基于卒中腦電信號的交叉頻率耦合分析

基于卒中腦電信號的交叉頻率耦合分析基于卒中腦電信號的交叉頻率耦合分析

摘要：卒中是一種常見的神經(jīng)系統(tǒng)疾病，其嚴重程度和預(yù)后問題導(dǎo)致許多人遭受嚴重的健康和社會影響。隨著技術(shù)的發(fā)展和研究的深入，越來越多的研究表明，卒中患者的腦電信號中存在著不同頻率之間的耦合。本文提出了一種基于卒中腦電信號的交叉頻率耦合分析方法，用于研究卒中患者腦電信號的特征和變化。

首先，本文介紹了卒中患者腦電信號的獲取和預(yù)處理方法，采用了快速傅里葉變換和濾波等技術(shù)，對腦電信號進行了預(yù)處理。然后，本文提出了一種基于互信息的交叉頻率耦合分析方法，旨在研究卒中患者不同頻率之間的關(guān)系和耦合。通過對卒中患者和正常對照組進行對比分析，研究發(fā)現(xiàn)，在卒中患者的腦電信號中存在著顯著的交叉頻率耦合，表現(xiàn)為不同頻率之間的關(guān)系和變化。同時，本文還探討了交叉頻率耦合在卒中患者腦電信號中的生理意義和應(yīng)用前景。

關(guān)鍵詞：卒中；腦電信號；交叉頻率耦合；互信息；生理意義。

Abstract:Strokeisacommonneurologicaldiseasewithsevereseverityandprognosisissuesthatleadtoserioushealthandsocialimpactsformanypeople.Withthedevelopmentoftechnologyandresearch,moreandmorestudieshaveshownthattherearecouplingsbetweendifferentfrequenciesinthebrainelectroencephalogram(EEG)signalsofstrokepatients.Thispaperproposesamethodofcross-frequencycouplinganalysisbasedontheEEGsignalsofstrokepatients,whichisusedtostudythecharacteristicsandchangesoftheEEGsignalsinstrokepatients.

Firstly,thispaperintroducestheacquisitionandpre-processingmethodsofEEGsignalsofstrokepatients,whichadoptsthetechniquessuchasFastFourierTransformandfilteringtopreprocesstheEEGsignals.Then,thispaperproposesacross-frequencycouplinganalysismethodbasedonmutualinformationwiththeaimofstudyingtherelationshipandcouplingbetweendifferentfrequenciesinstrokepatients.Throughcomparativeanalysisbetweenstrokepatientsandnormalcontrolgroup,itwasfoundthatthereweresignificantcross-frequencycouplingsintheEEGsignalsofstrokepatients,reflectingtherelationshipandchangebetweendifferentfrequencies.Meanwhile,thispaperalsodiscussesthephysiologicalsignificanceandapplicationprospectsofcross-frequencycouplinginEEGsignalsofstrokepatients.

Keywords:stroke;EEGsignals;cross-frequencycoupling;mutualinformation;physiologicalsignificanceStrokeisaneurologicaldisorderthataffectsmorethan15millionpeopleworldwide.Itcancauseseriousandlong-termdisabilityduetodamagetothebrain.Electroencephalography(EEG)isawell-establishedtechniqueformonitoringtheelectricalactivityofthebrain.Inrecentyears,studieshaveshownthattherearesignificantchangesinEEGsignalsofstrokepatients,especiallyintheformofcross-frequencycouplings.

Cross-frequencycouplingreferstotheinteractionbetweendifferentfrequencybandsinEEGsignals.Itisacomplexanddynamicprocessthatreflectsthefunctionalconnectivityofdifferentbrainregions.Studieshavefoundthatcross-frequencycouplingsareinvolvedinawiderangeofcognitiveprocesses,suchasattention,memory,andperception.Instrokepatients,cross-frequencycouplingsmayprovideimportantinformationabouttheprogressionofthediseaseandtheeffectivenessoftreatment.

Mutualinformationisapowerfultoolforanalyzingcross-frequencycouplingsinEEGsignals.Itmeasurestheamountofinformationsharedbetweendifferentfrequencybandsandcanrevealtheunderlyingfunctionalconnectivityinthebrain.Studieshaveshownthatmutualinformationofcross-frequencycouplingsissignificantlydifferentbetweenstrokepatientsandnormalcontrolgroups.Thissuggeststhatcross-frequencycouplingsareapromisingbiomarkerforidentifyingandmonitoringstrokepatients.

Thephysiologicalsignificanceofcross-frequencycouplingsinstrokepatientsisstillnotfullyunderstood.Onehypothesisisthattheabnormalcross-frequencycouplingsmayreflectthealteredneuralnetworkinthebraincausedbystroke.Anotherhypothesisisthattheymayberelatedtothecompensationmechanismorrecoveryprocessafterstroke.Furtherstudiesareneededtoinvestigatethemechanismandclinicalimplicationsofcross-frequencycouplingsinstroke.

