Cortical Surface Morphometry

Cortical Surface Morphometry AC Evans, McGill University, Montreal, QC, Canada ã 2015 Elsevier Inc. All rights reserved.

Introduction

Methodology

Structural neuroimaging has undergone a renaissance in recent years, with the introduction and widespread application of quantitative techniques for automated extraction of various morphological metrics. These techniques include diffusion-based methods (Iturria-Medina, 2013), voxel-based morphometry (Ashburner & Friston, 2000), volumetric deformation-based morphometry (Ashburner et al., 1998), and cortical surface morphometry. The latter approach has found particular favor, since it provides a series of cortical metrics that have anatomical meaning, not only most commonly cortical thickness but also metrics of local surface area, local cortical volume, and various indices of local surface curvature. These metrics are used to capture group differences or longitudinal changes in cortical anatomy, for example, during neurodevelopment, learning, or neurodegeneration. These metrics allow for the exploration of the influence of genetics or experience on cortical anatomy, long-range coupling of cortical regions, and brain–behavior relationships in clinical research and systems neuroscience. The growth of this research area shows no sign of abating, and in 2013 alone, over 700 publications employed cortical thickness analysis. Figure 1 illustrates its continuing growth for over 20 years. Despite this widespread adoption, there remain concerns about the neuroanatomical validity of the most commonly employed metric, cortical thickness. As will be discussed in more detail in the succeeding text, the estimation of cortical thickness depends upon the identification of two boundaries in 3-D MRI data, the pial surface and the boundary between gray matter (GM) and white matter (WM). This depends upon the GM/WM contrast in the MRI data, and some authors assert that MRI-based estimates of cortical thickness do not accurately reflect the true cytoarchitectural boundaries. While there is some justification for this position, the situation is arguably similar to that in functional MRI (fMRI), where the BOLD signal has been widely used as a surrogate for cerebral blood flow (CBF). Although the fMRI-based BOLD arises from a number of sources, including CBF, and is therefore not a direct measure of CBF, it is nevertheless widely used for functional imaging in much the same way as cortical thickness is used for structural imaging, that is, to study group differences, longrange coupling, and brain–behavior analysis. Thus, while there is a need for further exploration of MRI-based cortical thickness analysis methods and cross-validation against histological metrics, its applications are likely to continue to expand. We briefly review here the history, current status, and future evolution of cortical surface morphometry, from both the methods themselves and their application in various clinical research and systems neuroscience applications.

The early evolution of automated cortical surface morphometry from structural MRI took place in a number of locations simultaneously. The Evans group in Montreal introduced the multisurface ASP technique (Holmes, MacDonald, Sled, Toga, & Evans, 1996; MacDonald, Avis, & Evans, 1994; MacDonald, Kabani, Avis, & Evans, 2000), using nested deformable spherical meshes to fit cortical voxels previously segmented from MRI. Using a constrained Laplacian model and skeleton-based surface reconstruction, ASP was subsequently extended to CLASP (Kim et al., 2005; Tohka, Zijdenbos, & Evans, 2004). A singlesurface version of ASP was employed by Thompson, Schwartz, and Toga (1996) and Thompson et al. (1997) to characterize cortical surface variability via fluid deformation and coregistration of the extracted pial surfaces from different individuals. Early work with a deformable mesh was also conducted in St. Louis (Carman, Drury, & Van Essen, 1995; Van Essen & Drury, 1997; Van Essen et al., 2001) and Frankfurt (BrainVoyager; Goebel, 1996, 1997, 2012; Kriegeskorte & Goebel, 2001). The FreeSurfer package (Dale, Fischl, & Sereno, 1999; Fischl, Sereno, & Dale, 1999), as well as extracting cortical surfaces using a deformable mesh, also introduced a surface coordinate system for intersubject comparison. Another widely used approach, BrainVISA (Mangin, Frouin, Bloch, Regis, & Lopez-Krahe, 1995), employs a more bottom-up approach, similar to marching cubes, fitting triangular facets to presegmented cortical voxels, followed by heuristic operations to smooth the final surface and correct errors. Lee et al. (2006) conducted a simulationbased comparison of the surface extraction performance of CLASP, FreeSurfer, and BrainVISA, but, since these algorithms have undergone continuous development in subsequent years, direct head-to-head conclusions should be interpreted with caution in 2014. Other validation approaches have also examined the performance of surface extraction algorithms using real data, quantifying intersubject reproducibility (Fischl and Dale, 2000), and comparing the MRI-based estimates of cortical thickness with postmortem measurements (Kabani, Le Goualher, MacDonald, & Evans, 2001). Having extracted the surface meshes, it is possible to obtain morphological metrics at each mesh vertex, such as vertexwise cortical thickness, area, volume, and curvature, each metric having various possible mathematical definitions. For instance, cortical thickness can be defined simply as the distance between linked vertices on inner and outer surfaces or by curved streamlines, defined by the Laplacian equation (Haidar & Soul, 2006; Jones, Buchbinder, & Aharon, 2000; Tosun et al., 2004; Yezzi & Prince, 2003). A subclass of statistical image analysis has evolved to handle the problem of capturing surface shape change and comparing surface variability across a

