Anatomical abnormalities of the anterior cingulate and paracingulate cortex in patients with bipolar I disorder

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Psychiatry Research: Neuroimaging 162 (2008) 123 – 132 www.elsevier.com/locate/psychresns

Anatomical abnormalities of the anterior cingulate and paracingulate cortex in patients with bipolar I disorder Alex Fornitoa,b,⁎, Gin S. Malhic,d , Jim Lagopoulosc,d , Belinda Ivanovskic,d , Stephen J. Wooda , Michael M. Saling b , Christos Pantelisa,e , Murat Yücela,f a

Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia b Department of Psychology, The University of Melbourne, Melbourne, Australia c Department of Psychological Medicine, Northern Clinical School, The University of Sydney, Sydney, Australia d CADE clinic, Department of Psychiatry, Royal North Shore Hospital, Sydney, Australia e Howard Florey Institute, The University of Melbourne, Melbourne, Australia f ORYGEN Research Centre, The University of Melbourne, Melbourne, Australia Received 20 October 2006; received in revised form 17 April 2007; accepted 4 June 2007

Abstract Abnormalities of the anterior cingulate cortex (ACC) are thought to be involved in the pathophysiology of bipolar disorder, but structural Magnetic Resonance Imaging (MRI) studies have reported variable findings. Reasons for this include a failure to consider variability in regional cortical folding patterns and a reliance on relatively coarse measures (e.g., volume) to index anatomical change. We sought to overcome these limitations by combining a novel protocol for parcellating the ACC and adjacent paracingulate cortex (PaC) that accounts for inter-individual variations in sulcal and gyral morphology with a cortical surface-based approach that allowed calculation of regional grey matter volume, surface area and cortical thickness in 24 patients with bipolar I disorder and 24 matched controls. No changes in grey matter volume or surface area were identified in any region, but patients did show significant reductions in cortical thickness in the left rostral PaC and right dorsal PaC that were not attributable to group differences in cortical folding patterns. These findings suggest that bipolar disorder is associated with more pronounced changes in the PaC, and that reliance on volumetric measures alone may obscure more subtle differences. © 2007 Published by Elsevier Ireland Ltd. Keywords: Prefrontal; Depression; Mood; Voxel; Gyrification; Psychosis

1. Introduction

⁎ Corresponding author. Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Levels 2 and 3, National Neuroscience Facility, Alan Gilbert Building, 161 Barry St., Carlton South, Vic 3053, Australia. Tel.: +61 3 8344 1800; fax: +61 3 9348 0469. E-mail address: [email protected] (A. Fornito). 0925-4927/$ - see front matter © 2007 Published by Elsevier Ireland Ltd. doi:10.1016/j.pscychresns.2007.06.004

The anterior cingulate cortex (ACC) is a brain region critical for integrating cognitive and emotional functions in support of adaptive, goal-directed behaviour (Cohen et al., 2000; Phillips et al., 2003a; Ridderinkhof et al., 2004), and accumulating evidence suggests abnormalities in the region may represent a pathophysiological basis for many of the functional impairments seen in patients with

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bipolar disorder (Benes, 1993; Drevets et al., 1998; Harrison, 2002; Phillips et al., 2003b). Structural Magnetic Resonance Imaging (MRI) has frequently been used to characterize anatomical changes in the ACC of bipolar patients in vivo, although the studies conducted to date have yielded inconsistent findings. For example, initial studies using manual region-of-interest (ROI) techniques reported large (up to 39%) volumetric reductions in the grey matter of a subgenual region of the left ACC in familial bipolar patients (Drevets et al., 1997), and while this finding has subsequently been replicated in both imaging and post-mortem work (Hirayasu et al., 1999; Ongur et al., 1998), more recent studies have either failed to replicate (Brambilla et al., 2002; Sanches et al., 2005; Sharma et al., 2003; Zimmerman et al., 2006) or reported more generalized reductions across the entire anterior cingulate gyrus (Kaur et al., 2005; Sassi et al., 2004). Studies employing voxel-based approaches have also reported inconsistent results, with findings of either left-sided reductions (Lyoo et al., 2004), right-sided reductions (Bruno et al., 2004), bilateral changes (Adler et al., 2005; Lochhead et al., 2004; Nugent et al., 2006; Wilke et al., 2004), or no group differences at all (Dickstein et al., 2005; Farrow et al., 2005; Kubicki et al., 2002; McIntosh et al., 2004). Two major limitations restrict the conclusions that can be drawn from this literature. Firstly, the methods employed to date fail to account for the substantial interindividual variability in cortical folding patterns of the region (Paus et al., 1996b; Yücel et al., 2001). This variability has been shown to affect the volume of adjacent cortex (Fornito et al., 2006; Paus et al., 1996a); can cause registration errors and produce spurious results in voxel-based statistical comparisons (Bookstein, 2001; Crum et al., 2003); and can make it difficult to determine appropriate ROI boundaries in manual tracing studies. A second limitation of past research is that grey matter volume, the measure most commonly employed to quantify cortical changes, is a relatively coarse metric that reflects the product of a region's surface area and thickness. Consequently, focusing only on volume may prevent detection of more subtle changes in one of its constituent parameters (e.g., thickness) because the other (e.g., area) shows little or opposite change. It then becomes difficult to determine whether volumetric changes result from differences in surface area, thickness, or a combination of both variables, each of which may have distinct pathophysiological implications. For example, normal adolescent neurodevelopment is associated with both brain growth (i.e., surface area expansion) and cortical thickness reduction (Sowell

