Alterations in resting state connectivity along the autism trait continuum: a twin study

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.

Reduced brain connectivity along the autism spectrum controlled for familial confounding by co-twin design

Previous studies on brain connectivity correlates of autism have often focused on selective connections and yielded inconsistent results. By applying global fiber tracking and utilizing a within-twin pair design, we aimed to contribute to a more unbiased picture of white matter connectivity in association with clinical autism and autistic traits. Eighty-seven twin pairs (n = 174; 55% monozygotic; 24 with clinical autism) underwent diffusion tensor imaging. Linear regressions assessed within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism and autistic traits. These were explicitly adjusted for IQ, other neurodevelopmental/psychiatric conditions and multiple testing, and implicitly for biological sex, age, and all genetic and environmental factors shared by twins. Both clinical autism and autistic traits were associated with reductions in structural connectivity. Twins fulfilling diagnostic criteria for clinical autism had decreased brainstem-cuneus connectivity compared to their co-twins without clinical autism. Further, twins with higher autistic traits had decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations were also significant in dizygotic twins alone. Reduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while higher autistic traits seemed to be more associated with reduced connectivity in networks involving the hippocampus and the fusiform gyrus, crucial especially for processing of faces and other (higher order) visual processing. The observed associations were likely influenced by both genes and environment.

In search of environmental risk factors for obsessive-compulsive disorder: study protocol for the OCDTWIN project

BACKGROUND

The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD.

METHODS

OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited.

DISCUSSION

OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.

In search of environmental risk factors for obsessive-compulsive disorder: Study protocol for the OCDTWIN project

Background The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD. Methods OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited. Discussion OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.

Associations between Elemental Metabolic Dynamics and Default Mode Network Functional Connectivity Are Altered in Autism

Autism is a neurodevelopmental condition associated with atypical social communication, cognitive, and sensory faculties. Recent advances in exposure biology suggest that biomarkers of elemental uptake and metabolism measured in hair samples can yield an effective signal predictive of autism diagnosis. Here, we investigated if elemental biomarkers in hair were associated with functional connectivity in regions of the default mode network (DMN) previously linked to autism. In a study sample which included twin pairs with concordant and discordant diagnoses for autism, our analysis of hair samples and neuroimaging data supported two general findings. First, independent of autism diagnosis, we found a broad pattern of association between elemental biomarkers and functional connectivity in the DMN, which primarily involved dynamics in zinc metabolism. Second, we found that associations between the DMN and elemental biomarkers, particularly involving phosphorus, calcium, manganese, and magnesium, differed significantly in autistic participants from control participants. In sum, these findings suggest that functional dynamics in elemental metabolism relate broadly to persistent patterns of functional connectivity in the DMN, and that these associations are altered in the emergence of autism.

In Search of Biomarkers to Guide Interventions in Autism Spectrum Disorder: A Systematic Review

OBJECTIVE

The aim of this study was to catalog and evaluate response biomarkers correlated with autism spectrum disorder (ASD) symptoms to improve clinical trials.

METHODS

A systematic review of MEDLINE, Embase, and Scopus was conducted in April 2020. Seven criteria were applied to focus on original research that includes quantifiable response biomarkers measured alongside ASD symptoms. Interventional studies or human studies that assessed the correlation between biomarkers and ASD-related behavioral measures were included.

RESULTS

A total of 5,799 independent records yielded 280 articles for review that reported on 940 biomarkers, 755 of which were unique to a single publication. Molecular biomarkers were the most frequently assayed, including cytokines, growth factors, measures of oxidative stress, neurotransmitters, and hormones, followed by neurophysiology (e.g., EEG and eye tracking), neuroimaging (e.g., functional MRI), and other physiological measures. Studies were highly heterogeneous, including in phenotypes, demographic characteristics, tissues assayed, and methods for biomarker detection. With a median total sample size of 64, almost all of the reviewed studies were only powered to identify biomarkers with large effect sizes. Reporting of individual-level values and summary statistics was inconsistent, hampering mega- and meta-analysis. Biomarkers assayed in multiple studies yielded mostly inconsistent results, revealing a "replication crisis."

