Amygdala–hippocampal shape differences in schizophrenia: the application of 3D shape models to volumetric MR data

Abstract

Evidence suggests that some structural brain abnormalities in schizophrenia are neurodevelopmental in origin. There is also growing evidence to suggest that shape deformations in brain structure may reflect abnormalities in neurodevelopment. While many magnetic resonance (MR) imaging studies have investigated brain area and volume measures in schizophrenia, fewer have focused on shape deformations. In this MR study we used a 3D shape representation technique, based on spherical harmonic functions, to analyze left and right amygdala-hippocampus shapes in each of 15 patients with schizophrenia and 15 healthy controls matched for age, gender, handedness and parental socioeconomic status. Left/right asymmetry was also measured for both shape and volume differences. Additionally, shape and volume measurements were combined in a composite analysis. There were no differences between groups in overall volume or shape. Left/right amygdala–hippocampal asymmetry, however, was significantly larger in patients than controls for both relative volume and shape. The local brain regions responsible for the left/right asymmetry differences in patients with schizophrenia were in the tail of the hippocampus (including both the inferior aspect adjacent to parahippocampal gyrus and the superior aspect adjacent to the lateral geniculate nucleus and more anteriorly to the cerebral peduncles) and in portions of the amygdala body (including the anterior–superior aspect adjacent to the basal nucleus). Also, in patients, increased volumetric asymmetry tended to be correlated with increased left/right shape asymmetry. Furthermore, a combined analysis of volume and shape asymmetry resulted in improved differentiation between groups. Classification function analyses correctly classified 70% of cases using volume, 73.3% using shape, and 87% using combined volume and shape measures. These findings suggest that shape provides important new information toward characterizing the pathophysiology of schizophrenia, and that combining volume and shape measures provides improved group discrimination in studies investigating brain abnormalities in schizophrenia. An evaluation of shape deformations also suggests local abnormalities in the amygdala–hippocampal complex in schizophrenia.

Introduction

Magnetic resonance (MR) imaging studies of schizophrenia, which began only in 1984 (Smith et al., 1984), have evolved from the use of 1-cm slices that did not cover the whole brain to 1.5-mm slices of the entire brain (for a review, see Shenton et al., 1997, Shenton et al., 2001, McCarley et al., 1999). This improvement in spatial resolution was needed to analyze small brain changes between normal controls and schizophrenic patients. The volume reductions observed in schizophrenia are, in fact, relatively small, on the order of 10–20% difference from controls and, thus, improved measurement techniques were necessary before evidence could be accumulated to suggest small volume reductions in the brains of schizophrenic patients.

