Widespread Reductions of Cortical Thickness in Schizophrenia and Spectrum Disorders and Evidence of Heritability

doi:10.1001/archgenpsychiatry.2009.24

Arch Gen Psychiatry. Author manuscript; available in PMC 2009 July 31.

Published in final edited form as:

Arch Gen Psychiatry. 2009 May; 66(5): 467–477.

doi: 10.1001/archgenpsychiatry.2009.24

PMCID: PMC2719488

NIHMSID: NIHMS121354

Widespread Reductions of Cortical Thickness in Schizophrenia and Spectrum Disorders and Evidence of Heritability

Aaron L. Goldman, BSc, Lukas Pezawas, Priv Doz, Dr, Venkata S. Mattay, MD, Bruce Fischl, PhD, Beth A. Verchinski, BS, Qiang Chen, PhD, Daniel R. Weinberger, MD, and Andreas Meyer-Lindenberg, MD, PhD

Author Affiliations: Neuroimaging Core Facility, Genes, Cognition, and Psychosis Program, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland (Mr Goldman, Drs Pezawas, Mattay, Chen, Weinberger, and Meyer-Lindenberg, and Ms Verchinski); Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center, Harvard Medical School, Charlestown, and Department of Health Sciences and Technology and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge (Dr Fischl); and Central Institute of Mental Health, Mannheim, Germany (Dr Meyer-Lindenberg). Dr Pezawas is now with the Division of Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.

Correspondence: Daniel R. Weinberger, MD, National Institute of Mental Health, National Institutes of Health, 10 Center Dr, Bldg 10/Room 4S237B, Bethesda, MD20892 (Email: weinberd/at/mail.nih.gov)

The publisher's final edited version of this article is available at Arch Gen Psychiatry

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Abstract

Context

Schizophrenia is a brain disorder with predominantly genetic risk factors, and previous research has identified heritable cortical and subcortical reductions in local brain volume. To our knowledge, cortical thickness, a measure of particular interest in schizophrenia, has not previously been evaluated in terms of its heritability in relationship to risk for schizophrenia.

Objective

To quantify the distribution and heritability of cortical thickness changes in schizophrenia.

Design

We analyzed a large sample of normal controls, affected patients, and unaffected siblings using a surface-based approach. Cortical thickness was compared between diagnosis groups on a surfacewide node-by-node basis. Heritability related to disease risk was assessed in regions derived from an automated cortical parcellation algorithm by calculating the Risch λ.

Setting

Research hospital.

Participants

One hundred ninety-six normal controls, 115 affected patients with schizophrenia, and 192 unaffected siblings.

Main Outcome Measure

Regional cortical thickness.

Results

Node-by-node mapping statistics revealed widespread thickness reductions in the patient group, most pronouncedly in the frontal lobe and temporal cortex. Unaffected siblings did not significantly differ from normal controls at the chosen conservative threshold. Risch λ analysis revealed widespread evidence for heritability for cortical thickness reductions throughout the brain.

Conclusions

To our knowledge, the present study provides the first evidence of broadly distributed and heritable reductions of cortical thickness alterations in schizophrenia. However, since only trend-level reductions of thickness were observed in siblings, cortical thickness per se (at least as measured by this approach) is not a strong intermediate phenotype for schizophrenia.

Schizophrenia is a highly heritable disorder that has been linked to changes in brain morphology in a wide variety of studies.¹^–³ These studies have shown volumetric reductions in a number of cortical regions, particularly frontal and temporal, as well as several subcortical regions, most notably hippocampus.

The majority of these studies assess regional volumes through voxel-based approaches, estimating the volume of a given structure by summing up the number of voxels (volume elements) it encompasses. However, recent advances in morphological imaging now allow the reconstruction of structural features in a subvoxel range. This is of particular interest in cerebral cortex, where sophisticated algorithms designed to reconstruct subjects’ cortical surfaces offer an exciting opportunity to study cortical thickness, surface area, and sulcal depth.⁴^–⁸ Since abnormal architecture of cortex has been identified in postmortem studies in schizophrenia⁹^–¹² and occupies a privileged position in several influential theories of the pathogenesis of the disorder,¹³^–¹⁵ these measures are of particular interest for schizophrenia research. Recently emerging studies of cortical thickness in schizophrenia have in fact identified broad patterns of cortical thinning, particularly in prefrontal regions, temporal lobe, and cingulate gyrus, ¹⁶^–¹⁹ although studies on this aspect of schizophrenia morphology remain far less numerous than those analyzing volume.

Since genetic factors account for the majority of schizophrenia risk, for every finding of a schizophrenia-related abnormality in brain morphology, the question immediately arises whether this change in brain structure constitutes a heritable component of the disease. If true, this structural phenotype should be enriched in subjects at risk for schizophrenia. One strategy for examining disease-related heritability is therefore the analysis of unaffected siblings, who share 50% of their affected sibling’s genetic variants but do not themselves have schizophrenia. To our knowledge, this approach was previously pursued only in a pair of adult studies of thickness in schizophrenia, in which 19 first-degree relatives showed reduced cingulate thickness,²⁰ altered surface area in right cingulate and temporal regions,²⁰ and sulcal thickness alterations in the cingulate and superior temporal sulci.²¹ A further study focused on young siblings (aged 8–28 years) of patients with childhood-onset schizophrenia and found temporal and frontal thickness reductions only in siblings younger than 20 years.²² Observed volume differences between unaffected siblings and normal controls have varied, but a recent meta-analysis²³ reported significant reductions of hippocampal volume and cortical gray matter volume, and in a previous study of healthy siblings in our sample using voxel-based morphometry, we observed intermediate reductions in several cortical areas (including left medial frontal, left superior temporal, and left insula).²⁴ Other studies have reported reductions in hippocampus or other temporal areas²⁵^–²⁸ and frontal lobe,²⁸^,²⁹ though negative results have also been reported.³⁰^–³² Given the intuitive relation between volume and thickness, there is thus reason to investigate cortical thickness as an intermediate phenotype related to schizophrenia.

