White matter microstructure correlates with mathematics but not word reading performance in 13-year-old children born very preterm and full-term

[1]  J. Alexander.  Defining Nature , 2020, Perspectives on Place.

[2]  Jian Chen,et al.  Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum , 2019, NeuroImage.

[3]  Catherine Lebel,et al.  The development of brain white matter microstructure , 2018, NeuroImage.

[4]  J. Whitwell,et al.  Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.

[5]  S. Gathercole,et al.  Children's academic attainment is linked to the global organization of the white matter connectome , 2018, Developmental science.

[6]  S. Rose,et al.  Fixel-based analysis reveals alterations is brain microstructure and macrostructure of preterm-born infants at term equivalent age , 2018, NeuroImage: Clinical.

[7]  J. Oosterlaan,et al.  Academic performance of children born preterm: a meta-analysis and meta-regression , 2017, Archives of Disease in Childhood: Fetal and Neonatal Edition.

[8]  M. Lazar.  Working Memory , 2017, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[9]  G. S. Wilkinson,et al.  Wide Range Achievement Test 4 , 2016 .

[10]  Deanne K. Thompson,et al.  Axon density and axon orientation dispersion in children born preterm , 2016, Human brain mapping.

[11]  João Ricardo Sato,et al.  Children with Poor Reading Skills at the Word Level Show Reduced Fractional Anisotropy in White Matter Tracts of Both Hemispheres , 2016, Brain Connect..

[12]  Daniel C. Alexander,et al.  Multi-compartment microscopic diffusion imaging , 2016, NeuroImage.

[13]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[14]  Alexander Leemans,et al.  White matter abnormalities and impaired attention abilities in children born very preterm , 2016, NeuroImage.

[15]  Deanne K. Thompson,et al.  Accelerated corpus callosum development in prematurity predicts improved outcome , 2015, Human brain mapping.

[16]  Julien Cohen-Adad,et al.  In vivo histology of the myelin g-ratio with magnetic resonance imaging , 2015, NeuroImage.

[17]  Klaus Willmes,et al.  A review on functional and structural brain connectivity in numerical cognition , 2015, Front. Hum. Neurosci..

[18]  H. Feldman,et al.  Cerebellar white matter pathways are associated with reading skills in children and adolescents , 2015, Human brain mapping.

[19]  Deanne K. Thompson,et al.  The predictive validity of neonatal MRI for neurodevelopmental outcome in very preterm children. , 2015, Seminars in perinatology.

[20]  Daniel Ansari,et al.  Drawing connections between white matter and numerical and mathematical cognition: A literature review , 2015, Neuroscience & Biobehavioral Reviews.

[21]  Guilherme Wood,et al.  Considering structural connectivity in the triple code model of numerical cognition: differential connectivity for magnitude processing and arithmetic facts , 2014, Brain Structure and Function.

[22]  Klaus H. Maier-Hein,et al.  Methodological considerations on tract-based spatial statistics (TBSS) , 2014, NeuroImage.

[23]  M. Walshe,et al.  Preterm birth and structural brain alterations in early adulthood , 2014, NeuroImage: Clinical.

[24]  M. Quigley,et al.  School performance at age 7 years in late preterm and early term birth: a cohort study , 2014, Archives of Disease in Childhood: Fetal and Neonatal Edition.

[25]  Manuel Desco,et al.  White matter microstructure correlates of mathematical giftedness and intelligence quotient , 2014, Human brain mapping.

[26]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[27]  Bert De Smedt,et al.  Left fronto-parietal white matter correlates with individual differences in children's ability to solve additions and multiplications: A tractography study , 2014, NeuroImage.

[28]  P. Anderson,et al.  Neuropsychological outcomes of children born very preterm. , 2014, Seminars in fetal & neonatal medicine.

[29]  Paul M. Thompson,et al.  Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics , 2014, NeuroImage.

[30]  Gary F. Egan,et al.  Regional white matter microstructure in very preterm infants: Predictors and 7 year outcomes , 2014, Cortex.

[31]  Feiyan Chen,et al.  Individual structural differences in left inferior parietal area are associated with schoolchildrens' arithmetic scores , 2013, Front. Hum. Neurosci..

[32]  N. Marlow,et al.  Mathematics difficulties in children born very preterm: current research and future directions , 2013, Archives of Disease in Childhood: Fetal and Neonatal Edition.

[33]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[34]  Volkmar Glauche,et al.  Processing Pathways in Mental Arithmetic—Evidence from Probabilistic Fiber Tracking , 2013, PloS one.

[35]  Jason D. Yeatman,et al.  Language and reading skills in school-aged children and adolescents born preterm are associated with white matter properties on diffusion tensor imaging , 2012, Neuropsychologia.

[36]  Simon K. Warfield,et al.  Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI , 2012, PloS one.

