Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity

Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism

Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder.

Assessing autism at its social and developmental roots: A review of Autism Spectrum Disorder studies using functional near-infrared spectroscopy

We review a relatively new method for studying the developing brain in children and infants with Autism Spectrum Disorder (ASD). Despite advances in behavioral screening and brain imaging, due to paradigms that do not easily allow for testing of awake, very young, and socially-engaged children-i.e., the social and the baby brain-the biological underpinnings of this disorder remain a mystery. We introduce an approach based on functional near-infrared spectroscopy (fNIRS), which offers a noninvasive imaging technique for studying functional activations by measuring changes in the brain's hemodynamic properties. This further enables measurement of brain activation in upright, interactive settings, while maintaining general equivalence to fMRI findings. We review the existing studies that have used fNIRS for ASD, discussing their promise, limitations, and their technical aspects, gearing this study to the researcher who may be new to this technique and highlighting potential targets for future research.

Limited synapse overproduction can speed development but sometimes with long-term energy and discrimination penalties

Neural circuit development requires that synapses be formed between appropriate neurons. In addition, for a hierarchical network, successful development involves a sequencing of developmental events. It has been suggested that one mechanism that helps speed up development of proper connections is an early overproduction of synapses. Using a computational model of synapse development, such as adaptive synaptogenesis, it is possible to study such overproduction and its role in speeding up development; it is also possible to study other outcomes of synapse overproduction that are seemingly new to the literature. With a fixed number of neurons, adaptive synaptogenesis can control the speed of synaptic development in two ways: by altering the rate constants of the adaptive processes or by altering the initial number of rapidly but non-selectively accrued synapses. Using either mechanism, the simulations reveal that synapse overproduction appears as an unavoidable concomitant of rapid adaptive synaptogenesis. However, the shortest development times, which always produces the greatest amount of synapse overproduction, reduce adult performance by three measures: energy use, discrimination error rates, and proportional neuron allocation. Thus, the results here lead to the hypothesis that the observed speed of neural network development represents a particular inter-generational compromise: quick development benefits parental fecundity while slow development benefits offspring fecundity.

Intrinsic functional connectivity variance and state-specific under-connectivity in autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered brain connectivity. Previous neuroimaging research demonstrates inconsistent results, particularly in studies of functional connectivity in ASD. Typically, these inconsistent findings are results of studies using static measures of resting-state functional connectivity. Recent work has demonstrated that functional brain connections are dynamic, suggesting that static connectivity metrics fail to capture nuanced time-varying properties of functional connections in the brain. Here we used a dynamic functional connectivity approach to examine the differences in the strength and variance of dynamic functional connections between individuals with ASD and healthy controls (HCs). The variance of dynamic functional connections was defined as the respective standard deviations of the dynamic functional connectivity strength across time. We utilized a large multicenter dataset of 507 male subjects (209 with ASD and 298 HC, from 6 to 36 years old) from the Autism Brain Imaging Data Exchange (ABIDE) to identify six distinct whole-brain dynamic functional connectivity states. Analyses demonstrated greater variance of widespread long-range dynamic functional connections in ASD (P < 0.05, NBS method) and weaker dynamic functional connections in ASD (P < 0.05, NBS method) within specific whole-brain connectivity states. Hypervariant dynamic connections were also characterized by weaker connectivity strength in ASD compared with HC. Increased variance of dynamic functional connections was also related to ASD symptom severity (ADOS total score) (P < 0.05), and was most prominent in connections related to the medial superior frontal gyrus and temporal pole. These results demonstrate that greater intraindividual dynamic variance is a potential biomarker of ASD. Hum Brain Mapp 38:5740-5755, 2017. © 2017 Wiley Periodicals, Inc.

Large-scale network organization of EEG functional connectivity in newborn infants

The organization of functional brain networks changes across human lifespan. The present study analyzed functional brain networks in healthy full-term infants (N = 139) within 1-6 days from birth by measuring neural synchrony in EEG recordings during quiet sleep. Large-scale phase synchronization was measured in six frequency bands with the Phase Lag Index. Macroscopic network organization characteristics were quantified by constructing unweighted minimum spanning tree graphs. The cortical networks in early infancy were found to be significantly more hierarchical and had a more cost-efficient organization compared with MST of random control networks, more so in the theta and alpha than in other frequency bands. Frontal and parietal sites acted as the main hubs of these networks, the topological characteristics of which were associated with gestation age (GA). This suggests that individual differences in network topology are related to cortical maturation during the prenatal period, when functional networks shift from strictly centralized toward segregated configurations. Hum Brain Mapp 38:4019-4033, 2017. © 2017 Wiley Periodicals, Inc.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.

Brain network dynamics in high-functioning individuals with autism

Theoretically, autism should be underpinned by aberrant brain dynamics. However, how brain activity changes over time in individuals with autism spectrum disorder (ASD) remains unknown. Here we characterize brain dynamics in autism using an energy-landscape analysis applied to resting-state fMRI data. Whereas neurotypical brain activity frequently transits between two major brain states via an intermediate state, high-functioning adults with ASD show fewer neural transitions due to an unstable intermediate state, and these infrequent transitions predict the severity of autism. Moreover, in contrast to the controls whose IQ is correlated with the neural transition frequency, IQ scores of individuals with ASD are instead predicted by the stability of their brain dynamics. Finally, such brain–behaviour associations are related to functional segregation between brain networks. These findings suggest that atypical functional coordination in the brains of adults with ASD underpins overly stable neural dynamics, which supports both their ASD symptoms and cognitive abilities.

