Autism - The First Firm Finding Underconnectivity

Epilepsy & Behavior 11 (2007) 20–24 www.elsevier.com/locate/yebeh

Autism: The first firm finding = underconnectivity? John R. Hughes

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Department of Neurology, University of Illinois Medical Center at Chicago, 912 South Wood Street, Chicago, IL 60612, USA Received 14 December 2006; revised 13 March 2007; accepted 14 March 2007 Available online 24 May 2007

Abstract In January 2005, J.R. Hughes and M. Melyn published an electroencephalographic study on autistic children and found 46% with seizures and also a relatively high prevalence of 20% with epileptiform discharges but without any clinical seizures (Clin EEG Neurosci 2005;36:15–20). Because the discharges have always been viewed as focal events and the clinical seizures as requiring spread, the conclusion from these data was that children with autism may have a deficiency of corticocortical fibers. Since that time many MRI and functional MRI studies have been published confirming that one of the first findings in this devastating condition is underconnectivity. Specific findings are the thinning of the corpus callosum and the reduced connectivity, especially with the frontal areas and also the fusiform face area. Other studies involving positron emission tomography scans, magnetoencephalography, and perception have added to the evidence of underconnectivity. One final point is the initial overgrowth of white matter in the first 2 years of life in autistic children, followed later by arrested growth, resulting in aberrant connectivity; myelination of white matter will likely be significant in the etiology of autism. ! 2007 Elsevier Inc. All rights reserved. Keywords: Cerebral cortex; White matter; Magnetic resonance imaging; Functional magnetic resonance imaging; Fusiform face area; Corpus callosum; Frontal area; Brain circuits; Myelination; Autism

1. Introduction In January 2005, J.R. Hughes and M. Melyn [1] published an EEG study on autism, reporting seizures in 46%, as well as a relatively high prevalence of epileptiform discharges (20%) in others who did not have a history of clinical seizures. Although 20% may not seem high, this value was significantly different from that of the control group of matched children without autism. Because discharges are usually regarded as an exquisitely focal finding and the seizures require some spread from the focus, the final conclusion from these data was that a clue to one problem in autistic children may be a deficiency of corticocortical fibers to account for this presumed lack of spread from a focus. After that study, 19 studies—mainly MRI and functional MRI (fMRI) studies—have been published

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Fax: +1 312 996 4169. E-mail address: [email protected]

1525-5050/$ - see front matter ! 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2007.03.010

on autism and autistic spectrum disorders that are consistent with the original conclusions from the EEG study. The evidence is now clear that one major finding in autism is the underconnectivity within the brain. This finding is consistent with the behavior of these children, who tend to concentrate on some object, rather than on any person, but without any significant relationship to other sensory modalities, as may be expected from disconnected cerebral circuits. 2. Theoretical considerations and methods 2.1. Other theories Some general conclusions relevant to autism are that growth dysregulation [2] is likely involved and also that mechanical factors from cortical folding influencing the lamina [3] may also be implicated. A separate and different view of this disorder is that females are better than males as

J.R. Hughes / Epilepsy & Behavior 11 (2007) 20–24

‘‘empathizers,’’ dealing with people, and males are better ‘‘systemizers,’’ dealing with the rules governing the behavior of things [4]. Autism is then viewed as an ‘‘extreme male brain’’ [5]. Another theory relates to ‘‘mirror neurons,’’ action-coding cells that are activated both by passive observation of actions and by active execution of the same movements [6]. These neurons are considered to be reflected in mu waves (8–12 Hz) from central scalp areas, known to appropriately attenuate in children when either moving themselves or observing movement in others. The conclusion was that such an observation— execution matching system—exists even in the immature brain but that such a circuit in autism may be associated with a ‘‘faulty mirror neuron’’ system.

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density is an index of neural connectivity and that patients with autism exhibit less density in the genu, rostrum, and splenium of the corpus callosum, as a hypoplasia, rather than as an atrophy. In 2006, Vidal et al. [17] reported a significant reduction in both the splenium and genu of the corpus callosum, and in the same year, BogerMegiddo et al. [18] confirmed that the corpora callosa were disproportionately small, reflecting decreased interhemispheric connectivity. Finally, Alexander et al. [19] also reported small corpus callosum volumes, in addition to slow processing speeds. For example, the latter patients with small corpora callosa had significantly lower IQs than controls.

