As a number of investigators have speculated that schizophrenia is a disorder involving disconnectivity between distant brain regions (e.g., Weinberger et al., 1994; Kubicki et al., 2005), DTI has become the method of choice for quantifying white matter fiber tracts (see review in Kubicki et al., 2007). Early findings have shown reductions in integrity of whole white matter (Buchsbaum et al., 1998) as well as FA reduction in fronto-temporal connections including the uncinate fasciculus (Kubicki et al., 2002a, b), cingulum bundle (Kubicki et al., 2003; Wang et al., 2004), and arcuate fasciculus (Hubl et al., 2004). Loss of integrity has also been reported in the corpus callosum, particularly in the genu and splenium portions of this fiber tract (Foong et al., 2000; Agartz et al., 2001).

Another focus of attention has been thalamic frontal white matter projections, traveling through the internal capsule (IC) (Buchsbaum et al., 1998, 2006). The IC, in particular the anterior limb of the IC (AL-IC), is an important pathway involved in affect, attention, working memory, language and association brain functions, often reported to be abnormal in schizophrenia (Park et al., 2004; Buchsbaum et al., 2006; Zou et al., 2008). Also, and as noted by Nolte (2002), the thalamo-cortical projection through the AL-IC is the final pathway for all higher cognitive feedback loops, forming the thalamo-subcortical connections. Thus, structural abnormalities of fiber integrity (FA) or tissue microstructures (MD) in these fiber tracts could lead to functional disconnections of these networks, which, in turn, may underlie many of the behavioral and cognitive abnormalities observed in schizophrenia (Kubicki et al., 2005). In the study presented here, we further investigate FA and MD values in frontal projections of the mostly thalamic connections traveling through the AL-IC in schizophrenia.

Given that frontal projections of the IC include large bundles of fronto-subcortical fiber tracts that connect very distinct functional regions, we decided to use Brodmann areas (BA) as regions of interest (ROI) in order to divide IC projections into functionally-relevant parcellations. We thought that such an approach would provide more functional homogeneity compared with parcellation methods that are based on geometric divisions such as sulcal-gyral parcellation (e.g., Fischl et al., 1999; Park et al., 2008). In addition, we expected that this BA ROI approach would be useful for extracting fibers that are not only similar in function but also in appearance, thus improving parametrization. Furthermore, this approach is different from previous attempts to use cortical gray matter parcellation as a segmentation tool for reconstructing fiber tracts (Park et al., 2008; Thottakara et al., 2006), in that the focus is on parcellation of white matter structures in the brain.

With respect to fiber tractography, several issues need to be addressed. The most commonly-used tractography method consists of seeding fiber tracts in one region of the brain and employs a maximum likelihood orientation-based tracing technique to follow the path of the fibers. This deterministic method, however, is known to be very sensitive to noise and it lacks robustness, especially in constructing fiber tracts with crossing and fanning along their paths (Peled et al., 2006). Probabilistic tractography methods have been developed to try to resolve these issues (Behrens et al., 2003, 2007; Friman et al., 2003; Parker et al., 2003). Such methods are particularly good at addressing uncertainties in fiber bundle orientations, and at enabling researchers to use gray matter ROIs as seed points for fiber tractography. However, these methods usually involve tremendous computation in terms of estimation/sampling of random fiber orientations.

An alternative is to use deterministic tracing, and to seed the fibers in every location of the brain. This technique, called whole brain seeding-based tractography, generally has shown significantly better tracking results than regional seeding, as many more fibers are initiated (e.g., Conturo et al. 1999; Mori et al. 1999; Park et al., 2003; Huang et al., 2004). This tractography strategy also tends to be more efficient in terms of computation compared to probabilistic approaches. Many researchers thus employ whole brain tractography in order to construct group-based probabilistic maps of specific fiber tracts (e.g., Ciccarelli et al., 2003, 2005; Huang et al., 2005; Parker et al., 2005; Thottakara et al., 2006).

After tracing of fiber paths, information about their integrity needs to be computed so that it can be compared between groups. A common approach is to average values of FA and diffusivity for the entire fiber tracts. However, this method is not sensitive to local changes of the diffusion properties along the tracts. Thus more recently, methods of tract parametrization have been introduced in order to measure, and to compare, diffusion properties within subjects, and between populations (e.g., Gong et al., 2005; Lin et al., 2006; Reich et al, 2007; Oh et al., 2007). For example, Gong et al. (2005) established a cingulum bundle (CB)-specific “angular coordinate” system for tract parametrization. Others have focused on arc-length or normalized arc-length to subdivide (or parameterize) fiber tracts (e.g., Lin et al., 2006; Reich et al, 2007; Oh et al., 2007). The arc-length-based coordinate system is based on a transformation that converts rectangular Cartesian coordinate systems to a normalized coordinate system along the fiber bundle. These methods are similar to each other, in that they divide the fiber tracts into several segments, in order to provide better regional specificity than conventional methods (e.g., bulky ROI-based analysis). In particular, our previous study (Oh et al., 2007) demonstrated the usefulness of three-dimensional (3-D) parametrization of the corpus callosum (CC), in terms of more regional specificity. In this previous study, we successfully differentiated group-associated fiber integrity differences in the CC, the largest bundle of white matter tracts in human brain. As our objective was to have a quantitative DTI analysis based on a reliable fiber tract extraction with functional specificity, and also further parametrization with regional specificity, we decided to combine the BA ROI parcellation approach with the longitudinal tract parametrization.

The results of three-way ANOVAs for DTI measures (FA/MD values) are summarized in Table 2. Both DTI measures demonstrated a significant main effect for group, as well as a main effect for region, but no side effect, or group by region interaction.

The multiple one-way ANOVAs that followed revealed significant group effects for FA/MD values of IC projections to each frontal BA (also listed in Table 3). For the convenience of interpretation of functionally similar Brodmann regions, we describe the significant results of BA 9/46 together and BA 44/45 together in all the following descriptions:

Simultaneous decrease in FA and increase in MD were observed only for the dorsolateral prefrontal projections (DLPFC - BA 9/46) (F = 4.7, p = 0.03 for the FA), and (F = 14, p < 0.001 for the MD). Decreased FA without changes in MD was observed for the anterior cingulate projections (ACC - BA 32) (F = 6.31, p = 0.01) and BA 44/45 projections (that include Broca area on the left) (F = 5.86, p = 0.02). Finally, increased MD without FA changes was observed for the frontal pole projections (FP - BA 10) (F = 6.36, p = 0.01), the orbitofrontal projections (OFC - BA 11) (F = 9.52, p < 0.01) and the inferior-prefrontal projections (IPFC – BA 47) (F = 5.49, p = 0.02).

Finally, post hoc independent t-tests revealed regionally specific group differences for both DTI measures (FA and MD), localized as follows: FA showed decreases in the proximal (thalamic) part of the BA 9/46 projections, and in the middle part of the BA 9/46 and the BA 44 projections, while MD showed increases in the proximal and distal (cortical) parts of the BA 9/46, BA 47, BA 10 and BA 11 projections (See also Figure 2–3 for more details). According to our results, not all ROI connections were affected in the same way (see Figure 2 and Figure 3). More specifically, unlike FA differences (which were detected in DLPFC and BA 44/45) in proximal (near internal capsule) and middle parts, most MD differences were found in the distant (closer to the cortex) parts of the fiber projections to the rostral BA.

Figure 4 demonstrates results of FA analysis before and after removing possible confounds of age and mode from the analysis, demonstrating that neither fiber crossing nor age significantly affected the results.

Previous studies investigating these connections have used more generic approaches. For example, some studies have focused on the integrity and asymmetry of the AL-IC, using a voxel-based morphometry (VBM) approach, and reported decreased FA, altered magnetization transfer (MT) and diminished asymmetry within this region (Park et al., 2004; Kubicki et al., 2005). Others have used fiber tractography, measuring the length of fronto-thalamic fiber tracts and reporting their shortening in schizophrenia (Buchsbaum et al., 2006). Finally, Kim et al. (2008) used sulcal/gyral parcellation to separate and measure anatomically specific thalamo-cortical projections, and reported increased mean MD in thalamo-orbitofrontal and thalamo-parietal-occipital-temporal fiber tracts in patients with schizophrenia.

As opposed to other tractography approaches, our investigation attempted to separate fronto-thalamic tracts based on functional, rather than anatomical cortical segmentation. The advantage of this method is that extracted fiber tracts carry information to and from functionally homogenous regions of gray matter, and their integrity is inevitably related to particular brain function of specific BA that was used for tract segmentation. In this way, the relationship between anatomy and function, and their associated abnormalities in schizophrenia, are likely easier to establish. In addition, we included tract parametrization, which allows us to perform group comparison at any given position along the extracted fiber bundles, thus further increasing sensitivity and specificity. Our approach essentially combines the strengths of two different conventional methods: 1) In the VBM approach, group comparison of white matter integrity can be studied for every voxel of the human brain, but differences cannot be straight-forwardly assigned to specific fiber bundle (Park et al., 2004; Kubicki et al., 2005). Using inverse normalization of significant difference regions, however, one can conduct post-hoc tractography to assign fiber tracts to regional differences. Nonetheless, this approach will require additional efforts to warrant intersubject homology of each voxel in each subject. Alternatively, we tried to define intersubject homologous points using a tract-oriented method called tract parametrization. More importantly, our method is less affected by inter-subject registration of white matter features and its errors. 2) In the tractography approach, specific tracts are extracted, but group comparison is being carried on the diffusion properties averaged over the entire tract, rather than studied for each voxel of the tract separately (and thus the possible local, subtle differences might be washed out by the averaging) (Rosenberger et al., 2008; Kim et al., 2008).

