Absence of regional brain volume change in schizophrenia associated with short-term atypical antipsychotic treatment

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Schizophrenia Research 98 (2008) 29 – 39 www.elsevier.com/locate/schres

Absence of regional brain volume change in schizophrenia associated with short-term atypical antipsychotic treatment Robert K. McClure a,⁎, Khary Carew a , Stacy Greeter a , Emily Maushauer a , Grant Steen a , Daniel R. Weinberger b a

University of North Carolina at Chapel Hill, Department of Psychiatry, Chapel Hill, North Carolina, United States b Clinical Brain Disorders Branch, NIMH, Bethesda, Maryland, United States Received 27 January 2007; received in revised form 7 May 2007; accepted 11 May 2007 Available online 31 October 2007

Abstract The first aim of this pilot study was to determine if longitudinal change in caudate volume could be detected in chronic schizophrenic patients after 12 weeks of atypical antipsychotic treatment. A sub-aim of the first aim was to determine if similar results could be obtained from an operator-assisted segmentation tool for volumetric imaging (ITK-SNAP) and voxel-based morphometry (VBM) methods in the caudate. The second aim was to determine if frontal and temporal lobe grey matter, white matter, ventricular and sulcal cerebrospinal fluid volume change could be detected after 12 weeks of atypical antipsychotic treatment with VBM. Ten chronic schizophrenic inpatients, with illness duration averaging 10.6 years, underwent two MRI scans. The first scan was obtained after a mean of 39.4 days of antipsychotic withdrawal. The second MRI was obtained following twelve weeks of atypical antipsychotic treatment. Caudate volume change was first measured with ITK-SNAP. Then the location of grey matter volume change in the caudate was identified with VBM. Finally, the location of frontal and temporal lobe grey matter, white matter, ventricular and sulcal cerebrospinal fluid volume changes were identified with VBM. No longitudinal change in caudate volume or grey matter volume was observed after brief periods of atypical antipsychotic treatment. ITK-SNAP and VBM methods showed very similar results in the caudate. No statistically significant change was identified in the volume of frontal or temporal lobe grey matter, white matter, and lateral, third, or fourth ventricular cerebrospinal fluid. Although the results do not directly show that brief periods of atypical antipsychotic treatment are associated with basal ganglia and cortical volume change, there is much evidence to suggest that such an association exists. © 2007 Published by Elsevier B.V. Keywords: Treatment naïve; First episode; Chronic schizophrenia; Caudate; Atypical antipsychotic; ITK-SNAP; Voxel-based morphometry; VBM

1. Background Longitudinal MRI studies in schizophrenic patients have been performed primarily after periods of antipsy⁎ Corresponding author. UNC Department of Psychiatry, CB 7160, Room 247, Wing C, Chapel Hill, NC 27510-7160, United States. Tel.: +1 919 843 6629; fax: +1 919 966 4180. E-mail address: [email protected] (R.K. McClure). 0920-9964/$ - see front matter © 2007 Published by Elsevier B.V. doi:10.1016/j.schres.2007.05.012

chotic treatment greater than twelve weeks. There is a paucity of information about regional brain volume change associated with periods of antipsychotic treatment less than twelve weeks. Brain volume changes that occur over brief time periods are likely to be potentially reversible and are unlikely to reflect fixed structural abnormalities (McClure et al., 2006). A global objective of this pilot study was to reduce the paucity of scientific inquiry into the association of short-term