Such evidence has now accumulated and there has been a proliferation of MR studies documenting brain abnormalities in schizophrenia (e.g. Suddath et al., 1989, Suddath et al., 1990, Barta et al., 1990, Bogerts et al., 1990, Dauphinais et al., 1990, DeLisi et al., 1991, DeLisi et al., 1994, Shenton et al., 1992, Andreasen et al., 1994, Marsh et al., 1994, Marsh et al., 1997, Rossi et al., 1994a, Rossi et al., 1994b, Pearlson et al., 1997). In a recent review of the literature, the most robust MR findings in schizophrenia are: enlarged lateral ventricles (77% of studies); medial temporal lobe (amygdala–hippocampal complex and/or parahippocampal gyrus) volume reduction (77% of studies); and gray matter volume reduction of superior temporal gyrus (100% of studies) (see Shenton et al., 1997, Shenton et al., 2001, McCarley et al., 1999).¹ There is also growing evidence to suggest that at least some structural brain abnormalities observed in schizophrenia are neurodevelopmental in origin. The general approach taken is to assume that if certain brain abnormalities could only have occurred during neurodevelopment, then an abnormal finding at a later stage of development confirms a neurodevelopmental origin for that brain abnormality (e.g. Frangou and Murray, 1996, Bartley et al., 1997). For example, the sulco-gyral patterns in the brain are largely formed during the third trimester (e.g. Chi et al., 1977, Sadler, 1981, Ono et al., 1990). Thus, abnormalities in the sulco-gyral pattern of the temporal lobe in schizophrenic patients, reported in both post-mortem (e.g. Southard, 1910, Southard, 1915, Brown et al., 1986, Jakob and Beckmann, 1986) and MR studies (e.g. Kikinis et al., 1994), suggest that such alterations are the result of neurodevelopmental abnormalities. A further example of a neurodevelopmental abnormality in schizophrenia is the cavum septum pellucidi (CSP). The CSP fuses in the latter part of neural development, and therefore a space, or ‘cavum’, observed likely reflects deviations in neurodevelopment (e.g. Shaw and Alvord, 1969, Lewis and Mezey, 1985, Rossi et al., 1989, Sarwar, 1989, Nopoulos et al., 1996, Nopoulos et al., 1997, Kwon et al., 1998). Of note, this fusion is thought to result from the rapid growth of the corpus callosum and the hippocampus, further suggesting that abnormalities in these two structures may be related, at least in part, to neurodevelopmental abnormalities (e.g. Rakic and Yakovlev, 1968). Data from our laboratory confirm an association between CSP and hippocampus; large CSP was highly correlated with reduced hippocampal volume in chronic patients (Kwon et al., 1998). (Parenthetically, the hippocampus can be affected by environmental events—see review and discussion in Gurvits et al., 1996, McEwen and Magarinos, 1997.) Additionally, planum temporale asymmetry, an important biological substrate of language, is established during neural development and it, too, has been shown to be abnormal in schizophrenia, again suggesting the importance of neurodevelopmental influences in the etiology of schizophrenia (e.g. DeLisi et al., 1994, Rossi et al., 1994a, Rossi et al., 1994a, Barta et al., 1995, Barta et al., 1997, Petty et al., 1995, Kwon et al., 1999).

Given the importance of neurodevelopmental influences in schizophrenia, it is of interest to note that most studies in schizophrenia have investigated area, volume and asymmetry, but fewer (e.g. Csernansky et al., 1998) have evaluated shape, which may be importantly linked to neurodevelopmental influences. For example, there is evidence to suggest that shape deformations may be associated with the physical properties of morphogenetic mechanisms that directly impact on the particular shape of brain regions during neurodevelopment (Van Essen, 1997, Van Essen and Drury, 1997, Van Essen et al., 1998). The physical tension of brain growth during neurodevelopment may lead to shape deformations that might be observed using shape measures of brain structures.

Thus, midbrain structures, likely implicated in schizophrenia which, as noted above, include both the hippocampus and corpus callosum, are important brain regions to investigate in schizophrenia as they may show shape deformations that reflect neurodevelopmental anomalies. A recent study by Thompson et al. (2000) further suggests the importance of patterns of brain growth and development, which takes place post-natally as well, and may lead to changes in volume, shape and asymmetry of brain structures in both normal and abnormal development.

In the current study, we investigated shape deformations in the amygdala–hippocampal complex in 15 male patients diagnosed with chronic schizophrenia, and 15 male controls, group matched for handedness, parental socioeconomic status and age.² This brain region has figured prominently in many MR volume findings in schizophrenia (see reviews in Shenton et al., 1997, Shenton et al., 2001, McCarley et al., 1999), and, as noted previously, the posterior portion of the amygdala–hippocampal complex has been associated with large CSP in patients diagnosed with chronic schizophrenia (Kwon et al., 1998). This link further suggests an anomaly in neonatal development of midline brain structures. In the current study we focus on shape differences in the amygdala–hippocampal complex between groups.

