A framework for in vivo quantification of regional brain folding in premature neonates

Rodríguez-Carranza, Claudia E.; Mukherjee, Pratik; Vigneron, Daniel B.; Barkovich, A. James; Studholme, Colin

doi:10.1016/j.neuroimage.2008.01.008

Cited by 53 publications

(74 citation statements)

References 57 publications

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“…As the average of two boundaries, it is less error-prone to noise and it allows a better mapping of volumetric data (Liu et al, 2008;Van Essen et al, 2001). Generally, a surface reconstruction allows surface-based analysis that is not restricted to the grid and allows metrics, such as the gyrification index (Schaer et al, 2008) or other convolution measurements (Luders et al, 2006;Mietchen and Gaser, 2009;Rodriguez-Carranza et al, 2008;Toro et al, 2008), that can only be measured using surface meshes . It provides surface-based smoothing that gives results superior to that obtained from volumetric smoothing .…”

Section: Introductionmentioning

confidence: 99%

Cortical thickness and central surface estimation

2013

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Section: Introductionmentioning

confidence: 99%

Cortical thickness and central surface estimation

2013

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“…This is because cortical surface-based analysis, which explicitly reconstructs surface mesh representations of the highly-folded cerebral cortex, respects the intrinsic topological properties of the cortex and thus greatly facilitates the spatial normalization, analysis, comparison, and visualization of convoluted cortical regions (Fischl et al, 1999b; Goebel et al, 2006; Han et al, 2004; Li et al, 2009, 2010a; MacDonald et al, 2000; Mangin et al, 2004; Nie et al, 2007; Shattuck and Leahy, 2002; Shi et al, 2013; Shiee et al, 2014; Van Essen and Dierker, 2007; Xu et al, 1999). Moreover, cortical surface-based measurements, e.g., surface area (Hill et al, 2010b), cortical thickness (Fischl and Dale, 2000), and cortical folding/gyrification (Habas et al, 2012; Li et al, 2010b; Rodriguez-Carranza et al, 2008; Zhang et al, 2009; Zilles et al, 2013), each with distinct genetic underpinning, cellular mechanism, and developmental trajectory (Chen et al, 2013; Lyall et al, 2014; Panizzon et al, 2009), can comprehensively provide various detailed aspects of the cerebral cortex (Li et al, 2014a). Accordingly, several cortical surface atlases have been created and extensively used in current neuroimaging studies (Fischl et al, 1999b; Goebel et al, 2006; Hill et al, 2010a; Lyttelton et al, 2007; Van Essen, 2005), such as FreeSurfer surface atlas (Fischl et al, 1999b), PALS-B12 and PALS-term12 surface atlases (Hill et al, 2010a), and MNI surface atlas (Lyttelton et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…For precise revealing the detailed patterns of early cortex development by using advanced cortical surface-based analysis (Dubois et al, 2008; Hill et al, 2010a; Li et al, 2013; Rodriguez-Carranza et al, 2008; Xue et al, 2007), the infant age-matched cortical surface atlases are highly desired yet still lacking. Currently, there is only one neonate/infant dedicated cortical surface atlas, i.e., PALS-term12 atlas, which was constructed by landmark-constrained co-registration of 12 term born neonates (Hill et al, 2010a).…”

Section: Introductionmentioning

confidence: 99%

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Wang

Shi

et al. 2015

Medical Image Analysis

122

116

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In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two years of life, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at 7 time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.

