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      A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range


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          We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.

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          Most cited references16

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          Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

          Background Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size. Methodology To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-year-old, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between age-specific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies. Conclusions We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website, http://bric.unc.edu/ideagroup/free-softwares/.
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            SPM: A history

            Karl Friston began the SPM project around 1991. The rest is history
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              The ontogeny of human gyrification.

              During development the human cortex changes from a smooth lissencephalic structure to one that is highly convoluted. Increases in the degree of cortical folding are associated with brain size only for the first part of brain growth; during the second half, differences in cortical folding match those of brain size, resulting in no change in the degree of folding. When the degree of cortical folding is studied as a function of age, a brief postnatal overshoot, an effect of brain size, is observed. The analysis suggests that the mechanical hypothesis of cortical buckling can best explain the degree of cortical folding, but that other hypotheses, like gyrogenesis, are required to explain the placement and orientation of sulci. The adult asymptote in degree of cortical folding is associated with the onset and disappearance of single subplate lamina, suggesting that subplate:cortical plate associations should be examined as causal for gyrification. Areas whose sulci differ in length between the two hemispheres have similar degrees of convolutedness, supporting interpretations that the sizes of gyri are asymmetric in the two hemispheres. The ontogenetic data support the thesis that human cortical proportions evolved when the brain enlarged in size and that the process was not one of neoteny.

                Author and article information

                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                18 February 2015
                : 9
                : 21
                [1] 1Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital Boston, MA, USA
                [2] 2Laboratory of Computational Neuroimaging, AA Martinos Center, Massachusetts General Hospital Charlestown, MA, USA
                [3] 3Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital Boston, MA, USA
                [4] 4Department of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Cambridge, MA, USA
                Author notes

                Edited by: Lutz Jäncke, University of Zurich, Switzerland

                Reviewed by: Arun Bokde, Trinity College Dublin, Ireland; Jürgen Hänggi, University of Zurich, Switzerland

                *Correspondence: Lilla Zöllei, AA Martinos Center, Massachusetts General Hospital, 149 13th Street, Rm. 2301, Charlestown, MA 02129, USA e-mail: lzollei@ 123456nmr.mgh.harvard.edu

                This article was submitted to the journal Frontiers in Human Neuroscience.

                Copyright © 2015 de Macedo Rodrigues, Ben-Avi, Sliva, Choe, Drottar, Wang, Fischl, Grant and Zöllei.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 05 December 2014
                : 10 January 2015
                Page count
                Figures: 15, Tables: 2, Equations: 0, References: 26, Pages: 12, Words: 7591
                Technology Report Article

                mri imaging,pediatrics,neuroimaging,segmentation,atlas
                mri imaging, pediatrics, neuroimaging, segmentation, atlas


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