Author name cluster

Richard Beare

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

15 papers

1 author row

AAAI Conference 2023 Conference Paper

AUC Maximization for Low-Resource Named Entity Recognition

Ngoc Dang Nguyen
Wei Tan
Lan Du
Wray Buntine
Richard Beare
Changyou Chen

Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective functions for the NER problem generally produce adequate performance when the data distribution is balanced and there are sufficient annotated training examples. But since NER is inherently an imbalanced tagging problem, the model performance under the low-resource settings could suffer using these standard objective functions. Based on recent advances in area under the ROC curve (AUC) maximization, we propose to optimize the NER model by maximizing the AUC score. We give evidence that by simply combining two binary-classifiers that maximize the AUC score, significant performance improvement over traditional loss functions is achieved under low-resource NER settings. We also conduct extensive experiments to demonstrate the advantages of our method under the low-resource and highly-imbalanced data distribution settings. To the best of our knowledge, this is the first work that brings AUC maximization to the NER setting. Furthermore, we show that our method is agnostic to different types of NER embeddings, models and domains. The code of this work is available at https://github.com/dngu0061/NER-AUC-2T.

PDF Details DOI

YNICL Journal 2022 Journal Article

Assessment of intraoperative diffusion EPI distortion and its impact on estimation of supratentorial white matter tract positions in pediatric epilepsy surgery

Joseph Yuan-Mou Yang
Jian Chen
Bonnie Alexander
Kurt Schilling
Michael Kean
Alison Wray
Marc Seal
Wirginia Maixner

The effectiveness of correcting diffusion Echo Planar Imaging (EPI) distortion and its impact on tractography reconstruction have not been adequately investigated in the intraoperative MRI setting, particularly for High Angular Resolution Diffusion Imaging (HARDI) acquisition. In this study, we evaluated the effectiveness of EPI distortion correction using 27 legacy intraoperative HARDI datasets over two consecutive surgical time points, acquired without reverse phase-encoded data, from 17 children who underwent epilepsy surgery at our institution. The data was processed with EPI distortion correction using the Synb0-Disco technique (Schilling et al., 2019) and without distortion correction. The corrected and uncorrected b0 diffusion-weighted images (DWI) were first compared visually. The mutual information indices between the original T1-weighted images and the fractional anisotropy images derived from corrected and uncorrected DWI were used to quantify the effect of distortion correction. Sixty-four white matter tracts were segmented from each dataset, using a deep-learning based automated tractography algorithm for the purpose of a standardized and unbiased evaluation. Displacement was calculated between tracts generated before and after distortion correction. The tracts were grouped based on their principal morphological orientations to investigate whether the effects of EPI distortion vary with tract orientation. Group differences in tract distortion were investigated both globally, and regionally with respect to proximity to the resecting lesion in the operative hemisphere. Qualitatively, we observed notable improvement in the corrected diffusion images, over the typically affected brain regions near skull-base air sinuses, and correction of additional distortion unique to intraoperative open cranium images, particularly over the resection site. This improvement was supported quantitatively, as mutual information indices between the FA and T1-weighted images were significantly greater after the correction, compared to before the correction. Maximum tract displacement between the corrected and uncorrected data, was in the range of 7.5 to 10.0 mm, a magnitude that would challenge the safety resection margin typically tolerated for tractography-informed surgical guidance. This was particularly relevant for tracts oriented partially or fully in-line with the acquired diffusion phase-encoded direction. Portions of these tracts passing close to the resection site demonstrated significantly greater magnitude of displacement, compared to portions of tracts remote from the resection site in the operative hemisphere. Our findings have direct clinical implication on the accuracy of intraoperative tractography-informed image guidance and emphasize the need to develop a distortion correction technique with feasible intraoperative processing time.