Though individuals with autism spectrum disorder (ASD) show a number of neural abnormalities, the relationship between global dynamic neural patterns and ASD symptoms remains unclear. Here, authors describe such global brain dynamics, relate these to cognitive abilities, ASD symptoms, and predict ASD diagnosis.

Formulating autism systemically: Part 2 - A 12-year prospective case study

This prospective case study illustrates the ideas on the developmental origins of some cases of autism that were described in Part 1 of this article. We explored and found support for several systemic hypotheses regarding the experience of 'Graeme' and his mother. Graeme's story is told prospectively from the age of 3 until 15. Three independent sources of information are reported: hypotheses from the literature and case review in Part 1 of this article, the school's experience, and observations from the assessments of attachment when Graeme was turning 6. These three streams of information are integrated here. The review of the literature and clinical cases gives us a wide-angle lens, empirically supporting our focus on danger in relationships. The school's experience describes working with a child with undiagnosed autism. The assessments of attachment both provide information about family members' psychological strategies for protection from danger and also include the mother's childhood history of family threat. Their synthesis permits us to construct a developmental history, from Graeme's mother's childhood history of being triangulated into her parents' hidden marital discord to it's repetition for Graeme in her own marriage. New observations included mum's postural/gestural communication to Graeme and her possible ambivalence about males. Strikingly, Graeme was a case study (Attachment & Family Therapy, ch. 6) before he was diagnosed with autism. That presentation focused on Graeme's feeling of rejection by his father. In the same book, an interpersonal formulation of autism was offered (ch. 3), but it was not connected to Graeme, showing that when one lacks crucial information, the clinical formulation is distorted. The crucial information in this case was the mother's psychological history. Our case suggests that filling the blind spot in the research on autism with information about family functioning over at least two generations might change the understanding of autism in ways that could help families and their children.

Homogeneous Combinations of ASD-ADHD Traits and Their Cognitive and Behavioral Correlates in a Population-Based Sample

OBJECTIVE

Autism Spectrum Disorders (ASD) and ADHD are assumed to be the extreme manifestations of continuous heterogeneous traits that frequently co-occur. This study aims to identify subgroups of children with distinct ASD-ADHD trait profiles in the general population, using measures sensitive across both trait continua, and show how these subgroups differ in cognitive functioning.

METHOD

We examined 378 children (6-13 years) from a population-based sample.

RESULTS

Latent class analyses (LCA) detected three concordant classes with low (10.1%), medium (54.2%), or high (13.2%) scores on both traits, and two discordant classes with more ADHD than ASD characteristics (ADHD > ASD, 18.3%) and vice versa (ASD > ADHD, 4.2%). Findings suggest that ASD and ADHD traits usually are strongly related in the unaffected population, and that a minority of children displays atypical discordant trait profiles characterized by differential visual-spatial functioning.

CONCLUSION

This dissociation suggests that heterogeneity in ASD and ADHD is rooted in heterogeneity in the lower unaffected end of the distribution.

Autistic Traits Affect P300 Response to Unexpected Events, regardless of Mental State Inferences

Limited use of contextual information has been suggested as a way of understanding cognition in people with autism spectrum disorder (ASD). However, it has also been argued that individuals with ASD may have difficulties inferring others' mental states. Here, we examined how individuals with different levels of autistic traits respond to contextual deviations by measuring event-related potentials that reflect context usage. The Autism Spectrum Quotient (AQ) was used to quantify autistic-like traits in 28 university students, and 19 participants were defined as Low or High AQ groups. To additionally examine inferences about mental state, two belief conditions (with or without false belief) were included. Participants read short stories in which the final sentence included either an expected or an unexpected word and rated the word's degree of deviation from expectation. P300 waveform analysis revealed that unexpected words were associated with larger P300 waveforms for the Low AQ group, but smaller P300 responses in the High AQ group. Additionally, AQ social skill subscores were positively correlated with evaluation times in the Unexpected condition, whether a character's belief was false or not. This suggests that autistic traits can affect responses to unexpected events, possibly because of decreased availability of context information.

Shifting brain inhibitory balance and connectivity of the prefrontal cortex of adults with autism spectrum disorder

Currently, there are no effective pharmacologic treatments for the core symptoms of autism spectrum disorder (ASD). There is, nevertheless, potential for progress. For example, recent evidence suggests that the excitatory (E) glutamate and inhibitory (I) GABA systems may be altered in ASD. However, no prior studies of ASD have examined the 'responsivity' of the E-I system to pharmacologic challenge; or whether E-I modulation alters abnormalities in functional connectivity of brain regions implicated in the disorder. Therefore, we used magnetic resonance spectroscopy ([1H]MRS) to measure prefrontal E-I flux in response to the glutamate and GABA acting drug riluzole in adult men with and without ASD. We compared the change in prefrontal 'Inhibitory Index'-the GABA fraction within the pool of glutamate plus GABA metabolites-post riluzole challenge; and the impact of riluzole on differences in resting-state functional connectivity. Despite no baseline differences in E-I balance, there was a significant group difference in response to pharmacologic challenge. Riluzole increased the prefrontal cortex inhibitory index in ASD but decreased it in controls. There was also a significant group difference in prefrontal functional connectivity at baseline, which was abolished by riluzole within the ASD group. Our results also show, for we believe the first time in ASD, that E-I flux can be 'shifted' with a pharmacologic challenge, but that responsivity is significantly different from controls. Further, our initial evidence suggests that abnormalities in functional connectivity can be 'normalised' by targeting E-I, even in adults.

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.