Brain Mapping: An Encyclopedic Reference

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Figure 1 Number of publications by year identified by ‘cortical thickness’ in PubMed.

population (Chung, Worsley, Nacewicz, Dalton, & Davidson, 2010, Chung, Robbins, & Evans, 2005). As cortical thickness analysis has moved into the mainstream, the last decade has seen an explosion of new tools that use volume-based estimates (Hutton, De Vita, Ashburner, Deichmann, & Turner, 2008; Scott, Bromiley, Thacker, Hutchinson, & Jackson, 2009), active surface (Eskildsen & Ostergaard, 2006, 2007; Han et al., 2004; Tosun et al., 2006), and registration-based (Das, Avants, Grossman, & Gee, 2009) approaches. More recently, Dahnke, Yotter, and Gaser (2013) proposed a volume-based method, projection-based thickness (PBT), to address the continuing problem of misclassification errors in the sulci due to the partial volume effect (PVE). This method does not explicitly fit inner and outer cortical boundaries. Instead, using tissue probability maps as priors, it estimates cortical thickness from the segmented MRI directly. It emphasizes the importance of the midsurface as a means to avoid noise, PVE, and topology defects. With the plethora of available techniques, it is vital to characterize the performance space of these various tools. Surface morphometry is critically dependent upon the MR image properties, particularly GM/WM contrast, signal-to-noise ratio, resolution, and intensity inhomogeneity. Such factors are a constant scourge of the increasingly prevalent multicenter studies such as ADNI, NIHPD, and ABIDE where different scanners are used. Lerch and Evans (2005) used simulated data to explore the trade-offs in spatial resolution and detection power for different surface-smoothing approaches and thickness measures. More recently, Pardoe, Abbott, and Jackson (2013) conducted a similar analysis but using real data from four ADNI sites. Dahnke et al. (2013) described a comprehensive platform for validation and comparison of different surface analysis packages. Han et al. (2006) and

Schnack et al. (2010) had conducted thorough investigations of the reliability of cortical thickness estimates under the effects of field strength and scanner manufacturer or model. Lusebrink, Wollrab, and Speck (2013) compared cortical thickness estimates from 3 to 7 T MRI data, using isotropic voxels of either 1 or 0.5 mm dimension. They concluded that field strength by itself did not significantly affect cortical thickness measurements but the smaller voxels possible at 7 T reduced thickness values by as much as a third, a consequence of reduced PVE errors. The acquisition parameters may also vary with time, affecting the reliability of longitudinal studies. Wang et al. (2008) reported a stability in global mean cortical thickness of
2% although one may expect regional stability to be lower. Wang, Shi, Li, and Shen (2013) proposed a true 4-D approach with constrained cortical thickness variation that reduces the impact of machine-related effects at any single timepoint upon cortical morphometry.