3.1. MRI studies

3.1.2. Frontal area Another area of interest often mentioned in autistic children is the frontal area. Reduced connectivity was indicated in the (fMRI) finding that bilateral inferior frontal areas [20] were deficient in BOLD signal correlation with the time series in the visual area. These findings were viewed as compatible with the hypothesis of ‘‘mirror neuron’’ defects mentioned previously. A general comment was that a ‘‘dorsal stream connectivity’’ in autism was not likely fully functional, based on BOLD signals during visuomotor coordination, reporting reduced connectivity with bilateral frontal areas. Another study demonstrated significant localized gray matter reductions within the frontostriatal circuits and within the parietal networks. One other report on the frontal area, by Courchesne and Pierce [21], reviewed different methods. Evidence was found that connectivity within the frontal lobe was disorganized and inadequately selective, whereas connectivity between the frontal cortex and other systems was poorly synchronized and weakly responsive based on inferences from stimulation data. One final conclusion was that the reduced long-distance corticocortical reciprocal activity that appeared in their studies would impair frontal lobe function. Also reported were decreases in volume beyond the frontal area, namely, in ventral and superior temporal gray matter, left internal capsule, fornices, and cerebellar white matter [22]. The data were suggestive of abnormal anatomy and connections of the limbic–striatal social brain system in autistics.

3.1.1. Corpus callosum The first group of studies discussed here used MRI to determine underconnectivity. One of the most common findings in autism has been the decrease in the size of the corpus callosum. As early as 1993, Courchesne et al. [15] reported the thinning of the corpus callosum as part of white matter volume loss, in addition to the loss in the parietal lobes. Also, the cortical volume loss in the same parietal lobes was found to extend into the adjacent superior frontal and occipital lobes. The cortical volume loss either could be secondary to disconnection or could result in underconnectivity or possibly be unrelated. Eleven years later, Chung et al. [16] confirmed that white matter

3.1.3. Other studies Other MRI studies have shown that minicolumnar width and mean neuron and nucleolar cross sections are smaller in autistic children. The findings suggest a bias toward shorter connectivity fibers [23]. One other study demonstrated changes in cerebellar gray and white matter volume [24], and a final MRI study showed a decrease in the gray matter in the right thalamus [25], although with unexpected increases in the right fusiform gyrus, temporal–occipital region, and left frontal pole. The latter increases may be explained by an additional finding of increased total gray matter volume, which may reverse in later years.

2.2. Methods (fMRI) The next eight studies that are mentioned deal with fMRI as a method to investigate autism, determining functional neural connections. The first study used low-frequency oscillations that were temporally correlated in functionally related brain regions to quantify the degree of functional connectivity by detecting similarities in the oscillation, called ‘‘patterns with hierarchical clustering’’ [7]. The next study used a ‘‘time series to estimate frequency-dependent correlational matrices’’ [8]. The next study to determine functional connectivity used ‘‘concurrent spontaneous activity and spatial independent component analysis’’ [9]. Two other studies used the decrease in activity during various tasks [10], and one referred to a frequent finding of decreased cerebral blood flow during a given task [11]. Other studies used ‘‘network modulation from rest to a given task’’ [12], measuring ‘‘absolute brain state maps’’ from rest to a given activity [13] or ‘‘linear correlational coefficients’’ [14] as a means of determining functional connectivity. Thus, examining the complex multivariate correlational analyses in the rise and fall of the BOLD signal can help to determine connectivity. 3. Decreased connectivity

