The study of autism as a distributed disorder

Pre-autism: What a paediatrician should know about early diagnosis of autism‎

Autism, also known as an autism spectrum disorder, is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life. A range of symptoms characterizes it and can be diagnosed at any age, including adolescence and adulthood. However, early diagnosis is crucial for effective management, prognosis, and care. Unfortunately, there are no established fetal, prenatal, or newborn screening programs for autism, making early detection difficult. This review aims to shed light on the early detection of autism prenatally, natally, and early in life, during a stage we call as "pre-autism" when typical symptoms are not yet apparent. Some fetal, neonatal, and infant biomarkers may predict an increased risk of autism in the coming baby. By developing a biomarker array, we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient. These biomarkers could be genetic, immunological, hormonal, metabolic, amino acids, acute phase reactants, neonatal brainstem function biophysical activity, behavioral profile, body measurements, or radiological markers. However, every biomarker has its accuracy and limitations. Several factors can make early detection of autism a real challenge. To improve early detection, we need to overcome various challenges, such as raising community awareness of early signs of autism, improving access to diagnostic tools, reducing the stigma attached to the diagnosis of autism, and addressing various culturally sensitive concepts related to the disorder.

Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification

Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). When combined with dimension reduction techniques, such as principal component analysis, functional connectivity estimated with DTW showed greater predictive ability than functional connectivity estimated with PC. Our results suggest that DTW fcMRI can be a suitable alternative measure that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation. In studying different variants of cross validation (CV), our results suggest that, when it is necessary to tune model hyperparameters and assess model performance at the same time, a K-fold CV nested within leave-one-out CV may be a competitive contender in terms of performance and computational speed, especially when sample size is not large.

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Viruses and autism: A Bi-mutual cause and effect

Autism spectrum disorder (ASD) is a group of heterogeneous, multi-factorial, neurodevelopmental disorders resulting from genetic and environmental factors interplay. Infection is a significant trigger of autism, especially during the critical developmental period. There is a strong interplay between the viral infection as a trigger and a result of ASD. We aim to highlight the mutual relationship between autism and viruses. We performed a thorough literature review and included 158 research in this review. Most of the literature agreed on the possible effects of the viral infection during the critical period of development on the risk of developing autism, especially for specific viral infections such as Rubella, Cytomegalovirus, Herpes Simplex virus, Varicella Zoster Virus, Influenza virus, Zika virus, and severe acute respiratory syndrome coronavirus 2. Viral infection directly infects the brain, triggers immune activation, induces epigenetic changes, and raises the risks of having a child with autism. At the same time, there is some evidence of increased risk of infection, including viral infections in children with autism, due to lots of factors. There is an increased risk of developing autism with a specific viral infection during the early developmental period and an increased risk of viral infections in children with autism. In addition, children with autism are at increased risk of infection, including viruses. Every effort should be made to prevent maternal and early-life infections and reduce the risk of autism. Immune modulation of children with autism should be considered to reduce the risk of infection.

Catecholaminergic and cholinergic neuromodulation in autism spectrum disorder: A comparison to attention-deficit hyperactivity disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by social impairments and restricted, repetitive behaviors. Treatment of ASD is notoriously difficult and might benefit from identification of underlying mechanisms that overlap with those disturbed in other developmental disorders, for which treatment options are more obvious. One example of the latter is attention-deficit hyperactivity disorder (ADHD), given the efficacy of especially stimulants in treatment of ADHD. Deficiencies in catecholaminergic systems [dopamine (DA), norepinephrine (NE)] in ADHD are obvious targets for stimulant treatment. Recent findings suggest that dysfunction in catecholaminergic systems may also be a factor in at least a subgroup of ASD. In this review we scrutinize the evidence for catecholaminergic mechanisms underlying ASD symptoms, and also include in this analysis a third classic ascending arousing system, the acetylcholinergic (ACh) network. We complement this with a comprehensive review of DA-, NE-, and ACh-targeted interventions in ASD, and an exploratory search for potential treatment-response predictors (biomarkers) in ASD, genetically or otherwise. Based on this review and analysis we propose that (1) stimulant treatment may be a viable option for an ASD subcategory, possibly defined by genetic subtyping; (2) cerebellar dysfunction is pronounced for a relatively small ADHD subgroup but much more common in ASD and in both cases may point toward NE- or ACh-directed intervention; (3) deficiency of the cortical salience network is sizable in subgroups of both disorders, and biomarkers such as eye blink rate and pupillometric data may predict the efficacy of targeting this underlying deficiency via DA, NE, or ACh in both ASD and ADHD.

Processing efficiency in pediatric cancer survivors: A review and operationalization for outcomes research and clinical utility

OBJECTIVE

Childhood cancer and cancer-related treatments disrupt brain development and maturation, placing survivors at risk for cognitive late effects. Given that assessment tools vary widely across researchers and clinicians, it has been daunting to identify distinct patterns in outcomes across diverse cancer types and to implement systematic neurocognitive screening tools. This review aims to operationalize processing efficiency skill impairment-or inefficient neural processing as measured by working memory and processing speed abilities-as a worthwhile avenue for continued study within the context of childhood cancer.

METHODS

A comprehensive literature review was conducted to examine the existing research on cognitive late effects and biopsychosocial risk factors in order to conceptualize processing efficiency skill trends in childhood cancer survivors.

RESULTS

While a frequently reported pattern of neurobiological (white matter) and cognitive (working memory and processing speed) disruption is consistent with processing efficiency skill impairment, these weaknesses have not yet been fully operationalized in this population. We offer a theoretical model that highlights the impacts of a host of biological and environmental factors on the underlying neurobiological substrates of cancer survivors that precede and may even predict long-term cognitive outcomes and functional abilities following treatment.

CONCLUSION

The unified construct of processing efficiency may be useful in assessing and communicating neurocognitive skills in both outcomes research and clinical practice. Deficits in processing efficiency may serve as a possible indicator of cognitive late effects and functional outcomes due to the unique relationship between processing efficiency skills and neurobiological disruption following cancer treatment. Continued research along these lines is crucial for advancing childhood cancer outcomes research and improving quality of life for survivors.

