Network efficiency in autism spectrum disorder and its relation to brain overgrowth

Perinatal exposure to the immune-suppressant di-n-octyltin dichloride affects brain development in rats

Disruption of the immune system during embryonic brain development by environmental chemicals was proposed as a possible cause of neurodevelopmental disorders. We previously found adverse effects of di-n-octyltin dichloride (DOTC) on maternal and developing immune systems of rats in an extended one-generation reproductive toxicity study according to the OECD 443 test guideline. We hypothesize that the DOTC-induced changes in the immune system can affect neurodevelopment. Therefore, we used in-vivo MRI and PET imaging and genomics, in addition to behavioral testing and neuropathology as proposed in OECD test guideline 443, to investigate the effect of DOTC on structural and functional brain development. Male rats were exposed to DOTC (0, 3, 10, or 30 mg/kg of diet) from 2 weeks prior to mating of the F0-generation until sacrifice of F1-animals. The brains of rats, exposed to DOTC showed a transiently enlarged volume of specific brain regions (MRI), altered specific gravity, and transient hyper-metabolism ([18F]FDG PET). The alterations in brain development concurred with hyper-responsiveness in auditory startle response and slight hyperactivity in young adult animals. Genomics identified altered transcription of key regulators involved in neurodevelopment and neural function (e.g. Nrgrn, Shank3, Igf1r, Cck, Apba2, Foxp2); and regulators involved in cell size, cell proliferation, and organ development, especially immune system development and functioning (e.g. LOC679869, Itga11, Arhgap5, Cd47, Dlg1, Gas6, Cml5, Mef2c). The results suggest the involvement of immunotoxicity in the impairment of the nervous system by DOTC and support the hypothesis of a close connection between the immune and nervous systems in brain development.

The Cerebellum's Role in Affective Disorders: The Onset of Its Social Dimension

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) are the most frequent mental disorders whose indeterminate etiopathogenesis spurs to explore new aetiologic scenarios. In light of the neuropsychiatric symptoms characterizing Cerebellar Cognitive Affective Syndrome (CCAS), the objective of this narrative review is to analyze the involvement of the cerebellum (Cbm) in the onset of these conditions. It aims at detecting the repercussions of the Cbm activities on mood disorders based on its functional subdivision in vestibulocerebellum (vCbm), pontocerebellum (pCbm) and spinocerebellum (sCbm). Despite the Cbm having been, for decades, associated with somato-motor functions, the described intercellular pathways, without forgiving the molecular impairment and the alteration in the volumetric relationships, make the Cbm a new important therapeutic target for MDD and BD. Given that numerous studies have showed its activation during mnestic activities and socio-emotional events, this review highlights in the Cbm, in which the altered external space perception (vCbm) is strictly linked to the cognitive-limbic Cbm (pCbm and sCbm), a crucial role in the MDD and BD pathogenesis. Finally, by the analysis of the cerebellar activity, this study aims at underlying not only the Cbm involvement in affective disorders, but also its role in social relationship building.

Impact of brain overgrowth on sensorial learning processing during the first year of life

Macrocephaly is present in about 2–5% of the general population. It can be found as an isolated benign trait or as part of a syndromic condition. Brain overgrowth has been associated with neurodevelopmental disorders such as autism during the first year of life, however, evidence remains inconclusive. Furthermore, most of the studies have involved pathological or high-risk populations, but little is known about the effects of brain overgrowth on neurodevelopment in otherwise neurotypical infants. We investigated the impact of brain overgrowth on basic perceptual learning processes (repetition effects and change detection response) during the first year of life. We recorded high density electroencephalograms (EEG) in 116 full-term healthy infants aged between 3 and 11 months, 35 macrocephalic (14 girls) and 81 normocephalic (39 girls) classified according to the WHO head circumference norms. We used an adapted oddball paradigm, time-frequency analyses, and auditory event-related brain potentials (ERPs) to investigate differences between groups. We show that brain overgrowth has a significant impact on repetition effects and change detection response in the 10–20 Hz frequency band, and in N450 latency, suggesting that these correlates of sensorial learning processes are sensitive to brain overgrowth during the first year of life.