et al., 2004; Sowell et al., 2001; Sowell et al., 2004), and an exaggeration of these patterns has been found in the ACC of first episode schizophrenia patients (Fornito et al., in press-b). In contrast, as noted by Rettmann et al. (2006), age-related atrophy is associated with reduction in both thickness and area, leading to a global volumetric decrease. Differential changes in cortical thickness and area have also been associated with distinct neurodevelopmental conditions (e.g., Rakic, 2000), and recent evidence suggests cortical thickness may be a measure of particular relevance in studies of psychiatric populations, given findings of associations between thickness variations and cognitive and emotional functions (Fjell et al., 2006; Shaw et al., 2006). As such, moving beyond traditional volumetric measures may afford enhanced sensitivity for detecting anatomical differences, and may also offer the opportunity to identify patterns of change that may provide more information regarding underlying pathophysiology. In this study, we sought to address the limitations of past work by using a novel and reliable method for parcellating the ACC and adjacent paracingulate cortex (PaC) into functionally relevant regions while considering the morphological variability of the area (Fornito et al., 2006). Adapting this protocol for use with reconstructions of the cortical surface (Dale et al., 1999; Fischl et al., 1999a) allowed us to calculate the grey matter volume, surface area, and mean cortical thickness of each region separately, enabling a more detailed characterization of the regional anatomical abnormalities associated with bipolar disorder. We expected our patient sample would show a reduction in ACC grey matter, as indexed by either the volume, area, or thickness measures, given that this has been the most common finding in past research. However, as no prior study has considered the influence of sulcal variability on ACC morphometry in bipolar disorder, we made no firm predictions regarding the specific sub-region that would be affected. 2. Methods 2.1. Participants The sample comprised 24 patients with DSM-IV bipolar I disorder and 24 matched healthy controls. Patients were recruited from the Mood Disorders Unit at the Prince of Wales Hospital, Sydney, Australia. Diagnoses were made by research psychiatrists using the Structured Clinical Interview for DSM-IV (SCIDIV-P) (First et al., 1998), supplemented by case note review. Controls were recruited via advertisement and matched for age and education. They were screened for

A. Fornito et al. / Psychiatry Research: Neuroimaging 162 (2008) 123–132 Table 1 Demographic details Bipolar (n = 24) Male/female Age (years) NART-IQ Education (years) Illness duration (years) No. manic episodes No. depressive episodes

7/17 39.46 ± 10.45 113.96 ± 7.24 14.74 ± 2.93 14.25 ± 10.16 9.29 ± 10.44 11.62 ± 10.04

Controls (n = 24)

t

P

7/17 38.67 ± 11.07 0.26 0.80 115.08 ± 9.59 −0.46 0.65 15.58 ± 2.10 0.21 0.83

NART-IQ = National Adult Reading Test-estimated Intelligence Quotient.

a personal and family history of psychiatric or neurological disorder using the SCID-NP. Participants were excluded if they had a history of ongoing substance misuse, neurological disease or, in patients, a co-morbid Axis I or II DSM-IV diagnosis requiring treatment. Three patients had a family history of bipolar disorder, two had a family history of both bipolar disorder and unipolar depression, and five had a family history of unipolar depression only. Eleven patients had no family history of affective illness, and three had an unknown family illness history. Eight patients were taking lithium at the time of scanning, six were taking valproate, and four were taking a combination of both. One was taking valproate and carbemazepine and one was taking carbemazepine alone, while four patients were medication free at the time of scanning (none of these four had ever been stabilized on medication for a prolonged period, and the most recent exposure prior to entering the study was 5 years). All patients have previously been exposed to antipsychotic medication, although none within 12 months of entering the study. All participants provided written, informed consent before participating and the study was approved by the Prince of Wales Hospital and the University of New South Wales ethics committees. Additional demographic details are provided in Table 1. 2.2. Magnetic resonance imaging Scans were acquired on a 1.5 T GE Signa scanner located at the Royal Prince Alfred Hospital, Sydney, Australia. Imaging parameters were as follows: echo time (TE), 5.3 ms; Repetition Time (TR), 12.2 ms; field of view, 24.9 cm; voxel dimensions, 0.977× 0.977 ×1.6 mm thick coronal slices. All MRI data were transferred from CD to a Linux Debian 3.1 workstation and coded to ensure participants, confidentiality and blinded rating. The surface

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reconstruction algorithms employed (see below) are computer intensive, requiring ∼ 24 h per individual to generate accurate surfaces. Individual reconstructions were therefore performed in parallel on a networked cluster of 12 dual-processor Apple Mac G5 computers located at the National Neuroscience Facility, Melbourne, Australia (Kolbe et al., 2005). 2.3. Image pre-processing Each participant's image was stripped of extracerebral tissue (Smith, 2002) and aligned to the N27 template (Holmes et al., 1998) via a 6-degree rigid-body transformation (Jenkinson and Smith, 2001) using tools contained in the FSL software package (http://www. fmrib.ox.ac.uk/fsl). No re-scaling or warping was performed, but the images were resampled to 1 mm3 voxels in the process. 2.4. Classification of sulcal morphology Two major sulcal variations in the region affected ROI boundaries: the incidence and course of the paracingulate sulcus (PCS) and the continuity of the superior rostral sulcus (SRS) with the cingulate sulcus (CS). These variations were therefore classified in the sagittal plane of the N27-aligned images using established methods (Fornito et al., 2006; Yücel et al., 2001) prior to tracing in order to assist ROI delineation. Briefly, the PCS was classified as ‘present’ if there was a clearly identifiable sulcus running dorsal and parallel to the CS for ≥ 20 mm and which was present for ≥ 3 slices. If no such sulcus was apparent, an ‘absent’ classification was made. SRS classifications were based on guidelines provided by Ono et al. (1990), who noted that it connects with the CS in approximately 8% of right hemisphere and 24% of left hemisphere cases, and remains distinct from the CS in all other cases. Thus, a ‘continuous’ classification was assigned if the two sulci were connected rostral to the genu of the corpus callosum, and a ‘separate’ classification was made in all other cases (see Fornito et al., 2006; for more details, see also Fig. 1). All sulcal classifications were performed using Analyze 6.0 (Mayo Software). 2.5. Cortical surface reconstruction Cortical surface reconstructions were generated with the Freesurfer software package (http://surfer.nmr.mgh. harvard.edu), and detailed descriptions of the algorithms used have been provided elsewhere (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 1999a). Briefly, the

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Fig. 1. Examples of region-of-interest (ROI) boundaries and their relationship with local variations in cortical folding. Top row presents a case with an ‘absent’ paracingulate sulcus (PCS) and ‘continuous’ superior rostral sulcus (SRS) and bottom row presents a case with a ‘present’ PCS and ‘separate’ SRS. The left column presents the reconstructed pial surfaces with the delineated ROIs. The posterior red line represents the caudal border of the dorsal region. The anterior red line separates the rostral and dorsal regions dorsally, and rostral and subcallosal regions ventrally. The middle red line represents the posterior border of the subcallosal region. The left column presents the reconstructed white matter surfaces, expanded to illustrate ROI boundaries. Sulci on the white matter surface correspond to indentations or ‘crevasses’, whereas gyri correspond to protrusions or ‘ridges’. Note how the PaC ROIs are not visible on the pial surface in ‘absent’ cases, but are readily visualized on the white matter surface. ACC = anterior cingulate cortex; PaC = paracingulate cortex; d-, r-, and s-refer to the dorsal, rostral, and subcallosal divisions, respectively.

