Auer, T.

Doczi, T.

Frahm, J.

Merboldt, K.

Schwarcz, A.

English

MPG.PuRe

Atatrfmor©K1bdwootpsm1eR0dJournal of Neuroscience Methods  167 (2008) 335–339A novel group analysis for functional MRI of the humanbrain based on a two-threshold correlation (TTC) methodTibor Auer a, Attila Schwarcz a,b,∗, Tamas Doczi a,Klaus-Dietmar Merboldt c, Jens Frahm ca Department of Neurosurgery, University of Pécs, H-7624 Pécs, Hungaryb Pécs Diagnostic Institute, University of Pécs, H-7624 Pécs, Hungaryc Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalischeChemie, 37070 Göttingen, GermanyReceived 13 April 2007; received in revised form 1 August 2007; accepted 19 August 2007bstractThis work presents a new group analysis for functional MRI of human brain activation. The two-threshold correlation (TTC) method determineswo statistical thresholds by estimating the noise distribution underlying the summed histogram of correlation coefficients (CC) from all sectionsnd subjects. The probabilistic CC thresholds (p < 0.0001 for the identification of highly significant activation centers and p < 0.05 for limitinghe iterative addition of directly neighboring voxels to these centers) are applied to the group CC maps for each section. These maps may beeconstructed by taking the maximum (MAX) or mean (MEAN) CC value of all subjects for a particular voxel. Experimental analyses involvedunctional echo-planar imaging of sequential finger-to-thumb opposition and silent word generation at 3 T (eight subjects). Preprocessing includedotion correction, spatial filtering, and normalization to MNI space. While the results for the TTC MAX approach were very similar to thosebtained for a standard SPM analysis, the TTC MEAN approach turned out to be more conservative emphasizing voxels that are activated in mostather than in only a few subjects. The new method is simple, fast, and robust by linking two thresholds in a physiologically meaningful manner. 2007 Elsevier B.V. All rights reserved.csuptmlrdaeywords: Functional MRI; Group analysis; SPM. IntroductionFunctional magnetic resonance imaging (fMRI) of humanrain activation is a non-invasive method for monitoring hemo-ynamic responses to a functional challenge in single subjectsith high spatial and temporal resolution. However, in viewf increasingly complex cognitive paradigms that often involvenly subtle differences in neural activity, the response strengthsend to decrease and impose severe challenges for adequateost-processing. A common strategy is therefore to move fromingle subjects to group analyses and the most widely employedethod is statistical parametric mapping (SPM) (Friston et al.,995). Though elegantly designed for the statistically trained andxperienced researcher, a potential drawback of the method is its∗ Corresponding author at: Department of Neurosurgery, University of Pécs,ét u. 2, Pécs H-7624, Hungary. Tel.: +36 72 535 932; fax: +36 72 535 931.E-mail address: attila.schwarcz@aok.pte.hu (A. Schwarcz).bittuctf165-0270/$ – see front matter © 2007 Elsevier B.V. All rights reserved.oi:10.1016/j.jneumeth.2007.08.017omplexity. As a consequence, SPM applications require sub-tantial knowledge with the potential risk that the rank-and-fileser may be misguided to produce inadequate results.On the other hand, at the single-subject level, a fMRI datarocessing method has been established (Baudewig et al., 2003)hat emerges as a rather simple data-driven approach originallyotivated by detailed experimental observations and physio-ogic considerations (Kleinschmidt et al., 1995). The method iseferred to as two-threshold correlation (TTC) and has evolveduring the analysis of several thousands of fMRI data setscquired in multiple studies of the Göttingen laboratory. It isased on correlation coefficient (CC) maps that are thresholdedndividually by estimating the noise distribution underlyinghe distribution (or histogram) of correlation coefficients ofhe actual fMRI acquisition. In fact, studies without any stim-lation (Null experiment) demonstrated that the width of aorresponding CC distribution may be affected by alterations ofhe hemodynamic responsiveness (‘arousal’), respiration, per-usion, flow-induced tissue pulsations, or motions. Because the3 ciencbGiiodCTaaoeeaoarS22aeAsppofcW2gch(tpshBns21sahvtaftowtvf2(Kleinschmidt et al., 1995; Baudewig et al., 2003), correlationcoefficient maps were calculated for each subject with use of asimple boxcar reference function derived from the task proto-col and shifted by 5 s to account for the delayed hemodynamicFig. 