Details DOI

YNIMG Journal 2022 Journal Article

Brain tissue microstructural and free-water composition 13 years after very preterm birth

Claire Kelly
Thijs Dhollander
Ian H Harding
Wasim Khan
Richard Beare
Jeanie LY Cheong
Lex W Doyle
Marc Seal

There have been many studies demonstrating children born very preterm exhibit brain white matter microstructural alterations, which have been related to neurodevelopmental difficulties. These prior studies have often been based on diffusion MRI modelling and analysis techniques, which commonly focussed on white matter microstructural properties in children born very preterm. However, there have been relatively fewer studies investigating the free-water content of the white matter, and also the microstructure and free-water content of the cortical grey matter, in children born very preterm. These biophysical properties of the brain change rapidly during fetal and neonatal brain development, and therefore such properties are likely also adversely affected by very preterm birth. In this study, we investigated the relationship of very preterm birth (<30 weeks' gestation) to both white matter and cortical grey matter microstructure and free-water content in childhood using advanced diffusion MRI analyses. A total of 130 very preterm participants and 45 full-term control participants underwent diffusion MRI at age 13 years. Diffusion tissue signal fractions derived by Single-Shell 3-Tissue Constrained Spherical Deconvolution were used to investigate brain tissue microstructural and free-water composition. The tissue microstructural and free-water composition metrics were analysed using a voxel-based analysis and cortical region-of-interest analysis approach. Very preterm 13-year-olds exhibited reduced white matter microstructural density and increased free-water content across widespread regions of the white matter compared with controls. Additionally, very preterm 13-year-olds exhibited reduced microstructural density and increased free-water content in specific temporal, frontal, occipital and cingulate cortical regions. These brain tissue composition alterations were strongly associated with cerebral white matter abnormalities identified in the neonatal period, and concurrent adverse cognitive and motor outcomes in very preterm children. The findings demonstrate brain microstructural and free-water alterations up to thirteen years from neonatal brain abnormalities in very preterm children that relate to adverse neurodevelopmental outcomes.

Details DOI

YNIMG Journal 2021 Journal Article

Individual variation underlying brain age estimates in typical development

Gareth Ball
Claire E Kelly
Richard Beare
Marc L Seal

Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed 'brain age', this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n = 768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to identify sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regions brain development. However, while several regions are found to contribute to brain age prediction error, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.

Details DOI

YNIMG Journal 2020 Journal Article

Long-term development of white matter fibre density and morphology up to 13 years after preterm birth: A fixel-based analysis

Claire E. Kelly
Deanne K. Thompson
Sila Genc
Jian Chen
Joseph YM. Yang
Chris Adamson
Richard Beare
Marc L. Seal

Background It is well documented that infants born very preterm (VP) are at risk of brain injury and altered brain development in the neonatal period, however there is a lack of long-term, longitudinal studies on the effects of VP birth on white matter development over childhood. Most previous studies were based on voxel-averaged, non-fibre-specific diffusion magnetic resonance imaging (MRI) measures, such as fractional anisotropy. In contrast, the novel diffusion MRI analysis framework, fixel-based analysis (FBA), enables whole-brain analysis of microstructural and macrostructural properties of individual fibre populations at a sub-voxel level. We applied FBA to investigate the long-term implications of VP birth and associated perinatal risk factors on fibre development in childhood and adolescence. Methods Diffusion images were acquired for a cohort of VP (born <30 weeks’ gestation) and full-term (FT, ≥37 weeks’ gestation) children at two timepoints: mean (SD) 7. 6 (0. 2) years (n = 138 VP and 32 FT children) and 13. 3 (0. 4) years (n = 130 VP and 45 FT children). 103 VP and 21 FT children had images at both ages for longitudinal analysis. At every fixel (individual fibre population within an image voxel) across the white matter, we compared FBA metrics (fibre density (FD), cross-section (FC) and a combination of these properties (FDC)) between VP and FT groups cross-sectionally at each timepoint, and longitudinally between timepoints. We also examined associations between known perinatal risk factors and FBA metrics in the VP group. Results Compared with FT children, VP children had lower FD, FC and FDC throughout the white matter, particularly in the corpus callosum, tapetum, inferior fronto-occipital fasciculus, fornix and cingulum at ages 7 and 13 years, as well as the corticospinal tract and anterior limb of the internal capsule at age 13 years. VP children also had slower FDC development in the corpus callosum and corticospinal tract between ages 7 and 13 years compared with FT children. Within VP children, earlier gestational age at birth, lower birth weight z-score, and neonatal brain abnormalities were associated with lower FD, FC and FDC throughout the white matter at both ages. Conclusions VP birth and concomitant perinatal risk factors are associated with fibre tract-specific alterations to axonal development in childhood and adolescence.

