Elsevier

NeuroImage

Volume 60, Issue 1, March 2012, Pages 693-699
NeuroImage

Differential effects of surface area, gyrification and cortical thickness on voxel based morphometric deficits in schizophrenia

https://doi.org/10.1016/j.neuroimage.2011.12.058Get rights and content

Abstract

Voxel Based Morphometry (VBM) and Surface Based Morphometry (SBM) are the two most commonly used methods to study the structure of gray matter in various disease states such as schizophrenia. Though overlapping changes have been observed in same datasets using the two procedures, the proportional contribution of the anatomical properties of the cortical mantle such as thickness, surface area and gyrification to the group differences in gray matter volume (GMV) observed using VBM is unknown. In the present study, we investigate the relationship between the GMV and the anatomical properties of the cortical mantle in regions showing significant VBM changes in schizophrenia using a sample of 57 patients and 41 healthy controls. To this end, we obtained significant clusters showing VBM changes in schizophrenia and studied the contribution of the three anatomical properties derived from SBM to the observed group differences in the GMV using a multiple mediation analysis. Our results suggest that while SBM measures make distinct but regionally variable contribution to the VBM differences, a large proportion of the group difference observed using VBM is not explained by the individual surface anatomical properties. While VBM may be more sensitive in identifying the regions with gray matter abnormalities, studies investigating the pathophysiology of illnesses such as schizophrenia are better informed when both SBM and VBM analyses are performed concurrently.

Highlights

► Surface anatomical changes contribute to a proportion of gray matter deficits in VBM. ► The influence of surface anatomy on VBM deficits is regionally variable. ► Thickness, area and gyrification contribute to VBM deficits in schizophrenia.

Introduction

Voxel Based Morphometry (VBM) is a widely used method to quantify gray matter abnormalities in disease states (Ashburner and Friston, 2000). Since its initial application to study structural changes in schizophrenia (Wright et al., 1995), the technique of VBM has evolved significantly. One of the major criticisms of VBM is that the technique is very sensitive to the image registration procedures. Conditions that result in systematic differences in image alignment may produce spurious results (Bookstein, 2001), though the significance of this is refuted (Ashburner and Friston, 2001). This issue is especially relevant for conditions such as schizophrenia, where abnormalities of sulcal patterning around the lateral fissure (Csernansky et al., 2008) can produce group differences in aligning gray matter images for VBM, in particular affecting the perisylvian regions such as insula and the surrounding operculum that are commonly observed to have GMV deficit in schizophrenia (Koutsouleris et al., 2008, Meisenzahl et al., 2008). Recent updates of VBM improve on registration methods (Ashburner, 2007), thus addressing the criticisms raised by Bookstein (2001) to certain extent.

Surface based morphometric (SBM) methods such as Freesurfer circumvent some of the problems associated with VBM by undertaking computations of morphometric properties in the ‘native space’ (Dale et al., 1999). The computationally intensive surface based techniques provide distinct cortical thickness and surface area measures that are shown to be both genetically and phenotypically independent (Panizzon et al., 2009, Winkler et al., 2010). Group differences emerging in a VBM study could be variously attributed to cortical thinning, altered gyrification (cortical folding) or abnormalities in the surface area (Mechelli et al., 2005). Despite the increasing number of studies that employ both VBM and SBM together to study brain structure in disease states (Cerasa et al., 2011, Chee et al., 2011, Lehmann et al., 2011), the relationship between VBM derived gray matter volume (GMV) and SBM derived measures of thickness, gyrification and surface area is unclear.

The importance of structural changes in understanding the pathophysiology of schizophrenia has been well established (McCarley et al., 1999, Shenton et al., 2001). A significant number of VBM investigations of the neuroanatomy of schizophrenia have been published in the last two decades (Ellison-Wright et al., 2008, Glahn et al., 2008, Honea et al., 2005, Leung et al., 2011). Despite the significant spatial heterogeneity of the reported findings, meta-analysis of VBM studies estimates a high likelihood of gray matter deficits in specific regions such as bilateral insula, temporal cortex and anterior cingulate in schizophrenia (Ellison-Wright et al., 2008, Glahn et al., 2008, Honea et al., 2005, Leung et al., 2011). Cortical thickness (Kuperberg et al., 2003) and surface contraction maps (Palaniyappan et al., 2011a) identify regions of structural changes that overlap to some extent with the regions showing GMV reduction in VBM. Focused analyses of the spatial overlap between VBM and SBM in schizophrenia have previously revealed that either thickness or surface area changes were present in clusters that had significant reduction in GMV (Narr et al., 2005, Voets et al., 2008) with some suggestion that cortical folding differences could account for the some of the regional differences. These findings indicate that changes in the anatomical properties of the cortical mantle (thickness, cortical folding or surface area) may underlie the GMV reduction seen in schizophrenia.

In the present study, we investigate the relationship between the GMV and anatomical properties of the cortical mantle in regions showing significant VBM changes in schizophrenia. We hypothesized that SBM measures will mediate the group differences in GMV observed in the VBM analysis. Given the partial overlap in the spatial distribution of VBM and cortical thickness measured using both surface-based (Narr et al., 2005, Voets et al., 2008) and voxel-based methods (Hutton et al., 2009) in previous studies, we expected regional differences in the influence of surface based measures on VBM findings.

Section snippets

Subjects

Data acquired from 98 subjects (57 with schizophrenia and 41 healthy controls) were used in this study (Table 1). Regional Ethics Committees (Nottinghamshire and Derbyshire) approved the study and all participants provided written informed consent. This sample has been described in detail elsewhere in our reports on surface area and gyrification abnormalities (Palaniyappan et al., 2011a, Palaniyappan et al., 2011b). The diagnosis of schizophrenia was made in accordance with the procedure of

Voxel Based Morphometry

The VBM comparison between patients and controls revealed five clusters with significant reduction in GMV (Web Supplement DS1). There were no regions with increased GMV in schizophrenia. As expected, bilateral insula emerged as regions of significant GMV reduction, along with left thalamus, left precuneus and left middle temporal region. Binary masks were derived for right and left insula in addition to left middle temporal and left precuneus clusters. The MNI coordinates of the four cortical

Discussion

Using VBM and SBM on a cross sectional sample with schizophrenia, we have shown that the differences in GMV observed using VBM are partially mediated by surface anatomical properties such as gyrification, surface area and thickness. Reduced gyrification and surface area were observed in three out of four clusters examined, while reduced thickness was observed in two clusters. But the mediating effect of the surface anatomical features on GMV is regionally variable with a large proportion of the

Funding

This work was supported by a New Investigator grant from the University of Nottingham and an Interdisciplinary Research Award from the Nottingham Institute of Neuroscience, University of Nottingham.

Acknowledgments

We are grateful to the volunteers who participated in this study and would like to acknowledge Pavan Mallikarjun and Verghese Joseph for clinical recruitment. We would like to thank Thomas White, Kathrin Doege, Antonio Napolitano, Kay Head, Dawn-Marie Walker and Dorothee Auer for assisting the data acquisition.

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