Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes

Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology

BACKGROUND

Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together and sex differences are often overlooked. Population modeling, often referred to as normative modeling, provides a unified framework for studying age-specific and sex-specific divergences in brain development.

METHODS

Here, we used population modeling and a large, multisite neuroimaging dataset (N = 4255 after quality control) to characterize cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of more than 75,000 individuals. We also examined sex and age differences and relationship with autistic traits and explored the co-occurrence of autism and ADHD.

RESULTS

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume that was localized to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness but lower cortical volume and surface area across much of the cortex. The co-occurring autism+ADHD group showed a unique pattern of widespread increases in cortical thickness and certain decreases in surface area. We also found that sex modulated the neuroanatomy of autism but not ADHD, and there was an age-by-diagnosis interaction for ADHD only.

CONCLUSIONS

These results indicate distinct cortical differences in autism and ADHD that are differentially affected by age and sex as well as potentially unique patterns related to their co-occurrence.

Rightward brain structural asymmetry in young children with autism

To understand the neural mechanism of autism spectrum disorder (ASD) and developmental delay/intellectual disability (DD/ID) that can be associated with ASD, it is important to investigate individuals at an early stage with brain, behavioural and also genetic measures, but such research is still lacking. Here, using the cross-sectional sMRI data of 1030 children under 8 years old, we employed developmental normative models to investigate the atypical development of gray matter volume (GMV) asymmetry in individuals with ASD without DD/ID, ASD with DD/ID and individuals with only DD/ID, and their associations with behavioral and clinical measures and transcription profiles. By extracting the individual deviations of patients from the typical controls with normative models, we found a commonly abnormal pattern of GMV asymmetry across all ASD children: more rightward laterality in the inferior parietal lobe and precentral gyrus, and higher individual variability in the temporal pole. Specifically, ASD with DD/ID children showed a severer and more extensive abnormal pattern in GMV asymmetry deviation values, which was linked with both ASD symptoms and verbal IQ. The abnormal pattern of ASD without DD/ID children showed higher and more extensive individual variability, which was linked with ASD symptoms only. DD/ID children showed no significant differences from healthy population in asymmetry. Lastly, the GMV laterality patterns of all patient groups were significantly associated with both shared and unique gene expression profiles. Our findings provide evidence for rightward GMV asymmetry of some cortical regions in young ASD children (1-7 years) in a large sample (1030 cases), show that these asymmetries are related to ASD symptoms, and identify genes that are significantly associated with these differences.

Neuroanatomical basis of language ability in an autism subgroup with moderate language deficits

Children with autism spectrum disorder (ASD) are highly heterogenous in their language abilities. A number of studies have shown neural correlates of language deficits in children with ASD, but the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive. In this study, we analyzed MRI data from a cohort of Chinese children with ASD (n = 67) and typical development (TD, n = 37) aged 1.5 to 6.5 years. The ASD sample was classified into two subgroups based on the median of the language scores: ASD with moderate language deficits (ASDmoderate, n = 34) and ASD with severe language deficits (ASDsevere, n = 34). We tested the group differences in the brain volumes between TD and two ASD subgroups, and also examined the associations between cortical grey matter volume and language abilities in TD and ASD subgroups, separately. We observed significant group differences in grey matter and white matter volume, with post-hoc analyses specifically indicating significant differences between TD and ASDmoderate subgroup. Significant correlations between grey matter volume and language scores were observed exclusively within the ASDmoderate subgroup, including positive associations in the bilateral superior temporal gyrus, hippocampus, and left inferior parietal lobe, and negative correlations in the bilateral precuneus. These findings provide novel evidence for the neuroanatomical basis related to language ability in an ASD subgroup with moderate language deficits, and offer new insights into the heterogeneity of language deficits in children with ASD.

A 3D approach to understanding heterogeneity in early developing autisms

BACKGROUND

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology.

METHODS

Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work.

RESULTS

Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms.

LIMITATIONS

Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures.

