Hemispheric asymmetries in cortical and subcortical anatomy.

Laterality: Asymmetries of Body, Brain and Cognition

ISSN: 1357-650X (Print) 1464-0678 (Online) Journal homepage: http://www.tandfonline.com/loi/plat20

Hemispheric asymmetries in cortical and subcortical anatomy Xiaojian Kang, Timothy J. Herron, Marc Ettlinger & David L. Woods To cite this article: Xiaojian Kang, Timothy J. Herron, Marc Ettlinger & David L. Woods (2015) Hemispheric asymmetries in cortical and subcortical anatomy, Laterality: Asymmetries of Body, Brain and Cognition, 20:6, 658-684, DOI: 10.1080/1357650X.2015.1032975 To link to this article: http://dx.doi.org/10.1080/1357650X.2015.1032975

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Date: 05 November 2015, At: 15:33

Laterality: Asymmetries of Body, Brain and Cognition, 2015 Vol. 20, No. 6, 658–684, http://dx.doi.org/10.1080/1357650X.2015.1032975

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Hemispheric asymmetries in cortical and subcortical anatomy Xiaojian Kang1,2, Timothy J. Herron1, Marc Ettlinger1, and David L. Woods1,2,3 1

Human Cognitive Neurophysiology Lab, VA Research Service 151, VA-NCHCS, Martinez, CA, USA 2 Department of Neurology and Center for Neuroscience, UC Davis, Sacramento, CA, USA 3 UC Davis Center for Mind and Brain, UC Davis, Davis, CA, USA (Received 6 February 2015; accepted 18 March 2015)

Previous research studies have reported many hemispherical asymmetries in cortical and subcortical anatomy, but only a subset of findings is consistent across studies. Here, we used improved Freesurfer-based automated methods to analyse the properties of the cortex and seven subcortical structures in 138 young adult subjects. Male and female subjects showed similar hemispheric asymmetries in gyral and sulcal structures, with many areas associated with language processing enlarged in the left hemisphere (LH) and a number of areas associated with visuospatial processing enlarged in the right hemisphere (RH). In addition, we found greater (non-directional) cortical asymmetries in subjects with larger brains. Asymmetries in subcortical structures included larger LH volumes of thalamus, putamen and globus pallidus and larger RH volumes of the cerebellum and the amygdala. We also found significant correlations between the subcortical structural volumes, particularly of the thalamus and cerebellum, with cortical area. These results help to resolve some of the inconsistencies in previous studies of hemispheric asymmetries in brain anatomy.

Keywords: Frontal; Temporal; Parietal; Occipital; Insula.

Address correspondence to: Xiaojian Kang, Department of Neurology and Neurosciences Center, UC Davis, VANCHCS, Research Services (151), 150 Muir Road, Martinez, CA 94553, USA. E-mail: [email protected] The content is solely the responsibility of the authors and does not necessarily represent the official views of Department of Veterans Affairs or the U.S. Government. No potential conflict of interest was reported by the authors. This research was supported by a VA Merit Research [grant number CX000583], [grant number CX001000] to DLW. Xiaojian Kang and Timothy J. Herron contributed equally to this work. © 2015 Taylor & Francis


HEMISPHERIC ASYMMETRY

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Goldberg et al. (2013) described hemispheric asymmetry in the cerebral cortex as a “cardinal feature of cerebral organization”. Indeed, functional differences have long been associated with hemispheric asymmetries, first and foremost among them, language lateralization and the associated anatomical asymmetry of the planum temporale and Broca’s area (Geschwind & Levitsky, 1968). Among the most obvious anatomical differences is the petalia, i.e. protrusions in the right frontal and the left occipital regions (Narr et al., 2007; Watkins et al., 2001) that are seen in both grey and white matter (Good et al., 2001; Takao et al., 2011). Table 1 summarizes the results from nine recent relatively large-scale anatomical studies (Goldberg et al., 2013; Good et al., 2001; Herve, Crivello, Perchey, Mazoyer, & Tzourio-Mazoyer, 2006; Luders et al., 2006; Lyttelton et al., 2009; Szabo et al., 2006; Van Essen, Glasser, Dierker, Harwell, & Coalson, 2012; Watkins et al., 2001; Zhou, Lebel, Evans, & Beaulieu, 2013) and reveals a number of consistent and inconsistent findings. Some of these inconsistencies may reflect differences in subject age, since asymmetries may change over the lifespan. (Zhou et al., 2013). Asymmetries may also be influenced by the procedures used for brain parcellation and by the different anatomical quantities analysed (e.g., regional cortical volume, surface area and thickness).

Cortical asymmetries Studies using volumetric measures of cortical structure have produced a number of reliable findings (see Table 1), including enlarged left-hemisphere (LH) volumes of structures on the superior temporal plane and enlarged righthemisphere (RH) volumes of the superior temporal sulcus (Goldberg et al., 2013; Good et al., 2001; Herve, Zago, Petit, Mazoyer, & Tzourio-Mazoyer, 2013; Watkins et al., 2001). However, there are also substantial inconsistencies. For example, Watkins et al. (2001) and Good et al. (2001) found greater RH volume in the superior frontal gyrus, while Goldberg et al. (2013) found greater superior frontal volume in the left. Similarly, Watkins et al. (2001) reported greater volume in the right inferior temporal gyrus, while Herve et al. (2006) reported an opposite asymmetry. Furthermore, Watkins et al. (2001) and Herve et al. (2006) found a larger right pars opercularis, while Good et al. (2001) obtained an opposite result. A number of recent studies have also analysed hemispheric differences using measures of cortical thickness and surface area. Luders et al. (2006) found differences in the cortical thickness of homologous regions in the two hemispheres, with greater LH thickness in parts of the cingulate, orbital-frontal gyrus and temporal and parietal lobes, and greater RH thickness in the inferior frontal gyrus. However, Zhou et al. (2013) found a different pattern of thickness asymmetries (see Table 1). Studies of cortical surface area (Lyttelton et al., 2009; Van Essen et al., 2012) have generally corroborated the results of volumetric analysis, including greater LH area of the anterior temporal lobe, planum