Inter-trial Coherence of Medial Frontal Theta Oscillations Linked to Differential Feedback Processing in Youth and Young Adults with Autism

BACKGROUND

Impairment in prediction and appreciation for choice outcomes could contribute to several core symptoms of ASD. We examined electroencephalography (EEG) oscillations in 27 youth and young adults diagnosed with autism spectrum disorder (ASD) and 22 IQ-matched neurotypical controls while they performed a chance-based reward prediction task.

METHOD

We re-analyzed our previously published ERP data (Larson et al., 2011) and examined theta band oscillations (4-8 Hz) at frontal midline sites, within a timing window that overlaps with the feedback-related negativity (FRN). We focused on event-related changes after presentation of feedback for reward (WIN) and punitive (LOSE) outcomes, both for spectral power and inter-trial phase coherence.

RESULTS

In our reward prediction task, for both groups, medial frontal theta power and phase coherence were greater following LOSE compared to WIN feedback. However, compared to controls, inter-trial coherence of medial frontal theta was significantly lower overall (across both feedback types) for individuals with ASD. Our results indicate that while individuals with ASD are sensitive to the valence of reward feedback, comparable to their neurotypical peers, they have reduced synchronization of medial frontal theta activity during feedback processing.

CONCLUSIONS

This finding are consistent with previous studies showing neural variability in ASD and suggest that the processes underlying decision-making and reinforcement learning may be atypical and less efficient in ASD.

I tawt i taw a puddy tat: Gestures in canary row narrations by high-functioning youth with autism spectrum disorder

This study examined whether individuals with autism spectrum disorder (ASD) produce co-speech gestures similarly to typically developing (TD) peers. Participants were 20 youth ages 10-18 years with high-functioning ASD and 21 TD controls matched on age, gender, verbal IQ, and handedness. Gestures were elicited using a classic narrative-retelling task, in which participants watched a Tweety and Sylvester cartoon and retold the cartoon to a confederate. Analyses compared gesture rate, type, and viewpoint (character, observer, dual) across groups. Communicative utility of gestures was measured via naïve coder ratings of whether a movement was a gesture, and the clarity of a gesture's meaning. The ASD group produced shorter narratives and fewer total gestures than the TD group. Accounting for narrative length, the ASD group produced fewer gestures per clause than the TD group; however, proportions of gesture types (iconic, deictic, beat, metaphoric, emblems) did not differ. Most notably, the ASD group's gestures were rated as less clearly gestures in terms of timing and well formedness, with lower certainty ratings for gesture meaning. Gesture clarity and gesture meaning scores were related to diagnostic measures of gesture competence in ASD. Findings suggest that although fluent children and adolescents with ASD use the same type of gestures as controls, their gestures are more difficult to understand, which has significant implications for their communicative abilities more broadly. Autism Res 2017, 10: 1353-1363. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.

Disconnection from others in autism is more than just a feeling: whole-brain neural synchrony in adults during implicit processing of emotional faces

Background

Socio-emotional difficulties in autism spectrum disorder (ASD) are thought to reflect impaired functional connectivity within the “social brain”. Nonetheless, a whole-brain characterization of the fast responses in functional connectivity during implicit processing of emotional faces in adults with ASD is lacking.

Methods

The present study used magnetoencephalography to investigate early responses in functional connectivity, as measured by interregional phase synchronization, during implicit processing of angry, neutral and happy faces. The sample (n = 44) consisted of 22 young adults with ASD and 22 age- and sex-matched typically developed (TD) controls.

Results

Reduced phase-synchrony in the beta band around 300 ms emerged during processing of angry faces in the ASD compared to TD group, involving key areas of the social brain. In the same time window, de-synchronization in the beta band in the amygdala was reduced in the ASD group across conditions.

Conclusions

This is the first demonstration of atypical global and local synchrony patterns in the social brain in adults with ASD during implicit processing of emotional faces. The present results replicate and substantially extend previous findings on adolescents, highlighting that atypical brain synchrony during processing of socio-emotional stimuli is a hallmark of clinical sequelae in autism.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-017-0123-2) contains supplementary material, which is available to authorized users.

Reality Monitoring and Metamemory in Adults with Autism Spectrum Conditions

Studies of reality monitoring (RM) often implicate medial prefrontal cortex (mPFC) in distinguishing internal and external information, a region linked to autism-related deficits in social and self-referential information processing, executive function, and memory. This study used two RM conditions (self-other; perceived-imagined) to investigate RM and metamemory in adults with autism. The autism group showed a deficit in RM, which did not differ across source conditions, and both groups exhibited a self-encoding benefit on recognition and source memory. Metamemory for perceived-imagined information, but not for self-other information, was significantly lower in the autism group. Therefore, reality monitoring and metamemory, sensitive to mPFC function, appear impaired in autism, highlighting a difficulty in remembering and monitoring internal and external details of past events.

The Lag Structure of Intrinsic Activity is Focally Altered in High Functioning Adults with Autism

The behaviors that define autism spectrum disorders (ASDs) have been hypothesized to result from disordered communication within brain networks. Several groups have investigated this question using resting-state functional magnetic resonance imaging (RS-fMRI). However, the published findings to date have been inconsistent across laboratories. Prior RS-fMRI studies of ASD have employed conventional analysis techniques based on the assumption that intrinsic brain activity is exactly synchronous over widely separated parts of the brain. By relaxing the assumption of synchronicity and focusing, instead, on lags between time series, we have recently demonstrated highly reproducible patterns of temporally lagged activity in normal human adults. We refer to this analysis technique as resting-state lag analysis (RS-LA). Here, we report RS-LA as well as conventional analyses of RS-fMRI in adults with ASD and demographically matched controls. RS-LA analyses demonstrated significant group differences in rs-fMRI lag structure in frontopolar cortex, occipital cortex, and putamen. Moreover, the degree of abnormality in individuals was highly correlated with behavioral measures relevant to the diagnosis of ASD. In this sample, no significant group differences were observed using conventional RS-fMRI analysis techniques. Our results suggest that altered propagation of intrinsic activity may contribute to abnormal brain function in ASD.