[37]  K. Grill-Spector,et al.  White matter microstructure on diffusion tensor imaging is associated with conventional magnetic resonance imaging findings and cognitive function in adolescents born preterm , 2012, Developmental medicine and child neurology.

[38]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[39]  Derek K. Jones,et al.  Spatial and orientational heterogeneity in the statistical sensitivity of skeleton-based analyses of diffusion tensor MR imaging data , 2011, Journal of Neuroscience Methods.

[40]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[41]  Gro C. Christensen Løhaugen,et al.  Young adults born preterm with very low birth weight demonstrate widespread white matter alterations on brain DTI , 2011, NeuroImage.

[42]  B. Luna,et al.  Reading performance correlates with white‐matter properties in preterm and term children , 2010, Developmental medicine and child neurology.

[43]  Brian A Wandell,et al.  Frontoparietal white matter diffusion properties predict mental arithmetic skills in children , 2009, Proceedings of the National Academy of Sciences.

[44]  Alexander Leemans,et al.  The B‐matrix must be rotated when correcting for subject motion in DTI data , 2009, Magnetic resonance in medicine.

[45]  N Marlow,et al.  Academic attainment and special educational needs in extremely preterm children at 11 years of age: the EPICure study , 2009, Archives of Disease in Childhood Fetal and Neonatal Edition.

[46]  J. Volpe.  Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances , 2009, The Lancet Neurology.

[47]  Stephen M. Smith,et al.  Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference , 2009, NeuroImage.

[48]  Derek K. Jones,et al.  Gleaning multicomponent T1 and T2 information from steady‐state imaging data , 2008, Magnetic resonance in medicine.

[49]  Bruce D. McCandliss,et al.  White matter microstructures underlying mathematical abilities in children , 2008, Neuroreport.

[50]  Gehan Roberts,et al.  Rates of early intervention services in very preterm children with developmental disabilities at age 2 years , 2008, Journal of paediatrics and child health.

[51]  L. Doyle,et al.  Cognitive and educational deficits in children born extremely preterm. , 2008, Seminars in perinatology.

[52]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

[53]  A. Dale,et al.  Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. , 2007, Brain : a journal of neurology.

[54]  Bruce D. McCandliss,et al.  Left lateralized white matter microstructure accounts for individual differences in reading ability and disability , 2006, Neuropsychologia.

[55]  Paul A. Yushkevich,et al.  Deformable registration of diffusion tensor MR images with explicit orientation optimization , 2006, Medical Image Anal..

[56]  Anders M. Dale,et al.  Changes in white matter diffusion anisotropy in adolescents born prematurely , 2006, NeuroImage.

[57]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[58]  B. Wandell,et al.  Children's Reading Performance is Correlated with White Matter Structure Measured by Diffusion Tensor Imaging , 2005, Cortex.

[59]  Roland Bammer,et al.  Arithmetic ability and parietal alterations: a diffusion tensor imaging study in velocardiofacial syndrome. , 2005, Brain research. Cognitive brain research.

[60]  John B Carlin,et al.  Regression models for twin studies: a critical review. , 2005, International journal of epidemiology.

[61]  Derek K. Jones,et al.  The effect of filter size on VBM analyses of DT-MRI data , 2005, NeuroImage.

[62]  Luis Concha,et al.  Imaging brain connectivity in children with diverse reading ability , 2005, NeuroImage.

[63]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[64]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[65]  Terrie E Inder,et al.  Defining the nature of the cerebral abnormalities in the premature infant: a qualitative magnetic resonance imaging study. , 2003, The Journal of pediatrics.

[66]  S. Dehaene,et al.  THREE PARIETAL CIRCUITS FOR NUMBER PROCESSING , 2003, Cognitive neuropsychology.

[67]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[68]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[69]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[70]  L. Hildman,et al.  Kaufman Brief Intelligence Test , 1993 .

[71]  Melissa L. Allen,et al.  Kaufman Brief Intelligence Test , 2021, Encyclopedia of Autism Spectrum Disorders.

[72]  M. Descoteaux,et al.  A Generalized SMT-Based Framework for Diffusion MRI Microstructural Model Estimation , 2018, MICCAI 2018.

[73]  Lianne J. Woodward,et al.  Cognitive Development Trajectories of Very Preterm and Typically Developing Children. , 2017, Child development.

[74]  Jaap Oosterlaan,et al.  Development of preschool and academic skills in children born very preterm. , 2011, The Journal of pediatrics.

[75]  L. Papile,et al.  Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances , 2010 .

[76]  M. Jenkinson.  Non-linear registration aka Spatial normalisation , 2007 .

[77]  O. Castejón.  The Cerebellar White Matter , 2003 .

[78]  HighWire Press,et al.  Archives of disease in childhood. Fetal and neonatal edition , 1988 .