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Functional studies in schizophrenia demonstrate prominent abnormalities within the left inferior frontal gyrus(IFG) and also suggest the functional connectivity abnormalities in language network including left IFG and superior temporal gyrus during semantic processing. White matter connections between regions involved in the semantic network have also been indicated in schizophrenia. However, an association between functional and anatomical connectivity disruptions within the semantic network in schizophrenia has not been established. Functional (using Levels of Processing paradigm) as well as Diffusion Tensor Imaging data from 10 controls and 10 chronic schizophrenics were acquired and analyzed. First, semantic encoding specific activation was estimated, showing decreased activation within the left IFG in schizophrenia. Second, functional time series were extracted from this area, and left IFG specific functional connectivity maps were produced for each subject. In an independent analysis, Tract-Based Spatial Statistics(TBSS) was used to compare Fractional Anisotropy(FA) values between groups, and to correlate these values with functional connectivity maps. Schizophrenia patients showed weaker functional connectivity within the language network that includes left IFG and left superior temporal sulcus/middle temporal gyrus. FA was reduced in several white matter regions including left inferior frontal and left internal capsule. Finally, left inferior frontal white matter FA was positively correlated with connectivity measures of the semantic network in schizophrenics, but not in controls. Our results indicate an association between anatomical and functional connectivity abnormalities within the semantic network in schizophrenia, suggesting further that the functional abnormalities observed in this disorder might be directly related to white matter disruptions.
Wernicke (Wernicke 1894) was the first to posit that abnormal connections among brain regions may play a critical role in the etiology of schizophrenia. His early hypotheses have remained, however, only speculative, until very recent developments in functional neuroimaging. More specifically, correlational analysis exploring the relationship between various cortical areas activated during functional MRI experiments (Friston 1996; Friston and Büchel 2007) has opened new windows into understanding the way brains are wired. One measure, derived from fMRI, is a measure of “functional connectivity”, which estimates the strength of temporal correlations between brain sites. Several studies (using multiple modalities to detect brain function- such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG)), in fact, suggest that such connectivity may be weakened, or disrupted in schizophrenia (Meyer-Lindenberg, et al. 2005; Michelogiannis, et al. 1991; Morrison-Stewart, et al. 1991). These studies further point to fronto-temporal connectivity (Breakspear, et al. 2003; Friston, et al. 1996; Meyer-Lindenberg, et al. 2005; Michelogiannis, et al. 1991; Morrison-Stewart, et al. 1991), and the relationship between fronto-temporal connectivity disruptions and clinical and cognitive symptoms of schizophrenia such as verbal hallucinations (Ford, et al. 2002; Lawrie, et al. 2002; Norman, et al. 1997), auditory hallucinations, distractive speech, illogicality, incoherence and semantic priming abnormalities (Han, et al. 2007). Since language (semantic) disturbances have been consistently reported in schizophrenia (Ceccherini-Nelli, et al. 2007; Condray 2005; Kircher, et al. 2007; Phillips, et al. 2004), and have demonstrated superior diagnostic specificity over Schneider's first rank symptoms in schizophrenia diagnosis (Ceccherini-Nelli and Crow 2003; Ceccherini-Nelli, et al. 2007), it is important to understand further the relationship between language and functional connectivity in schizophrenia.