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

INTRODUCTION

Our flexible and adaptive interactions with the environment are guided by our individual representation of the physical world, estimated through sensation and evaluation of available information against prior knowledge. When linking sensory evidence with higher-level expectations for action, the central nervous system (CNS) in typically developing (TD) individuals relies in part on distributed and interacting cortical regions to communicate neuronal signals flexibly across the brain. Increasing evidence suggests that the balance between levels of signal and noise during information processing may be disrupted in individuals with Autism Spectrum Disorders (ASD).

METHODS

Participants with and without ASD performed a visuospatial interference task while undergoing functional Magnetic Resonance Imaging (fMRI). We empirically estimated parameters characterizing participants' latencies and their subtle fluctuations (noise accumulation) over the 16-min scan. We modeled hemodynamic activation and used seed-based analyses of neural coupling to study dysfunction in interference-specific connectivity in a subset of ASD participants who were nonparametrically matched to TD participants on age, male-to-female ratio, and magnitude of movement during the scan.

RESULTS

Stochastic patterns of response fluctuations reveal significantly higher noise-to-signal levels and a more random and noisy structure in ASD versus TD participants, and in particular ASD adults who have the greatest clinical autistic deficits. While individuals with ASD show an overall weaker modulation of interference-specific functional connectivity relative to TD individuals, in particular between the seeds of Anterior Cingulate Cortex (ACC) and Inferior Parietal Sulcus (IPS) and the rest of the brain, we found that in ASD, higher uncertainty during the task is linked to increased interference-specific coupling between bilateral anterior insula and prefrontal cortex.

CONCLUSIONS

Subtle and informative differences in the structure of experiencing information exist between ASD and TD individuals. Our findings reveal in ASD an atypical capacity to apply previously perceived information in a manner optimal for adaptive functioning, plausibly revealing suboptimal message-passing across the CNS.

Network over-connectivity differentiates autism spectrum disorder from other developmental disorders in toddlers: A diffusion MRI study

Advanced connectivity studies in toddlers with Autism Spectrum Disorder (ASD) are increasing and consistently reporting a disruption of brain connectivity. However, most of these studies compare ASD and typically developing subjects, thus providing little information on the specificity of the abnormalities detected in comparison with other developmental disorders (other-DD). We recruited subjects aged below 36 months who received a clinical diagnosis of Neurodevelopmental Disorder (32 ASD and 16 other-DD including intellectual disability and language disorder) according to DSM-IV TR. Structural and diffusion MRI were acquired to perform whole brain probabilistic and anatomically constrained tractography. Network connectivity matrices were built encoding the number of streamlines (D_NUM ) and the tract-averaged fractional anisotropy (D_FA ) values connecting each pair of cortical and subcortical regions. Network Based Statistics (NBS) was finally applied on the connectivity matrices to evaluate the network differences between the ASD and other-DD groups. The network differences resulted in an over-connectivity pattern (i.e., higher D_NUM and D_FA values) in the ASD group with a significance of P < 0.05. No contra-comparison results were found. The over-connectivity pattern in ASD occurred in networks primarily involving the fronto-temporal nodes, known to be crucial for social-skill development and basal ganglia, related to restricted and repetitive behaviours in ASD. To our knowledge, this is the first network-based diffusion study comparing toddlers with ASD and those with other-DD. Results indicate the detection of different connectivity patterns in ASD and other-DD at an age when clinical differential diagnosis is often challenging. Hum Brain Mapp 38:2333-2344, 2017. © 2017 Wiley Periodicals, Inc.

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.

Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.

Mice lacking GRIP1/2 show increased social interactions and enhanced phosphorylation at GluA2-S880

Glutamate receptor interacting proteins 1 and 2 (GRIP1/2) play an important role in regulating synaptic trafficking of AMPA receptor 2/3 (GluA2/3) and synaptic strength. Gain-of-function GRIP1 mutations are implicated in social behavioral deficits in autism. To study mechanisms of Grip1/2-mediated AMPA signaling in the regulation of social behaviors, we performed social behavioral testing on neuron-specific Grip1/2-double knockout (DKO) and wild type (WT) mice that are matched for age, sex, and strain background. We determined the expression profile of key signaling proteins in AMPAR, mGluR, mTOR, and GABA pathways in frontal cortex, striatum, and cerebellum of DKO mice. Compared to WT mice, DKO mice show increased sociability in a modified three-chamber social behavioral test [mean±sem for interaction time in seconds; WT: 44.0±5.0; n=10; DKO: 81.0±9.0; n=9; two factor repeated measures ANOVA: F(1,37)=14.45; p<0.01 and planned t-test; p<0.01] and in a dyadic male-male social interaction test (mean±sem for total time in seconds: sniffing, WT-WT, 18.9±1.1; WT-DKO, 42.5±2.1; t-test: p<0.001; following, WT-WT, 7.7±0.72; WT-DKO,14.4±1.8; t-test: p<0.001). Immunoblot studies identified an increase in phosphorylation at GluA2-Serine 880 (GluA2-pS880) in frontal cortex (mean±sem; WT: 0.69±0.06, n=5; DKO: 0.96±0.06, n=6; t-test; p<0.05) and reduced GABAβ3 expression in striatum (mean±sem; WT: 1.16±0.04, n=4; DKO: 0.95±0.06, n=4; t-test; p<0.05) in DKO mice. GluA2-S880 phosphorylation is known to regulate GluA2synaptic recycling, AMPA signaling strength and plasticity. GABAβ3 has been implicated in the etiology and pathogenesis in autism. These data support an important role of Grip1/2-mediated AMPA signaling in regulating social behaviors and disturbance of glutamate- and GABA-signaling in specialized brain regions in autism-related social behavioral deficits.