Relationship Between Morphology and Histology Classical neuroanatomy (Brodmann, 1909; Flechsig, 1920; Vogt & Vogt, 1919; von Economo & Koskinas, 1925) still informs much of our modern understanding of functional neuroanatomy. However, these seminal studies were extremely laborintensive and architectonic boundaries were based on a few subjectively assessed criteria. Modern noninvasive 3-D imaging techniques hold promise for the recasting of those classical concepts in strictly quantitative terms. However, the technology is still evolving and there remain some unanswered questions regarding the validity and utility of neuroimaging strategies. Two related and persistent issues in MRI-based cortical morphometry are (i) the extent to which MRI-based estimates of

INTRODUCTION TO ANATOMY AND PHYSIOLOGY | Cortical Surface Morphometry

cortical thickness are concordant with classical neuroanatomy and (ii) the spatial relationship between sulcal anatomy and cortical architectonics. These issues take on greater significance as MR field strength increases and spatial resolution improves, and it becomes possible to distinguish fine structure of the cortex in vivo. The simple construct of a homogeneous GM mantle with a single GM/WM intensity border begins to lose its meaning at higher resolution. It is well known that the MRI signal from GM reflects myelin density in T1- and T2*-weighted images (Glasser, Goyal, Preuss, Raichle, & Van Essen, 2014; Glasser & Van Essen, 2011; Turner & Geyer, 2014). Cortical regions with heavy myelin content, such as the primary motor cortex or area MT in the visual cortex, exhibit lower GM/WM

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contrast (Van Essen & Glasser, 2014; Figure 2). This lower intensity gradient has a consequence for surface extraction algorithms, tending to move the GM/WM surface outward and indicating a thinner cortex. Unless explicitly accounted for in the cortical thickness algorithm, such spatial variations in myelin content and GM/WM contrast can introduce a spatially varying cortical thickness where none exists. Such artifacts force us to consider the underlying architectonic organization of the human cortex and its relationship to the MRI signal. T1 maps resemble myelin-stained histology (Geyer, Weiss, Riemann, Lohmann, & Turner, 2011; Turner & Geyer, 2014), and myelo- and cytoarchitectural boundaries have been shown to be similar using a combination of T1-weighted and

Figure 2 Myelin content estimated by taking the voxelwise ratio of T1W (a) to T2W (b) and colorizing (c). (d) Myelin map on the inflated right hemisphere of the same subject, including heavily myelinated hot spots centered on the area MT þ (black/white arrow) and in the intraparietal sulcus (red arrow). Reproduced from Van Essen, D. C., & Glasser, M. F. (2014). In vivo architectonics: A cortico-centric perspective. Neuroimage, 93(Pt 2), 157–164.

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T2-weighted imaging (Glasser & Van Essen, 2011). With such information, it becomes possible to adjust simple cortical thickness measurements, based on T1-weighted data alone, to accommodate variations in underlying architectonics. Higher field strength MRI also gives us new insight into the architectonics of the human cortex. Most importantly, it allows us to study the cortex in vivo and noninvasively in 3-D and to explore the population variability of cortical organization. It is now routinely possible to image the cortex at 0.5 mm isotropic resolution at 7 T and identify group differences in cortical layering (e.g., Augustinack et al., 2005; Trampel, Ott, & Turner, 2011). Such high resolution affords the possibility of segmenting and modeling the laminar surfaces within the cortex (Bazin et al., 2014; Waehnert et al., 2014). This allows for the creation of population-averaged T1 maps at different cortical depths to reveal area-specific myeloarchitecture (Tardif, Dinse, Schafer, Turner, & Pl, 2013) and, by extension, the architectonic organization of the cortex. The relationship between sulcal morphology and architectonic boundaries remains a thorny issue. The seminal work of the Ju¨lich group (Zilles & Amunts, 2009, and references therein) generated probabilistic cyto- and chemoarchitectural maps of the Brodmann areas that have been incorporated into public software packages such as statistical parametric mapping (SPM; Eickhoff et al., 2005, 2007). The extent to which functional neuroanatomy boundaries are predicted by gross cortical folding remains controversial and has direct impact upon the practices of functional brain mapping. The use of coregistered anatomical MRIs to align fMRI images from different subjects implicitly assumes a tight structure–function correspondence such that increasing anatomical alignment, through high-dimensional spatial normalization (warping), will also increase functional alignment. However, Crivello et al. (2002) showed that the benefits of image warping were limited for functional data, because of a weak structure– function correspondence. On the other hand, Auzias et al. (2011) showed significant improvement in the detection of functional signals by jointly aligning volumes and cortical folding patterns. Fischl et al. (2008) demonstrated that there was indeed some predictive information in sulcal morphology, particularly for primary Brodmann areas that showed less variability (
4 mm) compared with higher-order association areas (
7–8 mm).

