Biomechanical Analysis of Normal Brain Development during the First Year of Life Using Finite Strain Theory

The first year of life is the most critical time period for structural and functional development of the human brain. Combining longitudinal MR imaging and finite strain theory, this study aimed to provide new insights into normal brain development through a biomechanical framework. Thirty-three normal infants were longitudinally imaged using MRI from 2 weeks to 1 year of age. Voxel-wise Jacobian determinant was estimated to elucidate volumetric changes while Lagrange strains (both normal and shear strains) were measured to reveal directional growth information every 3 months during the first year of life. Directional normal strain maps revealed that, during the first 6 months, the growth pattern of gray matter is anisotropic and spatially inhomogeneous with higher left-right stretch around the temporal lobe and interhemispheric fissure, anterior-posterior stretch in the frontal and occipital lobes, and superior-inferior stretch in right inferior occipital and right inferior temporal gyri. In contrast, anterior lateral ventricles and insula showed an isotropic stretch pattern. Volumetric and directional growth rates were linearly decreased with age for most of the cortical regions. Our results revealed anisotropic and inhomogeneous brain growth patterns of the human brain during the first year of life using longitudinal MRI and a biomechanical framework

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta [version 2; peer review: 2 approved]

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

Evaluación de la concordancia de la medición del cuerpo calloso en secuencias Espín Eco T1 en cortes sagitales de Resonancia Magnética en niños en edad escolar

Se trata de un estudio de pruebas diagnósticas de carácter prospectivo y analítico con el objetivo de estimar la concordancia de las mediciones del cuerpo calloso empleando un método disponible y reportado en la literatura científica; en escolares saludables explorados con RM cerebral, en Bogotá DC de enero a septiembre de 2018. Metodología: 49 resonancias cerebrales de niños sanos estudiados dentro proyecto "Caracterización clínica, ambiental y genética en pacientes infantiles colombianos con manifestaciones anormales de comportamiento" con código Hermes 24249, constituyeron la muestra, la cual fue evaluada por 3 médicos radiólogos con dedicación a la lectura de neuroimagenes pediátricas después de una capacitación corta en el procedimiento; el instrumento empleado fue un cuestionario que constituyó el registro primario. La información se procesó en SPSS 23 y Epidat 4.1, se presentó mediante distribuciones de frecuencia y coeficientes de correlación. Resultados: 49 estudios de niños entre 7 y 15 años, de los cuales fueron 26 (53%) varones, los datos presentaron distribución normal (test de Shapiro-Wilk 0.05), el valor de las medias del perímetro y área en relación con el sexo no presentaron diferencias significativas con 17.8 cm. (DE=1.32cm.) y 18.4cm. (DE=1.53cm.) p= 0.130 y 5.572 cm2. (DE=1.03 cm2.) y 5.739 cm2. (DE=0.96 cm2.) p=0.562, lo cual concuerdan con lo reportado; a excepción de la LVCC y el DET, el ICC inter e intra observador en promedio en todos los parámetros fue excelente con ICC = 0.88 (DE=0.1) en intervalos de confianza significativos. Conclusiones: existe concordancia intra e inter evaluador y valores normales similares a los reportado para la misma técnica en la literatura, no se recomienda usar la LVCC y la DET.Abstract: This study of diagnostic tests of prospective and analytical nature aims to estimate congruity of corpus callosum measurements by means of an available and indexed method in healthy schoolchildren explored with brain RMN in Bogota City, between January and September, 2018. Methodology: 49 cerebral resonances of healthy children studied as part of the project “Clinical, environmental and genetic characterization of infantile Colombian patients showing abnormal behavior patterns” with Hermes 24249 Code making up the sample evaluated by 3 pediatric radiologists with dedication to neuroimaging, upon brief training in the proceeding; the instrument was a questionnaire that stood as the primary record. Information was processed on SPSS 23 and Epidat 4.1, presented through frequency distributions and correlation coefficients. Results: 49 studies of children aged 7 to 15, 26 (53%) of them male; the measures presented a normal distribution (Shapiro-Wilk test 0.05), without significant sex-related differences in area or perimeter value 17.8cm. (DE=1.32cm.) vs. 18.4cm.(DE=1.53cm.) p= 0.130 and 5.572cm2. (DE=1.03cm2.) vs. 5.739cm2 (DE=0.96cm2) p=0.562. The results agree with the report, excluding LVCC and DET; average observed inter and intra ICC was excellent in all parameters, with ICC = 0.88 (DE=0.1), where all confidence intervals were significant. Conclusions: agreement exists both among intra and inter evaluators, with normal values, similar to those reported through the same technique, use of LVCC or DET is not recommended.Otr

Effect of Laser-Cut Slow-Flow Nipples on Preterm Feeding Performance

Background: Dysphagia of prematurity is a highly prevalent condition that carries negative developmental, social, and financial implications. Although the modification of bottle nipple properties is a widely used treatment for dysphagia of prematurity, there have been a paucity of investigations examining the effect of this intervention on refined measures of feeding performance. Methods: Healthy preterm infants were evaluated for measures of milk ingestion and respiratory performance during oral intake on a laser-cut slow-flow and standard-flow nipple. Time to achieve hospital discharge milestones was recorded. Results: Few differences were observed in feeding performance between slow-flow and standard-flow nipples. Characteristics of respiration during oral intake and at rest were correlated with time to hospital discharge. Conclusions: Slow-flow nipples may reduce the need for skilled feeders that are able to adapt feeding method based on infant feeding performance; when broadly applied to all infants by skilled feeders the clinical benefits are in question

Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants