Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan

Should We Continue to Tell Autistic People that Their Brains are Different?

Autism is often considered to reflect categorically 'different brains'. Neuropsychological research on autism spectrum disorder (ASD) however, has struggled to define this difference, or derive clear-cut boundaries between autism and non-autism. Consequently, restructuring or disbanding the ASD diagnosis is becoming increasingly advocated within research. Nonetheless, autism now exists as a salient social construction, of which 'difference' is a key facet. Clinical and educational professionals must influence this cautiously, as changes to autism's social construction may counterproductively affect the quality of life of autistic people. This paper therefore reviews ASD's value as both neuropsychological and social constructs. Although lacking neuropsychological validity, the autism label may be beneficial for autistic self-identity, reduction of stigma, and administering support. Whilst a shift away from case-control ASD research is warranted, lay notions of 'different brains' may be preserved.

Review of structural neuroimaging and genetic findings in autism spectrum disorder - a clinical perspective

Autism spectrum disorders (ASDs) are neurodevelopmental conditions characterized by deficits in social relationships and communication and restrictive, repetitive behaviors and interests. ASDs form a heterogeneous group from a clinical and genetic perspective. Currently, ASDs diagnosis is based on the clinical observation of the individual's behavior. The subjective nature of behavioral diagnoses, in the context of ASDs heterogeneity, contributes to significant variation in the age at ASD diagnosis. Early detection has been proved to be critical in ASDs, as early start of appropriate therapeutic interventions greatly improve the outcome for some children. Structural magnetic resonance imaging (MRI) is widely used in the diagnostic work-up of neurodevelopmental conditions, including ASDs, mostly for brain malformations detection. Recently, the focus of brain imaging shifted towards quantitative MRI parameters, aiming to identify subtle changes that may establish early detection biomarkers. ASDs have a strong genetic component; deletions and duplications of several genomic loci have been strongly associated with ASDs risk. Consequently, a multitude of neuroimaging and genetic findings emerged in ASDs in the recent years. The association of gross or subtle changes in brain morphometry and volumes with different genetic defects has the potential to bring new insights regarding normal development and pathomechanisms of various disorders affecting the brain. Still, the clinical implications of these discoveries and the impact of genetic abnormalities on brain structure and function are unclear. Here we review the literature on brain imaging correlated with the most prevalent genomic imbalances in ASD, and discuss the potential clinical impact.

Prenatal Exposure To Valproic Acid Induces Increased Autism-Like Behaviors and Impairment of Learning and Memory Functions in Rat Offspring by Upregulating ADAM10 Expression

Autism spectrum disorder (ASD) involves a complex neurodevelopmental pathogenesis. A disintegrin and metalloproteinase 10 (ADAM10) plays a crucial role in embryonic brain development and neural network stability. This study aimed to investigate the influence of ADAM10 on excitation/inhibition (E/I) balance, autism-like behaviors, and learning and memory dysfunction in rats prenatally exposed to valproic acid (VPA) and determine potential intervention strategies. The VPA-exposed group exhibited increased levels of ADAM10 and secreted amyloid precursor protein-α (sAPPα). Moreover, overexpression of glutamate decarboxylase 1 and N-methyl-D-aspartate receptors was observed. High-performance liquid chromatography-mass spectrometry revealed elevated levels of glutamate, glutamine, and γ-aminobutyric acid, as well as an E/I imbalance in the VPA group. Additionally, narrower synaptic clefts as well as increased postsynaptic density and synaptic vesicles were observed. Remarkably, intraperitoneal administration of ADAM10 inhibitor during the critical period of synaptic development significantly improved ASD-like behavior and learning and memory function in VPA-exposed rats. This intervention effectively reduced abnormally high sAPPα levels in the prefrontal cortex and corrected abnormal E/I balance. Thus, inhibiting ADAM10 overexpression may improve the E/I imbalance, alleviate core symptoms of ASD, and improve learning and memory dysfunction. The use of ADAM10 inhibitor represents a potential therapeutic strategy for treating ASD patients with intellectual disabilities.

Interbrain synchrony attenuation during a peer cooperative task in young children with autistic traits -an EEG hyperscanning study

Young children with autism spectrum disorder (ASD) traits frequently encounter difficulties in peer interaction. Assessing peer interaction performance is crucial but challenging within the clinical diagnostic paradigm of ASD. Hyperscanning, which simultaneously monitors brain activity in multiple individuals, has become a popular tool for assessing social interaction's neural features. The present study aims to investigate the brain-to-brain connectivity between child-dyads engaged in a game-like collaborative peer interaction task via the hyperscanning electroencephalogram (EEG) approach. The final sample comprised 66 young children: 18 child dyads with typical development (TD), TD-TD, and 15 with ASD traits matched to TD, TD-ASD. The study indicated a depressed level of connectivity in the dyad group with ASD as the responder, with a notable decrease observed in the beta oscillation over the right parietal to left temporal coupling between subjects. A pattern that differed from that observed in the TD-TD group was identified with regard to full-band connectivity over the right-to-right temporal region. It was observed that the TD-TD group exhibited enhanced connectivity following the completion of the task, which was not the case for the TD-ASD group. Significant correlations were observed between scores on the ASD symptom rating scale and the selected significant interbrain connectivity index. The application of a hyperscanning EEG paradigm demonstrated that the participating children with autistic traits exhibited an attenuated and apparently distinct alteration pattern of interbrain connectivity in comparison to a control group. These findings highlight the value of physiologically based measures in informing etiological and interventional studies in neuropsychiatry.

