Imaging the Brain in Autism


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Specifically, cortical grey matter can now be described in terms of cortical thickness and surface area, the product of which produces an estimate for cortical grey-matter volume. These two measures are of particular interest. Cortical thickness seems to reflect dendritic arborisation and pruning within grey matter [ 28 ], or changes in myelination at the interface of white and grey matter [ 29 ], whereas surface area varies with the degree of cortical folding or gyrification, and is thought to depend on division of progenitor cells in the periventricular area during embryogenesis [ 30 ].

Investigation of differences in these measures of cortical grey matter may provide important indications of very early neuroanatomical developmental events in the ASD population. In addition, as cortical thickness has been shown to vary during childhood, with changes occurring regionally in a developmental progression [ 31 , 32 ], detailed measures of cortical thickness can provide crucial information on cortical maturation and be an important index of an altered developmental trajectory in the brains of children with ASD.

Neurobiological changes in autism spectrum disorders

Complementary studies of diffusion tensor imaging DTI are providing increasing insights into the structure of white matter. DTI measures water diffusion within a tissue. The tightly organized white-matter tracts restrict the diffusion of water, producing anisotropic diffusion, whereas water diffusion in the grey matter tends to be less restricted [ 33 , 34 ]. In DTI, the shape of diffusion is represented by an ellipsoid with three eigenvectors describing the directions of the radii, and three eigenvalues describing their length. Diffusion along the primary eigenvector represents longitudinal diffusivity, and is thought to be related to axonal integrity, a complex construct that may include accumulation of cellular debris, disordered microtubule arrangement, aggregation of microfilaments, cellular swelling and decreased axonal transport [ 35 ].

By contrast, diffusion across the two other small directions is related to radial diffusivity, and is thought to be a marker of myelin integrity, although the sensitivity of radial diffusivity to detect myelin disruption may be decreased in the case of co-occurring axonal injury [ 36 — 38 ]. Mean diffusivity refers to the average diffusivity along all axes, whereas the shape of the ellipsoid is represented by the calculation of fractional anisotropy FA , a metric related to differences between the three eigenvalues.

Thus, several measures can be extracted from DTI neuroimaging studies, which will contribute to a fuller understanding of differences in white-matter development in ASD. Hadjikhani et al.

A number of regions were found to have thinner cortex in adults with ASD relative to controls, including regions in areas known to be important for social cognition and the mirror neuron system. By contrast, Hyde et al. Increased cortical thickness was also seen in primary sensory areas, and a small number of regions had thinner cortex in the autism group. In studies including children only, Hardan et al. In a follow-up study [ 18 ], the investigators reported that children with ASD had significant decreases in total grey matter with age relative to the control children, as well as decreases in cortical thickness with age.

However, only the difference in occipital cortical thickness remained significant after adjusting for multiple comparisons, and group differences disappeared after using IQ as a covariate. By contrast, with a sample ranging in age from 10 to 65 years, Raznahan et al. In addition, cortical thickness in typically developing children was increased relative to children with ASD at younger ages, but decreased relative to the ASD group at older ages.

Considerably fewer studies have investigated brain sulcation patterns, although atypicalities in sulcal position and depth have been reported in children and adolescents with ASD, with the most marked effects seen in the younger children [ 43 , 44 ]. Increased gyrification index in left frontal lobe in children but not adults, and decreased cortical folding with age in ASD relative to controls, were reported by Hardan et al. However, a recent study of surface area reported no age-by-group interactions for this measure [ 42 ]. Thus, most studies of cortical thickness in ASD have reported increases in values relative to typical controls, although exceptions have also been reported, with trends possibly suggesting that such differences are more pronounced in younger children, and that age-by-group interactions may be evident.

In addition, there has been considerable variability in the techniques used. Advances in automated cortical surface analyses [ 46 — 48 ] allow fine-grained examination of regional and sub-regional differences in cortical thickness, and investigation of developmental changes in more detail. Application of these techniques may yield greater consistency in findings for comparisons of children and adults with and without ASD.

Lastly, sample heterogeneity may have accounted for the heterogeneity of results as not all cohorts had been assessed with standardized diagnostic instruments Autism Diagnostic Observation Schedule ADOS and Autism Diagnostic Interview, Revised ADI-R consistently, and some did not match for IQ, although recent findings did find an association between IQ and cortical thickness [ 49 , 50 ]. Studies of cortical surface are still in their infancy, but given their potential to describe differential neurodevelopmental events, hold great promise.

