Abstract

Accumulated evidence suggests that schizophrenia is associated with subtle gray matter deficits throughout the cerebral cortex and regional cortical thinning. Although findings are not entirely consistent, healthy relatives of schizophrenia patients also show abnormalities in cortical gray matter volume, suggesting that this may be one aspect of an unexpressed genetic liability to the disorder. Cortical thickness and surface area are additional indicators of cortical cytoarchitectural integrity. To investigate the nature of cortical abnormalities in the healthy relatives of patients, this study used magnetic resonance imaging to evaluate gray matter volume, surface area, and thickness of 13 regions using an automated parcellation methodology. Compared with controls (n = 22), relatives (n = 19) had decreased volume and surface area in the right cingulate gyrus, a bilateral decrease in cingulate thickness, and decreased surface area in the superior temporal lobe. In addition, relatives had a subtle increase in gray matter volume and surface area in the left hemisphere, bilaterally in the parahippocampal gyri, and in the left middle temporal lobe. The results of this study suggest that the cortical regions most affected by the unexpressed genetic liability to schizophrenia may be the cingulate and temporal regions—regions associated with higher level cognitive, affective, and memory functions.

Introduction

Schizophrenia is a complex, heritable disorder associated with diffuse gray matter abnormalities throughout the cerebral cortex. The current study was designed to identify cortical structures affected by schizophrenia risk genes or the interaction between the risk genes and environmental factors in the healthy relatives of patients. Affected cortical structures may be candidate endophenotypes and help bridge the gap between the genes that cause schizophrenia and the disease manifestation (Gottesman and Gould 2003). Traditionally, regions of interest are manually defined in a labor-intensive, time-consuming process, which limits the number of regions measurable. Manual definitions also preclude quantification of cortical thickness as this requires defining the gray–white and pial boundaries. Recent advances in magnetic resonance imaging (MRI) analysis methods now allow for whole-brain morphometric analyses and cortical thickness measurements (e.g., Kuperberg and others 2003; Narr, Bilder, and others 2005). We examined gray matter volume, surface area, and thickness across cortical regions using a validated, automated region of interest approach (Kuperberg and others 2003; Fischl and others 2004). By examining both global and regional gray matter volume, surface area, and thickness in the healthy relatives of schizophrenia patients, the current study was able to investigate a wider range of potential abnormalities than previously possible.

Schizophrenia affects a number of brain structures. In a review of 193 MRI studies in schizophrenia, Shenton and others (2001) concluded the cortical volumes reliably found to be decreased in schizophrenia included the parahippocampal gyrus and superior temporal gyrus. There was also moderate evidence for decreased prefrontal gray matter, orbitofrontal cortex, and inferior parietal lobule volume. Many of the studies included in the review only evaluated lobes in their entirety, and this was especially the case for the frontal lobe. Assessing more specific regions of the cortex could be informative as larger lobes are heterogeneous in terms of development and function. Thus, assessing global volumes may mask more subtle abnormalities. For this reason, recent MRI studies of schizophrenia have looked at finer divisions of the cortex. This approach has additionally demonstrated volumetric abnormalities in the dorsolateral prefrontal cortex or middle frontal gyrus and the cingulate in schizophrenia (Goldstein and others 1999; Gur and others 2000; Zhou and others 2005).

Cortical volumes are related to surface area and thickness; therefore, both measurements contribute to understanding the volumetric differences found in schizophrenia. Assessing thickness and surface area may be useful as they provide additional clues to understanding the neurophysiology of schizophrenia, including the size, density, orientation, and location of neurons (Harrison 1999). Diffuse cortical abnormalities may be an index of fewer dendrites, dendritic spines, synapses, or changes in myelination (Harrison 1999; Benes 2003). Therefore, cortical thickness and surface area may be additional indicators of the possible genetic liability to schizophrenia.

To our knowledge, 5 MRI studies have reported cortical thickness findings in schizophrenia. Significant thinning has been observed overall in the brain and specifically in sulci in frontal, temporal, and parietal regions and in gyri in the temporal lobe in childhood and adolescent-onset schizophrenia (White and others 2003). In first-episode schizophrenia, cortical thinning has been noted in the dorsolateral prefrontal cortex, lateral temporal, and parietal areas (Narr, Bilder, and others 2005) and frontopolar, cingulate, and occipital regions (Narr, Toga, and others 2005). Findings have not always been consistent, with Wiegand and others (2004) reporting no significant differences in the prefrontal cortical thickness in first-episode schizophrenia. In a study methodologically similar to the current paper, chronic schizophrenia patients were found to have significant cortical thinning in prefrontal regions (specifically inferior frontal, orbitofrontal, and medial frontal) and temporal regions (specifically inferior temporal, medial temporal, and occipitotemporal) (Kuperberg and others 2003).

Noting that structural abnormalities exist in schizophrenia is only a piece of the puzzle. It is yet to be determined whether these abnormalities are due to the effects of illness, medication, environment, or a genetic predisposition to developing the disorder. Family studies can be informative in this regard. Such studies show that relatives of schizophrenia patients have reduced global gray matter and reductions in frontal, temporal, and parietal gray matter (Cannon and others 1998; Gogtay and others 2003).

