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White-matter relaxation time and myelin water fraction differences in young adults with autism

Published online by Cambridge University Press:  11 August 2014

S. C. L. Deoni ,
J. R. Zinkstok ,
E. Daly ,
C. Ecker ,
S. C. R. Williams ,
D. G. M. Murphy  and
MRC AIMS Consortium
S. C. L. Deoni
Affiliation:
Advanced Baby Imaging Laboratory, School of Engineering, Brown University, Providence, RI, USA
J. R. Zinkstok*
Affiliation:
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK
E. Daly
Affiliation:
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK
C. Ecker
Affiliation:
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK
S. C. R. Williams
Affiliation:
Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK
D. G. M. Murphy
Affiliation:
Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK The Sackler Institute for Translational Neurodevelopment, King's College London, London, UK
*
*Address for correspondence: Dr J. Zinkstok, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, P023, De Crespigny Park, Denmark Hill, London SE5 8AF, UK. (Email: [email protected])
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Abstract

Background

Increasing evidence suggests that autism is associated with abnormal white-matter (WM) anatomy and impaired brain ‘connectivity’. While myelin plays a critical role in synchronized brain communication, its aetiological role in autistic symptoms has only been indirectly addressed by WM volumetric, relaxometry and diffusion tensor imaging studies. A potentially more specific measure of myelin content, termed myelin water fraction (MWF), could provide improved sensitivity to myelin alteration in autism.

Method

We performed a cross-sectional imaging study that compared 14 individuals with autism and 14 age- and IQ-matched controls. T1 relaxation times (T1), T2 relaxation times (T2) and MWF values were compared between autistic subjects, diagnosed using the Autism Diagnostic Interview – Revised (ADI-R), with current symptoms assessed using the Autism Diagnostic Observation Schedule (ADOS) and typical healthy controls. Correlations between T1, T2 and MWF values with clinical measures [ADI-R, ADOS, and the Autism Quotient (AQ)] were also assessed.

Results

Individuals with autism showed widespread WM T1 and MWF differences compared to typical controls. Within autistic individuals, worse current social interaction skill as measured by the ADOS was related to reduced MWF although not T1. No significant differences or correlations with symptoms were observed with respect to T2.

Conclusions

Autistic individuals have significantly lower global MWF and higher T1, suggesting widespread alteration in tissue microstructure and biochemistry. Areas of difference, including thalamic projections, cerebellum and cingulum, have previously been implicated in the disorder; however, this is the first study to specifically indicate myelin alteration in these regions.

Keywords

Autismbrain connectivitybrain developmenthuman brain imagingmyelinwhite matter
Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 4 , March 2015 , pp. 795 - 805
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

†

These authors served as joint first authors.

‡

Members of the MRC AIMS Consortium are given in the Appendix.

