Identification of the Language Dominant Hemisphere using MEG Cortical Imaging and Permutation Tests

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1 Identification of the Language Dominant Hemisphere using MEG Cortical Imaging and Permutation Tests DIMITRIO PANTAZI 1 WARREN MERRIFIELD 2 FELIX DARVA 1 WILLIAM UTHERLING 2 RICHARD M. LEAHY 1 1 ignal and Image Processing Institute University of outhern California 3740 McClintock Ave, Los Angeles, CA , UA 2 Epilepsy and Brain Mapping, MEG Unit Huntington Medical Research Institutes 10 Pico treet, Pasadena, CA 91105, UA pantazis@sipi.usc.edu Abstract We present a new method to localize language-specific cortex in MEG event-related studies. We produce cortically constrained dynamic images of brain activity using regularized min-norm imaging. Then, we use a permutation test to optimally threshold the cortical maps while controlling false positive ratio. Once we establish significant activation, we perform a language asymmetry test based on average power, to identify the language dominant hemisphere. We demonstrate our method in application to MEG studies of language processing. Key-Words: Magnetoencephalography, Language Processing, Wada Test, Permutations 1 Introduction Patients with medically intractable epilepsy, tumors, or other neurological diseases often undergo neurosurgical procedures to remove pathological tissue. ince resection of language areas may compromise communication skills and cause receptive or expressive aphasia, surgical planning includes functional brain mapping that reveals the dominant hemisphere for language processing. The Wada test evaluates the language performance of one hemisphere by anesthetizing the other hemisphere with barbiturate agents injected in the corresponding carotid artery. Alternatively, electrocorticography performs direct cortical stimulation, either intraoperatively or extraoperatively, using implanted electrodes. Recent literature has focused on non-invasive alternatives to the Wada procedure and electrocorticography. Language asymmetry has been evaluated using fmri [1, 2, 3] and PET [4], by counting voxels of activation in each hemisphere; the hemisphere with more activation is considered dominant. Unlike fmri and PET that produce images of brain activity averaged over hundreds of milliseconds, MEG has excellent temporal resolution and offers invaluable information when we image cognitive tasks, such as language processing, involving both primary and associative cortical areas. everal MEG studies [5, 6, 7, 8] have localized language-specific cortical activity using either equivalent current dipoles or distributed cortical imaging, with promising results for clinical application. In this paper we present a new method to localize language-specific cortex in MEG event-related studies. ince complex cognitive tasks, such as language processing, involve simultaneous activation of multiple sources, we choose a cortical imaging approach based on regularized min-norm imaging, instead of an equivalent current dipole method that assumes only a few focal sources. We use a permutation test to optimally threshold the cortical maps while controlling 1

2 false positive ratio [9, 10]. Once we establish significant activation, we perform a language asymmetry test based on average power, to identify the language dominant hemisphere. We demonstrate our method in application to MEG studies of language processing. 2 Method Our goal is to identify the language dominant hemisphere using MEG cortical imaging. In this section we first describe our MEG data model. We then present a method based on non-parametric permutation tests for controlling the familywise error rate in regularized min-norm images in MEG experiments. Once significant areas have been established, a pairwise test of total activation between hemispheres determines the dominant hemisphere. 2.1 Model We assume that MEG data are collected as a set of J stimulus-locked event-related epochs (one per stimulus repetition), each consisting of a pre- and poststimulus interval. Each epoch consists of an array of data M (n channels n timepoints ) representing the measured magnetic field at each sensor as a function of time. The measurements M are linearly related with the brain activation X (n sources n timepoints ) as: M = GX + N (1) where G (n channels n sources ) is the forward operator and N represents additive noise in the channel measurements. The lead field matrix G depends on the shape and conductivity of the head and can be computed using a simplified spherical head model, or more accurately, using boundary or finite element methods that account for the true shape and conductivity within the head [11]. A cortical map can be computed for each epoch by applying a Tikhonov regularized minimum norm inverse method to produce an estimate of the temporal activity at each surface element in the cortex: X = (G T G + λi) G T M (2) We write the reconstructed cortical maps as {X itj }, where i = 1,...,, t = N 0 + 1,..., N, and j = 1,..., J are indices in space, time, and epoch, respectively. We let t = 1 correspond to the stimulus event time so that