Freesurfer processing pipeline involved intensity normalization of the T1-weighted images, automated removal of extracerebral tissue, and segmentation of volumes representing the white matter only, with cutting planes separating the two cerebral hemispheres from each other and from the brainstem and cerebellum. The white matter surfaces (i.e., the boundary between white and grey matter) of each hemisphere were then tessellated with a smoothed, triangular mesh comprising ∼150,000 vertices per hemisphere, and topological defects were corrected using an automated algorithm (Fischl et al., 2001) guided by atlas-based segmentation of cerebral structures (Fischl et al., 2002). The resulting white matter surface was then deformed outwards to estimate the pial surface (i.e., the boundary between grey matter and cerebrospinal fluid).

Reconstruction of the white and pial surfaces enabled calculation of regional surface area, cortical thickness, and grey matter volume. Surface area was calculated by summing the area of the triangle faces included in each ROI for the white and pial surfaces separately, and then averaging the two. Cortical thickness was calculated at each surface point by finding the vertex on the pial surface closest to a given point on the white surface (and vice-versa), and averaging these two values (Fischl and Dale, 2000). As the surfaces are generated with subvoxel resolution, the resulting thickness measures are estimated with sub-millimeter precision, and have been validated against post-mortem specimens (Rosas et al., 2002). The thickness values for each surface point in the ROIs were averaged to obtain the mean thickness for that region. Regional grey matter volume was calculated as the product of thickness and surface area (averaged across white and pial surfaces) at each surface point, summed across all surface points included in the ROI. All surfaces were visually inspected and inaccuracies were manually corrected as per guidelines on the Freesurfer website (http://surfer.nmr.mgh.harvard.edu). 2.6. Parcellation of the anterior cingulate and paracingulate cortices We adapted our previously described approach for volumetric parcellation of the ACC and PaC (Fornito et al., 2006) for use with the cortical surface reconstructions (as described in Fornito et al., in press-a). The method delineates a dorsal ACC and PaC (d-ACC and d-PaC, respectively), rostral ACC and PaC (r-ACC and r-PaC, respectively), and a subcallosal ACC and PaC (s-ACC and s-PaC, respectively). The borders between the dorsal, rostral, and subcallosal regions were identified on the N27aligned T1-weighted volumes. The border between the dorsal and rostral regions was identified as the first coronal slice, moving posteriorly, in which the genu of the corpus callosum could be clearly seen interconnecting the two hemispheres. This border bears reasonable correspondence with the boundary between areas 24 and 32 rostrally and 24V and 32V dorsally, identified in the post-mortem work of Vogt et al. (1995), and is consistent with functional imaging evidence that activations for affective tasks tend to cluster anterior to the genu whereas those for cognitive tasks cluster dorsal and posterior to the genu (Amodio and Frith, 2006; Bush et al., 2000; Steele and Lawrie, 2004). This plane also formed the anterior border of the subcallosal region, which was bordered posteriorly by the first coronal slice in which the internal capsule could be clearly seen separating the caudate from the putamen (following Drevets et al., 1997). This area approximates a

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fundus of the CS to that of the PCS. Similarly, in cases with a ‘separate’ SRS, the PaC extended from the fundus of the CS to that of the SRS, but was located on the rostro-ventral bank of the CS in ‘continuous’ cases. These relationships are illustrated in Fig. 1. A more detailed description of rules and justifications can be found in Fornito et al. (2006). We have previously assessed reliabilities for the surface-based adaptation of this protocol (Fornito et al., in press-a) and found both intra- and inter-rater reliability intra-class coefficients to be satisfactory across all ROIs, with none falling below 0.8, and most being above 0.9. All ROIs were traced using tools contained in the Freesurfer software package.

region commonly called the subgenual prefrontal cortex in structural imaging studies of bipolar disorder (Brambilla et al., 2002; Drevets et al., 1998; Hirayasu et al., 1999; Sharma et al., 2003), and appears to be primarily involved in the experiential aspects of emotion, particularly negative affect (Devinsky et al., 1995; Mayberg et al., 1999; Vogt et al., 2003). The posterior border of the dorsal region was taken as the first coronal slice, moving posteriorly, in which the anterior commissure was no longer visible, to separate it from a more caudal region involved in motor function (Picard and Strick, 1996). These planes were saved as binary images and overlaid onto the surface reconstructions by applying the inverse of the transform used to align raw image to the N27 template (which returned them back into native space). Borders distinguishing the ACC (approximating areas 24 and 24V) from the PaC (approximating areas 32 and 32V) were traced with reference to each individual's sulcal anatomy. The ACC was always located between the fundus of the CS and that of the callosal sulcus. Borders for the PaC were based on Vogt et al.'s (1995) finding that the region is buried on the dorsal bank of the CS when a PCS is absent and extends across the crown of the paracingulate gyrus when a PCS is present. Delineating ROIs according to this principle was facilitated by tracing on the reconstructed white matter surface, which opens up the medial wall sulci without introducing some of the distortions inherent in other surface representations, such as inflated surfaces or flat maps (Fischl et al., 1999a; Van Essen, 2005). Thus, in ‘absent’ cases, the PaC extended from the fundus of the CS up its dorsal bank, without continuing onto the gyral surface. In ‘present’ cases, the PaC extended from the

2.7. Intracranial volume In order to correct for group differences in head size, intracranial volume (ICV) was estimated for each individual using methods described by Eritaia et al. (2000). 2.8. Statistical analyses All analyses were performed using SPSS 12.0 for Windows. Regional grey matter volume, surface area, and cortical thickness were analyzed using mixed within- and between-subjects ANOVA, with hemisphere (left or right), region (dorsal, rostral, and subcallosal), and cortex (ACC or PaC) as within-subjects factors, and diagnosis as the between-subjects factor. Main effects and interactions were evaluated using Greenhouse–Geisser corrected degrees of freedom as sphericity assumptions were invariably violated. Model adequacy was assessed using standard assumption tests and graphical displays