CONCLUSIONS

This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Beyond the Spectrum: Subtype-Specific Molecular Insights into Autism Spectrum Disorder Via Multimodal Data Integration

Some toddlers with autism spectrum disorder (ASD) have mild social symptoms and developmental improvement in skills, but for others, symptoms and abilities are moderately or even severely affected. Those with profound autism have the most severe social, language, and cognitive symptoms and are at the greatest risk of having a poor developmental outcome. The little that is known about the underlying biology of this important profound autism subtype, points clearly to embryonic dysregulation of proliferation, differentiation and neurogenesis. Because it is essential to gain foundational knowledge of the molecular biology associated with profound, moderate, and mild autism clinical subtypes, we used well-validated, data-driven patient subtyping methods to integrate clinical and molecular data at 1 to 3 years of age in a cohort of 363 ASD and controls representative of the general pediatric population in San Diego County. Clinical data were diagnostic, language, cognitive and adaptive ability scores. Molecular measures were 50 MSigDB Hallmark gene pathway activity scores derived from RNAseq gene expression. Subtyping identified four ASD, typical and mixed diagnostic clusters. 93% of subjects in one cluster were profound autism and 93% in a different cluster were control toddlers; a third cluster was 76% moderate ability ASD; and the last cluster was a mix of mild ASD and control toddlers. Among the four clusters, the profound autism subtype had the most severe social symptoms, language, cognitive, adaptive, social attention eye tracking, social fMRI activation, and age-related decline in abilities, while mild autism toddlers mixed within typical and delayed clusters had mild social symptoms, and neurotypical language, cognitive and adaptive scores that improved with age compared with profound and moderate autism toddlers in other clusters. In profound autism, 7 subtype-specific dysregulated gene pathways were found; they control embryonic proliferation, differentiation, neurogenesis, and DNA repair. To find subtype-common dysregulated pathways, we compared all ASD vs TD and found 17 ASD subtype-common dysregulated pathways. These common pathways showed a severity gradient with the greatest dysregulation in profound and least in mild. Collectively, results raise the new hypothesis that the continuum of ASD heterogeneity is moderated by subtype-common pathways and the distinctive nature of profound autism is driven by the differentially added profound subtype-specific embryonic pathways.

Sensorimotor variability distinguishes early features of cognition in toddlers with autism

The potential role of early sensorimotor features to atypical human cognition in autistic children has received surprisingly little attention given that appropriate movements are a crucial element that connects us to other people. We examined quantitative and observation-based movements in over 1,000 toddlers diagnosed with autism spectrum disorder (ASD) with different levels of cognitive abilities (intelligence quotient, IQ). Relative to higher-IQ ASD toddlers, those with lower-IQ had significantly altered sensorimotor features. Remarkably, we found that higher IQ in autistic toddlers confers resilience to atypical movement, as sensorimotor features in higher-IQ ASD children were indistinguishable from those of typically developing healthy control toddlers. We suggest that the altered movement patterns may affect key autistic behaviors in those with lower intelligence by affecting sensorimotor learning mechanisms. Atypical sensorimotor functioning is a key feature in lower-IQ early childhood autism. These findings have implications for the development of individualized interventions for subtypes of autism.

Enhanced motor noise in an autism subtype with poor motor skills

BACKGROUND

Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise.

METHODS

This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task.

RESULTS

Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions.

LIMITATIONS

The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed.

CONCLUSIONS

Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms.

Polygenic adaptation of a cosmopolitan pest to a novel thermal environment

The fluctuation in temperature poses a significant challenge for poikilothermic organisms, notably insects, particularly in the context of changing climatic conditions. In insects, temperature adaptation has been driven by polygenes. In addition to genes that directly affect traits (core genes), other genes (peripheral genes) may also play a role in insect temperature adaptation. This study focuses on two peripheral genes, the GRIP and coiled-coil domain containing 2 (GCC2) and karyopherin subunit beta 1 (KPNB1). These genes are differentially expressed at different temperatures in the cosmopolitan pest, Plutella xylostella. GCC2 and KPNB1 in P. xylostella were cloned, and their relative expression patterns were identified. Reduced capacity for thermal adaptation (development, reproduction and response to temperature extremes) in the GCC2-deficient and KPNB1-deficient P. xylostella strains, which were constructed by CRISPR/Cas9 technique. Deletion of the PxGCC2 or PxKPNB1 genes in P. xylostella also had a differential effect on gene expression for many traits including stress resistance, resistance to pesticides, involved in immunity, trehalose metabolism, fatty acid metabolism and so forth. The ability of the moth to adapt to temperature via different pathways is likely to be key to its ability to remain an important pest species under predicted climate change conditions.

Individual Deviation-Based Functional Hypergraph for Identifying Subtypes of Autism Spectrum Disorder

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes.