Insummary,cross-frequencycouplingsinEEGsignalsofstrokepatientsareanimportantandpromisingresearchtopic.Theyprovidevaluableinformationaboutthealterationofneuralnetworkandpotentialcompensationorrecoveryafterstroke,whichmayfacilitatethedevelopmentofeffectivetreatmentandrehabilitationstrategiesforstrokepatientsOnepotentialdirectionforfutureresearchistoexaminetherelationshipbetweencross-frequencycouplingsandfunctionalconnectivityinstrokepatients.Functionalconnectivityreferstothecoordinationandsynchronizationofneuralactivitybetweendifferentbrainregions,whichiscrucialfornormalbrainfunction.Ithasbeenshownthatstrokecandisruptfunctionalconnectivity[33],butitisunclearhowthisdisruptionaffectscross-frequencycouplings.Understandingtheinterplaybetweenthesetwophenomenamayhelpustobetterunderstandtheoverallimpactofstrokeonthebrainandidentifynewtargetsfortherapy.

Anotherinterestingavenueforfutureresearchistoinvestigatethepotentialofcross-frequencycouplingsasbiomarkersforstrokediagnosisandprognosis.EEGisanon-invasiveandinexpensivetoolthatcanbeeasilyadministeredatthebedside,makingitanattractiveoptionforclinicaluse.However,currentmethodsforstrokediagnosisandmonitoringprimarilyrelyonneuroimagingtechniques,whichareexpensiveandnotwidelyavailableinmanycountries.Ifcross-frequencycouplingscanbeshowntoreliablypredictstrokeoutcomes,thismayleadtothedevelopmentofportable,low-costEEG-baseddiagnostictoolsthatcouldbeusedinawiderrangeofsettings.

Finally,itwillbeimportanttovalidatethefindingsfromEEGstudiesofcross-frequencycouplingsinstrokeusingothertechniques,suchasfunctionalMRI(fMRI)andmagnetoencephalography(MEG).EEGhasexcellenttemporalresolutionbutpoorspatialresolution,whereasfMRIandMEGprovidecomplementaryinformationwithhighspatialresolutionbutpoortemporalresolution.Combiningthesetechniquesmayallowustobetterunderstandthelarge-scalebrainnetworksthatareaffectedbystrokeandhowcross-frequencycouplingscontributetofunctionalrecovery.

Inconclusion,cross-frequencycouplingsinEEGsignalsareavaluableandpromisingtoolforstudyingtheneuralmechanismsunderlyingstrokeanditsrecovery.Bysheddinglightontheinteractionsbetweendifferentfrequencybandsinthebrain,cross-frequencycouplingsprovideimportantinformationaboutthecomplexneuralnetworksthatareinvolvedinstrokepathophysiology.Furtherresearchinthisareamayleadtonewdiagnostictools,therapeuticinterventions,andabetterunderstandingofhowthebrainrepairsitselfafterstrokeInadditiontocross-frequencycouplings,otherEEGmeasuressuchasevent-relatedpotentials(ERPs),spectralpowerdensityandcoherence,andphase-amplitudecoupling(PAC)havealsobeenusedtostudystrokeanditsrecovery.ERPsaretime-lockedtoaspecificstimulusorevent,andcanprovideinformationaboutthetimingandsequenceofneuralprocessing.Spectralpowerdensityandcoherencearemeasuresofthestrengthandcoherenceofoscillatoryactivitywithinaspecificfrequencyband,andcanrevealchangesinneuralconnectivityandsynchronization.PACisameasureoftherelationshipbetweenthephaseofaslowoscillationandtheamplitudeofafasteroscillation,andcanprovideinsightsintohowdifferentfrequencybandsinteracttosupportneuralprocessing.

AnumberofstudieshaveusedEEGmeasurestoinvestigatetheeffectsofstrokeonneuralprocessing.Onestudyfoundthatstrokepatientsshowedreducedfunctionalconnectivitybetweenbrainregionsinvolvedinsensorimotorprocessing,andthatthisdisruptionwasrelatedtotheseverityofmotorimpairment(Rossoetal.,2014).Otherstudieshaveshownthatstrokecanleadtochangesinoscillatoryactivityandconnectivitywithinandbetweenbrainregions,particularlyinthealphaandbetafrequencybands(Wangetal.,2019;Buchetal.,2018).

EEGmeasureshavealsobeenusedtoinvestigatetheneuralmechanismsunderlyingstrokerecovery.Onestudyfoundthatstrokepatientswithbettermotoroutcomesshowedincreasedbetabandcoherencewithintheipsilesionalmotorcortex,suggestingthatincreasedneuralsynchronizationmaysupportmotorrecovery(Rossiteretal.,2013).Anotherstudyfoundthatstrokepatientswhoshowedgreaterfunctionalconnectivitybetweentheipsilesionalmotorcortexandthecontralesionalcerebellumhadbettermotoroutcomes,suggestingthatthisneuralcircuitmayplayaroleinmotorrecovery(Schulzetal.,2015).

Overall,EEGmeasuresp