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3.2. Functional MRI studies 3.2.1. Pre-2006 investigations It should be made clear that these studies do not directly assess connectivity, and conclusions are drawn from indirect measurements. Most of these studies were published in 2005 and 2006. Exceptions include the report from 2003 [26] involving patients performing finger movements, which demonstrated abnormal variability and scatter of functional maps, suggesting early-onset disturbances in the development of cerebello-thalamo-cortical pathways in autism. Two other exceptions to very recent publications were reported in 2004. One showed less activation in Broca’s left inferior frontal gyrus, but especially less synchronization between various cortical areas, related to ‘‘neurobiological foundations of underconnectivity in autism’’ [27]. The patients were tested during sentence comprehension. The other study [28] showed reduced activation in responses to the faces of strangers in the fusiform face area, but also in the medial frontal lobe and the amygdala. The general conclusion was that autistic children exhibit defects in the systems that modulate the fusiform face area. However, it is impossible to distinguish whether the major issue is an abnormal area affecting other regions or a primary disorder of connectivity. 3.2.2. 2006 investigations of frontal areas As in the MRI studies, the 2006 fMRI studies emphasized the frontal areas. Silk et al. [29] studied patients performing a mental rotation task and reported a dysfunction in frontostriatal networks in autism by showing less activation in the lateral and medial frontal, dorsolateral prefrontal, and anterior cingulate cortex and also in the caudate nucleus. Turner et al. [30] studied patients during a visuomotor task and found that the circuits from BOLD signal cross correlation with this task were less pronounced or even absent in association with the orbitofrontal area and caudate nuclei, as well as the associative, oculomotor, and motor areas. These findings indicated an inefficiently organized functional connectivity between these regions. Similar results were reported by Just et al. [31], who studied patients performing a Tower of London test to assess executive function. They reported lower synchronization between the frontal and parietal areas, in addition to a smaller corpus callosum, indicating reduced intracortical connectivity or underconnectivity. Other reports, using patients processing sentences with different imagery content, were that functional connectivity among cortical regions, especially the language and spatial centers, is not well synchronized, again indicating underconnectivity [32]. Still another study [33] using a cognitive task reported loosely connected anterior–posterior circuits as an indication of cortical underconnectivity. A similar conclusion of ‘‘reduced corticocortical connectivity’’ was reached in another report [34] of patients performing a visuomotor task, even though the opposite was found for subcortical–cortical connectivity. The different areas implicated in

the latter studies may be a reflection of the short period during which investigators have studied this problem, but the emphasis on frontal areas seems to be clear. The present section indicates that most of these studies were done after 2005. 3.3. Other studies One positron emission tomography (PET) study was performed to demonstrate reduced functional connectivity by showing less activation in the medial prefrontal, anterior, and superior temporal cortex and the connections to the extrastriate region [35]. A magnetoencephalography (MEG) study revealed an abnormal left hemisphere gamma oscillation (40 Hz), ‘‘augmenting connectivity theories of autism by demonstrating deficiencies in long-range neural communication’’ [36]. In three studies involving perception, similar conclusions were drawn. In one study [37], increased amplitude of intrusive saccades and reduced latency of target fixation led to the conclusion of faulty functional connectivity in corticocerebellar networks. In another investigation [38], autistic children were found to be deficient in identifying complex texture-defined gratings, leading to the conclusion of atypical neural connectivity. In the third study [39], autistic children showed a lack of ‘‘attentional modulation,’’ especially for social stimuli, and the conclusion of poor connectivity between extrastriate and striate regions was drawn. One final study [40] concluded that the developmental process was disrupted in autistic children by showing ‘‘abnormal synthesis capacity’’ with respect to brain serotonin. It is clear that the latter studies were not directly measuring connectivity, but produced data that were only consistent with underconnectivity. 4. Emphasis on facial area One of the major clinical findings in the autistic spectrum is the lack of apparent recognition of faces, as these children tend to concentrate on things and not people. A number of studies have provided evidence to account for this phenomenon. In 2003, Frith [41] reported that autistic children failed to activate the fusiform face area during face perception tests, also demonstrating weak activation of medial frontal cortex and the superior temporal gyrus. The conclusion was that this finding was a marker for abnormal connectivity, possibly from lack of neural pruning. During the next year, Waiter et al. [25] reported an unexpected increase in gray matter volume in the right fusiform gyrus, as well as in the right temporal–occipital, left frontal, and medial frontal cortex. The conclusion was that this finding may ‘‘reflect an abnormally functioning social cognitive neural network.’’ Finally, in 2006 Bird et al. [39] reported a lack of attentional modulation of face-selective regions, concluding abnormal connectivity. Two other studies specifically involved fMRI. One was a study that autistic children exhibit less fusiform activity in response to strangers than to familiar faces. In general,

