Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-29T03:07:25.633Z Has data issue: false hasContentIssue false

Relations between White Matter Maturation and Reaction Time in Childhood

Published online by Cambridge University Press:  29 October 2013

Nadia Scantlebury
Affiliation:
Brain and Behavior Program, The Hospital for Sick Children, Toronto, Ontario
Todd Cunningham
Affiliation:
Brain and Behavior Program, The Hospital for Sick Children, Toronto, Ontario
Colleen Dockstader
Affiliation:
Brain and Behavior Program, The Hospital for Sick Children, Toronto, Ontario
Suzanne Laughlin
Affiliation:
Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario Department of Medical Imaging, University of Toronto, Toronto, Ontario
William Gaetz
Affiliation:
Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Conrad Rockel
Affiliation:
Department of Biomedical Engineering, McMaster University, Hamilton, Ontario
Jolynn Dickson
Affiliation:
Brain and Behavior Program, The Hospital for Sick Children, Toronto, Ontario
Donald Mabbott*
Affiliation:
Brain and Behavior Program, The Hospital for Sick Children, Toronto, Ontario Department of Psychology, University of Toronto, Toronto, Ontario
*
Correspondence and reprint requests to: Donald J. Mabbott, Program in Neurosciences and Mental Health, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, M5G 1X8. E-mail: donald.mabbott@sickkids.ca

Abstract

White matter matures with age and is important for the efficient transmission of neuronal signals. Consequently, white matter growth may underlie the development of cognitive processes important for learning, including the speed of information processing. To dissect the relationship between white matter structure and information processing speed, we administered a reaction time task (finger abduction in response to visual cue) to 27 typically developing, right-handed children aged 4 to 13. Magnetoencephalography and Diffusion Tensor Imaging were used to delineate white matter connections implicated in visual-motor information processing. Fractional anisotropy (FA) and radial diffusivity (RD) of the optic radiation in the left hemisphere, and FA and mean diffusivity (MD) of the optic radiation in the right hemisphere changed significantly with age. MD and RD decreased with age in the right inferior fronto-occipital fasciculus, and bilaterally in the cortico-spinal tracts. No age-related changes were evident in the inferior longitudinal fasciculus. FA of the cortico-spinal tract in the left hemisphere and MD of the inferior fronto-occipital fasciculus of the right hemisphere contributed uniquely beyond the effect of age in accounting for reaction time performance of the right hand. Our findings support the role of white matter maturation in the development of information processing speed. (JINS, 2013, 19, 1–14)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Basser, P.J. (1995). Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR in Biomedecine, 8(7–8), 333344.Google Scholar
Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system – A technical review. NMR in Biomedecine, 15(7–8), 435455.Google Scholar
Behrens, T.E., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A., Boulby, P.A., Matthews, P.M. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7), 750757.CrossRefGoogle ScholarPubMed
Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289300.Google Scholar
Bennett, C.M., Wolford, G.L., Miller, M.B. (2009). The principled control of false positives in neuroimaging. Social Cognitive and Affective Neuroscience, 4(4), 417422. doi:10.1093/scan/nsp053 nsp053 [pii] Google Scholar