Discovering the gene-brain-behavior link in autism via generative machine learning

Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variability is a challenge. We demonstrate a novel technique, 3D transport-based morphometry (TBM), to extract the structural brain changes linked to genetic copy number variation (CNV) at the 16p11.2 region. We identified two distinct endophenotypes. In data from the Simons Variation in Individuals Project, detection of these endophenotypes enabled 89 to 95% test accuracy in predicting 16p11.2 CNV from brain images alone. Then, TBM enabled direct visualization of the endophenotypes driving accurate prediction, revealing dose-dependent brain changes among deletion and duplication carriers. These endophenotypes are sensitive to articulation disorders and explain a portion of the intelligence quotient variability. Genetic stratification combined with TBM could reveal new brain endophenotypes in many neurodevelopmental disorders, accelerating precision medicine, and understanding of human neurodiversity.

Differences in regional brain structure in toddlers with autism are related to future language outcomes

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.

Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms

BACKGROUND

Social affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with "profound" autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known.

METHODS

Because ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we  tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions.

RESULTS

At the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences.

LIMITATIONS

Larger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes.

CONCLUSIONS

By embryogenesis, the biological bases of two subtypes of ASD social and brain development-profound autism and mild autism-are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler's social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.

A 3D approach to understanding heterogeneity in early developing autisms

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Using relatively large (n=615) publicly available data from early developing (24-68 months) standardized clinical tests tapping LIMA features, we show that stability-based relative cluster validation analysis can identify two robust and replicable clusters in the autism population with high levels of generalization accuracy (98%). These clusters can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression. This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Language Skill Differences Further Distinguish Social Sub-types in Children with Autism

This study investigated heterogeneity in language skills of children with autism and their relationship with different autistic social subtypes. Data from 90 autistic and 30 typically developing children were analyzed. Results showed that autistic social subtypes varied in language skill problems (aloof > passive > active-but-odd). There was a negative association between aloof dimension scores and language performance but positive for the active-but-odd dimension and no association in the passive one. Moreover, aloof dimension score was the main contributor to language performance. A receiver operating characteristic analysis suggested language vocabulary as an additional component in differentiating autistic social subtypes. These findings demonstrate that variations in language skills in autistic children provide additional information for discriminating their social subtype.

Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology

Background

Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development.

Methods

Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD).

Results

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only.

Conclusions

These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.

COVID-19 severity: does the genetic landscape of rare variants matter?

Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

Noncoding transcripts are linked to brain resting-state activity in non-human primates

Brain-derived transcriptomes are known to correlate with resting-state brain activity in humans. Whether this association holds in nonhuman primates remains uncertain. Here, we search for such molecular correlates by integrating 757 transcriptomes derived from 100 macaque cortical regions with resting-state activity in separate conspecifics. We observe that 150 noncoding genes explain variations in resting-state activity at a comparable level with protein-coding genes. In-depth analysis of these noncoding genes reveals that they are connected to the function of nonneuronal cells such as oligodendrocytes. Co-expression network analysis finds that the modules of noncoding genes are linked to both autism and schizophrenia risk genes. Moreover, genes associated with resting-state noncoding genes are highly enriched in human resting-state functional genes and memory-effect genes, and their links with resting-state functional magnetic resonance imaging (fMRI) signals are altered in the brains of patients with autism. Our results highlight the potential for noncoding RNAs to explain resting-state activity in the nonhuman primate brain.

Classification of autism based on short-term spontaneous hemodynamic fluctuations using an adaptive graph neural network

BACKGROUND

Short-term spontaneous hemodynamic fluctuations were collected by the functional near-infrared spectroscopy (fNIRS) system to classify children with autism spectrum disorder (ASD) and typical development (TD), and to explore abnormalities in the left inferior frontal gyrus in ASD.

METHODS

Using the fNIRS data of 25 children with ASD and 22 children with TD, a graph neural network combined with the temporal convolution module and the graph convolution module was used, to extract the spatio-temporal features of the data and achieve accurate classification of ASD.

RESULTS

The graph neural network was used to obtain a good classification result in the left inferior frontal gyrus, with an accuracy of 97.1%, precision of 95.1%, and specificity of 93.4%. It was found that the 5th channel (which is located in BA 10) and the 8th channel (which is located in BA 47) in the left inferior frontal gyrus were closely correlated with ASD.

COMPARISON WITH PREVIOUSLY USED METHOD(S)

Compared with the previous deep learning model using the same input, the accuracy of our model has increased by up to 13%, and the correlation between channels in the left inferior frontal gyrus area with the best classification effect was explored through the graph neural network.

CONCLUSION

The adaptive graph neural network (AGNN) model may be able to mine more valuable information to distinguish ASD from TD and in addition, the left inferior frontal gyrus may have greater investigative value.