Table 2 Means (and standard deviations) for each group in each region of interest d-ACC L Bipolar

Volume

1746.41 (418.85) Area 646.44 (128.84) Thickness 2.67 (0.19) Controls Volume 1558.34 (463.19) Area 591.97 (160.82) Thickness 2.63 (0.20)

d-PaC

r-ACC

r-PaC

R

L

R

L

R

L

2083.25 (627.70) 767.77 (217.29) 2.68 (0.19) 1975.05 (582.14) 710.16 (191.71) 2.73 (0.18)

1355.49 (667.76) 505.60 (226.90) 2.60 (0.27) 1369.46 (603.36) 488.53 (200.31) 2.74 (0.19)

1093.65 (504.76) 412.51 (167.60) 2.59 (0.28) 1152.11 (592.85) 412.61 (199.33) 2.73 (0.18)

1355.70 (799.51) 454.04 (262.89) 2.93 (0.22) 1111.53 (799.28) 381.72 (268.09) 2.79 (0.32)

1712.19 (783.45) 596.99 (267.80) 2.89 (0.34) 1827.98 (871.02) 620.85 (297.97) 2.92 (0.30)

2019.70 (955.40) 768.85 (294.56) 2.54 (0.30) 2456.62 (665.58) 885.04 (234.64) 2.72 (0.19)

s-ACC R

L

s-PaC R

L

R

1899.22 201.00 284.42 300.09 205.89 (688.60) (123.51) (190.21) (184.28) (143.20) 726.15 95.06 109.29 105.84 70.52 (236.48) (50.35) (72.18) (71.57) (47.91) 2.59 2.06 2.53 3.02 2.98 (0.24) (0.36) (0.40) (0.38) (0.48) 1894.10 191.73 274.50 271.17 174.44 (729.60) (146.65) (145.84) (124.38) (130.75) 690.31 88.94 102.85 96.68 59.32 (243.60) (52.90) (48.62) (47.40) (47.71) 2.69 2.08 2.55 2.94 3.07 (0.25) (0.46) (0.42) (0.52) (0.36)

ACC = anterior cingulate cortex; PaC = paracingulate cortex; d-, r-, and s-, denote dorsal, rostral, and subcallosal regions, respectively. Volume and area values are corrected for intracranial volume as detailed in Section 2. Volume reported as mm3, surface area as mm2, and thickness as mm.

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(Behrens, 1997). No observations were found to exert an undue influence on the analyses, as determined by Cook's Distance, a measure of the potential influence of outlying values. To strike a trade-off between retaining adequate power and protection of family-wise type 1 error rates in light of the large number of variables and limited sample size, we tested main effects and interactions in the overall model with α = 0.05 and followed up significant effects with post-hoc pairwise contrasts evaluated against a Bonferroni-adjusted threshold to correct for multiple comparisons. Effect sizes, expressed as Cohen's (1992) d, are also provided where appropriate. Only effects involving diagnosis are reported, as these were the primary focus of the current study. Initial analyses indicated that intracranial volume was a significant covariate for volume and surface area estimates, but that it interacted with some of the factors included in the models, suggesting violation of ANCOVA homogeneity of regression assumptions. We therefore corrected volume and area estimates for ICV using equations reported in Free et al. (1995). ICV was not a significant covariate for cortical thickness, and so was not included in the final model. 3. Results 3.1. Grey matter volume Group means for each measure in each region are presented in Table 2. There was no significant main effect of diagnosis for grey matter volume [F(1, 46) = 1.41 × 10− 11, P N 0.99], nor were there any interactions

between diagnosis and hemisphere [F(1, 46) = 0.009, P = 0.925], region [F(1.62, 74.55) = 0.992, P = 0.361], or cortex [F(1, 46) = 1.229, P = 0.273]. Similarly, diagnosis was not involved in any higher order three- or four-way interactions. 3.2. Surface area The results for surface area were nearly identical to those for grey matter volume; there was no significant main effect of diagnosis [F(1, 46) = 0.605, P N 0.441], nor were there any interactions between diagnosis and hemisphere [F(1, 46) = 0.086, P = 0.771], region [F (1.53, 70.34) = 0.744, P = 0.445], or cortex [F(1, 46) = 0.649, P = 0.425]. Once again, diagnosis was not involved in any higher order three- or four-way interactions. 3.3. Cortical thickness The analysis revealed no significant main effect of diagnosis [F(1, 46) = 1.054, P = 0.310], but there was a significant four-way diagnosis × hemisphere × region × cortex interaction [F(1.67, 77.02) = 3.38, P = 0.047]. Post-hoc pairwise contrasts comparing the two groups in each region in the left hemisphere indicated that patients showed significant thinning in the left r-PaC [F(1, 46) =6.637, P = 0.013, corrected; d = −0.76], with a trend towards a decrease in the left d-PaC [F(1, 46) = 3.985, P = 0.054; d = −0.59], and a thickness increase in the left rACC [F(1, 46) = 3.061, P = 0.087, corrected; d = 0.52]. In the right hemisphere, patients showed significant thinning in the d-PaC [F(1, 46) = 4.422, P = 0.041, corrected; d = 0.71], with no other differences approaching significance. Effect sizes for group differences in each ROI are presented in Fig. 2. 4. Discussion

Fig. 2. Effect sizes (Cohen's d) for cortical thickness differences between patients and controls in each region of interest. LH = left hemisphere; RH = right hemisphere; ACC = anterior cingulate cortex; PaC = paracingulate cortex; d-, r-, and s-, refer to the dorsal, rostral, and subcallosal divisions, respectively. ⁎ P b 0.05, corrected.

In this study, we used a surface-based approach to examine anatomical abnormalities of the ACC and PaC in bipolar disorder. We found that patients showed reduced cortical thickness in the left r-PaC and right d-PaC, with some evidence for an additional reduction in the left d-PaC that trended towards significance. In contrast, no significant group differences were identified for either grey matter volume or surface area estimates in any region. Taken together, these findings suggest that relying on volume alone as an index of cortical integrity may obscure important group differences, and that cortical thickness may provide a more sensitive means for mapping the neuroanatomical changes associated with bipolar disorder.

