Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies

Dentate Gyrus Morphogenesis is Regulated by an Autism Risk Gene Trio Function in Granule Cells

Autism Spectrum Disorders (ASDs) are reported as a group of neurodevelopmental disorders. The structural changes of brain regions including the hippocampus were widely reported in autistic patients and mouse models with dysfunction of ASD risk genes, but the underlying mechanisms are not fully understood. Here, we report that deletion of Trio, a high-susceptibility gene of ASDs, causes a postnatal dentate gyrus (DG) hypoplasia with a zigzagged suprapyramidal blade, and the Trio-deficient mice display autism-like behaviors. The impaired morphogenesis of DG is mainly caused by disturbing the postnatal distribution of postmitotic granule cells (GCs), which further results in a migration deficit of neural progenitors. Furthermore, we reveal that Trio plays different roles in various excitatory neural cells by spatial transcriptomic sequencing, especially the role of regulating the migration of postmitotic GCs. In summary, our findings provide evidence of cellular mechanisms that Trio is involved in postnatal DG morphogenesis.

Neurochemical differences in core regions of the autistic brain: a multivoxel 1H-MRS study in children

Autism spectrum disorder (ASD) is a neurodevelopmental condition which compromises various cognitive and behavioural domains. The understanding of the pathophysiology and molecular neurobiology of ASD is still an open critical research question. Here, we aimed to address ASD neurochemistry in the same time point at key regions that have been associated with its pathophysiology: the insula, hippocampus, putamen and thalamus. We conducted a multivoxel proton magnetic resonance spectroscopy (1H-MRS) study to non-invasively estimate the concentrations of total choline (GPC + PCh, tCho), total N-acetyl-aspartate (NAA + NAAG, tNAA) and Glx (Glu + Gln), presenting the results as ratios to total creatine while investigating replication for ratios to total choline as a secondary analysis. Twenty-two male children aged between 10 and 18 years diagnosed with ASD (none with intellectual disability, in spite of the expected lower IQ) and 22 age- and gender-matched typically developing (TD) controls were included. Aspartate ratios were significantly lower in the insula (tNAA/tCr: p = 0.010; tNAA/tCho: p = 0.012) and putamen (tNAA/tCr: p = 0.015) of ASD individuals in comparison with TD controls. The Glx ratios were significantly higher in the hippocampus of the ASD group (Glx/tCr: p = 0.027; Glx/tCho: p = 0.011). Differences in tNAA and Glx indices suggest that these metabolites might be neurochemical markers of region-specific atypical metabolism in ASD children, with a potential contribution for future advances in clinical monitoring and treatment.

Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement?

Objectives

Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods.

Materials & Methods

Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups.

Results

Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods.

Conclusion

The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.

Gray matter asymmetry alterations in children and adolescents with comorbid autism spectrum disorder and attention-deficit/hyperactivity disorder

Despite the high coexistence of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) (ASD + ADHD), the underlying neurobiological basis of this disorder remains unclear. Altered brain structural asymmetries have been verified in ASD and ADHD, respectively, making brain asymmetry a candidate for characterizing this coexisting disorder. Here, we measured the gray matter (GM) volume asymmetry in ASD + ADHD versus ASD without ADHD (ASD-only), ADHD without ASD (ADHD-only), and typically developing controls (TDc). High-resolution T1-weighted data from 48 ASD + ADHD, 63 ASD-only, 32 ADHD-only, and 211 matched TDc were included in our study. We also assessed brain-behavior relationships and the effects of age on GM asymmetry. We found that there were both shared and disorder-specific GM volume asymmetry alterations in ASD + ADHD, ASD-only, and ADHD-only compared with TDc. This finding demonstrates that ASD + ADHD is neither an endophenocopy nor an additive pathology of ASD and ADHD, but an entirely different neuroanatomical pathology. In addition, ASD + ADHD displayed altered GM volume asymmetries in the prefrontal regions responsible for executive function and theory of mind compared with ASD-only. We also found significant effects of age on GM asymmetry. The present study may provide structural insights into the neural basis of ASD + ADHD.

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.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes

Introduction

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan.

Methods

We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds.

Results

In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults.

Discussion

We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Examining the Dominant Presence of Brain Grey Matter in Autism During Functional Magnetic Resonance Imaging

Introduction

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with symptoms appearing from early childhood. Behavioral modifications, special education, and medicines are used to treat ASD; however, the effectiveness of the treatments depends on early diagnosis of the disorder. The primary approach in diagnosing ASD is based on clinical interviews and valid scales. Still, methods based on brain imaging could also be possible diagnostic biomarkers for ASD.

Methods

To identify the amount of information the functional magnetic resonance imaging (fMRI) reveals on ASD, we reviewed 292 task-based fMRI studies on ASD individuals. This study is part of a systematic review with the registration number CRD42017070975.

Results

We observed that face perception, language, attention, and social processing tasks were mainly studied in ASD. In addition, 73 brain regions, nearly 83% of brain grey matter, showed an altered activation between the ASD and normal individuals during these four tasks, either in a lower or a higher activation.

Conclusion

Using imaging methods, such as fMRI, to diagnose and predict ASD is a great objective; research similar to the present study could be the initial step.

Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey

Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.

Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8-12 years old with ASD were matched with 105 children aged 8-12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.