Details DOI

YNIMG Journal 2019 Journal Article

Changes in neonatal regional brain volume associated with preterm birth and perinatal factors

Bonnie Alexander
Claire E. Kelly
Chris Adamson
Richard Beare
Diana Zannino
Jian Chen
Andrea L. Murray
Wai Yen Loh

Background Preterm birth is associated with altered brain development, with younger gestational age (GA) at birth often associated with greater brain volume reduction. Such volume alterations at term equivalent age (TEA) have been found with differing magnitude across different brain regions, although this has mostly been investigated with regards to whole tissue volumes and large-scale subdivisions. In addition to degree of prematurity, many other perinatal factors have been found to influence brain structure and development in infants born preterm. We aimed to clarify the relationships between degree of prematurity and regional brain volumes at TEA, and between perinatal factors and regional brain volumes at TEA, in finer spatial detail. Methods 285 preterm and term-born infants (GA at birth 24. 6–42. 1 weeks; 145 female; 59 born at term) were scanned at TEA. Data on perinatal factors were obtained by chart review, including sex, multiple birth, birthweight standard deviation (SD) score, postnatal growth and social risk. The Melbourne Children's Regional Infant Brain (M-CRIB) atlas was registered to the current sample, then 100 brain regions were labelled for volumetric analyses. Linear regressions with generalised estimating equations and likelihood ratio tests were performed to investigate whether GA at birth or perinatal factors were associated with regional volumes at TEA. Results Younger GA at birth was associated with smaller volumes at TEA in some regions including bilateral cerebral white matter, middle temporal gyri, amygdalae, pallidum and brainstem. In other regions, younger GA at birth was associated with larger volumes, including in primary visual, motor and somatosensory cortices. Positive associations between perinatal factors and regional volumes at TEA were found in many brain regions for birthweight SD score, and male sex, independent of GA at birth. These associations were seen on both univariable analyses, and multivariable analyses controlling for other perinatal factors. Social risk and multiple birth were generally not associated with regional brain volumes, and postnatal growth was associated with volume in many regions only after adjusting for other perinatal factors. Conclusions These results elucidate regional brain volume differences associated with preterm birth and perinatal factors at a more detailed parcellated level than previously reported, and contribute to understanding of the complex array of correlates of preterm birth.

Details DOI

YNICL Journal 2019 Journal Article

Characterisation of brain volume and microstructure at term-equivalent age in infants born across the gestational age spectrum

Deanne K. Thompson
Claire E. Kelly
Jian Chen
Richard Beare
Bonnie Alexander
Marc L. Seal
Katherine J. Lee
Lillian G. Matthews

BACKGROUND: Risk of morbidity differs between very preterm (VP; <32 weeks' gestational age (GA)), moderate preterm (MP; 32-33 weeks' GA), late preterm (LP; 34-36 weeks' GA), and full-term (FT; ≥37 weeks' GA) infants. However, brain structure at term-equivalent age (TEA; 38-44 weeks) remains to be characterised in all clinically important GA groups. We aimed to compare global and regional brain volumes, and regional white matter microstructure, between VP, MP, LP and FT groups at TEA, in order to establish the magnitude and anatomical locations of between-group differences. METHODS: Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid (CSF), subcortical grey matter, brainstem and cerebellum. Global tissue volumes were analysed, and additionally, cortical grey matter and white matter volumes were analysed at the regional level using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Statistical analyses involved examining the overall effect of GA group on global volumes (using linear regressions) and regional volumes and microstructure (using non-parametric permutation testing), as well performing post-hoc comparisons between the GA sub-groups. RESULTS: On global analysis, cerebrospinal fluid (CSF) volume was larger in all preterm sub-groups compared with the FT group. On regional analysis, volume was smaller in parts of the temporal cortical grey matter, and parts of the temporal white matter and corpus callosum, in all preterm sub-groups compared with the FT group. FA was lower, and RD and MD were higher in voxels located in much of the white matter in all preterm sub-groups compared with the FT group. The anatomical locations of group differences were similar for each preterm vs. FT comparison, but the magnitude and spatial extent of group differences was largest for the VP, followed by the MP, and then the LP comparison. Comparing within the preterm groups, the VP sub-group had smaller frontal and temporal grey and white matter volume, and lower FA and higher MD and RD within voxels in the approximate location of the corpus callosum compared with the MP sub-group. There were few volume and microstructural differences between the MP and LP sub-groups. CONCLUSION: All preterm sub-groups had atypical brain volume and microstructure at TEA when compared with a FT group, particularly for the CSF, temporal grey and white matter, and corpus callosum. In general, the groups followed a gradient, where the differences were most pronounced for the VP group, less pronounced for the MP group, and least pronounced for the LP group. The VP sub-group was particularly vulnerable compared with the MP and LP sub-groups.