Interestingly, language related abnormalities in schizophrenia are not only reported in the functional domain. That is, in vivo structural MRI studies, also frequently report volumetric deficits in gray matter brain regions traditionally viewed as language related (including inferior frontal gyrus, superior temporal gyrus and inferior parietal lobule) (Kwon, et al. 1999; Niznikiewicz, et al. 2000; Torrey 2007; Venkatasubramanian, et al. 2008). Also, local white matter volume reduction in prefrontal (Breier, et al. 1992; Buchanan, et al. 1998; Hulshoff Pol, et al. 2004; Wible, et al. 2001) and temporal white matter (Okugawa, et al. 2002; Spalletta, et al. 2003) have been reported in schizophrenia. White matter myelinated axons provide communication between gray matter sites, and thus disruptions in their integrity, as suggested by white matter volume reductions, might underlie some of the functional deficits observed in schizophrenia. This hypothesis is further reinforced by recent post mortem, as well as genetic studies, suggesting myelin related abnormalities within white matter in schizophrenia (Karoutzou, et al. 2008; Lipska, et al. 2006), and refueled by recently developed Diffusion Tensor Imaging (DTI) (Kubicki, et al. 2005a), a method sensitive to white matter fiber tract integrity. Of note, DTI is a method that has consistently demonstrated disruptions in white matter integrity schizophrenic subjects within several fronto-temporal tracts, including the cingulum bundle (Kubicki, et al. 2003b; Manoach, et al. 2007; Nestor, et al. 2007), uncinate fasciculus (Price, et al. 2007; Szeszko, et al. 2007), and arcuate fasciculus (Burns, et al. 2003; Douaud, et al. 2007; Kubicki, et al. 2005b). However its relationship to clinical, cognitive and functional abnormalities observed in schizophrenia is still unclear.
As discussed above, while fMRI studies provide evidence for functional connectivity, disruptions within the semantic network in schizophrenia, and anatomical studies, especially DTI, provide evidence for disturbances in anatomical connectivity in this disorder, it is important to combine these two methodologies in order to understand better the relationship between anatomy and function, and the role of anatomical and functional abnormalities in the psychophysiology of schizophrenia. To date, however, only one study has combined fMRI and DTI to explore connectivity within the semantic network in healthy controls (Powell, et al. 2006). This study reports a correlation between functional activations related to verbal fluency (left frontal gyrus), and reading comprehension (left supramarginal gyrus), and integrity of superior longitudinal fasciculus (Powell, et al. 2006). No such studies exist in schizophrenia.
The goal of the current study is to investigate both functional and anatomical connectivity deficits within the semantic language network, as well as their relationship to schizophrenia.
Ten male patients with schizophrenia and ten male healthy control subjects participated in this study. Patients were recruited from the VA Boston Healthcare System, Brockton Division. Male control subjects were recruited through newspaper advertisements. Subjects were matched on handedness, parental socioeconomic status (PSES) (Hollingshead 1965), and age. Table 1 shows demographic and clinical characteristics of these two groups. Verbal IQ was significantly lower in the schizophrenia group, compared to control subjects. Since verbal IQ could affect measures of semantic processing (Elvevag, et al. 2001) and white matter integrity (Deary, et al. 2006; Mabbott, et al. 2006), we used it as a covariate in both group comparisons as well as in correlation analyses with white matter integrity. All patients were diagnosed with chronic schizophrenia, by trained interviewers, using DSM-IV criteria based on the Structured Clinical Interview Patient Edition (SCID-P) (First, et al. 1998b) and a review of the medical records. Control subjects were screened using the Structured Clinical Interview Non-Patient Edition (SCID) (First, et al. 1998a) by the same trained interviewers. No control subjects had an Axis-I psychiatric disorder or a first-degree relative with an Axis-I psychiatric disorder. The study was approved by the VA Boston Healthcare System Human Subjects Committee and by the Brigham and Women's Institutional Review Board. All subjects gave written informed consent prior to participation in the study, and all were compensated for their time.
The main purpose of this study was to investigate further the semantic encoding network in schizophrenia, by combining fMRI and DTI data. In the current analysis, we used semantic processing fMRI data published previously (Kubicki, et al. 2003a). Full details regarding data acquisition and behavioral paradigm can be found in original paper (Kubicki, et al. 2003a), thus here we describe them only briefly. During the fMRI session, patients and controls performed both semantic (“press the button if the word presented on the screen is abstract/concrete”) and perceptual (“press the button if the presented word is in lower/upper case”) encoding tasks. Stimuli were presented in 30 second blocks, separated by 30 sec “resting” baseline condition (for details see Kubicki, et al. 2003a). The words were presented with 2.5 second Stimulus Onset Asynchrony (SOA), to both patients and controls in two 9 minutes runs, and subjects' responses (judgment e.g., ABSTRACT or UPPERCASE) were collected.