A Review on Integrated ZnO-Based SERS Biosensors and Their Potential in Detecting Biomarkers of Neurodegenerative Diseases

Surface-enhanced Raman spectroscopy (SERS) applications in clinical diagnosis and spectral pathology are increasing due to the potential of the technique to bio-barcode incipient and differential diseases via real-time monitoring of biomarkers in fluids and in real-time via biomolecular fingerprinting. Additionally, the rapid advancements in micro/nanotechnology have a visible influence in all aspects of science and life. The miniaturization and enhanced properties of materials at the micro/nanoscale transcended the confines of the laboratory and are revolutionizing domains such as electronics, optics, medicine, and environmental science. The societal and technological impact of SERS biosensing by using semiconductor-based nanostructured smart substrates will be huge once minor technical pitfalls are solved. Herein, challenges in clinical routine testing are addressed in order to understand the context of how SERS can perform in real, in vivo sampling and bioassays for early neurodegenerative disease (ND) diagnosis. The main interest in translating SERS into clinical practice is reinforced by the practical advantages: portability of the designed setups, versatility in using nanomaterials of various matter and costs, readiness, and reliability. As we will present in this review, in the frame of technology readiness levels (TRL), the current maturity reached by semiconductor-based SERS biosensors, in particular that of zinc oxide (ZnO)-based hybrid SERS substrates, is situated at the development level TRL 6 (out of 9 levels). Three-dimensional, multilayered SERS substrates that provide additional plasmonic hot spots in the z-axis are of key importance in designing highly performant SERS biosensors for the detection of ND biomarkers.

The subcortical correlates of autistic traits in school-age children: a population-based neuroimaging study

BACKGROUND

There is emerging evidence that the neuroanatomy of autism forms a spectrum which extends into the general population. However, whilst several studies have identified cortical morphology correlates of autistic traits, it is not established whether morphological differences are present in the subcortical structures of the brain. Additionally, it is not clear to what extent previously reported structural associations may be confounded by co-occurring psychopathology. To address these questions, we utilised neuroimaging data from the Adolescent Brain Cognitive Development Study to assess whether a measure of autistic traits was associated with differences in child subcortical morphology, and if any observed differences persisted after adjustment for child internalising and externalising symptoms.

METHODS

Our analyses included data from 7005 children aged 9-10 years (female: 47.19%) participating in the Adolescent Brain Cognitive Development Study. Autistic traits were assessed using scores from the Social Responsiveness Scale (SRS). Volumes of subcortical regions of interest were derived from structural magnetic resonance imaging data.

RESULTS

Overall, we did not find strong evidence for an association of autistic traits with differences in subcortical morphology in this sample of school-aged children. Whilst lower absolute volumes of the nucleus accumbens and putamen were associated with higher scores of autistic traits, these differences did not persist once a global measure of brain size was accounted for.

LIMITATIONS

It is important to note that autistic traits were assessed using the SRS, of which higher scores are associated with general behavioural problems, and therefore may not be wholly indicative of autism-specific symptoms. In addition, individuals with a moderate or severe autism diagnosis were excluded from the ABCD study, and thus, the average level of autistic traits will be lower than in the general population which may bias findings towards the null.

CONCLUSIONS

These findings from our well-powered study suggest that other metrics of brain morphology, such as cortical morphology or shape-based phenotypes, may be stronger candidates to prioritise when attempting to identify robust neuromarkers of autistic traits.

Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks

Recently, functional brain network has been used for the classification of brain disorders, such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods either ignore the non-imaging information associated with the subjects and the relationship between the subjects, or cannot identify and analyze disease-related local brain regions and biomarkers, leading to inaccurate classification results. This paper proposes a local-to-global graph neural network (LG-GNN) to address this issue. A local ROI-GNN is designed to learn feature embeddings of local brain regions and identify biomarkers, and a global Subject-GNN is then established to learn the relationship between the subjects with the embeddings generated by the local ROI-GNN and the non-imaging information. The local ROI-GNN contains a self-attention based pooling module to preserve the embeddings most important for the classification. The global Subject-GNN contains an adaptive weight aggregation block to generate the multi-scale feature embedding corresponding to each subject. The proposed LG-GNN is thoroughly validated using two public datasets for ASD and AD classification. The experimental results demonstrated that it achieves the state-of-the-art performance in terms of various evaluation metrics.

Gray matter asymmetry atypical patterns in subgrouping minors with autism based on core symptoms

Abnormal gray matter (GM) asymmetry has been verified in autism spectrum disorder (ASD), which is characterized by high heterogeneity. ASD is distinguished by three core symptom domains. Previous neuroimaging studies have offered support for divergent neural substrates of different core symptom domains in ASD. However, no previous study has explored GM asymmetry alterations underlying different core symptom domains. This study sought to clarify atypical GM asymmetry patterns underlying three core symptom domains in ASD with a large sample of 230 minors with ASD (ages 7-18 years) and 274 matched TD controls from the Autism Brain Imaging Data Exchange I (ABIDE I) repository. To this end, the scores of the revised autism diagnostic interview (ADI-R) subscales were normalized for grouping ASD into three core-symptom-defined subgroups: social interaction (SI), verbal communication (VA), and restricted repetitive behaviors (RRB). We investigated core-symptom-related GM asymmetry alterations in ASD resulting from advanced voxel-based morphometry (VBM) by general linear models. We also examined the relationship between GM asymmetry and age and between GM asymmetry and symptom severity assessed by the Autism Diagnostic Observation Schedule (ADOS). We found unique GM asymmetry alterations underlying three core-symptom-defined subgroups in ASD: more rightward asymmetry in the thalamus for SI, less rightward asymmetry in the superior temporal gyrus, anterior cingulate and caudate for VA, and less rightward asymmetry in the middle and inferior frontal gyrus for RRB. Furthermore, the asymmetry indexes in the thalamus were negatively associated with ADOS_SOCIAL scores in the general ASD group. We also showed significant correlations between GM asymmetry and age in ASD and TD individuals. Our results support the theory that each core symptom domain of ASD may have independent etiological and neurobiological underpinnings, which is essential for the interpretation of heterogeneity and the future diagnosis and treatment of ASD.

Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data

Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.

Structural filtering of functional data offered discriminative features for autism spectrum disorder

This study attempted to answer the question, "Can filtering the functional data through the frequency bands of the structural graph provide data with valuable features which are not valuable in unfiltered data"?. The valuable features discriminate between autism spectrum disorder (ASD) and typically control (TC) groups. The resting-state fMRI data was passed through the structural graph's low, middle, and high-frequency band (LFB, MFB, and HFB) filters to answer the posed question. The structural graph was computed using the diffusion tensor imaging data. Then, the global metrics of functional graphs and metrics of functional triadic interactions were computed for filtered and unfiltered rfMRI data. Compared to TCs, ASDs had significantly higher clustering coefficients in the MFB, higher efficiencies and strengths in the MFB and HFB, and lower small-world propensity in the HFB. These results show over-connectivity, more global integration, and decreased local specialization in ASDs compared to TCs. Triadic analysis showed that the numbers of unbalanced triads were significantly lower for ASDs in the MFB. This finding may indicate the reason for restricted and repetitive behavior in ASDs. Also, in the MFB and HFB, the numbers of balanced triads and the energies of triadic interactions were significantly higher and lower for ASDs, respectively. These findings may reflect the disruption of the optimum balance between functional integration and specialization. There was no significant difference between ASDs and TCs when using the unfiltered data. All of these results demonstrated that significant differences between ASDs and TCs existed in the MFB and HFB of the structural graph when analyzing the global metrics of the functional graph and triadic interaction metrics. Also, these results demonstrated that frequency bands of the structural graph could offer significant findings which were not found in the unfiltered data. In conclusion, the results demonstrated the promising perspective of using structural graph frequency bands for attaining discriminative features and new knowledge, especially in the case of ASD.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Age-dependent white matter microstructural disintegrity in autism spectrum disorder

There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.

Children and neonates anesthesia in magnetic resonance environment in Italy: an active call survey

Background

Pediatric anesthesia care in the Magnetic Resonance Imaging is a challenge for clinicians. The recent debate about the role of anesthetic agent on neural development, encouraged an evaluation of their actual activity in this environment. In this active call survey, the authors sought to delineate the Italian situation regarding national centers, staff involved, monitoring tools available and sedation techniques.

Methods

A complete sample of all national centers performing almost a pediatric discharge in the 2014 was obtained from Health Ministry registers. All Institutions were contacted for a prospective phone investigation and a three-section survey was fill out with the Physician in charge. A descriptive and exploratory analyzes about the organization setting of the Centers were performed.

Results

Among 876 Institution screened, only 106 (37%) met minimal criteria for inclusion. Children are managed by anesthesiologists in the 95% of cases, while neonates in the 54%. A dedicated nurse is present in 74% of centers. While a pulse oximetry is present in 100% of centers, the rate of prevalence of other monitoring is lower. A specific MRI-compatible ventilator is available in the 95% of Centers, but many tools are not equally homogenously distributed. Pharmacological approach is preferred in pediatric age (98%), but its use for newborns is reduced to 43%.

Conclusions

We found significant heterogeneity in the daily clinical practice of sedation in MRI. Our results could be a starting point to evaluate the further evolution of approach to children and neonates in magnetic resonance setting.

Trial registration

ClinicalTrials.gov identifier: NCT04775641.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01821-3.

Modern Biomarkers for Autism Spectrum Disorder: Future Directions

Autism spectrum disorder is an increasingly prevalent neurodevelopmental disorder in the world today, with an estimated 2% of the population being affected in the USA. A major complicating factor in diagnosing, treating, and understanding autism spectrum disorder is that defining the disorder is solely based on the observation of behavior. Thus, recent research has focused on identifying specific biological abnormalities in autism spectrum disorder that can provide clues to diagnosis and treatment. Biomarkers are an objective way to identify and measure biological abnormalities for diagnostic purposes as well as to measure changes resulting from treatment. This current opinion paper discusses the state of research of various biomarkers currently in development for autism spectrum disorder. The types of biomarkers identified include prenatal history, genetics, neurological including neuroimaging, neurophysiologic, and visual attention, metabolic including abnormalities in mitochondrial, folate, trans-methylation, and trans-sulfuration pathways, immune including autoantibodies and cytokine dysregulation, autonomic nervous system, and nutritional. Many of these biomarkers have promising preliminary evidence for prenatal and post-natal pre-symptomatic risk assessment, confirmation of diagnosis, subtyping, and treatment response. However, most biomarkers have not undergone validation studies and most studies do not investigate biomarkers with clinically relevant comparison groups. Although the field of biomarker research in autism spectrum disorder is promising, it appears that it is currently in the early stages of development.

Potential protein markers in children with Autistic Spectrum Disorder (ASD) revealed by salivary proteomics

The lack of specific pharmacological therapy for Autistic Spectrum Disorder (ASD) and its clinical heterogeneity demand efforts directed toward the identification of biomarkers to aid in diagnosis. Proteomics offers a new perspective for studying the altered proteins associated with autism spectrum disorders (ASD) and we have saliva as an easy-to-collect biological fluid with important biomolecules for investigating biomarkers in various diseases. In this sense, saliva could be used to identify potential biomarkers of ASD. In the current work, saliva samples were collected from children with different degrees of ASD and healthy children and proteomics approaches were applied to generate data on differentially expressed proteins between groups which will serve as a basis for future validation studies as protein markers. Data are available via ProteomeXchange with identifier PXD030065. As results, 132 proteins were present in 80% of the saliva pools of all analyzed groups. Twenty-five proteins were identified as overexpressed in the group of severe and mild/moderate ASD carriers, among which, eight were identified as potential biomarkers for ASD.