660

W, H W G13 W, H

Area V L V

L

L V

L6

L6

L6

L6

L6 Z L6 L6 L6 Z

Area

2.18

Thickness

Current study (%) (P < 0.0001)

6.6

6.13

1.6 1.4 1.6

4.2

1.5

6.1 9.5

14.3

17.5 18.2 14.0 5.2


W, H W W, H G13 H W H H W, G, H

Thickness

18.1 17.7 14.4 4.3

12.9

8.8

29.7

6.52

Area

3.2 2.4

2.7 1.6

8.7

Thickness

OASIS data (%) (P < 0.0001)

KANG ET AL. TABLE 1 Hemispheric asymmetries in previous whole-brain MRI studies and the results of the current study

Volume

Previous studies Cortical structures

W, G13, G

G13 G13 W, G, H G

Frontal/cingulate Cingulate Cingulate sulcus Anterior cingulate Anterior cingulate sulcus Posterior cingulate Prefrontal cortex Anterior orbital gyrus Anterior orbital gyrus (BA13) Lateral orbital gyrus Medial orbital gyrus Frontal pole Ventro-medial Orbital-Frontal Superior rostral sulcus Middle frontal gyrus Inferior precentral sulcus Paracentral gyrus Precentral gyrus Superior precentral gyrus Inferior frontal gyrus Pars orbitalis Pars opercularis Pars triangularis Superior frontal gyrus

(Continued overleaf)

Volume

V

L

V V L L L, V

V

V

Area

Previous studies

G13, G

G13 W, H W G13 P G13, H W, G13, G, H W, G13 W, G13, G, H G W, H P G13 G13, H G13 G13, H W

TABLE 1 (Continued)

Thickness

L6 L6 Z L6 L6 L6 Z

L6 L6


2.9

Area

2.0

1.4

Thickness

2.8

Area

5.3

3.8

7.9 13.3 11.2 7.2

2.2

2.5

661

Thickness

OASIS data (%) (P < 0.0001)

3.6

1.7 4.5


5.8 33.6

3.9 1.9

2.2

18.1

2.6 4.9

6.2 32.5 9.5 2.5 10.2

2.3 1.4 5.4

19.2


Cortical structures Superior frontal sulcus Temporal/insular Insula Inferior insula Anterior insula Middle insula Superior insula Temporal lobe Anterior temporal lobe Superior temporal gyrus Heschl’s gyrus Planum temporale Temporal pole Superior temporal sulcus Middle temporal gyrus Inferior temporal gyrus Occipital/parietal Parietal lobe Post-central gyrus Sub-central gyrus Post-central sulcus Superior parietal gyrus Intraparietal sulcus Inferior parietal lobe

(Continued overleaf)

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Cortical structures Angular gyrus Supramarginal gyrus Parietal operculum Parietal-occipital fissure Occipital lobe Superior occipital gyrus Calcarine sulcus Lingual gyrus Occipital petalia Precuneus Middle occipital gyrus Subcortical structures Amygdala Uncus Caudate (Head) Hippocampus Cerebellum Lateral cerebellum Dorsal Thalamus Lateral thalamus Putamen Pallidum

Volume

V

L, V

L, V V

Area

Previous studies

W, G13, H H W W, G13 W, G13, G, H G13, H G H H Volume G W W, G,H,S H, S G, H, S G, H W, G G, H, S

Area

TABLE 1 (Continued)

Thickness

7.9

3.9

3.4

1.0

Thickness


Z L6

3.6

Volume 6.5

2.6 4.2 5.4 4.9

3.1 3.6

2.4

Thickness

OASIS data (%) (P < 0.0001)

Area

7.2

10.7

Volume 9.5

4.2

1.5

5.1


Studies shown in italic font reported significant rightward asymmetries, while studies shown in non-italic font reported leftward asymmetries. Studies in bold font included more than 100 subjects. Shading indicates regions where the results of the current study, the results of analysing the OASIS data, and those of previous studies are in agreement. (G = Good et al., 2001; G13 = Goldberg et al., 2013; H = Herve et al., 2006; L = Lyttelton et al., 2009; L6 = Luders et al., 2006; S = Szabo et al., 2006; V = Van Essen et al., 2012; W = Watkins et al., 2001; Z = Zhou et al., 2013)


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temporal and Heschl’s gyrus, as well as relatively increased areas in the LH supramarginal gyrus and the RH middle frontal gyrus.


Subcortical asymmetries Because of the difficulty associated with accurate parcellation, there are fewer studies of hemispheric asymmetries of sub-cortical structures such as the amygdala, thalamus, hippocampus and basal ganglia (Khan, Wang, & Beg, 2008). However, such studies are clinically relevant because volumetric changes in subcortical structures accompany many neuropsychiatric diseases such as Alzheimer’s disease (Cho et al., 2013; Roh et al., 2011), schizophrenia (Collinson et al., 2003; Kreczmanski et al., 2007), dementia (Unay, 2012) and temporal lobe epilepsy (Pulsipher et al., 2007). Thus, characterizing hemispheric asymmetries in subcortical structures can increase sensitivity to pathological changes in neurological disease (Gocmen-Mas et al., 2009; Kusbeci et al., 2009; Szabo et al., 2006). Some studies (see Table 1, bottom) have found no significant hemispheric differences in subcortical volumes (Ertekin, Acer, Icer, & Ilica, 2013). In contrast, Good et al. (2001) found that the amygdala, hippocampus and caudate head were larger in the LH while the thalamus was larger in the RH. The hippocampal and caudate asymmetries were corroborated by Szabo et al. (2006), who also reported opposite asymmetries in the amygdala (right larger) and thalamus (left larger). Moreover, an earlier study by Szabo, Lancaster, Xiong, Cook, and Fox (2003), using a different population and method, reported a rightward asymmetry in the caudate head, but failed to find significant asymmetries in either the hippocampus or thalamus. Thus, the study of subcortical asymmetries has also produced inconsistent results. In the present study, we used improved automated methods to analyse hemispheric asymmetries in brain structure in 138 young right-handed male and female subjects. By establishing consistent sample characteristics, image scan quality, segmentation and careful overall volume corrections, we were able to identify sources of inconsistency in previous studies and provide a clearer picture of hemispheric asymmetries in brain anatomy.

