Диссертация (Experimental study of several core concepts of theoretical morphology (on the material of russian) - regularity, syncretism, markedness), страница 10

Subjects were instructedto generate aloud as fast as possible the first person singular presenttense form if they saw a verb or the nominative plural form if theysaw a noun. All responses were recorded simultaneously with fMRIdata acquisition by means of the Persaio MRI Noise CancelationSystem (Psychology Software Tools, Inc.). Their correctness wasassessed offline. When a participant’s responses were no longerappropriate for the target’s category, the corresponding trials werediscarded in the subsequent fMRI analyses.MR IMAGING PROTOCOLTable 1 | Examples of stimuli in different conditions.Magnetic resonance imaging was performed on a 3 Tesla PhilipsAchieva scanner.

In addition to a scout sequence, participantsunderwent structural and functional imaging. Structural imageswere acquired applying a T1-weighted pulse sequence (T1W-3DFFE; TR = 2.5 ms; TE = 3.1 ms; 30◦ flip angle) measuring 130 axialslices (field of view, FOV = 240 mm × 240 mm; 256 × 256 scanmatrix) of 0.94 mm thickness. Functional images were obtainedusing an echo planar imaging (EPI) sequence (TE = 35 ms;90◦ flip angle; FOV = 208 mm × 208 mm; 128 × 128 scanmatrix). Thirty-two continuous 3.5 mm thick axial slices (voxelsize = 3 mm × 3 mm × 3.5 mm) covering the entire cerebrumand most of the cerebellum were oriented with reference to thestructural image.

The images were acquired with a repetition time(TR) of 2000 ms. In order to avoid extensive head motions weused an MR-compatible soft cervical collar.ConditionPresented formsCorrect answersCONNECTIVITY ANALYSISRegular verbs (RV)kivat’ ‘to nod’kivajuIrregular verbs (IV)kolot’ ‘to cleave, tokoljufMRI data preprocessing included realignment, slice-time correction, spatial normalization, and 8 mm full-width/half-maximumisotropic Gaussian smoothing (for details, see Slioussar et al.,2014). It was carried out using SPM8 software (Wellcome Department of Cognitive Neurology, London, UK). Artifact DetectionToolbox2 was used to remove fMRI outliers from the analysis.During the PPI analysis, ROIs were selected from the clusterin the LIFG, which exhibited greater BOLD values for the production of irregular forms (Slioussar et al., 2014).

Three ROIssting’Regular nonce verbs (RNV)vupat’vupajuIrregular nonce verbs (INV)xorot’xorjuRegular nouns (RN)sokol ‘falcon’sokolyIrregular nouns (IN)posol ‘embassador’poslyRegular nonce nouns (RNN)mokolmokoly (and mokly )Irregular nonce nouns (INN)fopolfoply (and fopoly )Frontiers in Human Neuroscience2 http://www.nitrc.org/projects/artifact_detect/www.frontiersin.org30February 2015 | Volume 9 | Article 36 | 4Kireev et al.A PPI study of Russian verb productionconnectivity induced by regular real verb production in the RV > IVcomparison for the LIFGop3 seed region.

(C) Covariance between the numbermistakes in irregular real verb production and functional connectivity inducedby irregular real verbs in the IV > RV comparison for the LIFGop3 seed region.FIGURE 1 | Location of ROIs in the LIFG and results of PPI analysis.(A) Three ROIs overlaid on the areas Slioussar et al. (2014) identified assensitive to the main effect of regularity (regular real and nonce verbs werecompared with irregular real and nonce verbs). (B) Increase in functionalwere created by centering a 4 mm radius sphere in the corresponding local maxima in the opercular part of the LIFG(BA 44, see Figure 1A), as defined by the Anatomy toolbox2.0 (Eickhoff et al., 2005). The analysis of functional connectivity changes was performed between each of the selected ROIsand the remaining voxels of the brain using the generalizedPPI toolbox3 (McLaren et al., 2012) and included the followingsteps.

First, neuronal activity underlying the observed BOLDchanges in every ROI was mathematically estimated (Gitelmanet al., 2003). Then the estimated neuronal activity was multiplied by the vectors of each condition’s ON times and convolvedwith the hemodynamic response function (McLaren et al., 2012;Cisler et al., 2013).As a result, PPI-regressors corresponding to every experimentaltrial were created, and the PPI analysis was performed using thegeneral linear model (GLM). Additionally, the GLM included thefollowing nuisance variables: (1) regressors modeling the BOLDsignal changes induced by eight experimental conditions and mistake trials (as in the conventional subtractive GLM analysis); (2)head motion parameters and the global mean fMRI outliers; (3) aregressor reflecting the time series of BOLD signal changes within3 http://www.nitrc.org/projects/gppiFrontiers in Human Neurosciencethe ROI to exclude context-dependent changes occurring at thehemodynamic level.To be able to compare our results to those of Stamatakis et al.(2005), we focused on the contrast between regular and irregular real word trials in the connectivity analysis, as these authorsdid.

