevisualsystem[46,52,53].Asaresult,each fixationonsetisrapidlyfollowedbyenhancedneuronalresponsivenessandphasealignment amongneuronaloscillations,especiallyinthethetaband,asshownforinstanceinV1andinthe hippocampus[52,54].Saccadesthusphase-resetcorticaloscillationstoendowthenewretinal inputwiththeoscillationphase,yieldingoptimalexcitability.Regardlessoftheprecise mecha-nismgeneratingsaccadeperiodicity,saccadesthusactinmanywaysasanintrinsic counter-parttothedelta-frequencystimulusentrainmentdiscussedabove.
Saccadesalsoappeartoimpactthecoherenceofneuronaloscillationsamongbrainregions,and thereby,possibly,thecommunicationbetweenthem.Specifically,interarealcoherenceofalpha oscillationshasbeenfoundtolinkcorticalareasthatencodetheretinallocationofavisualstimulus beforeandafterasaccade,whichsuggeststhatthesealphaoscillationssupporttheconstructionof stablevisualspacerepresentationsaroundthetimeofsaccadesandtheresultingshiftsinretinal imagelocation[53].Insum,itappearsthattherhythmicperceptualandattentionalsamplingis achievedviamultiplexingofdelta-bandeyemovementsandcorticaloscillationswiththetaand alpha oscillations,bothofwhichinfluencesaccadetimingandbecomephasealignedafterthesaccade. Alpha oscillations, and their functional roles, have been the topic of many theories and discussions.Recentyearshaveseenarevivedinterestinquestionsrelatingtotheneuronal mechanismsunderlyingalphaoscillations.Neuronalfiringismodulatedbythealphaphase[49];
andthepresenceofalpha-oscillationcurrentgeneratorsinallcorticallayersinprimarysensory areassuggeststhatalpha oscillationsare involvedinboth feedforward andfeedback pro-cesses[50,55,56],inlinewiththeideathatalphaoscillationsmayregulatebothintrinsicsensory processinganditsattentionalsampling.Thecontribution ofalphaphase synchronizationis exemplifiedbystudiesexaminingtherolesoffrontalareasinattentionandvisualperception.In theprimatefrontaleyefields(FEFs),saccade-relatedactivitypeaksaroundthetimeofsaccade initiation [57]. Synchronization and phase-coherence between FEF and visual cortices is associatedwithattentional modulation of neuronalactivity [58,59].During visualattention, corticalprocessinginthe visualareas ismodulatedby thalamic inputthroughalpha-band synchronization[60],suggesting that thalamic alpha oscillations may contribute to cortical alpha-bandrhythmicity inattentivestates.Studies usingTMS showedthatperturbing FEF activitycan‘break’thecouplingbetweenvisuospatialattentionandeyemovements[61]and slowdownperception[62].Inhumans,thesourcesofattention-relatedsynchronizationinthe prefrontalcortexaremorewidespread,but,overall,long-rangealphaphasesynchronization amongfrontal,parietal,andvisualcorticesseemstobesalient,asseenforinstanceinavisual attentiontask[36].Suchlinksprovideaputativenetwork-levelbasisforhowvisualperceptionis dependentonthephaseoflocalalphaoscillationsinoccipitalareas.
Therolesofrhythmicbrainactivityinactiongenerationgobeyondtheoculomotorsystem.Infact, adaptivemotor control involvingcorticaloscillationsentrained byenvironmentalperiodicities seemstobefairlywidespread.Thespeedofmotoractionsisdependentondelta-bandphase inbothhumanscalpEEG[8,63]andintracranialEEG[64],andmotorresponsesarefacilitated duringthehigh-excitabilityphases,similarlytowhatisseeninperceptualsampling.Slowfinger movementsarediscretizedintoalpha-frequencystepsthatarephasecoupledwithmotorcortical alphaoscillations[65],supportingtheideathatalpha-bandoscillationsunderlietemporalframing ofbothsensoryinformationandactions.Finally,coordinationbetweenthemotorcortexand musclesissupportedbycorticospinalcoherence,which(dependingonexperimentalconditions) wasshowntotakeplaceinthebeta-orgamma-bandsinhumans[66–68],andinthebeta-bandin monkeys[69,70].Themotorcortexisalsophase-synchronizedwithotherbrainregionsinatask dependentmanner.Forexample,concurrentsynchronizationindeltaandgammabands con-nectssensorimotor,frontal,andparietalbrainareasduringconscioussomatosensoryperception and action[71]. For speech perception, theta-band synchronizationbetween auditory and speech-motorregionswasobservedwhileparticipantslistenedtosyllables[72].Directevidence fortheimportanceofneuronaloscillationphaseinactioncoordinationcomesfromstudieswhere TMSwasappliedoverthemotorcortex.TheTMS-evokedmuscleactivationwasdependenton thephaseofcorticalslow[73]andbeta-band[74]oscillations.Hence,discretesamplingand phase-dependentcodingextendfrom perceptionandattention,as previouslydiscussed,to motorcontrolandactiongenerationaswell.
OscillationsContributetoEvidenceAccumulationforTemporalInformation Acquisition
Manyecologicallyvalidsituationsnecessitateexplicitestimationofthetimingofeventsinboth naturalenvironmentsandinonesshapedbyhumans.Explicitrepresentationoftemporal infor-mationinthebrainhasbeenfrequentlyinvestigatedusingintervaltimingtasks.Inthesetasks, subjectsareaskedtomakejudgmentsbasedontemporalintervalsbetweenpairsofevents. Explicitestimationoftimeinintervalsfromsecondstominutesinvolvesactivityinmanyareas relatedtomotorcontrolsuchasinpremotorandsupplementarymotorareas,basalganglia,and thecerebellum,butalsointheprefrontalcortex,frontaloperculum,andposteriorparietalcortex
[75].Theoreticalmodelsoftimeestimationsuggestthatthetimingprocessisdependentonthe accumulationofimplicitdurationinformationoverthetask-relevanttimewindow[76–78].Ithas
beenproposedthatthisaccumulationofinformationcouldbeachievedthroughrhythmicor periodicfluctuations(i.e.vianeuronaloscillations)[79].Thisideaissupportedbyfindingsshowing thatthesubjectiveperceptionofpassageoftimeisrhythmic[80].Yet,dataontheoscillatory mechanismunderlyingexplicittimeestimationarescarce.Temporalpredictionsinmonkeyshave beenrelatedto beta oscillations[81].Similarly,insource-modelledhumanMEGdata, time-estimationinaworkingmemory(WM)taskiscorrelatedwiththeamplitudeofbetaoscillationsin thesensorimotor(SM)areasandposteriorparietalcortex(PPC)[82].Asbetaoscillationsare generallylinkedtomotorcoordination,suchneuroanatomicallocalizationsupportstheideathat theexplicitestimationoftemporalintervalsistightlylinkedtoactiongeneration.Dynamic asso-ciationsandcouplingbetweentemporalestimationsandactionswouldallowfastandaccurate actiongenerationaccordingtoenvironmentaldemands.