Measures of shape are, nonetheless, complex. An entire field of computer science, in fact, has focused on quantitative descriptions of the shape of objects (e.g. Van Essen and Maunsell, 1980, Caviness et al., 1988, Kass et al., 1988, Bajcsy and Kovacic, 1989, Bookstein, 1989, Bookstein, 1997a, Bookstein, 1997b, Bookstein, 1997c, Evans et al., 1991, Cohen et al., 1992, Collins et al., 1992, Cootes and Taylor, 1992, Brechbühler et al., 1992, Hill and Taylor, 1992, Hill et al., 1992, Talbot and Vincent, 1992; Cootes et al., 1993; Gee et al., 1993, Grenander, 1993, Christensen et al., 1994, Christensen et al., 1996, Christensen et al., 1997, Attali and Montanvert, 1994, Brechbühler et al., 1995, Haller et al., 1996, Haller et al., 1997, Drury et al., 1996, Székely et al., 1996, Näf et al., 1996, Näf et al., 1997, Bookstein, 1997a, Bookstein, 1997b, Bookstein, 1997c, Joshi et al., 1997, Morse et al., 1998, Pizer et al., 1998, Angenent et al., 1999, Kelemen et al., 1999). Such descriptions have involved the use of a skeleton or medial axis to extract shape features (e.g. Blum, 1967, Blum, 1973, Bruce and Giblin, 1986, Talbot and Vincent, 1992, Ogniewicz, 1993, Attali and Montanvert, 1994, Kimia et al., 1995, Näf et al., 1996, Näf et al., 1997, August et al., 1999, Golland et al., 1999). Other approaches have included physically based shape representations such as thin-plate-splines and fiducials (e.g. Bookstein, 1989, Bookstein, 1997a, Bookstein, 1997b, Bookstein, 1997c, Pentland and Sclaroff, 1991, DeQuardo et al., 1996), surface or contour based representations (e.g. Kass et al., 1988, Brechbühler et al., 1992, Cohen et al., 1992, Cootes and Taylor, 1992, Cootes et al., 1993a, Cootes et al., 1993b, Hill and Taylor, 1992, Brechbühler et al., 1995, Hill et al., 1992, Hill et al., 1993, Kelemen et al., 1997, Pizer et al., 1998, Angenent et al., 1999), including elastically deformable contour and surface models (e.g. Bajcsy and Kovacic, 1989, Evans et al., 1991, Collins et al., 1992, Gee et al., 1993, Christensen et al., 1994, Christensen et al., 1996, Christensen et al., 1997, Kelemen et al., 1999), and pattern-matching methods derived from the theory of patterns by Grenander (1993) (e.g. Haller et al., 1997, Csernansky et al., 1998). A clear trend in shape analysis is toward the movement from summary measures of whole structures or objects to measures of regional differences in shape, thus incorporating more information about the properties of shape than more simple volumetric measures. Shape descriptions that are represented as high-dimensional features (Haller et al., 1996, Haller et al., 1997, Csernansky et al., 1998, Hogan et al., 2000, Wang et al., 2001) or features derived from a projection onto basis functions (Kelemen et al., 1999) are examples of this trend.

In this study we used an active, flexible deformable shape model to segment automatically the amygdala–hippocampal complex from MR image data. Models were trained from a set of volumes segmented manually by trained experts (derived from our previous study, Shenton et al., 1992). The surfaces of the training objects (amygdala–hippocampal complex) were then converted into parametric surface nets expanded into shape descriptions using spherical harmonic expansion (Brechbühler et al., 1995, Székely et al., 1996). The set of shapes characterized by parameter vectors led to a statistical shape model describing the average object shape and its major modes of variation. This statistical shape model was then used for the segmentation of new datasets. Here, the average shape model was initialized based on a manual selection of three anatomical landmarks (anterior/posterior commissure and a point in the interhemispheric fissure). This shape model was driven by the object boundaries of the new image, although deformation was constrained by the statistics learned from the training sample, which significantly improves the robustness of the method (Kelemen et al., 1999). The resulting objects were represented by a set of parameters, which were then used as input for subsequent classical multivariate analyses to detect group differences in volume and in object surface descriptions (i.e. shape).