Clearing Your Mind: Mechanisms of Debris Clearance After Cell Death During Neural Development

Neurodevelopment and efferocytosis have fascinated scientists for decades. How an organism builds a nervous system that is precisely tuned for efficient behaviors and survival and how it simultaneously manages constant somatic cell turnover are complex questions that have resulted in distinct fields of study. Although neurodevelopment requires the overproduction of cells that are subsequently pruned back, very few studies marry these fields to elucidate the cellular and molecular mechanisms that drive nervous system development through the lens of cell clearance. In this review, we discuss these fields to highlight exciting areas of future synergy. We first review neurodevelopment from the perspective of overproduction and subsequent refinement and then discuss who clears this developmental debris and the mechanisms that control these events. We then end with how a more deliberate merger of neurodevelopment and efferocytosis could reframe our understanding of homeostasis and disease and discuss areas of future study. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Human Self-Domestication and the Evolution of Pragmatics

As proposed for the emergence of modern languages, we argue that modern uses of languages (pragmatics) also evolved gradually in our species under the effects of human self-domestication, with three key aspects involved in a complex feedback loop: (a) a reduction in reactive aggression, (b) the sophistication of language structure (with emerging grammars initially facilitating the transition from physical aggression to verbal aggression); and (c) the potentiation of pragmatic principles governing conversation, including, but not limited to, turn-taking and inferential abilities. Our core hypothesis is that the reduction in reactive aggression, one of the key factors in self-domestication processes, enabled us to fully exploit our cognitive and interactional potential as applied to linguistic exchanges, and ultimately to evolve a specific form of communication governed by persuasive reciprocity-a trait of human conversation characterized by both competition and cooperation. In turn, both early crude forms of language, well suited for verbal aggression/insult, and later more sophisticated forms of language, well suited for persuasive reciprocity, significantly contributed to the resolution and reduction of (physical) aggression, thus having a return effect on the self-domestication processes. Supporting evidence for our proposal, as well as grounds for further testing, comes mainly from the consideration of cognitive disorders, which typically simultaneously present abnormal features of self-domestication (including aggressive behavior) and problems with pragmatics and social functioning. While various approaches to language evolution typically reduce it to a single factor, our approach considers language evolution as a multifactorial process, with each player acting upon the other, engaging in an intense mutually reinforcing feedback loop. Moreover, we see language evolution as a gradual process, continuous with the pre-linguistic cognitive abilities, which were engaged in a positive feedback loop with linguistic innovations, and where gene-culture co-evolution and cultural niche construction were the main driving forces.

Neurodevelopmental disorders: 2021 update

One of the current challenges in the field of neurodevelopmental disorders (NDDs) is still to determine their underlying aetiology and risk factors. NDDs comprise a diverse group of disorders primarily related to neurodevelopmental dysfunction including autism spectrum disorder (ASD), developmental delay, intellectual disability (ID), and attention-deficit/hyperactivity disorder (ADHD) that may present with a certain degree of cognitive dysfunction and high prevalence of neuropsychiatric outcomes. Last year, advances in human genomics have begun to shed light on the genetic architecture of these disorders and large-scale sequencing studies are starting to reveal mechanisms that range from unique genomic DNA methylation patterns (i.e. "episignatures") to highly polygenic conditions. In addition, the contribution of de novo somatic mutations to neurodevelopmental diseases is being recognized. However, progressing from genetic findings to underlying neuropathological mechanisms has proved challenging, due to the increased resolution of the molecular and genetic assays. Advancement in modelling tools is likely to improve our understanding of the origin of neurodevelopmental disorders and provide insight into their developmental mechanisms. Also, combined in vivo editing of multiple genes and single-cell RNA-sequencing (scRNA-seq) are bringing us into a new era of understanding the molecular neuropathology of NDDs.

Bridging global and local topology in whole-brain networks using the network statistic jackknife

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack.

Dietary Approaches to the Management of Autism Spectrum Disorders

This chapter reviews the literature surrounding autism spectrum disorders (ASD) and their relation to gastrointestinal (GI), behavioral, neurological, and immunological functioning. Individuals with ASD often have poor GI health, including bowel motility issues, autoimmune and/or other adverse responses to certain foods, and lack of necessary nutrient absorption. These issues may be caused or exacerbated by restrictive behavioral patterns (e.g., preference for sweet and salty foods and/or refusal of healthy foods). Those individuals with GI issues tend to demonstrate more behavioral deficits (e.g., irritability, agitation, hyperactivity) and also tend to have an imbalance in overall gut microbiome composition, thus corroborating several studies that have implicated brain-gut pathways as potential mediators of behavioral dysfunction.We examine the literature regarding dietary approaches to managing ASDs, including elimination diets for gluten, casein, or complex carbohydrates, a ketogenic diet, and a low oxalate diet. We also explore the research examining dietary supplements such as fatty acids, pro- and prebiotics, vitamins, minerals, glutathione, phytochemicals, and hormones. The research on dietary approaches to managing ASDs is limited and the results are mixed. However, a few approaches, such as the gluten-free/casein-free diet, fatty acid supplementation, and pre/probiotics have generally demonstrated improved GI and associated behavioral symptoms. Given that GI issues seem to be overrepresented in ASD populations, and that GI issues have been associated with a number behavioral and neurological deficits, dietary manipulation may offer a cheap and easily implemented approach to improve the lives of those with ASD.