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antipsychotic treatment with regional brain volume change in schizophrenia. Increased caudate volume in antipsychotic-naïve schizophrenia patients has been associated with typical antipsychotic treatment for time periods greater than twelve weeks (Chakos et al., 1995, 1994; Corson et al., 1999a,b; Dazzan et al., 2004; Dazzan et al., 2005; DeLisi et al., 1994, 1991, 1997; Gur et al., 1998; Heitmiller et al., 2004; Keshavan et al., 1998; Keshevan et al., 1994; Lang et al., 2001, 2004; Mathalon et al., 2001; Pressler et al., 2005; Scheepers et al., 2001; Shihabuddin et al., 1998; Tamagaki et al., 2005; TauscherWisniewski et al., 2005). Increased caudate volume in rats treated with atypical antipsychotics (Andersson et al., 2002) has also been reported. Few investigators (Christensen et al., 2004; Garver et al., 2005; Massana et al., 2005; McClure et al., 2006; Strungas et al., 2003; Tauscher-Wisniewski et al., 2005) have examined volume change in the caudate following briefer antipsychotic treatment periods (Massana et al., 2005; TauscherWisniewski et al., 2005). We sought to determine if regions of longitudinal volume change could be identified in the caudate following periods of antipsychotic treatment of twelve weeks or less in medication-free chronic schizophrenia patients. Specifically, we measured change in caudate volume in a small sample of chronic schizophrenic inpatients treated with atypical antipsychotics for twelve weeks following medication washout for a mean of 39.4 days. In the past, manual region-of-interest analysis (ROI) was considered the “gold standard” for determining regional volume with structural MRI studies (McCarley et al., 1999). The limitations of manual ROI analysis include the following. First, volume can be determined only for the ROIs drawn, providing no information on the regions that are not examined. Second, variability due to inter-rater reliability can be high. Third, elimination of inter-rater reliability requires extensive, time-consuming, and expensive rater training, which is beyond the capacity of many individual investigators. Voxel-based morphometry (VBM), a newer method for the analysis of structural magnetic resonance images, is not hampered by these limitations, because it does not introduce inter-rater bias and the entire brain is sampled in an unbiased manner. Since VBM requires estimation of smooth, low frequency deformation fields, it is within budgetary capabilities of many research units. There are some limitations to VBM (Mechelli et al., 2005). First, shape differences attributable to misregistration during spatial normalization, rather than actual group differences, can be detected. Second, global rather than local regional volume change may be more accurately

detected, because of the relatively imprecise registration used. Third, the accuracy of localization is negatively affected by smoothing, which may shift the peak of the SPM towards regions of low variance. Fourth, spatially complex, subtle, or changes that are related to changes that occur elsewhere in the brain may not be detected by this mass univariate approach. Fifth, the exact nature of tissue changes identified with VBM is still poorly understood. Consequently, the results of longitudinal MRI studies using VBM vary considerably(Ananth et al., 2002; Hulshoff et al., 2001; Job et al., 2002; Kubicki et al., 2002; Pantelis et al., 2002; Sowell et al., 2000; Thompson et al., 2001; Wilke et al., 2001; Wright et al., 1999a,b) and their interpretation is difficult. VBM as it is performed in theses studies and this study detects the presence and location of volume change, but not the magnitude. Validation of voxel-based studies has not been extensive, limited to a few brain regions, experimental conditions, and analysis methods. Recently, newer semi- and fully-automated methods of analyzing regional brain volume have been developed, since the advent of VBM. “Snake evolution” is one of these recently developed methodologies. “Snake” refers to a closed curve or surface in three dimensions that represents a segmentation. The snake begins as a very rough estimate of the anatomical structure of interest and evolves to a very close approximation of the structure. The evolution of the snake is governed by a mathematical equation describing the velocity of every point on the snake at any particular time. The velocity of each point depends on the shape of the snake and on the intensities of the image in the neighborhood of the point. Snake evolution has been implemented in a freely available program known as ITK-SNAP (http://www.ia. unc.edu/dev/download/index.htm). Validation studies suggest that this method has excellent efficiency and reliability(Yushkevich et al., 2006), but there are no published studies comparing the results of ITK-SNAP to VBM. A sub-aim of this pilot study was to determine if these two methods of detecting volume change-ITKSNAP and VBM-produced similar results in the caudate. ITK-SNAP-VBM comparison was purposefully limited to the caudate for several reasons. First, reducing the chance of false positive findings from the examination of multiple regions of interest is particularly important in structural neuroimaging studies. In this pilot study, the likelihood of false positives was greatly reduced, since repeated-measures ANOVA performing was performed only on the left and right caudate. Second, maximizing the chance of obtaining a true

