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Attention and Regional Gray Matter Development in Very Preterm Children at Age 12 Years

Published online by Cambridge University Press:  01 June 2017

Rachel E. Lean Open the ORCID record for Rachel E. Lean [Opens in a new window] ,
Tracy R. Melzer ,
Samudragupta Bora ,
Richard Watts  and
Lianne J. Woodward
Rachel E. Lean
Affiliation:
Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
Tracy R. Melzer
Affiliation:
Department of Medicine, University of Otago, Christchurch, New Zealand New Zealand Brain Research Institute, Christchurch, New Zealand
Samudragupta Bora
Affiliation:
Mothers, Babies and Women’s Health Program, Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia
Richard Watts
Affiliation:
Department of Radiology, University of Vermont, Burlington, Vermont
Lianne J. Woodward*
Affiliation:
Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts Department of Psychology, University of Canterbury, Christchurch, New Zealand
*
Correspondence and reprint requests to: Lianne Woodward, Department of Pediatric Newborn Medicine, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115. E-mail: ljwoodward@bwh.harvard.edu
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Abstract

Objectives: This study examines the selective, sustained, and executive attention abilities of very preterm (VPT) born children in relation to concurrent structural magnetic resonance imaging (MRI) measures of regional gray matter development at age 12 years. Methods: A regional cohort of 110 VPT (≤32 weeks gestation) and 113 full term (FT) born children were assessed at corrected age 12 years on the Test of Everyday Attention-Children. They also had a structural MRI scan that was subsequently analyzed using voxel-based morphometry to quantify regional between-group differences in cerebral gray matter development, which were then related to attention measures using multivariate methods. Results: VPT children obtained similar selective (p=.85), but poorer sustained (p=.02) and executive attention (p=.01) scores than FT children. VPT children were also characterized by reduced gray matter in the bilateral parietal, temporal, prefrontal and posterior cingulate cortices, bilateral thalami, and left hippocampus; and increased gray matter in the occipital and anterior cingulate cortices (family-wise error–corrected p<.05). Poorer sustained auditory attention was associated with increased gray matter in the anterior cingulate cortex (p=.04). Poor executive shifting attention was associated with reduced gray matter in the right superior temporal cortex (p=.04) and bilateral thalami (p=.05). Poorer executive divided attention was associated with reduced gray matter in the occipital (p=.001), posterior cingulate (p=.02), and left temporal (p=.01) cortices; and increased gray matter in the anterior cingulate cortex (p=.001). Conclusions: Disturbances in regional gray matter development appear to contribute, at least in part, to the poorer attentional performance of VPT children at school age. (JINS, 2017, 23, 539–550)

Keywords

Very pretermOutcomeMRIAttentionGray matterBrain
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 23 , Issue 7 , August 2017 , pp. 539 - 550
Copyright
Copyright © The International Neuropsychological Society 2017 