References

Abell, F, Krams, M, Ashburner, J, Passingham, R, Friston, K, Frackowiak, R, Happe, F, Frith, C, Frith, U (1999). The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport 10, 1647–1651.Google Scholar
Alexander, AL, Hurley, SA, Samsonov, AA, Adluru, N, Hosseinbor, AP, Mossahebi, P, Tromp do, PM, Zakszewski, E, Field, AS (2011). Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connectivity 6, 423–446.Google Scholar
Alexander, AL, Lee, JE, Lazar, M, Boudos, R, DuBray, MB, Oakes, TR, Miller, JN, Lu, J, Jeong, EK, McMahon, WM, Bigler, ED, Lainhart, JE (2007). Diffusion tensor imaging of the corpus callosum in Autism. Neuroimage 34, 61–73.Google Scholar
Allen, MC (2008). Neurodevelopmental outcomes of preterm infants. Current Opinion in Neurology 21, 123–128.10.1097/WCO.0b013e3282f88bb4Google Scholar
Avants, BB, Epstein, CL, Grossman, M, Gee, JC (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 12, 26–41.Google Scholar
Avants, BB, Yushkevich, P, Pluta, J, Minkoff, D, Korczykowski, M, Detre, J, Gee, JC (2010). The optimal template effect in hippocampus studies of diseased populations. Neuroimage 49, 2457–2466.Google Scholar
Aylward, EH, Minshew, NJ, Field, K, Sparks, BF, Singh, N (2002). Effects of age on brain volume and head circumference in autism. Neurology 59, 175–183.Google Scholar
Barnea-Goraly, N, Kwon, H, Menon, V, Eliez, S, Lotspeich, L, Reiss, AL (2004). White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biological Psychiatry 55, 323–326.Google Scholar
Baron-Cohen, S, Wheelwright, S, Hill, J, Raste, Y, Plumb, I (2001). The  Reading the Mind in the Eyes Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism. Journal of Child Psychology and Psychiatry 42, 241–251.Google Scholar
Beaulieu, C (2002). The basis of anisotropic water diffusion in the nervous system – a technical review. NMR in Biomedicine 15, 435–455.Google Scholar
Beaulieu, C, Fenrich, FR, Allen, PS (1998). Multicomponent water proton transverse relaxation and T2-discriminated water diffusion in myelinated and nonmyelinated nerve. Magnetic Resonance Imaging 16, 1201–1210.Google Scholar
Belmonte, MK, Allen, G, Beckel-Mitchener, A, Boulanger, LM, Carper, RA, Webb, SJ (2004). Autism and abnormal development of brain connectivity. Journal of Neuroscience 24, 9228–9231.Google Scholar
Ben Bashat, D, Kronfeld-Duenias, V, Zachor, DA, Ekstein, PM, Hendler, T, Tarrasch, R, Even, A, Levy, Y, Ben Sira, L (2007). Accelerated maturation of white matter in young children with autism: a high b value DWI study. Neuroimage 37, 40–47.10.1016/j.neuroimage.2007.04.060Google Scholar
Boddaert, N, Chabane, N, Gervais, H, Good, CD, Bourgeois, M, Plumet, MH, Barthelemy, C, Mouren, MC, Artiges, E, Samson, Y, Brunelle, F, Frackowiak, RS, Zilbovicius, M (2004). Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 23, 364–369.Google Scholar
Brown, C, Gruber, T, Boucher, J, Rippon, G, Brock, J (2005). Gamma abnormalities during perception of illusory figures in autism. Cortex 41, 364–376.Google Scholar
Buxhoeveden, DP, Semendeferi, K, Buckwalter, J, Schenker, N, Switzer, R, Courchesne, E (2006). Reduced minicolumns in the frontal cortex of patients with autism. Neuropathology and Applied Neurobiology 32, 483–491.10.1111/j.1365-2990.2006.00745.xGoogle Scholar
Carper, RA, Courchesne, E (2005). Localized enlargement of the frontal cortex in early autism. Biological Psychiatry 57, 126–133.Google Scholar
Casanova, MF (2004). White matter volume increase and minicolumns in autism. Annals of Neurology 56, 453; author reply 454.Google Scholar
Casanova, MF (2006). Neuropathological and genetic findings in autism: the significance of a putative minicolumnopathy. Neuroscientist 12, 435–441.Google Scholar
Castelli, F, Frith, C, Happe, F, Frith, U (2002). Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain 125, 1839–1849.Google Scholar
Catani, M, Jones, DK, Daly, E, Embiricos, N, Deeley, Q, Pugliese, L, Curran, S, Robertson, D, Murphy, DG (2008). Altered cerebellar feedback projections in Asperger syndrome. Neuroimage 41, 1184–1191.10.1016/j.neuroimage.2008.03.041Google Scholar