White-Matter fiber tract and resting-state functional connectivity abnormalities in young children with autism spectrum disorder

Autism spectrum disorder (ASD) is a complex developmental disorder characterized by difficulties in social interaction and communication and repetitive behaviors. Although abnormal brain development has been shown to exist in children with ASD, the link between structural brain abnormalities and resting-state functional connectivity (rsFC) disruptions in children with ASD remains understudied. To address this limitation, we utilized the population-based bundle-to-region connectome, providing a detailed understanding of the connectivity between cortical regions and white matter (WM) tracts. By precisely indexing WM-Gray Matter (GM) interactions, we investigated the rsFC of the cortex-associated ROIs to explore the association between structural and rsFC abnormalities and clinical symptoms in young children with ASD. This MRI study identified significant differences in WM structure and rsFC between children with ASD (n = 34) and typically developing children (TD, n = 43). Our results showed that decreased fractional anisotropy (FA) and increased mean diffusivity (MD) and radial diffusivity (RD) in ASD WM tracts compared to TD, particularly in left hemisphere tracts (anterior thalamic radiation [ATR], cingulum, inferior fronto-occipital fasciculus [IFOF], inferior longitudinal fasciculus [ILF], superior longitudinal fasciculus [SLF], and uncinate fasciculus [UF]). Abnormal rsFC was observed in GM areas connected by ATR, cingulum, IFOF, ILF, and SLF. Furthermore, abnormalities in the structural and functional connectivity index (SFCI) within the SLF and cingulum were identified. An association has been observed between these abnormalities and clinical symptoms. Specifically, SLF structural and functional connectivity appear to be associated with repetitive and restrictive behavior (RRB), while cingulum connectivity is associated with communication abilities. In conclusion, young children with ASD exhibit abnormal WM tract structures and associated rsFC abnormalities. These differences highlight significant disruptions in rsFC mapped from WM tracts to cortical areas in ASD, correlating with the severity of ASD symptoms, and suggest the importance of multi-modal imaging in capturing these variations.

Early identification of autism spectrum disorder in preschoolers by static and dynamic amplitude of low-frequency fluctuations features

Objectives

Early identification and timely intervention is critical for young children with autism spectrum disorder (ASD). The current study aims to explore potential disparities in static and dynamic intrinsic brain function in preschoolers with ASD, and uncover underlying neural underpinnings that can be used for facilitating the identification of ASD.

Materials and methods

Static and dynamic amplitude of low-frequency fluctuations (ALFF) of 73 ASD preschoolers and 43 age-matched typically developing individuals (TDs) were extracted and compared to identify differences in intrinsic brain local connectivity associated with ASD. The dynamic ALFF (dALFF) utilized a sliding window technique that integrates static ALFF (sALFF) to gauge the variance of local brain activity over time. A receiver operating characteristic (ROC) analysis was conducted to evaluate the potential diagnostic capability of the sALFF and dALFF metrics in identifying ASD.

Results

Compared with TDs, ASD preschoolers exhibited lower levels of sALFF in the left middle temporal gyrus, medial orbitofrontal cortex, precuneus and reduced dALFF values in the left inferior orbitofrontal cortex, middle temporal gyrus. ROC analysis indicated that sALFF and dALFF could distinguish preschoolers with ASD from TDs with the areas under the curve (AUC) of 0.848 and 0.744 (p < 0.001), and their combination showed an increased accuracy with the AUC of 0.866 (p < 0.001). Nevertheless, there were no linear correlation between the ALFF values in children with ASD and clinical scales.

Conclusion

The findings suggest an association of regional left brain dysfunction with ASD in preschoolers. The values of sALFF and dALFF, particularly in the middle temporal gyrus, could act as possible indicators for the early detection of ASD.

Mapping the coupling between tract reachability and cortical geometry of the human brain

The study of cortical geometry and connectivity is prevalent in research on the human brain. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aimed to elucidate fundamental links between cortical geometry and white matter tract connectivity. We developed the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. Specifically, spectral analyses of the cortical surface yielded a set of geometrical eigenmodes, which were then used to explain the locations on the cortical surface reached by specific white matter tracts, referred to as tract reachability. In two independent datasets, we confirmed that tract reachability was well characterized by cortical geometry. We further observed that TGC had high test-retest ability and was specific to each individual. Interestingly, low-frequency TGC was found to be heritable and more informative than the high-frequency components in behavior prediction. Finally, we found that TGC could reproduce task-evoked cortical activation patterns. Collectively, our study provides a new approach to mapping coupling between cortical geometry and connectivity, highlighting how these two aspects jointly shape the connected brain.