A network clustering based feature selection strategy for classifying autism spectrum disorder

BACKGROUND

Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.

METHODS

In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.

RESULTS

The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.

CONCLUSION

It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

Patient-Derived Stem Cells, Another in vitro Model, or the Missing Link Toward Novel Therapies for Autism Spectrum Disorders?

Autism Spectrum Disorders (ASDs) is a multigenic and multifactorial neurodevelopmental group of disorders diagnosed in early childhood, leading to deficits in social interaction, verbal and non-verbal communication and characterized by restricted and repetitive behaviors and interests. To date, genetic, descriptive and mechanistic aspects of the ASDs have been investigated using mouse models and post-mortem brain tissue. More recently, the technology to generate stem cells from patients' samples has brought a new avenue for modeling ASD through 2D and 3D neuronal models that are derived from a patient's own cells, with the goal of building new therapeutic strategies for treating ASDs. This review analyses how studies performed on mouse models and human samples can complement each other, advancing our current knowledge into the pathophysiology of the ASDs. Regardless of the genetic and phenotypic heterogeneities of ASDs, convergent information regarding the molecular and cellular mechanisms involved in these disorders can be extracted from these models. Thus, considering the complexities of these disorders, patient-derived models have immense potential to elucidate molecular deregulations that contributed to the different autistic phenotypes. Through these direct investigations with the human in vitro models, they offer the potential for opening new therapeutic avenues that can be translated into the clinic.

Brain Network Organization Correlates with Autistic Features in Preschoolers with Autism Spectrum Disorders and in Their Fathers: Preliminary Data from a DWI Analysis

Autism Spectrum Disorders (ASD) is a group of neurodevelopmental disorders that is characterized by an altered brain connectivity organization. Autistic traits below the clinical threshold (i.e., the broad autism phenotype; BAP) are frequent among first-degree relatives of subjects with ASD; however, little is known regarding whether subthreshold behavioral manifestations of ASD mirror also at the neuroanatomical level in parents of ASD probands. To this aim, we applied advanced diffusion network analysis to MRI of 16 dyads consisting of a child with ASD and his father in order to investigate: (i) the correlation between structural network organization and autistic features in preschoolers with ASD (all males; age range 1.5-5.2 years); (ii) the correlation between structural network organization and BAP features in the fathers of individuals with ASD (fath-ASD). Local network measures significantly correlated with autism severity in ASD children and with BAP traits in fath-ASD, while no significant association emerged when considering the global measures of brain connectivity. Notably, an overlap of some brain regions that are crucial for social functioning (cingulum, superior temporal gyrus, inferior temporal gyrus, middle frontal gyrus, frontal pole, and amygdala) in patients with ASD and fath-ASD was detected, suggesting an intergenerational transmission of these neural substrates. Overall, the results of this study may help in elucidating the neurostructural endophenotype of ASD, paving the way for bridging connections between underlying genetic and ASD symptomatology.

Age-dynamic networks and functional correlation for early white matter myelination

The maturation of the myelinated white matter throughout childhood is a critical developmental process that underlies emerging connectivity and brain function. In response to genetic influences and neuronal activities, myelination helps establish the mature neural networks that support cognitive and behavioral skills. The emergence and refinement of brain networks, traditionally investigated using functional imaging data, can also be interrogated using longitudinal structural imaging data. However, few studies of structural network development throughout infancy and early childhood have been presented, likely owing to the sparse and irregular nature of most longitudinal neuroimaging data, which complicates dynamic analysis. Here, we overcome this limitation and investigate through concurrent correlation the co-development of white matter myelination and volume, and structural network development of white matter myelination between brain regions as a function of age, using statistically well-supported methods. We show that the concurrent correlation of white matter myelination and volume is overall positive and reaches a peak at 580 days. Brain regions are found to differ in overall magnitudes and patterns of time-varying association throughout early childhood. We introduce time-dynamic developmental networks based on temporal similarity of association patterns in the levels of myelination across brain regions. These networks reflect groups of brain regions that share similar patterns of evolving intra-regional connectivity, as evidenced by levels of myelination, are biologically interpretable and provide novel visualizations of brain development. Comparing the constructed networks between different maternal education groups, we found that children with higher and lower maternal education differ significantly in the overall magnitude of the time-dynamic correlations.

