Clinical spectrum of MTOR-related hypomelanosis of Ito with neurodevelopmental abnormalities

PURPOSE: Hypomelanosis of Ito (HI) is a skin marker of somatic mosaicism. Mosaic MTOR pathogenic variants have been reported in HI with brain overgrowth. We sought to delineate further the pigmentary skin phenotype and clinical spectrum of neurodevelopmental manifestations of MTOR-related HI. METHODS: From two cohorts totaling 71 patients with pigmentary mosaicism, we identified 14 patients with Blaschko-linear and one with flag-like pigmentation abnormalities, psychomotor impairment or seizures, and a postzygotic MTOR variant in skin. Patient records, including brain magnetic resonance image (MRI) were reviewed. Immunostaining (n = 3) for melanocyte markers and ultrastructural studies (n = 2) were performed on skin biopsies. RESULTS: MTOR variants were present in skin, but absent from blood in half of cases. In a patient (p.[Glu2419Lys] variant), phosphorylation of p70S6K was constitutively increased. In hypopigmented skin of two patients, we found a decrease in stage 4 melanosomes in melanocytes and keratinocytes. Most patients (80%) had macrocephaly or (hemi)megalencephaly on MRI. CONCLUSION: MTOR-related HI is a recognizable neurocutaneous phenotype of patterned dyspigmentation, epilepsy, intellectual deficiency, and brain overgrowth, and a distinct subtype of hypomelanosis related to somatic mosaicism. Hypopigmentation may be due to a defect in melanogenesis, through mTORC1 activation, similar to hypochromic patches in tuberous sclerosis complex

Clinical spectrum of MTOR-related hypomelanosis of Ito with neurodevelopmental abnormalities.

PURPOSE: Hypomelanosis of Ito (HI) is a skin marker of somatic mosaicism. Mosaic MTOR pathogenic variants have been reported in HI with brain overgrowth. We sought to delineate further the pigmentary skin phenotype and clinical spectrum of neurodevelopmental manifestations of MTOR-related HI. METHODS: From two cohorts totaling 71 patients with pigmentary mosaicism, we identified 14 patients with Blaschko-linear and one with flag-like pigmentation abnormalities, psychomotor impairment or seizures, and a postzygotic MTOR variant in skin. Patient records, including brain magnetic resonance image (MRI) were reviewed. Immunostaining (n = 3) for melanocyte markers and ultrastructural studies (n = 2) were performed on skin biopsies. RESULTS: MTOR variants were present in skin, but absent from blood in half of cases. In a patient (p.[Glu2419Lys] variant), phosphorylation of p70S6K was constitutively increased. In hypopigmented skin of two patients, we found a decrease in stage 4 melanosomes in melanocytes and keratinocytes. Most patients (80%) had macrocephaly or (hemi)megalencephaly on MRI. CONCLUSION: MTOR-related HI is a recognizable neurocutaneous phenotype of patterned dyspigmentation, epilepsy, intellectual deficiency, and brain overgrowth, and a distinct subtype of hypomelanosis related to somatic mosaicism. Hypopigmentation may be due to a defect in melanogenesis, through mTORC1 activation, similar to hypochromic patches in tuberous sclerosis complex

A new Ralstonia solanacearum population affects Anthurium plantations and cucurbitaceous crops in Martinique (F.W.I)

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A post-processing pipeline to reconstruct the developing fetal brain using low-resolution MRI