All subjects underwent both fMRI and DTI procedures on 1.5-T whole body MRI Echospeed system (General Electric Medical Systems, Milwaukee, WI). For the fMRI acquisition, the total of 174 EPI BOLD scans (24 oblique coronal slices, 6 mm thick, TR 3 s; TE 40 ms; flip angle 90°; 64×64 matrix) were acquired perpendicular to the long axis of the hippocampus. For the DTI, line-scan diffusion tensor scans (LSDI)(Maier, et al. 1998) were collected in the coronal oblique plane, with the following parameters: 35 slices, 4 mm thick, 1mm gap, TR 3 s; TE 64 ms; B 1000 and 5 μm/s; 6 gradient directions (scanning procedures for DTI are described in detail elsewhere (Kubicki, et al. 2004)).
FMRI data was processed and reanalyzed with newer statistical parametric mapping package (SPM5, http://www.fil.ion.ucl.ac.uk/spm) than in the original paper reporting fMRI findings in this sample (Kubicki, et al. 2003a). In addition, additional preprocessing, also not carried in original analysis, that included artifacts removal, was performed using FSL software (http://www.fmrib.ox.ac.uk/fsl/melodic/index.html) - MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components). The first 4 scans of each run were discarded. The remaining 280 images (140 images per run) were spatially realigned to the first volume of the first run, and realignment parameters were saved to be used as covariates in the subsequent statistical analysis. In addition, both scanner-related and physiological artifacts were removed using MELODIC. Time-courses of all slices of each volume were adjusted to the time course of the 12th slice, to correct for the acquisition time delay between different slices. Next, the realigned images were spatially normalized to the Montreal Neurological Institute (MNI) EPI template, re-sampled to 2 mm cubic voxel and smoothed with an 8-mm FWHM Gaussian filter. The general linear model was used for further reducing the effects of head motion and regressing out the linear drift (Oakes, et al. 2005).
Activation maps for the contrast between semantic and non-semantic encoding conditions were constructed separately for each subject using the first session of data, and fixed effect analysis was performed using the general linear model in each group. To reduce non-white noise, an autoregression method for estimating serial autocorrelations in the model residuals was applied in first-level group analysis.(Smith, et al. 2007a) Fixed effect t tests were used for group comparison. Finally, P-values were subjected to cluster based permutation test, which generated the null distribution of the maximum t statistic for both functional connectivity and the fMRI-DTI correlational analyses. One reason for using nonparametric permutation test rather than parametric test was the fact that the noise in the data might not follow a Gaussian distribution (Nichols and Holmes 2002). The other was that some statistical assumptions (linearity, homogeneity of slope) of analysis of covariance could not be met for parametric test when the removal of a confounding effect of IQ was needed for the between-group comparison.
Before performing functional connectivity analysis, fMRI time series were preprocessed using high-pass filter (cut-off frequency: 1/128 sec) to remove low-frequency drift and signal fluctuation. FMRI analysis demonstrated decreased activity in left inferior frontal gyrus during the semantic encoding in schizophrenia subjects, compared to control subjects (see results for more details). Thus, for the purpose of functional connectivity analysis, mean time-series of this fMRI activation (left IFG) was extracted from the cubes of 27 (3×3×3) voxels around the maximal intensity (highest t values) pixels for each subject separately (Koshino, et al. 2007). The Pearson's correlation coefficient was calculated between the left IFG time-series and the time-series of each voxel in the whole-brain to create an r-map in each subject. For statistical analysis, the r-map was transformed into the z-map using Fisher's r-to-z transformation of z=0.5 × log[(1+r)/(1-r)]. The z-map of each subject was further fed into second-level analyses using a one-sample t-test to examine significant functional connectivity in each group. Two-sample t-tests were performed to examine significant differences in functional connectivity between groups after removing confounding factor of verbal IQ. Cluster-size statistical threshold (P < 0.005, which corresponds to z value of 2.81) was used to correct for multiple comparisons, by using the null distribution of the maximum test statistics (over 5000 permutations). Further, to identify significantly correlated brain regions, the clusters were thresholded at a level of P < 0.05, corrected for multiple comparisons and for a spatial extent of at least 50 voxels in both within group as well as between group comparisons. To further test for between group differences in correlation patterns, we applied threshold method followed by the Fisher exact test that was used previously in correlational analyses of fMRI studies (Auer, et al. 2008; Baudewig, et al. 2003). To define the threshold for the regions showing a correlation between IFG and whole brain in each subject, first, a group histogram of correlation coefficient maps of all 20 subjects was produced. Then, a Gaussian curve was fitted to the central portion of the group histogram. A true group distribution of correlation coefficients was rescaled into percentile ranks of the noise distribution (represented in the low correlation coefficient values in the distribution). Finally, the region of true correlation was defined as being a correlation coefficient above 0.374, which matched the 99.99 percentile rank of the noise distribution, and chosen as the statistical threshold in each subject (Auer, et al. 2008; Baudewig, et al. 2003). The number of subjects with true correlation was compared between two groups using Fisher's exact test in each region.
