Early functional brain development in autism and the promise of sleep fMRI

rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis

Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

Neuroimaging Markers of Risk and Pathways to Resilience in Autism Spectrum Disorder

Autism spectrum disorder is a complex, heterogeneous neurodevelopmental condition of largely unknown etiology. This heterogeneity of symptom presentation, combined with high rates of comorbidity with other developmental disorders and a lack of reliable biomarkers, makes diagnosing and evaluating life outcomes for individuals with autism spectrum disorder a challenge. We review the growing literature on neuroimaging-based biomarkers of risk for the development of autism and explore evidence for resilience in some autistic individuals. The current literature suggests that neuroimaging during early infancy, in combination with prebirth and early genetic studies, is a promising tool for identifying biomarkers of risk, while studies of gene expression and DNA methylation have provided some key insights into mechanisms of resilience. With genetics and the environment contributing to both risk for the development of autism spectrum disorder and conditions for resilience, additional studies are needed to understand how risk and resilience interact mechanistically, whereby factors of risk may engender conditions for adaptation. Future studies should prioritize longitudinal designs in global cohorts, with the involvement of the autism community as partners in research to help identify domains of functioning that hold value and importance to the community.

Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE)

Increased brain volume is a consistent finding in young children with autism spectrum disorders (ASD); however, the regional specificity and developmental course of abnormal brain structure are less clear. Small sample sizes, particularly among voxel-based morphometry (VBM) investigations, likely contribute to this difficulty. Recently established large-scale neuroimaging data repositories have helped clarify the neuroanatomy of neuropsychiatric disorders such as schizophrenia and may prove useful in ASD. Structural brain images from the Autism Brain Imaging Database Exchange (ABIDE), which contains over 1100 participants, were analyzing using DARTEL VBM to investigate total brain and tissue volumes, and regional brain structure abnormalities in ASD. Two, overlapping cohorts were analyzed; an 'All Subjects' cohort (n = 833) that included all individuals with usable MRI data, and a 'Matched Samples' cohort (n = 600) comprised of ASD and TD individuals matched, within each site, on age and sex. Total brain and grey matter volumes were enlarged by approximately 1-2 % in ASD; however, the effect reached statistical significance in only the All Subjects cohort. Within the All Subjects cohort, VBM analysis revealed enlargement of the left anterior superior temporal gyrus in ASD. No significant regional changes were detected in the Matched Samples cohort. There was a non-significant reduction in the correlation between IQ and TBV in ASD compared to TD. Brain structure abnormalities in ASD individuals age 6 and older consists of a subtle increase in total brain volume due to enlargement of grey matter with little evidence of regionally specific effects.

Toddlers later diagnosed with autism exhibit multiple structural abnormalities in temporal corpus callosum fibers

Interhemispheric functional connectivity abnormalities are often reported in autism and it is thus not surprising that structural defects of the corpus callosum (CC) are consistently found using both traditional MRI and DTI techniques. Past DTI studies however, have subdivided the CC into 2 or 3 segments without regard for where fibers may project to within the cortex, thus placing limitations on our ability to understand the nature, timing and neurobehavioral impact of early CC abnormalities in autism. Leveraging a unique cohort of 97 toddlers (68 autism; 29 typical) we utilized a novel technique that identified seven CC tracts according to their cortical projections. Results revealed that younger (<2.5 years old), but not older toddlers with autism exhibited abnormally low mean, radial, and axial diffusivity values in the CC tracts connecting the occipital lobes and the temporal lobes. Fractional anisotropy and the cross sectional area of the temporal CC tract were significantly larger in young toddlers with autism. These findings indicate that water diffusion is more restricted and unidirectional in the temporal CC tract of young toddlers who develop autism. Such results may be explained by a potential overabundance of small caliber axons generated by excessive prenatal neural proliferation as proposed by previous genetic, animal model, and postmortem studies of autism. Furthermore, early diffusion measures in the temporal CC tract of the young toddlers were correlated with outcome measures of autism severity at later ages. These findings regarding the potential nature, timing, and location of early CC abnormalities in autism add to accumulating evidence, which suggests that altered inter-hemispheric connectivity, particularly across the temporal lobes, is a hallmark of the disorder.