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they demonstrated a more limited network in response, even to familiar faces, at the same time showing a decrease in medial frontal lobe function. Their conclusion was that the dysfunction in the face area may reflect defects in systems that modulate the area, rather than a defect in the face area itself. The last study [42] reviewed the evidence that abnormal functional mechanisms are involved in face recognition, resulting in ‘‘dysfunctional reciprocal cortico-subcortical connections.’’ 5. Increase in white matter With considerable evidence indicating a decrease in the networks involving various areas, it may be somewhat surprising that an increase in white matter could be found. The earliest evidence came from Frith’s study [41] showing an increase in total brain volume during the first few years as a marker of abnormal connectivity from lack of pruning. In the next year (2004), Courchesne [43] was more specific by pointing out that the overgrowth occurred during the first 2 years of life, followed by abnormally slow or arrested growth in the next few years. This disruption was viewed as related to circuit formation, resulting in aberrant connectivity. In the same year, Herbert et al. [44] spelled out that the increased brain volume was an unexplained white matter enlargement, involving more the later or longer myelinating regions. These changes were viewed as affecting primarily intrahemispheric and corticocortical connections. In the next year, Herbert [45] confirmed that a growth spurt shortly after birth was primarily from white matter and that neuroinflammation was often present to explain impaired complex processing and underconnectivity. In 2006, Hendry et al. [46] reported that the increase in cerebral white matter, especially in the frontal and parietooccipital areas, suggested abnormalities in white matter tissue water content. Courchesne et al. [47] later discussed the early brain overgrowth and later arrest as a microstructural maldevelopment resulting in short-distance overconnectivity that was ineffective. In addition, the conclusion was a ‘‘failure of long distance cortico-cortical coupling and a reduction in frontal-posterior reciprocal connectivity.’’ 6. Myelination As white matter myelination generally reflects the progression of functional brain maturation [48], it is reasonable to explore the process of myelination as a putative etiology of autism. The time when this disorder usually reveals itself clinically is between 2 and 3 years of age [43], and, therefore, it is this same time interval that may lead us to discover the etiology of autism. The onset of myelination, as indicated by the expression of myelin basic protein, is usually at 1 year of age, and the progression to ‘‘adultlike’’ staining occurs at nearly 2 years of age [49]. Other data would indicate that adult values are reached during the third year [50]. Thus, a crucial time for myelination and also for the clinical expression of

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autism is between the second and third years of life. Consistent with the latter relationship, data have shown that the most significant decrease in (periventricular) white matter during maturation occurs during the first 2 years [51], and patients with developmental delay have, in fact, exhibited a significant reduction in the content of myelinated white matter [48]. As mentioned previously, early brain overgrowth occurs, but later arrest is then observed [47]. The factors that may play a significant role in this maturation include the myelin-forming oligodendrocytes, and injury to their progenitors likely would contribute to the pathogenesis of white matter development [52]. One other factor that may be significant is hypoglycemia, as the latter condition inhibits oligodendrocyte development and myelination, also triggering apoptotic cell death in oligodendrocyte precursor cells [53]. This reviewer is unaware of whether or not hypoglycemia is common in autism. These latter considerations are only clues to uncovering the etiology of autism. In summary, the first firm finding in children with autism or the autistic spectrum is underconnectivity within the brain. Both MRI and fMRI studies during the past 2 years have shown thinning of the corpus callosum and reduced connectivity, especially in the frontal and fusiform face areas, in addition to other circuits in the brain. The initial overgrowth of the white matter in the first 2 years is often followed by arrested growth, resulting in aberrant connectivity. The role of myelination of white matter is likely to be significant. The goal for scientists is now to provide further evidence that underconnectivity is indeed present with more definitive studies like postmortem neuroanatomical analyses and to discover the reasons for the development of this underconnectivity in the hope of eliminating the causes of this devastating disorder. References [1] Hughes JR, Melyn M. EEG and seizures in autistic children and adolescents: further findings with therapeutic implications. Clin EEG Neurosci 2005;36:15–20. [2] Akshoomoff N, Pierce K, Courchesne E. The neurobiological basis of autism from a developmental perspective. Dev Psychopathol 2002;14(3):613–34. [3] Hilgetag CC, Barbas H. Role of mechanical factors in the morphology of the primate cerebral cortex. PloS Comput Biol 2006;2(3):22. [4] Baron-Cohen S, Belmonte MK. Autism: a window onto the development of the social and the analytic brain. Annu Rev Neurosci 2005;28:109–26. [5] Baron-Cohen S, Knickmeyer RC, Belmonte MK. Sex differences in the brain: implications for explaining autism. Science 2005;310:819–23. [6] Lepage JF, Theoret H. EEG evidence for the presence of an action observation–execution matching system in children. Eur J Neurosci 2006;23:2505–10. [7] Cordes D, Haughton V, Carew JD, Arfanakis K, Maravilla K. Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 2002;20:305–17. [8] Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Neuroscience 2006;26:63–672.