Measures of tonic and phasic activity of the locus coeruleus-norepinephrine system in children with autism spectrum disorder: An event-related potential and pupillometry study

A growing body of research suggests that locus coeruleus-norepinephrine (LC-NE) system may function differently in individuals with autism spectrum disorder (ASD). Understanding the dynamics of both tonic (resting pupil diameter) and phasic (pupil dilation response [PDR] and event-related potential [ERP]) indices may provide meaningful insights about the nature of LC-NE function in ASD. Twenty-four children with ASD and 27 age- and nonverbal-IQ matched typically developing (TD) children completed two experiments: (1) a resting eye-tracking task to measure tonic pupil diameter, and (2) a three-stimulus oddball paradigm to measure phasic responsivity using PDR and ERP. Consistent with prior reports, our results indicate that children with ASD exhibit increased tonic (resting pupil diameter) and reduced phasic (PDR and ERP) activity of the LC-NE system compared to their TD peers. For both groups, decreased phasic responsivity was associated with increased resting pupil diameter. Lastly, tonic and phasic LC-NE indices were primarily related to measures of attention-deficit/hyperactivity disorder (ADHD), and not ASD, symptomatology. These findings expand our understanding of neurophysiological differences present in ASD and demonstrate that aberrant LC-NE activation may be associated with atypical arousal and decreased responsivity to behaviorally-relevant information in ASD.

Examining the Use of Game-Based Assessments for Hiring Autistic Job Seekers

Although people with autism are protected under the Americans with Disabilities Act of 1990, there is little theoretical or practical effort to determine whether traditional pre-employment assessments unfairly impact autistic job seekers. Due to the lack of emphasis on social communication, game-based assessments (GBAs) may offer a way of assessing candidate ability without disadvantaging autistic candidates. A total of 263 autistic job seekers took one of two game-based assessment packages designed to measure cognitive ability. After comparing their results to 323 college-graduate job seekers in the general population, we found that performance on the GBAs was generally similar in both populations, although some small differences were detected. Implications for hiring decisions are discussed.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Functional brain abnormalities associated with comorbid anxiety in autism spectrum disorder

Anxiety disorders are common in autism spectrum disorder (ASD) and associated with social-communication impairment and repetitive behavior symptoms. The neurobiology of anxiety in ASD is unknown, but amygdala dysfunction has been implicated in both ASD and anxiety disorders. Using resting-state functional magnetic resonance imaging, we compared amygdala-prefrontal and amygdala-striatal connections across three demographically matched groups studied in the Autism Brain Imaging Data Exchange (ABIDE): ASD with a comorbid anxiety disorder (N = 25; ASD + Anxiety), ASD without a comorbid disorder (N = 68; ASD-NoAnx), and typically developing controls (N = 139; TD). Relative to ASD-NoAnx and TD controls, ASD + Anxiety individuals had decreased connectivity between the amygdala and dorsal/rostral anterior cingulate cortex (dACC/rACC). The functional connectivity of these connections was not affected in ASD-NoAnx, and amygdala connectivity with ventral ACC/medial prefrontal cortex (mPFC) circuits was not different in ASD + Anxiety or ASD-NoAnx relative to TD. Decreased amygdala-dorsomedial prefrontal cortex (dmPFC)/rACC connectivity was associated with more severe social impairment in ASD + Anxiety; amygdala-striatal connectivity was associated with restricted, repetitive behavior (RRB) symptom severity in ASD-NoAnx individuals. These findings suggest comorbid anxiety in ASD is associated with disrupted emotion-monitoring processes supported by amygdala-dACC/mPFC pathways, whereas emotion regulation systems involving amygdala-ventromedial prefrontal cortex (vmPFC) are relatively spared. Our results highlight the importance of accounting for comorbid anxiety for parsing ASD neurobiological heterogeneity.

Changes in Electroencephalogram Coherence in Adolescents With Autism Spectrum Disorder After a Social Skills Intervention

Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavior. There is consensus that neurological differences are present in ASD. Further, theories emphasize the mixture of hypo- and hyper-connectivity as a neuropathologies in ASD [O'Reilly, Lewis, & Elsabbagh, 2017]; however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram [EEG], coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention, using a randomized controlled trial of two groups: an Experimental ASD (EXP) Group and a Waitlist Control ASD (WL) Group. The study had two purposes. First, the study aimed to determine whether changes in EEG coherence differed for adolescents that received PEERS® versus those that did not receive PEERS®. Results revealed a significant increase in connectivity in the occipital left to temporal left pair for the EXP group after intervention. Second, the study aimed to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills measured by questionnaires. At post-intervention, results indicated: (a) positive change in frontal right to parietal right coherence was linked to an increase in social skills scores; and (b) positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get-togethers. Results of this study support utilizing neurobehavioral domains as indicators of treatment outcome. Lay Summary: This study examined how well various areas of the brain communicate in adolescents with ASD before and after a social skills intervention. Results revealed increased connectivity in the adolescents that received the intervention. Secondly, the study aimed to determine if changes in connectivity of brain areas related to changes in behavior, friendships, and social skills. Results indicated that changes in connectivity were also linked to increased social skills. Autism Res 2021, 14: 787-803. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder

A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.

Hyperconnectivity of social brain networks in autism during action-intention judgment

Deficits in social communication in autism spectrum disorder (ASD) have been documented using neuroimaging techniques such as functional MRI over the past decade. More recently, functional connectivity MRI has revealed altered connectivity in face processing, mentalizing, and mirroring brain networks, networks involved in the social brain in ASD. However, to our knowledge, previous studies have not examined these three networks concurrently. The purpose of the current study was to investigate the functional connectivity of the face processing, mentalizing, and mirroring networks (within each network and across networks) in ASD during an action-intention task in which participants were asked to determine the means and intention of a model's actions. We examined: a) within-network connectivity of each network using an ROI-to-ROI analysis; b) connectivity of each network hub to the rest of the brain using a seed-to-voxel analysis; c) the between-network connectivity of each network hub using ROI-to-ROI analysis; and d) brain-behavior relationships by correlating autism symptoms with brain connectivity. Task-fMRI data were used from 21 participants with ASD and 20 typically developing participants. The ASD group consistently showed significantly greater connectivity between networks and between hub regions to the rest of the brain. Hyperconnectivity in ASD may entail more and widespread resource utilization for accomplishing action-intention judgment.