Studies have also examined more specific regions of the cortex, and healthy relatives have been shown to have less polar and dorsolateral prefrontal gray matter (Cannon and others 2002), more superior frontal and less fusiform gyrus gray matter (Marcelis and others 2003), and smaller right anterior and larger posterior parahippocampal volumes (Seidman and others 2003). The last finding is of particular interest in that the hippocampal formation, which lies within the parahippocampal gyrus, is the most consistently reduced structure in the healthy relatives of patients (for a review, see Seidman and others 2003). McDonald and others (2004) demonstrated that genetic risk for schizophrenia, but not bipolar disorder, was associated with smaller gray matter volumes bilaterally in the orbital, prefrontal, and premotor regions of the frontal lobe and in the lateral temporal lobe. However, other studies have failed to find differences in the healthy relatives of patients in global gray matter (Seidman and others 1997; Marcelis and others 2003; Schneider-Axmann and others 2005) or in the parahippocampal region (Staal and others 2000; Baare and others 2001). McIntosh and others (2004) using whole-brain voxel-based morphometry found no differences in gray matter between healthy relatives and controls. Studies investigating the genetic liability to schizophrenia suggest widespread gray matter abnormalities, though the regional specificity of these findings has been inconsistent.

To investigate the potential relationship between the unexpressed genetic liability to schizophrenia and brain morphology, we measured cortical gray matter volumes, surface areas, and thicknesses using MRI for the following regions: global cortex, superior frontal, middle frontal, inferior frontal (including the orbital prefrontal region), cingulate, superior temporal, middle temporal, inferior temporal, parahippocampal, inferior parietal, superior parietal, superior occipital, middle occipital, and inferior occipital gyri.

We hypothesized that there would be a smaller quantity of cortical global gray matter volume and consequently less surface area and/or a thinner cortex in the healthy relatives of schizophrenia patients compared with controls. Based on the most consistent regional findings in the literature, we hypothesized that specific abnormalities would be found in the middle frontal gyrus, inferior frontal gyrus, cingulate gyrus, superior temporal gyrus, parahippocampal gyrus, and inferior parietal lobule.

Materials and Methods

Subjects

Twenty-two first-degree healthy relatives of schizophrenia patients and 23 controls participated. All subjects were right handed as assessed by an abridged version of the Edinburgh Handedness Inventory (Oldfield 1971). First-degree relatives were recruited by first identifying probands receiving treatment through Western Psychiatric Institute and Clinic in Pittsburgh, Pennsylvania, and then recruiting their first-degree relatives. All probands had a Diagnostic and Statistical Manual of Mental Disorders, 4th edition, medical record diagnosis of schizophrenia or schizoaffective disorder, which was confirmed using the DSM-IV Structured Clinical Interview. Controls were recruited through newspaper advertisements and matched to the distributions of healthy relatives with regard to age, gender, ethnicity, and parental education. Subjects were excluded if they suffered from head trauma or seizures or had a diagnosis of substance abuse or dependence within the last 6 months. Controls were further excluded if they had a family history of psychosis. All assessments were conducted by trained research assistants under the supervision of 2 research clinicians (A.W.M. and C.S.C.). Healthy relatives and controls were screened for psychiatric symptoms or disorders using the Structured Clinical Interview, Non-Patient Version for DSM-IV. Healthy relatives were additionally assessed with Structured Interview for DSM-III Personality Disorders—Revised for Cluster A personality disorders (Pfohl and others 1982). All subjects participated voluntarily and provided written informed consent. The University of Pittsburgh Institution Review Board approved the protocol.

Data for the final sample are presented in Table 1. The imaging data from 3 relatives and 1 control had to be excluded due to poor quality, and these subjects were excluded from all analyses. As reported in Table 1, there were no significant differences between groups on any demographic variables. Although relatives tended to have slightly less educational attainment (14.2 vs. 15.7 years), parents were matched for education. One control met criteria for DSM-IV diagnosis in the past month (intermittent explosive disorder). Three healthy relatives and 3 controls met criteria for lifetime diagnosis for major depressive disorder. One healthy relative met criteria for schizotypal personality disorder; therefore, all analyses were conducted with and without this participant. None of the statistics changed significantly when the one subject with schizotypy was removed. There was no significant difference between groups for total intracranial volume.

Table 1

Relevant demographic information

Characteristic Healthy relatives (n = 19) Controls (n = 22) Test statistica P value 
Age (years) 34.2 (11) 34.1 (8.4) 0.02 0.98 
Male sex, n (%) 8 (42) 10 (46) 0.05 0.83 
Minority, n (%) 9 (47) 6 (27) 1.78 0.18 
Education (years) 14.2 (3.1) 15.7 (1.9) −1.79 0.08 
Father's education (years) 13.6 (2.6) 13 (2.8) 0.68 0.50 
Mother's education (years) 14.1 (2.7) 13.1 (2.2) 1.24 0.22 
Relationship to patient     
    Parent:sibling:offspring 1:13:5 — — — 
Intracranial volume (mm31 358 678 (150 938) 1 409 728 (131 135) 1.37 0.25 
Characteristic Healthy relatives (n = 19) Controls (n = 22) Test statistica P value 
Age (years) 34.2 (11) 34.1 (8.4) 0.02 0.98 
Male sex, n (%) 8 (42) 10 (46) 0.05 0.83 
Minority, n (%) 9 (47) 6 (27) 1.78 0.18 
Education (years) 14.2 (3.1) 15.7 (1.9) −1.79 0.08 
Father's education (years) 13.6 (2.6) 13 (2.8) 0.68 0.50 
Mother's education (years) 14.1 (2.7) 13.1 (2.2) 1.24 0.22 
Relationship to patient     
    Parent:sibling:offspring 1:13:5 — — — 
Intracranial volume (mm31 358 678 (150 938) 1 409 728 (131 135) 1.37 0.25 

Note: Values are mean and standard deviation unless otherwise noted. Minority = African American.

a

t-Test, except for sex and ethnicity (χ2-test) and intracranial volume (F-test).