Errant gardeners: glial-cell-dependent synaptic pruning and neurodevelopmental disorders

The final stage of brain development is associated with the generation and maturation of neuronal synapses. However, the same period is also associated with a peak in synapse elimination - a process known as synaptic pruning - that has been proposed to be crucial for the maturation of remaining synaptic connections. Recent studies have pointed to a key role for glial cells in synaptic pruning in various parts of the nervous system and have identified a set of critical signalling pathways between glia and neurons. At the same time, brain imaging and post-mortem anatomical studies suggest that insufficient or excessive synaptic pruning may underlie several neurodevelopmental disorders, including autism, schizophrenia and epilepsy. Here, we review current data on the cellular, physiological and molecular mechanisms of glial-cell-dependent synaptic pruning and outline their potential contribution to neurodevelopmental disorders.

Comparison of separation performance of independent component analysis algorithms for fMRI data

Independent component analysis, a data-driven analysis method, has found significant applications in task-based as well as resting state fMRI studies. There are numbers of independent component analysis algorithms available, but only a few of them have been used frequently so far for fMRI images. With a view that algorithms that are overlooked may outperform the most opted, a comparative study is taken up in this paper to analyze their abilities for the purpose of synthesis of fMRI images. In this paper, ten independent component algorithms: Fast ICA, INFOMAX, SIMBEC, JADE, ERICA, EVD, RADICAL, ICA-EBM, ERBM, and COMBI are compared. Their separation abilities are adjudged on both, synthetic and real fMRI images. Performance to decompose synthetic fMRI images is being monitored on the basis of spatial correlation coefficients, time elapsed to extract independent components and the visual appearance of independent components. Ranking of their performances on task-based real fMRI images are based on the closeness of time courses of identified independent components with model time course and the closeness of spatial maps of components with spatial templates while their competencies for resting state fMRI data are analyzed by examining how distinctly they decompose the data into the most consistent resting state networks. Sum of mutual information between all the permutations of decomposed components of resting state fMRI data are also calculated.

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.

Brains for birds and babies: Neural parallels between birdsong and speech acquisition

Language as a computational cognitive mechanism appears to be unique to the human species. However, there are remarkable behavioral similarities between song learning in songbirds and speech acquisition in human infants that are absent in non-human primates. Here we review important neural parallels between birdsong and speech. In both cases there are separate but continually interacting neural networks that underlie vocal production, sensorimotor learning, and auditory perception and memory. As in the case of human speech, neural activity related to birdsong learning is lateralized, and mirror neurons linking perception and performance may contribute to sensorimotor learning. In songbirds that are learning their songs, there is continual interaction between secondary auditory regions and sensorimotor regions, similar to the interaction between Wernicke's and Broca's areas in human infants acquiring speech and language. Taken together, song learning in birds and speech acquisition in humans may provide useful insights into the evolution and mechanisms of auditory-vocal learning.

Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

Diffusion Tensor Imaging Provides Evidence of Possible Axonal Overconnectivity in Frontal Lobes in Autism Spectrum Disorder Toddlers

BACKGROUND

Theories of brain abnormality in autism spectrum disorder (ASD) have focused on underconnectivity as an explanation for social, language, and behavioral deficits but are based mainly on studies of older autistic children and adults.

METHODS

In 94 ASD and typical toddlers ages 1 to 4 years, we examined the microstructure (indexed by fractional anisotropy) and volume of axon pathways using in vivo diffusion tensor imaging of fronto-frontal, fronto-temporal, fronto-striatal, and fronto-amygdala axon pathways, as well as posterior contrast tracts. Differences between ASD and typical toddlers in the nature of the relationship of age to these measures were tested.

RESULTS

Frontal tracts in ASD toddlers displayed abnormal age-related changes with greater fractional anisotropy and volume than normal at younger ages but an overall slower than typical apparent rate of continued development across the span of years. Posterior cortical contrast tracts had few significant abnormalities.

CONCLUSIONS

Frontal fiber tracts displayed deviant early development and age-related changes that could underlie impaired brain functioning and impact social and communication behaviors in ASD.

Rehabilitative Interventions and Brain Plasticity in Autism Spectrum Disorders: Focus on MRI-Based Studies

Clinical and research evidence supports the efficacy of rehabilitative intervention for improving targeted skills or global outcomes in individuals with autism spectrum disorder (ASD). However, putative mechanisms of structural and functional brain changes are poorly understood. This review aims to investigate the research literature on the neural circuit modifications after non-pharmacological intervention. For this purpose, longitudinal studies that used magnetic resonance imaging (MRI)-based techniques at the start and at the end of the trial to evaluate the neural effects of rehabilitative treatment in subjects with ASD were identified. The six included studies involved a limited number of patients in the active group (from 2 to 16), and differed by acquisition method (task-related and resting-state functional MRI) as well as by functional MRI tasks. Overall, the results produced by the selected investigations demonstrated brain plasticity during the treatment interval that results in an activation/functional connectivity more similar to those of subjects with typical development (TD). Repeated MRI evaluation may represent a promising tool for the detection of neural changes in response to treatment in patients with ASD. However, large-scale randomized controlled trials after standardized rehabilitative intervention are required before translating these preliminary results into clinical use.