CONCLUSIONS

There is currently no response biomarker with sufficient evidence to inform ASD clinical trials. This review highlights methodological imperatives for ASD biomarker research necessary to make definitive progress: consistent experimental design, correction for multiple comparisons, formal replication, sharing of sample-level data, and preregistration of study designs. Systematic "big data" analyses of multiple potential biomarkers could accelerate discovery.

Hypoconnectivity between anterior insula and amygdala associates with future vulnerabilities in social development in a neurodiverse sample of neonates

Altered resting state functional connectivity (FC) involving the anterior insula (aINS), a key node in the salience network, has been reported consistently in autism. Here we examined, for the first time, FC between the aINS and the whole brain in a sample of full-term, postmenstrual age (PMA) matched neonates (mean 44.0 weeks, SD = 1.5) who due to family history have high likelihood (HL) for developing autism (n = 12) and in controls (n = 41) without family history of autism (low likelihood, LL). Behaviors associated with autism were evaluated between 12 and 18 months (M = 17.3 months, SD = 2.5) in a subsample (25/53) of participants using the First Year Inventory (FYI). Compared to LL controls, HL neonates showed hypoconnectivity between left aINS and left amygdala. Lower connectivity between the two nodes was associated with higher FYI risk scores in the social domain (r(25) = -0.561, p = .003) and this association remained robust when maternal mental health factors were considered. Considering that a subsample of LL participants (n = 14/41) underwent brain imaging during the fetal period at PMA 31 and 34 weeks, in an exploratory analysis, we evaluated prospectively development of the LaINS-Lamy connectivity and found that the two areas strongly coactivate throughout the third trimester of pregnancy. The study identifies left lateralized anterior insula-amygdala connectivity as a potential target of further investigation into neural circuitry that enhances likelihood of future onset of social behaviors associated with autism during neonatal and potentially prenatal periods.

Interindividual variability in functional connectivity discovers differential development of cognition and transdiagnostic dimensions of psychopathology in youth

Cognitive and psychological development during adolescence is different from one another, which is rooted in individual differences in maturational changes in the adolescent brain. This study employed multi-modal MRI data and characterized interindividual variability in functional connectivity (IVFC) and its associations with cognition and psychopathology using the Philadelphia Neurodevelopmental Cohort (PNC) of 755 youth. We employed resting state functional MRI (rs-fMRI) and diffusion weighted images (DWIs) to estimate brain structural and functional networks. We computed the IVFC of individuals and examined its relation with structural and functional organizations. We further employed sparse partial least squares (sparse-PLS) and meta-analysis to examine the developmental associations of the IVFC with cognition and transdiagnostic dimensions of psychopathology in early, middle, and late adolescence. Our results revealed that the IVFC spatial topography reflects the brain functional integration and structure-function decoupling. Age effects on the IVFC of association networks were mediated by the FC among the triple networks, including frontoparietal, salience, and default mode networks (DMN), while those of primary and cerebellar networks were mediated by the cerebello-cortical FC. The IVFC of the triple and cerebellar networks explained the variance of executive functions and externalizing behaviors in early adolescence and then the variance of emotion and internalizing and psychosis in middle and late adolescence. We further evaluated this finding via meta-analysis on task-based studies on cognition and psychopathology. These findings implicate the emerging importance of the IVFC of the triple and cerebellar networks in cognitive, emotional, and psychopathological development during adolescence.

Altered Periodic Dynamics in the Default Mode Network in Autism and Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Altered resting-state functional connectivity in the default mode network (DMN) is characteristic of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Standard analytical pipelines for resting-state functional connectivity focus on linear correlations in activation time courses between neural networks or regions of interest. These features may be insensitive to temporally lagged or nonlinear relationships.

METHODS

In a twin cohort study comprising 292 children, including 52 with a diagnosis of ASD and 70 with a diagnosis of ADHD, we applied nonlinear analytical methods to characterize periodic dynamics in the DMN. Using recurrence quantification analysis and related methods, we measured the prevalence, duration, and complexity of periodic processes within and between DMN regions of interest. We constructed generalized estimating equations to compare these features between neurotypical children and children with ASD and/or ADHD while controlling for familial relationships, and we leveraged machine learning algorithms to construct models predictive of ASD or ADHD diagnosis.