In addition to being enriched in subjects at risk, a phenotype related to genetic risk must be heritable. While direct comparison of unaffected siblings with normal controls can be useful, other parameters are needed to establish this. One such measure is the Risch λ for siblings (λ_s), which compares the frequency of occurrence of a disease-related phenotype in healthy siblings of patients who exhibit that phenotype with the frequency seen in normal controls.³³^–³⁵ If the phenotype under study is heritable, siblings are more likely to exhibit the phenotype observed in their ill relative. In a previous study of brain volumetric changes, we used this measure to provide evidence of heritable components to hippocampal and overall cortical gray matter reductions in schizophrenia, whereas enlargements of the lateral ventricles and dorsal striatum did not show such familiality.³⁶

To investigate the presence and heritability of cortical thickness alterations in schizophrenia, herein we applied similar methods in a large surface-based data set. One hundred ninety-six normal controls, 115 affected patients, and 192 unaffected siblings were scanned within the framework of the Clinical Brain Disorders Branch/National Institute of Mental Health Sibling Study, a study aimed at identifying schizophrenia susceptibility genes and related intermediate biologic phenotypes.³⁷ Brain magnetic resonance images (MRIs) were processed using an automated surface reconstruction method and then automatically parcellated into a wide range of cortical regions. ⁴^,⁵^,³⁸^–⁴⁰ Cortical thickness was calculated at each node, and average thickness values were also extracted for each cortical region.⁷^,⁸

We contrasted cortical thickness between normal controls, patients with schizophrenia, and unaffected siblings using a surface-based general linear model (GLM) tool to map pairwise group contrasts on a node-by-node basis. Additionally, the Risch λ was calculated using the average thicknesses for each cortical region, providing further analysis of heritability. Based on previous volumetric data¹^,² and the importance of frontotemporal processing abnormalities for schizophrenia,⁴¹ we hypothesized that patients would exhibit heritable reductions in cortical thickness, particularly in frontal and temporal cortex. Hypothesizing that regional cortical thickness would be an intermediate phenotype for schizophrenia, we further expected unaffected siblings to show a similar but less pronounced pattern of reduction.

METHODS

PARTICIPANTS

A sample of 196 normal controls, 115 affected patients, and 192 unaffected siblings was included in this study. In previous studies, we analyzed local brain volume in largely overlapping but not identical samples to the one presently discussed.²⁴^,³⁶ Subjects were recruited nationwide as part of an ongoing family study of schizophrenia at the National Institute of Mental Health, Bethesda, Maryland, which included standard procedures such as a semistructured diagnostic interview (Structured Clinical Interview for DSM-IV) and a formal neurological examination. Subjects provided written informed consent and participated according to the guidelines of the National Institute of Mental Health institutional review board.

All patients met DSM-IV criteria for schizophrenia (79.1%) or related diagnoses including schizoaffective disorder (12.2%), psychosis (not otherwise specified) (1.7%), and schizoid, paranoid, and schizotypal personality disorders (7.0%). The majority (91.0%) were taking antipsychotic medication at the time of scan, and a minority (19.1%) had a lifetime history of comorbid mental illness or substance abuse/dependence (including alcohol). No normal controls currently had DSM-IV Axis I disorders, and no subjects in any group had a current history of alcohol or substance abuse within 6 months of being scanned. However, a minority of normal controls (17.3%) and unaffected siblings (41.7%) had a lifetime history of mental illness, substance abuse or dependence, personality disorders, or other disorders (Table 1).

Table 1

Demographic Results

For heritability analysis, 56 families were included with at least 1 affected and unaffected member, accounting for 59 affected patients and 72 unaffected siblings. The remaining patients (56) and siblings (120) did not have a relative included in the study.

IMAGING AND PREPROCESSING

Three-dimensional structural MRI scans were acquired on a 1.5-T GE scanner (GE Medical Systems, Milwaukee, Wisconsin) using a T1-weighted spoiled gradient recalled sequence (repetition time, 24 milliseconds; echo time, 5 milliseconds; number of excitations, 1; flip angle, 45°; matrix size, 256 × 256; field of view, 24 × 24 cm), with 124 sagittal slices (0.94 × 0.94 × 1.5-mm resolution). All participant groups were recruited and scanned throughout a 12-year period. Potential MRI system-specific effects were not apparent in ongoing quality control and examination of the effect of scan year using GLM-based analyses. Images were processed using the full stream in the Freesurfer stable release 3.0.2.⁴^–⁶^,⁴²^,⁴³ Preprocessing included resampling to a coronal 3-dimensional image with 1-mm isotropic voxel size, non-uniformity intensity normalization (N3),⁴⁴ an affine registration to Montreal Neurological Institute space,⁴⁵ further intensity normalization with a different algorithm, and an automated skull strip.⁴⁶ Images were then subcortically segmented as described in previous articles.³⁶^,⁴²^,⁴³ At this juncture, several careful visual quality checks were performed, and failures were manually corrected and reinspected as described previously.³⁶ These corrections were performed on 21 controls, 21 patients, and 40 siblings.

