<title>Analysis of event-related fMRI using nonlinear regression self-organizing map neural network</title>

Abstract

AnalysisofEvent-RelatedfMRIusingaNon-LinearRegressionSelf-OrganizingMapNeuralNetworkStephanG. Erb ericha, ManouLieb ert,Klaus WillmesbArmin Thronc,Walter Ob erschelpdaNeurofunctional Imaging Lab oratory,Interdisciplinary Centerfor Clinical Researchin theNeurosciences,Univ.Hospital, Univ.of TechnologyRWTH,Aachen,GermanybDept.ofNeuropsychology,Univ.Hospital, Univ.of TechnologyRWTH,Aachen,GermanycDept.of Neuroradiology,Univ.Hospital, Univ.of TechnologyRWTH,Aachen,GermanydDept.for Computer ScienceVI I,Univ.of TechnologyRWTH,Aachen,GermanyABSTRACTFunctional magnetic resonance imaging (fMRI) b ecomes a common metho d to study task induced brain activation.Using rapid Echo Planar Imaging (EPI) sequences one can obtain a higher MR-Signal under a task condition closebyactivated areas as a result of susceptibilitychanges in blo o d oxygenation (BOLD e ect).Beside the commonlyused blo cked task designs, event-related paradigms gain more imp ortance for activation of higher cognitive functionsenablingmoresophisticatedandcomplexparadigms.Fortheanalysisofevent-relatedfMRIdataonecanusestatisticaltests,inexamplet-testusedbySPMSoftware.Thetro ducedanalysismetho dbasedonanarti cialneural network algorithm, a self-organizing map(SOM),is capable to distinguish b eteen task related activation,deactivationandbaselinepatternsfromthetimeseries.Thisisachievedbytemp oralsortingpro jectionofall events from one condition into one combined hemo dynamic resp onse sampling for eachvoxel.These resp onses,having individual patterns can b e separated by their pattern features and is done by training of the neural network.After training the SOM consists of a pattern-to-voxel mapping which is sup erimp osed onto either an anatomical orEPI image of the sub ject for the task evaluation.Keywords:fMRI, non-linear regression, event-related fMRI, self-organizing map (SOM), time series analysis, un-sup ervised classi cation1.INTRODUCTIONEvent-Related functionalMRI(efMRI)hasb ecomeama jor paradigmtechniqueforBOLD(Blo o dOxygen LevelDep endent) fMRI studies.While an event-related design overcomes the lack of random stimuli presentation of b oxcar designs, one can presentstimuli without training e ects by the sub ject.Also random combinations of more thanone eventtyp e can b e investigated at once and failed evt reactions by the sub ject can b e indep endently remoedfrom the later analysis.Therefore efMRI techniques gain more and more imp ortance.A common to olb ox for the statistical analysis is the Statistical Parametric Mapping (SPM) software [1].SPM99,the latest release, uses a general linear mo del approach to describ e the time series of eachvoxel.While a linear mo delbased metho d can only explain e ects describ ed bythe chosen mo del, weinvestigated a new data-driven metho d toanalyze the fMRItime-series.This metho d is based on an self-organizing neural network, theself-organizing map[2].The neural network is trained by feature vectors, each consists of the sorted adjusted temp oral resp onse for oneeventtyp e of the time series from a voxel.This non-linear approach is based on the similarity measure, the Euclideandistance, b etween inputHRFand the trained resp onse intheneural netork.As cluster techniques [3],the SOMneeds this similarity measure to depict the b est matching neuron.Toaverage the vector p ointwecho ose the SPM99 HRF- tting function.The adjusted and tted resp onses fromeachvoxelarede ningthetrainingandlaterclassi cationinputforneuralnetwork.Afterneuron weights(vectors) representtheresp onse features,curvecharacteristics, ofpresented ttedtimeseries.*Corresp ondence: E-mail: Stephan.Erb erich@izkf.rwth-aachen.de; Phone:++49-241-80-88890; Fax: ++49-241-88-90505