Cross-frequencyInteractionsbetween OscillationsatMultipleScalesGovern NeuronalProcessingacrossCorticalHierarchies
Thestudiesreviewedaboveconvergetoshowthatphasedynamicsofoscillationsinmultiple frequencybands,withdistinctneuroanatomicalsourcesandfunctionalroles,areessentialfor perceptualandattentionalsampling,sensorimotorcoordination,andtemporalpredictions.The frequencies of neuronal oscillations underlying this temporal coordination and setting the temporalintegrationwindowsaredependentonthelevelofcognitivehierarchy(Figure2E). Coordination and functional integration of such multiscale neuronal oscillations demands cross-frequencycoupling(CFC)mechanismssuchascross-frequencyphasesynchronization (CFS)[83,84]orphase-amplitudecoupling(PAC)[85](Figure3A–C).Yet,whetherandhow thesemechanismscoordinateneuronalprocessing,fromrhythmicallysampledperception,to periodicallyimplementedstepsincontinuousactions,hasremainedincompletelyunderstood.
PAC has been repeatedly observed between slow (<1Hz) and fast (>1Hz) oscillations
(Figure3D) as wellas amongfast oscillations.Ithasbeensuggestedto provideageneric
temporalsegmentationmechanismforcognitivefunctions[86,87].Accordingtooneview,for instance,sensoryinformationisrepresentedingammaoscillations,whicharethensegmented byPACintogammaburstswithsloweroscillations[88,89].Inlinewiththeseideas,gamma oscillationamplitudesareindeedcoupledtothephaseofdelta,theta,and/oralphaoscillation phasesduringattentionalsamplingofsensoryinformation,bothinhumansandnon-human primates[7,9,90],as wellasduring workingmemory [91].Duringentrainmentofmultiscale neuronaloscillationsinspeechperception,theamplitudeofgammaoscillationsintheauditory cortexislockedtothethetaphase,whichhasbeenproposedtosupportspeechsegmentation
[92,93].Alsothephaseofaslow1Hzcomponentthatislockedtophrasalcontentshasbeen
foundto becoupledwiththeamplitudeofbeta-frequencyoscillations inmotorareas[14]. ObservationsofPAC duringattention andspeechperceptiontaskssuggestthat itplaysa functionalroleinexpressingtemporalpredictionsbysupportingtimewindowsofenhanced excitabilitydefinedbythephaseofdelta,theta,andalphaoscillations.
Cross-frequencyphasesynchronization(CFS)isanotherformofCFC,distinctfromPAC,andis characterizedbyastablephase-differencebetweentwoneuronalassembliesoscillatingwith anm:nfrequencyratio [83,94,95].Since,unlikeinPAC,the phasesofboththefaster and sloweroscillationarerelevantforCFS,itnecessarilyoperatesatthetemporalaccuracyofthe fasteroscillationandendowsthecoupledassemblieswithaconsistentspike-timing relation-ship. CFS may thus trigger neuronal coincidence detection mechanisms and serve the regulationofneuronalcommunicationsimilarlyto1:1synchronization.CFShasbeenobserved inhumansensor-levelMEG recordingsduringaWM intensivementalcalculationtask[83], duringaWM [96]task,aswellas duringattention demandingvisualperception[97].More
PAC 1:2 CFS (A) (E) (D) Am pl itu de (% ) Phase Hit rate 0 0 1:1 high-α network 1:1 low-γ network CFS 1:3 – 1:5 20 DescripƟve ISF cycle −20 −π/2 π/2 10 Hz 20 Hz 80 Hz A80 Hz θ10 Hz θ20 Hz (C) (B) (F) (G) (H) Task Rest
VISlat DMNppc DAN DMNvmpf SAL S2 M1 EXEC
−π π 40 Hz 20 Hz 10 Hz 5 Hz 2.5 Hz 1.25 Hz Broadband (10+20+80 Hz) β β β Figure3.
(Figurelegendcontinuedonthebottomofthenextpage.)