Supplementation of Diet With Different n-3/n-6 PUFA Ratios Ameliorates Autistic Behavior, Reduces Serotonin, and Improves Intestinal Barrier Impairments in a Valproic Acid Rat Model of Autism

The implication of different dietary n-3/n-6 polyunsaturated fatty acids (PUFAs) ratios has been investigated in some neurodevelopmental disorders (including autism and depression). However, the mechanisms underlying the effects of different PUFAs ratios on the autism are still poorly understood. In the present study, a valproic acid (VPA) rat model of autism was used to study the effects of diet with different n-3/n-6 PUFA ratios on the autism, and the underlying mechanisms explored. Our results showed that rats with prenatal administration of VPA took less response time to sniff three odorants in the olfactory habituation/dishabituation tests, had lower frequency of pinning and following patterns, and had decreased hippocampal 5-hydroxytryptamine (5-HT), increased serum 5-HT and downregulated expression of tight junction protein (occludin and claudin-1) in the colon. However, supplementation of n-3/n-6 PUFAs (1:5) in the VPA treated rats ameliorated the autistic behaviors, increased hippocampal 5-HT and tight junction expression in the colon, and decreased serum 5-HT. In conclusion, dietary supplementation of n-3/n-6 PUFAs (1:5) significantly improves VPA-induced autism-like behaviors in rats, which may be, at least partially, related to the increased hippocampal 5-HT. Furthermore, this diet can increase the expression of tight junction proteins to improve intestinal barrier impairment.

The language network in autism: Atypical functional connectivity with default mode and visual regions

Autism spectrum disorders (ASDs) are neurodevelopmental disorders associated with atypical brain connectivity. Although language abilities vary widely, they are impaired or atypical in most children with ASDs. Underlying brain mechanisms, however, are not fully understood. The present study examined intrinsic functional connectivity (iFC) of the extended language network in a cohort of 52 children and adolescents with ASDs (ages 8-18 years), using resting-state functional magnetic resonance imaging. We found that, in comparison to typically developing peers (n = 50), children with ASDs showed increased connectivity between some language regions. In addition, seed-to-whole brain analyses revealed increased connectivity of language regions with posterior cingulate cortex (PCC) and visual regions in the ASD group. Post hoc effective connectivity analyses revealed a mediation effect of PCC on the iFC between bilateral inferior frontal and visual regions in an ASD subgroup. This finding qualifies and expands on previous reports of recruitment of visual areas in language processing in ASDs. In addition, increased iFC between PCC and visual regions was linked to lower language scores in this ASD subgroup, suggesting that increased connectivity with visual cortices, mediated by default mode regions, may be detrimental to language abilities. Autism Res 2019, 12: 1344-1355. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We examined the functional connectivity between regions of the language network in children with autism spectrum disorders (ASDs) compared to typically developing peers. We found connectivity to be intact between core language in the ASD group, but also showed abnormally increased connectivity between regions of an extended language network. Additionally, connectivity was increased with regions associated with brain networks responsible for self-reflection and visual processing.

BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder

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Resting-state BOLD signal variability and complexity were examined.

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No significant group differences were observed in youth with and without autism.

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A continuum of brain-behavior relationships was observed across diagnostic groups.

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Positive correlations were found between brain measures, age and global efficiency.

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Negative correlations were found between the brain measures and behavioral severity.

Variability of neural signaling is an important index of healthy brain functioning, as is signal complexity, which relates to information processing capacity. Alterations in variability and complexity may underlie certain brain dysfunctions. Here, resting-state fMRI was used to examine variability and complexity in children and adolescents with and without autism spectrum disorder (ASD). Variability was measured using the mean square successive difference (MSSD) of the time series, and complexity was assessed using sample entropy. A categorical approach was implemented to determine if the brain measures differed between diagnostic groups (ASD and controls). A dimensional approach was used to examine the continuum of relationships between each brain measure and behavioral severity, age, IQ, and the global efficiency (GE) of each participant’s structural connectome, which reflects the structural capacity for information processing. Using the categorical approach, no significant group differences were found for neither MSSD nor entropy. The dimensional approach revealed significant positive correlations between each brain measure, GE, and age. Negative correlations were observed between each brain measure and the severity of ASD behaviors across all participants. These results reveal the nature of variability and complexity of BOLD signals in children and adolescents with and without ASD.

Targeting dynamic facial processing mechanisms in superior temporal sulcus using a novel fMRI neurofeedback target

The superior temporal sulcus (STS) encompasses a complex set of regions involved in a wide range of cognitive functions. To understand its functional properties, neuromodulation approaches such brain stimulation or neurofeedback can be used. We investigated whether the posterior STS (pSTS), a core region in the face perception and imagery network, could be specifically identified based on the presence of dynamic facial expressions (and not just on simple motion or static face signals), and probed with neurofeedback. Recognition of facial expressions is critically impaired in autism spectrum disorder, making this region a relevant target for future clinical neurofeedback studies. We used a stringent localizer approach based on the contrast of dynamic facial expressions against static neutral faces plus moving dots. The target region had to be specifically responsive to dynamic facial expressions instead of mere motion and/or the presence of a static face. The localizer was successful in selecting this region across subjects. Neurofeedback was then performed, using this region as a target, with two novel feedback rules (mean or derivative-based, using visual or auditory interfaces). Our results provide evidence that a facial expression-selective cluster in pSTS can be identified and may represent a suitable target for neurofeedback approaches, aiming at social and emotional cognition. These findings highlight the presence of a highly selective region in STS encoding dynamic aspects of facial expressions. Future studies should elucidate its role as a mechanistic target for neurofeedback strategies in clinical disorders of social cognition such as autism.

Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in Autism Spectrum Disorder

It is hypothesized that brain network abnormalities in autism spectrum disorder (ASD) reflect local overconnectivity and long-range underconnectivity. However, this is not a consistent finding in recent studies, which could reflect the developmental nature and the heterogeneity of ASD. Here, we tested 565 ASD and 602 neurotypical (NT) males, and 91 ASD and 233 NT females using local functional connectivity density (lFCD) mapping and seed-voxel correlation analyses to assess how local and long-range connectivities differ in ASD. Compared with NT males, ASD males had lower and weaker age-related increases in thalamic lFCD, which were associated with symptoms of autism. Post-hoc seed-voxel correlation analyses for the thalamus cluster revealed stronger connectivity with auditory, somatosensory, motoric, and interoceptive cortices for ASD than for NT, both in males and in females, which decreased with age in both ASD and NT. These results document the disruption of local thalamic connectivity and dysregulation of thalamo-cortical networks, which might contribute to perceptual, motoric, and interoceptive impairments, and are also consistent with a developmental delay in functional connectivity in ASD.

Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns ("states"), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole-brain, data-driven regions of interest were derived from group independent component analysis. Sliding window analysis and k-means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher-order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.

Neuromodulation and a Reconceptualization of Autism Spectrum Disorders: Using the Locus Coeruleus Functioning as an Exemplar

The Autism Spectrum Disorders (ASD) are a heterogeneous group of developmental disorders. Although, ASD can be reliably diagnosed, the etiology, pathophysiology, and treatment targets remain poorly characterized. While there are many atypical findings in anatomy, genetics, connectivity, and other biologic parameters, there remains no discreet hypothesis to explain the core signs as well as the very frequent comorbidities. Due to this, designing targets for treatments can only be done by assuming each symptom is a result of a discreet abnormality which is likely not the case. Neuronal circuity remains a major focus of research but rarely taking into account the functioning of the brain is highly dependent on various systems, including the neuromodulatory substances originating in the midbrain. A hypothesis is presented which explores the possibility of explaining many of the symptoms found in ASD in terms of inefficient neuromodulation using the functioning of the locus coeruleus and norepinephrine (LC/NE) as exemplars. The basic science of LC/NE is reviewed. Several functions found to be impaired in ASD including learning, attention, sensory processing, emotional regulation, autonomic functioning, adaptive and repetitive behaviors, sleep, language acquisition, initiation, and prompt dependency are examined in terms of the functioning of the LC/NE system. Suggestions about possible treatment directions are explored.

Local resting state functional connectivity in autism: site and cohort variability and the effect of eye status

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with prominent impairments in sociocommunicative abilities, which have been linked to anomalous brain network organization. Despite ample evidence of atypical long-distance connectivity, the literature on local connectivity remains small and divergent. We used resting-state functional MRI regional homogeneity (ReHo) as a local connectivity measure in comparative analyses across several well-matched low-motion subsamples from the Autism Brain Imaging Data Exchange and in-house data, with a grand total of 147 ASD and 184 typically developing (TD) participants, ages 7-18 years. We tested for group differences in each subsample, with additional focus on the difference between eyes-open and eyes-closed resting states. Despite selection of highest quality data and tight demographic and motion matching between groups and across samples, few effects in exactly identical loci (voxels) were found across samples. However, there was gross consistency across all eyes-open samples of local overconnectivity (ASD > TD) in posterior, visual regions. There was also gross consistency of local underconnectivity (ASD < TD) in cingulate gyrus, although exact loci varied between mid/posterior and anterior sections. While all eyes-open datasets showed the described gross similarities, the pattern of group differences for participants scanned with eyes closed was different, with local overconnectivity in ASD in posterior cingulate gyrus, but underconnectivity in some visual regions. Our findings suggest that fMRI local connectivity measures may be relatively susceptible to site and cohort variability and that some previous inconsistencies in the ASD ReHo literature may be reconciled by more careful consideration of eye status.

Distributed Intrinsic Functional Connectivity Patterns Predict Diagnostic Status in Large Autism Cohort

Diagnosis of autism spectrum disorder (ASD) currently relies on behavioral observations because brain markers are unknown. Machine learning approaches can identify patterns in imaging data that predict diagnostic status, but most studies using functional connectivity MRI (fcMRI) data achieved only modest accuracies of 60-80%. We used conditional random forest (CRF), an ensemble learning technique protected against bias from feature correlation (which exists in fcMRI matrices). We selected 252 low-motion resting-state functional MRI scans from the Autism Brain Imaging Data Exchange, including 126 typically developing (TD) and 126 ASD participants, matched for age, nonverbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification. In several runs, we achieved accuracies of 92-99% for classifiers with >300 features (most informative connections). Features, including pericentral somatosensory and motor regions, were disproportionately informative. Findings differed partially from a previous study in the same sample that used feature selection with random forest (which is biased by feature correlations). External validation in a smaller in-house data set, however, achieved only 67-71% accuracy. The large number of features in optimal models can be attributed to etiological heterogeneity under the clinical ASD umbrella. Lower accuracy in external validation is expected due to differences in unknown composition of ASD variants across samples. High accuracy in the main data set is unlikely due to noise overfitting, but rather indicates optimized characterization of a given cohort.

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

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

Generative models for clinical applications in computational psychiatry

Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto-/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us. This article is categorized under: Neuroscience > Computation Neuroscience > Clinical Neuroscience.