Introduction. Development of the fetal brain surface with concomitant gyrification is one of the major maturational processes of the human brain. First delineated by postmortem studies or by ultrasound, MRI has recently become a powerful tool for studying in vivo the structural correlates of brain maturation. However, the quantitative measurement of fetal brain development is a major challenge because of the movement of the fetus inside the amniotic cavity, the poor spatial resolution, the partial volume effect and the changing appearance of the developing brain. Today extensive efforts are made to deal with the “post-acquisition” reconstruction of high-resolution 3D fetal volumes based on several acquisitions with lower resolution (Rousseau, F., 2006; Jiang, S., 2007). We here propose a framework devoted to the segmentation of the basal ganglia, the gray-white tissue segmentation, and in turn the 3D cortical reconstruction of the fetal brain. Method. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences in fetuses aged from 29 to 32 gestational weeks (slice thickness 5.4mm, in plane spatial resolution 1.09mm). For each fetus, 6 axial volumes shifted by 1 mm were acquired (about 1 min per volume). First, each volume is manually segmented to extract fetal brain from surrounding fetal and maternal tissues. Inhomogeneity intensity correction and linear intensity normalization are then performed. A high spatial resolution image of isotropic voxel size of 1.09 mm is created for each fetus as previously published by others (Rousseau, F., 2006). B-splines are used for the scattered data interpolation (Lee, 1997). Then, basal ganglia segmentation is performed on this super reconstructed volume using active contour framework with a Level Set implementation (Bach Cuadra, M., 2010). Once basal ganglia are removed from the image, brain tissue segmentation is performed (Bach Cuadra, M., 2009). The resulting white matter image is then binarized and further given as an input in the Freesurfer software (http://surfer.nmr.mgh.harvard.edu/) to provide accurate three-dimensional reconstructions of the fetal brain. Results. High-resolution images of the cerebral fetal brain, as obtained from the low-resolution acquired MRI, are presented for 4 subjects of age ranging from 29 to 32 GA. An example is depicted in Figure 1. Accuracy in the automated basal ganglia segmentation is compared with manual segmentation using measurement of Dice similarity (DSI), with values above 0.7 considering to be a very good agreement. In our sample we observed DSI values between 0.785 and 0.856. We further show the results of gray-white matter segmentation overlaid on the high-resolution gray-scale images. The results are visually checked for accuracy using the same principles as commonly accepted in adult neuroimaging. Preliminary 3D cortical reconstructions of the fetal brain are shown in Figure 2. Conclusion. We hereby present a complete pipeline for the automated extraction of accurate three-dimensional cortical surface of the fetal brain. These results are preliminary but promising, with the ultimate goal to provide “movie” of the normal gyral development. In turn, a precise knowledge of the normal fetal brain development will allow the quantification of subtle and early but clinically relevant deviations. Moreover, a precise understanding of the gyral development process may help to build hypotheses to understand the pathogenesis of several neurodevelopmental conditions in which gyrification have been shown to be altered (e.g. schizophrenia, autism…). References. Rousseau, F. (2006), 'Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain images', IEEE Transactions on Medical Imaging, vol. 13, no. 9, pp. 1072-1081. Jiang, S. (2007), 'MRI of Moving Subjects Using Multislice Snapshot Images With Volume Reconstruction (SVR): Application to Fetal, Neonatal, and Adult Brain Studies', IEEE Transactions on Medical Imaging, vol. 26, no. 7, pp. 967-980. Lee, S. (1997), 'Scattered data interpolation with multilevel B-splines', IEEE Transactions on Visualization and Computer Graphics, vol. 3, no. 3, pp. 228-244. Bach Cuadra, M. (2010), 'Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain', ISMRM Conference. Bach Cuadra, M. (2009), 'Brain tissue segmentation of fetal MR images', MICCAI

A new Ralstonia solanacearum population affects Anthurium plantations and cucurbitaceous crops in Martinique (F.W.I)

Food production, marketing, and safety : Strategies for Carribbean food securityInternational audienc

