Differential growth and development of the upper and lower human thorax

The difficulties in quantifying the 3D form and spatial relationships of the skeletal components of the ribcage present a barrier to studies of the growth of the thoracic skeleton. Thus, most studies to date have relied on traditional measurements such as distances and indices from single or few ribs. It is currently known that adult-like thoracic shape is achieved early, by the end of the second postnatal year, with the circular cross-section of the newborn thorax transforming into the ovoid shape of adults; and that the ribs become inclined such that their anterior borders come to lie inferior to their posterior. Here we present a study that revisits growth changes using geometric morphometrics applied to extensive landmark data taken from the ribcage. We digitized 402 (semi) landmarks on 3D reconstructions to assess growth changes in 27 computed tomography-scanned modern humans representing newborns to adults of both sexes. Our analyses show a curved ontogenetic trajectory, resulting from different ontogenetic growth allometries of upper and lower thoracic units. Adult thoracic morphology is achieved later than predicted, by diverse modifications in different anatomical regions during different ontogenetic stages. Besides a marked increase in antero-posterior dimensions, there is an increase in medio-lateral dimensions of the upper thorax, relative to the lower thorax. This transforms the pyramidal infant thorax into the barrel-shaped one of adults. Rib descent is produced by complex changes in 3D curvature. Developmental differences between upper and lower thoracic regions relate to differential timings and rates of maturation of the respiratory and digestive systems, the spine and the locomotor system. Our findings are relevant to understanding how changes in the relative rates of growth of these systems and structures impacted on the development and evolution of modern human body shapeCGL2012-37279 (Spanish Ministry for Economy and Competition) Fyssen-foundation (http://www.fondationfyssen.fr

Microarchitectural changes during development of the cerebellar cortex

The cerebellum is a highly conserved structure in the Central Nervous System (CNS) of vertebrates, and is involved in the coordination of voluntary motor behaviour. Supporting this function, the cerebellar cortex presents a layered structure which requires a precise spatial and temporal coordination of proliferation, migration and differentiation events. One of the characteristics of the developing cortex is the formation of the external granule cell layer (EGL) in the outermost part. The EGL is a highly proliferative transient layer which disappears when cells migrate inwards to form the inner granule cell layer. The balance between proliferation and migration leads to changes in EGL thickness, and might be related to "indentations" observed in the surface of the developing chick cerebellum. We have extended the observation of this feature to quail and mouse, supporting the idea that this phenomenon forms part of the mechanisms of cerebellar morphogenesis. Different factors involved in both mitotic activity and migration were analyzed in this study. Our results indicate that proliferation, more than formation of raphes for cell migration, is involved in the formation of indentations in the EGL. In addition, we show that vessels penetrating from the pial surface divide the EGL into regular regions at the time of the appearance of bulges and furrows. We conclude that indentations are the result of a coincidence in time of both the increase in thickness of the EGL and the establishment of the embryonic vascular pattern, which confers a characteristic transitory morphology to the surface of folia.2.856 JCR (2010) Q2, 18/38 Developmental biolog

Analysis of cerebellar changes in shape and size along development by using geometric morphometrics coordinates

The cerebellar anlage is subject to a multifactorial process, with intrinsic and extrinsic factors, to acquire a complex adult morphological structure. The spectacular cortical development provokes dramatic changes in shape and size of the initial structure, that course with the formation of folia and fissures. The study of these transformations from a global point of view is the goal of the present study. For this approach, midsagittal sections of chick embryonic cerebella from stages HH36 to HH44 were used to obtain sets of landmarks analyzed by geometric morphometrics. This tool exploits not only the morphology per se, but the spatial relationships of anatomical structures in a quantitative and visual way. Preliminary results indicate that changes in the cerebellum are mainly observed at the transition between stages HH37-HH38, HH39-HH40 and HH40-HH41. The observed changes are summarized: 1)- the cavum ventricularis is progressively reduced to be converted into a cleft; 2)- centrifugal expansion of the folia, specially from HH40 onwards; 3)- fixation of the base of the fissures to the cerebellum core during the centrifugal expansion of the folia.No data (2008)UE

Properties of neuroepithelial cells during neurulation in normal and Pax3 mutant mice

During primary neurulation, the flat neural plate is transformed into a closed neural tube throughout a series of morphogenetic changes driven by a combination of intrinsic and extrinsic forces. Neuroepithelial cell properties like adhesion, polarity, movements and changes in cell shape act as intrinsic forces that determine morphogenesis of the neural tube. In particular, the formation of dorsolateral bendings in the neuroepithelium (dorsolateral hinge-points, DLHP) are crucial events for the neural tube to close in the cranial region. It has been suggested that the transcription factor Pax3 regulates cellular properties involved in the formation of DLHP, since null mutant embryos show flapping neural folds and failure of the neural tube closure in the cranial region. In this study, we analyze the expression pattern of Pax3 in relation to the formation of DLHP. The possible regulation of the cellular properties involved in the bending process is studied in the Sploth embryos (Pax3 mutants) showing exencephaly. The role of neural crest cells, as a dynamic population that affects the architecture of the neuroepithelium during neurulation in the mouse, was also investigated.No data (2008)UE

Expression of TGF-betas in the embryonic nervous system: analysis of interbalance between isoforms

Transforming growth factor-beta (TGF-beta) is a family of growth factors with essential and multiple roles during embryonic development. In mammals, three isoforms (TGF-beta1, TGF-beta2, TGF-beta3) have been described. In the nervous system, the presence of TGF-beta1 has remained undetectable in other structures than meninges and choroids plexus, while TGF-beta2 and TGF-beta3 were considered as the neural members of the family. In the present study, we have analysed the expression pattern of the three isoforms in the neural tube, brain, and spinal cord during development in both mouse and chicken. The data reveal specific patterns for each isoform. This work also shows that both TGF-beta1 and TGF-beta3 are expressed in neural crest cells. In addition, we demonstrate the existence of interbalance between TGF-beta1 and TGF-beta3 with possible functional implications, which, together with the expression of TGF-beta1 in the CNS, represents one of the most important contributions of this work.3.018 JCR (2008) Q1, 1/17 Anatomy & morphology; Q2, 14/38 Development biolog

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

Tractography dissection variability

Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Université de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council ofAustralia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 “Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universit? de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy ? EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Funda??o para a Ci?ncia e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 ?Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Publisher Copyright: © 2021White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols foreach fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.Peer reviewe