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positive finding is also particularly important in a structural neuroimaging study with a small sample size. The need for correction for multiple comparisons was avoided by limiting the manual ITK-SNAP analysis to left and right caudate only. Third, the caudate is an ideal candidate region to compare the results of image analysis methods. The direction of volume changes associated with atypical antipsychotic treatment can be directly predicted for the caudate, since the literature is more consistent then for other brain regions. Cortical grey matter, white matter, and cerebrospinal fluid volume change in schizophrenic patients has been associated with typical antipsychotic treatment for periods greater than twelve weeks using both typical (DeLisi et al., 1988, DeLisi, 1997, DeLisi et al., 1992, 1995; Gur, 1998; Ho et al., 2003; Mathalon, 2001; McCormick et al., 2005) and atypical (Gogtay et al., 2004; McCormick et al., 2005; Molina et al., 2005) antipsychotics. The complete absence of statistically significant volume changes in these regions has been reported. The association of volume change in cortical grey matter, white matter, and cerebrospinal fluid and antipsychotic treatment is not consistent (Chakos, 1994; James et al., 2004; Keshavan et al., 1994; Pressler et al., 2005). The direction of reported volume changes are also not consistent (Lieberman et al., 2005; Nopoulos et al., 2005), even in studies adequately powered to detect volume change (Gogtay et al., 2004; Ho et al., 2003; McCormick et al., 2005; Molina et al., 2005; Mathalon et al., 2001; Gur et al., 1998; DeLisi et al., 1994, 1991, 1997; Gur et al., 1998; Lieberman et al., 2005). It is also unclear if volume change in cortical grey matter, white matter or cerebrospinal fluid follows briefer periods of antipsychotic treatment, because so few investigators(Christensen et al., 2004; Garver et al., 2005; Massana et al., 2005; McClure et al., 2006; Strungas et al., 2003; Tauscher-Wisniewski et al., 2005; Garver et al., 2005; McClure, 2006; Strungas et al., 2003) have examined this question. A previous study from our research group demonstrated localized grey matter volume change in the frontal and temporal lobes following both withdrawal from, and chronic treatment with, antipsychotic medications ( p b .05, uncorrected for multiple comparisons). The changes did not persist after adjustment for multiple corrections with the false discovery rate (McClure, 2006). The results of this study also demonstrated good correlation between the results of Bonferroni corrected manual ROI and false discovery rate (FDR) adjusted VBM methods in frontal and temporal grey matter. A secondary aim of our current study was to determine if regions of longitudinal volume

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change could be identified in grey matter, white matter, ventricular-sulcal regions following short periods of antipsychotic treatment in chronic schizophrenia patients. We accomplished this aim by identifying significant regions of tissue volume change measured with VBM in the aforementioned sample of chronic schizophrenic inpatients treated with atypical antipsychotics for twelve weeks following medication washout for a mean of 39.4 days. Directional a priori hypothesize for antipsychoticassociated volume changes could be directly made for the caudate, based on previous findings described in the literature. Since reversal of caudate enlargement in schizophrenic patients previously treated with typical antipsychotics has been reported following treatment with atypicals (Chakos, 1995; Corson et al., 1999b; Dazzan et al., 2004; Gur, 1998; Scheepers, 2001) and no caudate volume change has also been reported after more than twelve weeks of atypical antipsychotic treatment (Heitmiller et al., 2004; Lang et al., 2001, 2004), we predicted that left and right caudate volume would either decrease or not change. We also predicted that either no change or decreases in grey matter volume would be observed in the caudate with VBM after adjustment for the false discovery rate. Based on the VBM results from our previous study (McClure, 2006), we predicted that if any localized frontal or temporal grey matter volume change were seen, it would not persist after false discovery rate adjustment. We hypothesized that no white matter and cerebrospinal fluid volume change would be observed after FDR adjustment. Although we did not expect changes in the larger grey, white and sulcal–cerebrospinal fluid regions, we expected VBM to sensitively detect these sorts of changes if they were present, since the method is designed to detect global volume change. With respect to the methods comparison, we predicted that localized caudate volume changes would be detected with the ITK-SNAP, but VBM methods would be less likely to detect localized caudate volume change. 2. Materials and methods 2.1. Subjects Ten psychiatric inpatients at NIMH Clinical Center, Bethesda, Maryland enrolled voluntarily in a research protocol between 1998–2002. Subjects were independently determined to have capacity to give, and subsequently gave informed consent to participate in the research protocol, which followed current ethical standards and was approved by the NIMH Internal