MRI Acquisition and Preprocessing

SPGR scans were acquired in the axial plane using a GE Signa 3T magnetic resonance scanner (0.9375 × 0.9375 × 1.5–mm voxel size, 124 slices). Whole-brain volumes were segmented from skull and meninges using an automated, consensus-based stripping algorithm (Minneapolis Consensus Stripping—McStrip), and defects were manually edited where necessary (Boesen and others 2004; Rehm and others 2004). To improve the accuracy of tissue classification and cortical surface extraction, whole-brain volumes were intensity corrected using nonparametric nonuniform intensity normalization (N3, Sled and others 1998). An automatic algorithm labeled the left and right hemispheres, cerebellum, and brain stem, and defects were manually edited (Rehm and others 2005). These stripped, intensity-corrected brain images and grossly subdivided volumes were used in the subsequent steps.

Surface Extraction

Surface extraction and cortical parcellation were conducted using FreeSurfer v. 1.3 (http://surfer.nmr.mgh.harvard.edu/) (see Fig. 1). Briefly, the stripped, intensity-corrected, subdivided volume was segmented to classify white matter and to approximate the gray–white matter boundary for each cortical hemisphere, from which a topologically correct gray–white matter boundary surface triangulation was generated (Dale and others 1999; Fischl and others 2001). Subsequently, a pial surface was generated using a deformable surface algorithm (Fischl and Dale 2000). All surfaces were visually inspected, and defects leading to major topological errors were manually corrected as recommended by the software guidelines. The gray–white boundary surface was inflated, and individual differences in curvature were normalized. Each subject's inflated brain was morphed into a sphere and registered to an average spherical surface that optimally aligned sulci and gyri across subjects (Fischl, Sereno, and Dale 1999; Fischl, Sereno, and others 1999). Surfaces were extracted for 41 subjects (22 controls and 19 relatives) using standard parameters.

Figure 1

Depiction of the cortical reconstruction process and region of interest definition for one control brain (left) and healthy relative brain (right) for the left hemisphere. (A) Depicts a sagittal section showing the gray matter–white matter boundary (yellow line) and the pial surface (red line). (B) Depicts the lateral view of inflated gray–white boundary surfaces created from the segmentation of the gray and white matter shown in (A). (C) Depicts the average brain (this brain is provided with FreeSurfer) to which each of individual brain was morphed and a selection of regions of interest labeled on this average brain (middle frontal gyrus and middle temporal gyrus). (D) Depicts the transformation in which the regions of interest from the average brain were transformed back onto the cortical surface of the individual brains.

Figure 1

Depiction of the cortical reconstruction process and region of interest definition for one control brain (left) and healthy relative brain (right) for the left hemisphere. (A) Depicts a sagittal section showing the gray matter–white matter boundary (yellow line) and the pial surface (red line). (B) Depicts the lateral view of inflated gray–white boundary surfaces created from the segmentation of the gray and white matter shown in (A). (C) Depicts the average brain (this brain is provided with FreeSurfer) to which each of individual brain was morphed and a selection of regions of interest labeled on this average brain (middle frontal gyrus and middle temporal gyrus). (D) Depicts the transformation in which the regions of interest from the average brain were transformed back onto the cortical surface of the individual brains.

Parcellation

Parcellation for each individual's left and right hemisphere surface was transmitted from the parcellation of the reference spherical surface to which they were aligned utilizing not only the location on the cortex but also local geometry and the local constellation of regions of interest. Each parcellated region was mapped back onto each individual subject's inflated surface by inverting the algorithm that morphed each subject's inflated surface to the average spherical surface representation (Kuperberg and others 2003; Fischl and others 2004). Eighty-five parcellation units were provided by FreeSurfer, based on the conventions of Duvernoy (1991). We analyzed the regions outlined a priori for this study. The cingulate region of interest was defined as the main part of the cingulate excluding the isthmus region.

This methodology has been extensively validated and utilized with schizophrenia patients and controls (Kuperberg and others 2003; Fischl and others 2004). For the current sample, a trained rater manually traced regions of interest on 10 white–gray boundary-inflated surfaces for both hemispheres, with reference to the original SPGR volume. We calculated the correspondence between the hand drawn and the automated region of interest by calculating an index of similarity. The index of similarity was defined as the ratio of twice the common area between the 2 methods to the sum of the individual areas (Zijdenbos and others 1994). Thus, it is a spatial analogue of the kappa statistic that corrects for chance agreement and is sensitive to differences in both size and location. We calculated the similarity index for each pair of measurements and then calculated the mean for the region of interest. A value above 0.7 is considered excellent correspondence (Zijdenbos and others 1994). All 3 regions of interest had excellent correspondence (middle frontal = 0.79, cingulate = 0.80, and middle temporal = 0.85).

Quantification

As FreeSurfer maintains a one-to-one relationship between triangles in the gray–white matter and pial surfaces, we were able to construct a closed mesh joining a pair of linked triangles (forming a “keystone”) and compute the enclosed volume (Eberly and others 1991). Individual keystone volumes were aggregated to compute cortical gray matter volume for each region of interest. Surface areas were calculated for both the white–gray boundary and pial surface by using the formula of Heron (Gellert and others 1975). The average of the 2 surface areas at each triangle was computed and was used in analyses to simulate an intermediate cortical surface. Thicknesses were calculated using FreeSurfer software; they were computed for each vertex in the triangulated surfaces by finding the point on the white matter surface that was closest to a given point of the pial surface (and vice versa), and the average was taken between these two values. This methodology for calculating thicknesses has been validated on postmortem brains (Rosas and others 2002).

Analyses

All dependent measures were assessed using the Kolmogorov–Smirnov test to evaluate normalcy. No measurement deviated significantly from normality, suggesting that no transformations were required for subsequent tests.