Autism Spectrum Disorder Related Functional Connectivity Changes in the Language Network in Children, Adolescents and Adults

Autism spectrum disorder (ASD) is a neurodevelopmental disability with global implication. Altered brain connectivity in the language network has frequently been reported in ASD patients using task-based functional magnetic resonance imaging (fMRI) compared to typically developing (TD) participants. Most of these studies have focused on a specific age group or mixed age groups with ASD. In the current study, we investigated age-related changes in functional connectivity related measure, degree centrality (DC), in the language network across three age groups with ASD (113 children, 113 adolescents and 103 adults) using resting-state fMRI data collected from the autism brain imaging data exchange repository. We identified regions with significant group-wise differences between ASD and TD groups for three age cohorts using DC based on graph theory. We found that both children and adolescents with ASD showed decreased DC in Broca's area compared to age-matched TD groups. Adults with ASD showed decreased DC in Wernicke's area compared to TD adults. We also observed increased DC in the left inferior parietal lobule (IPL) and left middle temporal gyrus (MTG) for children with ASD compared to TD children and for adults with ASD compared to TD adults, respectively. Overall, functional differences occurred in key language processing regions such as the left inferior frontal gyrus (IFG) and superior temporal gyrus (STG) related to language production and comprehension across three age cohorts. We explored correlations between DC values of our findings with autism diagnostic observation schedule (ADOS) scores related to severity of ASD symptoms in the ASD group. We found that DC values of the left IFG demonstrated negative correlations with ADOS scores in children and adolescents with ASD. The left STG showed significant negative correlations with ADOS scores in adults with ASD. These results might shed light on the language network regions that should be further explored for prognosis, diagnosis, and monitoring of ASD in three age groups.

Altered expression of circadian rhythm and extracellular matrix genes in the medial prefrontal cortex of a valproic acid rat model of autism

Autism spectrum disorders (ASD) are a highly heterogeneous group of neurodevelopmental disorders caused by complex interplay between various genes and environmental factors during embryonic development. Changes at the molecular, cellular and neuroanatomical levels are especially evident in the medial prefrontal cortex (mPFC) of ASD patients and are particularly contributing to social impairments. In the present study we tested the hypothesis that altered neuronal development and plasticity, as seen in the mPFC of ASD individuals, may result from aberrant expression of functionally connected genes. Towards this end, we combined transcriptome sequencing and computational gene ontology analysis to identify the molecular networks impaired in the mPFC of a valproic acid (VPA) rat model of autism. This investigation identified two subsets of genes differentially expressed in the mPFC of VPA rats: one group of genes being functionally involved in the regulation of the circadian rhythm, while the second group encompasses a set of differentially expressed collagen genes acting within the extracellular matrix. Ultimately, our integrated transcriptome analysis identified a distinct subset of altered gene networks in the mPFC of VPA rats, contributing to our understanding of autism at the molecular level, thus providing novel insight into the genetic alterations associated with this neurodevelopmental disorder.

Partial Agenesis and Hypoplasia of the Corpus Callosum in Idiopathic Autism

To test the hypothesis that developmental anomalies of the corpus callosum (CC), contribute to the pathogenesis of autism, we characterized the type, topography, and severity of CC pathology corresponding to reduced CC areas that are detected by magnetic resonance imaging in the brains of 11 individuals with autism and 11 controls. In the brains of 3 autistic subjects, partial CC agenesis resulted in complete or partial lack of interhemispheric axonal connections in CC segments III-V. In these cases, a combination of focal agenesis and uniform axonal deficit caused reduction of CC areas by 37%, of axon numbers by 62%, and of the numerical density of axons by 39%. In the CC of 8 autistic subjects without agenesis, there was an 18% deficit of the midsagittal CC area, 48.4% deficit of axon numbers, and 37% reduction of the numerical density of axons. The significantly thinner CC, reduced CC area, and uniform axonal deficit in all autistic subjects were classified as CC hypoplasia. Thus, the byproduct of partial CC agenesis and hypoplasia is reduction of axonal connections between cortical areas known to be involved in behavioral alterations observed in people with autism.

Current Practice and Future Avenues in Autism Therapy

Autism spectrum disorders (ASDs) are neurodevelopmental disorders with early onset, characterized by deficits in social communication and repetitive and restricted interests and activities. A growing number of studies over the last 10 years support the efficacy of behaviorally based interventions in ASD for the improvement of social communication and behavioral functioning. In contrast, research on neurobiological based therapies for ASD is still at its beginnings. In this article, we will provide a selective overview of both well-established evidence-based treatments and novel interventions and drug treatments based on neurobiological principles aiming at improving core symptoms in ASD. Directions and options for future research on treatment in ASD are discussed.

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.

From molecules to behavior: An integrative theory of autism spectrum disorder

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders for which various theories have been proposed. Each theory brings valuable insights and has experimental evidence backing it, yet none provides an overarching explanation for each of the pathological aspects involved in ASD. Here we present an integrative theory of ASD, centered on a sequence of events spanning from the molecular to the behavioral level. We propose that an abnormality in the interplay between retinoic acid and sex hormones predisposes an individual to specific molecular malfunctions. In turn, this molecular syndrome generates an altered brain connectivity between the cerebellum, the midbrain dopaminergic areas, and the prefrontal cortex. Lastly, this disconnection would generate specific behavioral traits traditionally involved in ASD. Therefore, this paper represents a step forward in unifying different levels of pathological features into novel integrated testable hypotheses.

Patterns of Atypical Functional Connectivity and Behavioral Links in Autism Differ Between Default, Salience, and Executive Networks

Autism spectrum disorder (ASD) is characterized by atypical brain network organization, but findings have been inconsistent. While methodological and maturational factors have been considered, the network specificity of connectivity abnormalities remains incompletely understood. We investigated intrinsic functional connectivity (iFC) for four "core" functional networks-default-mode (DMN), salience (SN), and left (lECN) and right executive control (rECN). Resting-state functional MRI data from 75 children and adolescents (37 ASD, 38 typically developing [TD]) were included. Functional connectivity within and between networks was analyzed for regions of interest (ROIs) and whole brain, compared between groups, and correlated with behavioral scores. ROI analyses showed overconnectivity (ASD > TD), especially between DMN and ECN. Whole-brain results were mixed. While predominant overconnectivity was found for DMN (posterior cingulate seed) and rECN (right inferior parietal seed), predominant underconnectivity was found for SN (right anterior insula seed) and lECN (left inferior parietal seed). In the ASD group, reduced SN integrity was associated with sensory and sociocommunicative symptoms. In conclusion, atypical connectivity in ASD is network-specific, ranging from extensive overconnectivity (DMN, rECN) to extensive underconnectivity (SN, lECN). Links between iFC and behavior differed between groups. Core symptomatology in the ASD group was predominantly related to connectivity within the salience network.