MATERIALS AND METHODS Overview We processed the anatomical images of 138 young, right-handed subjects using FreeSurfer, whose automated segmentation procedures also parcellated the cerebral cortical surface (Desikan et al., 2006) and subcortical structures (Fischl et al., 2002, 2004). We also incorporated a novel method (Kang, Herron, Cate, Yund, & Woods, 2012) to align the averaged FreeSurfer registered spheres of the LH and RH of the 138 subjects. Interhemispheric asymmetries in the cerebral

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cortex were examined using three cortical-surface metrics: surface curvature bending energy, surface area and cortical thickness. Differences in subcortical structure were evaluated with volume measures.


Subjects We studied the cortical and subcortical anatomy of 138 young subjects recruited from several colleges and military veteran sources in the northern California area, a cohort that we have studied previously (Kang, Herron, Cate, et al., 2012). All subjects were right-handed by self-report. There were 69 male and 69 female subjects, who were carefully matched in age (females: 18–38 years, mean 26.3 years; males: 18–38 years, mean 26.1 years) and education (approximately 15 years for both females and males). All subjects gave informed written consent following procedures approved by the Institutional Review Board of the Northern California Health Care System of the Department of Veterans Affairs and were paid for their participation. We also used an auxiliary group of high quality T1 images contained in a public repository, the OASIS data-set (oasis-brains.org; (Marcus et al., 2007)). This independent data-set was used to evaluate the reliability of the results. In particular, we used a subset of young right-handed OASIS subjects containing 65 males (mean age 22.9; range 18–32) and 65 females (mean age 22.8, range 18–32) and processed them identically to images from our primary data-set.

Imaging Two high-resolution T1 anatomical images (TR = 15 ms, TE = 4.47 ms, Flip Angle = 35°, voxel size 0.94 × 1.30 × 0.94 mm) were acquired on a 1.5 T Philips Eclipse scanner. These anatomical images were re-sampled to 1 × 1 × 1 mm resolution, averaged, segmented and then inflated to the cortical surface using FreeSurfer (Dale, Fischl, & Sereno, 1999; Fischl, Sereno, & Dale, 1999). The inflated cortical surfaces of LH and RH were then co-registered to a spherical coordinate system (Fischl, Sereno, Tootell, & Dale, 1999). Figure 1A–D shows the inflated cortical surfaces and spheres of LH averaged across all the 138 subjects. Six anatomical areas were identified (Kang, Herron, Cate, et al., 2012) on the cortical surfaces based on the neuroanatomical parcellation (Desikan et al., 2006): front lobe (FL), insular cortex (IC), limbic cortex (LC), occipital lobe (OL), parietal lobe (PL) and temporal lobe (TL). The spherical view of the averaged LH was further projected to a flat map, as shown in Figure 1E, using Mollweide projection to visualize the entire 3D cortical surface in two dimensions with minimal areal distortion (Kang, Herron, Cate, et al., 2012). FreeSurfer also provides the labels and statistical analysis of the segmented subcortical structures (Abe et al., 2010; Fischl et al., 2002, 2004). Figure 2 shows the seven FreeSurfer-segment subcortical structures discussed herein: i.e., the


(A)

(B)

PL

PL

TL

FL LC

FL IC

OL

OL

(C)

IHC

(D) PL

FL

FL


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PL LC

IC TL

OL

OL

IHC

(E) IC FL

TL

IHC

PL

OL

FL

LC

Figure 1. Inflation, spherical coregistration and projection of the cortical surface. Lateral (A) and medial views (B) of the inflated left hemisphere (LH) averaged across 138 subjects. (C) and (D) are the two views of the averaged LH sphere when coregistered to the spherical coordinate system by FreeSurfer. (E) shows the Mollweide projection of the averaged spherical surface of LH. The temporal and occipital lobes were positioned in the front/central area of the map. Anatomical areas were shown by the colour contours. FL, front lobe; IC, insular cortex; IHC, inter-hemispheric connection; LC, limbic cortex; OL, occipital lobe; PL, parietal lobe; TL, temporal lobe. [To view this figure in colour, please visit the online version of this Journal.]

cerebellum (light green), thalamus (dark green), caudate (grey), putamen (purple), pallidum (blue/grey), hippocampus (lime green) and amygdala (light blue).

Hemispheric asymmetries The new method developed by Kang, Herron, Cate, et al. (2012) was used to align the averaged FreeSurfer registered spheres of the LH and RH across all subjects by minimizing the curvature differences of two average hemispheres through rigid-body spherical transformation in spherical space. A common coordinate sphere was then generated by averaging the aligned mean LH and RH. This hemispherically unified coordinate system permits objective inter-hemispheric comparisons of anatomical curvature, structural variability and cortical thickness. It also permits interhemispheric comparisons of other co-registered images

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Ventricle

Amygdala Cerebellum Thalamus Caudate Putamen Pallidum Hippocampus

Figure 2. Segmentation of subcortical structures by FreeSurfer. The labels indicate the structures on the right hemisphere in one subject. [To view this figure in colour, please visit the online version of this Journal.]

including functional activations (Cate, Herron, Kang, Yund, & Woods, 2012; Kang, Herron, Turken, & Woods, 2012; Woods et al., 2009, 2010). Anatomical features on the cortical surface obtained in FreeSurfer, including surface curvature, bending energy, surface area and cortical thickness, were extracted and resampled from each individual subject into the hemispherically unified coordinate system on the Mollweide projection map. Cortical surface bending energy is the area-weighted square of mean curvature after subtracting hemispheric average mean curvature. Bending energy better reflects the number of gyri and sulci in a region compared to other common cortical folding measures (Pienaar, Fischl, Caviness, Makris, & Grant, 2008). Here we use the bending energy density (BED), dividing out by the total area of a region, in order to correct for the increase in bending energy with overall surface area. The


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surface area associated with a vertex is the averaged area of all the triangles associated with that vertex on the surface (Fischl, Sereno, & Dale, 1999). The total surface area of a region or hemisphere is the summation of the area of all vertices that it includes (Winkler et al., 2012). The volumes of subcortical structures were also compared across hemispheres using the volumetric measures provided by Freesurfer subcortical segmentation.