Cross-frequencyInteractionsforCouplingnearandfarFrequencies.(A)Schematicofoscillatory
corticalhierarchieswithanexamplebroadbandsignalcomposedofsimulatedoscillationsinthreefrequencybands(B).Note thattheseemingly nonsinusoidal andasymmetricappearanceof thebroadbandsignalis causedexclusivelyby the genuine 1:2 phasesynchronizationbetweenthesinusoidal10and20Hzoscillations.(C)Schematicillustrationofphase-amplitude coupling(PAC)andcross-frequencysynchronization(CFS).PACindicatesthattheamplitudeenvelopeofthefastoscillation (A80Hz,dottedline)iscorrelatedwiththephaseoftheslowoscillation(u10Hz).PACmaybeestimatedbyfilteringA80Hzatthe slowfrequency(here10Hz,filledline)andevaluating1:1-phasecouplingwiththephase-lockingvalue[120].CFSindicates cross-frequencycorrelationbetweenthephasesoftwosignalsatfrequenciesfxandfythathavea(usuallysmall)integerration: msothatnfx=mfy.UnlikePAC,CFSenablesconsistentspike-timerelationshipsintheunderlyingneuronalpopulations.Here weillustrate1:2CFSofbetaandalphaoscillations,whichinhumanbrainactivityisbyfarthemostprevalent[83,84].(D)PAC amonginfraslowoscillationsand1–40HzoscillationscharacterizesscalpEEGindetectiontasks,wheretheinfraslowphaseis alsocorrelatedwithbehavioralhitrate.NotethatsuchPACinherentlyalsointroducescross-frequencyamplitude–amplitude correlationsamongthe1–40Hzoscillations.Adaptedfrom[101].(E)CFSduringworkingmemoryretentionlinkshubsof1:1 within-frequency-synchronizednetworksandmayservecross-frequencyfunctionalintegration(ang,angular;C,central;CN, cuneus;F,frontal;G,gyrus;i,inferior;L,left;la,lateral;m,middle;O,occipital;P,parietal;po,post;pr,pre;R,right;S,sulcus;s, superior;sup,supramarginal;T,temporal;tr,transversal).Adaptedfrom[98].(F)Meanscalingexponents(b)oflong-range temporalcorrelations(LRTCs)inalpha-bandoscillationsduringstimulus-detectiontask(Task)andinaseparateresting-state
recently, CFS has also been observed in source-reconstructed MEG/EEG data during parametricvisualWM(VWM)tasks[98](Figure3E).There,VWMmaintenancewas character-izedbyCFSofthetawithalpha,beta,andgammaoscillationsaswellasbyCFSofhigh-alpha withbetaandgammaoscillations.ThesedatasuggestthatCFSisafunctionallysignificant communicationmechanismthat,atleasthypothetically,couldunderlietemporalsegmentation andcoordinationofneuronalprocessingacrossmultipletemporalscales.
Scale-FreeBehavioraland NeuronalFluctuations
Humancognitiveperformanceishighlyvariable,butfluctuatesinanautocorrelatedmannerso thatperformanceinanygiventrialissimilartotheprecedingtrials[99].Theseautocorrelations arepower-lawdistributed,long-rangetemporalcorrelations(LRTCs)thatlastuptohundredsof seconds (Figure1F) [100–102]. Similar slow fluctuations and LRTCsalso characterize the amplitudeenvelopesof>1Hzoscillationsinelectrophysiologicaldataandblood-oxygen-level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) data [101,
103–105]. Moreover,neuronal and behavioral LRTCsare correlated so that subjects with
strongLRTCsinneuronalfluctuations,bothatrestandduringthetask,alsohavestrongLRTCs in behavioral fluctuations [103] (Figure 3F,G), which shows that LRTCs reflect a trait-like dynamicofspontaneousbrainactivitythatispreservedduringtaskexecution.
LRTCsinneuronalactivityarereflectedinslowandinfraslow(0.01–1Hz)fluctuationsthatare observabledirectlyinslowEEGpotentials,inadditiontotheamplitudeenvelopesandfMRI BOLDsignals.Theseslowfluctuationsarescale-free(i.e.,haveapower-lawspectrum,butcan eitherbecomposedofarrhythmicnoiseorquasiperiodicoscillations[Figure1F,G]).Supporting thelatteralternative, inscalpEEG [101],thephase but nottheamplitude ofthe infraslow fluctuationspredictsthedetectionofweaksensorystimulisothatslowfluctuationssegment behavior intostreaks ofdetected andundetected stimuli (Figure1F). Theslow EEG scalp potentialfluctuationsaredirectelectrophysiologicalcorrelatesofthefMRIresting-statenetwork activity[106](Figure3H)andalsoBOLDfluctuationsarepredictiveofdetectionperformance variability[107–109].fMRIdatasuggestthattheperformancefluctuationsmayarisefromthe antagonisticinterplaybetweenthedefaultmodeandattentionalorcingulo-opercularcontrol networks[107,110,111].
BothinscalpEEG[101]andfMRI,theseslowscalp-potential(Figure1F)andhemodynamic fluctuationsare coupledvia PACwith 1–40Hz oscillations(Figure 3D). Thissuggeststhat similarly to delta, theta, and alpha oscillations, these slow fluctuations reflect excitability fluctuationsinneuronalnetworksleadingtotemporalsegmentationofneuronalprocessing, andconsequentlyalsobehavioralperformance,intheslowtimescales.
BrainCriticalityasaMechanismforSlowFluctuations
LRTCsandpower-lawscalingareaphenomenologicalsignatureofsystemsoperatinginor nearacriticalstate[4,112].Severallinesofevidencehaveshownsimilarindicationsofcritical dynamics inneuronal activity, including avalanche dynamics andLRTCs. Despite growing evidence that slow fluctuations and scale-free neuronal dynamics modulate behavioral
session(Rest)arecorrelatedwiththemeanbehavioralscalingexponentsoftaskdata(bbehav).(G)Correlationsbetween behavioralscalingexponents,b,andneuronalLRTCsintherestingstatewelldelineatedinspecificbrainareas.Adaptedfrom
[103].(H)Infraslowfluctuationsinfull-bandEEG(fbEEG)andfMRIBOLDsignalsexhibitcorrelationsinthesameanatomical regions.BOLDindependentcomponents(red–yellow)areoverlaidbycorrelationmapsbetweenfbEEGindependent componentsandfMRIvoxelsignals(green;VIS,visualnetwork;DMN,defaultmodenetwork;DAN,dorsalattentionnetwork; SAL,saliencynetwork;S2,secondarysomatosensorynetwork;M1,primarymotornetwork;EXEC,executivecontrol network).Adaptedfrom[106].