Learning-related brain hemispheric dominance in sleeping songbirds

There are striking behavioural and neural parallels between the acquisition of speech in humans and song learning in songbirds. In humans, language-related brain activation is mostly lateralised to the left hemisphere. During language acquisition in humans, brain hemispheric lateralisation develops as language proficiency increases. Sleep is important for the formation of long-term memory, in humans as well as in other animals, including songbirds. Here, we measured neuronal activation (as the expression pattern of the immediate early gene ZENK) during sleep in juvenile zebra finch males that were still learning their songs from a tutor. We found that during sleep, there was learning-dependent lateralisation of spontaneous neuronal activation in the caudomedial nidopallium (NCM), a secondary auditory brain region that is involved in tutor song memory, while there was right hemisphere dominance of neuronal activation in HVC (used as a proper name), a premotor nucleus that is involved in song production and sensorimotor learning. Specifically, in the NCM, birds that imitated their tutors well were left dominant, while poor imitators were right dominant, similar to language-proficiency related lateralisation in humans. Given the avian-human parallels, lateralised neural activation during sleep may also be important for speech and language acquisition in human infants.

The potential of infant fMRI research and the study of early life stress as a promising exemplar

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fMRI research with infants and toddlers has increased rapidly over the past decade.

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Infant fMRI has provided unique insight into early functional brain development.

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Complex methodological challenges associated with infant fMRI warrant careful consideration and ongoing research.

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Infant fMRI has potential to contribute to multiple fields of study.

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The study of early life stress is a prime example of a field that is ripe to benefit from this technique.

Functional magnetic resonance imaging (fMRI) research with infants and toddlers has increased rapidly over the past decade, and provided a unique window into early brain development. In the current report, we review the state of the literature, which has established the feasibility and utility of task-based fMRI and resting state functional connectivity MRI (rs-fcMRI) during early periods of brain maturation. These methodologies have been successfully applied beginning in the neonatal period to increase understanding of how the brain both responds to environmental stimuli, and becomes organized into large-scale functional systems that support complex behaviors. We discuss the methodological challenges posed by this promising area of research. We also highlight that despite these challenges, early work indicates a strong potential for these methods to influence multiple research domains. As an example, we focus on the study of early life stress and its influence on brain development and mental health outcomes. We illustrate the promise of these methodologies for building on, and making important contributions to, the existing literature in this field.

Neuroimaging-based methods for autism identification: a possible translational application?

Classification methods based on machine learning (ML) techniques are becoming widespread analysis tools in neuroimaging studies. They have the potential to enhance the diagnostic power of brain data, by assigning a predictive index, either of pathology or of treatment response, to the single subject's acquisition. ML techniques are currently finding numerous applications in psychiatric illness, in addition to the widely studied neurodegenerative diseases. In this review we give a comprehensive account of the use of classification techniques applied to structural magnetic resonance images in autism spectrum disorders (ASDs). Understanding of these highly heterogeneous neurodevelopmental diseases could greatly benefit from additional descriptors of pathology and predictive indices extracted directly from brain data. A perspective is also provided on the future developments necessary to translate ML methods from the field of ASD research into the clinic.

White matter development and early cognition in babies and toddlers

The normal myelination of neuronal axons is essential to neurodevelopment, allowing fast inter-neuronal communication. The most dynamic period of myelination occurs in the first few years of life, in concert with a dramatic increase in cognitive abilities. How these processes relate, however, is still unclear. Here we aimed to use a data-driven technique to parcellate developing white matter into regions with consistent white matter growth trajectories and investigate how these regions related to cognitive development. In a large sample of 183 children aged 3 months to 4 years, we calculated whole brain myelin volume fraction (VFM ) maps using quantitative multicomponent relaxometry. We used spatial independent component analysis (ICA) to blindly segment these quantitative VFM images into anatomically meaningful parcels with distinct developmental trajectories. We further investigated the relationship of these trajectories with standardized cognitive scores in the same children. The resulting components represented a mix of unilateral and bilateral white matter regions (e.g., cortico-spinal tract, genu and splenium of the corpus callosum, white matter underlying the inferior frontal gyrus) as well as structured noise (misregistration, image artifact). The trajectories of these regions were associated with individual differences in cognitive abilities. Specifically, components in white matter underlying frontal and temporal cortices showed significant relationships to expressive and receptive language abilities. Many of these relationships had a significant interaction with age, with VFM becoming more strongly associated with language skills with age. These data provide evidence for a changing coupling between developing myelin and cognitive development.