Cheng, Y, Chou, KH, Chen, IY, Fan, YT, Decety, J, Lin, CP (2010). Atypical development of white matter microstructure in adolescents with autism spectrum disorders. Neuroimage 50, 873–882.Google Scholar
Constable, RT, Ment, LR, Vohr, BR, Kesler, SR, Fulbright, RK, Lacadie, C, Delancy, S, Katz, KH, Schneider, KC, Schafer, RJ, Makuch, RW, Reiss, AR (2008). Prematurely born children demonstrate white matter microstructural differences at 12 years of age, relative to term control subjects: an investigation of group and gender effects. Pediatrics 121, 306–316.Google Scholar
Courchesne, E (2004). Brain development in autism: early overgrowth followed by premature arrest of growth. Mental Retardation and Developmental Disabilities Research Reviews 10, 106–111.Google Scholar
Courchesne, E, Karns, CM, Davis, HR, Ziccardi, R, Carper, RA, Tigue, ZD, Chisum, HJ, Moses, P, Pierce, K, Lord, C, Lincoln, AJ, Pizzo, S, Schreibman, L, Haas, RH, Akshoomoff, NA, Courchesne, RY (2001). Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57, 245–254.Google Scholar
Courchesne, E, Pierce, K, Schumann, CM, Redcay, E, Buckwalter, JA, Kennedy, DP, Morgan, J (2007). Mapping early brain development in autism. Neuron 56, 399–413.Google Scholar
Courchesne, E, Redcay, E, Morgan, JT, Kennedy, DP (2005). Autism at the beginning: microstructural and growth abnormalities underlying the cognitive and behavioral phenotype of autism. Development and Psychopathology 17, 577–597.Google Scholar
Deoni, SC (2007). High-resolution T1 mapping of the brain at 3T with driven equilibrium single pulse observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI). Journal of Magnetic Resonance Imaging 26, 1106–1111.Google Scholar
Deoni, SC (2009). Transverse relaxation time (T2) mapping in the brain with off-resonance correction using phase-cycled steady-state free precession imaging. Journal of Magnetic Resonance Imaging 30, 411–417.Google Scholar
Deoni, SC, Dean, DC, O'Muircheartaigh, J, Dirks, H, Jerskey, BA (2012). Investigating white matter development in infancy and early childhood using myelin water faction and relaxation time mapping. Neuroimage 63, 1038–1053.10.1016/j.neuroimage.2012.07.037Google Scholar
Deoni, SC, Matthews, L, Kolind, SH (2013). One component? Two components? Three? The effect of including a non-exchanging  free  water component in multicomponent driven equilibrium single pulse observation of T2 and T2. Magnetic Resonance Medicine 70, 147–154.10.1002/mrm.24429Google Scholar
Deoni, SC, Mercure, E, Blasi, A, Gasston, D, Thomson, A, Johnson, M, Williams, SC, Murphy, DG (2011). Mapping infant brain myelination with magnetic resonance imaging. Journal of Neuroscience 31, 784–791.Google Scholar
Deoni, SC, Rutt, BK, Arun, T, Pierpaoli, C, Jones, DK (2008). Gleaning multicomponent T1 and T2 information from steady-state imaging data. Magnetic Resonance Medicine 60, 1372–1387.Google Scholar
Durston, S, Casey, BJ (2006). What have we learned about cognitive development from neuroimaging? Neuropsychologia 44, 2149–2157.Google Scholar
Fatemi, SH, Folsom, TD, Reutiman, TJ, Abu-Odeh, D, Mori, S, Huang, H, Oishi, K (2009). Abnormal expression of myelination genes and alterations in white matter fractional anisotropy following prenatal viral influenza infection at E16 in mice. Schizophrenia Research 112, 46–53.Google Scholar
Fields, RD (2008). White matter in learning, cognition and psychiatric disorders. Trends in Neuroscience 31, 361–370.Google Scholar
Gareau, PJ, Rutt, BK, Karlik, SJ, Mitchell, JR (2000). Magnetization transfer and multicomponent T2 relaxation measurements with histopathologic correlation in an experimental model of MS. Journal of Magnetic Resonance Imaging 11, 586–595.Google Scholar
Geschwind, DH, Levitt, P (2007). Autism spectrum disorders: developmental disconnection syndromes. Current Opinion in Neurobiology 17, 103–111.Google Scholar
Grice, SJ, Spratling, MW, Karmiloff-Smith, A, Halit, H, Csibra, G, de Haan, M, Johnson, MH (2001). Disordered visual processing and oscillatory brain activity in autism and Williams syndrome. Neuroreport 12, 2697–2700.Google Scholar