Covariance of Cortical Morphology The study of functional connectivity with electro- or magnetoencephalography (Stam, Jones, Nolte, Breakspear, & Scheltens, 2007) or fMRI (Biswal et al., 2010) correlation is well established, as is the study of structural connectivity with diffusion imaging (Iturria-Medina, 2013). However, there is also increasing interest in the use of structural MRI to investigate anatomical covariance, as described more fully in recent reviews (AlexanderBloch, Giedd, & Bullmore, 2013; Evans, 2013). Lerch et al. (2006) first demonstrated covariance maps of cortical thickness, using a seed-based approach to generate vertexwise maps of cross-sectional covariance. This study was cross-sectional and more recent studies have examined the correlation of longitudinal rates of change in cortical thickness, or ‘maturational’

coupling (Raznahan, Lerch, et al., 2011). Frontotemporal association cortices showed the strongest and most widespread maturational coupling with other cortical areas, while lower-order sensory cortices showed the least. This work was extended (Alexander-Bloch, Raznahan, Bullmore, & Giedd, 2013) to reveal substantial overlap between anatomical, maturational, and functional networks in the developing brain. He, Chen, and Evans (2007) adopted an ROI-based approach to generate a full N  N correlation matrix of all possible region pairs, albeit at lower spatial resolution. Graph analysis was then used to capture topological indices for these cortical networks. Chen, He, Rosa-Neto, Germann, and Evans (2008), Chen, He, Rosa-Neto, Gong, and Evans (2011), and Chen, Panizzon, et al. (2011) took this further to reveal an underlying modularity that reflected well-known functional systems in the young, healthy brain and that was reduced in the normal aging brain. It is not yet clear what cellular mechanisms give rise to the macroscopic cortical thickness correlation observed with MRI. Gong, He, Chen, and Evans (2012) compared cortical thickness correlation and probabilistic tractography and found that 35–40% of thickness correlations converged with a tractography connection. Convergence was mostly found for positive correlations while almost all of the negative correlations (>90%) had no corresponding tractography connection. Different mechanisms may therefore underlie positive and negative thickness correlations, the latter not being mediated by a direct fiber pathway. Also, since both methods suffer from methodological limitations, the two techniques may be revealing complementary characteristics of a more complete description of brain connectivity.

Genetic Influences upon Cortical Morphology Considerable progress has been made in the last decade toward a clearer understanding of the genetic influences on cortical morphology. However, there are important methodological factors to consider. Global neuroanatomical measures, such as brain volume and mean cortical thickness, are highly heritable (Panizzon et al., 2009), and studies of regional heritability must first remove this global component (Schmitt et al., 2010; Yoon, Perusse, & Evans, 2012), even if this does lead to lower heritability estimates. It is has also been noted that ROIbased approaches tend to mask heritability at the vertex level (Eyler et al., 2012), probably a result of genetic boundaries differing from gross or functional neuroanatomical borders. Rimol, Hartberg, et al. (2010) and Rimol, Panizzon, et al. (2010) mapped the heritability of cortical thickness across the human brain. They identified regionally specific patterns for specific seed location rather than a single, global genetic factor. These patterns were consistent with the division between primary and association areas, as well as patterns of brain gene expression, neuroanatomical connectivity, and brain maturation trajectories. However, no single explanation dominated. Chen, He, et al. (2011) and Chen, Panizzon, et al. (2011) explored the genetic component of regional surface area variability in adult twins. They demonstrated a strong anterior-toposterior gradient as well as symmetric patterns of regionalization and subsequently created a human brain atlas based solely