ASD and ADHD Comorbidity: What Are We Talking About?

According to the scientific literature, 50 to 70% of individuals with autism spectrum disorder (ASD) also present with comorbid attention deficit hyperactivity disorder (ADHD). From a clinical perspective, this high rate of comorbidity is intriguing. What is the real significance of this dual diagnosis? Is ADHD in fact always present in such cases? Might the attentional impairment reported among our ASD patients actually be a distinct trait of their ASD-namely, impaired joint attention-rather than an ADHD attention deficit? Could their agitation be the consequence of this joint attention impairment or related to a physical restlessness etiologically very different from the agitation typical of ADHD? The neurobiological reality of ASD-ADHD comorbidity is a subject of debate, and amphetamine-based treatment can have paradoxical or undesirable effects in the ASD population. Consequently, does a dual diagnosis, notwithstanding its currency in the literature, prevent us from shedding sufficient light on major physiopathologic questions raised by the clinical picture of ASD?

Increased expression of SLC25A1/CIC causes an autistic-like phenotype with altered neuron morphology

N ε-lysine acetylation within the lumen of the endoplasmic reticulum is a recently characterized protein quality control system that positively selects properly folded glycoproteins in the early secretory pathway. Overexpression of the endoplasmic reticulum acetyl-CoA transporter AT-1 in mouse forebrain neurons results in increased dendritic branching, spine formation and an autistic-like phenotype that is attributed to altered glycoprotein flux through the secretory pathway. AT-1 overexpressing neurons maintain the cytosolic pool of acetyl-CoA by upregulation of SLC25A1, the mitochondrial citrate/malate antiporter and ATP citrate lyase, which converts cytosolic citrate into acetyl-CoA. All three genes have been associated with autism spectrum disorder, suggesting that aberrant cytosolic-to-endoplasmic reticulum flux of acetyl-CoA can be a mechanistic driver for the development of autism spectrum disorder. We therefore generated a SLC25A1 neuron transgenic mouse with overexpression specifically in the forebrain neurons. The mice displayed autistic-like behaviours with a jumping stereotypy. They exhibited increased steady-state levels of citrate and acetyl-CoA, disrupted white matter integrity with activated microglia and altered synaptic plasticity and morphology. Finally, quantitative proteomic and acetyl-proteomic analyses revealed differential adaptations in the hippocampus and cortex. Overall, our study reinforces the connection between aberrant cytosolic-to-endoplasmic reticulum acetyl-CoA flux and the development of an autistic-like phenotype.

Transdiagnostic symptom subtypes across autism spectrum disorders and attention deficit hyperactivity disorder: validated by measures of neurocognition and structural connectivity

BACKGROUNDS

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are neurodevelopmental disorders that exhibit within-disorder heterogeneity and cross-disorder phenotypic overlap, thus suggesting that the current disease categories may not fully represent the etiologic essence of the disorders, especially for highly comorbid neurodevelopmental disorders. In this study, we explored the subtypes of a combined sample of ASD and ADHD by integrating measurements of behavior, cognition and brain imaging.

METHODS

A total of 164 participants, including 65 with ASD, 47 with ADHD, and 52 controls, were recruited. Unsupervised machine learning with an agglomerative hierarchical clustering algorithm was used to identify transdiagnostic symptom clusters. Neurocognition and brain structural connectivity measurements were used to assess symptom clusters. Mediation analysis was used to explore the relationship between transdiagnostic symptoms, neurocognition and brain structural connectivity.

RESULTS

We identified three symptom clusters that did not fall within the diagnostic boundaries of DSM. External measurements from neurocognition and neuroimaging domains supported distinct profiles, including fine motor function, verbal fluency, and structural connectivity in the corpus callosum between these symptom clusters, highlighting possible biomarkers for ASD and ADHD. Additionally, fine motor function was shown to mediate the relationship between the corpus callosum and perseveration symptoms.

CONCLUSIONS

In this transdiagnostic study on ASD and ADHD, we identified three subtypes showing meaningful associations between symptoms, neurocognition and brain white matter structural connectivity. The fine motor function and structural connectivity of corpus callosum might be used as biomarkers for neurodevelopmental disorders with social skill symptoms. The results of this study highlighted the importance of precise phenotyping and further supported the effects of fine motor intervention on ASD and ADHD.

SLC13A5/sodium-citrate co-transporter overexpression causes disrupted white matter integrity and an autistic-like phenotype

Endoplasmic reticulum-based N ɛ-lysine acetylation serves as an important protein quality control system for the secretory pathway. Dysfunctional endoplasmic reticulum-based acetylation, as caused by overexpression of the acetyl coenzyme A transporter AT-1 in the mouse, results in altered glycoprotein flux through the secretory pathway and an autistic-like phenotype. AT-1 works in concert with SLC25A1, the citrate/malate antiporter in the mitochondria, SLC13A5, the plasma membrane sodium/citrate symporter and ATP citrate lyase, the cytosolic enzyme that converts citrate into acetyl coenzyme A. Here, we report that mice with neuron-specific overexpression of SLC13A5 exhibit autistic-like behaviours with a jumping stereotypy. The mice displayed disrupted white matter integrity and altered synaptic structure and function. Analysis of both the proteome and acetyl-proteome revealed unique adaptations in the hippocampus and cortex, highlighting a metabolic response that likely plays an important role in the SLC13A5 neuron transgenic phenotype. Overall, our results support a mechanistic link between aberrant intracellular citrate/acetyl coenzyme A flux and the development of an autistic-like phenotype.