Bryan, J., Luszcz, M.A., Crawford, J.R. (1997). Verbal knowledge and speed of information processing as mediators of age differences in verbal fluency performance among older adults. Psychology and Aging, 12(3), 473478.Google Scholar
Burzynska, A.Z., Preuschhof, C., Backman, L., Nyberg, L., Li, S.C., Lindenberger, U., Heekeren, H.R. (2010). Age-related differences in white matter microstructure: Region-specific patterns of diffusivity. Neuroimage, 49(3), 21042112. doi:10.1016/j.neuroimage.2009.09.041 S1053-8119(09)01020-9 [pii] Google Scholar
Casey, B.J., Giedd, J.N., Thomas, K.M. (2000). Structural and functional brain development and its relation to cognitive development. Biological Psychology, 54(1–3), 241257.CrossRefGoogle ScholarPubMed
Catani, M., Howard, R., Pajevic, S., Jones, D.K. (2002). Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage, 17(1), 7794.Google Scholar
Catani, M., Jones, D.K., Donato, R., Ffytche, D.H. (2003). Occipito-temporal connections in the human brain. Brain, 126(Pt 9), 20932107. doi:10.1093/brain/awg203 awg203 [pii] Google Scholar
Cheyne, D., Bakhtazad, L., Gaetz, W. (2006). Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach. Human Brain Mapping, 27(3), 213229. doi:10.1002/hbm.20178 Google Scholar
Cheyne, D., Bostan, A.C., Gaetz, W., Pang, E.W. (2007). Event-related beamforming: A robust method for presurgical functional mapping using MEG. Clinical Neurophysiology, 118(8), 16911704. doi:S1388-2457(07)00207-6 [pii] 10.1016/j.clinph.2007.05.064 Google Scholar
Clayden, J.D., Jentschke, S., Muñoz, M., Cooper, J.M., Chadwick, M.J., Banks, T., Vargha-Khadem, F. (2012). Normative development of white matter tracts: Similarities and differences in relation to age, gender, and intelligence. Cerebral Cortex, 22(8), 17381747.Google Scholar
Collins, L.F., Long, C.J. (1996). Visual reaction time and its relationship to neuropsychological test performance. Archives of Clinical Neuropsychology, 11(7), 613623. doi:0887-6177(97)81255-3 [pii] Google Scholar
Dockstader, C., Gaetz, W., Rockel, C., Mabbott, D.J. (2012). White matter maturation in visual and motor areas predicts the latency of visual activation in children. Human Brain Mapping, 33(1), 179191. doi:10.1002/hbm.21203 Google Scholar
Drew, M.A., Starkey, N.J., Isler, R.B. (2009). Examining the link between information processing speed and executive functioning in multiple sclerosis. Archives of Clinical Neuropsychology, 24(1), 4758. doi:acp007 [pii] 10.1093/arclin/acp007 Google Scholar
Eluvathingal, T.J., Hasan, K.M., Kramer, L., Fletcher, J.M., Ewing-Cobbs, L. (2007). Quantitative diffusion tensor tractography of association and projection fibers in normally developing children and adolescents. Cerebral Cortex, 17, 27602768.Google Scholar
Ferguson, A.N., Bowey, J.A. (2005). Global processing speed as a mediator of developmental changes in children's auditory memory span. Journal of Experimental Child Psychology, 91(2), 89112. doi:S0022-0965(05)00028-7 [pii] 10.1016/j.jecp.2004.12.006 Google Scholar
Fields, R.D. (2008). Oligodendrocytes changing the rules: Action potentials in glia and oligodendrocytes controlling action potentials. Neuroscientist, 14(6), 540543.Google Scholar
Forkel, S., Thiebaut de Schotten, M., Kawadler, J., Dell'Acqua, F., Danek, A., Catani, M. (2012). The anatomy of fronto-occipital connections from early blunt dissections to contemporary tractographycontemporary tractography. Cortex, [Epub ahead of print].Google Scholar
Gaetz, W., Cheyne, D. (2006). Localization of sensorimotor cortical rhythms induced by tactile stimulation using spatially filtered MEG. Neuroimage, 30(3), 899908. doi:S1053-8119(05)00785-8 [pii] 10.1016/j.neuroimage.2005.10.009 Google Scholar