Images then underwent a sophisticated and validated⁴^,⁸^,⁴⁷ surface reconstruction algorithm.⁴^–⁶ Briefly, white matter segmentations were first produced,⁴ using information from the subcortical segmentations to also fill in areas that commonly produce topological defects (such as basal ganglia and lateral ventricle). Cutting planes were used to isolate each cerebral hemisphere, and final binary white matter masks were produced. Tessellation was then performed to produce a triangle-based mesh of the white matter surface, and a smoothing algorithm was used to alleviate the voxel-based nature of the initial curvature. ⁴ Topological defects in the surfaces were then corrected using an automated topology fixer,⁴⁰ and the white matter surfaces were deformed outward to generate pial surfaces.⁴ As mentioned, preprocessing involved a registration to Montreal Neurological Institute space, producing a transformation matrix for use in several processing steps. Notwithstanding, the surfaces produced remained in native space, allowing direct, anatomically accurate measurements of thickness but also necessitating an algorithm for surface-based intersubject registration. To achieve this, surfaces were spherically inflated,⁵ and spherical surfaces for each subject were registered to a common space spherical deformation guided by automatically defined cortical features derived from a population atlas.⁴⁸

Final surface data were then parcellated into a variety of cortical regions using an automated algorithm (Figure 1), which used a manually labeled training data set, as well as knowledge of curvature and the spatial relationship between regions. ³⁸^,³⁹ The atlas used, detailed in Desikan et al,³⁸ included 33 gyral regions of interest, as well as 2 placeholder labels we did not analyze (corpus callosum and “unknown,” which includes insula as well as diencephalon and other noncortical regions). Using combined information from the pial and white matter surfaces, cortical thickness was calculated at each node, and average thickness was also calculated for each area of cortex parcellated.⁷^,⁸ At the end of this process, each subject was again visually inspected for gross topological inaccuracies, and subjects with defects (15.8%, listed sample sizes are for final sample) were excluded from the study.

Figure 1

Average surface with overlaid cortical parcellation labels. Using an automated algorithm, labels were generated for 33 gyral regions of interest. As an example, an average pial surface for the normal control group is displayed here, with the group’s (more ...)

STATISTICAL ANALYSIS

Node-by-node contrasts of cortical thickness were performed for normal controls vs affected patients, controls vs unaffected siblings, and patients vs siblings. For this, an average normal control surface was generated, and thickness data from each subject were mapped to this average surface and smoothed using a 10-mm full-width-at-half-maximum gaussian filter. Finally, each contrast was entered into a node-by-node GLM including diagnosis, sex, and exact age as covariates. Results were thresholded and false discovery rate (FDR) corrected at a conservative, surfacewide P <.05 significance level. To ensure that lack of an intracranial volume (ICV) covariate did not confound our results, analyses were also rerun with ICV added to the model. For the purpose of generating Figure 2 and Figure 3, these results were imported into SUMA (http://afni.nimh.nih.gov/afni/suma) and overlaid on the average normal control surface.

Figure 2

Mean thickness maps for normal controls (NC), patients with schizophrenia (SCZ), and siblings (SIB). For each subject, cortical thickness values were calculated on a node-by-node basis. Results were then mapped to a mean surface generated based on the (more ...)

Figure 3

Node-based contrasts of thickness between diagnosis groups. A surface-based general linear model approach was used to contrast thickness values between diagnosis groups on a node-by-node basis. Thickness values for each subject were mapped to an average (more ...)

In addition, heritability was analyzed using the Risch λ for siblings (λ_s), which quantitatively measures how frequently siblings of patients with pronounced cortical thickness reductions (the disease-associated phenotype) show such reductions themselves.³³^–³⁵ Since this measure is based on a categorical phenotype, it is necessary to determine cutoffs based on the distribution of average thickness in the normal control group. Given the sensitive nature of the heritability measure, and its lack of an inferential statistic, we chose to limit analysis to a discrete set of relatively stable regions of interest derived from an automated parcellation algorithm. Statistics of average thickness within each parcellation label were normalized to z scores using the mean and standard deviation of the normal controls, and subjects were assigned the reduced cortical thickness phenotype if their values were 1.5 SDs lower than the mean, corresponding to the outer 6.7% of the distribution. The Risch λ is then defined as the percentage of siblings of “patients with phenotype” who also show reductions exceeding the threshold, divided by the proportion of normal controls who show such reductions. While an inferential statistical test for this measure is not available (making this measure more similar to an effect size for heritability), nonfamilial traits are expected to have a λ_s value of 1, and relative risks of 2.0 or more are commonly considered evidence for heritability, barring shared effects of environment.⁵¹

RESULTS

DEMOGRAPHICS

Table 1 shows demographic data for the reported sample, which was typical overall of participants in this case-control study. To control for age and sex effects, these variables were included in all GLM analyses as confounding covariates. Furthermore, for all parcellation-based analyses, preliminary GLM tests were performed including sex × diagnosis and age × diagnosis interactions, and when significant, these interaction effects were included in the final analyses (see “Statistical Analysis” subsection).