Ionic Channels as Potential Targets for the Treatment of Autism Spectrum Disorder: A Review

Autism spectrum disorder (ASD) is a neurological condition that directly affects brain functions and can culminate in delayed intellectual development, problems in verbal communication, difficulties in social interaction, and stereotyped behaviors. Its etiology reveals a genetic basis that can be strongly influenced by socio-environmental factors. Ion channels controlled by ligand voltage-activated calcium, sodium, and potassium channels may play important roles in modulating sensory and cognitive responses, and their dysfunctions may be closely associated with neurodevelopmental disorders such as ASD. This is due to ionic flow, which is of paramount importance to maintaining physiological conditions in the central nervous system and triggers action potentials, gene expression, and cell signaling. However, since ASD is a multifactorial disease, treatment is directed only to secondary symptoms. Therefore, this research aims to gather evidence concerning the principal pathophysiological mechanisms involving ion channels in order to recognize their importance as therapeutic targets for the treatment of central and secondary ASD symptoms.

Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset

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Multi-site MRI data encode confounding information which may mask case-control differences.

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The impact of the NeuroHarmonize method is evaluated in the ASD-control classification.

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We verified the successful removal of the site effect by the harmonization protocol.

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The increment in the classification performance is quantified after data harmonization.

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We identified the anatomical features that contributed to the two-class separation.

Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 ± 0.04) to a still low, but reasonably significant AUC = 0.67 ± 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 ± 0.02, AUC = 0.65 ± 0.03 and AUC = 0.69 ± 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups.

Dysfunction of Trio GEF1 involves in excitatory/inhibitory imbalance and autism-like behaviors through regulation of interneuron migration

Autism spectrum disorders (ASDs) are a group of highly inheritable neurodevelopmental disorders. Functional mutations in TRIO, especially in the GEF1 domain, are strongly implicated in ASDs, whereas the underlying neurobiological pathogenesis and molecular mechanisms remain to be clarified. Here we characterize the abnormal morphology and behavior of embryonic migratory interneurons (INs) upon Trio deficiency or GEF1 mutation in mice, which are mediated by the Trio GEF1-Rac1 activation and involved in SDF1α/CXCR4 signaling. In addition, the migration deficits are specifically associated with altered neural microcircuit, decreased inhibitory neurotransmission, and autism-like behaviors, which are reminiscent of some features observed in patients with ASDs. Furthermore, restoring the excitatory/inhibitory (E/I) imbalance via activation of GABA signaling rescues autism-like deficits. Our findings demonstrate a critical role of Trio GEF1 mediated signaling in IN migration and E/I balance, which are related to autism-related behavioral phenotypes.

Brain exosomes as minuscule information hub for Autism Spectrum Disorder

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder initiating in the first three years of life. Early initiation of management therapies can significantly improve the health and quality of life of ASD subjects. Thus, indicating the need for suitable biomarkers for the early identification of ASD. Various biological domains were investigated in the quest for reliable biomarkers. However, most biomarkers are in the preliminary stage, and clinical validation is yet to be defined. Exosome based research gained momentum in various Central Nervous System disorders for biomarker identification. However, the utility and prospect of exosomes in ASD is still underexplored.

AREAS COVERED

In the present review, we summarized the biomarker discovery current status and the future of brain-specific exosomes in understanding pathophysiology and its potential as a biomarker. The studies reviewed herein were identified via systematic search (dated: June 2021) of PubMed using variations related to autism (ASD OR autism OR Autism spectrum disorder) AND exosomes AND/OR biomarkers.

EXPERT OPINION

As exosomess are highly relevant in brain disorders like ASD, direct access to brain tissue for molecular assessment is ethically impossible. Thus investigating the brain-derived exosomes would undoubtedly answer many unsolved aspects of the pathogenesis and provide reliable biomarkers.

Common and Distinct Disruptions of Cortical Surface Morphology Between Autism Spectrum Disorder Children With and Without SHANK3 Deficiency

SH3 and Multiple Ankyrin Repeat Domains 3 (SHANK3)-caused autism spectrum disorder (ASD) may present a unique opportunity to clarify the heterogeneous neuropathological mechanisms of ASD. However, the specificity and commonality of disrupted large-scale brain organization in SHANK3-deficient children remain largely unknown. The present study combined genetic tests, neurobehavioral evaluations, and magnetic resonance imaging, aiming to explore the disruptions of both local and networked cortical structural organization in ASD children with and without SHANK3 deficiency. Multiple surface morphological parameters such as cortical thickness (CT) and sulcus depth were estimated, and the graph theory was adopted to characterize the topological properties of structural covariance networks (SCNs). Finally, a correlation analysis between the alterations in brain morphological features and the neurobehavioral evaluations was performed. Compared with typically developed children, increased CT and reduced nodal degree were found in both ASD children with and without SHANK3 defects mainly in the lateral temporal cortex, prefrontal cortex (PFC), temporo-parietal junction (TPJ), superior temporal gyrus (STG), and limbic/paralimbic regions. Besides commonality, our findings showed some distinct abnormalities in ASD children with SHANK3 defects compared to those without. Locally, more changes in the STG and orbitofrontal cortex were exhibited in ASD children with SHANK3 defects, while more changes in the TPJ and inferior parietal lobe (IPL) in those without SHANK3 defects were observed. For the SCNs, a trend toward regular network topology was observed in ASD children with SHANK3 defects, but not in those without. In addition, ASD children with SHANK3 defects showed more alterations of nodal degrees in the anterior and posterior cingulate cortices and right insular, while there were more disruptions in the sensorimotor areas and the left insular and dorsomedial PFC in ASD without SHANK3 defects. Our findings indicate dissociable disruptions of local and networked brain morphological features in ASD children with and without SHANK3 deficiency. Moreover, this monogenic study may provide a valuable path for parsing the heterogeneity of brain disturbances in ASD.