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Indeed, given that the group differences in thickness and area observed in the left r-PaC and right d-PaC were not in opposite directions (Table 2), one explanation for why the significant thickness reductions were not sufficient to cause concomitant reductions in volume is that surface area tends to show greater variability across individuals. Thus, combining the two measures to represent grey matter volume yields a variable with greater variance than thickness alone, reducing sensitivity for detecting statistically significant differences. To our knowledge, the only other study of cortical thickness in bipolar disorder was conducted by Lyoo et al. (2006), who used similar surface reconstruction algorithms to those employed in the current study, combined with surface-based spatial normalization procedures (Fischl et al., 1999b) to map thickness changes across the entire cortex. They also reported thickness reductions in a region approximating our d-PaC, although their effects only reached significance in the left hemisphere. Whilst we also found evidence for left-sided reductions, our data suggest the differences are more pronounced in the right hemisphere. Differences in the frequency of a PCS in our two samples may explain this discrepancy as PCS variability can affect grey matter estimates in both ACC and PaC (Fornito et al., 2006; Paus et al., 1996a). We did not attempt to match patients and controls for PCS morphology, and while they were reasonably well matched in the right hemisphere (11/24 controls and 9/24 patients were classified as ‘present’), they were not matched as closely in the left (18/24 controls and 13/24 patients were classified as ‘present’). This may have affected our results since the PaC ROI in ‘present’ cases includes more cortex on the gyral crown than in ‘absent’ cases (by virtue of the way the ROI is defined; see Section 2), and cortex on the crown tends to be thicker than in fundal areas (Fischl and Dale, 2000; Welker, 1990). As such, left hemisphere thickness estimates in the patient group may have been biased downwards since there were proportionally more ‘absent’ cases in the bipolar group. Analyzing the combined effects of PCS incidence and diagnosis requires large samples and was beyond the scope of this article, but effect sizes for comparisons between patients and controls in the left r-PaC calculated separately for ‘present’ and ‘absent’ cases identified medium-to-large effects (d =−0.79 for ‘absent’ cases and d = −0.55 for ‘present’ cases), suggesting that PCS variability alone cannot account for our findings. The influence such variability had on Lyoo et al.'s findings is unclear, and an important avenue of future research will be to examine the effects of diagnosis and PCS variability on ACC and PaC morphometry concurrently in samples large enough to allow robust investigation of their inter-relationships.

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Our findings suggest that the most prominent thinning occurred in the PaC rather than the ACC, consistent with past findings of no volumetric change in the latter region (Brambilla et al., 2002; Dickstein et al., 2005; Farrow et al., 2005; Kubicki et al., 2002; McIntosh et al., 2004; Sanches et al., 2005; Sharma et al., 2003; Zimmerman et al., 2006). Reasons for inconsistencies with other studies that have identified significant ACC changes in bipolar disorder (Adler et al., 2005; Drevets et al., 1998; Hirayasu et al., 1999; Kaur et al., 2005) include the seldom-considered effect of PCS variability and differences in sample characteristics. With respect to the latter, family history may be a particularly important variable, given that volumetric reductions, especially in the subcallosal region, appear to be more prominent in familial patients (Drevets et al., 1997; Ongur et al., 1998). Cytoarchitectonically, the PaC is considered to represent a cingulofrontal transition zone between the ACC and adjacent prefrontal regions (Vogt et al., 1995). Its variable nature and close proximity to the ACC has made it difficult to characterize the PaC's precise functions, although there is increasing functional imaging evidence supporting a role for the r-PaC in social cognition and theory-of-mind (Amodio and Frith, 2006), and for the dPaC in monitoring the probability of reduced reward to facilitate reward-based decision-making (Bush et al., 2002; Ridderinkhof et al., 2004). Although it is tempting to speculate that our findings may represent a neuroanatomical basis for the theory-of-mind and decisionmaking deficits often reported in bipolar disorder (Bora et al., 2005; Kerr et al., 2003; Murphy et al., 2001; Olley et al., 2005), further work using concurrent structural and functional imaging would be needed to test this directly. It is unclear what cellular changes might be driving the PaC thickness reductions, since no post-mortem studies to date have examined this region. MRI-based measures of cortical thickness reflect the summation of several cellular characteristics, including the number and density of neurons, glia, and their processes, and may also be affected by the degree of regional myelination (Paus et al., 2001; Sowell et al., 2004). Unfortunately, neuropathological findings in the ACC have been variable; some studies have reported reductions in glial density (Ongur et al., 1998), while others have reported neuronal, but not glial, reductions (Benes et al., 2001) or have failed to find any density changes in either (Chana et al., 2003; Cotter et al., 2001). There is also evidence for decreased concentrations/expression of synaptic proteins in the ACC of bipolar patients (Bouras et al., 2001; Eastwood and Harrison, 2001), suggesting that a reduction in the inter-neuronal neuropil may also contribute to the thickness reductions

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observed herein (cf. Selemon and Goldman-Rakic, 1999). Further post-mortem work guided by imaging findings is needed to determine the histopathological changes underlying the anatomical abnormalities detected using in vivo techniques. The majority of our patients were taking lithium at the time of scanning. Some were taking valproate and a minority were being treated with carbemazepine. Past investigations of the effect of lithium treatment on ACC volume have been equivocal (Brambilla et al., 2002; Sassi et al., 2004). Atmaca et al. (2007) found no evidence of ACC volumetric decreases in patients being treated with valproate, but they did find reductions in an unmedicated patient group. In contrast, Lyoo et al. (2006) failed to find any differences in cortical thickness between treated and medication-naïve patients (where the treated group was composed of patients taking a variety of medications). Although it is possible that different medications exert differential effects on brain morphology across different regions, it is unlikely that such influences can account for our findings, given that, whenever they are detected (e.g., Sassi et al., 2004; Atmaca et al., 2007), they tend to be associated with a relative preservation (or potential increase) of volume, rather than a decrease. In this regard, while medication effects may partially explain the trend towards increased thickness seen in the left r-ACC in our sample, they may result in an underestimate of the true degree of illness-related reductions observed in PaC (or even ACC) regions, or of any pre-existing changes associated with illness vulnerability (e.g., McDonald et al., 2004). In summary, we used a novel, surface-based protocol that enabled a multi-level characterization of anatomical abnormalities of the ACC and PaC in patients with bipolar disorder. Our findings indicate that cortical thickness is a particularly sensitive measure for indexing illness-related changes in the region, and that patients display more prominent reductions in the PaC than in the ACC. An important goal of future work will be to recruit samples large enough to allow concurrent examination of the effects of both diagnosis and sulcal variability on regional measures. This would allow more definitive conclusions regarding whether the observed changes are the result of group differences in sulcal and gyral anatomy, represent a bona fide pathophysiological effect of the illness itself, or reflect a combination of both. Acknowledgements Neuroimaging analysis was facilitated by the Neuropsychiatry Imaging Laboratory at the Melbourne Neuropsychiatry Centre, which is managed by Ms. Bridget