Hazlett, HC, Poe, M, Gerig, G, Smith, RG, Provenzale, J, Ross, A, Gilmore, J, Piven, J (2005). Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Archives of General Psychiatry 62, 1366–1376.10.1001/archpsyc.62.12.1366Google Scholar
Hendry, J, DeVito, T, Gelman, N, Densmore, M, Rajakumar, N, Pavlosky, W, Williamson, PC, Thompson, PM, Drost, DJ, Nicolson, R (2006). White matter abnormalities in autism detected through transverse relaxation time imaging. Neuroimage 29, 1049–1057.Google Scholar
Herbert, MR, Ziegler, DA, Makris, N, Filipek, PA, Kemper, TL, Normandin, JJ, Sanders, HA, Kennedy, DN, Caviness, VS Jr. (2004). Localization of white matter volume increase in autism and developmental language disorder. Annals of Neurology 55, 530–540.Google Scholar
Hughes, JR (2007). Autism: the first firm finding=underconnectivity? Epilepsy and Behaviour 11, 20–24.Google Scholar
Jenkinson, M, Bannister, P, Brady, M, Smith, S (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841.Google Scholar
Johnson, MH, Griffin, R, Csibra, G, Halit, H, Farroni, T, de Haan, M, Tucker, LA, Baron-Cohen, S, Richards, J (2005). The emergence of the social brain network: evidence from typical and atypical development. Development and Psychopathology 17, 599–619.10.1017/S0954579405050297Google Scholar
Jones, TB, Bandettini, PA, Kenworthy, L, Case, LK, Milleville, SC, Martin, A, Birn, RM (2010). Sources of group differences in functional connectivity: an investigation applied to autism spectrum disorder. Neuroimage 49, 401–414.Google Scholar
Keller, TA, Kana, RK, Just, MA (2007). A developmental study of the structural integrity of white matter in autism. Neuroreport 18, 23–27.Google Scholar
Kitzler, HH, Su, J, Zeineh, M, Harper-Little, C, Leung, A, Kremenchutzky, M, Deoni, SC, Rutt, BK (2012). Deficient MWF mapping in multiple sclerosis using 3D whole-brain multi-component relaxation MRI. Neuroimage 59, 2670–2677.Google Scholar
Kleinhans, NM, Richards, T, Sterling, L, Stegbauer, KC, Mahurin, R, Johnson, LC, Greenson, J, Dawson, G, Aylward, E (2008). Abnormal functional connectivity in autism spectrum disorders during face processing. Brain 131, 1000–1012.Google Scholar
Kolind, S, Matthews, L, Johansen-Berg, H, Leite, MI, Williams, SC, Deoni, S, Palace, J (2012). Myelin water imaging reflects clinical variability in multiple sclerosis. Neuroimage 60, 263–270.Google Scholar
Koshino, H, Carpenter, PA, Minshew, NJ, Cherkassky, VL, Keller, TA, Just, MA (2005). Functional connectivity in an fMRI working memory task in high-functioning autism. Neuroimage 24, 810–821.Google Scholar
Laule, C, Kozlowski, P, Leung, E, Li, DK, Mackay, AL, Moore, GR (2008). Myelin water imaging of multiple sclerosis at 7T: correlations with histopathology. Neuroimage 40, 1575–1580.Google Scholar
Laule, C, Leung, E, Lis, DK, Traboulsee, AL, Paty, DW, MacKay, AL, Moore, GR (2006). Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Multiple Sclerosis 12, 747–753.Google Scholar
Lee, JE, Bigler, ED, Alexander, AL, Lazar, M, DuBray, MB, Chung, MK, Johnson, M, Morgan, J, Miller, JN, McMahon, WM, Lu, J, Jeong, EK, Lainhart, JE (2007). Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism. Neuroscience Letters 424, 127–132.Google Scholar
Lord, C, Rutter, M, Goode, S, Heemsbergen, J, Jordan, H, Mawhood, L, Schopler, E (1989). Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. Journal of Autism and Developmental Disorders 19, 185–212.Google Scholar
Lord, C, Rutter, M, Le Couteur, A (1994). Autism Diagnostic Interview – Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders 24, 659–685.10.1007/BF02172145Google Scholar
MacKay, AL, Vavasour, IM, Rauscher, A, Kolind, SH, Madler, B, Moore, GR, Traboulsee, AL, Li, DK, Laule, C (2009). MR relaxation in multiple sclerosis. Neuroimaging Clinics of North America 19, 1–26.Google Scholar
Madler, B, Drabycz, SA, Kolind, SH, Whittall, KP, MacKay, AL (2008). Is diffusion anisotropy an accurate monitor of myelination? Correlation of multicomponent T2 relaxation and diffusion tensor anisotropy in human brain. Magnetic Resonance Imaging 26, 874–888.Google Scholar