Cortical and subcortical correlations We also correlated subcortical volumes with cortical measures to evaluate whether enlarged subcortical structures (e.g., the thalamus) would be associated with increases in the cortical area and/or thickness. We further correlated cortical and subcortical asymmetries to determine if subjects with prominent subcortical asymmetries would show correspondingly large asymmetries in cortical measures. We also contrasted these asymmetry correlations with average subcortical volume and cortical measure correlations in order to see if the asymmetries in some structures were related to asymmetries in others.

Statistical analyses Multivariate linear regression (using R’s lm function v. 2.15.3 and mle4 library: r-project.org) over sex, age, intracranial volume (ICV0 and a dependent variable of overall subcortical structure volume or cortical area/thickness/bending, either hemispherically averaged or differenced, were used to analyse overall sex and hemispheric differences. Purely categorical within-subject factors were analysed using repeated measures analysis of variance (ANOVA; using CLEAVE: ebire. org/hcnlab/software) with Geisser-Greenhouse non-sphericity corrections uniformly applied. Correlations were computed using MATLAB (v7.14) Statistics toolbox (mathworks.com) with Pearson correlations used to analyse pairwise relationships and with partial correlations to control for covariates. Because we were looking at many effects simultaneously, we adjusted statistical thresholds so that p < 0.01 indicated a trend, p < 0.001 indicated weak statistical significance and p < 0.0001 indicated a strong statistical relationship. We also report effect size estimates (η) in ANOVA results, and regression coefficients to clarify effect magnitude.

RESULTS The results are presented as follows. We first give a whole-cortex overview of how the anatomical quantities are related to each other and to the relevant demographic variables. Then we report the results of detailed regional hemispheric comparisons and subcortical comparisons. Finally, we characterize how brain size is related to omnibus local cortical asymmetries and to

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asymmetries in subcortical structures. A further, more detailed discussion of sex differences in cortical and subcortical anatomy is presented in a companion manuscript (Herron, Kang, & Woods, 2015).


Hemispheric asymmetries in cortical structure The rightmost columns in Table 1 show the magnitude of cortical asymmetries (in percent) for those regions that showed significant (p < 0.0001; paired t-tests) interhemispheric asymmetries in cortical thickness and/or surface area. We found that two areas were relatively thicker and 11 areas had greater surface area in the LH, and that nine areas were relatively thicker, and 10 areas had greater surface area in the RH. Figure 3 shows the mean and standard deviations of surface curvature, cortical thickness and surface area averaged across 138 subjects and two hemispheres (A)

SMG HG1 CS IFG SF STG AG MFG MFG MTG IPS SFG OFC SFG ITG LOS TOS CG FG MedFG PreCun CalcS

(C)


(B)

SMG HG1 CS IFG SF STG AG MFG MFG MTG IPS SFG OFC SFG ITG LOS TOS CG FG MedFG

0.2 0

PreCun CalcS

–0.2

5 2.5

(D)


0

(E)

0.2

0

2 1 0

(F)


1.2 0.5 0


0.5

0

Figure 3. Mollweide projections maps of mean (left column) and standard deviation (right column) of surface curvature, cortical thickness and local surface area averaged across 138 subjects and two hemispheres. (A) Mean surface curvature, (B) SD of surface curvature, (C) mean cortical thickness (mm), (D) SD of cortical thickness (mm), (E) mean surface area (mm2) and (F) SD of surface area (mm2) Anatomical structures (white labels): AG, angular gyrus; CG, cingulate gyrus; CalcS, calcarine sulcus; CS, central sulcus; FG, fusiform gyrus; HG, Heschl’s gyrus; IFG, inferior frontal gyrus; IPS, intraparietal sulcus; ITG, inferior temporal gyrus; LOS, lateral occipital sulcus; MedFG, medial frontal gyrus; MFG, mid-frontal gyrus, MTG, middle temporal gyrus; OFC, orbitofrontal cortex; PreCun, precuneus; SF, Sylvian fissure; SFG, superior frontal gyrus; SMG, supramarginal gyrus; SPL, superior parietal lobule; STG, superior temporal gyrus; TOS, transverse occipital sulcus. [To view this figure in colour, please visit the online version of this Journal.]