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset

Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures, and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), autoencoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities, and evaluation pipelines rather than different machine learning models may explain variations in accuracy in the published literature. The highest accuracy in our framework was obtained when using ensemble models (p < 0.001), leading to an accuracy of 72.2% and AUC = 0.77 using GCN classifiers. However, an SVM classifier performed with an accuracy of 70.1% and AUC = 0.77, just marginally below GCN, and significant differences were not found when comparing different algorithms under the same testing conditions (p > 0.05). Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability in selecting relevant features contributing to model decision making. The code is available at https://github.com/YilanDong19/Machine-learning-with-ABIDE.

11C-UCB-J PET imaging is consistent with lower synaptic density in autistic adults

The neural bases of autism are poorly understood at the molecular level, but evidence from animal models, genetics, post-mortem studies, and single-gene disorders implicate synaptopathology. Here, we use positron emission tomography (PET) to assess the density of synapses with synaptic vesicle glycoprotein 2A (SV2A) in autistic adults using 11C-UCB-J. Twelve autistic (mean (SD) age 25 (4) years; six males), and twenty demographically matched non-autistic individuals (26 (3) years; eleven males) participated in a 11C-UCB-J PET scan. Binding potential, BPND, was the primary outcome measure and computed with the centrum semiovale as the reference region. Partial volume correction with Iterative Yang was applied to control for possible volumetric differences. Mixed-model statistics were calculated for between-group differences. Relationships to clinical characteristics were evaluated based on clinician ratings of autistic features. Whole cortex synaptic density was 17% lower in the autism group (p = 0.01). All brain regions in autism had lower 11C-UCB-J BPND compared to non-autistic participants. This effect was evident in all brain regions implicated in autism. Significant differences were observed across multiple individual regions, including the prefrontal cortex (-15%, p = 0.02), with differences most pronounced in gray matter (p < 0.0001). Synaptic density was significantly associated with clinical measures across the whole cortex (r = 0.67, p = 0.02) and multiple regions (rs = -0.58 to -0.82, ps = 0.05 to <0.01). The first in vivo investigation of synaptic density in autism with PET reveals pervasive and large-scale lower density in the cortex and across multiple brain areas. Synaptic density also correlated with clinical features, such that a greater number of autistic features were associated with lower synaptic density. These results indicate that brain-wide synaptic density may represent an as-yet-undiscovered molecular basis for the clinical phenotype of autism and associated pervasive alterations across a diversity of neural processes.

The epilepsy-autism phenotype associated with developmental and epileptic encephalopathies: New mechanism-based therapeutic options

Epilepsy and autism often co-occur in genetic developmental and epileptic encephalopathies (DEEs), but their underlying neurobiological processes remain poorly understood, complicating treatment. Advances in molecular genetics and understanding the neurodevelopmental pathogenesis of the epilepsy-autism phenotype may lead to mechanism-based treatments for children with DEEs and autism. Several genes, including the newly reported PPFIA3, MYCBP2, DHX9, TMEM63B, and RELN, are linked to various neurodevelopmental and epileptic disorders, intellectual disabilities, and autistic features. These findings underscore the clinical heterogeneity of genetic DEEs and suggest diverse neurobiological mechanisms influenced by genetic, epigenetic, and environmental factors. Mechanisms linking epilepsy and autism include γ-aminobutyric acidergic (GABAergic) signaling dysregulation, synaptic plasticity, disrupted functional connectivity, and neuroinflammatory responses. GABA system abnormalities, critical for inhibitory neurotransmission, contribute to both conditions. Dysregulation of the mechanistic target of rapamycin (mTOR) pathway and neuroinflammation are also pivotal, affecting seizure generation, drug resistance, and neuropsychiatric comorbidities. Abnormal synaptic function and connectivity further underscore the epilepsy-autism phenotype. New treatment options targeting specific mechanisms linked to the epilepsy-autism phenotype are emerging. Genetic variants in potassium channel genes like KCNQ2 and KCNT1 are frequent causes of early onset DEEs. Personalized treatments like retigabine and quinidine have been explored with heterogeneous responses. Efforts are ongoing to develop more effective KCNQ activators and KCNT1 blockers. SCN1A genetic variants, particularly in Dravet syndrome, show potential for treatment of autistic symptoms with low-dose clonazepam, fenfluramine, and cannabidiol, although human trials have yet to consistently replicate animal model successes. Early intervention before the age of 3 years, particularly in SCN1A- and tuberous sclerosis complex-related DEEs, is crucial. Additionally, targeting the mTOR pathway shows promise for seizure control and managing epilepsy-associated comorbidities. Understanding the distinct autism spectrum disorder phenotype in DEEs and implementing early behavioral interventions are essential for improving outcomes. Despite genetic advances, significant challenges persist in diagnosing and treating DEE-associated epilepsy-autism phenotypes. Future clinical trials should adopt precision health approaches to improve neurodevelopmental outcomes.

