Diffusion Tensor Imaging in Schizophrenia Richard A.A. Kanaan, Jin-Suh Kim, Walter E. Kaufmann, Godfrey D. Pearlson, Gareth J. Barker, and Philip K. McGuire Background: Diffusion tensor imaging (DTI) is a relatively new neuroimaging technique that can be used to examine the microstructure of white matter in vivo. A systematic review of DTI studies in schizophrenia was undertaken to test the hypothesis that DTI can detect white matter differences between schizophrenia patients and normal control subjects. Methods: EMBASE, PubMed, Medline, and PsychInfo were searched online and key journals were searched manually for studies comparing anisotropy (a measure of white matter integrity) between patients and control subjects. Nineteen articles were systematically reviewed. Results: Though 16 studies found differences, methodological and data differences prevented a meta-analysis. Fourteen studies found reduced anisotropy in patients; two studies found only a loss of normal asymmetry. The region of investigation varied across studies, however, and when the same region (for example, the cingulum) was examined in different studies, as many failed to find a difference as found one. These inconsistencies may be the result of small sample sizes and differences in methodology. Conclusions: Diffusion tensor imaging has yet to provide consistent findings of white matter abnormalities in schizophrenia. Its potential as a means of examining anatomical connectivity may be realized with the study of larger, more homogenous groups of subjects and with ongoing improvements in image analysis. Key Words: Diffusion, DTI, MRI, psychosis, schizophrenia, tensor
iffusion tensor imaging (DTI) is a relatively new imaging modality that is already widely employed in research. As with any new method, it is undergoing rapid development, and considerable controversies remain over the analysis of DTI data. Because of its particular sensitivity to white matter integrity, it is hoped that DTI will allow new insights into the pathophysiology of schizophrenia, particularly with respect to putative changes in cortico-cortical connectivity (McGuire and Frith 1996). The aim of this article is to examine how far that promise has been realized. Diffusion tensor imaging uses a conventional magnetic resonance imaging (MRI) scanner, but by imposing additional magnetic field gradients, the scanned images are sensitized to the diffusion of water in the direction of those gradients. If measurements in at least six noncolinear directions are acquired, along with a nondiffusion weighted image, the diffusion tensor can be calculated at each point (voxel) in the brain (Basser and Pierpaoli 1996). This characterizes the diffusion direction and displacement, irrespective of the subject’s orientation relative to the scanner, and since this diffusion is determined by the surrounding tissues, one can infer the orientation and coherence of the underlying cellular architecture. Interpreting tensor data is not straightforward, however, as the three-dimensional (3-D) information they contain cannot easily be represented in two-dimensional images, and their comparison is complex. This has led to the derivation of scalar measures such as fractional anisotropy (FA) (Basser and Pierpaoli 1996), which permits more traditional two-dimensional mapFrom the Section of Neuroimaging (RAAK, PKM) and Centre for Neuroimaging Sciences (GJB), Institute of Psychiatry, London, United Kingdom; Department of Radiology (J-SK), University of Wisconsin, Madison, Wisconsin; Johns Hopkins University School of Medicine (WEK, GDP) and Kennedy Krieger Institute (WEK), Baltimore, Maryland; OLIN Neuropsychiatry Research Centre (GDP), Hartford, and Yale University School of Medicine (GDP), New Haven, Connecticut. Address reprint requests to Dr. Richard Kanaan, Institute of Psychiatry, Section of Neuroimaging, P067, London, SE5 8AF, United Kingdom; E-mail: [email protected]
. Received February 8, 2005; revised May 2, 2005; accepted May 4, 2005.