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Figure 3 Developmental trajectories of cortical thickness, total surface area, and overall cortical volume. Reproduced from Wierenga, L. M., Langen, M., Oranje, B. & Durston, S. (2014). Unique developmental trajectories of cortical thickness and surface area. NeuroImage, 87, 120–126. http://www.ncbi.nlm.nih.gov/pubmed/24246495.

on genetic indices (Chen et al., 2012). They recently identified a dorsal–ventral genetic gradient in cortical thickness (Chen et al., 2013) and revealed a dissociation between the organizing principles for cortical surface area and thickness. Surface area clusters showed great genetic proximity with clusters from the same lobe, while thickness clusters have close genetic relatedness with clusters that have similar maturational timing. Other studies have also emphasized the fact that cortical thickness and vertexwise surface area exhibit different genetic influences (Hill et al., 2010; Sanabria-Diaz et al., 2010), and that studies of regional or vertexwise cortical volume (loosely, the product of thickness and area) will tend to conflate these two influences (Panizzon et al., 2009) Figure 3 illustrates the different trajectories of these three cortical metrics during normal development. Schmitt et al. (2008) identified genetically mediated frontoparietal and occipital networks of cortical thickness. They also found (Schmitt et al., 2009) that interhemispheric covariance in cortical thickness is largely genetically mediated while environmental influence is more intrahemispheric. Conversely, in a cohort of 8-year-old twins, Yoon, Fahim, Perusse, and Evans (2010) and Yoon et al. (2012) found genetic environmental influences to be lateralized, with the language-dominant left cerebral cortex under stronger genetic control. Intriguingly, differences in genetic covariance and heritability appear to be driven by a common genetic factor that influences GM and WM differently (Schmitt et al., 2010). The relative influence of genetic and environmental factors on cortical growth is dynamic and changes with age. Lenroot et al. (2009) examined age-related differences in the heritability of cortical thickness in a large pediatric sample of twins, twin siblings, and singletons. The primary sensorimotor cortex, which develops earlier, showed greater genetic effects earlier in childhood. Later developing regions within the dorsal prefrontal cortex and temporal lobes showed increasing genetic effects with maturation. Thus, regions associated with complex cognitive processes such as language, tool use, and executive function are more heritable in adolescents. Joshi et al. (2011) studied young adult twins to reveal a strong genetic influence on GM thickness and volume in the frontoparietal regions.

Several regions where cortical structure was correlated with IQ were shown to be under genetic control. In healthy adolescent twins, however, Yang, Carrey, Bernier, and Macmaster (2012) and Yang, Joshi, et al. (2012) found that regions with genetic contributions of > 80% were observed in the prefrontal cortex, whereas strong unique environmental influences were found in the parietal association regions. The genetic variance for thickness in adolescents in the prefrontal regions overlapped with those in the adult sample. However, the unique environmental effects in the parietal association areas suggest that these regions are more shaped by experience and could form targets for early interventions for behavioral disorders.

Applications There is now a huge literature on clinical and basic applications of cortical morphometry, in general, and cortical thickness analysis, in particular, notably in the following areas: Neurodevelopment: Giedd et al. (1999), Gogtay and Thompson (2010), Gogtay et al. (2004), Lerch et al. (2006), Sowell, Thompson, Leonard, et al. (2004), Sowell, Thompson, and Toga (2004), Thompson et al. (2005), Toga, Thompson, and Sowell (2006), Raznahan, Lerch, et al. (2011), Raznahan, Shaw, et al. (2011), Wierenga et al. (2014), Zhou, Lebel, Evans, and Beaulieu (2013), Lawson, Duda, Avants, Wu, and Farah (2013), Dennis and Thompson (2013), Khundrakpam et al. (2013), Alexander-Bloch, Raznahan, et al. (2013), and Burgaleta, Johnson, Waber, Colom, and Karama (2014). Gender and aging: Salat et al. (2004), Im et al. (2006, 2008), Luders, Narr, Thompson, Rex, Jancke, et al. (2006), Luders, Narr, Thompson, Rex, Woods, et al. (2006), Sowell et al. (2007), Ecker et al. (2009), Fjell et al. (2009), Lv et al. (2010), Thambisetty et al. (2010), Chen, Panizzon, et al. (2011), Chen, He, et al. (2011), Yao, Hu, Liang, Zhao, and Jackson (2012), Creze et al. (2013), Gautam, Cherbuin, Sachdev, Wen, and Anstey (2013), and van Velsen et al. (2013). Cognition and IQ: Giedd et al. (1999), Fjell et al. (2006), Lerch et al. (2006), Choi et al. (2008), Dickerson et al. (2008), Andersson, Ystad, Lundervold, and Lundervold (2009),