performance,thesephenomenahavebeenlargelythoughttobeintrinsicoreven epiphenom-enalpropertiesofbrainactivity, withpossiblynofunctionalrelationshiptothe dynamicsof natural environments. Yet, critical brain dynamics has been proposed to be essential for enablingrapid switching between metastablestates and to maximize information transfer andcapacityofthesystemaswellasitsdynamicrange[4,112].Criticaldynamicsemergein systemspoisedataphasetransitionbetweentwostates,here termedthe‘subcritical’ and ‘supercritical’states.Atthecriticalpoint,spatiotemporallyglobalcorrelationpatterns,suchas synchronization,canemergefromlocalinteractionmechanisms.Brains,andinparticularbrain oscillations,exhibitcritical-likedynamics[4,105,112,113].Criticalityendowsthesystemwith maximal information capacity, transmission capability [114], and dynamic range [115]. In particular,criticalityhasfundamentalimplicationstofunctionalpropertiesofneuronalnetwork oscillations,whereitindicatesabalancebetweendisorderandorder(i.e.,betweentoolowand excessivelyhighsynchronization).Despitesuchtheoreticalpredictions,researchfields explor-ingthefunctionalrolesofneuronaloscillations,asassessedhere,have beenonlyscarcely connectedwiththefieldofneuronalcriticality[116].Atthecriticalpointwithintermediatemean synchronization, the long-range correlations and power-law scaling emerge in oscillatory dynamics,andthevariabilityofsynchronizationismaximized[117,118](Figure4A,B).Itshould
(C) (B) (D) 0 80 Ph as e res pons e SƟmuli intensity CriƟcal SupercriƟcal SubcriƟcal 0 80 0.1 0.2 0.3 0.4 0.5 0.6 Ph as e res pons e Coupling CriƟcal SupercriƟcal SubcriƟcal (A) Sy nc hroniza Ɵon 0 0.8 0.5 0.6 0.7 0.8 0 0.8 0.1 0.2 0.3 0.4 0.5 0.6 Coupling DF A ( β ) Sy nc hroniza Ɵon Weak Strong CriƟcal SubcriƟcal SupercriƟcal
Figure4.CriticalDynamicsEndowsLocally CoupledOscillatorySystemsLong-RangeSpatiotemporal
CorrelationsandMaximizesPhaseResetResponses.(A)SimulatedsynchronizationtimeseriesinaKuramoto
model,anetworkofphasecoupledoscillators,poisedatsubcritical(blue),critical(black),andsupercritical(orange) regimes.(B)The‘controlparameter’inthismodelisstrengthofmeancouplingamongoscillators(x-axis).Synchronization (blackline),i.e.,the‘orderparameter’,increasesmonotonicallywithincreasingcoupling.Atthecriticalphasetransition betweendisordered(lowsynchronization)andordered(highsynchronization),thesystemexhibitsthegreatestvariability andexhibitsemergent,long-range,power-lawcorrelations,asindicatedbythepeakingofthescalingexponent,b,of detrendedfluctuationanalysis(DFA,magentaline).(C,D)Inthecriticalstate,thesystemexhibitsmorelong-lasting responsestoexternalphase-resettingstimulithaninsubcriticalorsupercriticalstates,showingthatchangesinthe system’soperatingpointalongthesubcriticaltosupercriticalaxismayhavesignificantconsequencesforphaseresetting andentrainmentinneuronalsystems.
benoted,however,thatatthewhole-brainscale,andunlikeinclassicaltheoreticalmodelsof criticalityinhomogenoussystems,bothneuronalsynchronizationandcriticalityare co-orga-nized in modular structures [116]. This is in line with theoretical studies showing that in heterogeneousor, in particular,hierarchically modular and realbrainnetworks, the critical pointis‘stretched’intoawidercriticalregime[119].Herethesystemoperatesina‘Griffiths phase’whereitexhibits‘critical-like’,ratherthanstrictlycritical,dynamics.
Weproposethatcriticaldynamicsofneuronaloscillations,despitebeingreflectedintheirslow fluctuations,are a prerequisitefor fast and dynamic adaptation to environmental, natural, statisticalregularities,andfortheunderlyingcapabilityforphaseresettingandentrainment.If theneuronalsystemis settoofarintothe subcriticaldomain,neuronaloscillationsare too weaklysynchronized.Inthiscase,neuronaloscillationswillbeunabletoimposetheintrinsic periodicity required for effective processing or to propagate the entrainment needed for synchronizationwithextrinsicsignals.Conversely,inasystemthatistoofarintosupercritical settings,neuronaloscillationsexhibitexcessivesynchronization.Here,informationengrainedin finerspatiotemporalstructuresislost,andneuralcircuitsareunabletoflexiblyaltertheirphase dynamics for entrainment. In sum,neuronal oscillations thatare near criticality and atthe optimalintermediatelevelsofsynchronizationaremostflexible;theyareabletoachieverapid transitionsintonovelstatesviaphase-resetting(Figure4C,D),andtoadapttotheperiodicities insensoryinformation.Thisisparticularlyimportantinthecontextofscale-free,possiblyonly quasiperiodic,environmentalrhythmicities(Figure1andBox1),wherebrainmechanismsmust extracttemporalinformationandadapttothedynamicallyvaryingperiodicityrapidly.
ConcludingRemarksandFuturePerspectives
Thestudiesreviewedhereshowthatacrossawiderangeoftimescales,braindynamicscan explicitly predicttemporal intervals as well as adapt to behaviorallyrelevant environmental rhythmicitiesbyphasepredictionandentrainmentofneuronaloscillations.Theyalsoshowthat even intheabsence of explicitenvironmental, temporalinformation, endogenous neuronal oscillationsimpose rhythmicityonperceptual,attentional,andaction-generationprocesses. Crucially,theintrinsicpacingofsensorimotorandcognitivefunctionsvia1–15Hzoscillations appearsto bea centralmechanismfor parsingcontinuoussensory inputstreams andfor achievingneuronalcoordinationinlarge-scalebraincircuits.Althoughanumberofstudieshave examinedthe relationshipbetweenbrainoscillations andtemporal predictionsinsimplified laboratorysettings,fewerstudieshaveaddressedsensorysamplinginmorenatural environ-mentswhereincomingsensoryinformationisscale-freeandcomprisedofavarietyof time-scales.Thisexcitinglineofresearchsuggeststhatthesamplingofnaturalsensoryinformation acrossmultiple scales correspondingly involvesneuronal oscillations in multiplefrequency bands and necessitates CFC mechanisms. We propose that CFC enables coordinated cooperationbetweentheoscillatingassembliesandcommunicationbetweenthem.Putatively, thesecouplingmechanismsencompassalpha-bandperceptual/motorsampling,beta-band attentionalsampling,anddeltaoscillationssupportingfunctionssuchasactivesensing and speech perception. In particular, while PAC readily couples these slow oscillations with gamma-bandactivities,ithasremainedlessclearhowthespectrallynearby 1–15Hz oscil-lationsaremutuallycoordinated.WeproposethatonepossiblemechanismcouldbeCFSthat flexiblycouplesoscillationswithsmall(e.g.,1:2and1:3)frequencyratioswiththetemporal accuracyofthe fasteroscillationandhenceparse multiscalesensorimotorinformation and cognitiveoperations.FuturestudiesarerequiredtoelucidatethemechanisticrolesofPACand CFSincross-scalecoordinationofneuronalprocessing.