ping (Figure 1A) and thus allows the usual armory of image analysis methods to be applied. Fractional anisotropy roughly represents the degree to which diffusion is directionally hindered (anisotropic). It is high in areas of high structural coherence, such as white matter (WM), lower in gray matter (GM), and close to zero in cerebrospinal fluid (CSF), where diffusion is expected to be equal in all directions (isotropic). A reduction in WM FA is therefore usually interpreted as reflecting a reduction in WM integrity. There are two principal methods employed in anisotropy analysis: region-of-interest (ROI) and whole-brain, voxel-based analysis (VBA). The majority of studies to date have adopted the former, manually defining ROIs on the unregistered images. This allows a powerful examination of regions selected on the basis of existing information. However, because the placement of ROIs is subjective, this should be guided by unambiguous criteria and with demonstrated interrater/intrarater reliability. Even then, there is a risk that the rater will be influenced by gross anatomical group differences into a systematic placement bias. In the case of schizophrenia, where the regions of abnormality are still being determined and where the abnormalities may be distributed rather than focal, VBA may be a useful exploratory alternative. However, the normalization algorithms available in widely used packages such as Statistical Parametric Mapping (SPM) (Wellcome Department of Cognitive Neurology, London, United Kingdom) were not designed to work with the high-contrast edges seen in the highly heterogeneous anisotropy images formed from DTI data, with the consequent risk of false-positive edge effects. Anisotropy analysis has been found to be highly sensitive to subtle WM differences, for example in diffuse axonal injury (Arfanakis et al 2002) and epilepsy (Rugg-Gunn et al 2001)— conditions where changes are often difficult to detect by other imaging modalities. It has been rapidly adopted in psychiatry to study age-related cognitive decline (Pfefferbaum et al 2000), chronic alcohol abuse (Pfefferbaum and Sullivan 2002), depression in the elderly (Taylor et al 2001), cocaine dependence (Lim et al 2002), reading difficulties (Klingberg et al 2000), and schizophrenia, the focus of this review. Interest in disconnectivity models of schizophrenia (Andreasen et al 1998; Crow 1998; McGuire and Frith 1996) has led to an increasing focus on white matter. With growing evidence for functional disconnectivity (Frith 2005), researchers are seekBIOL PSYCHIATRY 2005;58:921–929 © 2005 Society of Biological Psychiatry
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Figure 1. Representations of DTI data sets. (A) A fractional anisotropy (FA) axial slice from a single subject. (B) FA slice, colored to represent direction– red, left-right; green, anterior-posterior; blue, superior-inferior. (C) Tractography performed on the genu of the same subject, seen from above. DTI, diffusion tensor imaging; FA, fractional anisotropy.
ing evidence of abnormal anatomical connectivity that may underlie or even explain this. Structural imaging studies have found some evidence of global (Cannon et al 1998; Wright et al 2000) and regional (Buchanan et al 1998; Sigmundsson et al 2001) reductions in WM volume, and postmortem studies have found histological differences in neuronal arborization (Akbarian et al 1996), density (Selemon et al 1998), and myelination (Flynn et al 2003). But DTI appears to offer a tool for examining the integrity of the WM tracts implicated in this putative disconnectivity in vivo. The objective of the present article is to systematically review DTI studies of schizophrenia to determine whether there are WM abnormalities in schizophrenia that are detected using anisotropy analysis.
Methods and Materials Our inclusion criteria were that studies be published in peer-reviewed journals and make anisotropy-based statistical comparisons between schizophrenia patients and control subjects. Letters and abstracts were excluded. Online searches of the databases EMBASE, PubMed, PsychInfo, and Medline were performed, using the search terms schizophrenia, psychosis, diffusion, tensor, DTI, and MRI. An additional manual search was performed on the 2004 issues of those psychiatric and neuroimaging journals with an impact factor of 4 or above. The references of all selected articles were checked for further papers suitable for inclusion.