Network organization is globally atypical in autism: A graph theory study of intrinsic functional connectivity

BACKGROUND

Despite abundant evidence of brain network anomalies in autism spectrum disorder (ASD), findings have varied from broad functional underconnectivity to broad overconnectivity. Rather than pursuing overly simplifying general hypotheses ('under' vs. 'over'), we tested the hypothesis of atypical network distribution in ASD (i.e., participation of unusual loci in distributed functional networks).

METHODS

We used a selective high-quality data subset from the ABIDE datashare (including 111 ASD and 174 typically developing [TD] participants) and several graph theory metrics. Resting state functional MRI data were preprocessed and analyzed for detection of low-frequency intrinsic signal correlations. Groups were tightly matched for available demographics and head motion.

RESULTS

As hypothesized, the Rand Index (reflecting how similar network organization was to a normative set of networks) was significantly lower in ASD than TD participants. This was accounted for by globally reduced cohesion and density, but increased dispersion of networks. While differences in hub architecture did not survive correction, rich club connectivity (among the hubs) was increased in the ASD group.

CONCLUSIONS

Our findings support the model of reduced network integration (connectivity with networks) and differentiation (or segregation; based on connectivity outside network boundaries) in ASD. While the findings applied at the global level, they were not equally robust across all networks and in one case (greater cohesion within ventral attention network in ASD) even reversed.

Does WISC-IV Underestimate the Intelligence of Autistic Children?

Wechsler Intelligence Scale for Children (WISC) is widely used to estimate autistic intelligence (Joseph in The neuropsychology of autism. Oxford University Press, Oxford, 2011; Goldstein et al. in Assessment of autism spectrum disorders. Guilford Press, New York, 2008; Mottron in J Autism Dev Disord 34(1):19-27, 2004). However, previous studies suggest that while WISC-III and Raven's Progressive Matrices (RPM) provide similar estimates of non-autistic intelligence, autistic children perform significantly better on RPM (Dawson et al. in Psychol Sci 18(8):657-662, doi: 10.1111/j.1467-9280.2007.01954.x , 2007). The latest WISC version introduces substantial changes in subtests and index scores; thus, we asked whether WISC-IV still underestimates autistic intelligence. Twenty-five autistic and 22 typical children completed WISC-IV and RPM. Autistic children's RPM scores were significantly higher than their WISC-IV FSIQ, but there was no significant difference in typical children. Further, autistic children showed a distinctively uneven WISC-IV index profile, with a "peak" in the new Perceptual Reasoning Index. In spite of major changes, WISC-IV FSIQ continues to underestimate autistic intelligence.

Bimodal Virtual Reality Stroop for Assessing Distractor Inhibition in Autism Spectrum Disorders

Executive functioning deficits found in college students with ASD may have debilitating effects on their everyday activities. Although laboratory studies tend to report unimpaired inhibition in autism, studies of resistance to distractor inhibition reveal difficulties. In two studies, we compared a Virtual Classroom task with paper-and-pencil and computerized Stroop modalities in typically developing individuals and individuals with ASD. While significant differences were not observed between ASD and neurotypical groups on the paper-and-pencil and computerized task, individuals with ASD performed significantly worse on the virtual task with distractors. Findings suggest the potential of the Virtual Classroom Bimodal Stroop task to distinguish between prepotent response inhibition (non-distraction condition) and resistance to distractor inhibition (distraction condition) in adults with high functioning autism.

Disrupted development and imbalanced function in the global neuronal workspace: a positive-feedback mechanism for the emergence of ASD in early infancy

Autism spectrum disorder (ASD) is increasingly being conceptualized as a spectrum disorder of connectome development. We review evidence suggesting that ASD is characterized by a positive feedback loop that amplifies small functional variations in early-developing sensory-processing pathways into structural and functional imbalances in the global neuronal workspace. Using vision as an example, we discuss how early functional variants in visual processing may be feedback-amplified to produce variant object categories and disrupted top-down expectations, atypically large expectation-to-perception mismatches, problems re-identifying individual people and objects, socially inappropriate, generally aversive emotional responses and disrupted sensory-motor coordination. Viewing ASD in terms of feedback amplification of small functional variants allows a number of recent models of ASD to be integrated with neuroanatomical, neurofunctional and genetic data.

Can Mouse Imaging Studies Bring Order to Autism Connectivity Chaos?

Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted impaired or aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological underpinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to aberrant connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. Despite some obvious limitations in reliably modeling the full phenotypic spectrum of a complex developmental disorder like ASD, mouse models have played a central role in advancing our basic mechanistic and molecular understanding of this syndrome. Recent progress in mouse brain connectivity mapping via resting-state fMRI (rsfMRI) offers the opportunity to generate and test mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism. Here we discuss recent progress toward this goal, and illustrate initial examples of how the approach can be employed to establish causal links between ASD-related mutations, developmental processes, and brain connectional architecture. As the spectrum of genetic and pathophysiological components of ASD modeled in the mouse is rapidly expanding, the use of rsfMRI can advance our mechanistic understanding of the origin and significance of the connectional alterations associated with autism, and their heterogeneous expression across patient cohorts.