To investigate illness-related demographic variables that may have confounded or modulated observed reductions in our patient group, post hoc GLMs were performed for medication status (taking medication/not taking medication), years of illness, age at onset, and scores on the 3 major Positive and Negative Syndrome Scale symptom scales. Each variable was modeled along with age and sex, and effects were thresholded at P <.05, FDR corrected. The results were broadly negative, with only 1 small suprathreshold cluster appearing for Positive and Negative Syndrome Scale general psychopathology (in left superior temporal gyrus).

NODE-BASED ANALYSIS OF THICKNESS

Average thickness maps for normal controls, affected patients, and unaffected siblings are shown in Figure 2. Maps of the 3 groups show good correspondence to the classic postmortem thickness maps reported by Von Economo,⁴⁹ and reductions of prefrontal and lateral temporal cortical thickness are readily visible in the patient group.

Statistical contrasts of mean cortical thickness between diagnostic groups (Figure 3), thresholded at P <.05 and surfacewide FDR corrected for multiple comparisons, showed significant thickness reductions throughout the cortex in the patient group compared with controls. Differences were most pronounced in the frontal lobe but were also found in temporal, parietal, occipital, and limbic regions. Temporal lobe differences appeared especially pronounced in the right hemisphere, though we did not explicitly test for group × hemisphere effects. At the threshold applied, affected patients did not show increased cortical thickness in any brain area. Unaffected siblings, however, did not significantly differ from normal controls and showed relatively few effects even prior to FDR correction (eFigure 1, http://www.archgenpsychiatry.com). The pattern of reductions in patients compared with unaffected siblings was similar to that seen compared with normal controls, but less pronounced. While our statistical model did not account for the relatedness of some of the subjects, supplemental analyses using only the 120 unaffected siblings without a relative in the study showed equivalent results (eFigure 2). Similarly, adding ICV as a covariate did not appreciably alter our results (eFigure 3).

Effects of age and sex on cortical thickness are displayed in eFigure 4 and eFigure 5. Briefly, in each of the 3 groups (normal controls, affected patients, and unaffected siblings) similar widespread age effects were seen, most prominently including decreases in thickness with age in lateral frontal lobe, as well as lateral temporal areas, cingulate, precentral and postcentral gyrus, and parts of the parietal lobe. Interestingly, differences were less pronounced in the patient group, perhaps because of increased variance. Only a few sex effects were seen, which seemed most significant in the unaffected sibling group.

Figure 4

Average thickness distributions and heritability results for selected frontal regions. Thickness was also analyzed on a regionwide basis using automated cortical parcellations. Here, results are displayed for the 3 frontal regions in which patient thicknesses (more ...)

Figure 5

Average thickness distributions and heritability results for selected temporal regions. Similar to Figure 4, thickness distribution and heritability results are displayed for superior temporal gyrus, as well as regions of the medial temporal lobe (parahippocampal (more ...)

HERITABILITY ANALYSIS

The Risch λ was calculated for each parcellated region as a measure of heritability. In numerous areas, siblings of patients with pronounced thickness reductions indeed exhibited such reductions themselves, as shown in Table 2. Risch λ values of 2 or more, generally viewed as evidence for heritability, were seen in the majority of brain regions, and values as high as 7.88 were observed. Figure 4 and Figure 5 depict heritability results, as well as distributions of average thickness, for selected brain regions of special interest. With very few exceptions, regions noted earlier for especially pronounced thickness reductions also showed evidence for heritability.

Table 2

Risch λ (λ_s) in Cortical Parcellation Regions^a

COMMENT

The present study examined the heritability of cortical thickness changes associated with schizophrenia in a large cohort of normal controls, affected patients, and unaffected siblings analyzed using sophisticated surface extraction and cortical parcellation procedures. The study demonstrated marked thickness reductions in the patient sample, most notably in frontal and temporal lobes, and provided evidence for widespread heritability of these cortical alterations. Since no significant thickness reductions were found in unaffected siblings in this large sample, reduced cortical thickness per se is unlikely to be a major neural signature of the genetic risk architecture of schizophrenia. However, this conclusion must be considered in light of the technical limitations of MRI as an anatomical research method.

Perhaps the most striking thickness effects were seen in the frontal lobe, where patients showed widespread reductions. Prefrontal cortex is one of the most consistently implicated regions in morphometric studies of schizophrenia,¹^–³ and previous studies of cortical thickness have likewise shown widespread thinning in this area,¹⁶^,¹⁷^,¹⁹ although1study (using a non–surface-based method) found no reduction among first-episode patients.⁵² Furthermore, unaffected siblings showed widespread relative risk for thickness reductions within the frontal lobe, consistent with reports associating schizophrenia susceptibility genes with altered prefrontal structure⁵³ and function,⁵⁴ as well as studies showing intermediate prefrontally linked functional phenotypes in the first-degree relatives of patients with schizophrenia.⁵⁵

The temporal lobe has likewise exhibited reduced volume in a wide range of studies, particularly in the superior temporal gyrus and medial temporal lobe.¹^–³ Not surprisingly, therefore, and as hypothesized, thickness was found to be reduced in a variety of temporal regions in our present data, most pronouncedly on the lateral surface, replicating previous reports of temporal thickness reductions in schizophrenia.¹⁶^,¹⁷^,¹⁹ Furthermore, relative risk for reduced thickness was seen in a variety of temporal regions, suggesting a role of schizophrenia risk genes in temporal lobe cortical architecture.