Results There has been a remarkable proliferation of studies using DTI in schizophrenia. After the first in 1998, there followed one in 1999 and 2000, growing to eight studies in 2003 alone. A total of 19 studies met the inclusion criteria (see Table 1). Four other studies correlated FA values with impulsivity (Hoptman et al 2002, 2004), negative symptoms (Wolkin et al 2003), and neuropsychological measures (Nestor et al 2004) but did not compare FA with control subjects. One further study (Park et al 2004) made a novel anisotropy-based analysis of interhemispheric asymmetry in patients and control subjects separately but did not statistically compare the groups. All the included studies were of medicated patients; all the studies but one (Begré et al 2003) were of chronic patients; and all the studies but one (Kumra et al 2004) were of adult-onset patients. Of these 19 studies, 16 reported positive findings (Table 1). However, a review of Table 1 reveals that there was little overlap in the regions examined by those using an ROI approach and that only two studies used a whole-brain voxel-based group analysis (Agartz et al 2001; Ardekani et al 2003). Thus, for most of www.sobp.org/journal
R.A.A. Kanaan et al the studies, there was no common area examined. Furthermore, only eight studies presented the group-difference data in a form amenable to effect size calculation (Foong et al 2000; Kalus et al 2004; Kumra et al 2004; Okugawa et al 2004; Steel et al 2001; Sun et al 2003; Wang et al 2003, 2004). These limitations precluded a formal meta-analysis and the present study thus comprises a systematic review. Taking the six voxel-based group mapping studies first, two of these analyzed the whole brain and four co-registered whole brains but only analyzed specific regions (Table 1). Buchsbaum et al (1998) were the first to publish the application of DTI to schizophrenia using relative anisotropy (RA), a similarly defined metric to FA with perhaps inferior noise tolerance (Hasan et al 2004). Comparing five patients and six control subjects, they found significant differences in RA in multiple areas, largely in the right hemisphere, using cluster statistics at a threshold of p ⬍ .05. Restricting their analysis to two central slices, they found maximal differences frontally and around the putamen. They explored correlations with positron-emission tomography (PET) findings (glucose metabolism) from the same subjects performing a verbal memory task but found no correlations with task performance. In a much larger study, with an optimized acquisition, Agartz et al (2001) found reduced FA at the cluster level in a large area posterior to the ventricles, which encompassed the splenium of the corpus callosum and extended posteriorly, particularly in the right hemisphere. Foong et al (2002)) attempted a VBA of data originally analyzed with an ROI approach (Foong et al 2000); however, the normalization step in generic brain activation mapping (GBAM) (Brammer et al 1997) succeeded in only 14 and 19, respectively, of their 20 patients and 25 control subjects. These data sets were compared using SPM96, finding no significant differences, in contrast to their earlier ROI study. While this may have been due to the sharply reduced sample size, it also highlights the potential problem of type 2 error control introduced by using whole-brain VBA. Ardekani et al (2003) examined 7 patients with schizophrenia, 7 patients with schizoaffective disorder, and 14 control subjects. They co-registered using a nonlinear warp and found differences (cluster significance at p ⬍ .01) in the splenium and forceps occipitalis, the body of the corpus callosum, the anterior cingulum, and WM in the left superior temporal and bilateral parahippocampal, inferior parietal, and middle temporal gyri. Burns et al (2003) used SPM for co-registration and analysis in a study of 30 patients and 30 control subjects but restricted their analysis to small predetermined ROIs on the co-registered images in the anterior cingulum and the uncinate and arcuate fasciculi. They found reduced FA only in 2 voxels in the left arcuate, at a corrected significance level of .05. However, definition of the ROIs seemed to rely on the Talairach atlas, which offers a limited description of these tracts. Hubl et al (2004) designed their study for the comparison of patients with and without auditory hallucinations but included normal control subjects as a third group. They performed