Absence of associations with prefrontal cortex and cerebellum may link to early language and social deficits in preschool children with ASD

Introduction

Autism spectrum disorder (ASD) is a complex developmental disorder, characterized by language and social deficits that begin to appear in the first years of life. Research in preschool children with ASD has consistently reported increased global brain volume and abnormal cortical patterns, and the brain structure abnormalities have also been found to be clinically and behaviorally relevant. However, little is known regarding the associations between brain structure abnormalities and early language and social deficits in preschool children with ASD.

Methods

In this study, we collected magnetic resonance imaging (MRI) data from a cohort of Chinese preschool children with and without ASD (24 ASD/20 non-ASD) aged 12-52 months, explored group differences in brain gray matter (GM) volume, and examined associations between regional GM volume and early language and social abilities in these two groups, separately.

Results

We observed significantly greater global GM volume in children with ASD as compared to those without ASD, but there were no regional GM volume differences between these two groups. For children without ASD, GM volume in bilateral prefrontal cortex and cerebellum was significantly correlated with language scores; GM volume in bilateral prefrontal cortex was significantly correlated with social scores. No significant correlations were found in children with ASD.

Discussion

Our data demonstrate correlations of regional GM volume with early language and social abilities in preschool children without ASD, and the absence of these associations appear to underlie language and social deficits in children with ASD. These findings provide novel evidence for the neuroanatomical basis associated with language and social abilities in preschool children with and without ASD, which promotes a better understanding of early deficits in language and social functions in ASD.

Cross-sectional and longitudinal neuroanatomical profiles of distinct clinical (adaptive) outcomes in autism

Individuals with autism spectrum disorder (henceforth referred to as autism) display significant variation in clinical outcome. For instance, across age, some individuals' adaptive skills naturally improve or remain stable, while others' decrease. To pave the way for 'precision-medicine' approaches, it is crucial to identify the cross-sectional and, given the developmental nature of autism, longitudinal neurobiological (including neuroanatomical and linked genetic) correlates of this variation. We conducted a longitudinal follow-up study of 333 individuals (161 autistic and 172 neurotypical individuals, aged 6-30 years), with two assessment time points separated by ~12-24 months. We collected behavioural (Vineland Adaptive Behaviour Scale-II, VABS-II) and neuroanatomical (structural magnetic resonance imaging) data. Autistic participants were grouped into clinically meaningful "Increasers", "No-changers", and "Decreasers" in adaptive behaviour (based on VABS-II scores). We compared each clinical subgroup's neuroanatomy (surface area and cortical thickness at T1, ∆T (intra-individual change) and T2) to that of the neurotypicals. Next, we explored the neuroanatomical differences' potential genomic associates using the Allen Human Brain Atlas. Clinical subgroups had distinct neuroanatomical profiles in surface area and cortical thickness at baseline, neuroanatomical development, and follow-up. These profiles were enriched for genes previously associated with autism and for genes previously linked to neurobiological pathways implicated in autism (e.g. excitation-inhibition systems). Our findings suggest that distinct clinical outcomes (i.e. intra-individual change in clinical profiles) linked to autism core symptoms are associated with atypical cross-sectional and longitudinal, i.e. developmental, neurobiological profiles. If validated, our findings may advance the development of interventions, e.g. targeting mechanisms linked to relatively poorer outcomes.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

BACKGROUND

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations.

METHODS

This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model.

RESULTS

All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%).

CONCLUSIONS

The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD

BACKGROUND

Social and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown.

METHODS

We collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child's social and language abilities were examined.

RESULTS

While there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal-visual region connectivity and communication ability (r(49) = 0.55, p < 0.001) and between temporal-precuneus connectivity and expressive language ability (r(49) = 0.58, p < 0.001).

LIMITATIONS

The distinct connectivity-behavior correlation patterns may be related to different developmental stages in ASD and non-ASD subjects. The use of a prior 2-year-old template for spatial normalization may not be optimal for a few subjects beyond this age range.