Soulsby and supported by Neurosciences Victoria, the National Health and Medical Research Council (ID 236175), and the Ian Potter Foundation, Melbourne, Australia. AF was supported by a JN Peters Fellowship MY was supported by a National Health and Medical Research Program Grant (ID: 350241). References Adler, C.M., Levine, A.D., DelBello, M.P., Strakowski, S.M., 2005. Changes in gray matter volume in patients with bipolar disorder. Biological Psychiatry 58, 151–157. Amodio, D.M., Frith, C.D., 2006. Meeting of minds: the medial frontal cortex and social cognition. Nature Reviews Neuroscience 7, 268–277. Atmaca, M., Ozdemir, H., Cetinkaya, S., Parmaksiz, S., Belli, H., Poyraz, A.K., Tezcan, A.K., Ogur, E., 2007. Cingulate gyrus volumetry in drug free bipolar patients and patients treated with valproate or valproate and quetiapine. Journal of Psychiatric Research 41, 821–827. Behrens, J.T., 1997. Principles and procedures of exploratory data analysis. Psychological Methods 2, 131–160. Benes, F.M., 1993. Relationship of cingulate cortex to schizophrenia and other psychiatric disorders. In: Vogt, B.A., Gabriel, M. (Eds.), Neurobiology of Cingulate Cortex and Limbic Thalamus: A Comprehensive Handbook. Birkhäuser, Boston. Benes, F.M., Vincent, S.L., Todtenkopf, M., 2001. The density of pyramidal and nonpyramidal neurons in anterior cingulate cortex of schizophrenic and bipolar subjects. Biological Psychiatry 50, 395–406. Bookstein, F.L., 2001. “Voxel-based morphometry” should not be used with imperfectly registered images. NeuroImage 14, 1454–1462. Bora, E., Vahip, S., Gonul, A.S., Akdeniz, F., Alkan, M., Ogut, M., Eryavuz, A., 2005. Evidence for theory of mind deficits in euthymic patients with bipolar disorder. Acta Psychiatrica Scandinavica 112, 110–116. Bouras, C., Kovari, E., Hof, P.R., Riederer, B.M., Giannakopoulos, P., 2001. Anterior cingulate cortex pathology in schizophrenia and bipolar disorder. Acta Neuropathologica (Berlin) 102, 373–379. Brambilla, P., Nicoletti, M.A., Harenski, K., Sassi, R.B., Mallinger, A.G., Frank, E., Kupfer, D.J., Keshavan, M.S., Soares, J.C., 2002. Anatomical MRI study of subgenual prefrontal cortex in bipolar and unipolar subjects. Neuropsychopharmacology 27, 792–799. Bruno, S.D., Barker, G.J., Cercignani, M., Symms, M., Ron, M.A., 2004. A study of bipolar disorder using magnetization transfer imaging and voxel-based morphometry. Brain 127, 2433–2440. Bush, G., Luu, P., Posner, M.I., 2000. Cognitive and emotional influences in anterior cingulate cortex. Trends in Cognitive Sciences 4, 215–222. Bush, G., Vogt, B.A., Holmes, J., Dale, A.M., Greve, D., Jenike, M.A., Rosen, B.R., 2002. Dorsal anterior cingulate cortex: a role in rewardbased decision making. Proceedings of the National Academy of Sciences of the United States of America 99, 523–528. Chana, G., Landau, S., Beasley, C., Everall, I.P., Cotter, D., 2003. Two-dimensional assessment of cytoarchitecture in the anterior cingulate cortex in major depressive disorder, bipolar disorder, and schizophrenia: evidence for decreased neuronal somal size and increased neuronal density. Biological Psychiatry 53, 1086–1098. Cohen, J., 1992. A power primer. Psychological Bulletin 112, 155–159. Cohen, J.D., Botvinick, M., Carter, C.S., 2000. Anterior cingulate and prefrontal cortex: who's in control? Nature Neuroscience 3, 421–423.

A. Fornito et al. / Psychiatry Research: Neuroimaging 162 (2008) 123–132 Cotter, D., Mackay, D., Landau, S., Kerwin, R., Everall, I., 2001. Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Archives of General Psychiatry 58, 545–553. Crum, W.R., Griffin, L.D., Hill, D.L.G., Hawkes, D.J., 2003. Zen and the art of medical image registration: correspondence, homology, and quantity. NeuroImage 20, 1425–1437. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis I. Segmentation and surface reconstruction. NeuroImage 9, 179–194. Devinsky, O., Morrell, M.J., Vogt, B.A., 1995. Contributions of anterior cingulate cortex to behaviour. Brain 118, 279–306. Dickstein, D.P., Milham, M.P., Nugent, A.C., Drevets, W.C., Charney, D.S., Pine, D.S., Leibenluft, E., 2005. Frontotemporal alterations in pediatric bipolar disorder: results of a voxelbased morphometry study. Archives of General Psychiatry 62, 734–741. Drevets, W.C., Price, J.L., Simpson Jr., J.R., Todd, R.D., Reich, T., Vannier, M., Raichle, M.E., 1997. Subgenual prefrontal cortex abnormalities in mood disorders. Nature 386, 824–827. Drevets, W.C., Ongur, D., Price, J.L., 1998. Neuroimaging abnormalities in the subgenual prefrontal cortex: implications for the pathophysiology of familial mood disorders. Molecular Psychiatry 3, 220–226, 190–221. Eastwood, S.L., Harrison, P.J., 2001. Synaptic pathology in the anterior cingulate cortex in schizophrenia and mood disorders. A review and a Western blot study of synaptophysin, GAP-43 and the complexins. Brain Research Bulletin 55, 569–578. Eritaia, J., Wood, S.J., Stuart, G.W., Bridle, N., Dudgeon, P., Maruff, P., Velakoulis, D., Pantelis, C., 2000. An optimized method for estimating intracranial volume from magnetic resonance images. Magnetic Resonance in Medicine 44, 973–977. Farrow, T.F., Whitford, T.J., Williams, L.M., Gomes, L., Harris, A.W., 2005. Diagnosis-related regional gray matter loss over two years in first episode schizophrenia and bipolar disorder. Biological Psychiatry 58, 713–723. First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B., 1998. Structured Clinical Interview for DSM-IV. American Psychiatric Press, Washington, DC. Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America 97, 11050–11055. Fischl, B., Sereno, M.I., Dale, A.M., 1999a. Cortical surface-based analysis II: inflation, flattening, and a surface-based coordinate system. NeuroImage 9, 195–207. Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M., 1999b. Highresolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 8, 272–284. Fischl, B., Liu, A., Dale, A.M., 2001. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Transactions on Medical Imaging 20, 70–80. Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M., 2002. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355. Fjell, A.M., Walhovd, K.B., Reinvang, I., Lundervold, A., Salat, D., Quinn, B.T., Fischl, B., Dale, A.M., 2006. Selective increase of cortical thickness in high-performing elderly—structural indices of optimal cognitive aging. NeuroImage 29, 984–994.