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References

REFERENCES

Aarnoudse-Moens, C., Weisglas-Kuperus, N., van Goudoever, B., & Oosterlaan, J. (2009). Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics, 124, 717–728.CrossRefGoogle ScholarPubMed
Anderson, P.J., & Doyle, L.W. (2004). Executive Functioning in School-Aged Children Who Were Born Very Preterm or With Extremely Low Birth Weight in the 1990s. Pediatrics, 114(1), 50–57.CrossRefGoogle ScholarPubMed
Anderson, P.J., De Luca, C., Hutchinson, E., Spencer-Smith, M., Roberts, G., & Doyle, L. (2011). Attention problems in a representative sample of extremely preterm/extremely low birth weight children. Developmental Neuropsychology, 36, 57–73.CrossRefGoogle Scholar
Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38, 95–113.CrossRefGoogle ScholarPubMed
Ashburner, J. (2009). Computational Anatomy with the SPM Software. Magnetic Resonance Imaging, 27, 1163–1174.CrossRefGoogle ScholarPubMed
Ashburner, J., & Friston, K.J. (2000). Voxel-based morphometry--The methods. Neuroimage, 11, 805–821.CrossRefGoogle ScholarPubMed
Bayless, S., & Stevenson, J. (2007). Executive functions in school age children born very prematurely. Early Human Development, 83, 247–254.CrossRefGoogle ScholarPubMed
Berg, R.W., Alaburda, A., & Hounsgaard, J. (2007). Balanced inhibition and excitation drive spike activity in spinal half-centers. Science, 315(5810), 390–393.CrossRefGoogle ScholarPubMed
Bhutta, A.T., Cleves, M.A., Casey, P.H., Cradock, M.M., & Ananad, K.J.S. (2002). Cognitive and behavioral outcomes of school aged children who were born preterm. Journal of the American Medical Association, 288, 728–737.CrossRefGoogle ScholarPubMed
Boardman, J., Counsell, S., Rueckert, D., Kapellou, O., Bhatia, K., Aljabar, P., & Edwards, D. (2006). Abnormal deep grey matter development following very preterm birth detected using deformation-based morphometry. Neuroimage, 32, 70–78.CrossRefGoogle ScholarPubMed
Bora, S., Pritchard, V., Chen, Z., Inder, T., & Woodward, L. (2014). Neonatal cerebral morphometry and later risk of persistent inattention/hyperactivity in children born very preterm. Journal of Child Psychology and Psychiatry, 55, 828–838.CrossRefGoogle ScholarPubMed
Cohen, R. (2014). The neuropsychology of attention (2nd ed.), New York: Springer.CrossRefGoogle Scholar
Davidesco, I., Harel, M., Ramot, M., Kramer, U., Kipervasser, S., Andelman, F., & Malach, R. (2013). Spatial and object-based attention modulates broadband high-frequency responses across the human visual cortical hierarchy. The Journal of Neuroscience, 33, 1228–1240.Google Scholar
de Kieviet, J., van Elburg, R., Lafeber, H., & Oosterlaan, J. (2012). Attention problems of very preterm children compared with age-matched term controls at school age. Journal of Pediatrics, 161, 824–829.CrossRefGoogle ScholarPubMed
De Pisapia, N., & Braver, T. (2006). A model of dual control mechanisms through anterior cingulate and prefrontal cortex interactions. Neurocomputing, 69, 1322–1326.CrossRefGoogle Scholar
Dosenbach, N.U.F., Fair, D.A., Cohen, A.L., Schlaggar, B.L., & Petersen, S.E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12, 99–105.CrossRefGoogle ScholarPubMed
Elley, W.B., & Irving, J.C. (2003). The Elley-Irving Socio-Economic Index: 2001 Census Revision. New Zealand Journal of Educational Studies, 38, 3–17.Google Scholar
Fan, J., McCandliss, B., Sommer, T., Raz, A., & Posner, M. (2002). Testing the efficiency and independence of attentional networks. Journal of Cognitive Neuroscience, 14, 340–347.Google Scholar
Giedd, J., Blumenthal, J., Jeffries, N., Castellanos, F., Liu, H., Zijdenbos, A., & Rapoport, J. (1999). Brain development during childhood: A longitudinal MRI study. Nature Neuroscience, 2, 861–863.Google Scholar
Good, C., Johnsrude, J., Ashburner, J., Henson, R., Friston, K., & Frackowiak, R. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14, 21–36.CrossRefGoogle ScholarPubMed
Greicius, M.D., Krasnow, B., Reiss, A.L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 100(1), 253–258.Google Scholar
Heinze, H.J., Mangun, G.R., Burchert, W., Hinrichs, H., Scholz, M., Munte, T.F., & Hillyard, S.A. (1994). Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature, 272, 543–546.Google Scholar
Iwata, S., Nakamura, T., Kihara, H., Takashima, S., Matsushi, T., & Iwata, O. (2012). Qualitative brain MRI at term and cognitive outcomes at 9 years after very preterm birth. Pediatrics, 129, e1138–e1147.CrossRefGoogle ScholarPubMed
Jaekel, J., Wolke, D., & Bartmann, P. (2013). Poor attention rather than hyperactivity/ impulsivity predicts academic achievement in very preterm and full-term adolescents. Psychological Medicine, 43, 183–196.Google Scholar