CONCLUSIONS

Superior temporal cortex is known to have reduced activation to social affective speech in ASD at early ages, and here we find in ASD toddlers that it also has atypical connectivity with visual and precuneus cortices that is correlated with communication and language ability, a pattern not seen in non-ASD toddlers. This atypicality may be an early-age signature of ASD that also explains why the disorder has deviant early language and social development. Given that these atypical connectivity patterns are also present in older individuals with ASD, we conclude these atypical connectivity patterns persist across age and may explain why successful interventions targeting language and social skills at all ages in ASD are so difficult to achieve.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Language, Social, and Face Regions Are Affected in Toddlers with Autism and Predictive of Language Outcome

Identifying prognostic early brain alterations is crucial for autism spectrum disorder (ASD). Leveraging structural MRI data from 166 ASD and 109 typical developing (TD) toddlers and controlling for brain size, we found that, compared to TD, ASD toddlers showed larger or thicker lateral temporal regions; smaller or thinner frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most of these differences were replicated in an independent cohort of 38 ASD and 37 TD toddlers. Moreover, the identified brain alterations were related to ASD symptom severity and cognitive impairments at intake, and, remarkably, they improved the accuracy for predicting later language outcome beyond intake clinical and demographic variables. In summary, brain regions involved in language, social, and face processing were altered in ASD toddlers. These early-age brain alterations may be the result of dysregulation in multiple neural processes and stages and are promising prognostic biomarkers for future language ability.

Inter-individual heterogeneity of functional brain networks in children with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with considerable clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on inter-individual heterogeneity of functional brain networks.

METHODS

Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were used in this study for 105 children with ASD and 102 demographically matched typical controls (TC) children. Functional connectivity (FC) networks were first obtained for ASD and TC groups, and inter-individual deviation of functional connectivity (IDFC) from the TC group was then calculated for each individual with ASD. A k-means clustering algorithm was used to obtain ASD subtypes based on IDFC patterns. The FC patterns were further compared between ASD subtypes and the TC group from the brain region, network, and whole-brain levels. The relationship between IDFC and the severity of clinical symptoms of ASD for ASD subtypes was also analyzed using a support vector regression model.

RESULTS

Two ASD subtypes were identified based on the IDFC patterns. Compared with the TC group, the ASD subtype 1 group exhibited a hypoconnectivity pattern and the ASD subtype 2 group exhibited a hyperconnectivity pattern. IDFC for ASD subtype 1 and subtype 2 was found to predict the severity of social communication impairments and the severity of restricted and repetitive behaviors in ASD, respectively.

LIMITATIONS

Only male children were selected for this study, which limits the ability to study the effects of gender and development on ASD heterogeneity.

CONCLUSIONS

These results suggest the existence of subtypes with different FC patterns in ASD and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder

Gene expression covaries with brain activity as measured by resting state functional magnetic resonance imaging (MRI). However, it is unclear how genomic differences driven by disease state can affect this relationship. Here, we integrate from the ABIDE I and II imaging cohorts with datasets of gene expression in brains of neurotypical individuals and individuals with autism spectrum disorder (ASD) with regionally matched brain activity measurements from fMRI datasets. We identify genes linked with brain activity whose association is disrupted in ASD. We identified a subset of genes that showed a differential developmental trajectory in individuals with ASD compared with controls. These genes are enriched in voltage-gated ion channels and inhibitory neurons, pointing to excitation-inhibition imbalance in ASD. We further assessed differences at the regional level showing that the primary visual cortex is the most affected region in ASD. Our results link disrupted brain expression patterns of individuals with ASD to brain activity and show developmental, cell type, and regional enrichment of activity linked genes.

Rethinking Our Concepts and Assumptions About Autism

Autism is a clinical consensus diagnosis made based on behavioral symptoms of early developmental difficulties in domains of social-communication (SC) and restricted repetitive behaviors (RRB). Many readily assume that alongside being optimal for separating individuals based on SC and RRB behavioral domains, that the label should also be highly useful for explaining differential biology, outcomes, and treatment (BOT) responses. However, we also now take for granted the fact that the autism population is vastly heterogeneous at multiple scales, from genome to phenome. In the face of such multi-scale heterogeneity, here we argue that the concept of autism along with the assumptions that surround it require some rethinking. While we should retain the diagnosis for all the good it can do in real-world circumstances, we also call for the allowance of multiple other possible definitions that are better tailored to be highly useful for other translational end goals, such as explaining differential BOT responses.

Longitudinal assessment of network reorganizations and language recovery in postoperative patients with glioma

For patients with glioma located in or adjacent to the linguistic eloquent cortex, awake surgery with an emphasis on the preservation of language function is preferred. However, the brain network basis of postoperative linguistic functional outcomes remains largely unknown. In this work, 34 patients with left cerebral gliomas who underwent awake surgery were assessed for language function and resting-state network properties before and after surgery. We found that there were 28 patients whose language function returned to at least 80% of the baseline scores within 3 months after surgery or to 85% within 6 months after surgery. For these patients, the spontaneous recovery of language function synchronized with changes within the language and cognitive control networks, but not with other networks. Specifically, compared with baseline values, language functions and global network properties were the worst within 1 month after surgery and gradually recovered within 6 months after surgery. The recovery of connections was tumour location dependent and was attributed to both ipsihemispheric and interhemispheric connections. In contrast, for six patients whose language function did not recover well, severe network disruptions were observed before surgery and persisted into the chronic phase. This study suggests the synchronization of functional network normalization and spontaneous language recovery in postoperative patients with glioma.

Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD

Affective speech, including motherese, captures an infant's attention and enhances social, language and emotional development. Decreased behavioural response to affective speech and reduced caregiver-child interactions are early signs of autism in infants. To understand this, we measured neural responses to mild affect speech, moderate affect speech and motherese using natural sleep functional magnetic resonance imaging and behavioural preference for motherese using eye tracking in typically developing toddlers and those with autism. By combining diverse neural-clinical data using similarity network fusion, we discovered four distinct clusters of toddlers. The autism cluster with the weakest superior temporal responses to affective speech and very poor social and language abilities had reduced behavioural preference for motherese, while the typically developing cluster with the strongest superior temporal response to affective speech showed the opposite effect. We conclude that significantly reduced behavioural preference for motherese in autism is related to impaired development of temporal cortical systems that normally respond to parental affective speech.

Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice

Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.

Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism

Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., <24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predict 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predict the rate of skill growth, accounting for 13% of the variance in treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism postmortem cortical tissue, and are normatively highly expressed in a variety of subcortical and cortical areas important for social communication and language development. This work suggests that pre-treatment biological and clinical behavioral characteristics are important for predicting developmental change in the context of early intervention and that individualized pre-treatment biology related to histone acetylation may be key.

Genetic contributions to autism spectrum disorder

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous (de novo). More than 100 risk genes have been implicated by rare, often de novo, potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse, each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Atypical genomic cortical patterning in autism with poor early language outcome

Cortical regionalization develops via genomic patterning along anterior-posterior (A-P) and dorsal-ventral (D-V) gradients. Here, we find that normative A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT), present in typically developing and autistic toddlers with good early language outcome, is absent in autistic toddlers with poor early language outcome. Autistic toddlers with poor early language outcome are instead specifically characterized by a secondary and independent genomic patterning effect on CT. Genes involved in these effects can be traced back to midgestational A-P and D-V gene expression gradients and different prenatal cell types (e.g., progenitor cells and excitatory neurons), are functionally important for vocal learning and human-specific evolution, and are prominent in prenatal coexpression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be explained by atypical genomic cortical patterning starting in prenatal development, which may detrimentally affect later regional functional specialization and circuit formation.

Get SET Early to Identify and Treatment Refer Autism Spectrum Disorder at 1 Year and Discover Factors That Influence Early Diagnosis

OBJECTIVES

To examine the impact of a new approach, Get SET Early, on the rates of early autism spectrum disorder (ASD) detection and factors that influence the screen-evaluate-treat chain.

STUDY DESIGN

After attending Get SET Early training, 203 pediatricians administered 57 603 total screens using the Communication and Symbolic Behavior Scales Infant-Toddler Checklist at 12-, 18-, and 24-month well-baby examinations, and parents designated presence or absence of concern. For screen-positive toddlers, pediatricians specified if the child was being referred for evaluation, and if not, why not.

RESULTS

Collapsed across ages, toddlers were evaluated and referred for treatment at a median age of 19 months, and those screened at 12 months (59.4% of sample) by 15 months. Pediatricians referred one-third of screen-positive toddlers for evaluation, citing lack of confidence in the accuracy of screen-positive results as the primary reason for nonreferral. If a parent expressed concerns, referral probability doubled, and the rate of an ASD diagnosis increased by 37%. Of 897 toddlers evaluated, almost one-half were diagnosed as ASD, translating into an ASD prevalence of 1%.

CONCLUSIONS

The Get SET Early model was effective at detecting ASD and initiating very early treatment. Results also underscored the need for change in early identification approaches to formally operationalize and incorporate pediatrician judgment and level of parent concern into the process.