Region-by-region descriptions with respect to FA and/or MD differences are as follows: 1) The DLPFC related part of the AL-IC, that is the tract carrying connections mostly between DLPFC and medial-dorsal thalamic nucleus (MDN), demonstrated both decreased FA and increased MD values. This bundle, by virtue of its anatomical connections, likely plays an important role in affect, association and working memory functions, all reported to be abnormal in schizophrenia (Bunney and Bunney, 2000; Cannon et al., 2005; Schlosser et al., 2007). 2) Decreased fiber integrity in thalamic projection to BA 44/45, which includes Broca’s area, suggests a disruption in the language network (Felleman and Van Essen, 1991; Petrides and Pandya, 1988) that might elucidate language process abnormalities in schizophrenia (Burns et al., 2003; Hubl et al., 2004). The ACC, thalamic projection to which showed decreased FA values as well, is related to cognitive functions including executive attention, reward anticipation, decision-making and affective functions, which are also abnormal in schizophrenia (Quintana et al., 2004; Szeszko et al., 2007). 3) Although not frequently addressed in previous studies, increased MD values in IC projections to orbitofrontal cortex and inferior prefrontal cortex are likely important as they underlie cognitive processes, including decision-making in orbitofrontal cortex (Hutton et al., 2002), and syntax processing in spoken and signed languages in inferior prefrontal cortex (Kubicki et al., 2003), all reported to be abnormal in schizophrenia.

According to our results above, not all ROI connections were affected in the same way (see Figure 2 and Figure 3). Since we found significant FA (but not MD) changes in IC connections to area 9, and MD (but not FA) changes in IC projections to area 46, DLPFC connectivity results should be treated with caution. One possible source of such a differential pattern of changes in schizophrenia might be due to an anatomical difference in the way that the thalamocortical tracts are affected for these two ROIs in schizophrenia, as well as individual variability of Brodmann areas 9 and 46, which was demonstrated previously by Rajkowska and Goldman-Rakic (1995). Since the intersubject variability for most regions was equal or smaller than intrasubject (regional) variability, we believe it is appropriate to perform not only “bulky” tract analysis (i.e., ANOVAs) but also “regional” tract analysis (i.e., parameterization). However, since BA 47 showed greater intersubject variability of MD values, this region should be treated with more caution.

Interestingly, MD differences were seen along the tracts when reaching the cortex, and all correspond with rostral BAs. Indeed, we have utilized line scan diffusion imaging (LSDI) for DTI acquisition. Since this imaging technique is relatively free from image distortions observed with EPI-based DTIs, we think the results are not an artifact. Instead, we think that the nature of this dissociation could be due to the fact that the mean diffusivity (MD) measures, which basically reflect tissue micro structure disruption, could be more influenced by architectural, neuro-developmental, and more importantly, cortical reorganization than FA, and that MD might be more sensitive to changes in low coherence regions, and FA in more coherent regions.

The following limitations should be noted when interpreting our results. As noted by Smith et al. (2006), our parametrization method, similar to all parametrization-based approaches tends to be more reliable for large fiber tracts but less so for smaller bundles.

As mentioned above, our tracts of interest (frontal projection of the IC tracts) interconnect gray matter regions including frontal lobe and thalamus. To better construct these gray matter-relevant areas, we chose a relatively low FA threshold (0.1) for the seeding and stopping criteria of fiber tractography. Although low FA values, which also imply high fiber orientation uncertainties, can cause possible shape variability of the reconstructed white matter tracts, the parametrization has been regularized by cutting tracts between two ROIs (Figure 1).

Based on whole brain seeding, we were able to improve the robustness of the fiber tractography in the presence of uncertainty of the fiber orientation due to noise and fiber crossings. Whole brain tractography, however, is not an ultimate solution for these problems, and methods that explicitly model tract orientation uncertainty (such as stochastic approaches (Behrens et al., 2003; Parker et al., 2003; Friman et al., 2003)) need to be applied. Another consideration is to use high angular resolution diffusion imaging (HARDI), including Q-ball imaging (Tuch, 2004) as used by Schmahmann et al. (2007), although such an approach might be unrealistic in clinical studies due to the extremely long acquisition time.

As demonstrated by Rajkowska and Goldman-Rakic (1995), Amunts et al. (2007) and Malikovic et al. (2007), individual variability of Brodmann areas, not captured by a BA template approach, should be considered when interpreting the results. One study has also been conducted using gyral and sulcal borderlines for tract definition on the basis of cortical surface parametrization (Park et al., 2008). Since each approach has their pros and cons (more functional specificity for Brodmann ROIs vs. better anatomical precision for gyral ROIs), more experiments are needed in order to demonstrate the pros and cons of one method versus another. In addition, BA ROIs are easy-to-define using simple spatial transformation, which is the reason why voxel-based analyses are so popular in all neuroimaging studies.