RESULTS

In within-pair analyses of twins with discordant ASD diagnoses, we found that DMN signal dynamics were significantly different in dizygotic twins but not in monozygotic twins. Considering our full sample, we found that these patterns allowed a robust predictive classification of both ASD (81.0% accuracy; area under the curve = 0.85) and ADHD (82% accuracy; area under the curve = 0.87) cases.

CONCLUSIONS

These findings indicate that synchronized periodicity among regions comprising the DMN relates both to neurotypical function and to ASD and/or ADHD, and they suggest generally that a dynamical analysis of network interconnectivity may be a useful methodology for future neuroimaging studies.

EEG microstates suggest atypical resting-state network activity in high-functioning children and adolescents with Autism Spectrum Development

EEG microstates represent transient electrocortical events that reflect synchronized activities of large-scale networks, which allows investigations of brain dynamics with sub-second resolution. We recorded resting EEG from 38 children and adolescents with Autism Spectrum Development (ASD) and 48 age, IQ, sex, and handedness-matched typically developing (TD) participants. The EEG was segmented into a time series of microstates using modified k-means clustering of scalp voltage topographies. The frequency and global explained variance (GEV) of a specific microstate (type C) were significantly lower in the ASD group compared to the TD group while the duration of the same microstate was correlated with the presence of ASD-related behaviors. The duration of this microstate was also positively correlated with participant age in the TD group, but not in the ASD group. Further, the frequency and duration of the microstate were significantly correlated with the overall alpha power only in the TD group. The signal strength and GEV for another microstate (type G) was greater in the ASD group than the TD group, and the associated topographical pattern differed between groups with greater variations in the ASD group. While more work is needed to clarify the underlying neural sources, the existing literature supports associations between the two microstates and the default mode and salience networks. The current study suggests specific alterations of temporal dynamics of the resting cortical network activities as well as their developmental trajectories and relationships to alpha power, which has been proposed to reflect reduced neural inhibition in ASD. This article is protected by copyright. All rights reserved.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.

Glutamate and functional connectivity - support for the excitatory-inhibitory imbalance hypothesis in autism spectrum disorders

It has been proposed that the Glutamate (Glu) system is implicated in autism spectrum disorders (ASD) via an imbalance between excitatory and inhibitory brain circuits, which impacts on brain function. Here, we investigated the excitatory-inhibitory imbalance theory by measuring Glu-concentrations and the relationship with resting-state function. Nineteen adult males with ASD and 19 age and sex-matched healthy controls (HC) (23 - 58 years) underwent Proton Magnetic Resonance Spectroscopy of the dorsal anterior cingulate cortex (dACC) and resting-state functional Magnetic Resonance Imaging (fMRI). Glu and Glx concentrations were compared between groups. Seed-based functional connectivity was analyzed with a priori seeds of the right and left dACC. Finally, metabolite concentrations were related to functional connectivity coefficients and compared between both groups. Individuals with ASD showed significantly negative associations between increased Glx concentrations and reduced functional connectivity between the dACC and insular, limbic and parietal regions. In contrast, HC displayed a positive relationship between the same metabolite and connectivity measures. We provided new evidence to support the excitatory-inhibitory imbalance theory, where excitatory Glx concentrations were related to functional dysconnectivity in ASD. Future research is needed to investigate large-scale functional networks in association with both excitatory and inhibitory metabolites in subpopulations of ASD.

Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity

Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.

