Automatic whole brain MRI segmentation of the developing neonatal brain

Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.

Quantification of opioid receptor availability following spontaneous epileptic seizures: correction of [11C]diprenorphine PET data for the partial-volume effect

Previous positron emission tomography (PET) studies in refractory temporal lobe epilepsy (TLE) using the non-selective opioid receptor antagonist [(11)C]diprenorphine (DPN) did not detect any changes in mesial temporal structures, despite known involvement of the hippocampus in seizure generation. Normal binding in smaller hippocampi is suggestive of increased receptor concentration in the remaining grey matter. Correction for partial-volume effect (PVE) has not been used in previous DPN PET studies. Here, we present PVE-corrected DPN-PET data quantifying post-ictal and interictal opioid receptor availability in humans with mTLE. Eight paired datasets of post-ictal and interictal DPN PET scans and eleven test/retest control datasets were available from a previously published study on opioid receptor changes in TLE following seizures (Hammers et al., 2007a). Five of the eight participants with TLE had documented hippocampal sclerosis. Data were re-analyzed using regions of interest and a novel PVE correction method (structural functional synergistic-resolution recovery (SFS-RR); (Shidahara et al., 2012)). Data were denoised, followed by application of SFS-RR, with anatomical information derived via precise anatomical segmentation of the participants' MRI (MAPER; (Heckemann et al., 2010)). [(11)C]diprenorphine volume-of-distribution (VT) was quantified in six regions of interest. Post-ictal increases were observed in the ipsilateral fusiform gyri and lateral temporal pole. A novel finding was a post-ictal increase in [(11)C]DPN VT relative to the interictal state in the ipsilateral parahippocampal gyrus, not observed in uncorrected datasets. As for voxel-based (SPM) analyses, correction for global VT values was essential in order to demonstrate focal post-ictal increases in [(11)C]DPN VT. This study provides further direct human in vivo evidence for changes in opioid receptor availability in TLE following seizures, including changes that were not evident without PVE correction. Denoising, resolution recovery and precise anatomical segmentation can extract valuable information from PET studies that would be missed with conventional post-processing procedures.

Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions

We studied methods for the automatic segmentation of neonatal and developing brain images into 50 anatomical regions, utilizing a new set of manually segmented magnetic resonance (MR) images from 5 term-born and 15 preterm infants imaged at term corrected age called ALBERTs. Two methods were compared: individual registrations with label propagation and fusion; and template based registration with propagation of a maximum probability neonatal ALBERT (MPNA). In both cases we evaluated the performance of different neonatal atlases and MPNA, and the approaches were compared with the manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across regions, were 0.81±0.02 using label propagation and fusion for the preterm population, and 0.81±0.02 using the single registration of a MPNA for the term population. Segmentations of 36 further unsegmented target images of developing brains yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled age-specific brain atlases for neonates and the developing brain.

Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth

Cerebral white-matter injury is common in preterm-born infants and is associated with neurocognitive impairments. Identifying the pattern of connectivity changes in the brain following premature birth may provide a more comprehensive understanding of the neurobiology underlying these impairments. Here, we characterize whole-brain, macrostructural connectivity following preterm delivery and explore the influence of age and prematurity using a data-driven, nonsubjective analysis of diffusion magnetic resonance imaging data. T1- and T2-weighted and -diffusion MRI were obtained between 11 and 31 months postconceptional age in 49 infants, born between 25 and 35 weeks postconception. An optimized processing pipeline, combining anatomical, and tissue segmentations with probabilistic diffusion tractography, was used to map mean tract anisotropy. White-matter tracts where connection strength was related to age of delivery or imaging were identified using sparse-penalized regression and stability selection. Older children had stronger connections in tracts predominantly involving frontal lobe structures. Increasing prematurity at birth was related to widespread reductions in connection strength in tracts involving all cortical lobes and several subcortical structures. This nonsubjective approach to mapping whole-brain connectivity detected hypothesized changes in the strength of intracerebral connections during development and widespread reductions in connectivity strength associated with premature birth.

Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study.

The effect of preterm birth on thalamic and cortical development

Preterm birth is a leading cause of cognitive impairment in childhood and is associated with cerebral gray and white matter abnormalities. Using multimodal image analysis, we tested the hypothesis that altered thalamic development is an important component of preterm brain injury and is associated with other macro- and microstructural alterations. T₁- and T₂-weighted magnetic resonance images and 15-direction diffusion tensor images were acquired from 71 preterm infants at term-equivalent age. Deformation-based morphometry, Tract-Based Spatial Statistics, and tissue segmentation were combined for a nonsubjective whole-brain survey of the effect of prematurity on regional tissue volume and microstructure. Increasing prematurity was related to volume reduction in the thalamus, hippocampus, orbitofrontal lobe, posterior cingulate cortex, and centrum semiovale. After controlling for prematurity, reduced thalamic volume predicted: lower cortical volume; decreased volume in frontal and temporal lobes, including hippocampus, and to a lesser extent, parietal and occipital lobes; and reduced fractional anisotropy in the corticospinal tracts and corpus callosum. In the thalamus, reduced volume was associated with increased diffusivity. This demonstrates a significant effect of prematurity on thalamic development that is related to abnormalities in allied brain structures. This suggests that preterm delivery disrupts specific aspects of cerebral development, such as the thalamocortical system.

Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and thalamus

The basal ganglia and thalamus are involved in processing all physiological behaviors and affected by many diseases. Accurate localization is a crucial issue in neuroimaging, particularly when working with groups of normalized images in a standard stereotaxic space. Here, manual delineation of the central structures (thalamus; nucleus caudatus and accumbens; putamen, pallidum, substantia nigra) was performed on 30 high resolution MRIs of healthy young adults (15 female, median age 31 years) in native space. Protocol inter-rater reliabilities were quantified as structure overlap (similarity indices, SIs). Structural volumes were calculated in native space, and after spatial normalization to stereotaxic space (MNI/ICBM152) and in relation to hemispheric volumes. Spatial extents relative to the anterior commissure (AC) were extracted. The 30 resulting atlases were then used to create probabilistic maps in stereotaxic space. Inter-rater SIs were high at 0.85-0.92 except for the nucleus accumbens. In native space, caudate, nucleus accumbens and putamen were significantly larger on the left, and the globus pallidus larger in males. After normalizing for brain volume, the nucleus accumbens, putamen and thalamus were larger on the left, with the gender difference in the globus pallidus still detectable. Some of these volume differences translated into significantly different distances from the AC. The probabilistic maps showed that overall the central structures' boundaries are relatively unchanged after spatial normalization. We present a comprehensive assessment of thalamic and basal ganglia volumetric and geometric data in both native and stereotaxic spaces. Probabilistic maps in MNI/ICBM152 space will allow accurate localization in group analyses.