there are N 0 pre-stimulus time points. We use the pre-stim data to estimate the baseline mean ˆµ ij at each spatial element i at epoch j, by averaging over time t. We then estimate the centered data as: Y itj = X itj ˆµ ij (3) 2.2 Localization of ignificant Brain Activity While the NR in individual epochs j is too low to see any but the strongest components, averaging signal across epochs should reveal significant event-related activity across the entire cortex. ince interpretation of cortical maps is confounded by the presence of additive noise exhibiting highly nonuniform spatial dependence, we also noise-normalize the maps using the pre-stimulus variance estimate ˆσ i : T it = Ȳit ˆσ i / J (4) where the bar indicates an average over the dotted subscript. Assessment of T it maps can identify significant brain activation, and localize the language cortex. Based on Papanicolaou et al. [5], evoked responses have two types of components (Fig. 1): the early evoked response localizes approximately ms after stimulus, and corresponds to bilaterally symmetric activation of the primary sensory cortex; the late evoked response localizes approximately ms after stimulus, and reflects activation of the association cortex. ince language asymmetry is reflected predominantly on the late response, we focus our attention on the analysis of T it maps at ms. Objective assessment is crucial for the interpretation of cortical activation maps, because the choice of threshold can greatly affect the locus and spatial extent of significant activation. Evaluation of T it maps involves testing thousands of hypotheses (one per surface element and time instance) for statistically significant experimental effects. To effectively control the number of false positives over all tests we must therefore apply multiplicity adjustments by controlling the Familywise Error Rate (FWER), i.e. the chance of one or more false-positives under the null hypothesis H 0 of no activation. The FWER is directly related to the maximum statistic; one or more statistics T ik will exceed the threshold u under the null hypothesis H 0, if and only if the maximum ex- 2

3 curately. Our test is conservative, but we feel any false positives carry high cost and are unacceptable. Using the activated voxels, we define a set of symmetric spatial locations N R and N L for the right and left hemisphere. For example, if we find significant activity in Wernicke s area, we can choose a subset of the posterior temporal cortex in both hemispheres (Fig. 6). We further specify a time region T during which the significant voxels are active. We proceed by averaging activity over N R and N L spatial locations in each hemisphere: Figure 1: Channel Timeseries on a word identification MEG study. The early evoked response occurs at approximately ms after stimulus onset; the late evoked response occurs at approximately ms after stimulus onset. ceeds the threshold a : P (FWER) = P ( ik { T ik > u} H o ) = P (max ik T ik > u H o ) = 1 F max T Ho (u) (5) = 1 (1 α) = α We use the absolute value because negative values of T ik can also indicate significant changes in the language cortex, and we therefore need a two-tailed test statistic. As shown in Eq. 5, we can control the FWER if we choose the threshold u to be in the (1 α)100 th percentile of the maximum distribution. ince the complex spatial profile of noise makes it impossible to evaluate analytically the maximum distribution max ik T ik, in a later section we will demonstrate the use of permutation tests [9] to approximately estimate it. 2.3 Dominant Hemisphere Depending on the sensitivity of our statistical analysis and the actual activation of the language cortex, thresholding T it maps results in significant areas of activation that localize on right, left, or both hemispheres. In case no voxel exceeds the threshold it is not recommended to proceed to a subsequent language asymmetry test and use the results for neurosurgical planning; we should not base neurosurgical decisions on MEG analysis, unless we are absolutely sure that the language-cortex has been specified aca F X denotes the cumulative density function (CDF) of the random variable X E R j = mean t T,j N R Y itj 2 E L j = mean t T,j N L Y itj 2 (6) To identify the language-dominant hemisphere, we do a pairwise test between Ej R and Ej L using a standard t-statistic, as shown in Eq. 7. We assume a heteroscedastic model, i.e. we allow different variance between the two hemispheres, and the standard deviation σ is estimated as in Eq. 8. σ = = ĒR ĒL σ var{ej R} + var{el j } J (7) (8) Under the null hypothesis of no language asymmetry between hemispheres, the statistic should be close to zero. If we assume Gaussianity, we can use the t- distribution to establish significance, however this is not necessary with the permutation alternative discussed in the next section. 2.4 Permutation Tests In this section we demonstrate the use of permutation tests to evaluate the distribution under the null hypothesis of T = max ik T ik and in Eq. 4 and 7 respectively, where the tilde indicates a maximum over the dotted subscript. Beginning with the distribution of T, the standard approach to permutation tests is to find units exchangeable under the null hypothesis. By collecting equal pre- and post-stimulus data we can permute pre- and post-stim source timeseries, since these intervals are interchangeable under the null hypothesis of no activation at any post-stimulus timepoint. ince the permutations are applied over epochs (Fig. 2), which are typically regarded as independent, we do not destroy possible correlations in the data. 3