two analyses: first an ROI analysis, and secondly a voxel-based analysis on the 12 central slices they acquired. The latter was thresholded for clusters of ⬎99 voxels at p ⬍ .05 and found widespread reduced FA in the combined patient group compared with the control subjects in the arcuate, uncinate, and inferior longitudinal fasciculi bilaterally and the anterior and posterior corpus callosum. The ROI analysis was guided by a separate three-way analysis of variance (ANOVA) but restricted to the arcuate fasciculus, though the authors did not specify how these ROIs were defined. They found higher FA in patients with hallucinations in the left lateral arcuate than either patients
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Table 1. Study Characteristics and Results Summaries Study Name
Mean Age (Y) 34.7
Buchsbaum et al 1998
6 control subjects
Lim et al 1999
10 control subjects
25 control subjects
Agartz et al 2001
24 control subjects
Steel et al 2001
10 control subjects
Foong et al 2000
Foong et al 2002
19 control subjects
15 patients 18 control subjects 29 patients
43 43 28.5
20 control subjects Sun et al 2003
Ardekani et al 2003
Study Type VBA/ROI
Regions Examined 2 central axial slices
Prefrontal, temporo-parietal, parieto-occipital segmented WM on 8 slices Genu and splenium of CC
Prefrontal and occipital regions
6 slices above anterior commisure
19 control subjects
14 control subjects
Negative Findings in Patients
Reduced FA in frontotemporal, periputamen Whole brain WM FA lower
No differences elsewhere
Reduced FA in the splenium of the CC Reduced FA in the splenium of the CC
No difference in genu of CC
Middle and superior cerebellar peduncles
Internal capsule, AC, genu and splenium of CC, lobar WM
Reduced FA in anterior cingulum
Reduced FA in bilateral CC, AC, MTG, PHG, left STG
Positive Findings in Patients
Loss of asymmetry
No differences elsewhere No areas of significant difference No areas of significant difference No reduction of FA in uncinate No areas of significant difference No difference in internal capsule, CC, lobar WM No differences elsewhere
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Kubicki et al 2002 Wang et al 2003
Study Name Burns et al 2003 Minami et al 2003 Kubicki et al 2003
Mean Age (Y)
30 patients 30 control subjects 12 patients
15/15 15/15 5/7
36.4 35.7 30.8
11 control subjects 16 patients
18 control subjects
Begré et al 2003
Wang et al 2004
7 control subjects
20 control subjects Okugawa et al 2004
Kalus et al 2004
Kumra et al 2004
Hubl et al 2004
21 control subjects
15 control subjects
9 control subjects
13 control subjects
Uncinate and arcuate fasciculi and AC Multiple ROIs grouped into frontal, parietal, temporal, occipital regions Central cingulum
Anterior and posterior cìngulum
Middle Cerebellar Peduncles
Anterior and Posterior Hippocampus
7 levels of Bilateral Frontal WM, level occipital WM, genu and splenium of CC
12 slices segmented WM, plus ROIs on medial and lateral arcuate fasciculi
Positive Findings in Patients
Negative Findings in Patients
Reduced FA in left arcuate Reduced FA throughout
No difference in uncinate or AC
Reduced FA bilaterally in Cingulum
Reduced FA in bilateral AC, reduced asymmetry Reduced FA bilaterally in middle cerebellar peduncles Reduced FA in bilateral posterior hippocampus Reduced bilateral frontal and right occipital FA Reduced FA in bilateral arcuate, uncinate, CC, and ILF
No areas of significant difference No difference in posterior cingulum
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Table 1. (continued)
No difference in anterior hippocampus No difference in genu, splenium, left occipital FA No loss of asymmetry in uncinate
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Missing values indicates the data could not be extracted directly from the study. CPZ dose indicates antipsychotic Chlorpromazine equivalence in milligrams. Duration of illness was determined either directly from the paper or by subtraction of mean age of onset from mean patient age. AC, anterior cingulum; CC, corpus callosum; CPZ, chlorpromazine; FA, fractional anisotropy; ILF, inferior longitudinal fasciculus; IQ, intelligence quotient; MTG, middle temporal gyrus; PHG, parahippocampal gyrus; ROI, region of interest analysis; SES, socioeconomic status; STG, superior temporal gyrus; VBA, voxel-based analysis; VBA/ROI, voxel-based analysis restricted to a region of interest; WM, white matter. a Converted from Haloperidol equivalence.