Global Analyses

Global cortical gray matter volume, average cortical thickness, and total surface area were assessed using a mixed-model analysis of covariance (ANCOVA), with hemisphere (left, right) entered as a within-subject effect and group (control, healthy relative) entered as a between-subject effect. Age, gender, and intracranial volume were used as covariates for these models. Greenhouse–Geisser correction is reported for the mixed-model ANCOVAs. ANCOVAs were set to a significance threshold of P = 0.05.

Regional Analyses

Multivariate analyses of covariance (MANCOVAs) were used to assess the effect of group on the related subregions for a lobe and to control for multiple comparisons. Four subregional MANCOVAs tests were employed to analyze the volumes (left and right hemisphere were entered simultaneously), one for each of the lobes (frontal, temporal, parietal, and occipital); 4 MANCOVAs were used to assess the surface areas; and an additional 4 MANCOVAs were used to assess the associated thicknesses. Pillai's Trace test statistic is reported for the MANCOVA. To reduce the likelihood of a false positive, the MANCOVAs were set to a significance threshold of P = 0.005. Significant findings on a MANCOVA were followed up with individual mixed-model ANCOVAs of all the dependent measures with hemisphere entered as a within-subject effect and group entered as a between-subject effect. Greenhouse–Geisser correction is reported for mixed-model ANCOVAs. All mixed-model ANCOVAs were set to a significance threshold of P = 0.05. Age, gender, and intracranial volume were entered as covariates when analyzing regional volumes; age, gender, and total cortical surface area were entered as covariates when analyzing regional surface areas; and age, gender, and average cortical thickness were entered as covariates when analyzing regional thicknesses for both the MANCOVAs and ANCOVAs.

Planned Comparisons

Planned comparisons were assessed with mixed-model ANCOVAs using the model mentioned above for the following regions: middle frontal gyrus, inferior frontal gyrus, cingulate gyrus, superior temporal gyrus, parahippocampal gyrus, and inferior parietal lobule. Greenhouse–Geisser correction is reported for mixed-model ANCOVAs.

Partial eta-squared effect sizes are presented: 0.01 is considered a small effect, 0.06 is considered a medium effect, and 0.14 is considered a large effect (Stevens 2002).

Results

Global Cortical Measures

A mixed-model ANCOVA revealed a significant hemisphere by group interaction for total cortical gray volume (F1,36 = 6.42, P = 0.02, partial eta squared = 0.15), with relatives having 2.2% more cortical gray matter in their left hemisphere than controls. There were also significant effects of age (F1,36 = 14.44, P = 0.001), gender (F1,36 = 6.54, P = 0.02), and intracranial volume (F1,36 = 157.15, P < 0.001) covariates. There was a nearly significant hemisphere by group interaction for total cortical surface area (F1,36 = 3.94, P = 0.055, partial eta squared = 0.10), with relatives having a greater surface area in their left hemisphere. There was no significant main effect of group or hemisphere by group interaction for average cortical thickness (P values > 0.05) (see Table 2 for raw morphometry data).