Beneath the surface: hyper-connectivity between caudate and salience regions in ADHD fMRI at rest

Attention-Deficit/Hyperactivity Disorder (ADHD) comprises disturbances in attention, emotional regulation, and reward-related processes. In spite of the active efforts in researching neurofunctional correlates of these symptoms, how the activity of subcortical regions-such as basal ganglia-is related to ADHD has yet to be clarified. More specifically, how age may influence the critical changes observed in functional dynamics from childhood to adulthood remains relatively unexplored. We hence selected five core subcortical regions (amygdala, caudate, putamen, pallidum and hippocampus) as regions of interest from the previous literature, measuring their whole-brain voxel-wise rsFC in a sample of 95 ADHD and 90 neurotypical children and adolescents aged from 7 to 18. The only subcortical structure showing significant differences in rsFC was the caudate nucleus. Specifically, we measured increased rsFC with anterior cingulate and right insula, two mesolimbic regions pertaining to the Salience Network. The degree of hyper-rsFC positively correlated with ADHD symptomatology, and showed different patterns of evolution in ADHD vs neurotypical subjects. Finally, the rsFC scores allowed a fair discrimination of the ADHD group (Area Under the Curve ≥ 0.7). These findings shed further light on the fundamental role covered by subcortical structures in ADHD pathogenesis and neurodevelopment, providing new evidence to fill the gap between neurofunctional and clinical expressions of ADHD.

A co-twin-control study of altered sensory processing in autism

Autism spectrum disorder is associated with sensory processing alterations, such as sensory hyper- and hypo-responsiveness. Twin studies are scarce in this field, but they are necessary in order to disentangle the genetic and environmental contributions to this association. Furthermore, it is unclear how different neurodevelopmental/psychiatric conditions contribute to altering sensory processing. We investigated the association between autistic traits/autism spectrum disorder diagnosis and sensory processing alterations in twins (N = 269), using the adult/adolescent sensory profile, which differentiates four sub-domains: Low Registration, Sensation Seeking, Sensory Sensitivity, and Sensation Avoiding. While the associations between autistic traits and Low Registration and Sensation Avoiding persisted within monozygotic (genetically identical) twins, Sensory Sensitivity was only associated with autistic traits within dizygotic twins. In multivariate analyses with different neurodevelopmental/psychiatric diagnoses as predictor variables, autism spectrum disorder and attention deficit hyperactivity disorder were the strongest predictors for two adult/adolescent sensory profile sub-domains each. The results suggest that the association between autistic traits and Sensory Sensitivity is influenced by genetics while non-shared environmental factors influence the associations between autistic traits and Low Registration and Sensation Avoiding. They further indicate that altered sensory processing is not specific to autism spectrum disorder, while autism spectrum disorder is a strong predictor of certain sensory processing alterations, even when controlling for other (comorbid) neurodevelopmental/psychiatric conditions.

Autistic traits and individual brain differences: functional network efficiency reflects attentional and social impairments, structural nodal efficiencies index systemising and theory-of-mind skills

BACKGROUND

Autism is characterised not only by impaired social cognitive 'empathising' but also by superior rule-based 'systemising'. These cognitive domains intertwine within the categorical diagnosis of autism, yet behavioural genetics suggest largely independent heritability, and separable brain mechanisms. We sought to determine whether quantitative behavioural measures of autistic traits are dimensionally associated with structural and functional brain network integrity, and whether brain bases of autistic traits vary independently across individuals.

METHODS

Thirty right-handed neurotypical adults (12 females) were administered psychometric (Social Responsiveness Scale, Autism Spectrum Quotient and Systemising Quotient) and behavioural (Attention Network Test and theory-of-mind reaction time) measures of autistic traits, and structurally (diffusion tensor imaging) and functionally (500 s of 2 Hz eyes-closed resting fMRI) derived graph-theoretic measures of efficiency of information integration were computed throughout the brain and within subregions.

RESULTS

Social impairment was positively associated with functional efficiency (r = .47, p = .006), globally and within temporo-parietal and prefrontal cortices. Delayed orienting of attention likewise was associated with greater functional efficiency (r = - .46, p = .0133). Systemising was positively associated with global structural efficiency (r = .38, p = 0.018), driven specifically by temporal pole; theory-of-mind reaction time was related to structural efficiency (r = - .40, p = 0.0153) within right supramarginal gyrus.

LIMITATIONS

Interpretation of these relationships is complicated by the many senses of the term 'connectivity', including functional, structural and computational; by the approximation inherent in group functional anatomical parcellations when confronted with individual variation in functional anatomy; and by the validity, sensitivity and specificity of the several survey and experimental behavioural measures applied as correlates of brain structure and function.