R.A.A. Kanaan et al without hallucinations or healthy control subjects at p ⬍ .05 (uncorrected) but reduced FA in the left median arcuate in both patient groups. Two studies have defined ROIs comprising entire lobes. Lim et al (1999) segmented the images and found whole-brain WM median FA to be lower in the patient group, co-varying separately for age, WM volume, and GM volume. However, repeatedmeasures between-group ANOVAs did not further localize this by lobe or hemisphere. The subjects, particularly the patients, were somewhat older in this study than other studies, and though age was a covariate, this does not exclude the possibility of an age-by-group interaction. Minami et al (2003) placed 2-mm square ROIs throughout the WM of four slices and subdivided these into lobar collections, which they compared at the group level, also finding reduced WM in the patient group overall but no regional reductions. Fractional anisotropy was not correlated with scores on the Positive and Negative Syndrome Scale (PANSS) (Kay et al 1987), but left frontal WM FA, surprisingly, was positively correlated with antipsychotic dose, though this was not at a level (p ⫽ .047) that would have survived correction for multiple comparisons. The authors did not specify how their ROIs were aggregated nor if there were any group differences in their number or distribution. The remaining studies used more conventional ROI approaches. Foong et al (2000) placed ROIs on single slices of the genu and splenium of the corpus callosum. They found reduced FA in the splenium (p ⬍ .02) but not the genu; this was not correlated with age, duration of illness, medication dose, or PANSS scores, and there was no difference in FA between genders. However, the placement rules, maximal thickness of the genu or splenium, and the absence of a measure of reliability leave room for systematic bias (Narr et al 2000). Although the corpus callosum is arguably the “brightest” (highest FA) of the WM tracts, its FA varies considerably throughout its length (Chepuri et al 2002) such that the effect of bias in placement of a small single-slice ROI may be significant. Steel et al (2001) performed both DTI and magnetic resonance spectroscopy (MRS), predicting that N-acetyl aspartate (NAA) concentration, an index of neuronal density and integrity, would be correlated with FA. Bilateral frontal and occipital ROIs were placed by a radiologist to coincide with MRS regions. There were no between-group differences in FA found in any of the four areas, though when subdivided by gender, the five female patients showed increased FA in the right occipital ROI compared with the six female control subjects. N-acetyl aspartate was negatively correlated with left frontal FA but positively correlated with right frontal FA in patients; the expected correlation of NAA and FA could not therefore be supported. Begré et al (2003) examined FA in hippocampal ROIs and 19-channel electroencephalogram (EEG) data in seven firstepisode patients and control subjects. There were no betweengroup differences in FA, but there was a correlation between lower hippocampal FA and increased anterior alpha rhythm. The ROI placement was not described in detail, but the region appeared to be large and may have encompassed adjacent structures in addition to the hippocampus. Kalus et al (2004) also defined ROIs on both the anterior and posterior hippocampus, individually tracing volumes in 3-D with excellent interrater and intrarater reliability and normalizing these volumes for anisotropy comparison. They used intervoxel coherence (COH) (Pierpaoli and Basser 1996) as their anisotropy measure on the grounds of its robust noise characteristics (Skare et al 2000). They
BIOL PSYCHIATRY 2005;58:921–929 925 found reduced COH in the posterior hippocampus bilaterally and in the left hippocampus overall at p ⬍ .05 (uncorrected). Kubicki et al (2002, 2003) examined the uncinate fasciculus and the central cingulum in two separate studies of what appears to be the same all-male subject group (with the addition of one patient for the latter study). Patients were well matched on age, parental socioeconomic status, handedness, and gender but differed in intelligence quotient (IQ), years of education, or socioeconomic status. The authors chose single voxels within the tracts as their regions of interest. Around each such voxel, they then defined areas of probable WM (segmenting by FA) and took the mean and maximal FA of these areas as further regions of interest. In the uncinate study, they found no differences between groups but found a loss of the left-greater-than-right FA asymmetry in the patient group. They also found a correlation of right uncinate FA with Trail Making Test score (Lezak 1983) and the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) similarities subtest (Wechsler 1997), and of left uncinate FA with recall of word pairs in the patients (though they do not specify which of the three FA measures– chosen voxel, maximum voxel, or cross-section mean–they correlated). For their cingulum study, the ROI was averaged over eight slices and bilaterally reduced FA was found in the patients, as well as reduced area. Fractional anisotropy in the left cingulum correlated with Wisconsin Card Sort Test (Heaton 1981) score in the patients. Though a great number of statistical comparisons were made, particularly in the uncinate study, these were not corrected for, on the grounds that the analyses were hypothesis-driven. Their method for ROI placement employed FA maps colored to reflect the principal direction of the diffusion tensor (Figure 1B). While this is potentially very helpful, interpretation of these maps still requires knowledge of the distribution of WM tracts in each plane and cannot discriminate between adjacent tracts with a parallel orientation. Thus, for example, the ROI intended to sample the uncinate may have included the inferior fronto-occipital fasciculus (Crosby 1962). A group from Peking produced three ROI studies examining the cerebellar peduncles (Wang