DTI was preprocessed using the FMRIB Software Library (FSL, Oxford, U.K.), including skull stripping and eddy current correction. Fractional Anisotropy (FA) images of each subject were created using FSL. Next, Tract-Based Spatial Statistics (TBSS) was used for the data analysis (Smith, et al. 2007b). TBSS has been previously reported to be hypothesis free, automatic, and more precise than conventional voxel-based approach (VBM) with respect to defining and aligning anatomical white matter structures (tracts) between subjects (Smith, et al. 2006). In addition, this method does not require smoothing, which has been shown previously to skew voxel based morphometry (VBM) results (Jones, et al. 2005). Here, TBSS was used to calculate tract-based differences in FA values between the schizophrenia group and the control group, and these differences were later further subjected to correlational analyses with the fMRI results. The whole preprocessing procedure is automatic, and has been described elsewhere (Smith, et al. 2007b). Resulting FA maps were analyzed using a General Linear Model where verbal IQ was used as a covariate. False discovery rate multiple comparison correction with q < 0.05 (Nichols and Hayasaka 2003), was applied to statistical results in order to minimize type I errors when the significant region was not identified at permutation testing using cluster-based thresholding.
From the group comparison maps, we extracted areas within the left inferior frontal white matter (IFWM), where schizophrenics had significantly lower FA values than control subjects in TBSS analysis (see results section for more detail). Then, to examine the relationship between anatomical and functional connectivity, the correlation coefficients were computed between correlation map created by functional connectivity analysis (represented by Pearson correlation coefficient r values) and the averaged FA values of left IFWM separately for controls and schizophrenia patients. Only voxels above 2.81 of z value (P < 0.005) in each z-map of each subject were selected. The significant correlated regions were identified using permutation-based testing on cluster size (P < 0.05 corrected) in each group.
There were no group differences in age, handedness or parental socioeconomic status. Groups differed in verbal IQ, which was then used as a covariate in the data analysis (Table 1).
There were no statistical differences between groups in task accuracy during encoding (t(16) = 1.4, P = 0.18) experiment (Kubicki, et al. 2003a), with all subjects reaching at least 75% accuracy in judgments (Kubicki, et al. 2003a).
When contrasting deep and shallow encoding conditions, control subjects demonstrated (P<0.005 at cluster level) activation in pars triangularis of left IFG and the right middle orbital gyrus (see Table 2 and Figure 1). For the same contrast, schizophrenic subjects showed activation in pars triangularis of right IFG, pars orbitalis of right IFG, right medial temporal pole, right inferior temporal gyrus and left superior temporal gyrus. When contrasting groups, control subjects showed significantly (uncorrected P<0.005 at cluster level) more activation in pars triangularis of left IFG and right thalamus, while schizophrenic subjects showed significantly more activation in the left inferior temporal gyrus, pars opercularis of right IFG, right middle frontal gyrus and right precentral gyrus. (Table 2 and Figure 1: Right side regions were not displayed in figure 1.)