1. Group distributions of CC values from all subjects and sections (dotted36 T. Auer et al. / Journal of Neurosasic form of these distributions is adequately described by aaussian curve, true brain activations, that are voxels represent-ng paradigm-associated fMRI signal alterations, may be easilydentified: the CC histogram of a fMRI study emerges as the sumf a dominating noise distribution and a second much smalleristribution of activated voxels with high positive (or negative)C values. In contrast to methods based on a single threshold, theTC method employs two probabilistic thresholds for their sep-ration: a high value for the identification of highly significantctivations and a lower value for limiting the iterative additionf directly neighboring voxels to these centers. The approachnsures both specificity and sensitivity for defining the spatialxtent of significant activation spots.The aim of this study was to extend the TTC method to groupnalyses in order to offer an easy-to-use and robust alternative tother more complex post-processing strategies for fMRI. Brainctivation was elicited with standard task paradigms and theesults were compared to corresponding group analyses withPM.. Materials and methods.1. Participants and paradigmsEight healthy normal volunteers (male, right-handed, meange: 31 ± 4 years) participated in the study. Handedness wasxamined by the Edinburgh Inventory Test (Oldfield, 1971).pproval by the Institutional Review Board was obtained and allubjects gave written informed consent before each examination.Mapping of sensorimotor and language areas was accom-lished with use of a block design alternating active and passiveeriods each lasting for 50 s. The paradigms involved five cyclesf unilateral sequential finger-to-thumb opposition (right hand,requency 1–2 Hz) and motor rest (Baraldi et al., 1999) and sevenycles of silent word generation and rest (Maldjian et al., 2002;oermann et al., 2003)..2. MRIMRI was conducted at 2.89 T (Siemens Tim Trio, Erlan-en, Germany). Excitation was performed with the bodyoil, while signal reception was achieved with an 8-channelead coil. Functional MRI involved a standard EPI sequenceTR/TE = 2500/36 ms, flip angle 80◦) with an isotropic spa-ial resolution of 3 mm (24 sections). Motion correction waserformed as offered by the manufacturer. All images wereubjected to a spatial Gaussian filter of 5 mm full width atalf maximum and normalized to MNI space (Collins, 1994).oth SPM and TTC post-processing steps were applied to theseormalized and spatially smoothed images using MatLab 6.5oftware..3. SPM group analysisGroup analyses were performed with SPM5 (Friston et al.,995). We have specified our fixed-effect model based on defaultettings of SPM with the eight subjects together using first-levellfidaVe Methods  167 (2008) 335–339nalysis: Basic hemodynamic response function, canonical hrf,igh-pass filter: cutoff of 128 s; global calculation: mean voxelalue; grand mean scaling: session specific; global normaliza-ion: none. AR(1) model was used to account for substantialutocorrelations in fMRI data. We used contrasts “1 1 1 1 1 1 1 1”or activation and “−1 −1 −1 −1 −1 −1 −1 −1” for deactiva-ion for the eight subjects respectively. A statistical thresholdf p < 0.05 with FWE correction (Nichols and Hayasaka, 2003)as applied resulting in t = 4.66 for silent word generation and= 4.62 for sequential finger opposition tasks, respectively. Acti-ation and deactivation are shown on the same maps derivedrom t score maps produced by SPM5 software..4. TTC group analysisSimilar to the established single-subject TTC analysisine) for sequential finger-to-thumb opposition and silent word generation. Thet of a Gaussian function to the central portion of the CC distributions (solid line)efines the underlying noise distribution. It reveals deviations for high positivend high negative CC values indicating activation and deactivation, respectively.ertical lines refer to the 99.99 and 95 percentile ranks of the noise distributions.osciergtaTradCdaveaDavvaamt(mofTttmr3stfippCrb(sgtatFMT. Auer et al. / Journal of Neuresponse (Bandettini et al., 1993). Two CC thresholds for theroup analysis were determined by fitting a Gaussian functiono the central portion of the group distribution of CC values fromll subjects and sections (i.e., from all individual CC maps).his part of the CC distribution