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J.R. Hughes / Epilepsy & Behavior 11 (2007) 20–24

[9] Van de Ven VG, Formisano E, Prvulovic D, Roeder CH, Linden DE. Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum Brain Mapp 2004;22:165–78. [10] Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003;22:165–78. [11] Binder JR, Frost JA, Hammeke TA, Bellgowan PS, Rao SM, Cox RW. Conceptual processing during the conscious resting state: a functional MRI study. J Cogn Neurosci 1999;11:80–95. [12] Jiang T, He Y, Zang Y, Weng X. Modulation of functional connectivity during the resting state and the motor task. Hum Brain Mapp 2004;22:63–71. [13] Waites AB, Stanislavsky A, Abbott DF, Jackson GD. Effect of prior cognitive state on resting state networks measured with functional connectivity. Hum Brain Mapp 2005;24:59–68. [14] Bokde AL, Lopez-Bayo P, Meindl T, et al. Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment. Brain 2006;129:1113–24. [15] Courchesne E, Press GA, Yeung-Courchesne R. Parietal lobe abnormalities with MR in patients with infantile autism. Am J Roentgenol 1993;160:387–93. [16] Chung MK, Dalton KM, Alexander AL, Davidson RJ. Less white matter concentration in autism: D voxel-based morphometry. NeuroImage 2004;23:242–51. [17] Vidal CN, Nicolson R, DeVito TJ, et al. Mapping corpus callosum deficits in autism: an index of aberrant cortical connectivity. Biol Psychiatry 2006;60:218–25. [18] Boger-Megiddo I, Shaw DW, Friedman SD, et al. Corpus callosum morphometrics in young children with autism spectrum disorder. J Autism Dev Disord 2006;36:733–9. [19] Alexander AL, Lee JE, Lazar M, et al. Diffusion tensor imaging of the corpus callosum in autism. NeuroImage 2007;34:61–73. [20] Villalobos ME, Mizuno A, Dahl BC, Kemmotsu N, Muller RA. Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. NeuroImage 2005;25:916–25. [21] Courchesne E, Pierce K. Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection. Curr Opin Neurobiol 2005;15:225–30. [22] McAlonan GM, Cheung V, Cheung C, et al. Mapping the brain in autism: a voxel-based MRI study of volumetric differences and intercorrelations in autism. Brain 2005;128:268–76. [23] Casanova MF, van Kooten IA, Switala AE, et al. Minicolumnar abnormalities in autism. Acta Neuropathol (Berl) 2006;112:287–303. [24] Kates WR, Burnette CP, Eliez S, et al. Neuroanatomic variation in monozygotic twin pairs discordant for the narrow phenotype for autism. Am J Psychiatry 2004;161:539–46. [25] Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A. A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. NeuroImage 2004;22:619–25. [26] Muller RA, Kleinhans N, Kemmotsu N, Pierce K, Courchesne E. Abnormal variability and distribution of functional maps in autism: an fMRI study of visuomotor learning. Am J Psychiatry 2003;160:1847–62. [27] Just MA, Cherkassky VL, Keller TA, Minshew NJ. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain 2004;127:1811–21. [28] Pierce K, Haist F, Sedaghat F, Courchesne E. The brain response to personally familiar faces in autism: finding of fusiform activity and beyond. Brain 2004;127:2703–16. [29] Silk TJ, Rinehart N, Bradshaw JL, et al. Visuospatial processing and the function of prefrontal-parietal networks in autism spectrum disorders: a functional MRI study. Am J Psychiatry 2006;163:1440–3. [30] Turner KC, Frost L, Linsenbardt D, McIlroy JR, Muller RA. Atypically diffuse functional connectivity between caudate nuclei and cerebral cortex in autism. Behav Brain Funct 2006;2:34.