A systematic review of brain MRI findings in monogenic disorders strongly associated with autism spectrum disorder

BACKGROUND

Research on monogenic forms of autism spectrum disorder (autism) can inform our understanding of genetic contributions to the autism phenotype; yet, there is much to be learned about the pathways from gene to brain structure to behavior. This systematic review summarizes and evaluates research on brain magnetic resonance imaging (MRI) findings in monogenic conditions that have strong association with autism. This will improve understanding of the impact of genetic variability on brain structure and related behavioral traits in autism.

METHODS

The search strategy for this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias (ROB) assessment was completed on included studies using the Newcastle-Ottawa Scales.

RESULTS

Of 4,287 studies screened, 69 were included pertaining to 13 of the top 20 genes with the strongest association with autism. The greatest number of studies related to individuals with PTEN variants and autism. Brain MRI abnormalities were reported for 12 of the 13 genes studied, and in 51.7% of participants across all 13 genes, including 100% of participants with ARID1B variants. Specific MRI findings were highly variable, with no clear patterns emerging within or between the 13 genes, although white matter abnormalities were the most common. Few studies reported specific details about methods for acquisition and processing of brain MRI, and descriptors for brain abnormalities were variable. ROB assessment indicated high ROB for all studies, largely due to small sample sizes and lack of comparison groups.

CONCLUSIONS

Brain abnormalities are common in this population of individuals, in particular, children; however, a range of different brain abnormalities were reported within and between genes. Directions for future neuroimaging research in monogenic autism are suggested.

Cortical Gyrification Morphology in ASD and ADHD: Implication for Further Similarities or Disorder-Specific Features?

Shared etiological pathways are suggested in ASD and ADHD given high rates of comorbidity, phenotypic overlap and shared genetic susceptibility. Given the peak of cortical gyrification expansion and emergence of ASD and ADHD symptomology in early development, we investigated gyrification morphology in 539 children and adolescents (6–17 years of age) with ASD (n=197) and ADHD (n=96) compared to typically developing controls (n=246) using the local Gyrification Index (lGI) to provide insight into contributing etiopathological factors in these two disorders. We also examined IQ effects and functional implications of gyrification by exploring the relation between lGI and ASD and ADHD symptomatology beyond diagnosis. General Linear Models yielded no group differences in lGI, and across groups, we identified an age-related decrease of lGI and greater lGI in females compared to males. No diagnosis-by-age interactions were found. Accounting for IQ variability in the model (n=484) yielded similar results. No significant associations were found between lGI and social communication deficits, repetitive and restricted behaviours, inattention or adaptive functioning. By examining both disorders and controls using shared methodology, we found no evidence of atypicality in gyrification as measured by the lGI in these conditions.

The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.

Dissecting autism and schizophrenia through neuroimaging genomics

Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.

Relationship between local gyrification index and age, intelligence quotient, symptom severity with Autism Spectrum Disorder: A large-scale MRI study

Gyrification is one of the most important characteristics in the cerebral cortex and the local gyrification index (LGI) was used to quantify the regional changes in gyrification. The aim of this study was to evaluate LGI alterations in autism spectrum disorder (ASD) individuals in comparison with typically developing (TD) controls and the association of the LGI with age, intelligence quotient (IQ), and symptom severity in a large multicenter dataset. Structural MRI datasets selected from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) repository (606 ASD individuals and 765 age-matched TD controls) were used to calculate LGI values. The correlation between the LGI and age, IQ, and other clinical measurements were assessed. No differences in LGI were found between ASD individuals and TD controls after FDR multiple comparison correction, however, LGI decreased with age in both ASD and TD groups. In the TD group, a significant positive correlation was found between the LGI and full IQ (FIQ) in the parahippocampal gyrus, parsopercularis of left hemisphere and entorhinal cortex, parahippocampal, superior temporal gyrus of right hemisphere, but was not observed in the ASD group. Furthermore, a positive correlation between the LGI and Autism Diagnostic Interview-Revised (ADI-R) Repetitive and Restrictive Behaviors (RRB) score was found in the left inferior parietal lobule, lateral occipital cortex, superior frontal gyrus and right superior frontal gyrus, inferior temporal gyrus. In summary, these results demonstrate that the ASD is a truly heterogeneous neurodevelopmental disorder. Future investigations are required that group ASD patients into more homogeneous subtypes.

Brain Volumes in Mice are Smaller at Birth After Term or Preterm Cesarean Section Delivery

The rate of cesarean section (CS) delivery has steadily increased over the past decades despite epidemiological studies reporting higher risks of neonatal morbidity and neurodevelopmental disorders. Yet, little is known about the immediate impact of CS birth on the brain, hence the need of experimental studies to evaluate brain parameters following this mode of delivery. Using the solvent clearing method iDISCO and 3D imaging technique, we report that on the day of birth, whole-brain, hippocampus, and striatum volumes are reduced in CS-delivered as compared to vaginally-born mice, with a stronger effect observed in preterm CS pups. These results stress the impact of CS delivery, at term or preterm, during parturition and at birth. In contrast, cellular activity and apoptosis are reduced in mice born by CS preterm but not term, suggesting that these early-life processes are only impacted by the combination of preterm birth and CS delivery.

Longitudinal stability of salivary microRNA biomarkers in children and adolescents with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurological condition with increasing prevalence. Few tools accurately predict the developmental trajectory of children with ASD. Such tools would allow clinicians to provide accurate prognoses and track the efficacy of therapeutic interventions. Salivary RNAs that reflect the genetic-environmental interactions underlying ASD may provide objective measures of symptom severity and developmental outcomes. This study investigated whether salivary RNAs previously identified in childhood ASD remain perturbed in older children. We also explored whether RNA candidates changed with therapeutic intervention.