Details DOI

YNIMG Journal 2019 Journal Article

Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum

Deanne K. Thompson
Claire E. Kelly
Jian Chen
Richard Beare
Bonnie Alexander
Marc L. Seal
Katherine Lee
Lillian G. Matthews

Background It is well established that preterm infants have altered brain development compared with full-term (FT; ≥37 weeks' gestational age [GA]) infants, however the perinatal factors associated with brain development in preterm infants have not been fully elucidated. In particular, perinatal predictors of brain development may differ between very preterm infants (VP; <32 weeks' GA) and infants born moderate (MP; 32–33 weeks' GA) and late (LP; 34–36 weeks' GA) preterm, but this has not been studied. This study aimed to investigate the effects of early life predictors on brain volume and microstructure at term-equivalent age (TEA; 38–44 weeks), and whether these effects differ for GA groups (VP, MP, LP or FT). Methods Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid, subcortical grey matter, brainstem and cerebellum. Cortical grey matter and white matter images were analysed using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Relationships between early life predictors (birthweight standard deviation score [BWSDS], multiple birth, sex, postnatal growth and social risk) and global brain volumes were analysed using linear regressions. Relationships between early life predictors and regional brain volumes and diffusion measures were analysed using voxelwise non-parametric permutation testing. Results Male sex was associated with higher global volumes of all tissues and higher regional volumes throughout much of the cortical grey matter and white matter, particularly in the FT group. Male sex was also associated with lower FA and higher AD, RD and MD in the optic radiation, external and internal capsules and corona radiata, and these associations were generally similar between GA groups. Higher BWSDS was associated with higher global volumes of all tissues and higher regional volumes in much of the cortical grey matter and white matter in all GA groups, as well as higher FA and lower RD and MD in many major tracts (corpus callosum, optic radiation, internal and external capsules and corona radiata), particularly in the MP and LP groups. Multiple birth and social risk also showed associations with global and regional volumes and regional diffusion values which varied by GA group, but these associations were not independent of the other early life predictors. Postnatal growth was not associated with brain volumes or diffusion values. Conclusion Early life predictors of brain volumes and microstructure at TEA include sex, BWSDS, multiple birth and social risk, which have different effects based on GA group at birth. This study improves knowledge of the perinatal factors associated with brain abnormalities in infants born across the prematurity spectrum.

Details DOI

YNIMG Journal 2019 Journal Article

Structural covariance networks in children and their associations with maternal behaviors

Sally Richmond
Richard Beare
Katherine A. Johnson
Nicholas B. Allen
Marc L. Seal
Sarah Whittle

There is a substantial body of research documenting the influence of early adverse experience on brain development. In contrast, relatively little attention has been directed toward the influence of ‘normative’ variation in parenting behaviors. This study investigated associations between parenting behaviors and structural brain networks, as measured by structural covariance, in a community sample of children. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed parent-child interaction tasks. Structural MRI scans were also obtained from the children. Structural covariance networks based on partial correlation between cortical thickness estimates were constructed, and estimates of efficiency were obtained using graph theoretical analysis. Associations between affective and communicative maternal behaviors and these network metrics were investigated. High levels of observed negative affective and communicative maternal behaviors were associated with decreased local efficiency, whereas high levels of positive affective maternal behaviors were associated with increased local efficiency. The regions implicated (including the cingulate cortex, temporal pole, and temporo-parietal junction) are thought to be involved in the processing of social information. Minimal support was found for an association between global efficiency and maternal behaviors. Our findings suggest that variations in parenting behaviors are associated with structural organization of socio-emotional brain networks in children.

Details DOI

YNIMG Journal 2017 Journal Article

A new neonatal cortical and subcortical brain atlas: the Melbourne Children's Regional Infant Brain (M-CRIB) atlas

Bonnie Alexander
Andrea L. Murray
Wai Yen Loh
Lillian G. Matthews
Chris Adamson
Richard Beare
Jian Chen
Claire E. Kelly