Uncovering the Social Deficits in the Autistic Brain. A Source-Based Morphometric Study

Autism is a neurodevelopmental disorder that mainly affects social interaction and communication. Evidence from behavioral and functional MRI studies supports the hypothesis that dysfunctional mechanisms involving social brain structures play a major role in autistic symptomatology. However, the investigation of anatomical abnormalities in the brain of people with autism has led to inconsistent results. We investigated whether specific brain regions, known to display functional abnormalities in autism, may exhibit mutual and peculiar patterns of covariance in their gray-matter concentrations. We analyzed structural MRI images of 32 young men affected by autistic disorder (AD) and 50 healthy controls. Controls were matched for sex, age, handedness. IQ scores were also monitored to avoid confounding. A multivariate Source-Based Morphometry (SBM) was applied for the first time on AD and controls to detect maximally independent networks of gray matter. Group comparison revealed a gray-matter source that showed differences in AD compared to controls. This network includes broad temporal regions involved in social cognition and high-level visual processing, but also motor and executive areas of the frontal lobe. Notably, we found that gray matter differences, as reflected by SBM, significantly correlated with social and behavioral deficits displayed by AD individuals and encoded via the Autism Diagnostic Observation Schedule scores. These findings provide support for current hypotheses about the neural basis of atypical social and mental states information processing in autism.

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.

Autistic-Like Traits and Cerebellar Dysfunction in Purkinje Cell PTEN Knock-Out Mice

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by impaired social interaction, isolated areas of interest, and insistence on sameness. Mutations in Phosphatase and tensin homolog missing on chromosome 10 (PTEN) have been reported in individuals with ASDs. Recent evidence highlights a crucial role of the cerebellum in the etiopathogenesis of ASDs. In the present study we analyzed the specific contribution of cerebellar Purkinje cell (PC) PTEN loss to these disorders. Using the Cre-loxP recombination system, we generated conditional knockout mice in which PTEN inactivation was induced specifically in PCs. We investigated PC morphology and physiology as well as sociability, repetitive behavior, motor learning, and cognitive inflexibility of adult PC PTEN-mutant mice. Loss of PTEN in PCs results in autistic-like traits, including impaired sociability, repetitive behavior and deficits in motor learning. Mutant PCs appear hypertrophic and show structural abnormalities in dendrites and axons, decreased excitability, disrupted parallel fiber and climbing fiber synapses and late-onset cell death. Our results unveil new roles of PTEN in PC function and provide the first evidence of a link between the loss of PTEN in PCs and the genesis of ASD-like traits.

Subregional differences in intrinsic amygdala hyperconnectivity and hypoconnectivity in autism spectrum disorder

The amygdala is a complex structure with distinct subregions and dissociable functional networks. The laterobasal subregion of the amygdala is hypothesized to mediate the presentation and severity of autism symptoms, although very little data are available regarding amygdala dysfunction at the subregional level. In this study, we investigated the relationship between abnormal amygdalar intrinsic connectivity, autism symptom severity, and anxiety and depressive symptoms. We collected resting state fMRI data on 31 high functioning adolescents and adults with autism spectrum disorder and 38 typically developing (TD) controls aged 14-45. Twenty-five participants with ASD and 28 TD participants were included in the final analyses. ASD participants were administered the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule. Adult participants were administered the Beck Depression Inventory II and the Beck Anxiety Inventory. Functional connectivity analyses were conducted from three amygdalar subregions: centromedial (CM), laterobasal (LB) and superficial (SF). In addition, correlations with the behavioral measures were tested in the adult participants. In general, the ASD group showed significantly decreased connectivity from the LB subregion and increased connectivity from the CM and SF subregions compared to the TD group. We found evidence that social symptoms are primarily associated with under-connectivity from the LB subregion whereas over-connectivity and under-connectivity from the CM, SF and LB subregions are related to co-morbid depression and anxiety in ASD, in brain regions that were distinct from those associated with social dysfunction, and in different patterns than were observed in mildly symptomatic TD participants. Our findings provide new evidence for functional subregional differences in amygdala pathophysiology in ASD. Autism Res 2016, 9: 760-772. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Reduced integration and differentiation of the imitation network in autism: A combined functional connectivity magnetic resonance imaging and diffusion-weighted imaging study

OBJECTIVE

Converging evidence indicates that brain abnormalities in autism spectrum disorder (ASD) involve atypical network connectivity, but few studies have integrated functional with structural connectivity measures. This multimodal investigation examined functional and structural connectivity of the imitation network in children and adolescents with ASD, and its links with clinical symptoms.

METHODS

Resting state functional magnetic resonance imaging and diffusion-weighted imaging were performed in 35 participants with ASD and 35 typically developing controls, aged 8 to 17 years, matched for age, gender, intelligence quotient, and head motion.

RESULTS

Within-network analyses revealed overall reduced functional connectivity (FC) between distributed imitation regions in the ASD group. Whole brain analyses showed that underconnectivity in ASD occurred exclusively in regions belonging to the imitation network, whereas overconnectivity was observed between imitation nodes and extraneous regions. Structurally, reduced fractional anisotropy and increased mean diffusivity were found in white matter tracts directly connecting key imitation regions with atypical FC in ASD. These differences in microstructural organization of white matter correlated with weaker FC and greater ASD symptomatology.