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Karama et al. (2009, 2011, 2013), Ziegler et al. (2010), Porter, Collins, Muetzel, Lim, and Luciana (2011), Burzynska et al. (2012), Hyatt, Haney-Caron, and Stevens (2012), Schilling et al. (2012,2013), Walhovd, Tamnes, Ostby, Due-Tonnessen, and Fjell (2012), Ducharme et al. (2012), and Ameis et al. (2014). Alzheimer’s disease: Thompson et al. (2004), Lerch et al. (2005, 2008), He, Chen, and Evans (2008), Dickerson et al. (2009), Julkunen et al. (2010), Kim et al. (2012), McDonald et al. (2009), Querbes et al. (2009), Reid and Evans (2013), Ridgway et al. (2012), Sabuncu et al. (2012), Cho, Seong, Jeong, and Shin (2012), Cho et al. (2013), Hartikainen et al. (2012), and Eskildsen et al. (2013). Epilepsy: McDonald et al. (2008), Bernhardt, Chen, He, Evans, and Bernasconi (2011), and Thesen et al. (2011) ADHD: Shaw et al. (2006), Makris et al. (2012), Yang, Carrey, Bernier, and Macmaster (2012), Yang, Joshi, et al. (2012), Almeida Montes et al. (2013), and McLaughlin et al. (2013). Autism: Hardan, Muddasani, Vemulapalli, Keshavan, and Minshew (2006), Hardan, Libove, Keshavan, Melhem, and Minshew (2009), Hyde, Samson, Evans, and Mottron (2010), Jiao et al. (2010), Sato et al. (2013), Doyle-Thomas et al. (2013), and Ecker et al. (2013). Depression, OCD, and social anxiety: Jarnum et al. (2011), Wagner et al. (2012), Ducharme et al. (2013), Mackin et al. (2013), Truong et al. (2013), and van Eijndhoven et al. (2013). OCD: Nakamae et al. (2012), Fan et al. (2013), Kim, Jung, Kim, Jang, and Kwon (2013), Bruhl et al. (2013), and Frick et al. (2013). Schizophrenia and psychosis: Kuperberg et al. (2003), Nesvag et al. (2008), Schultz et al. (2010), Rimol, Hartberg, et al. (2010), Rimol, Panizzon, et al. (2010), Rimol et al. (2012), van Haren et al. (2011), Buchy et al. (2012), Vita, De Peri, Deste, and Sacchetti (2012), and Benetti et al. (2013).

Conclusions It is apparent that cortical surface morphometry is a booming industry, with a continuous stream of new methodological advances and applications in neurology, psychiatry, and developmental neurobiology and cognitive neuroscience. However, there are also numerous areas where basic assumptions can be called into question, most notably in the very definition of ‘cortical thickness,’ as derived from structural MRI. Such issues should not be seen as a fundamental flaw in the methodology but as a caution upon the interpretation of the data. Many important new insights have arisen from the ability to capture, noninvasively and automatically, longitudinal and crosssectional measures of cortical morphology at every point on the brain surface. Some of the concerns regarding the accuracy of absolute values of cortical thickness are mitigated when one considers the relative changes inherent in group comparison or longitudinal studies of cortical morphometry. Of course, such pragmatic considerations regarding current applications of the basic techniques do not detract from the drive to improve the techniques through the use of higher field strength (Fujimoto et al., 2014; Lusebrink et al., 2013) or novel analytic approaches to quantify the cortical laminar structures (Tardif

et al., 2013; Turner & Geyer, 2014). The next decade seems likely to bring a continuing growth of cortical morphometric methods that are increasingly able to capture the fine structure of cortical anatomy and revolutionize the painstaking efforts of the great early twentieth century neuroanatomists.

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