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning. However, data augmentation strategies employed in these approaches may damage the original blood-oxygen-level-dependent (BOLD) signals, thus hindering subsequent fMRI feature extraction. In this paper, we propose a self-supervised graph contrastive learning framework with diffusion augmentation (GCDA) for functional MRI analysis. The GCDA consists of a pretext model and a task-specific model. In the pretext model, we first augment each brain functional connectivity network derived from fMRI through a graph diffusion augmentation (GDA) module, and then use two graph isomorphism networks with shared parameters to extract features in a self-supervised contrastive learning manner. The pretext model can be optimized without the need for labeled training data, while the GDA focuses on perturbing graph edges and nodes, thus preserving the integrity of original BOLD signals. The task-specific model involves fine-tuning the trained pretext model to adapt to downstream tasks. Experimental results on two rs-fMRI cohorts with a total of 1230 subjects demonstrate the effectiveness of our method compared with several state-of-the-arts.

Impact of high maternal body mass index on fetal cerebral cortical and cerebellar volumes

OBJECTIVES

Maternal obesity increases a child's risk of neurodevelopmental impairment. However, little is known about the impact of maternal obesity on fetal brain development.

METHODS

We prospectively recruited 20 healthy pregnant women across the range of pre-pregnancy or first-trimester body mass index (BMI) and performed fetal brain magnetic resonance imaging (MRI) of their healthy singleton fetuses. We examined correlations between early pregnancy maternal BMI and regional brain volume of living fetuses using volumetric MRI analysis.

RESULTS

Of 20 fetuses, there were 8 males and 12 females (median gestational age at MRI acquisition was 24.3 weeks, range: 19.7-33.3 weeks, median maternal age was 33.3 years, range: 22.0-37.4 years). There were no significant differences in clinical demographics between overweight (OW, 25≤BMI<30)/obese (OB, BMI≥30 kg/m2) (n=12) and normal BMI (18.5≤BMI<25) (n=8) groups. Fetuses in the OW/OB group had significantly larger left cortical plate (p=0.0003), right cortical plate (p=0.0002), and whole cerebellum (p=0.049) compared to the normal BMI group. In the OW/OB BMI group, cortical plate volume was larger relative to other brain regions after 28 weeks.

CONCLUSIONS

This pilot study supports the concept that maternal obesity impacts fetal brain volume, detectable via MRI in living fetuses using quantitative analysis.

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning

OBJECTIVES

To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.

MATERIALS AND METHODS

This retrospective study collected data from 110 children with ASD (80 exploratory, 43 validation) and 68 typically developing children (50 exploratory, 18 validation) from two centers. The automated diffusion tensor imaging along the perivascular space (aDTI-ALPS), fractional anisotropy (FA), cerebrospinal fluid volume, and perivascular space (PVS) volume indices were extracted from DTI, three-dimensional T1-weighted, and T2-weighted images. Intergroup comparisons were conducted using t-tests, Mann-Whitney U-test, and tract-based spatial statistics. Correlation analysis assessed the relationship between glymphatic function, white matter integrity, and clinical scales. Machine learning models based on MRI indices were developed using the AutoGluon framework.

RESULTS

The PVS volume (p < 0.001) was larger, and aDTI-ALPS index (p < 0.001) was lower in children with ASD compared to typically developing children. FA values were reduced in the ASD group and positively correlated with aDTI-ALPS index. The aDTI-ALPS index correlated with ASD severity (r = -0.27, p = 0.02) and developmental delays (r = 0.63, p < 0.001). Mediation analysis indicated the aDTI-ALPS index partially mediated the relationship between white matter integrity and developmental delay. The MRI-based model achieved an area under the curve of 0.84 for ASD diagnosis.

CONCLUSION

Analyzing glymphatic function and white matter integrity enhances understanding of ASD's neurobiological underpinnings. The multi-parametric MRI, combined with machine learning, can facilitate the early detection of ASD.

KEY POINTS

Question How can multi-parametric MRI based on the glymphatic system improve early diagnosis of autism spectrum disorder (ASD) beyond the limitations of current behavioral assessments? Findings Glymphatic dysfunction and disruptions in white matter integrity were associated with clinical symptoms of ASD. Multi-parametric MRI with machine learning can improve early ASD detection. Clinical relevance Multi-parametric MRI, focusing on glymphatic function and white matter integrity, enhances the diagnostic accuracy of ASD by serving as an objective complement to clinical scales.

Autism spectrum disorder common variants associated with regional lobe volume variations at birth: cross-sectional study in 273 European term neonates in developing human connectome project

Increasing lines of evidence suggest cerebral overgrowth in autism spectrum disorder (ASD) children in early life, but few studies have examined the effect of ASD common genetic variants on brain volumes in a general paediatric population. This study examined the association between ASD polygenic risk score (PRS) and volumes of the frontal, temporal, parietal, occipital, fronto-temporal and parieto-occipital lobes in 273 term-born infants of European ancestry in the developing Human Connectome Project. ASD PRS was positively associated with frontal (β = 0.027, pFDR = 0.04) and fronto-temporal (β = 0.024, pFDR = 0.01) volumes, but negatively with parietal (β = -0.037, pFDR = 0.04) and parieto-occipital (β = -0.033, pFDR = 0.01) volumes. This preliminary result suggests the potential involvement of ASD common genetic variants in early structural variations linked to ASD.