4 Figure 2: Illustration of the summarizing procedures used to construct FWER-corrected thresholds: the original epochs Y itj produce M permutation samples Yitj by exchanging preand post-stimulus data. The epochs are then averaged and normalized to produce T it and Tit. Finally, Tit are summarized in time and space to produce epochwise thresholds ˆF (1 α). Figure 3: Empirical probability density distribution of max it T it.we use this distribution to estimate a threshold ˆF (1 α) that controls the FWER at the α = 0.05 level. Given J original epochs Y itj, j = {1,, J}, we can create M 2 J permutation samples Yitj, each consisting of J new epochs (Fig. 2). The symbol (*) indicates that the values Yitj are created by permutation. By applying Eq. 4 to the permuted data, we create permutation samples of T ik : T it = Ȳ it ˆσ i / J (9) To obtain thresholds that control the FWER over space and time, we compute the permutation distribution of the maximum statistic: T = max T ik (10) ik We use this distribution to define a level α threshold, ˆF (1 α) (Fig. 3). A voxel i has statistically significant activation if T ik (the original statistic) exceeds this threshold. Learning the distribution of in Eq. 7 requires a simpler procedure. Under the null hypothesis that there are no task-related differences between right and left hemispheres, we can permute Ej R and Ej L (Fig. 4) to create permutation samples Ej R and Ej L. We use these samples to create permutation samples of : = ĒR ĒL σ (11) The empirical distribution of (called ˆF ) allows us to define a level α threshold using the right and left tail of the distribution, ˆF (1 α) and ˆF (α) Figure 4: Illustration of the permutation procedure used to estimate the distribution of the statistic. We first permute the energy statistics Ej R and Ej L to create permutation samples Ej R and Ej L. We then use the permutation samples to estimate the empirical distribution of, and estimate the thresholds ˆF (α) and ˆF (1 α). respectively. The subject is right dominant if > ˆF (1 α), and left dominant if < ˆF (α), where is the original statistic from Eq Results We illustrate our method in a language processing MEG study. The experiment consisted of two phases: the study phase and recognition phase. In the study phase, participants were asked to listen to and remember 30 words. During the recognition phase, participants were presented new words in addition to the words from the study list for a total of 240 words. Words were presented in six blocks (40 each), which included 30 words from the study list and 10 new words. Participants were instructed to deflect their index finger (left first half, right second half) when they recognized a word from the study list. Prior to the start of the experiment, participants were instructed to keep their eyes focused on a target in front 4

5 ubject ˆF (0.95) C C P P P P C1 C2 Right Left Table 1: patiotemporal thresholds ˆF (0.95), used to identify significant activation in the T it maps. P1 of them and refrain from excessive movement during data collection. The MEG measurements were recorded at the time interval {-150 ms, 800 ms} with zero corresponding to the word presentation, using a 68-channel MEG system manufactured by CTF MEG ystems. The MEG recordings of the recognition phase were mapped onto the cortical surface using Tikhonov regularized min-norm imaging (Eq. 2). We used Eqs. (4) and (9) to form t-statistics T ik from our original data, and Tik from M = 1000 permutation samples. ince the permutation scheme requires equal number of pre- and post-stimulus timepoints and we have only 150ms of pre-stimulus data, we used the post-stimulus interval {250 ms, 400ms} during permutation. Using the permutation distribution of max ik Tik, we estimated thresholds ˆF (1 α) that controls the FWER at the α = 0.05 level for two control subjects (C1-C2) and four epileptic patients (P1- P4) (Table 1). Figure 5 shows all voxels that where deemed significant with our permutation analysis. ubjects P1 and P2 had no significant voxels, indicating that their language cortex does not demonstrate considerable variation from the baseline. All remaining subjects had significant voxels close to Wernicke s area. We use the results from Fig. 5 to define regions N R and N L to all subjects that had significant languagespecific activity. ince all significant voxels were in Wernicke s area, we define N R and N L to be the right and left posterior temporal lobe respectively (Fig. 6). We use the total energy E R j and E L j during the time interval T = {250ms, 600ms} to estimate the asymmetry test statistic (Eq. 7). By applying a permutation test as described