OutstandingQuestions
The prestimulus phase can bias behavioral outcomes. What are the brainstructuresimplicatedinthis pro-cess?Doesthebiasariseinsensoryor attentional systems,and howis the localizationdependentontherelevant oscillationfrequency?
Large-scale networks of interareal phasecoupling coordinate attention and conscious perception. Are the prestimulusphasebiasesassociated withthehubsofthesenetworksand aretheyrelatedtospecificinterareal phaserelationships?
What is the role of thalamocortical alpha-band rhythmicityinperceptual sampling?
Aretemporalpredictionsarisingfrom oscillatoryprocesses atmultiple fre-quencies coordinated via cross-fre-quency interactions? Does cross-frequencycouplingplayan instrumen-talroleinthiscontextorisitmoreofan epiphenomenon?
Docriticalbraindynamicsenable,or conversely limit, phase-locking and entrainment of neuronal activity accordingtosensorimotordemands? Areinfraslowfluctuationsentrainable oradaptive,and,ifso,dothey contrib-utetosensorimotorpredictionsinthe timescales of seconds to tens of seconds?
Whatisthe mechanisticrelationship betweenexplicittimeestimationand implicittemporalpredictions?
Thefunctionalsignificanceofscale-freeneuronalactivityandbehavioralperformance fluctua-tionsbelow1Hzremainanotherfocaltopicforfutureresearch.Theseslowfluctuationsare observed in the fMRI BOLD signal as well as in the amplitude envelopes of fast >1Hz oscillationsand<1Hzslowcorticalpotentialsinelectrophysiologicaldata.Thesefluctuations arelikelyto becausedbyemergentcritical-like braindynamics. Inthecontextoftemporal expectations,thedynamicstateoftheneuronalsystemsathand(whethercriticalorsubcritical/ supercritical)israrelyconsidered.Thedynamicstatecouldcontributesignificantlytotheability ofneuronaloscillationstoexpressflexiblesynchronization,phaseresetting,andentrainment.In addition,itistemptingtospeculatethatthedynamicstateofneuralsystems,apartfromits variationacrossspecificbrainsystemsanddifferentexternalconditions,alsovariesbetween individuals,possiblythroughheritableprocessesandmaythuscontributetotheinterindividual variabilityincorrespondingperceptualandattentionalmeasures(seeOutstandingQuestions).
Acknowledgements
ThisworkwasfundedbytheAcademyofFinland.WearegratefultoHamedHaqueandSamiKaradenizfortheillustration inFigure2EandthecorrespondingTableS1inthesupplementalinformationonline,andtoShengWangfortheschematic simulationsforFigure4.
SupplementalInformation
Supplementalinformationassociatedwiththisarticlecanbefoundonlineathttps://doi.org/10.1016/j.tins.2018.08.008. References
1. vanderSchaaf,A.andvanHateren,J.H.(1996)Modellingthe powerspectraofnaturalimages:statisticsandinformation.Vis. Res.36,2759–2770
2. Singh,N.C.andTheunissen,F.E.(2003)Modulationspectraof naturalsoundsandethologicaltheoriesofauditoryprocessing. J.Acoust.Soc.Am.114,3394–3411
3. Shew,W.L.andPlenz,D.(2012)Thefunctionalbenefitsof criticalityinthecortex.Neuroscientist19,88–100 4. Chialvo,D.R.(2010)Emergentcomplexneuraldynamics.Nat.
Phys.6,744–750
5. Fries,P.(2015)Rhythmsforcognition:communicationthrough coherence.Neuron88,220–235
6. Herrmann,B.etal.(2016)Temporalexpectationsandneural amplitudefluctuationsinauditorycortexinteractivelyinfluence perception.Neuroimage124,487–497
7. Besle,J.etal.(2011)Tuningofthehumanneocortextothe temporaldynamicsofattendedevents.J.Neurosci.31,3176– 3185
8. Stefanics,G.etal.(2010)Phaseentrainmentofhumandelta oscillationscanmediatetheeffectsofexpectationonreaction speed.J.Neurosci.30,13578–13585
9. Lakatos,P.etal.(2008)Entrainmentofneuronaloscillationsasa mechanismofattentionalselection.Science320,110–113 10. Gong,G.etal.(2009)Mappinganatomicalconnectivitypatterns
ofhumancerebralcortexusinginvivodiffusiontensorimaging tractography.Cereb.Cortex19,524–536
11. Ding,N.etal.(2017)Temporalmodulationsinspeechand music.Neurosci.Biobehav.Rev.81,181–187
12. Arnal,L.H.etal.(2015)Delta-betacoupledoscillationsunderlie temporalpredictionaccuracy.Cereb.Cortex25,3077–3085 13. Teng,X.etal.(2017)Concurrenttemporalchannelsforauditory
processing:oscillatoryneuralentrainmentrevealssegregationof functionatdifferentscales.PLoSBiol.15,e2000812 14. Keitel,A.etal.(2018)Perceptuallyrelevantspeechtrackingin
auditoryandmotorcortexreflectsdistinctlinguisticfeatures. PLoSBiol.16,e2004473
15. Ding,N.etal.(2016)Corticaltrackingofhierarchicallinguistic structuresinconnectedspeech.Nat.Neurosci.19,158–164
16. Wilsch,A.etal.(2018)Transcranialalternatingcurrent stimula-tionwithspeechenvelopesmodulatesspeechcomprehension. Neuroimage172,766–774
17. Keitel,C.etal.(2017)Visualcortexresponsesreflecttemporal structureof continuousquasi-rhythmicsensory stimulation. Neuroimage146,58–70