INTERPRETATION

Findings demonstrate atypical connectivity of the brain network supporting imitation in ASD, characterized by a highly specific pattern. This pattern of underconnectivity within, but overconnectivity outside the functional network is in contrast with typical development and suggests reduced network integration and differentiation in ASD. Our findings also indicate that atypical connectivity of the imitation network may contribute to ASD clinical symptoms, highlighting the role of this fundamental social cognition ability in the pathophysiology of ASD.

Corticospinal tract anatomy and functional connectivity of primary motor cortex in autism

OBJECTIVE

Growing evidence indicates that autism spectrum disorder (ASD) stems from abnormal structural and functional connectivity of neural networks. Although diagnostic symptoms are sociocommunicative, motor-related functions (beyond repetitive mannerisms) are also impaired. However, evidence on connectivity at the level of basic motor execution is limited, which we address here.

METHOD

We compared right-handed children and adolescents (aged 7-18 years) with ASD (n = 44) to matched typically developing participants (TD, n = 36) using magnetic resonance imaging (MRI). Diffusion-weighted imaging and probabilistic tractography measured microstructure of the corticospinal tract (CST). Intrinsic functional connectivity MRI examined whole-brain voxelwise correlations, both with identical precentral gyrus (PCG) seeds.

RESULTS

In the group with ASD, radial and mean diffusivity were increased bilaterally in the CST, particularly in superior segments, and a leftward asymmetry of CST volume detected in the TD group was reversed. Functionally, overconnectivity was found for both left and right PCG with prefrontal, parietal, medial occipital, and cingulate cortices. The group with ASD also showed significantly reduced asymmetry of functional connectivity for both left and right PCG seeds. Finally, in the group with ASD, significant correlations were found for functional overconnectivity of the right PCG seed with anisotropy and mean diffusivity in the right CST.

CONCLUSION

The findings, implicating both functional and anatomical connectivity of the primary motor cortex, suggest that network anomalies in ASD go well beyond sociocommunicative domains, affecting basic motor execution. They also suggest that even in right-handed adolescents with ASD, typical left hemisphere dominance is reduced, both anatomically and functionally, with an unusual degree of right hemisphere motor participation.

Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

IMPORTANCE

Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks.

OBJECTIVES

To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories.

DESIGN, SETTING, AND PARTICIPANTS

We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012.

MAIN OUTCOMES AND MEASURES

We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale.

RESULTS

Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ -0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ -0.13, P < .009 corrected).

CONCLUSIONS AND RELEVANCE

Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Robust disruptions in electroencephalogram cortical oscillations and large-scale functional networks in autism

BACKGROUND

Autism spectrum disorders (ASD) are increasingly prevalent and have a significant impact on the lives of patients and their families. Currently, the diagnosis is determined by clinical judgment and no definitive physiological biomarker for ASD exists. Quantitative biomarkers obtainable from clinical neuroimaging data - such as the scalp electroencephalogram (EEG) - would provide an important aid to clinicians in the diagnosis of ASD. The interpretation of prior studies in this area has been limited by mixed results and the lack of validation procedures. Here we use retrospective clinical data from a well-characterized population of children with ASD to evaluate the rhythms and coupling patterns present in the EEG to develop and validate an electrophysiological biomarker of ASD.

METHODS

EEG data were acquired from a population of ASD (n = 27) and control (n = 55) children 4-8 years old. Data were divided into training (n = 13 ASD, n = 24 control) and validation (n = 14 ASD, n = 31 control) groups. Evaluation of spectral and functional network properties in the first group of patients motivated three biomarkers that were computed in the second group of age-matched patients for validation.

RESULTS

Three biomarkers of ASD were identified in the first patient group: (1) reduced posterior/anterior power ratio in the alpha frequency range (8-14 Hz), which we label the "peak alpha ratio", (2) reduced global density in functional networks, and (3) a reduction in the mean connectivity strength of a subset of functional network edges. Of these three biomarkers, the first and third were validated in a second group of patients. Using the two validated biomarkers, we were able to classify ASD subjects with 83 % sensitivity and 68 % specificity in a post-hoc analysis.

CONCLUSIONS

This study demonstrates that clinical EEG can provide quantitative biomarkers to assist diagnosis of autism. These results corroborate the general finding that ASD subjects have decreased alpha power gradients and network connectivities compared to control subjects. In addition, this study demonstrates the necessity of using statistical techniques to validate EEG biomarkers identified using exploratory methods.

Development of the uncinate fasciculus: Implications for theory and developmental disorders

The uncinate fasciculus (UF) is a long-range white matter tract that connects limbic regions in the temporal lobe to the frontal lobe. The UF is one of the latest developing tracts, and continues maturing into the third decade of life. As such, individual differences in the maturational profile of the UF may serve to explain differences in behavior. Indeed, atypical macrostructure and microstructure of the UF have been reported in numerous studies of individuals with developmental and psychiatric disorders such as social deprivation and maltreatment, autism spectrum disorders, conduct disorder, risk taking, and substance abuse. The present review evaluates what we currently know about the UF's developmental trajectory and reviews the literature relating UF abnormalities to specific disorders. Additionally, we take a dimensional approach and critically examine symptoms and behavioral impairments that have been demonstrated to cluster with UF aberrations, in an effort to relate these impairments to our speculations regarding the functionality of the UF. We suggest that developmental disorders with core problems relating to memory retrieval, reward and valuation computation, and impulsive decision making may be linked to aberrations in uncinate microstructure.

Alterations of local spontaneous brain activity and connectivity in adults with high-functioning autism spectrum disorder

Background

Previous autism research has hypothesized that abnormalities of functional connectivity in autism spectrum disorder (ASD) may vary with the spatial distance between two brain regions. Although several resting-state functional magnetic resonance imaging (rsfMRI) studies have extensively examined long-range (or distant) connectivity in the adult ASD brain, short-range (or local) connectivity has been investigated in less depth. Furthermore, the possible relationship between functional connectivity and brain activity level during the resting state remains unclear.