Kastner, S., Saalmann, Y., & Schneider, K. (2012). Thalamic control of visual attention. In G. Mangun (Ed.), The neuroscience of attention: Attention control and selection. Oxford: Oxford University Press.Google Scholar
Kesler, S., Ment, L., Vohr, B., Pajot, S., Schneider, K., Katz, K., & Reiss, A. (2004). Volumetric analysis of regional cerebral development in preterm children. Pediatric Neurology, 31, 318–325.CrossRefGoogle ScholarPubMed
Kesler, S., Reiss, L., Vohr, B., Watson, C., Schneider, K., Katz, K., & Ment, L. (2008). Brain volume reductions within multiple cognitive systems in male preterm children at age twelve. Journal of Pediatrics, 152, 513–520.CrossRefGoogle ScholarPubMed
Kinomura, S., Larsson, J., Gulyás, B., & Roland, P.E. (1996). Activation by attention of the human reticular formation and thalamic intralaminar nuclei. Science, 271, 512–515.Google Scholar
Konrad, K., Neufang, S., Thiel, C., Specht, K., Hanisch, C., Fan, J., & Fink, G. (2005). Development of attentional networks: An fMRI study with children and adults. Neuroimage, 28, 429–439.CrossRefGoogle ScholarPubMed
Leech, R., Braga, R., & Sharp, D. (2012). Echoes of the brain within the posterior cingulate cortex. Journal of Neuroscience, 32, 215–222.Google Scholar
Leech, R., Kamourieh, S., Beckmann, C.F., & Sharp, D.J. (2011). Fractionating the default mode network: Distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. Journal of Neuroscience, 31, 3217–3224.Google Scholar
Leviton, A., & Gressens, P. (2007). Neuronal damage accompanies perinatal white matter damage. Trends in Neurosciences, 30, 473–478.Google Scholar
Manly, T., Anderson, V., Nimmo-Smith, I., Turner, A., Watson, P., & Robertson, I. (2001). The differential assessment of children’s attention: The Test of Everyday Attention for Children (TEA-Ch), normative sample and ADHD performance. Journal of Child Psychology and Psychiatry, 42, 1065–1081.Google Scholar
Marshall, W.A., & Tanner, J.M. (1969). Variations in pattern of pubertal changes in girls. Archives of Disease in Childhood, 44, 291–303.CrossRefGoogle ScholarPubMed
Marshall, W.A., & Tanner, J.M. (1970). Variations in the pattern of pubertal changes in boys. Archives of Disease in Childhood, 45, 13–23.Google Scholar
Mechelli, A., Price, C., Friston, K., & Ashburner, J. (2005). Voxel-based morphometry of the human brain: Methods and applications. Current Medical Imaging Reviews, 1, 1–9.Google Scholar
Mulder, H., Pitchford, N., Hagger, M., & Marlow, N. (2009). Development of executive function and attention in preterm children: A systematic review. Developmental Neuropsychology, 34, 393–421.CrossRefGoogle ScholarPubMed
Mulder, H., Pitchford, N., & Marlow, N. (2011). Processing speed mediates executive function difficulties in very preterm children in middle childhood. Journal of the International Neuropsychological Society, 17, 445–454.Google Scholar
Murner-Lavanchy, I., Steinlin, M., Nelle, C., Rummel, W, Perrig, G., Schroth, G., & Everts, R. (2014). Delay of cortical thinning in very preterm born children. Early Human Development, 90, 443–450.CrossRefGoogle ScholarPubMed
Murray, A., Scratch, S., Thompson, D., Inder, T., Doyle, L., Anderson, J., & Anderson, P. (2014). Neonatal brain pathology predicts adverse attention and processing speed outcomes in very preterm children and/or very low birth weight children. Neuropsychology, 28, 552–562.Google Scholar
Murray, A., Thompson, D., Pascoe, L., Leemans, A., Inder, T., Doyle, L., & Anderson, P. (2016). White matter abnormalities and impaired attention abilities in children born very preterm. Neuroimage, 124, 75–84.Google Scholar
Nagy, Z., Ashburner, J., Andersson, J., Jbabdi, S., Draganski, B., Skare, S., & Largercrantz, H. (2009). Structural correlates of preterm birth in the adolescent brain. Pediatrics, 214, 964–972.Google Scholar
Nosarti, C., Giouroukou, E., Healy, E., Rifkin, L., Walshe, M., Reichenberg, A., & Murray, R. (2008). Gray and white matter distribution in very preterm adolescents mediates neurodevelopmental outcome. Brain, 131, 205–217.CrossRefGoogle ScholarPubMed
Nosarti, C., Mechelli, A., Herrera, A., Walshe, M., Shergill, S., Murray, R., & Allin, P. (2011). Structural covariance in the cortex of very preterm adolescents: A voxel-based morphometry study. Human Brain Mapping, 32, 1615–1625.CrossRefGoogle ScholarPubMed
Petersen, S., & Posner, M. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 73–89.CrossRefGoogle ScholarPubMed
Peterson, B., Vohr, B., Staib, L., Cannistraci, C., Dolberg, A., Schneider, K., & Ment, L. (2000). Regional volume abnormalities and long-term cognitive outcome in preterm infants. Journal of American Medical Association, 284, 1939–1947.CrossRefGoogle ScholarPubMed
Posner, M., & Petersen, S. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.CrossRefGoogle ScholarPubMed
Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., & Petersen, S.E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.CrossRefGoogle ScholarPubMed
Reuter, M., Tisdall, M., Qureshi, A., Buckner, R., van der Kouwe, A., & Fischl, B. (2015). Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage, 107, 107–115.CrossRefGoogle ScholarPubMed
Sadaghiani, S., & D’Esposito, M. (2014). Functional characterization of the cingulo-opercular network in the maintenance of tonic alertness. Cerebral Cortex, 25, 2763–2773.CrossRefGoogle ScholarPubMed
Sarter, M., Givens, B., & Bruno, J. (2001). The cognitive neuroscience of sustained attention: Where top-down meets bottom-up. Brain Research Reviews, 35, 146–160.Google Scholar
Shah, D.K., Guinane, C., August, P., Austin, N.C., Woodward, L.J., Thompson, D.K., & Inder, T.E. (2006). Reduced occipital regional volumes at term predict impaired visual function in early childhood in very low birth weight infants. Investigative Ophthalmology & Visual Science, 47, 3366–3373.Google Scholar
Shapiro, K., Hillstrom, A.P., & Husain, M. (2002). Control of visuotemporal attention by inferior parietal and superior temporal cortex. Current Biology, 12, 1320–1325.Google Scholar
Shenhav, A., Botvinick, M.M., & Cohen, J.D. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79, 217–240.Google Scholar
Shum, D., Neulinger, K., O’Callaghn, M., & Mohay, H. (2008). Attentional problems in children born very preterm or with extremely low birth weight at 7 – 9 years. Archives of Clinical Neuropsychology, 23, 103–112.Google Scholar
Soria-Pastor, S., Padilla, N., Zubiaurre-Elorza, L., Ibarretxe-Bilbao, N., Botet, F., Costas-Moragas, C., & Junque, C. (2009). Decreased regional brain volume and cognitive impairment in preterm children at low risk. Pediatrics, 124, e1161–e1170.Google Scholar
Statistics New Zealand. (2001). 2001 Census: Regional Summary. Retrieved from http://www.stats.govt.nz/Census/2001-census-data.aspx.Google Scholar
Thompson, D.K., Chen, J., Beare, R., Adamson, C., Ellis, R., Ahmadzai, Z., & Anderson, P.J. (2016). Structural connectivity relates to perinatal factors and functional impairments at 7 years in children born very preterm. Neuroimage, 134, 328–337.Google Scholar
Thompson, D.K., Lee, K.J., van Bijnen, L., Leemans, A., Pascoe, L., Scratch, S.E., & Anderson, P.J. (2015). Accelerated corpus callosum development in prematurity predicts improved outcome: Longitudinal corpus callosum development. Human Brain Mapping, 36, 3733–3748.Google Scholar
Urben, S., Van Hanswijck De Jonge, L., Barishnikov, R., Pizzo, R., Monnier, M., Lazeyras, F., & Hüppi, P. (2015). Gestational age and gender influence on executive control and its related neural structures in preterm-born children at age 6 years. Child Neuropsychology, 23, 188–207.Google Scholar
Volpe, J. (2009a). Brain injury in premature infants: A complex amalgam of destructive and developmental disturbances. Lancet Neurology, 8, 110–124.Google Scholar
Volpe, J. (2009b). The encephalopathy of prematurity—Brain injury and impaired brain development inextricably intertwined. Seminars in Pediatric Neurology, 16, 167–178.Google Scholar
Vossel, S., Geng, J.J., & Fink, G.R. (2014). Dorsal and ventral attention systems. The Neuroscientist, 20, 150–159.Google Scholar
Waber, D., Moor, C., Forbes, P., Almli, C., Botteron, K., Leonard, G., & Rumsey, J. (2007). The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological test battery. Journal of the International Neuropsychological Society, 13, 1–18.Google Scholar
Wilson-Ching, M., Molloy, C., Anderson, V., Burnett, A., Roberts, G., Cheong, J., & Anderson, P.J. (2013). Attention difficulties in a contemporary geographic cohort of adolescents born extremely preterm/extremely low birth weight. Journal of the International Neuropsychology Society, 19, 1097–1108.Google Scholar
Woldorff, M.G., Liotti, M., Seabolt, M., Busse, L., Lancaster, J.L., & Fox, P.T. (2002). The temporal dynamics of the effects in occipital cortex of visual-spatial selective attention. Cognitive Brain Research, 15, 1–15.Google Scholar
Woodward, L.J., Anderson, P.J., Austin, N.C., Howard, K., & Inder, T.E. (2006). Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. New England Journal of Medicine, 355, 685–694.Google Scholar
Woodward, L.J., Clark, C., Bora, S., & Inder, T.E. (2012). Neonatal white matter abnormalities. An Important predictor of neurocognitive outcome for very preterm children. PLoS One, 7, e51879.Google Scholar
Zhang, Y., Inder, T.E., Neil, J., Dierker, D., Alexopoulos, D., Anderson, P.J., & Van Essen, D. (2015). Cortical structural abnormalities in very preterm children at 7 years of age. Neuroimage, 109, 469–479.Google Scholar
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