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using the hemispherically unified coordinate system. Increased variance of surface curvature (Figure 3B, green regions) reflects two factors: (1) withinhemisphere intersubject variability in sulcal structure and (2) hemispheric asymmetries. Curvature variance was low over most of the cortical surface indicating low sulcal variability, low asymmetry and accurate sulcal alignment. Curvature variance was increased in regions surrounding the posterior Sylvian fissure due primarily to hemispheric asymmetries, while increased variance in other regions of the temporal, parietal, and frontal lobes, primarily reflected increased variability in sulcal folding patterns (Ono, Kubik, & Abernathey, 1990), which were associated with corresponding reductions in parcellation accuracy in Destrieux, Fischl, Dale, and Halgren’s (2010) study. The thickness measurements were in good agreement with those of Fischl and Dale (2000) and Salat et al. (2004). Whole hemispheric asymmetries were analysed in the hemispherically unified space using repeated measures ANOVA on regional thickness, area and BED (Table 2). There were no significant whole hemisphere asymmetries for mean thickness or area, but there was a trend towards increased BED values (+0.5%) in the LH. There were no hemisphere × sex differences in any of the three quantities. Lobar asymmetries were analysed with hemisphere × lobe interactions. Thickness and area both had moderately strong (η = 0.22 and η = 0.13, respectively) asymmetries in certain lobes, with weaker significant difference found in BED lobar values (η = 0.06). For thickness, the strongest asymmetry (η = 0.35) was seen in the temporal lobe, where RH >LH by 2.0%. A more moderately significant (η = 0.20) LH > RH thickness asymmetry (2.9%) was seen in the limbic cortex. Two weaker but still significant effects were found in the insula (η = 0.22; RH > LH by 1.4%) and in occipital cortex (η = 0.22; RH > LH by 1.0%). There were also consistent, but opposite, areal asymmetries in temporal and parietal lobes. The area of the temporal lobe was increased in the LH (by 3.6%, η = 0.36), while the area of the parietal lobe was increased in the RH (by 2.3%, η = 0.29). The parietal lobe also showed a trend towards interaction with sex, with male parietal-lobe asymmetry (3.0%) being twice that of female asymmetry (1.5%). A more modestly significant lobar area asymmetry was found in the insula (η = 0.18; LH > RH by 2.9%). BED values also showed significant asymmetries in three lobes, with greater folding in the left temporal lobe (2.7%, η = 0.22), left parietal lobe (1.3%, η = 0.15) and right frontal lobe (1.1%, η = 0.14). We next examined finer-grained asymmetries in hemisphere × Desikan parcel (nested within lobes) analyses using post-hoc pairwise t-tests on each parcel within a lobe (thresholded at p < 10–6). The results can be seen in the Mollweide asymmetry maps shown in Figure 4. We should first mention that there were no significant hemi × parcel × sex results to discuss. There were few significant asymmetries in Desikan parcel thickness, with only the occipital lobe showing reasonably strong and consistent parcel heterogeneity in asymmetry (η = 0.25),

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Thickness (mm)

F1,136 = 1.8; η = 0.01 LH = 892, RH = 893 F1,136 = 2.0; η = 0.01 F5,680 = 20.0***; η = 0.13 F: F1,136 = 0.0; η = 0.00 I: F1,136 = 29.5***; η = 0.18 L: F1,136 = 1.3; η = 0.01 O: F1,136 = 8.3*; η = 0.06 P: F1,136 = 54.4***; η = 0.29 T: F1,136 = 77.1***; η = 0.36 F: F9,1224 = 65.2***; η = 0.32 L: F5,680 = 53.1***; η = 0.28 O: F3,408 = 57.6***; η = 0.30 P: F6,816 = 25.5***; η = 0.16 T: F1,136 = 135.6***; η = 0.50

Area (cm2)

F1,136 = 6.7*; η = 0.05 LH = .0587 RH = .0584 F1,136 = 0.0; η = 0.00 F5,680 = 8.1**; η = 0.06 F: F1,136 = 22.3**; η = 0.14 I: F1,136 = 4.1; η = 0.03 L: F1,136 = 2.5; η = 0.02 O: F1,136 = 0.2; η = 0.00 P: F1,136 = 23.3***; η = 0.15 T: F1,136 = 38.0***; η = 0.22 F: F9,1224 = 3.8; η = 0.02 L: F5,680 = 13.6***; η = 0.09 O: F3,408 = 6.1*; η = 0.04 P: F6,816 = 18.0***; η = 0.12 T: F1,136 = 3.7; η = 0.03

BED

TABLE 2 ANOVA results for hemispheric comparisons of thickness, area and BED across whole hemispheres, lobes and Desikan parcels

Hemisphere Hemisphere × Sex Hemisphere × Lobe

Hemisphere × Parcel (nested in lobe)

F1,136 = 2.0; η = 0.01 LH = 2.60 RH = 2.61 F1,136 = 1.9; η = 0.01 F5,680 = 38.3***; η = 0.22 F: F1,136 = 0.3; η = 0.00 I: F1,136 = 16.0**; η = 0.11 L: F1,136 = 34.8***; η = 0.20 O: F1,136 = 12.0**; η = 0.08 P: F1,136 = 9.2*; η = 0.06 T: F1,136 = 73.8***; η = 0.35 F: F9,1224 = 3.5*; η = 0.03 L: F5,680 = 17.9***; η = 0.12 O: F3,408 = 44.2***; η = 0.25 P: F6,816 = 4.4*; η = 0.03 T: F1,136 = 3.4; η = 0.02


Thickness and BED were computed using area-weighted means across hemispheres (hemi and hemi × sex), lobes (hemi × lobe) and Desikan parcels (hemi × parcel, nested inside lobes). Areas were analysed as cross-hemisphere percentage differences to normalise parcels with substantially different sizes. Lobes: F = frontal, L = Limbic, O = occipital, P = parietal, T = temporal. Significant effects shown in bold, along with effect sizes (η). *p < 0.01, **p < 0.001, ***p < 0.0001.