has low CC values that do notefer to stimulus-correlated events. It may therefore be regardeds a representation of the noise distribution underlying the CCistribution of the actual experiment (Kleinschmidt et al., 1995).With use of the estimated noise distribution the actualC distribution is rescaled into percentile ranks of the noiseistribution. Thus, the upper threshold chosen in the TTCnalysis corresponds to the CC that matches the 99.99% (palue = 0.0001) percentile rank of the noise distribution. Vox-ls exceeding this value become automatically accepted asctivated—they form the highly significant activation centers.irectly neighboring voxels are iteratively added to improve therea delineation around an activation center as long as their CCalues exceed a lower threshold corresponding to the 95% (palue = 0.05) percentile rank of the noise distribution.After establishment of the CC thresholds for all sections,voxelwise determination of respective group CC maps ischieved in two different ways: by a selection of either theaximum CC value (MAX) from the range of CC values ofhe individual CC maps or by calculating a mean CC valueMEAN) for each individual voxel. The resulting group CCaps are then analyzed with the previously determined thresh-lds in the prescribed manner to yield a group activation mapor each section—similar to the procedure for a single-subjectttttig. 2. Group activation maps obtained for sequential finger-to-thumb opposition oEAN approaches. Activations and deactivations are indicated by red and blue colornce Methods 167 (2008) 335–339 337TC analysis. Activations and deactivations are visualized onhe same map. In order to characterize negative correlations,he group maps are reconstructed with use of the minimum orean negative CC values for the MAX and MEAN approach,espectively.. ResultsFig. 1 shows the group distributions of CC values from allubjects and sections for the two tasks studied. When comparinghe actual distribution and the estimated noise distribution, activenger movements were mainly characterized by deviations in theositive CC range indicating activations. The word generationaradigm revealed deviations for both high positive and negativeC values that correspond to activated and deactivated voxels,espectively.Figs. 2 and 3 summarize group activation maps obtainedy SPM and TTC for finger movements and word generationfour sections each), respectively. In either case, the first rowhows SPM group maps, while the bottom two rows refer to theroup maps obtained with TTC MAX and TTC MEAN, respec-ively. All post-processing methods identified the sensorimotornd supplementary motor areas for the motor task as wells ashe Broca region for the word generation paradigm. The loca-ions and the extent of activations/deactivations were similar forhe maps analyzed with SPM and TTC MAX, whereas activa-ions/deactivations appeared much more restricted when usinghe TTC MEAN approach. A brief quantitative comparison off the right hand. Group maps are calculated with SPM, TCC MAX and TTCs, respectively.338 T. Auer et al. / Journal of Neuroscience Methods  167 (2008) 335–339on. Taos4tmfsttdfcmmatTTT##siabdercirmloiotFig. 3. Group activation maps obtained for silent word generatictivated voxels obtained for silent word generation with usef SPM, TTC MAX, and TTC MEAN is given in Table 1 andupports the qualitative findings from the images.. DiscussionDespite a widespread acceptance and availability of SPM,he analysis of fMRI data sets remains a field open for improve-ents. The idea of this work is not to design a new mathematicalramework, but to describe a simple and transparent technique.While most of the proposed analysis strategies rely on aingle statistical threshold (sometimes supplemented by a clus-er threshold), the key of the TTC method is a combination ofwo thresholds in a physiologically meaningful manner. In moreetail, the resulting advantageous features may be explained asollows. First of all, in the absence of a functional challenge,orrelation analyses have been demonstrated to result in a sym-etric distribution of CC values (centered around zero) whichay be characterized by a Gaussian function (Kleinschmidt etl., 1995). This noise distribution reflects physiologic fluctua-ions and/or ‘resting state’ brain activity of the subject and asable 1otal activations and deactivations obtained for silent word generation with SPM,TC MAX, and TTC MEANSPM TTC MAX TTC MEANActivated voxels 