Oxygen profile in the hyporheic zone: wooden stake methodology improvement

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Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in its early stage is today possible thanks to in vivo fetal magnetic resonance imaging (MRI) techniques. A quantitative analysis of fetal cortical surface represents a new approach which can be used as a marker of the cerebral maturation (as gyration) and also for studying central nervous system pathologies [1]. However, this quantitative approach is a major challenge for several reasons. First, movement of the fetus inside the amniotic cavity requires very fast MRI sequences to minimize motion artifacts, resulting in a poor spatial resolution and/or lower SNR. Second, due to the ongoing myelination and cortical maturation, the appearance of the developing brain differs very much from the homogenous tissue types found in adults. Third, due to low resolution, fetal MR images considerably suffer of partial volume (PV) effect, sometimes in large areas. Today extensive efforts are made to deal with the reconstruction of high resolution 3D fetal volumes [2,3,4] to cope with intra-volume motion and low SNR. However, few studies exist related to the automated segmentation of MR fetal imaging. [5] and [6] work on the segmentation of specific areas of the fetal brain such as posterior fossa, brainstem or germinal matrix. First attempt for automated brain tissue segmentation has been presented in [7] and in our previous work [8]. Both methods apply the Expectation-Maximization Markov Random Field (EM-MRF) framework but contrary to [7] we do not need from any anatomical atlas prior. Data set & Methods. Prenatal MR imaging was performed with a 1-T system (GE Medical Systems, Milwaukee) using single shot fast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms, FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatial resolution 1.09mm). Each fetus has 6 axial volumes (around 15 slices per volume), each of them acquired in about 1 min. Each volume is shifted by 1 mm with respect to the previous one. Gestational age (GA) ranges from 29 to 32 weeks. Mother is under sedation. Each volume is manually segmented to extract fetal brain from surrounding maternal tissues. Then, in-homogeneity intensity correction is performed using [9] and linear intensity normalization is performed to have intensity values that range from 0 to 255. Note that due to intra-tissue variability of developing brain some intensity variability still remains. For each fetus, a high spatial resolution image of isotropic voxel size of 1.09 mm is created applying [2] and using B-splines for the scattered data interpolation [10] (see Fig. 1). Then, basal ganglia (BS) segmentation is performed on this super reconstructed volume. Active contour framework with a Level Set (LS) implementation is used. Our LS follows a slightly different formulation from well-known Chan-Vese [11] formulation. In our case, the LS evolves forcing the mean of the inside of the curve to be the mean intensity of basal ganglia. Moreover, we add local spatial prior through a probabilistic map created by fitting an ellipsoid onto the basal ganglia region. Some user interaction is needed to set the mean intensity of BG (green dots in Fig. 2) and the initial fitting points for the probabilistic prior map (blue points in Fig. 2). Once basal ganglia are removed from the image, brain tissue segmentation is performed as described in [8]. Results. The case study presented here has 29 weeks of GA. The high resolution reconstructed volume is presented in Fig. 1. The steps of BG segmentation are shown in Fig. 2. Overlap in comparison with manual segmentation is quantified by the Dice similarity index (DSI) equal to 0.829 (values above 0.7 are considered a very good agreement). Such BG segmentation has been applied on 3 other subjects ranging for 29 to 32 GA and the DSI has been of 0.856, 0.794 and 0.785. Our segmentation of the inner (red and blue contours) and outer cortical surface (green contour) is presented in Fig. 3. Finally, to refine the results we include our WM segmentation in the Freesurfer software [12] and some manual corrections to obtain Fig.4. Discussion. Precise cortical surface extraction of fetal brain is needed for quantitative studies of early human brain development. Our work combines the well known statistical classification framework with the active contour segmentation for central gray mater extraction. A main advantage of the presented procedure for fetal brain surface extraction is that we do not include any spatial prior coming from anatomical atlases. The results presented here are preliminary but promising. Our efforts are now in testing such approach on a wider range of gestational ages that we will include in the final version of this work and studying as well its generalization to different scanners and different type of MRI sequences. References. [1] Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau, Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4] Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio. Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7] Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009. [9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans. Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEE Trans. Img. Proc, 10(2), 2001 [12] Freesurfer, http://surfer.nmr.mgh.harvard.edu

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality

Unexpected diagnosis of a SHH nonsense variant causing a variable phenotype ranging from familial coloboma and Intellectual disability to isolated microcephaly

IF 3.326Lettre à l'éditeur ("Clinical Genetics" : 94(1):182-184)