Gaetz, W., Scantlebury, N., Widjaja, E., Rutka, J., Bouffet, E., Rockel, C., Mabbott, D. (2010). Mapping of the cortical spinal tracts using magnetoencephalography and diffusion tensor tractography in pediatric brain tumor patients. Child's Nervous System, 26(11), 16391645. doi:10.1007/s00381-010-1189-8 Google Scholar
Giedd, J.N., Blumenthal, J., Jeffries, N.O., Castellanos, F.X., Liu, H., Zijdenbos, A., Rapoport, J.L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2(10), 861863.CrossRefGoogle ScholarPubMed
Hasan, K.M., Kamali, A., Abid, H., Kramer, L.A., Fletcher, J.M., Ewing-Cobbs, L. (2010). Quantification of the spatiotemporal microstructural organization of the human brain association, projection and commissural pathways across the lifespan using diffusion tensor tractography. Brain Structure and Function, 214(4), 361373.Google Scholar
Hasan, K.M., Kamali, A., Iftikhar, A., Kramer, L.A., Papanicolaou, A.C., Fletcher, J.M., Ewing-Cobbs, L. (2009). Diffusion tensor tractography quantification of the human corpus callosum fiber pathways across the lifespan. Brain Research, 1249, 91100. doi:S0006-8993(08)02516-X [pii] 10.1016/j.brainres.2008.10.026 CrossRefGoogle ScholarPubMed
Helmuth, L. (2001). Neuroscience. Glia tell neurons to build synapses. Science, 291(5504), 569570.Google Scholar
Kail, R. (2000). Speed of information processing: Developmental change and links to intelligence. Journal of School Psychology, 38, 5161.Google Scholar
Kail, R., Hall, L.K. (2001). Distinguishing short-term memory from working memory. Memory & Cognition, 29(1), 19.Google Scholar
Kail, R., Park, Y.S. (1994). Processing time, articulation time, and memory span. Journal of Experimental Child Psychology, 57(2), 281291.Google Scholar
Konrad, A., Vucurevic, G., Musso, F., Stoeter, P., Winterer, G. (2009). Correlation of brain white matter diffusion anisotropy and mean diffusivity with reaction time in an oddball task. Neuropsychobiology, 60(2), 5566. doi:000236445 [pii] 10.1159/000236445 CrossRefGoogle Scholar
Lebel, C., Beaulieu, C. (2011). Longitudinal development of human brain wiring continues from childhood into adulthood. Journal of Neuroscience, 31(30), 1093710947. doi:31/30/10937 [pii] 10.1523/JNEUROSCI.5302-10.2011 Google Scholar
Lebel, C., Walker, L., Leemans, A., Phillips, L., Beaulieu, C. (2008). Microstructural maturation of the human brain from childhood to adulthood. Neuroimage, 40(3), 10441055. doi:S1053-8119(07)01177-9 [pii] 10.1016/j.neuroimage.2007.12.053 Google Scholar
Loenneker, T., Klaver, P., Bucher, K., Lichtensteiger, J., Imfeld, A., Martin, E. (2011). Microstructural development: Organizational differences of the fiber architecture between children and adults in dorsal and ventral visual streams. Human Brain Mapping, 32(6), 935946.Google Scholar
Luciano, M., Wright, M.J., Geffen, G.M., Geffen, L.B., Smith, G.A., Martin, N.G. (2004). A genetic investigation of the covariation among inspection time, choice reaction time, and IQ subtest scores. Behavior Genetics, 34(1), 4150. doi:10.1023/B:BEGE.0000009475.35287.9d474994 [pii] Google Scholar
Luszcz, M.A., Bryan, J., Kent, P. (1997). Predicting episodic memory performance of very old men and women: Contributions from age, depression, activity, cognitive ability, and speed. Psychology and Aging, 12(2), 340351.CrossRefGoogle ScholarPubMed
Mabbott, D.J., Noseworthy, M., Bouffet, E., Laughlin, S., Rockel, C. (2006). White matter growth as a mechanism of cognitive development in children. Neuroimage, 33(3), 936946. doi:S1053-8119(06)00786-5 [pii] 10.1016/j.neuroimage.2006.07.024 Google Scholar
Mabbott, D.J., Rovet, J., Noseworthy, M.D., Smith, M.L., Rockel, C. (2009). The relations between white matter and declarative memory in older children and adolescents. Brain Research, 1294, 8090. doi:S0006-8993(09)01480-2 [pii] 10.1016/j.brainres.2009.07.046 Google Scholar