Different aberrant mentalizing networks in males and females with autism spectrum disorders: Evidence from resting-state functional magnetic resonance imaging

Previous studies have found that individuals with autism spectrum disorders show impairments in mentalizing processes and aberrant brain activity compared with typically developing participants. However, the findings are mainly from male participants and the aberrant effects in autism spectrum disorder females and sex differences are still unclear. To address these issues, this study analyzed intrinsic functional connectivity of mentalizing regions using resting-state functional magnetic resonance imaging data of 48 autism spectrum disorder males and females and 48 typically developing participants in autism brain imaging data exchange. Whole-brain analyses showed that autism spectrum disorder males had hyperconnectivity in functional connectivity of the bilateral temporal-parietal junction, whereas autism spectrum disorder females showed hypoconnectivity in functional connectivity of the medial prefrontal cortex, precuneus, and right temporal-parietal junction. Interaction between sex and autism was found in both short- and long-distance functional connectivity effects, confirming that autism spectrum disorder males showed overconnectivity, while autism spectrum disorder females showed underconnectivity. Furthermore, a regression analysis revealed that in autism spectrum disorder, males and females demonstrated different relations between the functional connectivity effects of the mentalizing regions and the core autism spectrum disorder deficits. These results suggest sex differences in the mentalizing network in autism spectrum disorder individuals. Future work is needed to examine how sex interacts with other factors such as age and the sex differences during mentalizing task performance.

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder found to have widespread alterations in the function and synchrony of brain regions. These differences may underlie alterations in microstructural organization, such as in white matter pathways. To investigate the diffusion of major white matter tracts, the current study examined multiple indices of white matter diffusion in 42 children and adults with ASD and 44 typically developing (TD) age- and IQ-matched peers using diffusion tensor imaging. Diffusivity measures were compared between groups for the following tracts: bilateral cingulum bundle, corpus callosum, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. Results indicate a significant reduction in fractional anisotropy (FA) for the left superior longitudinal fasciculus (LSLF) in ASD children and adults compared with TD peers. A significant increase in radial diffusivity for ASD participants was also found in the same cluster along the LSLF. In addition, a significant positive correlation emerged for all subjects between FA for the LSLF and age, with FA increasing with age. These findings point to a significant alteration in long-distance white matter connectivity in children and adults with ASD, potentially underscoring the relationship between alterations in white matter diffusion and the ASD phenotype. These results also suggest that the white matter alterations in autism may be subtle and related to the developmental trajectory.

Preserved Self-other Distinction During Empathy in Autism is Linked to Network Integrity of Right Supramarginal Gyrus

Autism spectrum disorder (ASD) shows deficits in self-other distinction during theory of mind (ToM). Here we investigated whether ASD patients also show difficulties in self-other distinction during empathy and if potential deficits are linked to dysfunctional resting-state connectivity patterns. In a first study, ASD patients and controls performed an emotional egocentricity paradigm and a ToMtask. In the second study, resting-state connectivity of right temporo-parietal junction and right supramarginal gyrus(rSMG) were analysed using a large-scale fMRI data set. ASD patients exhibited deficient ToM but normal emotional egocentricity, which was paralleled by reduced connectivity of regions of the ToM network and unimpaired rSMG network connectivity. These results suggest spared self-other distinction during empathy and an intact rSMG network in ASD.

Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity

BACKGROUND

Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group.

METHODS

EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one's own, close-other's, famous, and unknown.

RESULTS

Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other's name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections-they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group.

CONCLUSIONS

Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.

Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity

Background

Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group.

Methods

EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one’s own, close-other’s, famous, and unknown.

Results

Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other’s name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections—they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group.

Conclusions

Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-016-0102-z) contains supplementary material, which is available to authorized users.

Basal ganglia impairments in autism spectrum disorder are related to abnormal signal gating to prefrontal cortex

Research on the biological basis of autism spectrum disorder has yielded a list of brain abnormalities that are arguably as diverse as the set of behavioral symptoms that characterize the disorder. Among these are patterns of abnormal cortical connectivity and abnormal basal ganglia development. In attempts to integrate the existing literature, the current paper tests the hypothesis that impairments in the basal ganglia's function to flexibly select and route task-relevant neural signals to the prefrontal cortex underpins patterns of abnormal synchronization between the prefrontal cortex and other cortical processing centers observed in individuals with autism spectrum disorder (ASD). We tested this hypothesis using a Dynamic Causal Modeling analysis of neuroimaging data collected from 16 individuals with ASD (mean age=25.3 years; 6 female) and 17 age- and IQ-matched neurotypical controls (mean age=25.6, 6 female), who performed a Go/No-Go test of executive functioning. Consistent with the hypothesis tested, a random-effects Bayesian model selection procedure determined that a model of network connectivity in which basal ganglia activation modulated connectivity between the prefrontal cortex and other key cortical processing centers best fit the data of both neurotypicals and individuals with ASD. Follow-up analyses suggested that the largest group differences were observed for modulation of connectivity between prefrontal cortex and the sensory input region in the occipital lobe [t(31)=2.03, p=0.025]. Specifically, basal ganglia activation was associated with a small decrease in synchronization between the occipital region and prefrontal cortical regions in controls; however, in individuals with ASD, basal ganglia activation resulted in increased synchronization between the occipital region and the prefrontal cortex. We propose that this increased synchronization may reflect a failure in basal ganglia signal gating mechanisms, resulting in a non-selective copying of signals to prefrontal cortex. Such a failure to prioritize and filter signals to the prefrontal cortex could result in the pervasive impairments in cognitive flexibility and executive functioning that characterize autism spectrum disorder, and may offer a mechanistic explanation of some of the observed abnormalities in patterns of cortical synchronization in ASD.