Neurobiological abnormalities in the first few years of life in individuals later diagnosed with autism spectrum disorder: a review of recent data

Background. Despite the widely-held understanding that the biological changes that lead to autism usually occur during prenatal life, there has been relatively little research into the functional development of the brain during early infancy in individuals later diagnosed with autism spectrum disorder (ASD). Objective. This review explores the studies over the last three years which have investigated differences in various brain regions in individuals with ASD or who later go on to receive a diagnosis of ASD. Methods. We used PRISMA guidelines and selected published articles reporting any neurological abnormalities in very early childhood in individuals with or later diagnosed with ASD. Results. Various brain regions are discussed including the amygdala, cerebellum, frontal cortex, and lateralised abnormalities of the temporal cortex during language processing. This review discusses studies investigating head circumference, electrophysiological markers, and interhemispheric synchronisation. All of the recent findings from the beginning of 2009 across these different aspects of defining neurological abnormalities are discussed in light of earlier findings. Conclusions. The studies across these different areas reveal the existence of atypicalities in the first year of life, well before ASD is reliably diagnosed. Cross-disciplinary approaches are essential to elucidate the pathophysiological sequence of events that lead to ASD.

Revisiting the relationship between autism and schizophrenia: toward an integrated neurobiology

Schizophrenia and autism have been linked since their earliest descriptions. Both are disorders of cerebral specialization originating in the embryonic period. Genetic, molecular, and cytologic research highlights a variety of shared contributory mechanisms that may lead to patterns of abnormal connectivity arising from altered development and topology. Overt behavioral pathology likely emerges during or after neurosensitive periods in which resource demands overwhelm system resources and the individual's ability to compensate using interregional activation fails. We are at the threshold of being able to chart autism and schizophrenia from the inside out. In so doing, the door is opened to the consideration of new therapeutics that are developed based upon molecular, synaptic, and systems targets common to both disorders.

Speech and Language Impairments in Autism: Insights from Behavior and Neuroimaging

A failure to develop language is one of the earliest signs of autism. The ability to identify the neural signature of this deficit in very young children has become increasingly important, given that the presence of speech before five years of age is the strongest predictor for better outcomes in autism. This review consolidates what is known about verbal and preverbal precursors of language development as a framework for examining behavioral and brain anomalies related to speech and language in autism spectrum disorders. Relating the disruptions in the speech network to the social deficits observed will provide promising targets for behavioral and pharmacological interventions in ASD.

Training Self-Control: A Domain-General Translational Neuroscience Approach

Self-control plays an important role in healthy development and has been shown to be amenable to intervention. This article presents a theoretical framework for the emerging area of "brain-training" interventions that includes both laboratory-based direct training methods and ecologically valid school-, family-, and community-based interventions. Although these approaches have proliferated in recent years, evidence supporting them is just beginning to emerge, and conceptual models underlying many of the techniques they employ tend to be underspecified and imprecise. Identifying the neural systems responsible for improvements in self-control may be of tremendous benefit not only for overall intervention efficacy but also for basic science issues related to underlying shared biological mechanisms of psychopathology. This article reviews the neurodevelopment of self-control and explores its implications for theory, intervention, and prevention. It then presents a neurally informed framework for understanding self-control development and change and discusses how this framework may inform future intervention strategies for individuals suffering with psychopathology or drug abuse/dependence, or for young children with delays in cognitive or emotional functioning.

Diagnosing autism in neurobiological research studies

Autism spectrum disorder (ASD) is by definition a complex and heterogeneous disorder. Variation in factors such as developmental level, language ability and IQ further complicate the presentation of symptoms. Clinical research and basic science must continue to inform each other's questions to help address the heterogeneity inherent to the disorder. This review uses a clinical perspective to outline the common tools and best practices for diagnosing and characterizing ASD in a research setting. We discuss considerations for classifying research populations, including language ability and IQ and examine the advantages and disadvantages of different psychometric measurements. Ultimately, the contribution of multiple sources of data representing different perspectives is crucial for interpreting and understanding the ASD phenotype.