CONCLUSIONS

Functional connectivities highlight distributed networks associated with domain-general properties such as attentional orienting and social cognition broadly, associating more impaired behaviour with more efficient brain networks that may reflect heightened feedforward information flow subserving autistic strengths and deficits alike. Structural connectivity results highlight specific anatomical nodes of convergence, reflecting cognitive and neuroanatomical independence of systemising and theory-of-mind. In addition, this work shows that individual differences in theory-of-mind related to brain structure can be measured behaviourally, and offers neuroanatomical evidence to pin down the slippery construct of 'systemising' as the capacity to construct invariant contextual associations.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

Sex Differences in Functional Connectivity of the Salience, Default Mode, and Central Executive Networks in Youth with ASD

Autism spectrum disorder (ASD) is associated with the altered functional connectivity of 3 neurocognitive networks that are hypothesized to be central to the symptomatology of ASD: the salience network (SN), default mode network (DMN), and central executive network (CEN). Due to the considerably higher prevalence of ASD in males, however, previous studies examining these networks in ASD have used primarily male samples. It is thus unknown how these networks may be differentially impacted among females with ASD compared to males with ASD, and how such differences may compare to those observed in neurotypical individuals. Here, we investigated the functional connectivity of the SN, DMN, and CEN in a large, well-matched sample of girls and boys with and without ASD (169 youth, ages 8-17). Girls with ASD displayed greater functional connectivity between the DMN and CEN than boys with ASD, whereas typically developing girls and boys differed in SN functional connectivity only. Together, these results demonstrate that youth with ASD exhibit altered sex differences in these networks relative to what is observed in typical development, and highlight the importance of considering sex-related biological factors and participant sex when characterizing the neural mechanisms underlying ASD.

A Review of Default Mode Network Connectivity and Its Association With Social Cognition in Adolescents With Autism Spectrum Disorder and Early-Onset Psychosis

Recent studies have demonstrated substantial phenotypic overlap, notably social impairment, between autism spectrum disorder (ASD) and schizophrenia. However, the neural mechanisms underlying the pathogenesis of social impairments across these distinct neuropsychiatric disorders has not yet been fully examined. Most neuroimaging studies to date have focused on adults with these disorders, with little known about the neural underpinnings of social impairments in younger populations. Here, we present a narrative review of the literature available through April 2020 on imaging studies of adolescents with either ASD or early-onset psychosis (EOP), to better understand the shared and unique neural mechanisms of social difficulties across diagnosis from a developmental framework. We specifically focus on functional connectivity studies of the default mode network (DMN), as the most extensively studied brain network relevant to social cognition across both groups. Our review included 29 studies of DMN connectivity in adolescents with ASD (Mean age range = 11.2-21.6 years), and 14 studies in adolescents with EOP (Mean age range = 14.2-24.3 years). Of these, 15 of 29 studies in ASD adolescents found predominant underconnectivity when examining DMN connectivity. In contrast, findings were mixed in adolescents with EOP, with five of 14 studies reporting DMN underconnectivity, and an additional six of 14 studies reporting both under- and over-connectivity of the DMN. Specifically, intra-DMN networks were more frequently underconnected in ASD, but overconnected in EOP. On the other hand, inter-DMN connectivity patterns were mixed (both under- and over-connected) for each group, especially DMN connectivity with frontal, sensorimotor, and temporoparietal regions in ASD, and with frontal, temporal, subcortical, and cerebellar regions in EOP. Finally, disrupted DMN connectivity appeared to be associated with social impairments in both groups, less so with other features distinct to each condition, such as repetitive behaviors/restricted interests in ASD and hallucinations/delusions in EOP. Further studies on demographically well-matched groups of adolescents with each of these conditions are needed to systematically explore additional contributing factors in DMN connectivity patterns such as clinical heterogeneity, pubertal development, and medication effects that would better inform treatment targets and facilitate prediction of outcomes in the context of these developmental neuropsychiatric conditions.