18. Montemurro,M.A.etal.(2008)Phase-of-firingcodingofnatural visualstimuliinprimaryvisualcortex.Curr.Biol.18,375–380 19. VanRullen,R.(2016)Perceptualcycles.TrendsCogn.Sci.20,
723–735
20. Haegens,S.etal.(2011)Betaoscillationsinthemonkey sen-sorimotornetwork reflectsomatosensory decision making. Proc.Natl.Acad.Sci.U.S.A.108,10708––10713 21. Dugue,L.etal.(2011)Thephaseofongoingoscillations
medi-atesthecausalrelationbetweenbrainexcitationandvisual perception.J.Neurosci.31,11889–11893
22. Drewes,J.andVanrullen,R.(2011)Thisistherhythmofyour eyes:thephaseofongoingelectroencephalogramoscillations modulatessaccadicreactiontime.J.Neurosci.31,4698– 4708
23. Busch, N.A.and VanRullen,R. (2010) Spontaneous EEG oscillationsrevealperiodicsamplingofvisualattention.Proc. Natl.Acad.Sci.U.S.A.107,16048–16053
24. Mathewson,K.E.etal.(2009)Toseeornottosee:prestimulus alphaphasepredictsvisualawareness.J.Neurosci.29,2725– 2732
25. Hanslmayr,S.etal.(2013)Prestimulusoscillatoryphaseat7Hz gatescorticalinformationflowandvisualperception.Curr.Biol. 23,2273–2278
26. Sherman,M.T.etal.(2016)Rhythmicinfluenceoftop–down perceptualpriorsinthephaseofprestimulusoccipitalalpha oscillations.J.Cogn.Neurosci.28,1318–1330
27. Samaha,J.andPostle,B.R.(2015)Thespeedofalpha-band oscillationspredictsthetemporalresolutionofvisualperception. Curr.Biol.25,2985–2990
28. Sokoliuk,R.andVanRullen,R.(2013)Theflickeringwheel illu-sion:whenalpharhythmsmakeastaticwheelflicker.J. Neuro-sci.33,13498–13504
29. VanRullen,R.andMacdonald,J.S.(2012)Perceptualechoesat 10Hzinthehumanbrain.Curr.Biol.22,995–999 30. VanRullen,R.etal.(2005)Attention-drivendiscretesampling
ofmotionperception.Proc.Natl.Acad.Sci.U.S.A.102,5291– 5296
31. VanRullen,R.etal.(2007)Theblinkingspotlightofattention. Proc.Natl.Acad.Sci.U.S.A.104,19204–19209 32. Landau,A.N.etal.(2015)Distributedattentionisimplemented
throughtheta-rhythmic gammamodulation. Curr. Biol.25, 2332–2337
33. Landau,A.N.andFries,P.(2012)Attentionsamplesstimuli rhythmically.Curr.Biol.22,1000–1004
34. Fiebelkorn,I.C.etal.(2013)Rhythmicsamplingwithinand betweenobjectsdespitesustainedattentionatacuedlocation. Curr.Biol.23,2553–2558
35. Samaha,J.etal.(2015)Top–downcontrolofthephaseof alpha-bandoscillationsasamechanismfortemporalprediction. Proc.Natl.Acad.Sci.U.S.A.112,8439–8444
36. Lobier,M.etal.(2017)High-alphabandsynchronizationacross frontal, parietalandvisual cortex mediatesbehavioral and neuronaleffectsofvisuospatialattention.Neuroimage165, 222–237
37. Ai,L. andRo, T. (2014)The phaseof prestimulus alpha oscillationsaffectstactileperception.J.Neurophysiol.111, 1300–1307
38. Baumgarten,T.J.etal.(2015)Betaoscillationsdefinediscrete perceptualcyclesinthesomatosensorydomain.Proc.Natl. Acad.Sci.U.S.A.112,12187–12192
39. Ng,B.S.etal.(2012)Aprecludingbutnotensuringroleof entrainedlow-frequencyoscillationsforauditoryperception. J.Neurosci.32,12268–12276
40. Strauss,A.etal.(2015)Alphaphasedeterminessuccessful lexicaldecisioninnoise.J.Neurosci.35,3256–3262 41. Zoefel,B.andVanRullen,R.(2017)Oscillatorymechanismsof
stimulusprocessingandselectioninthevisualandauditory systems:state-of-the-art,speculationsandsuggestions.Front. Neurosci.11,296
42. Dugue,L. etal.(2011) The phaseof ongoing oscillations mediatesthecausalrelationbetweenbrainexcitationandvisual perception.J.Neurosci.31,11889–11893
43. Emrich,S.M.etal.(2017)Comparingtheeffectsof10-Hz repetitiveTMSontasksofvisualSTMandattention.J.Cogn. Neurosci.29,286–297
44. Neuling,T. etal.(2012)Goodvibrations:oscillatoryphase shapesperception.Neuroimage63,771–778
45. Melloni,L.etal.(2009)(Micro)saccades,corollaryactivityand corticaloscillations.TrendsCogn.Sci.13,239–245 46. Bosman,C.A.etal.(2009)Amicrosaccadicrhythmmodulates
gamma-bandsynchronizationandbehavior.J.Neurosci.29, 9471–9480
47. Chen,C.Y.etal.(2015)Neuronalresponsegainenhancement priortomicrosaccades.Curr.Biol.25,2065–2074 48. Amit,R.etal.(2017)Temporaldynamicsofsaccadesexplained
byaself-pacedprocess.Sci.Rep.7,886
49. Haegens,S.etal.(2011)Alpha-oscillationsin themonkey sensorimotor network influence discrimination performance byrhythmicalinhibitionofneuronalspiking.Proc.Natl.Acad. Sci.U.S.A.108,19377–19382
50. Bollimunta,A.etal.(2008)Neuronalmechanismsofcortical alphaoscillationsinawake-behavingmacaques.J.Neurosci. 28,9976–9988
51. Maldonado, P. et al. (2008) Synchronization of neuronal responsesinprimaryvisualcortexofmonkeysviewingnatural images.J.Neurophysiol.100,1523–1532
52. Rajkai,C.etal.(2008)Transientcorticalexcitationattheonsetof visualfixation.Cereb.Cortex18,200–209