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Review Board. Patient inclusion criteria were: (1.) normal brain MRI, defined as the absence of structural abnormality, prominent anatomical variants, or technical artifacts, including any motion detectable by human observation; (2.) absence of criteria for substance abuse six months before, or criteria for substance dependence one year before enrollment; (3.) absence of a seizure disorder or history of head trauma involving loss of consciousness; (4.) absence of any medical condition known to affect MRI of the brain; (5.) presence of DSMIV criteria for schizophrenia or schizoaffective disorder (First et al., 1994). Diagnosis was confirmed by a boardcertified psychiatrist following structured clinical interview for DSM-IV disorders. Patient exclusionary criteria included (1.) presence of another neurological condition (besides schizophrenia or schizoaffective disorder) or a medical condition that affects brain MRI. As part of a larger research protocol designed to assess the effects of antipsychotic medications on neurocognitive and neuroimaging phenotypes, patients elected to withdraw from medications during a three to six week period of close psychiatric, nursing, and medical monitoring. After the period of antipsychotic withdrawal, subjects were restarted on antipsychotics. Two MRI studies were performed, at baseline off medications, and twelve weeks after restarting medications. Twelve subjects originally completed the protocol. Since there were only two subjects treated with typical antipsychotics, they were dropped from the analysis. Ten subjects, nine males and one female, were treated with second-generation atypical antipsychotics at clinical doses (olanzapine 10–20 mg per day, risperidone 4– 6 mg per day, quetiapine 300–800 mg per day, clozapine 500–1000 mg per day). Other adjunctive treatments included cogentin 2 mg per day and valproic acid 1500 mg per day, and fluoxetine 40 mg per day in three different subjects respectively. All patients except one were previously treated with the same second-generation

atypical antipsychotics. Ethnicity of the subjects was Caucasian of European decent. Of note, all ten subjects also participated in the previously mentioned study (McClure, 2006) and analysis of this follow up data was planned in an a priori manner. Time, resources and ethical considerations precluded recruitment of additional subjects. The demographic composition of the sample is described in Table 1. Premorbid IQ was estimated from the by Wide Range Achievement TestReading subscale (Wilkinson, 1993) administered by a neuropsychologist. Handedness was determined by the Edinburgh Handedness Inventory (Oldfield, 1971) by a trained research assistant. 2.2. MRI acquisition MRI was performed on one of two 1.5 T GE Sigma scanners at the NIMH Clinical Center, Bethesda, MD. The same scanner, running the same operating system and sequence (3D capital SPGR, TE 5.0, TR 24, Flip angle 45°, FOV 24, Matrix size 256 × 192 × 124 slices) was used for each scan. 2.3. Image processing for caudate ITK-SNAP analysis In MRIcro (http://people.cas.sc.edu/rorden/mricro. html) images were converted from DICOM to Analyze format on Dell microcomputers. Bilinear interpolation was used to convert the anisotropic 0.938 × 0.938 × 1.5 mm to 8-bit isotropic .94 × .94 × .94 mm voxels. Images were reoriented along the AC-PC line and the inter-hemispheric fissure by visual inspection. Bias correction was performed using itkEMS version 1.5.4. (http://www.ia.unc.edu/dev/download/itkems/index. htm). Boundaries for the caudate were established using standard anatomical landmarks, available upon request. Caudate segmentation was performed with InsightITKSNAP version 1.4 (http://www.itksnap.org/download/

Table 1 Demographic and clinical variables Atypicals

Sample size Gender Age in years Duration of illness in years Education (years) Intelligence Quotient Handedness Quotient Days off medications

Mean

Range

S.D.