Intriguingly, despite the prevalence of middle temporal lobe reductions in volumetric studies of schizophrenia, ¹^–³^,³⁶ our sample did not show significant thickness reductions in parahippocampal gyrus or entorhinal cortex. The lack of results in this area could indicate that medial temporal volume reductions are limited to subcortical regions (hippocampus and/or amygdala). Alternatively, since thickness is not directly synonymous to volume, cortical reductions could exist in the form of reduced cortical area or some other morphometric difference, such as abnormal cytoarchitecture. Previous results in the parahippocampal gyrus have been equivocal, with at least 1 study reporting reduced thickness¹⁶ but others failing to find thinning in first-episode schizophrenia¹⁸ or over the first 5 years of childhood-onset schizophrenia.⁵⁶

Beyond these a priori hypothesized regions, thickness reductions were seen in lingual gyrus, supramarginal gyrus, inferior parietal lobule, right precuneus, lateral occipital lobe, postcentral gyrus, paracentral lobule, and most of the cingulate gyrus. As with temporal and frontal lobe, many of these regions showed evidence of heritability of volume reductions. Cortical abnormalities in the cingulate might be a structural correlate of convergent reports of abnormal error-related processing in this region in schizophrenia.⁵⁷ Thus, while dorsolateral prefrontal and lateral temporal regions are generally the most strongly implicated in schizophrenia, morphometric effects were not exclusive to these regions, again mirroring findings using other methods, such as regional volumes. ¹^,²⁴^,³⁶ This supports the idea that the predominant functional impairment of prefrontal and medial temporal structures in schizophrenia may not be exclusively due to localized structural-functional effects, but also mediated through the extensive interconnections that these regions maintain with the rest of the brain.¹¹^,⁴¹^,⁵⁸ The majority of patients were treated with antipsychotic medications at the time of scanning. While supplemental analysis did not show any significant effects of medication use on cortical thickness (see the “Demographics” subsection in the “Results” section), it is impossible to fully rule out medication effects in the context of this study design. Indeed, studies in monkeys treated long-term with antipsychotic drugs have demonstrated that cortical volume is reduced by these agents.⁵⁹

The Risch λ, the measure of heritability used in this study, has previously been used in a variety of schizophrenia studies, including analyses of N-acetylaspartate level⁶⁰ and local brain volume,³⁶ among others. In a previous study, our group used this method in an analysis of brain morphology using automated subcortical segmentations. Importantly, while this study provided evidence for heritable reductions in cortical gray matter as a whole and hippocampus in particular, it also demonstrated that marked and well-replicated phenotypes such as ventricle and dorsal striatal enlargements (the latter of which has been linked primarily to use of typical antipsychotics) did not show increased relative risk using this measure,³⁶ illustrating that the Risch λ can be used to help dissect heritable disease-related factors from those that are environmental.³⁶

In terms of cortical thickness, the Risch λ indeed showed evidence for heritability of thickness reductions in a wide variety of brain areas studied. Therefore, cortical thickness reduction as measured herein has a significant heritable component. Cortical thickness reductions do not necessarily reflect a loss of neurons but could also be related to a loss of local circuit connections and cortico-cortical connections through reductions in neuropil volume. This has indeed been suggested by previous postmortem studies of prefrontal cortex⁶¹ and conforms with the “dysconnectivity” concept discussed earlier. However, postmortem evidence of cortical thickness reductions is not consistent and the possibility that changes observed with MRI reflect changes that are not related to cellular elements, eg, changes in fluid compartments or vascularity, cannot be ruled out. Methodologically, one strength of the present study is the large sample assessed through a reliable semiautomated technology. Even so, the robustness of the Risch λ varies depending on the distribution of thickness values in each region and can depend on very few siblings exceeding the chosen phenotype-defining threshold. However, even using an excessively stringent limit to the most robust analyses (for the sake of example, those containing ≥20 siblings), areas of marked heritability were seen throughout the brain. These included findings in left superior and middle temporal gyrus, rostral middle frontal gyrus, inferior frontal gyrus (opercular part), banks of the superior temporal sulcus, inferior parietal lobule, and posterior cingulate and right caudal anterior cingulate, highlighting the majority of regions hypothesized at the onset of the study. While the use of parcellation labels may have obscured effects that did not conform to gyral anatomy, we feel false negatives are unlikely given the multitude of our positive findings.

Widespread heritability of cortical thickness reductions alone does not demonstrate that reduced thickness is a reflection of the risk architecture of schizophrenia, because findings like the ones presented herein could also be obtained if the siblings of healthy subjects with thin cortices were examined. To show that reduced thickness is also a marker of increased genetic risk for schizophrenia, it has to be enriched in the sample of people who are at increased genetic risk for this disease, ie, the sibling group. In our data, thickness reductions in siblings did not exceed the chosen surfacewide threshold, although smaller studies²⁰^–²² did find reductions in siblings, and in our sample, differences between the sibling and patient groups were consistently less pronounced than those between controls and patients, suggesting that siblings did occupy an intermediate position between patients and controls. Even larger samples might still uncover a significant reduction of thickness in healthy subjects at risk, or intermediate thickness reductions may be observed in subjects assessed to be at particularly high risk for the disorder.⁶²