53. Neupane,S.etal.(2017)Coherentalphaoscillationslinkcurrent andfuturereceptivefieldsduringsaccades.Proc.Natl.Acad. Sci.U.S.A.114,E5979–E5985
54. Hoffman,K.L.etal.(2013)Saccadesduringvisualexploration alignhippocampal3–8Hzrhythmsinhumanandnon-human primates.Front.Syst.Neurosci.7,43
55. Bollimunta, A. et al. (2011) Neuronal mechanisms and attentionalmodulationofcorticothalamicalphaoscillations.J. Neurosci.31,4935–4943
56. Haegens,S.etal.(2015)Laminarprofileandphysiologyofthe alpharhythminprimaryvisual,auditory,andsomatosensory regionsofneocortex.J.Neurosci.35,14341–14352 57. Peel,T.R.etal.(2016)Acausalroleforthecorticalfrontaleye
fieldsinmicrosaccadedeployment.PLoSBiol.14,e1002531 58. Buschman,T.J. andMiller, E.K. (2007) Top–downversus bottom–upcontrolofattentionintheprefrontalandposterior parietalcortices.Science315,1860–1862
59. Gregoriou,G.G.etal.(2012)Cell-type-specificsynchronization ofneuralactivityinFEFwithV4duringattention.Neuron73, 581–594
60. Saalmann,Y.B.etal.(2012)Thepulvinarregulatesinformation transmission between cortical areas based on attention demands.Science337,753–756
61. Neggers,S.F.etal.(2007)TMSpulsesonthefrontaleyefields breakcouplingbetweenvisuospatialattentionandeye move-ments.J.Neurophysiol.98,2765–2778
62. Marshall,T.R.etal.(2015)Frontaleyefieldscontrolattentional modulationofalphaandgammaoscillationsincontralateral occipitoparietalcortex.J.Neurosci.35,1638–1647 63. Hamel-Thibault,A.etal.(2018)Delta-bandoscillationsinmotor
regionspredicthandselectionforreaching.Cereb.Cortex28, 574–584
64. Saleh,M.etal.(2010)Fastandslowoscillationsinhuman primarymotorcortexpredictoncomingbehaviorallyrelevant cues.Neuron65,461–471
65. Gross,J.etal.(2002)Theneuralbasisofintermittentmotor controlinhumans.Proc.Natl.Acad.Sci.U.S.A.99,2299– 2302
66. Salenius,S.andHari,R.(2003)Synchronouscorticaloscillatory activityduringmotoraction.Curr.Opin.Neurobiol.13,678–684 67. Schoffelen,J.M.etal.(2011)Selectivemovementpreparationis subservedbyselectiveincreasesincorticomuscular gamma-bandcoherence.J.Neurosci.31,6750–6758
68. Bourguignon,M.(2017)MEGinsightintothespectraldynamics underlyingsteadyisometricmusclecontraction.J.Neurosci.37, 10421–10437
69. Witham,C.L.etal.(2010)Corticomuscularcoherencebetween motorcortex,somatosensoryareasandforearmmusclesinthe monkey.Front.Syst.Neurosci.4,38
70. Mackay,W.A.(1997)Synchronizedneuronaloscillationsand theirroleinmotorprocesses.TrendsCogn.Sci.1,176–183 71. Hirvonen,J.etal.(2018)Dynamiclarge-scalenetwork
synchro-nizationfromperceptiontoaction.Netw.Neurosci.Published onlineJanuary 10, 2018. http://dx.doi.org/10.1162/netn_a_ 00039
72. Assaneo,M.F.andPoeppel,D.(2018)Thecouplingbetween auditoryandmotorcorticesisrate-restricted:evidenceforan intrinsicspeech-motorrhythm.Sci.Adv.4, eaao3842 73. Bergmann,T.O.etal.(2012)EEG-guidedtranscranialmagnetic
stimulationrevealsrapidshiftsinmotorcorticalexcitability dur-ingthehumansleepslowoscillation.J.Neurosci.32,243–253 74. vanElswijk,G.etal.(2010)Corticospinalbeta-band synchroni-zationentailsrhythmicgainmodulation.J.Neurosci.30,4481– 4488
75. Merchant,H.etal.(2013)Neuralbasisoftheperceptionand estimationoftime.Annu.Rev.Neurosci.36,313–336 76. Treisman,M.etal.(1990)Theinternalclock:evidencefora
temporaloscillatorunderlyingtimeperceptionwithsome esti-matesofitscharacteristicfrequency.Perception19,705–743 77. Matell,M.S.andMeck,W.H.(2004)Cortico-striatalcircuitsand intervaltiming:coincidencedetectionofoscillatoryprocesses. BrainRes.Cogn.BrainRes.21,139–170
78. Buhusi,C.V.andMeck,W.H.(2009)Relativitytheoryandtime perception:singleormultipleclocks?PLoSOne4,e6268 79. vanRijn,H.etal.(2011)Contingentnegativevariationandits
relationtotimeestimation:atheoreticalevaluation.Front.Integr. Neurosci.5,91
80. Kosem,A.etal.(2014)Encodingofeventtiminginthephaseof neuraloscillations.Neuroimage92,274–284
81. Bartolo,R.andMerchant,H.(2015)Betaoscillationsarelinked totheinitiationofsensory-cuedmovementsequencesandthe internalguidanceofregulartappinginthemonkey.J.Neurosci. 35,4635–4640
82. Kulashekhar,S.etal.(2016)Theroleofcorticalbetaoscillations intimeestimation.Hum.BrainMapp.37,3262–3281 83. Palva,J.M.etal.(2005)Phasesynchronyamongneuronal
oscillationsinthehumancortex.J.Neurosci.25,3962–3972 84. Palva,J.M.andPalva,S.(2017)Functionalintegrationacross
oscillationfrequenciesbycross-frequencyphase synchroniza-tion.Eur.J.Neurosci.PublishedonlineNovember2,2017. http://dx.doi.org/10.1111/ejn.13767
85. Lakatos,P.etal.(2005)Anoscillatoryhierarchycontrolling neuronalexcitabilityandstimulusprocessingintheauditory cortex.J.Neurophysiol.94,1904–1911