A Network Neuroscience Approach to Typical and Atypical Brain Development

Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions.

Structural neuroimaging as clinical predictor: A review of machine learning applications

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.

Variations of the Functional Brain Network Efficiency in a Young Clinical Sample within the Autism Spectrum: A fNIRS Investigation

Autism is a neurodevelopmental disorder with dimensional behavioral symptoms and various damages in the structural and functional brain. Previous neuroimaging studies focused on exploring the differences of brain development between individuals with and without autism spectrum disorders (ASD). However, few of them have attempted to investigate the individual differences of the brain features among subjects within the Autism spectrum. Our main goal was to explore the individual differences of neurodevelopment in young children with Autism by testing for the association between the functional network efficiency and levels of autistic behaviors, as well as the association between the functional network efficiency and age. Forty-six children with Autism (ages 2.0-8.9 years old) participated in the current study, with levels of autistic behaviors evaluated by their parents. The network efficiency (global and local network efficiency) were obtained from the functional networks based on the oxy-, deoxy-, and total-Hemoglobin series, respectively. Results indicated that the network efficiency decreased with age in young children with Autism in the deoxy- and total-Hemoglobin-based-networks, and children with a relatively higher level of autistic behaviors showed decreased network efficiency in the oxy-hemoglobin-based network. Results suggest individual differences of brain development in young children within the Autism spectrum, providing new insights into the psychopathology of ASD.

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.

The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral features that emerge during the first years of life. Research indicates that abnormalities in brain connectivity are associated with these behavioral features. However, the inclusion of individuals past the age of onset of the defining behaviors complicates interpretation of the observed abnormalities: they may be cascade effects of earlier neuropathology and behavioral abnormalities. Our recent study of network efficiency in a cohort of 24-month-olds at high and low familial risk for ASD reduced this confound; we reported reduced network efficiencies in toddlers classified with ASD. The current study maps the emergence of these inefficiencies in the first year of life.

METHODS

This study uses data from 260 infants at 6 and 12 months of age, including 116 infants with longitudinal data. As in our earlier study, we use diffusion data to obtain measures of the length and strength of connections between brain regions to compute network efficiency. We assess group differences in efficiency within linear mixed-effects models determined by the Akaike information criterion.

RESULTS

Inefficiencies in high-risk infants later classified with ASD were detected from 6 months onward in regions involved in low-level sensory processing. In addition, within the high-risk infants, these inefficiencies predicted 24-month symptom severity.

CONCLUSIONS

These results suggest that infants with ASD, even before 6 months of age, have deficits in connectivity related to low-level processing, which contribute to a developmental cascade affecting brain organization and eventually higher-level cognitive processes and social behavior.

Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies

BACKGROUND

Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.

OBJECTIVES

1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).

METHODS

Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.

RESULTS

Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.

CONCLUSIONS

The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

Epilepsy and Autism

Epilepsy and autistic spectrum disorder frequently coexist in the same individual. Electroencephalogram (EEG) epileptiform activity is also present at a substantially higher rate in children with autism than normally developing children. As with epilepsy, there are a multitude of genetic and environmental factors that can result in autistic spectrum disorder. There is growing consensus from both animal and clinical studies that autism is a disorder of aberrant connectivity. As measured with functional magnetic resonance imaging (MRI) and EEG, the brain in autistic spectrum disorder may be under- or overconnected or have a mixture of over- and underconnectivity. In the case of comorbid epilepsy and autism, an imbalance of the excitatory/inhibitory (E/I) ratio in selected regions of the brain may drive overconnectivity. Understanding the mechanism by which altered connectivity in individuals with comorbid epilepsy and autistic spectrum disorder results in the behaviors specific to the autistic spectrum disorder remains a challenge.

Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

Background

Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.

Methods

We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.

Results

In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.

Conclusions

Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0058-4) contains supplementary material, which is available to authorized users.