However, at the present time, while our data clearly show that reduced cortical thickness is heritable, our findings do not support the presence of thin cortex per se as a strong intermediate phenotype, or endophenotype, related to genetic risk for schizophrenia. This raises the question of the intermediate phenotype status of gray matter structure in schizophrenia in general. While the evidence is variable, some studies, including our own in this sample, have found heritable reductions in gray matter volume in first-degree relatives of schizophrenic patients. If it is accepted that volume can be reduced while thickness is unaltered in genetically high-risk individuals, this could indicate a reduction in cortical area, possibly as a signature of abnormal neurodevelopment, and it would be of interest to examine this feature of brain structure. If on the other hand one takes a more skeptical view of the literature regarding gray matter volume reduction, one could also conclude that gray matter structure, while heritable, lies outside the core genetic risk architecture of schizophrenia and would then be more related to peristatic and environmental effects related to illness state. In support of this latter skeptical view, significant structural differences in monozygotic twins discordant for schizophrenia are well established.⁶³^,⁶⁴ Further studies should examine the effects of specifically disease-related allelic variation on cortical thickness, analogous to studies performed using regional brain volume as the phenotype,⁵³^,⁶⁵^–⁶⁷ and investigate the effect of illness-related variables, such as treatment and duration of illness, on the phenotype in patients.

Acknowledgments

Funding/Support: This work was supported by the National Institute of Mental Health Intramural Research Program and used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health (NIH) (http://biowulf.nih.gov). Support for this research was also provided in part by National Center for Research Resources grants P41-RR14075, R01 RR16594-01A1, and NCRR BIRN Morphometric Project BIRN002, U24 RR021382; National Institute for Biomedical Imaging and Bioengineering grant R01 EB001550, and National Institute for Neurological Disorders and Stroke grant R01 NS052585-01 as well as the Mental Illness and Neuroscience Discovery Institute and is part of the National Alliance for Medical Image Computing, funded by NIH through the NIH Roadmap for Medical Research, grant U54 EB005149.

Footnotes

Financial Disclosure: None reported.

Additional Information: The eFigures are available at http://www.archgenpsychiatry.com. Information on the National Centers for Biomedical Computing can be obtained at http://nihroadmap.nih.gov/bioinformatics.

REFERENCES

1. Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162(12):2233–2245. [PubMed]

2. Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET. Meta-analysis of regional brain volumes in schizophrenia. Am J Psychiatry. 2000;157(1):16–25. [PubMed]

3. Henn FA, Braus DF. Structural neuroimaging in schizophrenia: an integrative view of neuromorphology. Eur Arch Psychiatry Clin Neurosci. 1999;249 suppl 4:48–56. [PubMed]

4. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage. 1999;9(2):179–194. [PubMed]

5. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis, II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9(2):195–207. [PubMed]

6. Fischl B, Liu A, Dale AM. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans Med Imaging. 2001;20(1):70–80. [PubMed]

7. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97(20):11050–11055. [PubMed]

8. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco M, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage. 2006;32(1):180–194. [PubMed]

9. Selemon LD, Mrzljak J, Kleinman JE, Herman MM, Goldman-Rakic PS. Regional specificity in the neuropathologic substrates of schizophrenia: a morphometric analysis of Broca’s area 44 and area 9. Arch Gen Psychiatry. 2003;60(1):69–77. [PubMed]

10. Rajkowska G, Selemon LD, Goldman-Rakic PS. Neuronal and glial somal size in the prefrontal cortex: a postmortem morphometric study of schizophrenia and Huntington disease. Arch Gen Psychiatry. 1998;55(3):215–224. [PubMed]

11. Weinberger DR, Lipska BK. Cortical maldevelopment, anti-psychotic drugs, and schizophrenia: a search for common ground. Schizophr Res. 1995;16(2):87–110. [PubMed]

12. Weinberger DR. Schizophrenia as a neurodevelopmental disorder. In: Hirsch SR, Weinberger DR, editors. Schizophrenia. London, England: Blackwood; 1995. pp. 295–323.

13. Lewis DA, Lieberman JA. Catching up on schizophrenia: natural history and neurobiology. Neuron. 2000;28(2):325–334. [PubMed]

14. Murray RM, Lewis SW. Is schizophrenia a neurodevelopmental disorder? Br Med J (Clin Res Ed) 1987;295(6600):681–682. [PMC free article] [PubMed]

15. Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44(7):660–669. [PubMed]

16. Kuperberg GR, Broome MR, McGuire PK, David AS, Eddy M, Ozawa F, Goff D, West WC, Williams SC, van der Kouwe AJ, Salat DH, Dale AM, Fischl B. Regionally localized thinning of the cerebral cortex in schizophrenia. Arch Gen Psychiatry. 2003;60(9):878–888. [PubMed]

17. Narr KL, Bilder RM, Toga AW, Woods RP, Rex DE, Szeszko PR, Robinson D, Sevy S, Gunduz-Bruce H, Wang YP, DeLuca H, Thompson PM. Mapping cortical thickness and gray matter concentration in first episode schizophrenia. Cereb Cortex. 2005;15(6):708–719. [PubMed]

18. Narr KL, Toga AW, Szeszko P, Thompson PM, Woods RP, Robinson D, Sevy S, Wang Y, Schrock K, Bilder RM. Cortical thinning in cingulate and occipital cortices in first episode schizophrenia. Biol Psychiatry. 2005;58(1):32–40. [PubMed]

19. White T, Andreasen NC, Nopoulos P, Magnotta V. Gyrification abnormalities in childhood- and adolescent-onset schizophrenia. Biol Psychiatry. 2003;54(4):418–426. [PubMed]

20. Goghari VM, Rehm K, Carter CS, MacDonald AW., III Regionally specific cortical thinning and gray matter abnormalities in the healthy relatives of schizophrenia patients. Cereb Cortex. 2007;17(2):415–424. [PubMed]