A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI

Autism spectrum disorder (ASD) is a neurodegenerative disorder characterized by lingual and social disabilities. The autism diagnostic observation schedule is the current gold standard for ASD diagnosis. Developing objective computer aided technologies for ASD diagnosis with the utilization of brain imaging modalities and machine learning is one of main tracks in current studies to understand autism. Task-based fMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in response to certain tasks. It is believed to hold discriminant features for autism. A novel computer aided diagnosis (CAD) framework is proposed to classify 50 ASD and 50 typically developed toddlers with the adoption of CNN deep networks. The CAD system includes both local and global diagnosis in a response to speech task. Spatial dimensionality reduction with region of interest selection and clustering has been utilized. In addition, the proposed framework performs discriminant feature extraction with continuous wavelet transform. Local diagnosis on cingulate gyri, superior temporal gyrus, primary auditory cortex and angular gyrus achieves accuracies ranging between 71% and 80% with a four-fold cross validation technique. The fused global diagnosis achieves an accuracy of 86% with 82% sensitivity, 92% specificity. A brain map indicating ASD severity level for each brain area is created, which contributes to personalized diagnosis and treatment plans.

Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children

Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

Neural Biomarkers Distinguish Severe From Mild Autism Spectrum Disorder Among High-Functioning Individuals

Several previous studies have reported atypicality in resting-state functional connectivity (FC) in autism spectrum disorder (ASD), yet the relatively small effect sizes prevent us from using these characteristics for diagnostic purposes. Here, canonical correlation analysis (CCA) and hierarchical clustering were used to partition the high-functioning ASD group (i.e., the ASD discovery group) into subgroups. A support vector machine (SVM) model was trained through the 10-fold strategy to predict Autism Diagnostic Observation Schedule (ADOS) scores within the ASD discovery group (r = 0.30, P < 0.001, n = 260), which was further validated in an independent sample (i.e., the ASD validation group) (r = 0.35, P = 0.031, n = 29). The neuroimage-based partition derived two subgroups representing severe versus mild autistic patients. We identified FCs that show graded changes in strength from ASD-severe, through ASD-mild, to controls, while the same pattern cannot be observed in partitions based on ADOS score. We also identified FCs that are specific for ASD-mild, similar to a partition based on ADOS score. The current study provided multiple pieces of evidence with replication to show that resting-state functional magnetic resonance imaging (rsfMRI) FCs could serve as neural biomarkers in partitioning high-functioning autistic individuals based on their symptom severity and showing advantages over traditional partition based on ADOS score. Our results also indicate a compensatory role for a frontocortical network in patients with mild ASD, indicating potential targets for future clinical treatments.

A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI

PURPOSE

Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment.

METHODS

To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step.

RESULTS

Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%).

CONCLUSION

The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Gene-expression correlates of the oscillatory signatures supporting human episodic memory encoding

In humans, brain oscillations support critical features of memory formation. However, understanding the molecular mechanisms underlying this activity remains a major challenge. Here, we measured memory-sensitive oscillations using intracranial electroencephalography recordings from the temporal cortex of patients performing an episodic memory task. When these patients subsequently underwent resection, we employed transcriptomics on the temporal cortex to link gene expression with brain oscillations and identified genes correlated with oscillatory signatures of memory formation across six frequency bands. A co-expression analysis isolated oscillatory signature-specific modules associated with neuropsychiatric disorders and ion channel activity, with highly correlated genes exhibiting strong connectivity within these modules. Using single-nucleus transcriptomics, we further revealed that these modules are enriched for specific classes of both excitatory and inhibitory neurons, and immunohistochemistry confirmed expression of highly correlated genes. This unprecedented dataset of patient-specific brain oscillations coupled to genomics unlocks new insights into the genetic mechanisms that support memory encoding.

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders

Neurodevelopmental disorders have a heritable component and are associated with region specific alterations in brain anatomy. However, it is unclear how genetic risks for neurodevelopmental disorders are translated into spatially patterned brain vulnerabilities. Here, we integrated cortical neuroimaging data from patients with neurodevelopmental disorders caused by genomic copy number variations (CNVs) and gene expression data from healthy subjects. For each of the six investigated disorders, we show that spatial patterns of cortical anatomy changes in youth are correlated with cortical spatial expression of CNV genes in neurotypical adults. By transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link anatomical change maps in each analysed disorder to specific cell classes as well as the CNV-region genes they express. Our findings reveal organizing principles that regulate the mapping of genetic risks onto regional brain changes in neurogenetic disorders. Our findings will enable screening for candidate molecular mechanisms from readily available neuroimaging data.