Lower FA in a group comparison has typically been interpreted to reflect decreased organization and coherence within fibre tracts. Although the locations have varied orbitofrontal, medial prefrontal, temporal lobe, corpus callosum, cingulate cortex, arcuate fasciculus, ILF, uncinate fasciculus, cerebellar outflow tracts, internal capsule , and different techniques have been used, including tractography and voxel-based techniques, most studies have found evidence of reduced FA in various regions in children and adults with ASD compared with control groups [ 51 — 66 ].

Consistent with the variable findings in other structural brain measures in this field, some recent studies have also found evidence of regions of increased FA in ASD, in samples of young children [ 67 ] and adolescents [ 68 ]. However, because FA represents the relative magnitudes of parallel and perpendicular diffusion shape of ellipsoid , investigation of the individual eigenvalues are needed to better interpret any differences in DTI that may exist between ASD and control groups.

Recent studies have begun to report the sources of these differences in radial, axial and mean diffusivity. Widespread increases in mean diffusivity have been described [ 69 — 71 ], suggesting a poorly organized white matter, although little consensus exists on the relative contribution of axial versus radial diffusivity in this population.

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In fact, abnormalities in radial diffusivity have been reported [ 59 , 60 , 72 ], implicating the myelin component of white matter in ASD pathophysiology. Despite the variability in the brain areas in which differences are reported in DTI measures between individuals with and without ASD, many groups have found evidence of atypical white-matter measures, lending credence to these findings [ 51 — 71 ].

However, more detailed studies analysing the more specific measures from DTI, such as radial and axial diffusivity, carefully controlling for age, may allow a more reliable picture to emerge that will further help our understanding of the aetiology of these abnormalities and the link between these changes in the brain structure and associated behavioral profiles in the ASD population. In summary, structural studies of grey and white matter suggest an abnormal developmental trajectory of brain growth, with evidence of poorly organized white matter, increased cortical thickness and atypicalities in gyration patterns, possibly implicating abnormalities in neuronal migration, cortical organization and myelination in ASD.

The abundance of inconsistent findings in the published literature on autism might reflect differences between study populations, such as age, level of impairment, and presence of medical and behavioral comorbidities in the selected groups. There has been a plethora of functional neuroimaging studies in ASD, examining aspects of social cognition, language and executive functions, among others. The contribution of this method to understanding aspects of the biology of ASD has been invaluable, as it helped established that ASD is a neurological disorder for an extensive review, see Minshew and Keller [ 73 ].

The extensive literature explored patterns of activation in response to tasks related to social cognition such as face processing [ 74 — 78 ], theory of mind tasks [ 79 , 80 ], imitation [ 81 , 82 ], language processing such as semantic sentence comprehension [ 83 ], lexical semantic processing tasks, or tasks involving sentences of variable imagery content [ 84 ]. Repetitive behaviors have been a more difficult domain to study with scans. Cognitive paradigms used as proxies for repetitive behaviors have included executive function tasks such as those involved with inhibitory control, conflict resolution or oddball detection [ 85 , 86 , 73 ].

Other aspects of executive function, motor control and planning have also been studied, with some convergent results. For example, in a variety of tasks, atypical activation in the anterior cingulate is commonly reported in participants with ASD [ 87 — 89 ], suggesting a difference in ASD that can be reliably detected and can affect a range of cognitive processes. Although a detailed review of fMRI studies in this population is beyond the scope of this review, this approach deconstructed 'unusual' behavior into recognizable neural components [ 73 ], suggested decreased cortical specialization in ASD mild shifting of cortical location areas in response to a variety of tasks , and proposed that autism is a distributed brain systems disorder.

Young Children with Autism May Have Abnormal Brain Connections

A series of studies followed, examining functional connectivity Fc MRI. There are at least two approaches to conceptualizing connectivity, the more prominent of which focuses on evaluating connection strength, whereas the other examines the number of connections. Consistent with the first approach, in the original study by Just et al. In the many studies that followed, the concept of cortical-cortical under-connectivity has been reported with a variety of tasks related to core symptom domains, such as social cognition [ 90 ], language [ 91 ] or executive function [ 92 ] as a proxy for repetitive behaviors, and visual guided saccades and basic motor tasks [ 93 ].