(A)

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SMG IFG

HG1 SF STG

CS AG

MFG

MFG MTG SFG

IPS

OFC

SFG ITG FG

CG

LOS TOS MedFG

0.1 0 –0.1

PreCun


CalcS

–0.3

(B)

0.3 SMG IFG

SF

HG1 STG

CS AG

MFG

MFG MTG SFG

0.1

IPS

OFC

SFG ITG FG

CG

LOS TOS MedFG

0 –0.1

PreCun CalcS

–0.3

(C)

10

SMG IFG

HG1 SF STG

CS AG

MFG

MFG MTG SFG CG

2

IPS

OFC

SFG ITG FG

LOS TOS MedFG

0 –2

PreCun CalcS

–10

Figure 4. Interhemispheric differences (LH–RH) for curvature-adjusted cortical thickness and surface area. (A) Cortical thickness, (B) curvature-adjusted thickness difference (mm), 50 mm FWHM Gaussian smoothing and (C) area difference (%), 50 mm FWHM Gaussian smoothing. Only z-scores > 4.0 are shown. Red to yellow colours indicate greater regional values in the left hemisphere, blue to cyan colours indicate greater values in the right hemisphere. Gyral and sulcal structures are shown in the background (light grey = gyri, dark grey = sulci). See Figure 3 for anatomical labels. [To view this figure in colour, please visit the online version of this Journal.]

with increased pericalcarine thickness (3%) in the LH and increased lateral occipital thickness (4%) in the RH. Less reliable heterogeneity in thickness asymmetry was found in limbic cortex (η = 0.12), with no specific parcels showing significant asymmetries. Substantial heterogeneous locations in the


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otherwise uniform (η = 0.02) temporal lobe were found including increased RH thickness in the transverse temporal gyrus (4%) and bank of the STS (4%). Both of those asymmetries may be influenced by folding differences between the hemispheres (Figure 4A vs 4B). In the homogeneous (η = 0.03) frontal lobe and parietal lobe, there were no reliable parcel-level asymmetries in thickness. In contrast to thickness, strong areal asymmetries in Desikan parcels were seen in every lobe, particularly the temporal lobe (η = 0.50), but with substantial differences also evident in the frontal (η = 0.32), limbic (η = 0.28) and occipital (η = 0.30) lobes. However, asymmetries of within-lobar parcels may be due to subtle, consistent differences in how parcel boundaries were determined in each hemisphere, or may reflect systematic differences in hemispheric curvature or folding (Ono et al., 1990). Therefore, it is important to verify that parcel asymmetries are also evident in voxel-based surface maps (Figure 4). For example, in the temporal lobe there was an above-average (for the temporal lobe) LH areal expansion in the transverse temporal parcel (30%), while an opposite RH areal expansion was seen in the middle temporal parcel (13%), which includes portions of the superior temporal sulcus. Both asymmetries can be seen in the voxel surface maps in Figure 4C. In the occipital lobe, only the pericalcarine region shows a significant regional area asymmetry (13% greater in RH). The only significant cingulate cortex areal variation reflected opposing regional asymmetries in the rostral cingulate (25% expansion in the LH) and the caudal-anterior parcel (17% expansion in the RH). The frontal lobe contained several asymmetries in parcel areas, including regional LH expansion in superior frontal (5%), caudal middle frontal (9%) and pars opercularis (18%; Keller et al., 2007), and RH expansion in more ventral areas, such as the frontal pole (37%), medial orbital (6%), pars orbitalis (18%), pars triangularis (14%) and the paracentral parcel (14%). However, the lack of nearby opposing colours in Figure 4C in the inferior frontal locations calls into question the pars asymmetries: perhaps they are due to boundary placements. Finally, in the parietal lobe, there was a substantial RH expansion in the inferior parietal parcel (17%) and significant LH expansion of the supramarginal (10%) and postcentral (8%) parcels, all of which are evident in Figure 4C. In addition, there were two BED asymmetries at the parcel level in the temporal lobe: greater BED in the left middle temporal (7%) and left superior temporal (5%) parcels in comparison with corresponding regions of the RH. We found a reliable asymmetry of the parietal lobe in the postcentral area (6%), also favouring the LH. In the limbic lobe, there was one final parcel level asymmetry in BED values favouring the RH in the isthmus of the cingulate (8%).

Asymmetries and brain size Based on observations that functional brain organization varies with brain size (Razoumnikova, 2000), we also evaluated the influence of ICV on hemispheric


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(A) SMG IFG

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CS AG

MFG

MFG MTG SFG

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SFG ITG FG

CG

LOS TOS MedFG

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SMG IFG

SF

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MFG MTG SFG CG

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Figure 5. Correlations of absolute areal asymmetries (i.e., squared areal differences) (A) with intracranial volume (ICV) without correction for subject sex, and (B) similar correlations after eliminating covariance due to subject sex. [To view this figure in colour, please visit the online version of this Journal.]

asymmetries. We found that overall asymmetry, i.e., the square root of the sum of squared hemispheric Desikan parcel differences, increased in magnitude in larger brains (Herve et al., 2013). Thus, there was a significant correlation of overall asymmetry with ICV (r = 0.27); one that increased to r = 0.40 when the 74 finerscale Destrieux cortical parcels (Destrieux et al., 2010) were used. Figure 5 shows the spatial distribution of squared hemispheric area differences, smoothed with a 50 mm FWHM kernel Gaussian filter: summed areal asymmetries on this scale correlated strongly with ICV (r = 0.57). This indicates that local areal asymmetries increased in larger brains, particularly in the insula, frontal lobe and parietal lobe. In contrast, similar calculations with asymmetries in cortical thickness showed no significant correlations with ICV at any level of anatomical detail (all |r| < 0.2).

Hemispheric asymmetries in subcortical structures Table 3 shows the results of an ANOVA with the factors of sex and hemisphere to capture within-subject subcortical asymmetries. The significant (p < 0.001) results show that the thalamus (4.2%, η = 0.50), putamen (5.4%, η = 0.58) and

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KANG ET AL. TABLE 3 Subcortical volume asymmetries for each of seven structures Hemispheric asymmetry

Cerebellum Thalamus Caudate


Putamen Pallidum Hippocampus Amygdala

t136 = –11.4*** RH: +2.6% t136 = +11.7*** LH: +4.2% t136 = –2.6* RH: +1.1% t136 = +13.6*** LH: +5.4% t136 = +7.1*** LH: +4.9% t136 = +1.1 t136 = –8.9*** RH: +6.5%

Asymmetry × Sex t136 = 1.4 t136 = 4.5** Male LH + 5.7% t136 = 2.1 t136 = 1.5 t136 = 0.3 t136 = 0.5 t136 = +0.6

A mixed between-within ANOVA over sex and hemisphere was used to identify factors contributing to volume values. Significant effects shown in bold. *p < 0.01; **p < 0.001; ***p < 0.0001.

globus pallidus (4.9%, η = 0.27) were significantly larger in the LH, while the cerebellum (2.6%, η = 0.49) and amygdala (6.5%, η = 0.36) were significantly larger in the RH. There was only one significant hemisphere × sex interaction (η = 0.13); males showed a more pronounced LH enlargement of the thalamus (5.7%) than females (2.7%).