An ALE meta-analysis of the neural evidence of facial emotion processing in autism

OBJECTIVE

Facial emotion recognition is central to successful social interaction. People with autism spectrum disorder (ASD) have difficulties in this area. However, neuroimaging evidence on facial emotion processing in ASD has been diverse. This study aims to identify common and consistent brain activity patterns during facial emotion processing in autism.

METHODS

Following PRISMA guidelines, 22 fMRI studies (539 ASD, 502 typically developing participants (TD) were included.

RESULTS

Both groups showed significant activation in the right fusiform gyrus (FG) and left fusiform face area (FFA). In addition, TD participants showed increased left amygdala activity. Compared to TD, ASD individuals had increased activation in the right cerebellum lobule VI and left secondary visual cortex. Age-based subgroup analysis showed that ASD children showed increased activity in bilateral FG, and ASD adults and TD children in the right FG. Finally, adults from both groups had increased activity in the right FG in the within-group and conjunction analyses.

CONCLUSIONS

These results suggest that ASD and TD engage core face processing areas similarly while TD may use core and an extended social brain network. Findings of this study underscore the role of fusiform face area in facial emotion processing along with more insights into the neural processing of facial emotions.

Links between brain structure and function in children with autism spectrum disorder by parallel independent component analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder accompanied by structural and functional changes in the brain. However, the relationship between brain structure and function in children with ASD remains largely obscure. In the current study, parallel independent component analysis (pICA) was performed to identify inter-modality associations by drawing on information from different modalities. Structural and resting-state functional magnetic resonance imaging data from 105 children with ASD and 102 typically developing children (obtained from the open-access Autism Brain Imaging Data Exchange database) were combined through the pICA framework. Features of structural and functional modalities were represented by the voxel-based morphometry (VBM) and amplitude of low-frequency fluctuations (ALFF), respectively. The relationship between the structural and functional components derived from the pICA was investigated by Pearson's correlation analysis, and between-group differences in these components were analyzed through the two-sample t-test. Finally, multivariate support vector regression analysis was used to analyze the relationship between the structural/functional components and Autism Diagnostic Observation Schedule (ADOS) subscores in the ASD group. This study found a significant association between VBM and ALFF components in ASD. Significant between-group differences were detected in the loading coefficients of the VBM component. Furthermore, the ALFF component loading coefficients predicted the subscores of communication and repetitive stereotypic behaviors of the ADOS. Likewise, the VBM component loading coefficients predicted the ADOS communication subscore in ASD. These findings provide evidence of a link between brain function and structure, yielding new insights into the neural mechanisms of ASD.

A neuronal marker of eye contact spontaneously activated in neurotypical subjects but not in autistic spectrum disorders

Attention to faces and eye contact are key behaviors for establishing social bonds in humans. In Autism Spectrum Disorders (ASD), a disturbance in neurodevelopment, impaired face processing and gaze avoidance are key clinical features for ASD diagnosis. The biological alterations underlying these impairments are not yet clearly established. Using high-density electroencephalography coupled with multi-variate pattern classification and group blind source separation methods we searched for face- and-face components-related neural signals that could best discriminate visual processing of neurotypical subjects (N = 38) from ASD participants (N = 27). We isolated a face-specific neural signal in the superior temporal sulcus peaking at 240 msec after face-stimulus onset. A machine learning algorithm applied on the extracted neural component reached 74% decoding accuracy at the same latencies, discriminating the neurotypical population from ASD subjects in whom this signal was weak. By manipulating attention on different parts of the face, we also found that the power of the evoked signal in neurotypical subjects varied depending on the region observed: it was strong when the eye region fell on the fovea to decrease on regions further away and outside the stimulus face. Such face and face-components selective neural modulations were not found in ASD, although they did show typical early face-related P100 and N170 signals. These results show that specialized cortical mechanisms for face perception show higher responses for eyes when attention is focused on gaze and that these mechanisms may be particularly affected in autism spectrum disorders.

Molecular architecture of the altered cortical complexity in autism

Autism spectrum disorder (ASD) is characterized by difficulties in social interaction, communication challenges, and repetitive behaviors. Despite extensive research, the molecular mechanisms underlying these neurodevelopmental abnormalities remain elusive. We integrated microscale brain gene expression data with macroscale MRI data from 1829 participants, including individuals with ASD and typically developing controls, from the autism brain imaging data exchange I and II. Using fractal dimension as an index for quantifying cortical complexity, we identified significant regional alterations in ASD, within the left temporoparietal, left peripheral visual, right central visual, left somatomotor (including the insula), and left ventral attention networks. Partial least squares regression analysis revealed gene sets associated with these cortical complexity changes, enriched for biological functions related to synaptic transmission, synaptic plasticity, mitochondrial dysfunction, and chromatin organization. Cell-specific analyses, protein-protein interaction network analysis and gene temporal expression profiling further elucidated the dynamic molecular landscape associated with these alterations. These findings indicate that ASD-related alterations in cortical complexity are closely linked to specific genetic pathways. The combined analysis of neuroimaging and transcriptomic data enhances our understanding of how genetic factors contribute to brain structural changes in ASD.