Investigating neonatal brain structure and function can offer valuable insights into behaviour and cognition in healthy and clinical populations; both at term age, and longitudinally in comparison with later time points. Parcellated brain atlases for adult populations are readily available, however warping infant data to adult template space is not ideal due to morphological and tissue differences between these groups. Several parcellated neonatal atlases have been developed, although there remains strong demand for manually parcellated ground truth data with detailed cortical definition. Additionally, compatibility with existing adult atlases is favourable for use in longitudinal investigations. We aimed to address these needs by replicating the widely-used Desikan-Killiany (2006) adult cortical atlas in neonates. We also aimed to extend brain coverage by complementing this cortical scheme with basal ganglia, thalamus, cerebellum and other subcortical segmentations. Thus, we have manually parcellated these areas volumetrically using high-resolution neonatal T 2-weighted MRI scans, and initial automated and manually edited tissue classification, providing 100 regions in all. Linear and nonlinear T 2-weighted structural templates were also generated. In this paper we provide manual parcellation protocols, and present the parcellated probability maps and structural templates together as the Melbourne Children's Regional Infant Brain (M-CRIB) atlas.

Details DOI

YNIMG Journal 2017 Journal Article

Neurite density index is sensitive to age related differences in the developing brain

Sila Genc
Charles B. Malpas
Scott K. Holland
Richard Beare
Timothy J. Silk

Purpose White matter development during childhood and adolescence is characterised by increasing white matter coherence and organisation. Commonly used scalar metrics, such as fractional anisotropy (FA), are sensitive to multiple mechanisms of white matter change and therefore unable to distinguish between mechanisms that change during development. We investigate the relationship between age and neurite density index (NDI) from neurite orientation dispersion and density imaging (NODDI), and the age-classification accuracy of NDI compared with FA, in a developmental cohort. Method Diffusion-weighted imaging data from 72 children and adolescents between the ages of 4–19 was collected (M=10. 42, SD=3. 99, 36 male). We compared NODDI metrics against conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD] and radial diffusivity [RD]) in terms of their relationship to age. An ROC analysis was also performed to assess the ability of each metric to classify older and younger participants. Results NDI exhibited a stronger relationship with age (median R 2=. 60) compared with MD (median R 2=. 39), FA (median R 2=. 27), AD (median R 2=. 14), and RD (median R 2=. 35) in a high proportion of white matter tracts. When participants were divided into an older and younger group, NDI achieved the best classification (median area under the curve [AUC]=. 89), followed by MD (median AUC=. 81), FA (median AUC=. 80), RD (median AUC=. 81), and AD (median AUC=. 64). Conclusion Our results demonstrate the sensitivity of NDI to age-related differences in white matter microstructural organisation over development. Importantly, NDI is more sensitive to such developmental changes compared to commonly used DTI metrics. This knowledge provides justification for implementing NODDI metrics in developmental studies.

Details DOI

YNIMG Journal 2016 Journal Article

Structural connectivity relates to perinatal factors and functional impairment at 7 years in children born very preterm

Deanne K. Thompson
Jian Chen
Richard Beare
Christopher L. Adamson
Rachel Ellis
Zohra M. Ahmadzai
Claire E. Kelly
Katherine J. Lee

Objective To use structural connectivity to (1) compare brain networks between typically and atypically developing (very preterm) children, (2) explore associations between potential perinatal developmental disturbances and brain networks, and (3) describe associations between brain networks and functional impairments in very preterm children. Methods 26 full-term and 107 very preterm 7-year-old children (born <30weeks' gestational age and/or <1250g) underwent T 1 - and diffusion-weighted imaging. Global white matter fibre networks were produced using 80 cortical and subcortical nodes, and edges were created using constrained spherical deconvolution-based tractography. Global graph theory metrics were analysed, and regional networks were identified using network-based statistics. Cognitive and motor function were assessed at 7years of age. Results Compared with full-term children, very preterm children had reduced density, lower global efficiency and higher local efficiency. Those with lower gestational age at birth, infection or higher neonatal brain abnormality score had reduced connectivity. Reduced connectivity within a widespread network was predictive of impaired IQ, while reduced connectivity within the right parietal and temporal lobes was associated with motor impairment in very preterm children. Conclusions This study utilised an innovative structural connectivity pipeline to reveal that children born very preterm have less connected and less complex brain networks compared with typically developing term-born children. Adverse perinatal factors led to disturbances in white matter connectivity, which in turn are associated with impaired functional outcomes, highlighting novel structure–function relationships.