McAlonan, GM, Cheung, V, Cheung, C, Suckling, J, Lam, GY, Tai, KS, Yip, L, Murphy, DG, Chua, SE (2005). Mapping the brain in autism. A voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 128, 268–276.Google Scholar
Murphy, DG, Critchley, HD, Schmitz, N, McAlonan, G, Van Amelsvoort, T, Robertson, D, Daly, E, Rowe, A, Russell, A, Simmons, A, Murphy, KC, Howlin, P (2002). Asperger syndrome: a proton magnetic resonance spectroscopy study of brain. Archives of General Psychiatry 59, 885–891.Google Scholar
Nagy, Z, Westerberg, H, Klingberg, T (2004). Maturation of white matter is associated with the development of cognitive functions during childhood. Journal of Cognitive Neuroscience 16, 1227–1233.Google Scholar
Palmen, SJ, van Engeland, H, Hof, PR, Schmitz, C (2004). Neuropathological findings in autism. Brain 127, 2572–2583.Google Scholar
Paus, T, Collins, DL, Evans, AC, 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, 255–266.Google Scholar
Ramoz, N, Reichert, JG, Smith, CJ, Silverman, JM, Bespalova, IN, Davis, KL, Buxbaum, JD (2004). Linkage and association of the mitochondrial aspartate/glutamate carrier SLC25A12 gene with autism. American Journal of Psychiatry 161, 662–669.Google Scholar
Rose, SE, Hatzigeorgiou, X, Strudwick, MW, Durbridge, G, Davies, PS, Colditz, PB (2008). Altered white matter diffusion anisotropy in normal and preterm infants at term-equivalent age. Magnetic Resonance Medicine 60, 761–767.Google Scholar
Skranes, J, Vangberg, TR, Kulseng, S, Indredavik, MS, Evensen, KA, Martinussen, M, Dale, AM, Haraldseth, O, Brubakk, AM (2007). Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. Brain 130, 654–666.Google Scholar
Smith, SM (2002). Fast robust automated brain extraction. Human Brain Mapping 17, 143–155.Google Scholar
Villalobos, ME, Mizuno, A, Dahl, BC, Kemmotsu, N, Muller, RA (2005). Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. Neuroimage 25, 916–925.Google Scholar
Waber, DP, De Moor, C, Forbes, PW, Almli, CR, Botteron, KN, Leonard, G, Milovan, D, Paus, T, 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 battery. Journal of the International Neuropsychological Society 13, 729–746.Google Scholar
Wassink, TH, Hazlett, HC, Epping, EA, Arndt, S, Dager, SR, Schellenberg, GD, Dawson, G, Piven, J (2007). Cerebral cortical gray matter overgrowth and functional variation of the serotonin transporter gene in autism. Archives of General Psychiatry 64, 709–717.Google Scholar
Webb, S, Munro, CA, Midha, R, Stanisz, GJ (2003). Is multicomponent T2 a good measure of myelin content in peripheral nerve? Magnetic Resonance Medicine 49, 638–645.Google Scholar
Wechsler, D (1999). Wechsler Abbreviated Scale of Intelligence. The Psychological Corporation: San Antonio, TX.Google Scholar
Weng, SJ, Wiggins, JL, Peltier, SJ, Carrasco, M, Risi, S, Lord, C, Monk, CS (2010). Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Research 1313, 202–214.Google Scholar
Whittall, KP, MacKay, AL, Graeb, DA, Nugent, RA, Li, DK, Paty, DW (1997). In vivo measurement of T2 distributions and water contents in normal human brain. Magnetic Resonance Medicine 37, 34–43.Google Scholar
WHO (2004). International Statistical Classification of Diseases and Health Related Problems, 10th revision. World Health Organization: Geneva.Google Scholar
Wibom, R, Lasorsa, FM, Tohonen, V, Barbaro, M, Sterky, FH, Kucinski, T, Naess, K, Jonsson, M, Pierri, CL, Palmieri, F, Wedell, A (2009). AGC1 deficiency associated with global cerebral hypomyelination. New England Journal of Medicine 361, 489–495.Google Scholar
Wolff, JJ, Gu, H, Gerig, G, Elison, JT, Styner, M, Gouttard, S, Botteron, KN, Dager, SR, Dawson, G, Estes, AM, Evans, AC, Hazlett, HC, Kostopoulos, P, McKinstry, RC, Paterson, SJ, Schultz, RT, Zwaigenbaum, L, Piven, J (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. American Journal of Psychiatry 169, 589–600.Google Scholar
Woodbury-Smith, MR, Robinson, J, Wheelwright, S, Baron-Cohen, S (2005). Screening adults for Asperger Syndrome using the AQ: a preliminary study of its diagnostic validity in clinical practice. Journal of Autism and Developmental Disorders 35, 331–335.Google Scholar