N = 10 Male = 9 Female = 1 36.7 10.6 14.4 92.7 0.6 39.4

25–46 1–26 12–20 78–102 − 0.8–1.0 12–105

7.7 9.7 3.3 9.1 0.69 33

tvalue

df

p

1.8 − 0.2 − 0.2 − 1.7 2.2 0.6

7 7 7 5 6 7

0.12 0.85 0.84 0.16 0.07 0.56

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snap/) and Linux version 6.0 using a laboratory protocol adjusted for our schizophrenic population (http://www. psychiatry.unc.edu/autismresearch/mri/ROIs). Two raters blinded to medication status attained good inter-rater reliability by segmenting caudate on 10 MRI scans of a similar group of schizophrenic patients. Intraclass correlation coefficients (Bartko, 1966) were 0.94 for the right caudate and 0.89 for the left caudate. To further reduce inter-rater variance, a single rater measured caudate ROIs on the two scans of each subject. Covariance for total brain volume was unnecessary since each subject was compared to his or herself as a repeated measure. 2.4. Image processing for caudate, grey, white and sulcal–cerebrospinal fluid VBM Optimized VBM (Good et al., 2001) was performed in SPM2, using a protocol similar to that described in our previous study (McClure, 2006), using publicly available scripts to automate the process (http://dbm. neuro.uni-jena.de/home). A whole brain T1 template was constructed for this study population (McClure et al., 2006) as follows. A T1 MRI study of twenty chronic schizophrenic subjects treated with typical and second-generation atypical antipychotics was spatially normalized to the ICBM 152 of the Montreal Neurological Institute, a template derived from 152 normal subjects, approximating Talairach space. The SPM2 normalization algorithm uses a 12-parameter affine, followed by non-linear, transformation. The twenty normalized images were smoothed with a 8-mm full width at half-maximum (FWHM) isotropic Gaussian kernel which was followed by image averaging. Grey matter templates were constructed as follows. The T1 MRIs of the twenty treated chronic schizophrenic were spatially normalized to the whole brain template using 12-parameter affine, followed by nonlinear transformation. Voxel size was reformatted to 1.5 × 1.5 × 1.5 mm. The images were segmented into grey matter probability maps, smoothed with a 12-mm FWHM isotropic Gaussian kernel, and then averaged. The subjects' MRIs obtained off and then on atypical antipsychotics medications were then processed to create statistical parametric maps. The second MRI study of the ten subjects were coregistered to the first. The first and second MRI of the ten subjects were segmented. Extraneous tissue removed by creating a mask of brain tissue for each scan using the Extract Brain option. The grey matter image of each subject was set to i1, the white matter image to i2, cerebrospinal fluid to i3, non-brain voxels to i4; and applying the expression i1./ (i1 + i2 + i3 + eps). ⁎ i4, using the IMCALC option. Parameters for

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spatially normalization of the “cleaned up” grey matter images to the grey matter template were estimated. These spatial normalization parameters were then applied to the T1 MRI images. The normalized T1 images were segmented into grey matter, white matter and cerebrospinal fluid images, and the images “cleaned up” with IMCALC. In order to sensitize the images to change in volume, as opposed to concentration–the amount of tissue in a given volume–a Matlab function that adjusts for volume change due to spatial normalization was applied to each grey matter image (Good et al., 2001), which were then smoothed with a 12-mm full width at half-maximum isotropic Gaussian kernel. VBM as it is performed in this study detects the presence and location of volume change, but not the magnitude. The statistical analysis was conducted using the general linear model with a multi-group conditions and covariates model, originally designed for positron emission tomography. The MRI scans at baseline off medications were used for the first condition, whereas the MRI scans off medications, were used for the second condition. Covarying for total brain volume or for mean voxel tissue volume was not necessary for the following reasons: the two groups were composed of the same subjects scanned at two different times, making it very unlikely that the two images differed in any global intensity measures; our sample size was quite small, and; identifying total grey matter, white matter and cerebrospinal fluid volume was planned and would likely identify change in mean volume would occur. This was confirmed in our analysis (see Results). The analysis was performed without any other covariates or nuisance variables. The use of global normalization for total tissue matter intensity was calculated and did not alter the results. We present the results without global normalization for tissue intensity. Scaling for the global image signal was not performed, since this correction is appropriate for PET studies. Grand mean scaling was completed by multiplying voxel value– the probability (0 to 1) of assignment to tissue class–by a scale factor of 100, so voxel value could be more intuitively interpreted as percent likelihood of assignment to grey matter tissue class (0% to 100%). A proportional threshold of 0.5 was applied per SPM2 routine, excluding voxels with probability less than 50% of the mean voxel value from the analysis. Similarly, a global calculation was performed that excludes voxels with values less than a two pass mean, integrated over the entire image/8 (http:// www.mrc-cbu.cam.ac.uk/Imaging/Common). These corrections eliminated voxels with a very low likelihood of being in a tissue category from the statistical analysis. The SPM2 non-sphericity algorithm was not stable in this small data set, so it was not used.