Mesial temporal lobe and memory function in autism spectrum disorder: an exploration of volumetric findings

Studies have shown that individuals with autism spectrum disorder (ASD) tend to perform significantly below typical developing individuals on standardized measures of memory, even when not significantly different on measures of IQ. The current study sought to examine within ASD whether anatomical correlates of memory performance differed between those with average-to-above-average IQ (AIQ group) and those with low-average to borderline ability (LIQ group) as well as in relations to typically developing comparisons (TDC). Using automated volumetric analyses, we examined regional volume of classic memory areas including the hippocampus, parahippocampal gyrus, entorhinal cortex, and amygdala in an all-male sample AIQ (n = 38) and LIQ (n = 18) individuals with ASD along with 30 typically developing comparisons (TDC). Memory performance was assessed using the Test of Memory and Learning (TOMAL) compared among the groups and then correlated with regional brain volumes. Analyses revealed group differences on almost all facets of memory and learning as assessed by the various subtests of the TOMAL. The three groups did not differ on any region of interest (ROI) memory-related brain volumes. However, significant size-memory function interactions were observed. Negative correlations were found between the volume of the amygdala and composite, verbal, and delayed memory indices for the LIQ ASD group, indicating larger volume related to poorer performance. Implications for general memory functioning and dysfunctional neural connectivity in ASD are discussed.

State-Dependent Differences in Functional Connectivity in Young Children With Autism Spectrum Disorder

Background

While there is increasing evidence of altered brain connectivity in autism, the degree and direction of these alterations in connectivity and their uniqueness to autism has not been established. The aim of the present study was to compare connectivity in children with autism to that of typically developing controls and children with developmental delay without autism.

Methods

We assessed EEG spectral power, coherence, phase lag, Pearson and partial correlations, and epileptiform activity during the awake, slow wave sleep, and REM sleep states in 137 children aged 2 to 6 years with autism (n = 87), developmental delay without autism (n = 21), or typical development (n = 29).

Findings

We found that brain connectivity, as measured by coherence, phase lag, and Pearson and partial correlations distinguished children with autism from both neurotypical and developmentally delayed children. In general, children with autism had increased coherence which was most prominent during slow wave sleep.

Interpretation

Functional connectivity is distinctly different in children with autism compared to samples with typical development and developmental delay without autism. Differences in connectivity in autism are state and region related. In this study, children with autism were characterized by a dynamically evolving pattern of altered connectivity.

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We used EEG to examine the connectivity in young children in awake, rapid eye movement, and slow wave sleep (SWS) states.

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Differences in coherence between the autism group and the other groups were maximal during SWS.

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Sleep may be the most sensitive time to measure differences in neuro-development before they are observable in behavior.

Shared mechanisms underlie normal healthy sleep and normal brain development. Therefore, we suspect that differences in the way that the brain is behaving during sleep have the potential to tell us about what might be developing incorrectly in people with neurodevelopmental disorders. In addition, sleep evaluations of children allow us to filter out waking distractions, so that we are truly measuring the brain working offline. For instance, the differences in the way the brain was connected that we observed between our three study groups were by far the most notable during slow wave sleep. This finding highlights the importance of taking the state of the brain into account when commenting on the ‘connectedness’ of the brain as a potential biomarker for neurodevelopmental disorders. Future attempts to classify developmental disorders by using differences in connectivity must take into account brain state, whether awake or asleep as well as developmental stage.

Regional specificity of aberrant thalamocortical connectivity in autism

Preliminary evidence suggests aberrant (mostly reduced) thalamocortical (TC) connectivity in autism spectrum disorder (ASD), but despite the crucial role of thalamus in sensorimotor functions and its extensive connectivity with cerebral cortex, relevant evidence remains limited. We performed a comprehensive investigation of region-specific TC connectivity in ASD. Resting-state functional MRI and diffusion tensor imaging (DTI) data were acquired for 60 children and adolescents with ASD (ages 7-17 years) and 45 age, sex, and IQ-matched typically developing (TD) participants. We examined intrinsic functional connectivity (iFC) and anatomical connectivity (probabilistic tractography) with thalamus, using 68 unilateral cerebral cortical regions of interest (ROIs). For frontal and parietal lobes, iFC was atypically reduced in the ASD group for supramodal association cortices, but was increased for cingulate gyri and motor cortex. Temporal iFC was characterized by overconnectivity for auditory cortices, but underconnectivity for amygdalae. Occipital iFC was broadly reduced in the ASD group. DTI indices (such as increased radial diffusion) for regions with group differences in iFC further indicated compromised anatomical connectivity, especially for frontal ROIs, in the ASD group. Our findings highlight the regional specificity of aberrant TC connectivity in ASD. Their overall pattern can be largely accounted for by functional overconnectivity with limbic and sensorimotor regions, but underconnectivity with supramodal association cortices. This could be related to comparatively early maturation of limbic and sensorimotor regions in the context of early overgrowth in ASD, at the expense of TC connectivity with later maturing cortical regions.