Cortical and subcortical correlations We also examined the correlations between subcortical and cortical volumes (above diagonal, Table 4), and between subcortical and cortical asymmetries (below diagonal, Table 4). Overall subcortical volumes correlated strongly with each other and with cortical area, with particularly strong correlations seen between the volume of the thalamus and cortical area. In contrast, there were no significant correlations between subcortical volumes and cortical thickness. There were only a few weakly significant correlations in cortical and subcortical asymmetries, most notably between the putamen and pallidum, which may follow from their close pairwise proximity and functional relationship. There was also a similar weak correlation between amygdala volume asymmetry and total cortical surface area asymmetry as well as a negative correlation between cortical-area and cortical-thickness asymmetries.

Cerebellum Thalamus Caudate Putamen Pallidum Hippocampus Amygdala Cortical area Cortical thickness

Thalamus

Caudate 0.63 0.57 0.54

Putamen

Pallidum

0.59 0.62 0.53 0.48 0.45

Hippocampus

Amygdala


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Cortical area

−0.32 −0.27

Cortical BED

0.71 0.74 0.62 0.65 0.58 0.64 0.59

−0.39

Cortical thickness

−0.31

TABLE 4 Correlations between cortical and subcortical volumes and asymmetries

0.63

0.60 0.60

0.60 0.73 0.59 0.68 0.28

0.52 0.47 0.49 0.52 0.48 0.59 0.28


Values above the diagonal line show significant correlations between subcortical volumes and total cortical area, mean cortical thickness and mean cortical BED. Values below the diagonal line show those correlations between hemispheric asymmetries that were significant in the main analysed image data-set (Table 4).

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DISCUSSION Asymmetries in cortical structure We found no hemispheric asymmetries in overall cortical thickness or area (Table 3), consistent with the results of Salat et al. (2004), but in contrast with previous reports of overall LH increases in thickness (Hamilton et al., 2007; Luders et al., 2006). However, we did find a trend for the LH to have slightly more bending energy, presumably reflecting an increase in the gyri and sulci. On the other hand, we found substantial asymmetries between the lobes in thickness and area, with the temporal lobe being the most asymmetric overall (η > 0.3 for thickness, area, and BED as well). The largest lobar thickness asymmetry was found in the temporal lobe (RH > LH; 2.0%) with the largest differences localized to the superior temporal area, and consistent with previous results (Meyer, Liem, Hirsiger, Jancke, & Hanggi, 2013). The second most consistent thickness difference was in the limbic cortex (LH > RH; 2.9%), due primarily to the rostral cingulate, agreeing with asymmetries previously reported by Huster, Westerhausen, Kreuder, Schweiger, and Wittling (2007) and several others (Table 1). We also found a substantial asymmetry in the area of the temporal lobe (LH > RH; 3.6%) in agreement with the studies of Lyttelton et al. (2009) and Van Essen et al. (2012). However, the parietal lobe areal asymmetry we observed (RH > LH; 2.3%) conflicts with the reports of Lyttelton et al. (2009) and Van Essen et al. (2012), although we did find an LH enlargement of the supramarginal gyrus as they reported. There have also been reports of RH > LH volume asymmetries in the superior parietal cortex in postmortem data (Scheperjans et al., 2008). We also found a moderately reliable asymmetry in the insula area (LH > RH; 2.9%), agreeing with most previous studies (Table 1). Overall, area measures showed somewhat greater asymmetries, i.e., larger effect sizes, than thickness or BED measures. However, in a number of cases area and thickness asymmetries showed opposite polarities, e.g., as in the temporal lobe as a whole, and in the superior temporal gyrus and Heschl’s gyrus. In so far as functions are topographically mapped onto the cortical surface, this trade-off suggests that in some areas map resolution, indexed by cortical area, may be greater in one hemisphere, while the complexity of local intracortical processing, indexed by cortical thickness, may be greater in the other.

Asymmetries and brain size We found that the overall magnitude of cortical areal asymmetries correlated with brain size, particularly in the insula, inferior frontal and parietal areas. This strong result is unlikely to be artifactual result of Freesurfer ROI boundary determinations since the correlation was evident with both an ROI and a surface voxel analysis (and did not hold for thickness). The results are in accord with the


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hypothesis that asymmetries increase in larger brains (Herve et al., 2013). However, the increased asymmetries in larger brains appeared in spatially diverse locations and differed in directionality in different individuals—i.e. it was only revealed in the cumulative absolute asymmetry, and not in any particular parcels/ lobes with a consistent sign. This apparent randomness of asymmetric loci area might simply be due to the development of additional sulci (side branches, sulcal interruptions) in somewhat random locations as larger brains develop.