131

Fornito, A., Whittle, S., Wood, S.J., Velakoulis, D., Pantelis, C., Yucel, M., 2006. The influence of sulcal variability on morphometry of the human anterior cingulate and paracingulate cortex. NeuroImage 33, 843–854. Fornito, A., Wood, S.J., Whittle, S., Fuller, J., Adamson, C., Saling, M.M., Velakoulis, D., Pantelis, C., Yücel, M., in press-a. Variability of the paracingulate sulcus and morphometry of the medial frontal cortex: associations with cortical thickness, surface area, volume, and sulcal depth. Human Brain Mapping (Epub ahead of print). Fornito, A., Yücel, M., Wood, S.J., Adamson, C., Velakoulis, D., Saling, M.M., McGorry, P.D., Pantelis, C., in press-b. Surface-based morphometry of the anterior cingulate cortex in first episode schizophrenia. Human Brain Mapping (Epub ahead of print). Free, S.L., Bergin, P.S., Fish, D.R., Cook, M.J., Shorvon, S.D., Stevens, J.M., 1995. Methods for normalization of hippocampal volumes measured with MR. AJNR American Journal of Neuroradiology 16, 637–643. Harrison, P.J., 2002. The neuropathology of primary mood disorder. Brain 125, 1428–1449. Hirayasu, Y., Shenton, M.E., Salisbury, D.F., Kwon, J.S., Wible, C.G., Fischer, I.A., Yurgelun-Todd, D., Zarate, C., Kikinis, R., Jolesz, F.A., McCarley, R.W., 1999. Subgenual cingulate cortex volume in first-episode psychosis. American Journal of Psychiatry 156, 1091–1093. Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C., 1998. Enhancement of MR images using registration for signal averaging. Journal of Computer Assisted Tomography 22, 324–333. Jenkinson, M., Smith, S., 2001. A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5, 143–156. Kaur, S., Sassi, R.B., Axelson, D., Nicoletti, M., Brambilla, P., Monkul, E.S., Hatch, J.P., Keshavan, M.S., Ryan, N., Birmaher, B., Soares, J.C., 2005. Cingulate cortex anatomical abnormalities in children and adolescents with bipolar disorder. American Journal of Psychiatry 162, 1637–1643. Kerr, N., Dunbar, R.I., Bentall, R.P., 2003. Theory of mind deficits in bipolar affective disorder. Journal of Affective Disorders 73, 253–259. Kolbe, S., Ma, T., Liu, W., Soh, W.S., Buyya, R., Egan, G.F., 2005. A platform for distributed analysis of neuroimaging data on global grids. Proceedings of the 1st IEEE International Conference on eScience and Grid Computing. IEEE CS Press, Melbourne, Australia. Kubicki, M., Shenton, M.E., Salisbury, D.F., Hirayasu, Y., Kasai, K., Kikinis, R., Jolesz, F.A., McCarley, R.W., 2002. Voxel-based morphometric analysis of gray matter in first episode schizophrenia. NeuroImage 17, 1711–1719. Lochhead, R.A., Parsey, R.V., Oquendo, M.A., Mann, J.J., 2004. Regional brain gray matter volume differences in patients with bipolar disorder as assessed by optimized voxel-based morphometry. Biological Psychiatry 55, 1154–1162. Lyoo, I.K., Kim, M.J., Stoll, A.L., Demopulos, C.M., Parow, A.M., Dager, S.R., Friedman, S.D., Dunner, D.L., Renshaw, P.F., 2004. Frontal lobe gray matter density decreases in bipolar I disorder. Biological Psychiatry 55, 648–651. Lyoo, I.K., Sung, Y.H., Dager, S.R., Friedman, S.D., Lee, J.Y., Kim, S.J., Kim, N., Dunner, D.L., Renshaw, P.F., 2006. Regional cerebral cortical thinning in bipolar disorder. Bipolar Disorders 8, 65–74. Mayberg, H.S., Liotti, M., Brannan, S.K., McGinnis, S., Mahurin, R.K., Jerabek, P.A., Silva, J.A., Tekell, J.L., Martin, C.C., Lancaster, J.L., Fox, P.T., 1999. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. American Journal of Psychiatry 156, 675–682.