Autism genetics: searching for specificity and convergence

Advances in genetics and genomics have improved our understanding of autism spectrum disorders. As many genes have been implicated, we look to points of convergence among these genes across biological systems to better understand and treat these disorders.

Autism genetics: searching for specificity and convergence

Advances in genetics and genomics have improved our understanding of autism spectrum disorders. As many genes have been implicated, we look to points of convergence among these genes across biological systems to better understand and treat these disorders.

GABAergic activity in autism spectrum disorders: an investigation of cortical inhibition via transcranial magnetic stimulation

Mounting evidence suggests a possible role for γ-aminobutyric acid (GABA) in the neuropathophysiology of autism spectrum disorders (ASD), but the extent of this impairment is unclear. A non-invasive, in vivo measure of GABA involves transcranial magnetic stimulation (TMS) of the primary motor cortex to probe cortical inhibition. Individuals diagnosed with ASD (high-functioning autism or Asperger's disorder) (n = 36 [28 male]; mean age: 26.00 years) and a group of healthy individuals (n = 34 [23 male]; mean age: 26.21 years) (matched for age, gender, and cognitive function) were administered motor cortical TMS paradigms putatively measuring activity at GABAA and GABAB receptors (i.e., short and long interval paired pulse TMS, cortical silent period). All cortical inhibition paradigms yielded no difference between ASD and control groups. There was, however, evidence for short interval cortical inhibition (SICI) deficits among those ASD participants who had experienced early language delay, suggesting that GABA may be implicated in an ASD subtype. The current findings do not support a broad role for GABA in the neuropathophysiology of ASD, but provide further indication that GABAA could be involved in ASD where there is a delay in language acquisition. This article is part of the Special Issue entitled 'Neurodevelopmental Disorders'.

A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism

Failure to develop normal language comprehension is an early warning sign of autism, but the neural mechanisms underlying this signature deficit are unknown. This is because of an almost complete absence of functional studies of the autistic brain during early development. Using functional magnetic resonance imaging, we previously observed a trend for abnormally lateralized temporal responses to language (i.e. greater activation on the right, rather than the expected left) in a small sample (n = 12) of sleeping 2-3 year olds with autism in contrast to typically developing children, a finding also reported in autistic adults and adolescents. It was unclear, however, if findings of atypical laterality would be observed in a larger sample, and at even earlier ages in autism, such as around the first birthday. Answers to these questions would provide the foundation for understanding how neurofunctional defects of autism unfold, and provide a foundation for studies using patterns of brain activation as a functional early biomarker of autism. To begin to examine these issues, a prospective, cross-sectional design was used in which brain activity was measured in a large sample of toddlers (n = 80) during the presentation of a bedtime story during natural sleep. Forty toddlers with autism spectrum disorder and 40 typically developing toddlers ranging in age between 12-48 months participated. Any toddler with autism who participated in the imaging experiment prior to final diagnosis was tracked and diagnoses confirmed at a later age. Results indicated that at-risk toddlers later diagnosed as autistic display deficient left hemisphere response to speech sounds and have abnormally right-lateralized temporal cortex response to language; this defect worsens with age, becoming most severe in autistic 3- and 4-year-olds. Typically developing children show opposite developmental trends with a tendency towards greater temporal cortex response with increasing age and maintenance of left-lateralized activation with age. We have now demonstrated lateralized abnormalities of temporal cortex processing of language in autism across two separate samples, including a large sample of young infants who later are diagnosed with autism, suggesting that this pattern may reflect a fundamental early neural developmental pathology in autism.

RECENT DEVELOPMENTS IN NEUROPATHOLOGY OF AUTISM SPECTRUM DISORDERS

Autism spectrum disorders (ASD) represent complex neurodevelopmental disorders characterized by impairments in reciprocal social interactions, abnormal development and use of language, and monotonously repetitive behaviors. With an estimated heritability of more than 90%, it is the most strongly genetically influenced psychiatric disorder of the young age. In spite of the complexity of this disorder, there has recently been much progress in the research on etiology, early diagnosing, and therapy of autism. Besides already advanced neuropathologic research, several new technological innovations, such as sleep functional MRI, diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy imaging ((1)H-MRS) divulged promising breakthroughs in exploring subtle morphological and neurochemical changes in the autistic brain. This review provides a comprehensive summary of morphological and neurochemical alterations in autism known to date, as well as a short introduction to the functional research that has begun to advance in the last decade. Finally, we mention the progress in establishing new standardized diagnostic measures and its importance in early recognition and treatment of ASD.