Madden, D.J., Whiting, W.L., Huettel, S.A., White, L.E., MacFall, J.R., Provenzale, J.M. (2004). Diffusion tensor imaging of adult age differences in cerebral white matter: Relation to response time. Neuroimage, 21(3), 11741181. doi:10.1016/j.neuroimage.2003.11.004 S1053811903007249 [pii] Google Scholar
Madsen, K.S., Baaré, W.F., Skimminge, A., Vestergaard, M., Siebner, H.R., Jernigan, T.L. (2011). Brain microstructural correlates of visuospatial choice reaction time in children. Neuroimage, 58(4), 10901100.Google Scholar
Makris, N., Papadimitriou, G.M., Sorg, S., Kennedy, D.N., Caviness, V.S., Pandya, D.N. (2007). The occipitofrontal fascicle in humans: A quantitative, in vivo, DT-MRI study. Neuroimage, 37(4), 11001111.Google Scholar
Martino, J., Vergani, F., Robles, S.G., Duffau, H. (2010). New insights into the anatomic dissection of the temporal stem with special emphasis on the inferior fronto-occipital fasciculus: Implications in surgical approach to left mesiotemporal and temporoinsular structures. Neurosurgery, 66(3 Suppl Operative), 412. doi:10.1227/01.NEU.0000348564.28415.FA00006123-201003002-00002 [pii] Google Scholar
Nelson, L.A., Yoash-Gantz, R.E., Pickett, T.C., Campbell, T.A. (2009). Relationship between processing speed and executive functioning performance among OEF/OIF veterans: Implications for postdeployment rehabilitation. Journal of Head Trauma Rehabilitation, 24(1), 3240. doi:10.1097/HTR.0b013e318195701600001199-200901000-00005 [pii] Google Scholar
Pang, E.W., Drake, J.M., Otsubo, H., Martineau, A., Strantzas, S., Cheyne, D., Gaetz, W. (2008). Intraoperative confirmation of hand motor area identified preoperatively by magnetoencephalography. Pediatric Neurosurgery, 44(4), 313317.Google Scholar
Paus, T., Collins, D.L., Evans, A.C., Leonard, G., Pike, B., Zijdenbos, A. (2001). Maturation of white matter in the human brain: A review of magnetic resonance studies. Brain Research Bulletin, 54(3), 255266. doi:S0361-9230(00)00434-2 [pii] CrossRefGoogle Scholar
Paus, T., Zijdenbos, A., Worsley, K., Collins, D.L., Blumenthal, J., Giedd, J.N., Evans, A.C. (1999). Structural maturation of neural pathways in children and adolescents: In vivo study. Science, 283(5409), 19081911.Google Scholar
Reed, T.E., Vernon, P.A., Johnson, A.M. (2004). Sex difference in brain nerve conduction velocity in normal humans. Neuropsychologia, 42(12), 17091714.Google Scholar
Rose, S.A., Feldman, J.F., Jankowski, J.J., Caro, D.M. (2002). A longitudinal study of visual expectation and reaction time in the first year of life. Child Development, 73(1), 4761.Google Scholar
Schmahmann, J.D., Pandya, D.N. (2007). The complex history of the fronto-occipital fasciculus. Journal of the History of the Neurosciences, 16, 362377.Google Scholar
Schmahmann, J.D., Pandya, D.N. (2009). Chapter 19: Fronto-occipital fasciculus Fiber Pathways of the Brain. New York: Oxford Scholarship Online.Google Scholar
Schmithorst, V.J., Wilke, M., Dardzinski, B.J., Holland, S.K. (2002). Correlation of white matter diffusivity and anisotropy with age during childhood and adolescence: A cross–sectional diffusion-tensor MR imaging study. Radiology, 222(1), 212218.Google Scholar
Song, S.K., Sun, S.W., Ramsbottom, M.J., Chang, C., Russell, J., Cross, A.H. (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage, 17(3), 14291436.Google Scholar
Song, S.K., Yoshino, J., Le, T.Q., Lin, S.J., Sun, S.W., Cross, A.H., Armstrong, R.C. (2005). Demyelination increases radial diffusivity in corpus callosum of mouse brain. Neuroimage, 26(1), 132140. doi:S1053-8119(05)00022-4 [pii] 10.1016/j.neuroimage.2005.01.028 Google Scholar