et al 2003), a number of cortical WM areas (Sun et al 2003), and the cingulum (Wang et al 2004), respectively. For the cerebellum, a neuroradiologist placed the ROIs in superior and middle cerebellar peduncles, but no between-group differences were found. For the cortical study, a mixed-gender group of 30 patients and 19 control subjects had ROIs placed by two raters with good interrater reliability on one to three consecutive slices of the genu and splenium of corpus callosum, bilateral anterior forceps, anterior cingulum, anterior and posterior limbs of the internal capsule, and areas of temporal, parietal, and occipital WM. The only significant difference (by ANOVA with post hoc t test) was in the anterior cingulum, with the patient group having a large FA reduction that was significant at p ⬍ .001. It should be noted that the cingulum ROI was defined on only a single axial slice with a smaller ROI than the others; defining the cingulum as described, without contamination either by GM or by the corpus callosum, would be extremely difficult. If the cingula of the two groups were different sizes, as they were found to be in Kubicki et al (2003), this could have led to a systematic bias in FA results. In the third study, the authors appeared to follow up the above, restricting the subject group to 21 male patients and 20 male control subjects. Regions of interest were placed on three slices of the anterior and posterior cingulum by a radiologist, permitting the same potential confound as above. They again found reduced FA in the anterior, but not www.sobp.org/journal
926 BIOL PSYCHIATRY 2005;58:921–929 posterior, cingulum in the patient group; reduced left-greaterthan-right asymmetry was also found in the patients. In a study of 25 patients and 21 control subjects, Okugawa et al (2004) placed 2-mm squares on the middle cerebellar peduncles, finding reduced FA in patients. Placing such a small ROI in such a small tract leaves considerable room for partial volume error (contamination of the ROI by surrounding tissues), however, and this may lead to systematic bias, particularly when it is suggested there are volume differences between groups in this area. Kumra et al (2004) examined 12 adolescents with schizophrenia and 9 well-matched control subjects. They placed standardized ROIs on seven slices, frontally, and on a single slice for the corpus callosum and occipital WM. Analysis of variance with post hoc t test found reduced FA in patients in only the frontal WM slice at the level of the anterior commissure bilaterally and the right occipital ROI; however, the authors point out these results did not survive a Bonferroni correction.
Summary Voxel-Based Studies Overall, though 14 studies found reduced FA in patients with schizophrenia, there was a lack of consistent findings. The variation in the regions examined limits direct comparison in most cases, so we shall focus on the two tracts which have been most studied: the corpus callosum and the cingulum. First, we shall compare the voxel-based studies, which perforce include every region examined by the ROI studies. Foong et al (2002) found no significant differences in their sample of 14 patients, whereas Ardekani et al (2003) found widespread differences in a demographically similar group of the same size using DTI acquired with a similar scheme. However, Ardekani et al (2003) used a nonlinear (as opposed to linear) registration and clusterlevel (as opposed to voxel-level) statistics. A nonlinear registration may provide a better match between subjects and therefore increase the power of the study, while cluster-level tests are usually more sensitive (Bullmore et al 1999). Agartz et al (2001) performed the largest voxel-based study using a linear registration and cluster-level statistics, confirming the reduced FA in the splenium and forceps major in Ardekani et al (2003) but not the other areas. The voxel-based analysis in Hubl et al (2004)) found extensive reductions in FA, including areas of the genu and splenium. Corpus Callosum Four articles defined ROIs that included the corpus callosum (Buchsbaum et al 1998; Foong et al 2000; Kumra et al 2004; Sun et al 2003) in addition to the four voxel-based studies above. Of the eight, four found reduced FA (Agartz et al 2001; Ardekani et al 2003; Foong et al 2000; Hubl et al 2004) but four did not (Buchsbaum et al 1998; Foong et al 2002; Kumra et al 2004; Sun et al 2003), accounting for 80 and 62 patients, respectively. Foong et al (2000, 2002) used an ROI and a voxel-based approach in the same sample, finding differences with the former approach but not the latter. An ROI analysis is particularly suited to the corpus callosum, as it is relatively easy to define and avoids some of the risk of contamination by CSF that may result from registration errors in voxel-based studies. If the ROI studies are given preeminence, then the balance is against a difference in the corpus callosum. www.sobp.org/journal
R.A.A. Kanaan et al Cingulum The cingulum bundle has been examined in five ROI studies (Buchsbaum et al 1998; Burns et al 2003; Kubicki et al 2003; Sun et al 2003; Wang et al 2004), in addition to the whole brain studies, making nine in total: four of these (Ardekani et al 2003; Kubicki et al 2003; Sun et al 2003; Wang et al 2004) found reduced FA in 81 patients, but five (Agartz et al 2001; Buchsbaum et al 1998; Burns et al 2003; Foong et al 2002; Hubl et al 2004) did not find a reduction in 98 patients. Three of the positive studies were ROI based, and two of these considered only the cingulum, which would suggest that they be given greater weighting. However, Wang et al (2004) was a follow-on study from their earlier study (Sun et al 2003) and involved most of the original subjects (D. Zhang, M.D., Ph.D., personal communication, April 4, 2005); it may thus be misleading to regard these as two separate positive studies. Similar calculations show smaller numbers of studies evenly divided on the uncinate and arcuate fasciculi, the cerebellar peduncles, and the hippocampus.