Table 2

Raw values for gray matter volumes, surface areas, and thicknesses

Region Volume (mm3Surface area (mm2Thickness (mm) 
 Healthy relatives Controls Healthy relatives Controls Healthy relatives Controls 
Total cortex       
    Left hemisphere 220 950 (32 442) 225 478 (26 143) 93 298 (9513) 95 502 (8533) 2.37 (0.15) 2.36 (0.10) 
    Right hemisphere 221 591 (31 748) 228 935 (27 342) 93 821 (9315) 97 138 (9387) 2.36 (0.16) 2.37 (0.12) 
Frontal regions       
    Left inferior 6691 (1167) 6858 (1217) 2409 (293) 2481 (306) 2.75 (0.24) 2.73 (0.23) 
    Right inferior 6009 (1228) 5999 (993) 2177 (298) 2247 (337) 2.74 (0.31) 2.66 (0.22) 
    Left middle 7807 (1640) 7997 (1780) 2798 (451) 2885 (562) 2.75 (0.24) 2.71 (0.16) 
    Right middle 8231 (1962) 8580 (1974) 3119 (644) 3358 (690) 2.60 (0.23) 2.51 (0.19) 
    Left superior 16 151 (2677) 16 868 (2792) 5591 (724) 5878 (942) 2.83 (0.24) 2.80 (0.15) 
    Right superior 15 480 (2780) 16 646 (2451) 5578 (669) 6034 (830) 2.72 (0.28) 2.72 (0.16) 
Cingulate regions       
    Left 6093 (1008) 6318 (949) 1881 (276) 1890 (246) 3.17 (0.27) 3.25 (0.12) 
    Right 5733 (1099) 6658 (1031) 1779 (296) 1995 (303) 3.17 (0.23) 3.25 (0.19) 
Temporal regions       
    Left inferior 5075 (885) 4990 (867) 2278 (313) 2225 (330) 2.26 (0.17) 2.28 (0.22) 
    Right inferior 4645 (978) 5047 (1116) 2023 (326) 2192 (420) 2.30 (0.23) 2.31 (0.15) 
    Left middle 6666 (1481) 6045 (1398) 2379 (397) 2231 (425) 2.75 (0.29) 2.67 (0.27) 
    Right middle 5723 (1179) 5688 (1231) 2242 (287) 2312 (403) 2.52 (0.31) 2.45 (0.24) 
    Left superior 10 306 (1730) 10 879 (2102) 3764 (445) 4144 (672) 2.73 (0.27) 2.61 (0.27) 
    Right superior 9272 (2294) 9850 (1734) 3682 (569) 3992 (603) 2.46 (0.29) 2.44 (0.25) 
    Left hippocampal 4355 (1019) 4128 (688) 1782 (295) 1717 (247) 2.47 (0.18) 2.42 (0.18) 
    Right hippocampal 4310 (1222) 3879 (766) 1712 (427) 1598 (326) 2.49 (0.16) 2.47 (0.17) 
Parietal regions       
    Left inferior 10 685 (2172) 11 177 (1903) 4022 (537) 4167 (508) 2.61 (0.29) 2.67 (0.20) 
    Right inferior 11 154 (2023) 11 484 (2104) 4299 (517) 4441 (721) 2.57 (0.26) 2.61 (0.26) 
    Left superior 4661 (1106) 4891 (898) 2091 (362) 2222 (320) 2.22 (0.25) 2.18 (0.15) 
    Right superior 4198 (971) 4356 (767) 1860 (342) 1987 (293) 2.23 (0.21) 2.18 (0.19) 
Occipital regions       
    Left inferior 1527 (338) 1467 (437) 754 (123) 735 (155) 2.06 (0.20) 2.03 (0.22) 
    Right inferior 1779 (477) 1828 (381) 863 (203) 888 (182) 2.12 (0.25) 2.12 (0.25) 
    Left middle 3962 (1115) 3943 (887) 1663 (359) 1737 (328) 2.39 (0.23) 2.28 (0.18) 
    Right middle 3547 (739) 3578 (734) 1603 (322) 1605 (248) 2.23 (0.19) 2.24 (0.25) 
    Left superior 1928 (443) 1874 (396) 1047 (179) 1058 (179) 1.90 (0.20) 1.84 (0.13) 
    Right superior 1987 (421) 2061 (504) 1027 (158) 1080 (222) 1.99 (0.23) 1.97 (0.18) 
Region Volume (mm3Surface area (mm2Thickness (mm) 
 Healthy relatives Controls Healthy relatives Controls Healthy relatives Controls 
Total cortex       
    Left hemisphere 220 950 (32 442) 225 478 (26 143) 93 298 (9513) 95 502 (8533) 2.37 (0.15) 2.36 (0.10) 
    Right hemisphere 221 591 (31 748) 228 935 (27 342) 93 821 (9315) 97 138 (9387) 2.36 (0.16) 2.37 (0.12) 
Frontal regions       
    Left inferior 6691 (1167) 6858 (1217) 2409 (293) 2481 (306) 2.75 (0.24) 2.73 (0.23) 
    Right inferior 6009 (1228) 5999 (993) 2177 (298) 2247 (337) 2.74 (0.31) 2.66 (0.22) 
    Left middle 7807 (1640) 7997 (1780) 2798 (451) 2885 (562) 2.75 (0.24) 2.71 (0.16) 
    Right middle 8231 (1962) 8580 (1974) 3119 (644) 3358 (690) 2.60 (0.23) 2.51 (0.19) 
    Left superior 16 151 (2677) 16 868 (2792) 5591 (724) 5878 (942) 2.83 (0.24) 2.80 (0.15) 
    Right superior 15 480 (2780) 16 646 (2451) 5578 (669) 6034 (830) 2.72 (0.28) 2.72 (0.16) 
Cingulate regions       
    Left 6093 (1008) 6318 (949) 1881 (276) 1890 (246) 3.17 (0.27) 3.25 (0.12) 
    Right 5733 (1099) 6658 (1031) 1779 (296) 1995 (303) 3.17 (0.23) 3.25 (0.19) 
Temporal regions       
    Left inferior 5075 (885) 4990 (867) 2278 (313) 2225 (330) 2.26 (0.17) 2.28 (0.22) 
    Right inferior 4645 (978) 5047 (1116) 2023 (326) 2192 (420) 2.30 (0.23) 2.31 (0.15) 
    Left middle 6666 (1481) 6045 (1398) 2379 (397) 2231 (425) 2.75 (0.29) 2.67 (0.27) 
    Right middle 5723 (1179) 5688 (1231) 2242 (287) 2312 (403) 2.52 (0.31) 2.45 (0.24) 
    Left superior 10 306 (1730) 10 879 (2102) 3764 (445) 4144 (672) 2.73 (0.27) 2.61 (0.27) 
    Right superior 9272 (2294) 9850 (1734) 3682 (569) 3992 (603) 2.46 (0.29) 2.44 (0.25) 
    Left hippocampal 4355 (1019) 4128 (688) 1782 (295) 1717 (247) 2.47 (0.18) 2.42 (0.18) 
    Right hippocampal 4310 (1222) 3879 (766) 1712 (427) 1598 (326) 2.49 (0.16) 2.47 (0.17) 
Parietal regions       
    Left inferior 10 685 (2172) 11 177 (1903) 4022 (537) 4167 (508) 2.61 (0.29) 2.67 (0.20) 
    Right inferior 11 154 (2023) 11 484 (2104) 4299 (517) 4441 (721) 2.57 (0.26) 2.61 (0.26) 
    Left superior 4661 (1106) 4891 (898) 2091 (362) 2222 (320) 2.22 (0.25) 2.18 (0.15) 
    Right superior 4198 (971) 4356 (767) 1860 (342) 1987 (293) 2.23 (0.21) 2.18 (0.19) 
Occipital regions       
    Left inferior 1527 (338) 1467 (437) 754 (123) 735 (155) 2.06 (0.20) 2.03 (0.22) 
    Right inferior 1779 (477) 1828 (381) 863 (203) 888 (182) 2.12 (0.25) 2.12 (0.25) 
    Left middle 3962 (1115) 3943 (887) 1663 (359) 1737 (328) 2.39 (0.23) 2.28 (0.18) 
    Right middle 3547 (739) 3578 (734) 1603 (322) 1605 (248) 2.23 (0.19) 2.24 (0.25) 
    Left superior 1928 (443) 1874 (396) 1047 (179) 1058 (179) 1.90 (0.20) 1.84 (0.13) 
    Right superior 1987 (421) 2061 (504) 1027 (158) 1080 (222) 1.99 (0.23) 1.97 (0.18) 

Note: All values are raw mean and standard deviation.