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For the caudate, which is primarily a grey matter structure, the grey matter statistical parametric maps were used to assess change in tissue volume. In order to reduce the voxels analyzed to those within these regions of standardized space lying within the caudate, masks for left and right caudate were applied to the grey matter statistical parametric maps with the WFU Pickatlas (Maldjian et al., 2003). The coordinates in the standardized space of the Montreal Neurological Institute of areas that showed statistically significant tissue volume change were converted to Talairach coordinates using the matlab function mni2tal.m (http://imaging.mrccbu.cam.ac.uk/ imaging/MniTal-airach#head-794ffcf1c22fe255b159c2a95db607e7cdd58b20). The location of the Talairach coordinates were identified first using the Talairach Deamon (Maldjian et al., 2003)) and were verified using the atlas of Talairach and Tournoux (Lancaster et al., 1997, 2000; Talairacha and Tournoux, 1988). VBM detected the presence and location of volume change, not the magnitude. For the grey matter, white matter, and cerebrospinal fluid statistical parametric maps, we assessed volume change in much larger regions, but used the WFU Pickatlas to reduce the number of voxels examined. Frontal and temporal lobe masks were applied to the grey matter statistical parametric maps to assess grey matter volume change where treatment effects were previously found without adjustment for FDR in an earlier study (McClure, 2006), in order to reduce inference to frontal and temporal lobe. No mask was applied to the white matter images. A mask for the 3rd, 4th, and lateral ventricle, as well as for cerebral cortical sulci was applied to the cerebrospinal fluid statistical parametric maps with the WFU Pickatlas, in order to reduce the voxels analyzed to only those in these structures. VBM detected the presence and location of volume change, not the magnitude.

maps masked to include only voxels of right and left caudate. The effect of treatment status was determined using the statistical maps and appropriate contrasts with small volume correction in SPM2. Since an F-test was performed in all caudate voxels, some correction of the thresholds was necessary. Since standard procedures for multiple hypothesis testing such as the Bonferroni correction are relatively insensitive, use of the false discovery rate (FDR) correction has become common (Genovese et al., 2002). These investigators have demonstrated that the FDR controls the expected proportion of rejected hypotheses that are falsely rejected. FDR adjustment is performed automatically in SPM2. A threshold of significance at the p b .05 level was used. For grey matter, white matter and cerebrospinal fluid, the general linear model was used and Ftests were performed. The effect of treatment status was determined using the statistical maps and appropriate contrasts in SPM2. The same adjustment for multiple tests was performed using the false discovery rate. A threshold of significance at the p b .05 level was used. 3. Results 3.1. Caudate ITK-SNAP Mean right caudate volume off medications was 3.788 cc's (S.D. = 0.598 cc's) and on medications was 3.724 cc's (S.D. = 0.630 cc's). Change in mean right caudate volume did not approach statistical significance ( p = .80, df, = 1, F(1,7) = .0636). Mean left caudate off medication was 3.885 cc's (S.D. = 0.509 cc's) and on medications was 3.608 cc's (S.D. = 0.529 cc's). Similarly, change in left mean caudate volume also did not reach statistical significance ( p = .253, df = 1 F(1,7) = .254) (see Figs. 1 and 2).

2.5. Statistical design for caudate ITK-SNAP analysis Using Statistica version 7.0, a repeated-measures ANOVA was performed on caudate volume. Treatment status (off versus on atypical antipsychotics) was the within-subject factor. Right and left caudate volume was the dependent variable. Results were considered significant if p b .05 (uncorrected). 2.6. Statistical design for VBM caudate, grey, white and sulcal–cerebrospinal fluid In SPM2, the general linear model was used. F-tests were performed on the grey matter statistical parametric

Fig. 1. ANOVA right caudate.

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3.2. Caudate, grey, white and sulcal–cerebrospinal fluid VBM

since the method is designed to detect global volume change.

No change in right or left caudate grey matter volume identified with VBM, adjusted with the false discovery rate. Increases in grey matter volume were observed in two right caudate regions, unadjusted with FDR, but not in any left caudate regions, either unadjusted or adjusted for FDR. No effect of treatment status was observed in frontal or temporal grey matter volume. No effect of treatment status was detected in white matter volume. Similarly no effect of treatment status was observed in the cerebrospinal fluid volume in 3rd, 4th, or lateral ventricles and in cortical sulci. Our a priori hypothesize were confirmed, since it was predicted that either no change in frontal or temporal grey matter volume grey matter volume would be observed with VBM after adjustment for the false discovery rate. We also predicted that no white matter and cerebrospinal fluid volume change would be observed after FDR adjustment, and this was confirmed.