Age-related differences in autism: The case of white matter microstructure

Autism spectrum disorder (ASD) is typified as a brain connectivity disorder in which white matter abnormalities are already present early on in life. However, it is unknown if and to which extent these abnormalities are hard-wired in (older) adults with ASD and how this interacts with age-related white matter changes as observed in typical aging. The aim of this first cross-sectional study in mid- and late-aged adults with ASD was to characterize white matter microstructure and its relationship with age. We utilized diffusion tensor imaging with head motion control in 48 adults with ASD and 48 age-matched controls (30-74 years), who also completed a Flanker task. Intra-individual variability of reaction times (IIVRT) measures based on performance on the Flanker interference task were used to assess IIVRT-white matter microstructure associations. We observed primarily higher mean and radial diffusivity in white matter microstructure in ASD, particularly in long-range fibers, which persisted after taking head motion into account. Importantly, group-by-age interactions revealed higher age-related mean and radial diffusivity in ASD, in projection and association fiber tracts. Subtle dissociations were observed in IIVRT-white matter microstructure relations between groups, with the IIVRT-white matter association pattern in ASD resembling observations in cognitive aging. The observed white matter microstructure differences are lending support to the structural underconnectivity hypothesis in ASD. These reductions seem to have behavioral percussions given the atypical relationship with IIVRT. Taken together, the current results may indicate different age-related patterns of white matter microstructure in adults with ASD. Hum Brain Mapp 38:82-96, 2017. © 2016 Wiley Periodicals, Inc.

Anatomical imbalance between cortical networks in autism

Influential psychological models of autism spectrum disorder (ASD) have proposed that this prevalent developmental disorder results from impairment of global (integrative) information processing and overload of local (sensory) information. However, little neuroanatomical evidence consistent with this account has been reported. Here, we examined relative grey matter volumes (rGMVs) between three cortical networks, how they changed with age, and their relationship with core symptomatology. Using public neuroimaging data of high-functioning ASD males and age-/sex-/IQ-matched controls, we first identified age-associated atypical increases in rGMVs of the regions of two sensory systems (auditory and visual networks), and an age-related aberrant decrease in rGMV of a task-control system (fronto-parietal network, FPN) in ASD children. While the enlarged rGMV of the auditory network in ASD adults was associated with the severity of autistic socio-communicational core symptom, that of the visual network was instead correlated with the severity of restricted and repetitive behaviours in ASD. Notably, the atypically decreased rGMV of FPN predicted both of the two core symptoms. These findings suggest that disproportionate undergrowth of a task-control system (FPN) may be a common anatomical basis for the two ASD core symptoms, and relative overgrowth of the two different sensory systems selectively compounds the distinct symptoms.

Bayesian Community Detection in the Space of Group-Level Functional Differences

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Similar white matter but opposite grey matter changes in schizophrenia and high-functioning autism

OBJECTIVE

High-functioning autism (HFA) and schizophrenia (SZ) are two of the main neurodevelopmental disorders, sharing several clinical dimensions and risk factors. Their exact relationship is poorly understood, and few studies have directly compared both disorders. Our aim was thus to directly compare neuroanatomy of HFA and SZ using a multimodal MRI design.

METHODS

We scanned 79 male adult subjects with 3T MRI (23 with HFA, 24 with SZ and 32 healthy controls, with similar non-verbal IQ). We compared them using both diffusion-based whole-brain tractography and T1 voxel-based morphometry.

RESULTS

HFA and SZ groups exhibited similar white matter alterations in the left fronto-occipital inferior fasciculus with a decrease in generalized fractional anisotropy compared with controls. In grey matter, the HFA group demonstrated bilateral prefrontal and anterior cingulate increases in contrast with prefrontal and left temporal reductions in SZ.

CONCLUSION

HFA and SZ may share common white matter deficits in long-range connections involved in social functions, but opposite grey matter abnormalities in frontal regions that subserve complex cognitive functions. Our results are consistent with the fronto-occipital underconnectivity theory of HFA and the altered connectivity hypothesis of SZ and suggest the existence of both associated and diametrical liabilities to these two conditions.

Frontal networks in adults with autism spectrum disorder

It has been postulated that autism spectrum disorder is underpinned by an 'atypical connectivity' involving higher-order association brain regions. To test this hypothesis in a large cohort of adults with autism spectrum disorder we compared the white matter networks of 61 adult males with autism spectrum disorder and 61 neurotypical controls, using two complementary approaches to diffusion tensor magnetic resonance imaging. First, we applied tract-based spatial statistics, a 'whole brain' non-hypothesis driven method, to identify differences in white matter networks in adults with autism spectrum disorder. Following this we used a tract-specific analysis, based on tractography, to carry out a more detailed analysis of individual tracts identified by tract-based spatial statistics. Finally, within the autism spectrum disorder group, we studied the relationship between diffusion measures and autistic symptom severity. Tract-based spatial statistics revealed that autism spectrum disorder was associated with significantly reduced fractional anisotropy in regions that included frontal lobe pathways. Tractography analysis of these specific pathways showed increased mean and perpendicular diffusivity, and reduced number of streamlines in the anterior and long segments of the arcuate fasciculus, cingulum and uncinate--predominantly in the left hemisphere. Abnormalities were also evident in the anterior portions of the corpus callosum connecting left and right frontal lobes. The degree of microstructural alteration of the arcuate and uncinate fasciculi was associated with severity of symptoms in language and social reciprocity in childhood. Our results indicated that autism spectrum disorder is a developmental condition associated with abnormal connectivity of the frontal lobes. Furthermore our findings showed that male adults with autism spectrum disorder have regional differences in brain anatomy, which correlate with specific aspects of autistic symptoms. Overall these results suggest that autism spectrum disorder is a condition linked to aberrant developmental trajectories of the frontal networks that persist in adult life.