Regional Cortical Measures

Frontal Lobe Measures

No significant effect of group was found in the MANCOVA of the frontal gray matter volumes (F6,31 = 0.84, P = 0.55), surface areas (F6,31 = 1.49, P = 0.21), or thicknesses (F6,31 = 1.07, P = 0.40). Planned comparisons of middle and inferior frontal gray matter volume, surface area, or thickness revealed no significant main effect of group or hemisphere by group interaction (P values > 0.05).

Cingulate Measures

A significant hemisphere by group interaction was found in cingulate gray matter volume (F1,36 = 5.09, P = 0.03, partial eta squared = 0.12), with healthy relatives having 11.4% less right cingulate gray matter volume. A significant hemisphere by group interaction was found in cingulate surface area (F1,36 = 4.63, P = 0.04, partial eta squared = 0.11), with healthy relatives having 8.3% smaller right cingulate surface area. There was also a significant effect of total cortical surface area covariate (F1,36 = 49.60, P < 0.001). A significant effect of group was found in cingulate thickness (F1,36 = 4.54, P = 0.04, partial eta squared = 0.11), with healthy relatives having 2.5% thinner cingulate bilaterally. There was also a significant effect of the average cortical thickness covariate (F1,36 = 43.12, P < 0.001) (see Fig. 2).

Figure 2

Mean ± standard error of the mean (SEM) z-scores for surface areas and thicknesses that demonstrated a significant difference between controls (n = 22, mean = 0 ± SEM) and healthy relatives of schizophrenia patients (n = 19; left and right hemisphere, mean ± SEM). The data presented have been corrected for age, gender, and surface area or average cortical thickness. In the figure, yellow represents regions that show a greater left hemisphere difference, and blue represents regions that show a greater right hemisphere difference. Volumetric differences were found in the cingulate, middle temporal, and parahippocampal gyri. Statistics are presented in the text.

Figure 2

Mean ± standard error of the mean (SEM) z-scores for surface areas and thicknesses that demonstrated a significant difference between controls (n = 22, mean = 0 ± SEM) and healthy relatives of schizophrenia patients (n = 19; left and right hemisphere, mean ± SEM). The data presented have been corrected for age, gender, and surface area or average cortical thickness. In the figure, yellow represents regions that show a greater left hemisphere difference, and blue represents regions that show a greater right hemisphere difference. Volumetric differences were found in the cingulate, middle temporal, and parahippocampal gyri. Statistics are presented in the text.

Temporal Lobe Measures

A significant effect of group was observed in the MANCOVA of the temporal gray matter volumes (F8,29 = 4.77, P = 0.001) and of the intracranial volume covariate (F8,29 = 20.64, P < 0.001). Therefore, follow-up mixed-model ANCOVAs were conducted on all the subregions. A significant effect of group was found in the parahippocampal gyri (F1,36 = 7.28, P = 0.01, partial eta squared = 0.17), with healthy relatives of schizophrenia patients having 12.5% larger volumes bilaterally. There was also a significant effect of the intracranial volume covariate (F1,36 = 28.98, P < 0.001). A significant effect of group (F1,36 = 6.41, P = 0.02, partial eta squared = 0.15) and a significant hemisphere by group interaction (F1,36 = 6.78, P = 0.01, partial eta squared = 0.16) were found in the middle temporal gyri with healthy relatives of schizophrenia patients having 10.6% larger volumes bilaterally, with the left hemisphere affected more (15.9% larger). There were also significant effects of age (F1,36 = 8.32, P = 0.007) and intracranial volume (F1,36 = 43.13, P < 0.001) covariates.

A significant effect of group was observed in the MANCOVA of the temporal surface areas (F8,29 = 3.66, P = 0.005) and total cortical surface area covariate (F8,29 = 46.74, P < 0.001). Therefore, follow-up mixed-model ANCOVAs were conducted on all the subregions. A significant effect of group was found in the parahippocampal gyri (F1,36 = 5.22, P = 0.03, partial eta squared = 0.13), with healthy relatives of schizophrenia patients having 8.5% greater surface area bilaterally. There was a significant effect of the total cortical surface area covariate (F1,36 = 31.95, P < 0.001). A significant hemisphere by group interaction was found in the middle temporal gyri (F1,36 = 7.49, P = 0.01, partial eta squared = 0.17), with healthy relatives of schizophrenia patients having 10.7% greater surface area in their left hemisphere. There was also a significant effect of the total cortical surface area covariate (F1,36 = 55.80, P < 0.001). A significant effect of group was found in the superior temporal surface area (F1,36 = 4.99, P = 0.03, partial eta squared = 0.12), with healthy relatives of schizophrenia patients having 6% less surface area bilaterally. There was also a significant effect of the total cortical surface area covariate (F1,36 = 45.52, P < 0.001). No significant differences were found in the other temporal subregional surface areas (P values > 0.05).

No significant effect of group was found in the MANCOVA of the temporal subregional thicknesses (F8,29 = 0.99, P = 0.46). Planned comparisons of parahippocampal, superior, and middle temporal thickness revealed no significant main effect of group or hemisphere by group interaction (P values > 0.05).

Parietal Lobe Measures

No significant effect of group was found in the MANCOVA of the parietal gray volumes (F4,33 = 0.05, P = 0.99), surface areas (F4,33 = 0.42, P = 0.80), or associated thicknesses (F4,33 = 0.93, P = 0.46). Planned comparisons of the inferior parietal gray matter volume, surface area, and thickness revealed no significant main effect of group or hemisphere by group interaction (P values > 0.05).