The contribution of environmental exposure to the etiology of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition of heterogeneous etiology. While it is widely recognized that genetic and environmental factors and their interactions contribute to autism phenotypes, their precise causal mechanisms remain poorly understood. This article reviews our current understanding of environmental risk factors of ASD and their presumed adverse physiological mechanisms. It comprehensively maps the significance of parental age, teratogenic compounds, perinatal risks, medication, smoking and alcohol use, nutrition, vaccination, toxic exposures, as well as the role of extreme psychosocial factors. Further, we consider the role of potential protective factors such as folate and fatty acid intake. Evidence indicates an increased offspring vulnerability to ASD through advanced maternal and paternal age, valproate intake, toxic chemical exposure, maternal diabetes, enhanced steroidogenic activity, immune activation, and possibly altered zinc-copper cycles and treatment with selective serotonin reuptake inhibitors. Epidemiological studies demonstrate no evidence for vaccination posing an autism risk. It is concluded that future research needs to consider categorical autism, broader autism phenotypes, as well as autistic traits, and examine more homogenous autism variants by subgroup stratification. Our understanding of autism etiology could be advanced by research aimed at disentangling the causal and non-causal environmental effects, both founding and moderating, and gene-environment interplay using twin studies, longitudinal and experimental designs. The specificity of many environmental risks for ASD remains unknown and control of multiple confounders has been limited. Further understanding of the critical windows of neurodevelopmental vulnerability and investigating the fit of multiple hit and cumulative risk models are likely promising approaches in enhancing the understanding of role of environmental factors in the etiology of ASD.

Neurometabolic and functional connectivity basis of prosocial behavior in early adolescence

Human prosocial behavior (PB) emerges in childhood and matures during adolescence. Previous task-related functional magnetic resonance imaging (fMRI) studies have reported involvement of the medial prefrontal cortex including the anterior cingulate cortex (ACC) in social cognition in adolescence. However, neurometabolic and functional connectivity (FC) basis of PB in early adolescence remains unclear. Here, we measured GABA levels in the ACC and FC in a subsample (aged 10.5–13.4 years) of a large-scale population-based cohort with MR spectroscopy (MEGA-PRESS) and resting-state fMRI. PB was negatively correlated with GABA levels in the ACC (N = 221), and positively correlated with right ACC-seeded FC with the right precentral gyrus and the bilateral middle and posterior cingulate gyrus (N = 187). Furthermore, GABA concentrations and this FC were negatively correlated, and the FC mediated the association between GABA levels and PB (N = 171). Our results from a minimally biased, large-scale sample provide new insights into the neurometabolic and neurofunctional correlates of prosocial development during early adolescence.

Increased scale-free dynamics in salience network in adult high-functioning autism

Autism spectrum disorder (ASD) is clinically characterized by extremely slow and inflexible behavior. The neuronal mechanisms of these symptoms remain unclear though. Using fMRI, we investigate the resting state's temporal structure in the frequency domain (scale-free activity as measured with Power-Law Exponent, PLE, and Spectral Entropy, SE) and temporal variance (neural variability) in high-functioning, adult ASD comparing them with schizophrenic and neurotypical subjects. We show that ASD is characterized by high PLE in salience network, especially in dorsal anterior cingulate. This increase in PLE was 1) specific for salience network; 2) independent of other measures such as neuronal variability/SD and functional connectivity, which did not show any significant difference; 3) detected in two independent samples of ASD but not in the schizophrenia sample. Among salience network subregions, dorsal anterior cingulate cortex exhibited PLE differences between ASD and neurotypicals in both samples, showing high robustness in ROC curves values. Salience network abnormal temporal structure was confirmed by SE, which was strongly anticorrelated with PLE and thus decreased in ASD. Taken together, our findings show abnormal temporal structure (but normal temporal variance) in resting state salience network in adult high-functioning ASD. The abnormally high PLE indicates a relative predominance of slower over faster frequencies, which may underlie the slow adaptation to unexpected changes and the inflexible behavior observed in autistic individuals. The specificity of abnormal PLE in salience network suggests its potential utility as biomarker in ASD.

Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project

BACKGROUND

Resting-state functional magnetic resonance imaging-based studies on functional connectivity in autism spectrum disorder (ASD) have generated inconsistent results. Interpretation of findings is further hampered by small samples and a focus on a limited number of networks, with networks underlying sensory processing being largely underexamined. We aimed to comprehensively characterize ASD-related alterations within and between 20 well-characterized resting-state networks using baseline data from the EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project.

METHODS

Resting-state functional magnetic resonance imaging data was available for 265 individuals with ASD (7.5-30.3 years; 73.2% male) and 218 typically developing individuals (6.9-29.8 years; 64.2% male), all with IQ > 70. We compared functional connectivity within 20 networks-obtained using independent component analysis-between the ASD and typically developing groups, and related functional connectivity within these networks to continuous (overall) autism trait severity scores derived from the Social Responsiveness Scale Second Edition across all participants. Furthermore, we investigated case-control differences and autism trait-related alterations in between-network connectivity.

RESULTS

Higher autism traits were associated with increased connectivity within salience, medial motor, and orbitofrontal networks. However, we did not replicate previously reported case-control differences within these networks. The between-network analysis did reveal case-control differences showing on average 1) decreased connectivity of the visual association network with somatosensory, medial, and lateral motor networks, and 2) increased connectivity of the cerebellum with these sensory and motor networks in ASD compared with typically developing subjects.

CONCLUSIONS

We demonstrate ASD-related alterations in within- and between-network connectivity. The between-network alterations broadly affect connectivity between cerebellum, visual, and sensory-motor networks, potentially underlying impairments in multisensory and visual-motor integration frequently observed in ASD.

Atypical longitudinal development of functional connectivity in adolescents with autism spectrum disorder

Autism spectrum disorder (ASD) is consistently associated with alterations in brain connectivity, but there are conflicting results as to where and when individuals with ASD display increased or reduced functional connectivity. Such inconsistent findings may be driven by atypical neurodevelopmental trajectories in ASD during adolescence, but no longitudinal studies to date have investigated this hypothesis. We thus examined the functional connectivity of three neurocognitive resting-state networks-the default mode network (DMN), salience network, and central executive network (CEN)-in a longitudinal sample of youth with ASD (n = 16) and without ASD (n = 22) studied during early/mid- and late adolescence. Functional connectivity between the CEN and the DMN displayed significantly altered developmental trajectories in ASD: typically developing (TD) controls-but not youth with ASD-exhibited an increase in negative functional connectivity between these two networks with age. This significant interaction was due to the ASD group displaying less negative functional connectivity than the TD group during late adolescence only, with no significant group differences in early/mid-adolescence. These preliminary findings suggest a localized age-dependency of functional connectivity alterations in ASD and underscore the importance of considering age when examining brain connectivity. Autism Research 2019, 12: 53-65. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain connectivity may develop differently during adolescence in youth with autism spectrum disorder (ASD). We looked at changes in brain connectivity over time within individuals and found that, for some brain regions, adolescents with ASD did not show the same changes in brain connectivity that typically developing adolescents did. This suggests it is important to consider age when studying brain connectivity in ASD.

EU-AIMS Longitudinal European Autism Project (LEAP): the autism twin cohort

EU-AIMS is the largest European research program aiming to identify stratification biomarkers and novel interventions for autism spectrum disorder (ASD). Within the program, the Longitudinal European Autism Project (LEAP) has recruited and comprehensively phenotyped a rare sample of 76 monozygotic and dizygotic twins, discordant, or concordant for ASD plus 30 typically developing twins. The aim of this letter is to complete previous descriptions of the LEAP case-control sample, clinically characterize, and investigate the suitability of the sample for ASD twin-control analyses purposes and share some 'lessons learnt.' Among the twins, a diagnosis of ASD is associated with increased symptom levels of ADHD, higher rates of intellectual disability, and lower family income. For the future, we conclude that the LEAP twin cohort offers multiple options for analyses of genetic and shared and non-shared environmental factors to generate new hypotheses for the larger cohort of LEAP singletons, but particularly cross-validate and refine evidence from it.