Suzuki, Y., Matsuzawa, H., Kwee, I.L., Nakada, T. (2003). Absolute eigenvalue diffusion tensor analysis for human brain maturation. NMR in Biomedecine, 16(5), 257260.Google Scholar
Taki, Y., Thyreau, B., Hashizume, H., Sassa, Y., Takeuchi, H., Wu, K., Kawashima, R. (2012). Linear and curvilinear correlations of brain white matter volume, fractional anisotropy, and mean diffusivity with age using voxel-based and region-of-interest analyses in 246 healthy children. Human Brain Mapping, 34(8), 18421856. doi:10.1002/hbm.22027 Google Scholar
Tamnes, C.K., Fjell, A.M., Westlye, L.T., Østby, Y., Walhovd, K.B. (2012). Becoming consistent: Developmental reductions in intraindividual variability in reaction time are related to white matter integrity. Journal of Neuroscience, 32(3), 972982.Google Scholar
Thiebaut de Schotten, M., Dell'Acqua, F., Valabregue, R., Catani, M. (2012). Monkey to human comparative anatomy of the frontal lobe association tracts. Cortex, 48, 8296.Google Scholar
Tsuda, M., Inoue, K., Salter, M.W. (2005). Neuropathic pain and spinal microglia: A big problem from molecules in “small” glia. Trends in Neurosciences, 28(2), 101107.Google Scholar
Tsuda, M., Shigemoto-Mogami, Y., Koizumi, S., Mizokoshi, A., Kohsaka, S., Salter, M.W., Inoue, K. (2003). P2X4 receptors induced in spinal microglia gate tactile allodynia after nerve injury. Nature, 424(6950), 778783.Google Scholar
Tuch, D.S., Salat, D.H., Wisco, J.J., Zaleta, A.K., Hevelone, N.D., Rosas, H.D. (2005). Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proceedings of the National Academy of Sciences of the United States of America, 102(34), 1221212217. doi:0407259102 [pii] 10.1073/pnas.0407259102 Google Scholar
Turken, A., Whitfield-Gabrieli, S., Bammer, R., Baldo, J.V., Dronkers, N.F., Gabrieli, J.D. (2008). Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies. Neuroimage, 42(2), 10321044. doi:S1053-8119(08)00286-3 [pii] 10.1016/j.neuroimage.2008.03.057 Google Scholar
Ullian, E.M., Sapperstein, S.K., Christopherson, K.S., Barres, B.A. (2001). Control of synapse number by glia. Science, 291(5504), 657661.Google Scholar
Urbanski, M., Thiebaut de Schotten, M., Rodrigo, S., Catani, M., Oppenheim, C., Touze, E., Bartolomeo, P. (2008). Brain networks of spatial awareness: Evidence from diffusion tensor imaging tractography. Journal of Neurology, Neurosurgery, and Psychiatry, 79(5), 598601. doi:jnnp.2007.126276 [pii] 10.1136/jnnp.2007.126276 Google Scholar
Verhoeven, J.S., Sage, C.A., Leemans, A., Van Hecke, W., Callaert, D., Peeters, R., Sunaert, S. (2010). Construction of a stereotaxic DTI atlas with full diffusion tensor information for studying white matter maturation from childhood to adolescence using tractography-based segmentations. Human Brain Mapping, 31(3), 470486. doi:10.1002/hbm.20880 Google Scholar
Wang, J.Y., Abdi, H., Bakhadirov, K., Diaz-Arrastia, R., Devous, M.D.S. (2012). A comprehensive reliability assessment of quantitative diffusion tensor tractography. Neuroimage, 60(2), 11271138.Google Scholar
Welford, A.T. (1977). Motor performance Handbook of the psychology of aging (pp. 450–496). New York: Van Nostrand Reinhold.Google Scholar
Western, S.L., Long, C.J. (1996). Relationship between reaction time and neuropsychological test performance. Archives of Clinical Neuropsychology, 11(7), 557571. doi:0887-6177(95)00043-7 [pii] Google Scholar
Woods, R.P., Grafton, S.T., Watson, J.D., Sicotte, N.L., Mazziotta, J.C. (1998). Automated image registration: II. Intersubject validation of linear and nonlinear models. Journal of Computer Assisted Tomography, 22(1), 153165.Google Scholar
Yousry, T.A., Schmid, U.D., Alkadhi, H., Schmidt, D., Peraud, A., Buettner, A., Winkler, P. (1997). Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain, 120(Pt1), 141157.Google Scholar