Hemispheric asymmetries in subcortical structures Significant hemispheric asymmetries were also found in subcortical structures including the thalamus, putamen and pallidum (all LH > RH), as well as the amygdala and cerebellum (both RH > LH). The thalamic and basal ganglia results are in agreement with some previous reports (Table 1), while the amygdala asymmetry was opposite that of Good et al. (2001). We also found significant interhemispheric asymmetry in the cerebellum consistent with Herve et al. (2006) and Fan et al. (2010), whereas stereological methods used by Ertekin et al. (2013) and Gocmen-Mas et al. (2009) failed to find asymmetries. Additionally, our study did not find the significant RH enlargement of the hippocampus that has been reported in a number of previous studies in the Internet Brain Volume Database (Kennedy, Hodge, Gao, Frazier, & Haselgrove, 2012; see Supplementary Table 1), most often in the anterior hippocampus, e.g. Rogers, Sheffield, Luksik, and Heckers (2012). Some of these inconsistencies may reflect the different methods used for parcellation. While Freesurfer slightly overestimates hippocampal volume in young controls (Wenger et al., 2014), it is fully automated and so, unlike the various semi-manual and manual protocols for hippocampal delineation (Konrad et al., 2009), it is unlikely to introduce asymmetric biases that can occur with manual methods (Maltbie et al., 2012; Rogers et al., 2012). However, given the difficulty of delineating the amygdala from the hippocampus (Konrad et al., 2009), it might be the case that the unexpected RH enlargement of the amygdala that we found might be more properly assigned to the anterior hippocampus. However, when we analysed the OASIS-130 subcortical volume data as a replication, we again found significant RH increases in the volume of the amygdala, cerebellum and caudate, as well as a trend towards increased RH hippocampal volume (Supplementary Table 2).

Correlations of asymmetries between structures Of the subcortical volume asymmetries, two were significantly correlated with one another: the putamen and the pallidum (Table 4). This correlation was also seen in the OASIS data-set (Table 5: r = +0.20 vs. Table 4: r = +0.28). The other two weak correlations involving cortical surface area asymmetries (see above)

KANG ET AL.

Thalamus

Caudate

0.47 0.61 0.56

Putamen

Pallidum

0.52 0.63 0.54 0.46 0.49

Hippocampus

Amygdala

Cortical area

−0.12

0.65 0.77 0.61 0.63 0.56 0.60 0.62

TABLE 5 Correlations between cortical and subcortical volumes and asymmetries in the OASIS data-set Cerebellum 0.66

0.47 0.59

0.55 0.65 0.61 0.74 0.20

0.48 0.62 0.48 0.62 0.47 0.62

−0.01


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Cerebellum Thalamus Caudate Putamen Pallidum Hippocampus Amygdala Cortical area Cortical thickness

Cortical thickness


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were not significant when the OASIS data-set was analysed. In particular, even though total cortical surface area (but not thickness or BED) varies quite closely with hemisphere-averaged subcortical volume values (Tables 4 and 5), there was no substantial covariation in the overall cortical and subcortical asymmetry. However, this may be because we were looking at the entire cortex where we found little cortical asymmetry overall: this lack of correlation may not hold regionally, but larger data-sets would be needed to investigate such questions.


Functional implications of anatomical asymmetries In general, speech-related regions (e.g., the transverse temporal lobe, insula, superior temporal gyrus, temporal pole and superior temporal sulcus) were relatively enlarged in the LH. In addition, the lenticular nucleus and lateral thalamus were larger in the LH, and the right cerebellum (receiving inputs from the LH) was also enlarged. In contrast, a number of regions implicated in visuospatial processing (e.g., the parietal lobe and prefrontal cortex) were relatively enlarged in the RH. Thus, the results are generally consistent with dominant role of the LH in linguistic processes and in movement control (e.g., praxis) and the dominant role of the RH in visuospatial processing (Herve et al., 2013).

Sex differences in anatomical asymmetries In contrast to some previous reports (Luders et al., 2006), but in accord with others (Roldan-Valadez, Rios, Suarez-May, Favila, & Aguilar-Castaneda, 2013), we found minimal sex differences in systematic cortical or subcortical asymmetries. No significant sex differences in asymmetry were found at the Desikan parcel level for thickness, area or BED when doing direct statistical interaction comparisons. However, a trend was seen at the lobar level towards greater male area asymmetries in the parietal lobe, and males also showed greater asymmetry in the thalamus. In addition, the fact that larger brains showed greater (non-directional) omnibus asymmetries, suggests that, on average, the male brain shows somewhat greater anatomical asymmetry than the female brain.

Limitations The primary limitation is that our results may only apply to young, healthy, welleducated adults. In particular, different patterns of asymmetry have been found in children (Brain Development Cooperative Group, 2012), and asymmetries in cortical and subcortical volume have been reported to change as adults age (Crivello, Tzourio-Mazoyer, Tzourio, & Mazoyer, 2014). For example, the considerable interstudy disagreement in morphological asymmetry (Table 1) shown in many cortical and subcortical locations may be due to the use of groups

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with uncontrolled age and handedness as well as due to small group sizes. Further, different surface analysis techniques used in Table 1 have not been thoroughly tested against one another for accuracy or reliability. Finally, the automated subcortical segmentation and volume estimation technique used here performs at around 60–85% overlap with gold standard manual segmentation: we expect that future developments in more accurate automated subcortical parcellation might show discrepant results even in a similar population.

CONCLUSIONS We examined hemispheric asymmetries in cortical area, thickness and BED using Freesurfer cortical parcellations analysed on a hemispherically unified coordinate system and found significant lobar and sub-lobar asymmetries. Perisylvian language areas including the superior temporal plane, superior temporal lobe and insula were relatively enlarged in the LH as was the cingulate, while several regions implicated in visuospatial processing (e.g., the parietal lobe and prefrontal cortex) were relatively enlarged in the RH. In addition, we found that absolute (i.e., non-directional) cortical asymmetries were more pronounced in subjects with in larger overall brain volume. Finally, the volumes of automatically parcellated subcortical structures correlated strongly with cortical area (but not thickness) and revealed LH enlargement of the thalamus, putamen and pallidum, and RH enlargement of the cerebellum and the amygdala.

Supplementary material Supplementary content is available via the ‘Supplementary’ tab on the article’s online page (http://dx.doi.org/10.1080/1357650X.2015.1032975).

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Developing human brain: age-related changes in cortical, subcortical, and cerebellar anatomy.

Erratum: The developing human brain: age-related changes in cortical, subcortical, and cerebellar anatomy.

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Connectivity measures in the Poffenberger paradigm indicate hemispheric asymmetries.

Abnormal asymmetries in subcortical brain volume in schizophrenia.

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