Occipital Lobe Measures

No significant effect of group was found in the MANCOVA of the occipital gray matter volumes (F6,31 = 0.83, P = 0.56), surface areas (F6,31 = 0.48, P = 0.82), or associated thicknesses (F6,31 = 0.82, P = 0.57).

Discussion

The current study evaluated whether structural abnormalities were associated with a possible genetic vulnerability to schizophrenia by measuring 13 brain regions bilaterally using a validated, automated parcellation methodology in the healthy relatives of schizophrenia patients. These measurements yielded not only cortical brain volumes but also surface area and thickness. The principal findings were that healthy relatives of patients demonstrated decreased gray matter volume, surface area, and thickness in the cingulate and decreased surface area in the superior temporal lobe compared with controls. This study also found that healthy relatives of patients demonstrated increased gray matter volume and surface area in the cortex and more specifically in the parahippocampal and middle temporal lobe compared with controls. These data were controlled for age, gender, and brain size. The findings remained unchanged when control for ethnicity was included.

Regions Decreased in Healthy Relatives Compared With Controls

The right cingulate gyrus was shown to have less gray matter volume and surface area in the healthy relatives of schizophrenia patients compared with controls. The cingulate gyrus is part of the limbic system and performs functions such as affective regulation, motor control, and performance monitoring (Vogt and others 1992). Studies have observed decreased volume in the cingulate in schizophrenia (Goldstein and others 1999; Job and others 2002; Kubicki and others 2002; Zhou and others 2005). Differences have also been noted in cingulate morphology in those at high risk for psychosis (Job and others 2003; Yucel and others 2003) and in drug-naive schizophrenia patients (Jayakumar and others 2005). In contrast, Marcelis and others (2003) using computational morphometry described a significant decrease in the cingulate gyrus in schizophrenia compared with controls and healthy relatives but not in healthy relatives compared with controls. Differences in published results are likely due to the nature of the relatives population studied, part of the cingulate studied (i.e., anterior, posterior, paracingulate, or whole), and methodology (computational morphology vs. region of interest).

We also found a bilateral decrease in cingulate thickness. Recent MRI studies of cortical thickness have noted the cingulate to be thinner in first-episode (Narr, Toga, and others 2005) and chronic schizophrenia (Kuperberg and others 2003). Postmortem work in the anterior cingulate of schizophrenia patients has demonstrated glial cell loss (Stark and others 2004), fewer pyramidal neurons in layer IV (Benes and others 2001), smaller neurons separated with wider interspaces in layer II (Benes and Bird 1987; Benes and others 1991), lower neuronal density in layer V (Benes and others 1986), and a greater number of axons in layer II and sublaminae IIIA (Benes and others 1987). Thus, these findings generally suggest that reduced cingulate volume, surface area, and thickness are associated with schizophrenia. Postmortem and imaging work argue that cingulate abnormalities are one of the key aspects of the disease. The current finding adds to this literature by showing that a thinner cingulate is possibly related to the genetic liability to schizophrenia.

Consistent with the literature, we found reduced surface area in the superior temporal lobe in the healthy relatives of schizophrenia patients compared with controls. The superior temporal gyrus contains Heschl's gyrus and Wernicke's area and is thought to be associated with disordered thinking, auditory hallucinations, and language abnormalities in schizophrenia (Shenton and others 2001). In their review of MRI studies, Shenton and others (2001) concluded that all studies that investigated superior temporal lobe gray matter found volume reductions. In addition, superior temporal lobe volume has also been shown to be reduced in the young offspring of patients (Rajarethinam and others 2004).

Reduced gray matter volume, surface area, and thickness in the healthy relatives of patients may represent brain regions influenced by the familial, environmental, and probably the genetic, underpinning of schizophrenia. Although these findings could be explained by shared environmental effects, there is little evidence to suggest that shared environmental effects can account for the predisposition to schizophrenia (Pogue-Geile and Gottesman 1999).

Regions Increased in Healthy Relatives Compared With Controls

Not all the differences found in patients' relatives were decreases. Contrary to our expectations, healthy relatives of patients were found to have a subtle increase in cortical gray matter in their left hemisphere (approximately 2%). This increase in cortical gray matter was due to increased surface area rather than increased thickness or the combination of the two. Previous studies in the healthy relatives of patients have described decreased global gray matter (Cannon and others 1998; Gogtay and others 2003) or no significant difference between groups in global gray matter (Seidman and others 1997; Marcelis and others 2003; Schneider-Axmann and others 2005). However, Seidman and others (1997) in a small sample found an increased cerebral volume in the healthy relatives of schizophrenia patients compared with the controls.

We found increased gray matter volume in the parahippocampal gyrus. The parahippocampal gyrus is crucial to medial temporal lobe memory functions, performing functions such as encoding, retrieving context-appropriate and emotionally salient memories, and probabilistic reasoning (Schacter and Wagner 1999; Burgess and others 2001; Parsons and Osherson 2001). Thus, this region plays a key role in the communication between the hippocampus and cortex. Most postmortem studies of the parahippocampal gyrus have demonstrated decreased parahippocampal area, volume, and cortical thickness in patients (for a review, see Arnold 2000). Shenton and others (2001) in her review concluded that decreased parahippocampal volume is one of the more reliable findings in schizophrenia. The parahippocampal gyrus volume has also been shown to be decreased in drug-naive (Jayakumar and others 2005) and first-episode schizophrenia (Job and others 2002), suggesting the abnormalities were less likely to be affected by illness processes or medication effects; however, these results have not been entirely consistent (Prasad and others 2004).

