This study utilized extensive structural magnetic resonance and neurocognitive data from a unique twin and family data set, and state of the art statistical methods to quantify the genetic overlap between selected structural regions in prefrontal cortex grey matter and liability to schizophrenia. This allowed us, for the first time, to define the validity of selected prefrontal cortex grey matter regions of interest as markers of the genetic risk for schizophrenia and so inform future genetic studies. Our key findings were that increased liability to schizophrenia was phenotypically associated with reduced whole brain and grey matter volume in the superior, inferior and orbital frontal cortices. Total prefrontal cortex grey matter volume and the orbital frontal cortex were moderately heritable; however, neither whole brain volume nor any of the prefrontal cortex regions had significant genetic correlations with schizophrenia.
Our data suggest that higher genetic loading for schizophrenia is associated with subtle whole brain and prefrontal cortex grey matter volume reductions. Previous studies also found non-significant trends of prefrontal cortex volume reductions in unaffected relatives (Lawrie et al., 1999
; Bhojraj et al., 2010
) or no difference at all (Lawrie et al., 2001
; Steel et al., 2002
; Toulopoulou et al., 2004
). Using voxel-based morphometry, we previously found subtle frontal cortical volume loss in relatives (McDonald et al., 2004
) while others did not (Goghari et al., 2007
; Honea et al., 2008
). However, the picture remains unclear as a cortical mapping study using twin data found that genetic proximity was associated with reductions in the polar and dorsolateral prefrontal cortex (Cannon et al., 2002
). Overall these findings suggest that there is a family association of very subtle frontal lobe grey matter reduction in schizophrenia.
We went further to estimate the heritability of prefrontal cortex grey matter regions and to quantify their aetiological relationship to schizophrenia. Genetic factors accounted for a large proportion of total variance in whole brain volume, in line with the majority of such studies, ranging from 67% to 99% (Baare et al., 2001a
; White et al., 2002
; Wright et al., 2002
; Winkler et al., 2010
). Significant heritability estimates were also found for the total prefrontal cortex grey matter and the orbital frontal cortex, while the heritability of superior, middle and inferior frontal cortices were small and non-significant. Our findings are similar to other work that used healthy families, with heritability estimates ranging from 0.54 to 0.57 for the orbital frontal cortex (Wright et al. 2002
; Winkler et al. 2010
) and the inferior frontal gyrus (0.00–0.38; Wright et al., 2002
However, our findings differ compared with the estimates for the superior (0.47–0.80; Hulshoff Pol et al., 2006
; Winkler et al., 2010
); and middle rostral frontal (0.28–0.44) and caudal frontal cortices (0.42–0.43; Winkler et al., 2010
). Our data are in line with a large voxel-based morphometry sibling study (Honea et al., 2008
) that similarly found non-significant grey matter decreases in the frontal lobe in unaffected siblings, with intra-class correlations between the sibling pairs suggesting no heritability. Familial association and heritability are two necessary properties of an endophenotype, so taken together, our results suggest that prefrontal grey matter reduction is actually only a weak endophenotype marker for schizophrenia.
Increased liability to schizophrenia was phenotypically associated with reduced whole brain and grey matter volume in the superior, inferior and orbital frontal cortices; and poorer performance on IQ and executive functioning factors. However, neither whole brain volume nor any of the prefrontal cortex regions had significant genetic correlations with schizophrenia. In accordance with previous findings, IQ and putative functional measures of frontal lobe functioning were shown to be heritable and related to the disorder (Greenwood et al., 2007
; Toulopoulou et al., 2007
; Aukes et al., 2008
; Chen et al., 2009
; Husted et al., 2009
; Quiñones et al., 2009
; Owens et al., 2011
). Common environmental correlations were not significant for any regions of interest. In contrast, shared unique environment correlations were significant for total prefrontal cortex and for IQ. The results suggest that we did not have adequate power to detect small to moderate genetic and environmental correlations.
While findings from ours and other family studies (Honea et al., 2008
) suggest that prefrontal cortex grey matter volume may not be a useful endophenotype, it could be hypothesized that alternative parcellation methods, other aspects of morphology (e.g. cortical thickness, surface area and gyrification) or other imaging techniques (e.g. DTI, PET and functional MRI) may prove to be more useful biological markers of genetic risk. For example, evidence for functional neuroimaging endophenotypes has been found within the frontal and temporal cortices (Callicott et al., 1998
; Cannon et al., 2001
). These studies have implicated various prefrontal regions; however, it is unclear whether these functional abnormalities are related to the presence of structural abnormalities in the prefrontal cortex or represent a secondary consequence to a primary lesion elsewhere in the brain (for a review see Callicott et al., 2003
; Manoach, 2003
). Further investigation into functional MRI abnormalities with healthy relatives or twins might prove useful for the identification and clarification of endophenotypes in schizophrenia.