Social behavior in ASD males: The interplay between cognitive flexibility, working memory, and functional connectivity deviations

Autism spectrum disorder (ASD) is highly heterogeneous in presentation. While abnormalities in brain functional connectivity are consistently observed in autistic males, the neurobiological basis underlying the different domains of autism symptoms is unclear. In this study, we evaluated whether individual variations in functional connectivity deviations map to social behavior in ASD males. Using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE), we modeled normative trajectories of between-network resting-state functional connectivity (rsFC) in non-ASD males across childhood (n = 321). These normative charts were then applied to ASD males (n = 418) to calculate individual deviation scores (z-scores) that reflect the degree of rsFC atypicality. Deviations in rsFC patterns among the default mode network (DMN), ventral attention network (VAN), frontoparietal network (FPN), and somatomotor network (SMN) were associated with distinct dimensions of social behavior. Cognitive flexibility and working memory mediated the association between VANxDMN z-scores and social behavioral problems. Our findings underscore the potential of normative models to identify atypical brain connectivity at an individual level, revealing the neurobiological patterns associated with social behavioral problems in ASD that are critical for precision diagnosis and intervention. Social outcomes in ASD males may be improved by targeting cognitive flexibility and working memory.

Decomposing the Brain in Autism: Linking Behavioral Domains to Neuroanatomical Variation and Genomic Underpinnings

BACKGROUND

Autism is accompanied by highly individualized patterns of neurodevelopmental differences in brain anatomy. This variability makes the neuroanatomy of autism inherently difficult to describe at the group level. Here, we examined interindividual neuroanatomical differences using a dimensional approach that decomposed the domains of social communication and interaction (SCI), restricted and repetitive behaviors (RRBs), and atypical sensory processing (ASP) within a neurodiverse study population. Moreover, we aimed to link the resulting neuroanatomical patterns to specific molecular underpinnings.

METHODS

Neurodevelopmental differences in cortical thickness (CT) and surface area (SA) were correlated with SCI, RRB, and ASP domain scores by regression of a general linear model in a large neurodiverse sample of 288 autistic individuals and 140 nonautistic individuals, ages 6 to 30 years, recruited within the European Autism Interventions Longitudinal European Autism Project (EU-AIMS LEAP). The domain-specific patterns of neuroanatomical variability were subsequently correlated with cortical gene expression profiles via the Allen Human Brain Atlas.

RESULTS

Across groups, behavioral variations in SCI, RRBs, and ASP were associated with interindividual differences in CT and SA in partially non-overlapping frontoparietal, temporal, and occipital networks. These domain-specific imaging patterns were enriched for genes that 1) are differentially expressed in autism, 2) mediate typical brain development, and 3) are associated with specific cortical cell types. Many of these genes were implicated in pathways governing synaptic structure and function.

CONCLUSIONS

Our study corroborates the close relationship between neuroanatomical variation and interindividual differences in autism-related symptoms and traits within the general framework of neurodiversity and links domain-specific patterns of neuroanatomical differences to putative molecular underpinnings.

Mapping the structure of biomarkers in autism spectrum disorder: a review of the most influential studies

BACKGROUND

Autism spectrum disorder is a distinctive developmental condition which is caused by an interaction between genetic vulnerability and environmental factors. Biomarkers play a crucial role in understanding disease characteristics for diagnosis, prognosis, and treatment. This study employs bibliometric analysis to identify and review the 100 top-cited articles' characteristics, current research hotspots and future directions of autism biomarkers.

METHODS

A comprehensive search of autism biomarkers studies was retrieved from the Web of Science Core Collection database with a combined keyword search strategy. A comprehensive analysis of the top 100 articles was conducted with CiteSpace, VOSviewer, and Excel, including citations, countries, authors, and keywords.

RESULTS

The top 100 cited studies were published between 1988 and 2021, with the United States led in productivity. Core biomarkers such as genetics, children, oxidative stress, and mitochondrial dysfunction are well-established. Potential trends for future research may include brain studies, metabolomics, and associations with other psychiatric disorders.

CONCLUSION

This pioneering bibliometric analysis provides a comprehensive compilation of the 100 most-cited studies on autism, which not only offers a valuable resource for doctors, and researchers but shedding insights into current shortcomings and future endeavors. Future research should prioritize the application of emerging technologies for biomarkers, longitudinal study of biomarkers, and specificity of autism biomarkers to advance the precision of ASD diagnosis and treatment.