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“…If an accurate mesh of the cortical surface can be provided, multiple characteristics of shape can be obtained, including cortical thickness [Hutton et al, 2008;Jones et al, 2000;Lerch et al, 2008;Martinussen et al, 2005] to identify pachygyria, and cortical folding [Chung et al, 2003;Joshi et al, 1999a;Tosun et al, 2006;White et al, 2003] and sulcal depth van Essen et al, 2006] to quantify the extent of several malformations, including schizencephaly, lissencephaly and polymicrogyria. All three of these cortical measures have been used to characterise healthy cortical development or quantify interrupted development in children born preterm [Dubois et al, 2008a;Dubois et al, 2008b;Martinussen et al, 2005;Rodriguez-Carranza et al, 2008;Xue et al, 2007;Zhang et al, 2015], illustrating the potential use of these measures in the CP setting. Comparison of these shape measures to the spatially corresponding regions of healthy brains may assist in the identification and quantification of cortical injury in children with CP.…”

Section: Shape Analysis Of Subcortical Structuresmentioning

confidence: 99%

“…Cortical thickness has been used in many studies in order to characterise healthy development [Sowell et al, 2004], abnormal development [Moeskops et al, 2015], and to investigate cortical thinning due to schizophrenia [Rimol et al, 2012] and Alzheimer's disease [Haidar and Soul, 2006]. The curvature of the cortex [Rodriguez-Carranza et al, 2008] and sulcal depth [van Essen, 2005] are alternative measures that reflect the changes in cortical surface area arising from gyrification during development [Dubois et al, 2008a]. Consequently, these measures are important for identifying changes in cortical folding related to several brain malformations such as lissencepahly, polymicrogyria or schizencephaly, with multiple studies highlighting the sensitivity of these measures in detecting cortical abnormalities [van Essen et al, 2006;Nordahl et al, 2007;White et al, 2003;Zhang et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Automated injury segmentation to assist in the treatment of children with cerebral palsy

Pagnozzi¹

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Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. It is the most common physical disability of children worldwide and can lead to a wide range of functional impairments. Accurate diagnosis and prognosis, in terms of the type and severity of functional impairment, is difficult due to the wide range of injuries that may occur and the variable effect of plasticity; which leads to inconsistency in the clinical outcomes of children with CP. The use of Magnetic resonance imaging (MRI) to identify and locate brain lesions has facilitated the diagnosis and qualitative classification of children with CP.Currently, the quantification of image findings from structural MRIs is not automated and remains labour intensive, hence is not widely performed for clinical assessment. Automated brain image segmentation techniques could reduce the clinical time required toprovide an accurate and reproducible quantification of injury. Although such approaches have been used to study other neurological disorders, the heterogeneous appearance of injury and the large anatomical distortion that occurs in CP require modification of existing algorithms to be sufficiently reliable. As a result, they have not been widely applied toMRIs of children with CP.In this thesis, a series of automated image quantification techniques are presented for analysing the MRI data obtained from children with CP. These techniques identify and quantify the severity of the three main types of injury observed in children with CP; including ventricular enlargement, cortical malformations and white and grey matter injury.The developed automated pipeline involves a number of technical developments and contributions to the automated analysis of CP MRI data. A brain tissue segmentation approach based on the Expectation Maximisation (EM)/Markov Random Field (MRF) approach was developed, with a modified MRF implementation that expects different tissue labels within a given neighbourhood to have a corresponding intensity gradient.Following this segmentation step, three different approaches were used to identify and quantify the three distinct types of injury observed from children with CP, which are all important for clinical assessment. Biomarkers from each type of injury obtained from these approaches were used as independent variables in a devised statistical methodology designed to elucidate significant and generalisable correlations between image-derived measures of injury and patient function.ii Ventricular enlargement was quantified using a model of lateral ventricular shape that encapsulates healthy variation observed in ventricular shape. The residual of this model to a target shape reveals a volume of injury, allowing a measure of involvement of critical adjacent anatomies, such as the thalamus, caudate nucleus and lenticular nucleus, to be computed. This measure of involvement was devised as a way to translate the indirect injury of ventricular enlargement to the direct in...

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A framework for in vivo quantification of regional brain folding in premature neonates

Cited by 53 publications

References 57 publications

Cortical thickness and central surface estimation

Cortical thickness and central surface estimation

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

Automated injury segmentation to assist in the treatment of children with cerebral palsy

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