Many studies (Haller, et al. 2007; Shtyrov and Pulvermuller 2007; Vigneau, et al. 2006) including our previous investigation (Kubicki, et al. 2003a) have reported the left IFG as a key structure that is activated during semantic versus nonsemantic encoding. Thus in this analysis, we explored functional connectivity between left IFG (pars triangularis), which showed decreased activation in schizophrenia (MNI: x=-48, y=28, z=12), and other regions of the brain. Results indicated that in control subjects, the mean time series of left IFG was correlated with mean time series of several regions, including left middle temporal gyrus/left superior temporal sulcus, pars triangularis of right IFG, bilateral superior frontal gyrus, left superior parietal lobule/left supramarginal gyrus, bilateral cerebellum and left thalamus. In schizophrenic subjects, the mean time series of left IFG were correlated with bilateral superior frontal gyrus, left cuneus. In a between-group comparison of the correlation maps, the mean time series of left IFG were significantly less correlated with the time course of several parietal, temporal and frontal regions in schizophrenic subjects compared to control subjects. In particular, the mean time series of the left IFG were less correlated with the left middle temporal gyrus/left superior temporal sulcus, left superior parietal lobule/intraparietal sulcus/supramarginal gyrus, right superior parietal lobule/intraparietal sulcus, left globus pallidus, left thalamus, superior frontal gyrus, precentral gyrus, cerebellum and the right IFG in patients than in control subjects (See Table 3, Figure 2: Note that both the cerebellum and the right side of the brain are not included in the view in Figure 2). These group differences were shown in all regions described above at the level of the correction of multiple comparisons using cluster level permutation test after controlling for verbal IQ.
The Fisher's exact test further confirmed group differences in correlation patterns, showing that a significantly higher number of control subjects (compared to schizophrenic subjects) demonstrated significant functional connectivity within the left perisylvian language areas during semantic processing (Table 3).
TBSS analysis revealed significant (q<0.05; false discovery rate correction for multiple comparisons) FA reduction in several white matter areas, including left anterior limb of internal capsule and left IFWM in schizophrenia, when compared with control subjects. There were no regions characterized by higher FA values in schizophrenia (see Table 4, Figure 3-A). Probabilistic tractography using FSL was then used to qualitatively determine which anatomical structures (fiber tracts) are involved in connections between regions showing less activation and less functional connectivity in schizophrenia, and at the same time pass through left IFWM of regions with lower FA values in schizophrenia. Two tracts originating from left IFG reached left middle temporal gyrus/left superior temporal sulcus and crossed the left IFWM regions with reduced FA values in schizophrenia - left arcuate fasciculus, and left inferior occipito-frontal fasciculus and/or left inferior longitudinal fasciculus (Figure 3-B).
Finally, we investigated the relationship between mean FA value in left IFWM regions showing decreased white matter integrity within the fiber tracts interconnecting between left IFG and left middle temporal gyrus/left superior temporal sulcus in schizophrenia and the correlation coefficients of correlation map created by the functional correlation analysis. We hypothesized that the white matter disruption affecting anatomical connectivity would predict functional connectivity disruptions observed in patients. As expected, schizophrenic subjects (but not controls) showed significant, positive correlation between left IFWM integrity, FA value, and the correlation coefficient of several regions including left middle temporal gyrus/left superior temporal sulcus, left supramarginal gyrus, left thalamus, right insula, and bilateral cerebellum created by the functional correlation analysis (see Table 5 and Figure 4-A). Specifically, we observed positive relationship between disruptions of white matter integrity of left IFWM, and the functional connectivity abnormalities between left IFG and left middle temporal gyrus during semantic encoding in schizophrenia (Figure 4-B).
Our study demonstrated two previously hypothesized, but never reported phenomena in schizophrenia. First, we detected deficits in functional connectivity using fMRI measures as well as anatomical connectivity using DTI measures within the left language network that involves left IFG, left middle temporal gyrus/left superior temporal sulcus and white matter tracts interconnecting these regions in schizophrenia. Second, we demonstrated that these deficits are closely related to each other. More specifically, during semantic encoding, control subjects showed a high degree of connectivity between the pars triangularis of left IFG and the left middle temporal gyrus/left superior temporal sulcus. Schizophrenic patients did not show statistically significant connectivity between these regions. Anatomical connectivity analysis using DTI measures revealed a disruption within the white matter interconnecting left hemisphere language regions in schizophrenia. Finally correlation analysis demonstrated that the functional deficits observed within the language network, might be related to white matter disruption within this network.