4. Discussion

3.3. Comparison of caudate ITK-SNAP to VBM analysis Neither the ITK-SNAP analysis, nor the VBM analysis, demonstrated significant effects of treatment status in right or left caudate. The prediction that caudate volume would decrease or not change was confirmed. The prediction that localized caudate volume changes would be detected with the ITK-SNAP, but would be less likely to be detected with VBM, was not substantiated, since both methods confirmed the absence of any significant caudate volume change. It is likely that VBM would sensitively detect changes in large grey, white and sulcal–cerebrospinal fluid regions if they were present,

Fig. 2. ANOVA left caudate.

No significant caudate volume change was observed in medication-free chronic schizophrenic patients treated briefly with atypical antipsychotics. This finding stands in contrast to the findings of previous crosssectional (Corson et al., 1999a,b; Dazzan et al., 2005; Dazzan et al., 2004; Shihabuddin et al., 1998; TauscherWisniewski et al., 2005) and long-term(Chakos, 1995; Corson et al., 1999a,b; Keshavan et al., 1994; Lang et al., 2001, 2004; Massana et al., 2005; Scheepers, 2001) as well as short-term (Massana et al., 2005; McClure, 2006; Tauscher-Wisniewski et al., 2005; Christensen et al., 2004; Garver et al., 2005; Massana et al., 2005; McClure et al., 2006; Strungas et al., 2003; Tauscher-Wisniewski et al., 2005) longitudinal studies. Caudate volume change was most likely absent because the subjects were treated previously with atypical antipsychotics only, and because the treatment period was only a few months. It is possible that caudate volume change occurred in our subjects, but small sample size/ low power precluded detection of very small volume changes. It is also possible that the results were influenced by confounding variables. There are many factors associated with physiological alterations in the brain, and hence brain volume change (Weinberger and McClure, 2002). For example, two of our subjects were treated with either a mood stabilizer (valproic acid) and an SSRI (fluoxetine), psychotropic medication that have been associated with brain volume change. In marked contrast to the cortical grey matter volume changes reported in long-term (Gogtay et al., 2004; Gur, 1998; Lieberman et al., 2005; Mathalon, 2001; Molina et al., 2005; Nopoulos et al., 2005; Gur et al., 1998; DeLisi et al., 1994, 1991, 1997; Gur et al., 1998) and short-term (Garver et al., 2005; Strungas et al., 2003; Christensen et al., 2004; Massana et al., 2005; McClure et al., 2006; Tauscher-Wisniewski et al., 2005) longitudinal studies, no volume change was seen in frontal or temporal grey matter regions where treatment effects were observed in our previous VBM study(McClure, 2006). Although volume changes in white matter have been previously observed in both long-term (Ho et al., 2003; Molina et al., 2005; Nopoulos et al., 2005; Gur et al., 1998; DeLisi et al., 1994, 1991, 1997; Gur et al., 1998; Mathalon et al., 2001) and short-term (Christensen et al., 2004; Garver et al., 2005; Massana et al., 2005; McClure et al., 2006; Strungas et al., 2003; TauscherWisniewski et al., 2005) longitudinal treatment studies,