Inability to activate Rac1-dependent forgetting contributes to behavioral inflexibility in mutants of multiple autism-risk genes

The etiology of autism is so complicated because it involves the effects of variants of several hundred risk genes along with the contribution of environmental factors. Therefore, it has been challenging to identify the causal paths that lead to the core autistic symptoms such as social deficit, repetitive behaviors, and behavioral inflexibility. As an alternative approach, extensive efforts have been devoted to identifying the convergence of the targets and functions of the autism-risk genes to facilitate mapping out causal paths. In this study, we used a reversal-learning task to measure behavioral flexibility in Drosophila and determined the effects of loss-of-function mutations in multiple autism-risk gene homologs in flies. Mutations of five autism-risk genes with diversified molecular functions all led to a similar phenotype of behavioral inflexibility indicated by impaired reversal-learning. These reversal-learning defects resulted from the inability to forget or rather, specifically, to activate Rac1 (Ras-related C3 botulinum toxin substrate 1)-dependent forgetting. Thus, behavior-evoked activation of Rac1-dependent forgetting has a converging function for autism-risk genes.

Resting-State Functional Connectivity Changes Between Dentate Nucleus and Cortical Social Brain Regions in Autism Spectrum Disorders

Autism spectrum disorders (ASDs) are known to be characterized by restricted and repetitive behaviors and interests and by impairments in social communication and interactions mainly including "theory of mind" (ToM) processes. The cerebellum has emerged as one of the brain regions affected by ASDs. As the cerebellum is known to influence cerebral cortex activity via cerebello-thalamo-cortical (CTC) circuits, it has been proposed that cerebello-cortical "disconnection" could in part underlie autistic symptoms. We used resting-state (RS) functional magnetic resonance imaging (fMRI) to investigate the potential RS connectivity changes between the cerebellar dentate nucleus (DN) and the CTC circuit targets, that may contribute to ASD pathophysiology. When comparing ASD patients to controls, we found decreased connectivity between the left DN and cerebral regions known to be components of the ToM network and the default mode network, implicated in specific aspects of mentalizing, social cognition processing, and higher order emotional processes. Further, a pattern of overconnectivity was also detected between the left DN and the supramodal cerebellar lobules associated with the default mode network. The presented RS-fMRI data provide evidence that functional connectivity (FC) between the dentate nucleus and the cerebral cortex is altered in ASD patients. This suggests that the dysfunction reported within the cerebral cortical network, typically related to social features of ASDs, may be at least partially related to an impaired interaction between cerebellum and key cortical social brain regions.

Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder

White matter microstructure forms a complex and dynamical system that is critical for efficient and synchronized brain function. Neuroimaging findings in children with autism spectrum disorder (ASD) suggest this condition is associated with altered white matter microstructure, which may lead to atypical macroscale brain connectivity. In this study, we used diffusion tensor imaging measures to examine the extent that white matter tracts are interrelated within ASD and typical development. We assessed the strength of inter-regional white matter correlations between typically developing and ASD diagnosed individuals. Using hierarchical clustering analysis, clustering patterns of the pairwise white matter correlations were constructed and revealed to be different between the two groups. Additionally, we explored the use of graph theory analysis to examine the characteristics of the patterns formed by inter-regional white matter correlations and compared these properties between ASD and typical development. We demonstrate that the ASD sample has significantly less coherence in white matter microstructure across the brain compared to that in the typical development sample. The ASD group also presented altered topological characteristics, which may implicate less efficient brain networking in ASD. These findings highlight the potential of graph theory based network characteristics to describe the underlying networks as measured by diffusion magnetic resonance imaging and furthermore indicates that ASD may be associated with altered brain network characteristics. Our findings are consistent with those of a growing number of studies and hypotheses that have suggested disrupted brain connectivity in ASD.

Attention and Working Memory in Adolescents with Autism Spectrum Disorder: A Functional MRI Study

The present study examined attention and memory load-dependent differences in the brain activation and deactivation patterns between adolescents with autism spectrum disorders (ASDs) and typically developing (TD) controls using functional magnetic resonance imaging. Attentional (0-back) and working memory (WM; 2-back) processing and load differences (0 vs. 2-back) were analysed. WM-related areas activated and default mode network deactivated normally in ASDs as a function of task load. ASDs performed the attentional 0-back task similarly to TD controls but showed increased deactivation in cerebellum and right temporal cortical areas and weaker activation in other cerebellar areas. Increasing task load resulted in multiple responses in ASDs compared to TD and in inadequate modulation of brain activity in right insula, primary somatosensory, motor and auditory cortices. The changes during attentional task may reflect compensatory mechanisms enabling normal behavioral performance. The inadequate memory load-dependent modulation of activity suggests diminished compensatory potential in ASD.