Details DOI

YNIMG Journal 2012 Journal Article

Application of principal component analysis to study topography of hypoxic–ischemic brain injury

Shaloo Singhal
Jian Chen
Richard Beare
Henry Ma
John Ly
Thanh G. Phan

The regions at risk of ischemia following cardio-respiratory arrest have not been systematically analysed. This knowledge may be of use in determining the mechanism of ischemic injury at vulnerable sites. The aim of this study is to evaluate the use of principal component analysis to analyse the covariance patterns of hypoxic ischemic injury. The inclusion criteria were: age≥17years, cardio-respiratory arrest and coma on admission (2003–2011). Regions of ischemic injury were manually segmented on fluid attenuated inversion recovery (FLAIR) and diffusion weighted (DWI) sequences and linearly registered into common stereotaxic coordinate space. Topography of ischemic injury was assessed using principal component analysis (covariance data) and compared qualitatively against current method of topography analysis, the probabilistic method (frequency data). For the probabilistic data, subgroup analyses were performed using t-statistics while for the covariance data, subgroup analyses were performed by calculating the angle between the principle components. To account for bias due to a higher frequency of coma survivors in the studied group, we performed sensitivity analysis by sequentially removing coma survivors such that the final data set contained higher rate of death. Quantitative analysis between these methods could not be performed as they have different units of measurement. Forty one patients were included in this series (mean age±SD=51. 5±18. 9years). In our probabilistic map, the highest frequency of ischemic injury on the DWI and FLAIR sequences was putamen (0. 250), caudate (0. 225), temporal lobes (0. 175), occipital (0. 150) and hippocampus (0. 125). The first 6 principal components contained 77. 7% of the variance of the data. The first component showed covariance between the deep grey matter nuclei and posterior cortical structures (contains 50. 2% of the variance of the data). There was similarity in the findings of the subgroup analyses by the downtime whether it was assessed by t-statistics for probabilistic data or angle between the principal components for the covariance data. The sensitivity analysis showed that the pattern of ischemic injury did not change when the analysis was restricted to patients who died. In conclusion, PCA method has many advantages over probabilistic method. In the context of this dataset, PCA showed covariance between deep grey matter nuclei and the posterior cortical structures whereas the probabilistic map provided complementary information on the frequency of occurrence at these locations.

Details DOI

YNIMG Journal 2011 Journal Article

Segmentation of the mouse hippocampal formation in magnetic resonance images

Kay Richards
Charles Watson
Rachel F. Buckley
Nyoman D. Kurniawan
Zhengyi Yang
Marianne D. Keller
Richard Beare
Perry F. Bartlett

The hippocampal formation plays an important role in cognition, spatial navigation, learning, and memory. High resolution magnetic resonance (MR) imaging makes it possible to study in vivo changes in the hippocampus over time and is useful for comparing hippocampal volume and structure in wild type and mutant mice. Such comparisons demand a reliable way to segment the hippocampal formation. We have developed a method for the systematic segmentation of the hippocampal formation using the perfusion-fixed C57BL/6 mouse brain for application in longitudinal and comparative studies. Our aim was to develop a guide for segmenting over 40 structures in an adult mouse brain using 30μm isotropic resolution images acquired with a 16. 4T MR imaging system and combined using super-resolution reconstruction.

Details DOI

YNIMG Journal 2009 Journal Article

Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities

Richard Beare
Velandai Srikanth
Jian Chen
Thanh G. Phan
Jennifer Stapleton
Rebecca Lipshut
David Reutens

Accurate automated segmentation of age-related white matter hyperintensity (WMH) is desirable for topological studies and those involving large samples. We assessed the accuracy of a novel automated method for segmentation of WMH on magnetic resonance imaging (MRI) in a randomly selected population-based sample of older people aged >60 years. The method combined morphological segmentation and statistical classifiers. Validation of this method was performed against expert manual segmentation in a sample of 30 scans, and against semi-automated segmentation in 202 scans. Its performance was also compared with those of other known methods derived from simple thresholding or Gaussian mixture modelling. Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation, with an intraclass correlation coefficient (ICC) of 0. 90 (95% confidence interval [CI], 0. 80–0. 95) for WMH volume and median similarity index (SI) of 0. 58 (interquartile range [IQR] 0. 50–0. 65). The method also showed similarly high levels of agreement with semi-automated segmentation, with ICC 0. 92 (95% CI 0. 89–0. 93) and median SI 0. 56 (IQR 0. 49–0. 66). Its best performance was observed for the highest tertile of WMH volume. Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study.

Details DOI