The fronto-temporal connectivity observed by Stamatakiset al. (2005) is most reasonably described as a frontal modulation of lexical access processes, which is obviously not applicableto nonce stimuli. However, the findings from other comparisonsare also reported. As in Slioussar et al.

(2014), we analyzed verbsand nouns separately rather than putting them together and treating word category as the third factor, primarily because the typeof irregularity we were able to find for nouns was very minorcompared to what we had in the case of verbs. Thus, RV > IVand IV > RV contrasts of PPI-parameters were estimated with theuse of one-sample t-tests. Additionally, PPI-parameters for all realand nonce verb trials were analyzed using the ANOVA with tworepeated measure factors: lexicality (real vs. nonce) and regularity.The same was done for nouns.Statistical parametric mappings were computed using thep < 0.001 voxel-wise uncorrected threshold.

To avoid false positivefindings, the FWE p < 0.05 correction for multiple comparisonswas applied at the cluster level. Since two t-tests were calculatedwww.frontiersin.org31February 2015 | Volume 9 | Article 36 | 5Kireev et al.A PPI study of Russian verb productionfor each of the three ROIs, an additional Bonferroni–Holm correction for multiple comparisons was used.

The anatomical locationof the functional connectivity changes revealed was identified bythe Anatomy toolbox. The REX toolbox4 was used to demonstratedifferences between beta values reflecting functional connectivitychanges in the revealed clusters.RESULTSIn the RV > IV comparisons, the PPI analysis revealed clustersbilaterally located in the anterior portion of the superior temproralgyri (STG, see Table 2; Figure 1B). This effect was observed onlyfor the LIFGop3 ROI seed, RV > IV PPI-contrasts for the othertwo ROI seeds were not significant.

Calculating the mean values ofPPI-parameters within the obtained clusters pointed to a relativeincrease in connectivity in RV trials in comparison to IV trials.In the IV > RV comparisons, no significant changes in functional connectivity were found for all selected ROI seeds. Since wehad concluded in Slioussar et al.

(2014) that IV trials were characterized by an increase in processing load in comparison to RVtrials, and this conclusion relied not only on neuroimaging, butalso on behavioral data (number of mistakes in different conditions), we undertook the following subsidiary analysis to revealprocessing load effects. We took the number of mistakes committed by every participant in the IV trials as an individual measureof task difficulty.

As we reported in Slioussar et al. (2014), participants made significantly more mistakes in the IV condition thanin the RV condition (96 out of 735 vs. 22 out of 735 responses intotal, or 13.1% vs. 3.0%, respectively). If we look at each participant separately, the number of mistakes in the IV trials varies from1 out of 35, or 2.9%, to 9 out of 35, or 25.7%.Then we submitted IV > RV contrasts of PPI parameters calculated for every participant to the second level group analysis. Aone-sample t-test, as it is implemented in SPM8, was used withthe percentage of mistakes committed by every participant as avariable of interest and estimates of individual IV > RV PPI contrasts as a dependent variable. The results were significant onlyfor one ROI seed, LIFGop3, the same as in the RV > IV PPIanalysis above.

For this ROI seed, we observed a significant positive covariance between the number of mistakes in the IV trialsand the difference in functional connectivity between the LIFG4 http://www.nitrc.org/projects/rex/Table 2 | RV > IV PPI-contrast for a ROI seed in the LIFG (BA 44,LIFGop3).Peak MNI coordinatesBrain regionp-valueKXYZL. STG (BA 22/21)0.007*83−54−13−5L. putamen0.02862−272R. STG/ insula (BA 22/21)0.0001*15254−114−1*Significant clusters after Bonferroni–Holm correction.BA, approximate Brodmann’s area; L/R, left/right hemisphere; k, cluster size invoxels; STG, superior temporal gyrus.Frontiers in Human NeuroscienceTable 3 | The effect of task difficulty in the IV > RV PPI-contrast for aROI seed in the LIFG (BA 44, LIFGop3).Peak MNI coordinatesBrain regionp-valueKXYZR.

ACC (BA 32)0.013*29122031*Significant clusters after Bonferroni-Holm correction.ACC, anterior cingulate cortex; BA, approximate Brodmann’s area; L/R, left/righthemisphere; k, cluster size in voxels.and the right ACC (BA 32; see Table 3; Figure 1C).

Notably,when error rates are low, the difference is negative, i.e., connectivity between the LIFG and the ACC is greater in RV trials. Aserror rates grow, the difference approaches zero and then becomespositive, i.e., for participants who made more errors than the others, connectivity between the LIFG and the ACC is greater in theIV trials.Comparisons involving real noun stimuli (RN > IN andIN > RN), as well as factorial analyses for verb and nounconditions, did not yield significant results.DISCUSSIONWe believe that the most noteworthy outcome of the present studyis that the connectivity analysis allowed us to dissociate regularity and processing difficulty effects and, as we hope to showbelow, gain a deeper understanding of their nature.