Correlations of brain structure with the social behavior of 15q11-13 duplication mice, an animal model of autism

Duplication of chromosome 15q11-13 has been reported to be one of the most frequent cytogenetic copy number variations in autism spectrum disorder (ASD), and a mouse model of paternal 15q11-13 duplication was generated, termed 15q dup mice. While previous studies have replicated some of the behavioral and brain structural phenotypes of ASD separately, the relationship between brain structure and behavior has rarely been examined. In this study, we performed behavioral experiments related to anxiety and social behaviors and magnetic resonance imaging (MRI) using the same set of 15q dup and wild-type mice. 15q dup mice showed increased anxiety and a tendency toward alterations in social behaviors, as reported previously, as well as variability in terms of sociability. MRI analysis revealed that a lower sociability index was correlated with a smaller gray matter volume in the right medial entorhinal cortex. These results may help to understand how variability in behavioral phenotypes of ASD arises even in individuals with the same genetic background and to determine the individual differences in neurodevelopmental trajectory correlated with specific brain structures that underlie these phenotypes.

Transdiagnostic and Diagnosis-Specific Morphological Similarity Related Transcriptional Profile in Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder

OBJECTIVE

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are both highly heritable developmental psychiatric disorders and exhibit a high degree of comorbidity. Our objective is to enhance understanding of the transdiagnostic and diagnosis-specific structural alterations and related cellular and genetic pathophysiological mechanisms between ADHD and ASD.

METHOD

We used structural magnetic resonance imaging data of 247 subjects from the publicly available 1000 Functional Connectomes Project, including 91 individuals with ADHD, 49 individuals with ASD, and 107 age- and sex-matched controls. We performed morphological similarity networks (MSN) and gene transcriptional profile analysis on these image data to identify the anatomical changes and MSN-related genes. Enrichment analysis was further conducted on ADHD/ASD risk genes and MSN-related genes.

RESULTS

Individuals with ADHD showed the diagnosis-specific MSN changes distributing in areas related to high-level cognitive functions, whereas ASD had MSN changes in areas related to language comprehension and spatial location. ADHD and ASD exhibited the transdiagnostic morphological increase in the right middle temporal gyrus. Gene transcriptional profile analysis showed enrichment of ADHD and ASD risk genes in more than 10 biological processes, primarily including function of synapse transmission and development. Genes in excitatory and inhibitory neurons also enriched in pathways with similar function.

CONCLUSION

The transdiagnostic morphological dedifferentiation in the right middle temporal gyrus might indicate the shared motion impairments in ADHD and ASD. Evidence from the transcription of MSN-related genes further indicates a potential imbalance in excitatory and inhibitory neural pathways in ADHD and ASD.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. We worked to ensure diversity in experimental samples through the selection of the cell lines. We worked to ensure diversity in experimental samples through the selection of the genomic datasets. Diverse cell lines and/or genomic datasets were not available. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list.

Age-related differences in delta-beta phase-amplitude coupling in autistic individuals

OBJECTIVE

We aim to investigate the relationship between the core symptoms of autism, anxiety levels, and attention deficit hyperactivity disorder (ADHD) traits, and a non-autism-specific, neurophysiological metric, the Delta-Beta phase-amplitude coupling (PAC), extracted from the resting-state EEG for autistic and non-autistic populations across three different age groups (children, adolescents, and adults).

METHODS

We analyze the eyes-open resting-state EEG of 371 individuals. We applied a phase de-biasing PAC algorithm expected to result in a more accurate PAC estimate than other PAC methodologies available in the literature.

RESULTS

In the adult group, we found a significant increase of the delta-beta PAC in the autistic subgroup who met the Autism Diagnostic Observation Schedule-2 (ADOS-2) Autism Diagnostic Interview-Revised (ADR-R) ADOS-2/ADI-R threshold compared to non-autistic individuals. The differences seem age-specific since we found no statistically significant differences in the children and adolescent populations. Moreover, we found a significant positive correlation with the restricted and repetitive behaviours score of the ADOS-2 diagnostic instrument and with ADHD hyperactivity/impulsivity in the entire autistic cohort.

CONCLUSIONS

The neurophysiological differences we found only in the autistic individuals that meet the thresholds also point out the need for future studies that look for autistic neurodiverse subgroups beyond age.

SIGNIFICANCE

The delta-beta debiasing PAC (dPAC) may potentially serve as a severity biomarker in the autistic population.

Exploring the Dynamic Changes of Brain Lipids, Lipid Rafts, and Lipid Droplets in Aging and Alzheimer's Disease

Aging induces complex changes in the lipid profiles across different areas of the brain. These changes can affect the function of brain cells and may contribute to neurodegenerative diseases such as Alzheimer's disease. Research shows that while the overall lipid profile in the human brain remains quite steady throughout adulthood, specific changes occur with age, especially after the age of 50. These changes include a slow decline in total lipid content and shifts in the composition of fatty acids, particularly in glycerophospholipids and cholesterol levels, which can vary depending on the brain region. Lipid rafts play a crucial role in maintaining membrane integrity and facilitating cellular signaling. In the context of Alzheimer's disease, changes in the composition of lipid rafts have been associated with the development of the disease. For example, alterations in lipid raft composition can lead to increased accumulation of amyloid β (Aβ) peptides, contributing to neurotoxic effects. Lipid droplets store neutral lipids and are key for cellular energy metabolism. As organisms age, the dynamics of lipid droplets in the brain change, with evidence suggesting a decline in metabolic activity over time. This reduced activity may lead to an imbalance in lipid synthesis and mobilization, contributing to neurodegenerative processes. In model organisms like Drosophila, studies have shown that lipid metabolism in the brain can be influenced by diet and insulin signaling pathways, crucial for maintaining metabolic balance. The interplay between lipid metabolism, oxidative stress, and inflammation is critical in the context of aging and Alzheimer's disease. Lipid peroxidation, a consequence of oxidative stress, can lead to the formation of reactive aldehydes that further damage neurons. Inflammatory processes can also disrupt lipid metabolism, contributing to the pathology of AD. Consequently, the accumulation of oxidized lipids can affect lipid raft integrity, influencing signaling pathways involved in neuronal survival and function.