METHOD

A case-control design was used to characterize levels of 78 saliva RNA candidates among 96 children (48 ASD, 48 non-ASD, mean age: 11 years). Thirty-one children (22 ASD, 9 non-ASD developmental delay, mean age: 4 years) were followed longitudinally to explore changes of RNA candidates during early intervention. Saliva RNA and standardized behavioral assessments were collected for each participant. Associations between candidate RNAs and behavioral scores were determined in both groups via Spearman Correlation. Changes in candidate RNAs across two time-points were assessed in the younger cohort via Wilcoxon rank-sum test.

RESULTS

Seven RNAs were associated with VABS-II and BASC scores in the older group ([R] >0.25, FDR< 0.15). Within the younger cohort, 12 RNAs displayed significant changes over time (FDR< 0.05). Three microRNAs were associated with behavioral scores and changed over time (miR-182-5p, miR-146b-5p, miR-374a-5p).

CONCLUSION

Several salivary RNAs are strongly associated with autistic behaviors in older individuals with ASD and change as early as three months after therapy initiation in younger children. These molecules could be used to track treatment effectiveness and provide prognoses. Further validation is necessary.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals

BACKGROUND

Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

MATERIALS AND METHOD

We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection.

RESULTS

A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model.

CONCLUSIONS

The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.

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.

Palmitoylethanolamide and Its Biobehavioral Correlates in Autism Spectrum Disorder: A Systematic Review of Human and Animal Evidence

Autism spectrum disorder (ASD) pathophysiology is not completely understood; however, altered inflammatory response and glutamate signaling have been reported, leading to the investigation of molecules targeting the immune-glutamatergic system in ASD treatment. Palmitoylethanolamide (PEA) is a naturally occurring saturated N-acylethanolamine that has proven to be effective in controlling inflammation, depression, epilepsy, and pain, possibly through a neuroprotective role against glutamate toxicity. Here, we systematically reviewed all human and animal studies examining PEA and its biobehavioral correlates in ASD. Studies indicate altered serum/brain levels of PEA and other endocannabinoids (ECBs)/acylethanolamines (AEs) in ASD. Altered PEA signaling response to social exposure and altered expression/activity of enzymes responsible for the synthesis and catalysis of ECBs/AEs, as well as downregulation of the peroxisome proliferator activated receptor-α (PPAR-α) and cannabinoid receptor target GPR55 mRNA brain expression, have been reported. Stress and exposure to exogenous cannabinoids may modulate ECBs/AEs levels and expression of candidate genes for neuropsychiatric disorders, with implications for ASD. Limited research suggests that PEA supplementation reduces overall autism severity by improving language and social and nonsocial behaviors. Potential neurobiological underpinnings include modulation of immune response, neuroinflammation, neurotrophy, apoptosis, neurogenesis, neuroplasticity, neurodegeneration, mitochondrial function, and microbiota activity, possibly through peroxisome proliferator-activated receptor-α (PPAR-α) activation.

Cortical Gyrification Morphology in Individuals with ASD and ADHD across the Lifespan: A Systematic Review and Meta-Analysis

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification

Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In this study, we first investigated statistical differences in large-scale resting-state networks (RSNs) on 192 healthy controls (HCs) and 103 ASD patients by using independent component analysis (ICA). Second, an image-based meta-analysis (IBMA) was applied to discover the consistency of spatial patterns from different sites. Last, utilizing these patterns as features, we used Support Vector Machine (SVM) classifier to identify whether a subject was suffering from ASD or not. As a result, six RSNs were obtained with ICA. In each RSN, we identified altered functional connectivity between ASD and HC across the multi-site data. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine the classification performance. The AUC value of classification reaches 0.988. In conclusion, the present study indicates that intrinsic connectivity patterns produced from rsfMRI data could yield a possible biomarker of ASD and contributed to the neurobiology of ASD.

Peripheral blood neurotrophic factor levels in children with autism spectrum disorder: a meta-analysis

Increasing evidence suggests that abnormal regulation of neurotrophic factors is involved in the etiology and pathogenesis of Autism Spectrum Disorder (ASD). However, clinical data on neurotrophic factor levels in children with ASD were inconsistent. Therefore, we performed a systematic review of peripheral blood neurotrophic factors levels in children with ASD, and quantitatively summarized the clinical data of peripheral blood neurotrophic factors in ASD children and healthy controls. A systematic search of PubMed and Web of Science identified 31 studies with 2627 ASD children and 4418 healthy controls to be included in the meta-analysis. The results of random effect meta-analysis showed that the peripheral blood levels of brain-derived neurotrophic factor (Hedges’ g = 0.302; 95% CI = 0.014 to 0.591; P = 0.040) , nerve growth factor (Hedges’ g = 0.395; 95% CI = 0.104 to 0.686; P = 0.008) and vascular endothelial growth factor (VEGF) (Hedges’ g = 0.097; 95% CI = 0.018 to 0.175; P = 0.016) in children with ASD were significantly higher than that of healthy controls, whereas blood neurotrophin-3 (Hedges’ g =  − 0.795; 95% CI =  − 1.723 to 0.134; P = 0.093) and neurotrophin-4 (Hedges’ g = 0.182; 95% CI =  − 0.285 to 0.650; P = 0.445) levels did not show significant differences between cases and controls. Taken together, these results clarified circulating neurotrophic factor profile in children with ASD, strengthening clinical evidence of neurotrophic factor aberrations in children with ASD.

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to segments of resting ECG data collected from school-age children with ASD, age-matched typically developing controls, and children with other psychiatric conditions characterized by altered HRV (conduct disorder, depression). We use machine learning to identify time, frequency, and geometric signal-analytical domains that are specific to ASD (receiver operating curve area = 0.89). This is the first study to differentiate children with ASD from other disorders characterized by altered HRV. Despite a small cohort and lack of external validation, results warrant larger prospective studies.