Tumor grade-related language and control network reorganization in patients with left cerebral glioma

Language processing relies on both a functionally specialized language network and a domain-general cognitive control network. Yet, how the two networks reorganize after damage resulting from diffuse and progressive glioma remains largely unknown. To address this issue, 130 patients with left cerebral gliomas, including 77 patients with low-grade glioma (LGG, WHO grade Ⅰ/II), 53 patients with high-grade glioma (HGG, WHO grade III/IV) and 38 healthy controls (HC) were adopted. The changes in resting-state functional connectivity (rsFC) of the language network and the cingulo-opercular/fronto-parietal (CO-FP) network were examined using network-based statistics. We found that tumor grade negatively correlated with language scores and language network integrity. Compared with HCs, patients with LGGs exhibited slight language deficits, both decreased and increased changes in rsFC of language network, and nearly normal CO-FP network. Patients with HGGs had significantly lower language scores than those with LGG and exhibited more severe language and CO-FP network disruptions than HCs or patients with LGGs. Moreover, we found that in patients with HGGs, the decreased rsFCs of language network were positively correlated with language scores. Together, our findings suggest tumor grade-related network reorganization of both language and control networks underlie the different levels of language impairments observed in patients with gliomas.

Prenatal Origins of ASD: The When, What, and How of ASD Development

Autism spectrum disorder (ASD) is a largely heritable, multistage prenatal disorder that impacts a child's ability to perceive and react to social information. Most ASD risk genes are expressed prenatally in many ASD-relevant brain regions and fall into two categories: broadly expressed regulatory genes that are expressed in the brain and other organs, and brain-specific genes. In trimesters one to three (Epoch-1), one set of broadly expressed (the majority) and brain-specific risk genes disrupts cell proliferation, neurogenesis, migration, and cell fate, while in trimester three and early postnatally (Epoch-2) another set (the majority being brain specific) disrupts neurite outgrowth, synaptogenesis, and the 'wiring' of the cortex. A proposed model is that upstream, highly interconnected regulatory ASD gene mutations disrupt transcriptional programs or signaling pathways resulting in dysregulation of downstream processes such as proliferation, neurogenesis, synaptogenesis, and neural activity. Dysregulation of signaling pathways is correlated with ASD social symptom severity. Since the majority of ASD risk genes are broadly expressed, many ASD individuals may benefit by being treated as having a broader medical disorder. An important future direction is the noninvasive study of ASD cell biology.

Transcriptome signatures from discordant sibling pairs reveal changes in peripheral blood immune cell composition in Autism Spectrum Disorder

Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.

Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties

Social visual engagement difficulties are hallmark early signs of autism (ASD) and are easily quantified using eye tracking methods. However, it is unclear how these difficulties are linked to atypical early functional brain organization in ASD. With resting state fMRI data in a large sample of ASD toddlers and other non-ASD comparison groups, we find ASD-related functional hypoconnnectivity between ‘social brain’ circuitry such as the default mode network (DMN) and visual and attention networks. An eye tracking-identified ASD subtype with pronounced early social visual engagement difficulties (GeoPref ASD) is characterized by marked DMN-occipito-temporal cortex (OTC) hypoconnectivity. Increased DMN-OTC hypoconnectivity is also related to increased severity of social-communication difficulties, but only in GeoPref ASD. Early and pronounced social-visual circuit hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development and represent new treatment targets for early intervention in these individuals.

Many parents of children with autism spectrum disorder (ASD) spot the first signs when their child is still a toddler, by noticing that their child is less interested than other toddlers in people and in social play. These early differences in behavior can have long-term implications for brain development. The brains of toddlers with little interest in social stimuli will receive less social input than those of other toddlers. This will make it even harder for the brain to develop the circuits required to support social skills.

But even among children with ASD, there are large differences in children's interest in the social world. One way of measuring these differences is to track eye movements. Lombardo et al. presented toddlers with and without ASD with images of moving colorful geometric shapes next to videos of dancing children. The majority of toddlers, including most of those with ASD, spent more time looking at the children than the shapes. But about 20% of the toddlers with ASD spent most of their time looking at the shapes. These toddlers also had the most severe social symptoms.

To find out why, Lombardo et al. measured the toddlers' brain activity while they slept. During sleep, or when at rest, the brain shows stereotyped patterns of activity. Groups of brain regions that work together – such as those involved in vision – fire in synchrony. Lombardo et al. found that toddlers who preferred looking at shapes over people showed different patterns of brain activity while asleep compared to other children. In the toddlers who preferred shapes, brain networks involved in social skills were less likely to coordinate their activity with networks that support vision and attention.

These findings suggest there may be multiple subtypes of ASD, with different symptoms resulting from different patterns of brain activity. At present, all children who receive a diagnosis of ASD receive much the same behavioral therapy. But in the future, studies of brain networks could allow children to receive more specific diagnoses. This could in turn lead to more effective and personalized treatments.