The few cases of over-connectivity that have been reported, primarily affecting cortical or subcortical connections [ 94 , 95 ], still support the idea of atypical connectivity in ASD. The second approach was used by Noonan et al. The implications of these findings are that inefficient connectivity may be the hallmark of ASD, arising from both decreased signal in connections, and possibly overabundant connection between 'non-essential' regions, allowing for low-level cross talk and resulting in increased noise in the system [ 96 ].

There has also been an increased interest in studying connectivity in non-task dependent paradigms.

Brain MRI of People with Genetic Autism | Diagnostic Imaging

This approach addresses the concerns that findings of under-connectivity in ASD were partially driven by activation effects, which may be confounded by task performance [ 97 ]. The presence of a default-mode network was first hypothesized in studies that identified areas of increased BOLD signal at rest compared with the active task state [ 98 , 99 ].

This network has now been thoroughly investigated, and includes medial structures such as the medial prefrontal cortex, anterior and posterior cingulates, medial parietal cortex and precuneus, and medial temporal regions such as the parahippocampal gyri.

The resting-state network provides the opportunity to study long-distance connectivity in ASD, without complications related to task activation. Results are now emerging from such investigations with ASD, and most of the data available are consistent with decreased long-distance connectivity frontal-posterior [ — ] in these populations, with some evidence for increased connectivity within posterior regions.

This is a relatively new area of exploration that requires significant technical development, but the early data seem consistent with other fMRI-related approaches that support the idea of poor cortical-cortical long-distance connectivity in ASD.

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In summary, multiple imaging techniques based on the BOLD signal have provided evidence for decreased cortical-cortical connectivity, with possibly increased connectivity between subcortical regions and cortex, and within primary sensory areas such as the visual cortex. These results, in combination with findings of decreased cortical specialization, and supported by structural imaging studies that indicate abnormal growth and organization of both grey and white matter, reinforce the model of atypical connectivity in ASD, possibly resulting in an inefficient system with altered signal-to-noise ratio [ 73 ], that is, decreased signal with under-connectivity or increased noise with over-connectivity when defined as increased numbers of connections.

In the context of accumulating data suggesting poor structural and functional connectivity in autism, the question arises of whether techniques that provide biochemical markers of the integrity of white and grey matter, such as magnetic resonance spectroscopy MRS may be useful in this population. MRS allows quantification of a range of brain metabolites. The technique has been useful in other neuropsychiatric disorders, offering insights into dysfunction of grey and white matter. In addition, structural imaging and MRS studies may reflect different mechanisms of abnormal pathologies in grey and white matter, with MRS measurements being determined by the biochemical profile of underlying pathologies.

Hence, MRS is a good canditate to contribute information to the study of grey and white matter in autism, which we can not possibly obtain with structural and BOLD-based techniques. N -acetyl-aspartate NAA is the most prominent metabolite detected in the typical human brain, and is located within neurons, neuronal projections and mature oligodendrocytes. In grey matter, NAA levels are considered to reflect neuronal density, whereas in white matter, decreases in NAA are traditionally interpreted as axonal loss [ ]. Decreased neuronal density in grey-matter regions that are the origin for long-range fibre tracts would result in decreased NAA in both the grey and the corresponding white matter.

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  • Acknowledgement.
  • Brain Imaging Studies in Autism Spectrum Disorders.
  • The Celestial Selenite Scry (The Moon God Trilogy Book 1)!
  • Mystery of Gods Mathematics in Human Life (10+3 MDGC Book Book 5).
  • Saisons sauvages (LITTER GENERALE) (French Edition).

To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies. Menstrual phase: Female participants will be tested only during the follicular phase of their menstrual cycle defined as days after the first day of the last menstrual period.

Head trauma with loss of consciousness in the last year or any evidence of functional impairment due to and persisting after head trauma. Having a known risk from exposure to high magnetic fields e.

Brain imaging shows how nonverbal children with autism have slower response to sounds

Past or present psychiatric, neurological, or severe chronic medical illness. This includes the absence of substance abuse histories, learning disabilities and all DSM IV disorders. The investigators will evaluate medical histories. Potential subjects with medical conditions that are judged not to interfere with the study may be allowed to participate.

Imaging the Brain in Autism Imaging the Brain in Autism
Imaging the Brain in Autism Imaging the Brain in Autism
Imaging the Brain in Autism Imaging the Brain in Autism
Imaging the Brain in Autism Imaging the Brain in Autism
Imaging the Brain in Autism Imaging the Brain in Autism

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