4326 5292 2449Deactivated voxels 7268 6439 2814mybvtrTriuvahe order and color coding of maps is identical to that of Fig. 2.uch may vary in broadness from experiment to experiment. Its therefore mandatory to estimate the noise distribution for thectual fMRI acquisition to be analyzed. This is accomplishedy restricting the fit to the central portion of the actual CCistribution which contains only low CC values of voxels notngaged in task performance. This procedure ensures substantialobustness of the TTC analysis and certainly facilitates intertrialomparisons by removing systemic alterations not related to thenterrogated functional state.Secondly, the actual CC distribution is rescaled in percentileanks of the noise distribution. This step is followed by an auto-atic definition of two statistical thresholds which are tightlyinked by neighborhood principles when used for calculationf an activation map. While the upper threshold focuses on thedentification of highly significant activations, the lower thresh-ld is only used to better delineate their spatial extent rather thano incorporate new activations of low significance. This strategyay be characterized as defining the mountain around its peak. Itields robust statistical parametric maps of activated voxels withoth high specificity and sensitivity under the assumption thatoxels within a truly activated area bear a tight topographic rela-ionship to a smaller number of voxels which exceed a thresholdelated to a very low type-one error probability (false positives).he method should not be mistaken for a ‘cluster analysis’ thatelies on assumptions about the cluster size in relation to themage resolution. Specifically, the spatial response delineationsed here does not exclude small – even single voxel – acti-ations which are necessarily omitted in a conventional clusternalysis.oscieoTitmatMvtaarotaatvtitiaRBBBCFKMNOldfield RC. The assessment and analysis of handedness: the Edinburgh Inven-T. Auer et al. / Journal of NeurTwo different group CC maps have been tested (bottom rowsf Figs. 2 and 3). A maximum sensitivity is obtained for theTC MAX approach which is based on group CC maps thatncorporate the maximum (or minimum) CC values observed inhe entire group of subjects. The general appearance of theseaps is similar to what may be obtained with a standard SPMnalysis (top row of Figs. 2 and 3). Conversely, when usinghe mean value rather than the extreme CC values, the TTCEAN results appear more conservative and demonstrate acti-ated (or deactivated) clusters in a very restrictive manner. Takenogether, the TTC MAX approach may include voxels that arective in only few of the subjects (or even a single subject withsingle high CC value), whereas the TTC MEAN approachelies on activations more common to the group (or in mostf the subjects yielding a high mean CC value). In this sensehe TTC MEAN method successfully eliminates false positivectivations.In conclusion, the proposed TTC group analysis for fMRIppears to be advantageous in several respects: (i) it is simple,ransparent, and fast, (ii) it is robust with respect to physiologicariability by estimating the noise distribution and respectivehresholds from the actual experiment, and (iii) it may min-mize the problem of false positive activations by combiningwo thresholds for the identification of activations and by tak-ng the mean of individual CC values for the respective groupnalysis.Wnce Methods 167 (2008) 335–339 339eferencesandettini PA, Jesmanowicz A, Wong EC, Hyde JS. 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InternJ Imag Syst Technol 1995;6:238–44.aldjian JA, Laurienti PJ, Driskill L, Burdette JH. Multiple reproducibilityindices for evaluation of cognitive functional MR imaging paradigms. AJNR2002;23:1030–7.ichols T, Hayasaka S. Controlling the familywise error rate in functional neu-roimaging: a comparative review. Stat Methods Med Res 2003;12:419–46.tory. Neuropsychologia 1971;9:97–113.oermann FG, Jokeit H, Luerding R, Freitag H, Schulz R, Guertler S, et al.Language lateralization by Wada test and fMRI in 100 patients with epilepsy.Neurology 2003;61:699–701.

A novel group analysis for functional MRI of the human brain based on a two-threshold correlation (TTC) method.

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A novel group analysis for functional MRI of the human brain based on a two-threshold correlation (TTC) method.

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