Hot and cold executive functions in the brain: A prefrontal-cingular network

Executive functions, or cognitive control, are higher-order cognitive functions needed for adaptive goal-directed behaviours and are significantly impaired in majority of neuropsychiatric disorders. Different models and approaches are proposed for describing how executive functions are functionally organised in the brain. One popular and recently proposed organising principle of executive functions is the distinction between hot (i.e. reward or affective-related) versus cold (i.e. purely cognitive) domains of executive functions. The prefrontal cortex is traditionally linked to executive functions, but on the other hand, anterior and posterior cingulate cortices are hugely involved in executive functions as well. In this review, we first define executive functions, their domains, and the appropriate methods for studying them. Second, we discuss how hot and cold executive functions are linked to different areas of the prefrontal cortex. Next, we discuss the association of hot versus cold executive functions with the cingulate cortex, focusing on the anterior and posterior compartments. Finally, we propose a functional model for hot and cold executive function organisation in the brain with a specific focus on the fronto-cingular network. We also discuss clinical implications of hot versus cold cognition in major neuropsychiatric disorders (depression, schizophrenia, anxiety disorders, substance use disorder, attention-deficit hyperactivity disorder, and autism) and attempt to characterise their profile according to the functional dominance or manifest of hot-cold cognition. Our model proposes that the lateral prefrontal cortex along with the dorsal anterior cingulate cortex are more relevant for cold executive functions, while the medial-orbital prefrontal cortex along with the ventral anterior cingulate cortex, and the posterior cingulate cortex are more closely involved in hot executive functions. This functional distinction, however, is not absolute and depends on several factors including task features, context, and the extent to which the measured function relies on cognition and emotion or both.

Altered striatum centered brain structures in SHANK3 deficient Chinese children with genotype and phenotype profiling

SHANK3 deficiency represents one of the most replicated monogenic risk factors for autism spectrum disorder (ASD) and SHANK3 caused ASD presents a unique opportunity to understand the underlying neuropathological mechanisms of ASD. In this study, genetic tests, comprehensive clinical and neurobehavioral evaluations, as well as multimodal structural MRI using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were conducted in SHANK3 group (N = 14 with SHANK3 defects), ASD controls (N = 26 with idiopathic ASD without SHANK3 defects) and typically developing (TD) controls (N = 32). Phenotypically, we reported several new features in Chinese SHANK3 deficient children including anteverted nares, sensory stimulation seeking, dental abnormalities and hematological problems. In SHANK3 group, VBM revealed decreased grey matter volumes mainly in dorsal striatum, amygdala, hippocampus and parahippocampal gyrus; TBSS demonstrated decreased fractional anisotropy in multiple tracts involving projection, association and commissural fibers, including middle cerebral peduncle, corpus callosum, superior longitudinal fasciculus, corona radiata, external and internal capsule, and posterior thalamic radiation, etc. We report that the disrupted striatum centered brain structures are associated with SHANK3 deficient children. Study of subjects with monogenic cause offer specific insights into the neuroimaging studies of ASD. The discovery may support a path for future functional connectivity studies to allow for more in-depth understandings of the abnormal neural circuits and the underlying neuropathological mechanisms for ASD.

Altered White Matter Organization in the TUBB3 E410K Syndrome

Seven unrelated individuals (four pediatric, three adults) with the TUBB3 E410K syndrome, harboring identical de novo heterozygous TUBB3 c.1228 G>A mutations, underwent neuropsychological testing and neuroimaging. Despite the absence of cortical malformations, they have intellectual and social disabilities. To search for potential etiologies for these deficits, we compared their brain's structural and white matter organization to 22 controls using structural and diffusion magnetic resonance imaging. Diffusion images were processed to calculate fractional anisotropy (FA) and perform tract reconstructions. Cortical parcellation-based network analysis and gyral topology-based FA analyses were performed. Major interhemispheric, projection and intrahemispheric tracts were manually segmented. Subjects had decreased corpus callosum volume and decreased network efficiency. While only pediatric subjects had diffuse decreases in FA predominantly affecting mid- and long-range tracts, only adult subjects had white matter volume loss associated with decreased cortical surface area. All subjects showed aberrant corticospinal tract trajectory and bilateral absence of the dorsal language network long segment. Furthermore, pediatric subjects had more tracts with decreased FA compared with controls than did adult subjects. These findings define a TUBB3 E410K neuroimaging endophenotype and lead to the hypothesis that the age-related changes are due to microscopic intrahemispheric misguided axons that are pruned during maturation.

Integrated Systems Analysis Explores Dysfunctional Molecular Modules and Regulatory Factors in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a genetic neurodevelopmental disorder involving multiple genes that occurs in early childhood, and a number of risk genes have been reported in previous studies. However, the molecular mechanism of the polygenic regulation leading to pathological changes in ASD remains unclear. First, we identified 8 dysregulated gene coexpression modules by analyzing blood transcriptome data from 96 children with ASD and 42 controls. These modules are rich in ASD risk genes and function related to metabolism, immunity, neurodevelopment, and signaling. The regulatory factors of each module including microRNA (miRNA) and transcription factors (TFs) were subsequently predicted based on transcriptional and posttranscriptional regulation. We identified a set of miRNAs that regulate metabolic and immune modules, as well as transcription factors that cause dysregulation of the modules, and we constructed a coregulatory network between the regulatory factors and modules. Our work reveals dysfunctional modules in children with ASD, elucidates the role of miRNA and transcription factor dysregulation in the pathophysiology of ASD, and helps us to further understand the underlying molecular mechanism of ASD.