Future research is also needed to better understand the aetiology, course and impact of the prefrontal cortex grey matter reduction in patients. The neuropathological underpinnings of the volumetric differences in schizophrenia have yet to be confirmed and are thought to be heterogeneous within different brain structures, but reduction in grey matter volumes may reflect decrease of interneuronal neuropil in schizophrenia. Post-mortem studies in schizophrenia have found evidence of abnormal migration (Akbarian et al., 1993
) and altered cell (Selemon et al., 1995
) and interneuronal density (Daviss and Lewis, 1995
) in the dorsolateral and altered interneuronal density in the orbital prefrontal cortex (Benes et al., 1991
) have been reported in patients with schizophrenia.
The neurobiological abnormalities seen in schizophrenia may have their basis in early brain development (Murray and Lewis, 1987
; Weinberger, 1987
), yet elucidating the nature, timing and course of the underlying neurobiological changes has proved difficult (Harrison and Lewis, 2003
). Studies from pre-psychosis onset indicate that abnormalities are not greatly evident and that changes are dynamic around the time of onset and over the first few years of illness (Pantelis et al., 2005
; Wood et al., 2008
). This is also a time when dynamic brain changes occur in the normal brains of adolescents. Capturing these changes requires prospective, longitudinal studies that take account of normal and abnormal trajectories of development (Gogtay et al., 2004
; Pantelis and Wood, 2009
). Dynamic changes provide an important context and temporal dimension to identifying potential premorbid markers of subsequent illness.
One attractive theory attempting to explain grey matter reduction in patients is that there is an exaggerated action of the normal CNS pruning process (Feinberg, 1982
; Hoffman and Dobscha, 1989
; Keshavan et al., 1994
; Hoffman and McGlashan, 1997
). There is some emerging support for this hypothesis (for a recent discussion see Boksa, 2012
). One reason to consider exaggerated pruning as the main process in grey matter reduction is the similar time course of the pruning process and onset of illness in schizophrenia. Another reason is the fact that brain tissue of patients with schizophrenia contains no histological evidence of gliosis (scarring) or abnormal degeneration (Rosenthal, 2011
). This lack of a histological marker would be expected with the normal physiological process of pruning. There are some suggestions, for example of exaggerated pruning due to epigenetic differences (Rosenthal, 2011
), that might explain the lack of familial effects on grey matter in our sample.
Several study limitations should be acknowledged. The majority of patients in this study were pharmacologically treated, and many were ill for a number of years, allowing an effect of antipsychotic treatment and disease chronicity on brain volume. This study was not designed to address medication effects; it was cross-sectional and only self-report data were recorded, rather than blood levels or other objective measures of medication status. That neurocognitive differences were also observed in unaffected relatives suggests that medication effects alone cannot account for these findings. However, the opposite applies to the prefrontal cortex reductions that were observed in patients but not relatives. Indeed, some evidence points towards the possibility that antipsychotic drugs reduce the volume of brain matter. Antipsychotics may contribute to the genesis of some of the abnormalities usually attributed to schizophrenia (Moncrieff and Leo, 2010
). More longitudinal studies beginning at first episode and comparing medication conditions are needed to answer this question.
A large number of studies attribute prefrontal cortex dysfunction to the type of executive deficits that we measured. However, it is not proven that all of these tests are adequate markers of frontal lobe function and as such the validity of our conclusions, on the disparity between frontal lobe structure and function, hinges on the adequacy of the tests reflecting frontal lobe function. Other limitations are the general assumptions of genetic modelling (Toulopoulou et al., 2007
) and the particular assumptions used in combined twin and family samples. Specifically, we equated the shared environmental effects across all relatives who share 50% additive genetic effects, which leads to underestimation of any shared environment effects. Several regions of interest heritability estimates were moderate yet failed to reach significance, suggesting that the study lacked power.
We incorporated data that were sometimes collected using different but compatible instruments that could increase methodological heterogeneity. To address this, we standardized the data within each cohort relative to their own control cohort. We used magnetic resonance data from two scanners and three acquisition protocols, introducing possible sources of noise to the data. However, the scanners were identical and the effects of scanner and protocol were controlled for statistically. The segmentation of the prefrontal cortex was based on validated methods, but the regions are relatively large in volume and it remains possible that subtle genetic effects could be present at a subregional level and be identified by alternative neuroimaging analysis techniques incorporating voxel level analysis, finer grained parcellation or greater anatomical precision (Nakamura et al., 2008
The strengths of this study include that the sample, to our knowledge, represents the largest examination of prefrontal cortex subregions using a region of interest technique, in families and twins with schizophrenia and used highly sophisticated multivariate genetic analytical models. The combination of twins and families has the further advantages of increasing the sample size, reducing sample variance due to differences in allelic frequency and representing the true population more accurately than would a twin sample alone while also increasing the power to differentiate between additive genetic effects and shared environmental effects.
In conclusion, while grey matter volumes of the orbital frontal cortex and total prefrontal cortex were moderately heritable, neither shared a genetic overlap with the disorder. The well recognized prefrontal cortex reductions observed in patients with schizophrenia are not related to the same familial influences that increase schizophrenia liability and instead may be attributable to illness-related biological changes or indeed confounded by illness trajectory, chronicity, medication or substance abuse, or in fact a combination of some or all of these. This is the first study to show that prefrontal cortex grey matter deviations are disease specific and not part of the genetic vulnerability. It is certain that the neuropathology, be it atrophic or neurodevelopmental, is part of the disease process.