Methods

We acquired rsfMRI data from 50 adults with high-functioning ASD and 50 matched controls to examine the properties of spontaneous brain activity using measures of local and distant connectivity together with a measure of the amplitude of brain activity, known as fractional amplitude of low-frequency fluctuation (fALFF). The two connectivity measures were calculated using a common graph-theoretic framework. We also examined the spatial overlaps between these measures and possible relationships of these disrupted functional measures with autistic traits assessed by the Autism-Spectrum Quotient (AQ).

Results

Compared to the controls, participants with ASD exhibited local over-connectivity in the right superior frontal gyrus and middle frontal gyrus, accompanied by local under-connectivity in the bilateral fusiform gyri (FG) and right middle temporal gyrus (MTG). On the other hand, we did not find any significant alterations in distant connectivity. Participants with ASD also exhibited reduced fALFF in the right middle occipital gyrus, lingual gyrus, and FG. Further conjunction and spatial overlap analyses confirmed that the spatial pattern of reduced fALFF substantially overlapped with that of local under-connectivity, demonstrating the co-occurrence of disrupted connectivity and spontaneous activity level in the right inferior occipital gyrus, posterior MTG (pMTG), and FG. Finally, within the ASD group, disrupted local connectivity in the right pMTG significantly correlated with the “social interaction” subscale score of the AQ.

Conclusions

These findings revealed local functional disruptions in the occipital and temporal regions, especially the right FG and pMTG, in the form of co-occurrence of spontaneous brain activity level and local connectivity, which may underline social and communicative dysfunctions in adult ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0026-z) contains supplementary material, which is available to authorized users.

Elevated microRNA-181c and microRNA-30d levels in the enlarged amygdala of the valproic acid rat model of autism

Autism spectrum disorders are severe neurodevelopmental disorders, marked by impairments in reciprocal social interaction, delays in early language and communication, and the presence of restrictive, repetitive and stereotyped behaviors. Accumulating evidence suggests that dysfunction of the amygdala may be partially responsible for the impairment of social behavior that is a hallmark feature of ASD. Our studies suggest that a valproic acid (VPA) rat model of ASD exhibits an enlargement of the amygdala as compared to controls rats, similar to that observed in adolescent ASD individuals. Since recent research suggests that altered neuronal development and morphology, as seen in ASD, may result from a common post-transcriptional process that is under tight regulation by microRNAs (miRs), we examined genome-wide transcriptomics expression in the amygdala of rats prenatally exposed to VPA, and detected elevated miR-181c and miR-30d expression levels as well as dysregulated expression of their cognate mRNA targets encoding proteins involved in neuronal system development. Furthermore, selective suppression of miR-181c function attenuates neurite outgrowth and branching, and results in reduced synaptic density in primary amygdalar neurons in vitro. Collectively, these results implicate the small non-coding miR-181c in neuronal morphology, and provide a framework of understanding how dysregulation of a neurodevelopmentally relevant miR in the amygdala may contribute to the pathophysiology of ASD.

Differential Local Connectivity and Neuroinflammation Profiles in the Medial Prefrontal Cortex and Hippocampus in the Valproic Acid Rat Model of Autism

Autism spectrum disorders (ASD) are a group of developmental disabilities characterized by impaired social interaction, communication deficit and repetitive and stereotyped behaviors. Neuroinflammation and synaptic alterations in several brain areas have been suggested to contribute to the physiopathology of ASD. Although the limbic system plays an important role in the functions found impaired in ASD, reports on these areas are scarce and results controversial. In the present study we searched in the medial prefrontal cortex (mPFC) and hippocampus of rats exposed to the valproic acid (VPA) model of ASD for early structural and molecular changes, coincident in time with the behavioral alterations. After confirming delayed growth and maturation in VPA rats, we were able to detect decreased exploratory activity and social interaction at an early time point (postnatal day 35). In mPFC, although typical cortical column organization was preserved in VPA animals, we found that interneuronal space was wider than in controls. Hippocampal CA3 (cornu ammonis 3) pyramidal layer and the granular layer of the dentate gyrus both showed a disorganized spatial arrangement in VPA animals. Neuronal alterations were accompanied with increased tomato lectin and glial fibrillary acidic protein (GFAP) immunostainings both in the mPFC and hippocampus. In the latter region, the increased GFAP immunoreactivity was CA3 specific. At the synaptic level, while mPFC from VPA animals showed increased synaptophysin (SYN) immunostaining, a SYN deficit was found in all hippocampal subfields. Additionally, both the mPFC and the hippocampus of VPA rats showed increased neuronal cell adhesion molecule (NCAM) immunostaining together with decreased levels of its polysialylated form (PSA-NCAM). Interestingly, these changes were more robust in the CA3 hippocampal subfield. Our results indicate that exploratory and social deficits correlate with region-dependent neuronal disorganization and reactive gliosis in the mPFC and hippocampus of VPA rats. While microgliosis is spread in these two limbic areas, astrogliosis, although extended in the mPFC, is circumscribed to the CA3 hippocampal subfield. Our work indicates that neuroinflammation and synaptic alterations do coexist in VPA rats, making this model suitable for studying novel aspects of neuron-glia interactions. Moreover, it suggests that the mPFC and hippocampus might behave differently in the context of the local hyperconnectivity and synaptic hypotheses of autism.

Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism

Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets <70%), diagnostic classification reached a high accuracy of 91% with random forest (RF), a nonparametric ensemble learning method. Among the 100 most informative features (connectivities), for which this peak accuracy was achieved, participation of somatosensory, default mode, visual, and subcortical regions stood out. Whereas some of these findings were expected, given previous findings of default mode abnormalities and atypical visual functioning in ASD, the prominent role of somatosensory regions was remarkable. The finding of peak accuracy for 100 interregional functional connectivities further suggests that brain biomarkers of ASD may be regionally complex and distributed, rather than localized.

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Machine learning of resting fMRI attains high diagnostic accuracy for autism.

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Peak accuracy is seen for a complex pattern of 100 connectivities.

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Somatosensory regions are overall most informative.

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Default mode and visual regions also contribute to diagnostic accuracy.