21. Goghari VM, Rehm K, Carter CS, MacDonald AW. Sulcal thickness as a vulner-ability indicator for schizophrenia. Br J Psychiatry. 2007;191:229–233. [PubMed]

22. Gogtay N, Greenstein D, Lenane M, Clasen L, Sharp W, Gochman P, Butler P, Evans A, Rapoport J. Cortical brain development in nonpsychotic siblings of patients with childhood-onset schizophrenia. Arch Gen Psychiatry. 2007;64(7):772–780. [PubMed]

23. Boos HB, Aleman A, Cahn W, Hulshoff Pol H, Kahn RS. Brain volumes in relatives of patients with schizophrenia: a meta-analysis. Arch Gen Psychiatry. 2007;64(3):297–304. [PubMed]

24. Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, Verchinski B, Passingham RE, Weinberger DR, Callicott JH. Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiatry. 2008;63(5):465–474. [PMC free article] [PubMed]

25. Tepest R, Wang L, Miller MI, Falkai P, Csernansky JG. Hippocampal deformities in the unaffected siblings of schizophrenia subjects. Biol Psychiatry. 2003;54(11):1234–1240. [PubMed]

26. Van Erp TG, Saleh PA, Rosso IM, Huttunen M, Lönnqvist J, Pirkola T, Salonen O, Valanne L, Poutanen VP, Standertskjöld-Nordenstam CG, Cannon TD. Contributions of genetic risk and fetal hypoxia to hippocampal volume in patients with schizophrenia or schizoaffective disorder, their unaffected siblings, and healthy unrelated volunteers. Am J Psychiatry. 2002;159(9):1514–1520. [PubMed]

27. Seidman LJ, Faraone SV, Goldstein JM, Kremen WS, Horton NJ, Makris N, Toomey R, Kennedy D, Caviness VS, Tsuang MT. Left hippocampal volume as a vulner-ability indicator for schizophrenia: a magnetic resonance imaging morphometric study of nonpsychotic first-degree relatives. Arch Gen Psychiatry. 2002;59(9):839–849. [PubMed]

28. Cannon TD, van Erp TG, Huttunen M, Lönnqvist J, Salonen O, Valanne L, Poutanen VP, Standertskjöld-Nordenstam CG, Gur RE, Yan M. Regional gray matter, white matter, and cerebrospinal fluid distributions in schizophrenic patients, their siblings, and controls. Arch Gen Psychiatry. 1998;55(12):1084–1091. [PubMed]

29. McIntosh AM, Job DE, Moorhead WJ, Harrison LK, Whalley HC, Johnstone EC, Lawrie SM. Genetic liability to schizophrenia or bipolar disorder and its relationship to brain structure. Am J Med Genet B Neuropsychiatr Genet. 2006;141B(1):76–83. [PubMed]

30. McDonald C, Marshall N, Sham PC, Bullmore ET, Schulze K, Chapple B, Bramon E, Filbey F, Quraishi S, Walshe M, Murray RM. Regional brain morphometry in patients with schizophrenia or bipolar disorder and their unaffected relatives. Am J Psychiatry. 2006;163(3):478–487. [PubMed]

31. Schulze K, McDonald C, Frangou S, Sham P, Grech A, Toulopoulou T, Walshe M, Sharma T, Sigmundsson T, Taylor M, Murray RM. Hippocampal volume in familial and nonfamilial schizophrenic probands and their unaffected relatives. Biol Psychiatry. 2003;53(7):562–570. [PubMed]

32. Staal WG, Hulshoff Pol HE, Schnack HG, Hoogendoorn ML, Jellema K, Kahn RS. Structural brain abnormalities in patients with schizophrenia and their healthy siblings. Am J Psychiatry. 2000;157(3):416–421. [PubMed]

33. Risch N. Linkage strategies for genetically complex traits, I: multilocus models. Am J Hum Genet. 1990;46(2):222–228. [PubMed]

34. Risch N. Linkage strategies for genetically complex traits, II: the power of affected relative pairs. Am J Hum Genet. 1990;46(2):229–241. [PubMed]

35. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273(5281):1516–1517. [PubMed]

36. Goldman AL, Pezawas L, Mattay VS, Fischl B, Verchinski BA, Zoltick B, Weinberger DR, Meyer-Lindenberg A. Heritability of brain morphology related to schizophrenia: a large-scale automated magnetic resonance imaging segmentation study. Biol Psychiatry. 2008;63(5):475–483. [PubMed]

37. Egan MF, Hyde TM, Bonomo JB, Mattay VS, Bigelow LB, Goldberg TE, Weinberger DR. Relative risk of neurological signs in siblings of patients with schizophrenia. Am J Psychiatry. 2001;158(11):1827–1834. [PubMed]

38. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968–980. [PubMed]

39. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11–22. [PubMed]

40. Ségonne F, Grimson E, Fischl B. A genetic algorithm for the topology correction of cortical surfaces. Inf Process Med Imaging. 2005;19:393–405. [PubMed]

41. Meyer-Lindenberg AS, Olsen RK, Kohn PD, Brown T, Egan MF, Weinberger DR, Berman KF. Regionally specific disturbance of dorsolateral prefrontal-hippocampal functional connectivity in schizophrenia. Arch Gen Psychiatry. 2005;62(4):379–386. [PubMed]

42. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–355. [PubMed]

43. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Ségonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage. 2004;23 suppl 1:S69–S84. [PubMed]

44. Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17(1):87–97. [PubMed]

45. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192–205. [PubMed]

46. Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B. A hybrid approach to the skull stripping problem in MRI. Neuroimage. 2004;22(3):1060–1075. [PubMed]

47. Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der Kouwe A, Jenkins BG, Dale AM, Fischl B. Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology. 2002;58(5):695–701. [PubMed]

48. Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8(4):272–284. [PubMed]

49. Von Economo C. The Cytoarchitechtonics of the Human Cerebral Cortex. London, England: Oxford Medical Publications; 1929.

50. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci. 2004;24(38):8223–8231. [PubMed]

51. Egan MF, Weinberger DR. Neurobiology of schizophrenia. Curr Opin Neurobiol. 1997;7(5):701–707. [PubMed]

52. Wiegand LC, Warfield SK, Levitt JJ, Hirayasu Y, Salisbury DF, Heckers S, Dickey CC, Kikinis R, Jolesz FA, McCarley RW, Shenton ME. Prefrontal cortical thickness in first-episode psychosis: a magnetic resonance imaging study. Biol Psychiatry. 2004;55(2):131–140. [PMC free article] [PubMed]

53. Pezawas L, Verchinski BA, Mattay VS, Callicott JH, Kolachana BS, Straub RE, Egan MF, Meyer-Lindenberg A, Weinberger DR. The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J Neurosci. 2004;24(45):10099–10102. [PubMed]

54. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE, Goldman D, Weinberger DR. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci U S A. 2001;98(12):6917–6922. [PubMed]

55. Callicott JH, Egan MF, Mattay VS, Bertolino A, Bone AD, Verchinksi B, Weinberger DR. Abnormal fMRI response of the dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia. Am J Psychiatry. 2003;160(4):709–719. [PubMed]

56. Thompson PM, Hayashi KM, Sowell ER, Gogtay N, Giedd JN, Rapoport JL, de Zubicaray GI, Janke AL, Rose SE, Semple J, Doddrell DM, Wang Y, van Erp TG, Cannon TD, Toga AW. Mapping cortical change in Alzheimer’s disease, brain development, and schizophrenia. Neuroimage. 2004;23 suppl 1:S2–S18. [PubMed]

57. Carter CS, MacDonald AW, III, Ross LL, Stenger VA. Anterior cingulate cortex activity and impaired self-monitoring of performance in patients with schizophrenia: an event-related fMRI study. Am J Psychiatry. 2001;158(9):1423–1428. [PubMed]

58. Harrison PJ, Weinberger DR. Schizophrenia genes, gene expression, and neuropathology: on the matter of their convergence. Mol Psychiatry. 2005;10(1):40–68. 5. [PubMed]

59. Dorph-Petersen KA, Pierri JN, Perel JM, Sun Z, Sampson AR, Lewis DA. The influence of chronic exposure to antipsychotic medications on brain size before and after tissue fixation: a comparison of haloperidol and olanzapine in macaque monkeys. Neuropsychopharmacology. 2005;30(9):1649–1661. [PubMed]

60. Callicott JH, Egan MF, Bertolino A, Mattay VS, Langheim FJ, Frank JA, Weinberger DR. Hippocampal N-acetyl aspartate in unaffected siblings of patients with schizophrenia: a possible intermediate neurobiological phenotype. Biol Psychiatry. 1998;44(10):941–950. [PubMed]

61. Selemon LD, Goldman-Rakic PS. The reduced neuropil hypothesis: a circuit based model of schizophrenia. Biol Psychiatry. 1999;45(1):17–25. [PubMed]

62. Yung AR, Stanford C, Cosgrave E, Killackey E, Phillips L, Nelson B, McGorry PD. Testing the ultra high risk (prodromal) criteria for the prediction of psychosis in a clinical sample of young people. Schizophr Res. 2006;84(1):57–66. [PubMed]

63. Suddath RL, Christison GW, Torrey EF, Casanova MF, Weinberger DR. Anatomical abnormalities in the brains of monozygotic twins discordant for schizophrenia. N Engl J Med. 1990;322(12):789–794. [PubMed]

64. Noga JT, Bartley AJ, Jones DW, Torrey EF, Weinberger DR. Cortical gyral anatomy and gross brain dimensions in monozygotic twins discordant for schizophrenia. Schizophr Res. 1996;22(1):27–40. [PubMed]

65. Pezawas L, Meyer-Lindenberg A, Drabant EM, Verchinski BA, Munoz KE, Kolachana BS, Egan MF, Mattay VS, Hariri AR, Weinberger DR. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Nat Neurosci. 2005;8(6):828–834. [PubMed]

66. Callicott JH, Straub RE, Pezawas L, Egan MF, Mattay VS, Hariri AR, Verchinski BA, Meyer-Lindenberg A, Balkissoon R, Kolachana B, Goldberg TE, Weinberger DR. Variation in DISC1 affects hippocampal structure and function and increases risk for schizophrenia. Proc Natl Acad Sci U S A. 2005;102(24):8627–8632. [PubMed]

67. Meyer-Lindenberg A, Straub RE, Lipska BK, Verchinski BA, Goldberg T, Callicott JH, Egan MF, Huffaker SS, Mattay VS, Kolachana B, Kleinman JE, Weinberger DR. Genetic evidence implicating DARPP-32 in human frontostriatal structure, function, and cognition. J Clin Invest. 2007;117(3):672–682. [PMC free article] [PubMed]