In terms of the functional abnormalities observed in our study, underactivation of left IFG in schizophrenia during deep encoding has been reported previously by us (Kubicki, et al. 2003a), and was attributed to different encoding memory strategy used by patients (Bonner-Jackson, et al. 2007; Bonner-Jackson, et al. 2005). Ragland et al. (2005) demonstrated no difference in the activation of left IFG between schizophrenics and controls when performing levels-of-processing paradigm. There is some evidence, however, that left IFG function is abnormal in schizophrenia. Kuperberg, for example, has demonstrated that schizophrenics show an abnormal increase in the activity of left IFG during semantic priming while controls showed a decrease in activity in response to semantically related words (Kuperberg, et al. 2007). Conversely, underactivation of the left IFG has also been reported in patients with schizophrenia during various language tasks including verbal learning (Eyler, et al. 2008), language comprehension (Dollfus, et al. 2005) and word encoding (Ragland, et al. 2004). Also, several fMRI studies demonstrate a relationship between left inferior frontal gyrus and semantic processing (Baker, et al. 2001; Marinkovic, et al. 2003; Savage, et al. 2001). Finally, Bonner-Jackson et al. (Bonner-Jackson, et al. 2005) demonstrated that when being provided with semantic processing strategy, schizophrenics show increased left prefrontal activation, including left IFG, which reached and exceeded activation levels seen in control subjects. Commenting on those findings, the authors suggested that the wider prefrontal cortex activation during deep (successful) encoding represented a potential endophenotype marker of genetic liability for schizophrenia. We would argue that intact functional connectivity between left IFG and other parts of the frontal cortex, and decreased communication between left IFG and temporal or parietal lobe (as shown by both functional and anatomical connectivity results), might underlie the described results.
Of note, functional connectivity is defined as the synchronization between the concurrent activities of different cortical regions. This synchronization is usually based on quantifying covariances/correlations among the brain activation time series. A functional connectivity group comparison can provide some insight into the neural circuits' abnormalities of the diseased brain. Previous studies with schizophrenia, for example, have demonstrated altered fronto-temporal functional connectivity during cognitive tasks (Lawrie, et al. 2002; Wolf, et al. 2007). While the studies using ROI based-functional connectivity analysis (Cheung, et al. 2007; Ford, et al. 2002) are usually restricted to the network of an a priori hypothesized set of ROIs, whole brain correlation analysis, as used here, is free from such limitations. On the other hand, results of such an analysis should still be interpreted with caution. Our analysis can not, for example, infer causality, and the confounding factors, such as incompletely removed task-related movement, can influence the results. Thus, in the effect, regions that are not part of the same functional network can still express similar time courses, and appear as functionally connected in this analysis.
As the results from functional connectivity analyses simply reflect the observed temporal correlations between brain regions, interpretation of such results would greatly benefit from additional anatomical data, such as DTI. Our DTI results revealed local white matter integrity disruption within the left frontal lobe in schizophrenia, which was reported previously by several investigators (Andreone, et al. 2007; Buchsbaum, et al. 2006; Buchsbaum, et al. 1998). Subsequent exploratory probabilistic tractography suggested that the disrupted white matter region might belong to the left superior longitudinal fasciculus, presumably left arcuate fasciculus, and left inferior occipito-frontal fasciculus and/or left inferior longitudinal fasciculus, tracts that form anatomical connections between the left IFG and the left middle/superior temporal gyrus (Figure 3).
Traditionally, it has been suggested that the left arcuate fasciculus is the only connection within the semantic network (Catani, et al. 2005). Few recent studies, however, suggest a relationship between semantic function and left inferior longitudinal fasciculus, as well as left inferior occipito-frontal fasciculus (Duffau, et al. 2005; Mandonnet, et al. 2007).