Discussion Most DTI studies in schizophrenia found some positive results, though there were many negative results; indeed, for any region where studies found a positive result, at least as many failed to find one. While it is thus possible that there are no real differences in FA between patients with schizophrenia and control subjects, it is also possible that there are real but small (or highly variable) differences but at least some of the studies did not have enough power to detect them (type 2 error). There are three considerations in addressing this question of power: sample size, effect size, and variance. The sample size of these studies may well favor the lack-ofpower explanation, as most have involved modest numbers of subjects (median 15 patients). Taking the Foong et al (2000) ROI study of the corpus callosum as an example of a positive result, only six of the studies we examined would have been large enough to give even 50% power to detect a difference of that magnitude with the variances described. The second consideration—the size of any difference—is more difficult to assess. While there are many descriptions of WM abnormalities in schizophrenia, there is no a priori reason to believe that these will always result in reduced FA and certainly no prescription for how large any differences should be. So, while increased neuronal density (Selemon et al 1998), for example, might be expected to lead to higher FA (Beaulieu 2002), decreased myelination (Flynn et al 2003) would tend to reduce it (Beaulieu 2002) and neuronal redistribution (Akbarian et al 1996) might increase FA, lower it, or leave it unchanged, depending on the final WM architecture (Pierpaoli et al 1996). In favor of a real difference in schizophrenia, however, is the fact that virtually all the positive results were consistent in finding reduced rather than increased FA. A finding of higher FA is almost unknown in pathological studies, though it has been noted in the period immediately following ischemia in rats (Carano et al 2000). The finding of raised arcuate FA in Hubl et al (2004), though exciting in its suggestion of heightened frontotemporal connectivity in auditory hallucinations, is thus unusual and requires replication. Third, there is the question of variance. Potential sources of this are the sample (demographic and illness variables), the acquisition (including the gradient scheme and problems associated with motion and distortion), and the analysis (including
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R.A.A. Kanaan et al the registration method employed in voxel-based approaches and the way ROIs are defined). In terms of the samples, most of the studies attempted to match at least for age and some for other variables (see Table 1). There is evidence that age (Pfefferbaum et al 2000), IQ (Schmithorst et al, in press), gender, and handedness (Westerhausen et al 2003) all affect FA, so a lack of matching between groups in these respects may have a bearing on the DTI findings. All of the studies were of medicated patients and all but one were of chronic patients, so the patient samples may have differed with respect to the severity and duration of illness and the dose and duration of antipsychotic treatment. However, in the studies where they were considered, FA was not found to be correlated with PANSS score (Foong et al 2000; Minami et al 2003), duration of illness (Foong et al 2000; Kubicki et al 2002, 2003), or medication dose (Foong et al 2000; Kubicki et al 2002, 2003; Minami et al 2003). Diffusion tensor imaging is still a relatively immature research technique, and the methods of image acquisition and analysis are still evolving. Some of the inconsistency of the findings across studies to date may therefore be related to differences in methodology (see Basser and Jones 2002 for a detailed review). In terms of acquisition, only 4 of the 19 studies (Agartz et al 2001; Sun et al 2003; Wang et al 2003, 2004) used “optimized” gradient schemes, which have been shown to greatly reduce FA error (Jones et al 1999). Some made specific correction for subject motion using line-scan acquisition (Gudbjartsson et al 1996; Hubl et al 2004; Kubicki et al 2002, 2003), cardiac gating (Agartz et al 2001; Foong et al 2000, 2002), or in postprocessing (Ardekani et al 2003; Steel et al 2001), and some corrected for geometric distortion (Agartz et al 2001; Ardekani et al 2003; Burns et al 2003; Steel et al 2001; Sun et al 2003; Wang et al 2003, 2004). With regard to analysis, one thing that distinguishes DTI from that of other types of imaging is that it seeks to treat WM as nonhomogenous, as a conglomeration of tracts rather than as a volume. It is often insufficient to define a tract within WM by using WM-GM boundaries or by reference to neuroanatomical atlases. For example, Ardekani et al (2003) rightly only report differences “in the vicinity” of the cingulum, as the area identified also possibly includes the corpus callosum and the superior fronto-occipital fasciculus. Partly, this is a problem of voxel-based clusters not respecting anatomical boundaries, but it is also a problem of distinguishing WM tracts when they run together. Kubicki et al (2002, 2003) made use of additional directional information acquired in DTI to better define their ROIs, which goes some way to addressing this problem. The findings that have found most support, reduced FA in the corpus callosum and the anterior cingulum, appear to have face validity. The corpus callosum is the major interhemispheric commissure (Crosby et al 1962), and its dysfunction has been implicated in auditory hallucinations (Rossell et al 2001) and negative symptoms (Gunther et al 1991). The anterior cingulum is central to limbic circuitry (Tamminga et al 2000), and its dysfunction linked to attentional deficits in schizophrenia (Carter et al 1997). The most common interpretation of reduced FA is that it reflects lower WM “integrity.” Reduced FA in these tracts would therefore be consistent with the idea that schizophrenia involves the disconnection of cortical regions, which is supported by studies in other modalities (reviewed in Innocenti et al 2003 and Tamminga et al 2000). However, given that other modalities have already implicated these tracts, we may question the utility of FA analysis. To realize its full potential, DTI must either detect differences that other modalities cannot or engender an interpretation of those differences.
Future Directions A definitive answer to our question may be provided by studies involving larger, more homogeneous patient groups, such as those defined by a common phase of illness and treatment (e.g., Kumra et al 2004) or by symptom profile (e.g., Hubl et al 2004). Combining DTI with other imaging may shed light on the nature of FA deficits detected and allow the correlation of structure and function (Toosy et al 2004). Solutions are being developed for many of the methodological problems outlined above: the move to higher magnetic field strengths holds the promise of higher resolution, albeit at the cost of greater susceptibility artefacts (Hunsche et al 2001); parallel image acquisition techniques (Bammer et al 2002) should allow more robust data acquisition or reduced scanning time; optimized acquisition schemes improve the precision of diffusion tensor (DT) measurements (Jones et al 1999); and improved nonlinear registration methods (Park et al 2003) are being prepared for DTI, permitting a closer match of WM structures. It is important to remember that DTI data offer a wealth of information in addition to FA. We have seen some of this directional information used to guide ROI placement (Kubicki et al 2002, 2003), but the promise of DTI in schizophrenia research may be more fully realized through fiber tractography (Basser et al 2000). This takes an initial ROI and traces the fiber tracts from there using the diffusion tensor (Figure 1C). The validity of these putative “tracts” is difficult to ascertain, however, due to a lack of neuroanatomical standards (Catani et al 2002). They also suffer from the operator dependency of their production, difficulties in generalizing three-dimensional data, and problems resolving direction where fibers cross. However, developments in the analysis of high angular resolution data (Zhan et al 2003) and multiple tensor modeling (Alexander et al 2001) may allow resolution of crossing tracts, while probabilistic fiber tracking (Parker et al 2003) and group tractography (Ciccarelli et al 2003) should improve validity. With tractography, researchers may be able to define WM tracts and examine measures of WM integrity (such as FA) on a tract-by-tract basis (Jones et al 2003).
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