Atypical functional brain connectivity during rest in autism spectrum disorders

OBJECTIVE

Connectivity atypicalities in autism spectrum disorders (ASD) have been extensively proposed. The default mode network (DMN) is critical in this study, given the insight it provides for long-distance connectivity, and the importance of regions in this network for introspection and social emotion processing, areas affected in ASD. However, study of this network has largely been limited to adults; research earlier in development is lacking. The objective of this study was to examine DMN connectivity in children/adolescents with ASD.

METHODS

A total of 115 children/adolescents, aged 6 to 17 years (71 males with ASD and 44 group age-matched TD males) were included in these analyses. We examined group differences in (1) functional connectivity between the posterior cingulate cortex and regions across the brain, (2) connectivity within the DMN as a function of age and intelligence quotient (IQ), and (3) the association between DMN connectivity and empathic accuracy.

RESULTS

Individuals with ASD, relative to controls, showed either stronger or weaker connectivity between the posterior cingulate cortex (PCC) and DMN regions, depending on the region, but also showed stronger connectivity with non-DMN regions. A significant group-by-age interaction was observed in functional connectivity between the PCC and medial prefrontal cortex; connectivity increased with age in controls, but decreased in individuals with ASD. No effects of IQ were found. There was a significant group difference in the relation between DMN connectivity and empathic accuracy.

INTERPRETATION

Differences in functional connectivity may suggest the presence of neural atypicalities that impact the development of typical connectivity in ASD. In addition to affecting DMN dynamics, these atypicalities may also impact social-cognitive abilities.

Dyspraxia, motor function and visual-motor integration in autism

This project assessed dyspraxia in high-functioning school aged children with autism with a focus on Ideational Praxis. We examined the association of specific underlying motor function including eye movement with ideational dyspraxia (sequences of skilled movements) as well as the possible role of visual-motor integration in dyspraxia. We found that compared to IQ-, sex- and age-matched typically developing children, the children with autism performed significantly worse on: Ideational and Buccofacial praxis; a broad range of motor tests, including measures of simple motor skill, timing and accuracy of saccadic eye movements and motor coordination; and tests of visual-motor integration. Impairments in individual children with autism were heterogeneous in nature, although when we examined the praxis data as a function of a qualitative measure representing motor timing, we found that children with poor motor timing performed worse on all praxis categories and had slower and less accurate eye movements while those with regular timing performed as well as typical children on those same tasks. Our data provide evidence that both motor function and visual-motor integration contribute to dyspraxia. We suggest that dyspraxia in autism involves cerebellar mechanisms of movement control and the integration of these mechanisms with cortical networks implicated in praxis.

Network inefficiencies in autism spectrum disorder at 24 months

Autism spectrum disorder (ASD) is a developmental disorder defined by behavioral symptoms that emerge during the first years of life. Associated with these symptoms are differences in the structure of a wide array of brain regions, and in the connectivity between these regions. However, the use of cohorts with large age variability and participants past the generally recognized age of onset of the defining behaviors means that many of the reported abnormalities may be a result of cascade effects of developmentally earlier deviations. This study assessed differences in connectivity in ASD at the age at which the defining behaviors first become clear. There were 113 24-month-old participants at high risk for ASD, 31 of whom were classified as ASD, and 23 typically developing 24-month-old participants at low risk for ASD. Utilizing diffusion data to obtain measures of the length and strength of connections between anatomical regions, we performed an analysis of network efficiency. Our results showed significantly decreased local and global efficiency over temporal, parietal and occipital lobes in high-risk infants classified as ASD, relative to both low- and high-risk infants not classified as ASD. The frontal lobes showed only a reduction in global efficiency in Broca's area. In addition, these same regions showed an inverse relation between efficiency and symptom severity across the high-risk infants. The results suggest delay or deficits in infants with ASD in the optimization of both local and global aspects of network structure in regions involved in processing auditory and visual stimuli, language and nonlinguistic social stimuli.