So far, three DTI studies demonstrate anatomical disruption of the frontal part of left arcuate fasciculus in schizophrenia (Burns, et al. 2003; Douaud, et al. 2007; Kubicki, et al. 2005b), and one additional paper suggests increased integrity of the temporoparietal part of left arcuate fasciculus in schizophrenia patients experiencing auditory hallucinations (Hubl, et al. 2004), while one study points to the left inferior occipito-frontal fasciculus/inferior longitudinal fasciculus as related to schizophrenia (Ashtari, et al. 2007; Cheung, et al. 2007; Szeszko, et al. 2008). Our study, however, is the first to investigate the relationship between these deficits and semantic abnormalities observed in schizophrenia.
The deficit in functional connectivity of left IFG during semantic encoding condition and its relationship with a disruption of interconnecting white matter tracts in schizophrenia not only suggests a strong relationship between functional and anatomical deficits observed in this disease, but it also suggests a need for such an integrating approach in other cognitive domains affected in schizophrenia, such as, for example, language lateralization(Oakes, et al. 2005), visual processing (Toosy, et al. 2004), and working memory (Olesen, et al. 2003).
This is one of the first studies that combines functional and anatomical connectivity analysis in schizophrenia population. As such, it is not free from limitations. First and foremost, sample size is small but we believe that the convergence of the results obtained independently with different modalities (DTI and FMRI) increases the strength of our findings. Next, only chronic, medicated males were involved in the study, thus some of the disease related connectivity abnormalities could be masked by small sensitivity, and/or medication effects, or possible gender effects. In the effect, both small sample size and inclusion of males only could limit the generalizability of our results. Moreover, specificity of diffusion findings is also limited, since white matter FA could be decreased for many different reasons, including axonal and myelin abnormalities, as well as fiber tract coherence. Also, IQ also could affect the activation of left IFG as it is related to the left inferior frontal (Broca's area) activation during semantic task in healthy children (Schmithorst and Holland 2006). Low IQ has been also shown to be related to reduced whole brain volume in schizophrenia, however, the same study did not show left inferior frontal gyrus volume and IQ relationship in schizophrenia subjects, but did show relationship between verbal memory and this region volume in healthy controls. Thus while low IQ might, to some extent, be related to schizophrenia itself, it may have only a limited effect on the underactivation of left IFG. IQ and its low values in patients with schizophrenia could be still another possible source of functional group differences. Even though our subjects showed no difference in WRAT3 Reading score, the relatively low average IQ of the patients might still affect the brain activation. Since many imaging studies using various modalities do show correlations between IQ and functional as well as anatomical brain changes (Choi, et al. 2008; Deary, et al. 2006; Thatcher, et al. 2007), the low average IQ observed in our study should be controlled for. It is important not only because of the possible impact of IQ on semantic encoding and IFG activation, but also on functional connectivity in general.
In addition, there were also several limitations specific to the analytic methodology used-TBSS methodology. More specifically, even though skeleton based registration (Smith, et al. 2006) performs much better than the whole white matter registration, it is still based on the premise that it is possible to find and warp corresponding structures in every individual brain. Since this is usually not the case, the method can suffer from misregistration errors, especially since partial volume effects with 4 mm thick slices can be significant. Small number of diffusion gradient directions DTI could also affect the accuracy of FA estimation, introducing additional noise to the analysis. In addition, TBSS does not assign abnormalities to anatomical structure, thus additional localization tools, such as tractography, need to be used in order to understand better the relationship between functional and anatomical connectivity within the semantic network.
In summary, our study demonstrates the additional analytic power in combining functional and anatomical information, and suggests association between semantic encoding related functional deficit and abnormal anatomical connectivity in schizophrenia.
We gratefully acknowledge the support of the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No.2006-05372: B.S.J.), the National Institute of Health (NIH) grant (R01 MH067080-01A2: C.G.W.) and the Harvard NeuroDiscovery Center (formally HCNR). This work is also, in part, funded by the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149 (MK).
We also thank Dr. Martha Shenton for reviewing this manuscript and providing feedback to the investigators throughout the project.
Conflict of Interest Statements: None