In contrast to findings in schizophrenia, healthy relatives from families with only one member with schizophrenia were described as having larger posterior parahippocampal volumes (Seidman and others 2003). In the same study, healthy relatives from families with multiple members with schizophrenia were shown to have smaller right anterior parahippocampal gyrus, suggesting a complex relationship between genetic loading and phenotype (however, for negative findings, see also Staal and others 2000). In addition, Harris and others (2002) noted that parents of probands had increased hippocampal volumes compared with controls (however, it is important to note that most family studies of the hippocampus have found reduced volumes; for a review, see Seidman and others 2003). A study of monozygotic and dizygotic twins discordant for schizophrenia found that regardless of zygosity, discordant twins had reduced parahippocampal gyri compared with controls, suggesting that environmental effects play a role in this region (Baare and others 2001). Differences in findings may be due to differences in sample size, differences in methodology, or sample studied. However, like the current study, other studies have shown increases in the healthy relatives of patients in this region.

We also demonstrated increased gray matter volume in the middle temporal lobe. The middle temporal lobe is thought to play a role in cognitive processes such as language and semantic memory processes. The middle temporal gyrus may be associated with auditory hallucinations. For example, one study found that a predisposition to auditory verbal hallucinations in schizophrenia was related to the inability to activate the left middle temporal gyrus (McGuire and others 1995). Studies examining the morphology of the middle temporal gyrus have found decreased gray matter in schizophrenia (Job and others 2002; Onitsuka and others 2004), though not consistently (Goldstein and others 1999).

Although studies have found increased gray matter in schizophrenia patients and also their healthy relatives, it is unclear how these findings are related to the pathophysiology of schizophrenia. Increased gray matter in the healthy relatives could represent abnormal cell migration or deficient pruning. An alternative explanation for increased gray matter in healthy relatives is that it represents a compensatory factor that protects against psychosis.

A study that investigated global gene expression profiles across the brain found that the superior temporal gyrus followed by the cingulate and hippocampus had the greatest number of transcripts with altered gene expression, accounting for 63–73% of all differentially expressed genes in schizophrenia (Katsel and others 2005). The parahippocampal gyrus also ranked high in terms of regions shown to have significant aberrations. These findings provide convergent evidence for the validity of the current constellation of morphological findings.

Limitations

Our sample size is modest and may not have been sufficient to detect small and moderate group differences. We observed no reliable differences in the frontal regions in the healthy relatives of patients compared with controls. Cannon and others (2002) studied monozygotic and dizygotic twins discordant for schizophrenia and control twins and found that increased genetic liability to schizophrenia was associated with deficits primarily in polar and dorsolateral prefrontal cortex; however, they were unable to assess the cingulate and medial temporal lobe regions due to their methodology. Analysis of effect size enables a determination of the magnitude of the difference between groups, regardless of sample size. Our effect sizes indicated that the magnitude of the difference between the groups in the inferior and middle frontal volume, surface area, and thickness ranged from small to medium (partial eta squared = 0–0.09). Thus, the current study had at best a power of 0.20 to detect a group difference at standard levels of significance. Given our modest sample size and moderate significance levels, the findings of this study need to be replicated in a larger sample (this is currently ongoing).

Our group of relatives was heterogeneous including 1 parent, 13 siblings, and 5 offspring of probands. Although it is likely that all first-degree relatives share a similar proportion of genes, the shared environmental effects may be potentially different between siblings, parents, and offspring, which makes environmental factors affecting the relatives more heterogeneous in this study than in twin or sibling study designs. Also, it would have also been advantageous to have data on the probands and the ability to assess the potential relationship between genetic loading and brain abnormalities.

We used an automated methodology to measure cortical thickness and to parcellate the cerebral cortex. There are limitations associated with assessing cortical thickness from MRI data. The accuracy of the thickness values will depend on the accuracy of the gray–white segmentation and therefore can be influenced by various artifacts. We followed guidelines provided by the FreeSurfer manual to ensure production of topologically accurate surfaces. In addition, the data processing was conducted in consultation with a trained physicist (K.R.) well versed with structural imaging tools. It is important to note that our volume, surface area, and thickness measurements were not independent of one another and volume and surface area measurements were best approximations.

Conclusions

In contrast to previous studies, this study was able to more fully probe the nature of cortical structural abnormalities in schizophrenia by measuring volume, surface area, and thickness in healthy relatives. The cingulate and the temporal lobe were the most affected. We found that most volumetric differences in our sample were associated with abnormalities in surface area rather than cortical thickness. Decreased cortical surface area could represent fewer interneurons, more cell packing, or smaller cells, whereas greater surface area could represent increased interneurons, more diffuse cell packing, or larger cells. These regions may be associated with the unexpressed genetic liability to schizophrenia and aid in elucidating the genetic underpinnings. The increases in gray matter may also represent protective or compensatory factors against the development of psychosis or loss of associated functioning. Evaluation of schizophrenia patients, their healthy relatives, and protective factors may be useful in fully elucidating the nature of the manifestation of the illness.

Ms Goghari was supported by a PGS Master's Award from the Natural Sciences and Engineering Research Council of Canada and by the Graduate Research Partnership Program, University of Minnesota. Additional support was provided by National Institute of Mental Health grant # MH45156 and the University of Minnesota. The authors gratefully acknowledge the assistance of the International Neuroimaging Consortium: Dr David Rottenberg, Kirt Schaper, Kristi Boesen, and Tim Jarvis; the University of Pittsburgh Medical Center, Department of Radiology; the Cognitive Control Neuroscience Laboratory; Western Psychiatric Institute and Clinic, University of Pittsburgh; and Theresa Becker, Jim Porter, Jill Stanton, Ryan Walter, Christopher Kallie, and Bruce Fischl. Preliminary data from this study were presented at the biennial meeting of the International Congress on Schizophrenia Research, Savannah Georgia, April 2–6, 2005, and at the 11th Annual Meeting of the Organization for Human Brain Mapping, Toronto, Ontario, June 12–16, 2005. Conflict of Interest: None declared.

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