[31] Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. Functional and anatomical cortical underconnectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cereb Cortex 2007;17:951–61. [32] Kana RK, Teller TA, Cherkassky VL, Minshew NJ, Just MA. Sentence comprehension in autism: thinking in pictures with decreased functional connectivity. Brain 2006;129:2484–93. [33] Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. NeuroReport 2006;17:1687–90. [34] Mizuno A, Villalobos ME, Davies MM, Dahl BC, Muller RA. Partially enhanced thalamocortical functional connectivity in autism. Brain Res 2006;1104:160–74. [35] Castelli F, Frith C, Happe F, Frith U. Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain 2002;125:1839–49. [36] Wilson TW, Rojas DC, Reite ML, Teale PD, Rogers SJ. Children and adolescents with autism exhibit reduced MEG steady-state gamma responses. Biol Psychiatry 2007;in press. [37] Nowinski CV, Minshew NJ, Luna B, Takarae Y, Sweeney JA. Oculomotor studies of cerebellar function in autism. Psychiatry Res 2005;127:11–9. [38] Bertone A, Mottron L, Jelenic P, Faubert J. Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain 2005;128:2430–41. [39] Bird G, Catmur C, Silani G, Frith C, Frith U. Attention does not modulate neural responses to social stimuli in autism spectrum disorders. NeuroImage 2006;31:1614–24. [40] Chugani DC, Muzik O, Behen M, et al. Developmental changes in brain serotonin synthesis capacity in autistic and nonautistic children. Ann Neurol 1999;45:287–95. [41] Frith C. What do imaging studies tell us about the neural basis of autism? Novartis Found Symp 2003;251:149–66. [42] Brambilla P, Hardan AY, di Nemi SU, et al. The functional neuroanatomy of autism. Funct Neurol 2004;19:9–17. [43] Courchesne E. Brain development in autism: early overgrowth followed by premature arrest of growth. Ment Retard Dev Disabil Res Rev 2004;10:106–11. [44] Herbert MR, Ziegler DA, Makris N, et al. Localization of white matter volume increase in autism and developmental language disorder. Ann Neurol 2004;55:530–40. [45] Herbert MR. Large brains in autism: the challenge of pervasive abnormality. Neuroscientist 2005;11:417–40. [46] Hendry J, DeVito T, Gelman N, et al. White matter abnormalities in autism detected through transverse relaxation time imaging. NeuroImage 2006;29:1049–57. [47] Courchesne E, Redcay E, Morgan JT, Kennedy DP. Autism at the beginning: microstructural and growth abnormalities underlying the cognitive and behavioral phenotype of autism. Dev Psychopathol 2005;17:577–97. [48] Pujol J, Lopez-Sala A, Sebastian-Galles N, et al. Delayed myelination in children with developmental delay detected by volumetric MRI. NeuroImage 2004;22:897–903. [49] Haynes RL, Borenstein NS, Desilva TM, et al. Axonal development in the cerebral white matter of the human fetus and infant. J Comp Neurol 2005;484:156–67. [50] Schneider JF, Il’yasov KA, Hennig J, Martin E. Fast quantitative diffusion-tensor imaging of cerebral white matter from the neonatal period to adolescence. Neuroradiology 2004;46:258–66. [51] Zhang L, Thomas KM, Davidson MC, Casey BJ, Heier LA, Ulug AM. MR quantitation of volume and diffusion changes in the developing brain. Am J Neuroradiol 2005;26:45–9. [52] Back SA. Perinatal white matter injury: the changing spectrum of pathology and emerging insights into pathogenetic mechanisms. Ment Retard Dev Disabil Res Rev 2006;12:129–40. [53] Yan H, Rivkees SA. Hypoglycemia influences oligodendrocyte development and myelin formation. NeuroReport 2006;17:55–9.