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we did not observe an effect of treatment status in white matter. Our negative findings in third, fourth or lateral ventricle and cerebral cortical sulci, contrast with the findings of previous long-term (DeLisi et al., 1988, 1992, 1995, 1997; Mathalon, 2001; Gur et al., 1998; DeLisi et al., 1994, 1991, 1997, 1998a,b; Mathalon et al., 2001; Gogtay et al., 2004; Ho et al., 2003; Lieberman et al., 2005; McCormick et al., 2005; Molina et al., 2005) and short-term (Christensen et al., 2004; Garver et al., 2005; Massana et al., 2005; McClure et al., 2006; Strungas et al., 2003; Tauscher-Wisniewski et al., 2005) longitudinal treatment studies examining volume change in cerebrospinal fluid containing structures. The inconsistencies in both direction and region of findings of our study compared to previous studies are less perplexing when one considers that previous longitudinal studies have a unique pattern of findings (Marenco et al., 2002; McClure et al., 2006; Styner et al., 2005; Weinberger and McClure, 2002). Certainly, our findings are no more or less inconsistent than those of other investigators. Our findings are of considerable clinical significance given the proposed use of brain volume change as biomarkers or intermediate phenotypes. Our negative findings should be interpreted in light of the weaknesses of this pilot study, particularly the small sample size, and low power to detect small changes. The absence of random assignment of subjects is a potential weakness, although our subjects appear to have similar characteristics to other chronic schizophrenic patients. The absence of an appropriate control group could be considered a weakness. However, in designing the study, we considered what an appropriate control group would represent: a longitudinal sample of age-matched normal controls was deemed not to be appropriate, since they would not have not been treated with antipsychotics; obtaining a group of healthy controls treated with atypical antipsychotics, followed by a medication-free period of 4 weeks, and retreated with antipsychotics for three months, was neither possible nor ethical; data from the comparison group of chronically treated patients from our previous study (McClure, 2006) has already been published, so we did not consider it appropriate to publish the same data twice, and so; the within-subject repeatedmeasures design seemed to best address the experimental question. We cannot comment on the potential effects of gender on volume change related to antipsychotic treatment, which could be considered a weakness. We did not stratify our analysis by gender, since our sample was very small, and it did not seem appropriate to compare our sample to larger samples in other studies. This report is the first comparison of ITK-SNAP to VBM methods in the caudate using the same MRI data.

We observed little inconsistency between the two methods in the caudate. This is surprising, given that VBM and ITK-SNAP methods differ in their potential sources of variance, error, and sensitivity to volume change. It has been suggested that voxel-based morphometry is especially useful for detecting grey matter reductions that are very small, are located in areas with high variability of volume or are inconsistent in location (Wright et al., 1999a,b), although this is not entirely consistent with its original design (Mechelli et al., 2005). Although VBM may be vulnerable to partial volume effects when grey or white matter voxels are misclassified (Job et al., 2002), and the location of maximal change may be altered by the use of smoothing (Kubicki et al., 2002), this method produced identical results when compared to a new, efficient, reliable useroperated tool. Our results do not directly show that brief periods of atypical antipsychotic treatment are associated with basal ganglia and cortical volume change, but there is much evidence to suggest that such an association does exist. However, even with adequately powered unbiased studies, the presence and the direction of changes observed after short-term treatment are unlikely to be consistent in region or direction, as with studies of longterm treatment. Volume change during short-term antipsychotic treatment is likely to reflect transient physiological changes, such as those attributable to alteration in blood flow or metabolic demands. Our study makes a valuable contribution to the scientific literature because it is one of the few published that examines short-term atypical antipsychotic treatment and directly compares results of ITK-SNAP to voxel-based methods. Role of funding source The funding for this study was provided by the Clinical Brain Disorders Branch, National Institutes of Mental Health, which provided for the study design and data collection. CBDB-NIMH had no role in the analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. The STRT grant from NIDDK provided stipends for Stacy Greeter and Khary Carew, who performed data analysis, but NIDDK had no other role in the study.

Contributors Robert K. McClure designed the study with Dr. Weinberger, gathered the MRI data, performed the ROI and VBM analysis, statistical analysis, created the tables and figures, and wrote the manuscript. Khary Carew and Stacy Greeter contributed equally and performed the caudate ROI analysis. Emily Maushauer established methods for obtaining inter-rater reliability for the caudate ROIs. Grant Steen assisted with preparation of the manuscript. Daniel R. Weinberger designed the study with Dr. McClure.

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Conflict of interest Dr. McClure has contracted with X Marketing Consulting LLC to do several non-promotional presentations for patients, families, advocates, and mental health professionals on behalf of Jannsen pharmaceuticals within the past three years.

Acknowledgments I would like to gratefully acknowledge Beth Verchinski, BS for her assistance in obtaining and archiving the MRI data. This work was supported by the Clinical Brain Disorders Branch, National Institutes of Mental Health, and by a Short-Term Research Training (STRT) grant from the National Institute of Diabetes, Digestive and Kidney diseases (NIDDK) for Stacy Greeter and Khary Carew.

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