in the method section, we establish significance for all subjects that had significant voxels (Table 2). ince P1 and P2 had no significant voxels, we should not do an asymmetry analysis for these subjects, as explained in the Method sec- P2 P3 P4 Figure 5: Voxels with significant activity during ms after the word presentation. ubjects P1 and P2 had no significant voxels. tion; our test is conservative, but we want to avoid false positives. Table 3 summarizes the results for all subjects, together with Edinburgh profile and Wada score when available. For subjects C1, C2, and P4 a Wada test is not available, however their high Edinburgh profile indicates right handedness, and consequently the probability of left hemisphere language dominance is very high [12]. Our MEG methodology also indicates left dominance. Edinburgh profile, Wada test, and our MEG methodology indicates left dominance for P3. Our analysis for P1 and P2 was inconclusive. Figure 6: The posterior temporal cortex is used to define areas N R and N L for the language asymmetry test of all subjects that had significant voxels. 5

6 ubject ˆF (0.05) ˆF (0.95) C C P P P P ˆF Table 2: Thresholds (α) and ˆF (1 α) at 5% significance level, and original statistic for all subjects. The subject is right dominant if > ˆF (0.95), and left dominant if < ˆF (0.05). For subject P1 the test is inconclusive. *: We should not proceed to a laterality test for subjects P1 and P2, because they did not have significant voxels (Fig. 5). However we show the results for completeness. ubject Edinburgh Wada ig. Activity Dom. Hemisphere C Left Left C Left Left P1-100 Right No activity Inconclusive P Left No activity Left P Left Left Left P Left Left Table 3: Edinburgh profile, Wada test, location of significant voxels after thresholding T it maps, and asymmetry analysis using statistic. *: The same comments as Table 2 apply. 4 Conclusion We have developed a two-step procedure to identify the language dominant hemisphere using MEG cortical imaging and permutation tests. The first step identifies language-specific cortical locations that demonstrate significant variation against the baseline. Once the language cortex is specified, we perform an asymmetry test based on average power, to identify the language dominant hemisphere. ince the number of subjects is very small, our results are only preliminary. We are currently processing more subjects to validate the accuracy of our methodology. Acknowledgement This work was supported in part by grants from NIBIB (R01 EB002010), NCRR (P41 RR013642), and PH (N20806). References [1] Woermann F., Jokeit H., Luerding R., Freitag H., chulz R., Guertler., Okujava M., Wolf P., Tuxhorn I., and Ebner A. Language lateralization by Wada test and fmri in 100 patients with epilepsy. Neurology, vol. 61, 2003, pp [2] Binder J., wanson., Hammeke T., Morris G., Mueller W., Fischer M., Benbadis., Frost J., Rao., and Haughton V. Determination of language dominance using functional MRI: a comparison with the Wada test. Neurology, vol. 46, 1996, pp [3] Gaillard W., Balsamo L., Xu B., Grandin C., Braniecki., Papero P., Weinstein., Conry J., Pearl P., achs B., ato., Jabbari B., Vezina L., Frattali C., and Theodore. W. Language dominance in partial epilepsy patients identified with an fmri reading task. Neurology, vol. 59, 2002, pp [4] Gross G., Duara R., Barker W., Loewenstein D., Chang J., Yoshii F., Apicella A., Pascal., Boothe T., evush., and et al. Positron emission tomographic studies during serial word-reading by normal and dyslexic adults. J Clin Exp Neuropsychol, vol. 13, 1991, pp [5] Papanicolaou A., imos P., Breier J., Zouridakis G., Willmore L., Wheless J., Constantinou J., Maggio W., and Gormley W. Magnetoencephalographic mapping of the language-specific cortex. J Neurosurg, vol. 90, 1999, pp [6] Papanicolaou A., imos P., Castillo E., Breier J., arkari., Pataraia E., Briilingsley R., Buchanan., Wheless J., Maggio V., and Maggio W. Magnetoencephalography: a noninvasive alternative to the Wada procedure. J Neurosurg, vol. 100, 2004, pp [7] Bowyer., Moran J., Weiland B., Mason K., Greenwald M., mith B., Barkley G., and Tepley N. Language laterality determined by MEG mapping with MR-FOCU. Epilepsy and Behavior, vol. 6, 2005, pp [8] zymanski M., Perry D., Gage N., Rowley H., Walker J., Berger M., and Roberts T. Magnetic source imaging of late evoked field responses to vowels: toward an assessment of hemispheric dominance for language. J Neurosurg, vol. 94, 2001, pp [9] Pantazis D., Nichols T.E., Baillet., and Leahy R.M. A Comparison of Random Field Theory and Permutation Methods for the tatistical Analysis of MEG data. Neuroimage, vol. 25, 2005, pp [10] Nichols T.E. and Holmes A.P. Nonparametric Permutation Tests for Functional Neuroimaging: A Primer with Examples. Human Brain Mapping, vol. 15, 2001, pp [11] Baillet., Mosher J.C., and Leahy R.M. Electromagnetic Brain Mapping. IEEE ignal Processins Magazine, vol. 18, 2001, pp [12] Knecht., Drager B., Deppe M., Bobe L., Lohmann H., Floel A., Ringelstein E., and Henningsen H. Handedness and hemispheric language dominance in healthy humans. Brain, vol. 123, 2000, pp