132

A. Fornito et al. / Psychiatry Research: Neuroimaging 162 (2008) 123–132

McDonald, C., Bullmore, E.T., Sham, P.C., Chitnis, X., Wickham, H., Bramon, E., Murray, R.M., 2004. Association of genetic risks for schizophrenia and bipolar disorder with specific and generic brain structural endophenotypes. Archives of General Psychiatry 61, 974–984. McIntosh, A.M., Job, D.E., Moorhead, T.W., Harrison, L.K., Forrester, K., Lawrie, S.M., Johnstone, E.C., 2004. Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives. Biological Psychiatry 56, 544–552. Murphy, F.C., Rubinsztein, J.S., Michael, A., Rogers, R.D., Robbins, T.W., Paykel, E.S., Sahakian, B.J., 2001. Decision-making cognition in mania and depression. Psychological Medicine 31, 679–693. Nugent, A.C., Milham, M.P., Bain, E.E., Mah, L., Cannon, D.M., Marrett, S., Zarate, C.A., Pine, D.S., Price, J.L., Drevets, W.C., 2006. Cortical abnormalities in bipolar disorder investigated with MRI and voxel-based morphometry. NeuroImage 30, 485–497. Olley, A.L., Malhi, G.S., Bachelor, J., Cahill, C.M., Mitchell, P.B., Berk, M., 2005. Executive functioning and theory of mind in euthymic bipolar disorder. Bipolar Disorders 7 (Suppl 5), 43–52. Ongur, D., Drevets, W.C., Price, J.L., 1998. Glial reduction in the subgenual prefrontal cortex in mood disorders. Proceedings of the National Academy of Sciences of the United States of America 95, 13290–13295. Ono, M., Kubik, S., Abernathy, C.D., 1990. Atlas of the Cerebral Sulci. Thieme, New York. Paus, T., Otkay, N., Caramanos, Z., MacDonald, D., Zijdenbos, A., D'Avirro, D., Gutmans, D., Holmes, C., Tomaiuolo, F., Evans, A.C., 1996a. In vivo morphometry of the intrasulcal gray matter in the human cingulate, paracingulate, and superior rostral sulci: hemispheric asymmetries, gender differences and probability maps. Journal of Comparative Neurology 376, 664–673. Paus, T., Tomaiuolo, F., Otkay, N., MacDonald, D., Petrides, M., Atlas, J., Morris, R., Evans, A.C., 1996b. Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map. Cerebral Cortex 6, 207–214. Paus, T., Collins, D.L., Evans, A.C., Leonard, G., Pike, B., Zijdenbos, A., 2001. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Research Bulletin 54, 255–266. Phillips, M.L., Drevets, W.C., Rauch, S.L., Lane, R., 2003a. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biological Psychiatry 54, 504–514. Phillips, M.L., Drevets, W.C., Rauch, S.L., Lane, R., 2003b. Neurobiology of emotion perception II: implications for major psychiatric disorders. Biological Psychiatry 54, 515–528. Picard, N., Strick, P.L., 1996. Motor areas of the medial wall: a review of their location and functional activation. Cerebral Cortex 6, 342–353. Rakic, P., 2000. Radial unit hypothesis of neocortical expansion. In: Bock, G.R., Cardew, G. (Eds.), Evolutionary Developmental Biology of the Cerebral Cortex. John Wiley & Sons, Ltd., West Sussex, pp. 30–45. Rettmann, M.E., Kraut, M.A., Prince, J.L., Resnick, S.M., 2006. Crosssectional and longitudinal analyses of anatomical sulcal changes associated with aging. Cerebral Cortex 16, 1584–1594. Ridderinkhof, K.R., Ullsperger, M., Crone, E.A., Nieuwenhuis, S., 2004. The role of the medial frontal cortex in cognitive control. Science 306, 443–447. Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., van der Kouwe, A., Jenkins, B.G., Dale, A.M., Fischl, B., 2002. Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology 58, 695–701.

Sanches, M., Sassi, R.B., Axelson, D., Nicoletti, M., Brambilla, P., Hatch, J.P., Keshavan, M.S., Ryan, N.D., Birmaher, B., Soares, J.C., 2005. Subgenual prefrontal cortex of child and adolescent bipolar patients: a morphometric magnetic resonance imaging study. Psychiatry Research: Neuroimaging 138, 43–49. Sassi, R.B., Brambilla, P., Hatch, J.P., Nicoletti, M.A., Mallinger, A.G., Frank, E., Kupfer, D.J., Keshavan, M.S., Soares, J.C., 2004. Reduced left anterior cingulate volumes in untreated bipolar patients. Biological Psychiatry 56, 467–475. Selemon, L.D., Goldman-Rakic, P.S., 1999. The reduced neuropil hypothesis: a circuit based model of schizophrenia. Biological Psychiatry 45, 17–25. Sharma, V., Menon, R., Carr, T.J., Densmore, M., Mazmanian, D., Williamson, P.C., 2003. An MRI study of subgenual prefrontal cortex in patients with familial and non-familial bipolar I disorder. Journal of Affective Disorders 77, 167–171. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., Evans, A., Rapoport, J., Giedd, J., 2006. Intellectual ability and cortical development in children and adolescents. Nature 440, 676–679. Smith, S.M., 2002. Fast robust automated brain extraction. Human Brain Mapping 17, 143–155. Sowell, E.R., Thompson, P.M., Tessner, K.D., Toga, A.W., 2001. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. Journal of Neuroscience 21, 8819–8829. Sowell, E.R., Thompson, P.M., Leonard, C.M., Welcome, S.E., Kan, E., Toga, A.W., 2004. Longitudinal mapping of cortical thickness and brain growth in normal children. Journal of Neuroscience 24, 8223–8231. Steele, J.D., Lawrie, S.M., 2004. Segregation of cognitive and emotional function in the prefrontal cortex: a stereotactic metaanalysis. NeuroImage 21, 868–875. Van Essen, D.C., 2005. A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. NeuroImage 28, 635–662. Vogt, B.A., Nimchinsky, E.A., Vogt, L.J., Hof, P.R., 1995. Human cingulate cortex: surface features, flat maps, and cytoarchitecture. Journal of Comparative Neurology 359, 490–506. Vogt, B.A., Berger, G.R., Derbyshire, S.W., 2003. Structural and functional dichotomy of human midcingulate cortex. European Journal of Neuroscience 18, 3134–3144. Welker, W., 1990. Why does cerebral cortex fissure and fold? A review of determinants of sulci an gyri. In: Jones, E.G., Peters, A. (Eds.), Cerebral Cortex. Plenum Press, New York, pp. 3–136. Wilke, M., Kowatch, R.A., DelBello, M.P., Mills, N.P., Holland, S.K., 2004. Voxel-based morphometry in adolescents with bipolar disorder: first results. Psychiatry Research: Neuroimaging 131, 57–69. Yücel, M., Stuart, G.W., Maruff, P., Velakoulis, D., Crowe, S.F., Savage, G., Pantelis, C., 2001. Hemispheric and gender-related differences in the gross morphology of the anterior cingulate/ paracingulate cortex in normal volunteers: an MRI morphometric study. Cerebral Cortex 11, 17–25. Zimmerman, M.E., DelBello, M.P., Getz, G.E., Shear, P.K., Strakowski, S.M., 2006. Anterior cingulate subregion volumes and executive function in bipolar disorder. Bipolar Disorders 8, 281–288.