86. Jensen,O.etal.(2014)Temporalcodingorganizedbycoupled alpha and gamma oscillations prioritize visual processing. TrendsNeurosci.37,357–369
87. Sporns,O.andKotter,R.(2004)Motifsinbrainnetworks.PLoS Biol.2, e369
88. Watrous,A.J.etal.(2015)Phase-amplitudecouplingsupports phasecodinginhumanECoG.Elife4,e07886
89. Honkanen,R.etal.(2015)Gammaoscillationsunderliethe maintenanceoffeature-specificinformationandthecontents ofvisualworkingmemory.Cereb.Cortex25,3788–3801 90. Lakatos,P.etal.(2016)Globaldynamicsofselectiveattention
anditslapsesinprimaryauditorycortex.Nat.Neurosci.19, 1707–1717
91. Axmacher,N.etal.(2010)Cross-frequencycouplingsupports multi-itemworkingmemoryinthehumanhippocampus.Proc. Natl.Acad.Sci.U.S.A.107,3228–3233
92. Gross,J.etal.(2013)Speechrhythmsandmultiplexed oscil-latorysensorycoding in thehumanbrain.PLoSBiol.11, e1001752
93. Giraud,A.L.andPoeppel,D.(2012)Corticaloscillationsand speechprocessing: emergingcomputationalprinciplesand operations.Nat.Neurosci.15,511–517
94. Tass,P.etal.(1998)Detectionofn:mphaselockingfromnoisy data:applicationtomagnetoencephalography.Phys.Rev.Lett. 81,3291–3294
95. Fell,J.andAxmacher,N.(2011)Theroleofphase synchroni-zationinmemoryprocesses.Nat.Rev.Neurosci.12,105–118 96. Akiyama,M.etal.(2017)Theta-alphaEEGphasedistributionsin thefrontalareafordissociationofvisualandauditoryworking memory.Sci.Rep.7, 42776
97. Sauseng,P.etal.(2008)Cross-frequencyphase synchroniza-tion:abrainmechanismofmemorymatchingandattention. Neuroimage40,308–317
98. Siebenhühner,F.etal.(2016)Cross-frequencysynchronization connectsnetworksoffastandslowoscillationsduringvisual workingmemorymaintenance.Elife5,e13451
99. Verplanck,W.S.etal.(1952)Nonindependenceofsuccessive responsesinmeasurementsofthevisualthreshold.J.Exp. Psychol.44,273–282
100.Gilden,D.L.andWilson,S.G.(1995)Onthenatureofstreaksin signaldetection.Cognit.Psychol.28,17–64
101.Monto,S.etal.(2008)VeryslowEEGfluctuationspredictthe dynamicsofstimulusdetectionandoscillationamplitudesin humans.J.Neurosci.28,8268–8272
102.Simola,J. et al. (2017) Critical dynamics of endogenous fluctuationspredictcognitiveflexibilityintheGo/NoGotask. Sci.Rep.7, 2909-017-02750-9
103.Palva,J.M.etal.(2013)Neuronallong-rangetemporal correla-tionsandavalanchedynamicsarecorrelatedwithbehavioral scalinglaws.Proc.Natl.Acad.Sci.U.S.A.110,3585–3590 104.He,B.J.(2011)Scale-freepropertiesofthefunctionalmagnetic
resonanceimagingsignalduringrestandtask.J.Neurosci.31, 13786–13795
105.Linkenkaer-Hansen, K. et al. (2001) Long-range temporal correlationsandscalingbehaviorinhumanbrainoscillations. J.Neurosci.21,1370–1377
106.Hiltunen,T.etal.(2014)Infra-slowEEGfluctuationsare corre-latedwithresting-statenetworkdynamicsinfMRI.J.Neurosci. 34,356–362
107.Boly,M.etal.(2007)Baselinebrainactivityfluctuationspredict somatosensoryperceptioninhumans.Proc.Natl.Acad.Sci. U.S.A.104,12187–12192
108.Sadaghiani,S.etal.(2009)Distributedandantagonistic contri-butionsofongoingactivityfluctuationstoauditorystimulus detection.J.Neurosci.29,13410–13417
109.Spadone,S.etal.(2015)Dynamicreorganizationofhuman resting-statenetworksduringvisuospatialattention.Proc.Natl. Acad.Sci.U.S.A.112,8112–8117
110.Fox,M.D.etal.(2005)Thehumanbrainisintrinsicallyorganized intodynamic,anticorrelatedfunctionalnetworks.Proc.Natl. Acad.Sci.U.S.A.102,9673–9678
111.Sadaghiani,S.etal.(2010)Therelationofongoingbrainactivity, evokedneuralresponses,andcognition.Front.Syst.Neurosci. 4,20
112.Cocchi,L.etal.(2017)Criticalityinthebrain:asynthesisof neurobiology,modelsandcognition.Prog.Neurobiol.158,132– 152
113.Beggs,J.M. andPlenz,D.(2003) Neuronalavalanchesin neocorticalcircuits.J.Neurosci.23,11167–11177 114.Shew,W.L.etal.(2011)Informationcapacityandtransmission
aremaximizedinbalanced corticalnetworkswithneuronal avalanches.J.Neurosci.31,55–63
115.Shew,W.L.etal.(2009)Neuronalavalanchesimplymaximum dynamicrangeincorticalnetworksatcriticality.J.Neurosci.29, 15595–15600
116.Zhigalov,A.etal.(2017)Modularco-organizationoffunctional connectivityandscale-freedynamicsinthehumanbrain.Netw. Neurosci.1,143–165
117.Yang,H.etal.(2012)Maximalvariabilityofphasesynchronyin corticalnetworkswithneuronalavalanches.J.Neurosci.32, 1061–1072
118.Poil,S.S.etal.(2012)Critical-statedynamicsofavalanchesand oscillationsjointlyemergefrombalancedexcitation/inhibitionin neuronalnetworks.J.Neurosci.32,9817–9823
119.Moretti,P.andMunoz,M.A.(2013)Griffithsphasesandthe stretchingofcriticalityinbrainnetworks.Nat.Commun.4,2521 120.Vanhatalo,S.etal.(2004)Infraslowoscillationsmodulate excit-abilityandinterictalepilepticactivityinthehumancortexduring sleep.Proc.Natl.Acad.Sci.U.S.A.101,5053–5057