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D DL applied to minimally processed structural MRI scans. We trained 3D DL models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous machine learning (ML) studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template. Our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, encompassing a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism and site effects, and to enable the extension of our method to other neuropsychiatric conditions.

Structural and functional whole brain changes in autism spectrum disorder at different age stages

Autism spectrum disorder (ASD) is a developmental disorder involving regional changes and local neural disturbances. However, few studies have investigated the dysfunctional phenomenon across different age stages. This study explores the structural and functional brain changes across different developmental stages in individuals with ASD, focusing on childhood (6-12 years), adolescence (12-18 years), and adulthood (18 + years). Using a comprehensive set of neuroimaging metrics, including modulated and non-modulated voxel-based morphometry (VBM), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF), we identified significant stage-specific alterations in both VBM and functional measurements. Our results reveal that ASD is associated with progressive and stage-specific abnormalities in brain structure and function, with distinct patterns emerging at each developmental stage. Specifically, we observed significant modulated VBM reductions in the precuneus, lentiform nucleus, and inferior parietal lobule, accompanied by increases in the midbrain and sub-gyral regions. Moreover, we observed unmodulated VBM increment in regions including lentiform nucleus and thalamus. Functionally, ReHo analyses demonstrated disrupted local synchronization in the medial frontal gyrus, while ALFF and fALFF metrics highlighted altered spontaneous brain activity in the sub-gyral and sub-lobar. Finally, correlation analyses revealed that stage-specific findings are closely linked to clinical social- and behavior-related scores, with VBM in the inferior parietal lobule and putamen as well as ReHo in supplemental motor area being significantly associated with restrictive repetitive behaviors in childhood. These findings underscore the importance of considering age-specific brain changes when studying ASD and suggest that targeted interventions may be necessary at different developmental stages.

Longitudinal changes in executive function in autism spectrum disorder: A systematic review and meta-analyses

Individuals with an autism spectrum disorder (ASD) diagnosis show impairment in executive function (EF). However, findings are mixed regarding differences in the age effect on EF between autistic individuals and persons with typical development (TD). Questions remain regarding whether the age-related trajectories of EF in ASD are the same as or different from those in TD. To bridge this knowledge gap, we conducted a systematic review and meta-analyses of longitudinal studies that compared age-related changes in EF between ASD and TD groups (preregistration: osf.io/j5764). A literature search was conducted using PubMed, PsycINFO, and Web of Science on January 29, 2024. After screening by two independent reviewers, 14 longitudinal studies were included. Random-effects meta-analyses of studies involving a maximum total of 518 autistic and 3558 TD children and adolescents (mean baseline ages: 5.7-12.0 years) showed that ASD had significantly poorer EF than TD at both baseline and follow-up. However, there was no significant group difference in the age-related change in EF across domains, including working memory, inhibition, shifting, and planning. Robust Bayesian meta-analyses also provided substantial evidence in favor of the null hypothesis that ASD and TD groups showed similar changes over time for most EF processes. Limitations of the literature included the limited number of longitudinal studies and a narrow range of developmental stages and EF constructs analyzed across studies. Altogether, these findings suggest that autistic children and adolescents generally can improve in EF over time similarly to their neurotypical peers. This has important implications for parents and educators, encouraging appropriate EF training and intervention for autistic children and adolescents at an early stage.

Neurocognitive features in childhood & adulthood in autism spectrum disorder: A neurodiversity approach

OBJECTIVES

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a diverse profile of cognitive functions. Heterogeneity is observed among both baseline and comorbid features concerning the diversity of neuropathology in autism. Symptoms vary depending on the developmental stage, level of severity, or comorbidity with other medical or psychiatric diagnoses such as intellectual disability, epilepsy, and anxiety disorders.

METHOD

The neurodiversity movement does not face variations in neurological and cognitive development in ASD as deficits but as normal non-pathological human variations. Thus, ASD is not identified as a neurocognitive pathological disorder that deviates from the typical, but as a neuro-individuality, a normal manifestation of a neurobiological variation within the population.

RESULTS

In this light, neurodiversity is described as equivalent to any other human variation, such as ethnicity, gender, or sexual orientation. This review will provide insights about the neurodiversity approach in children and adults with ASD. Using a neurodiversity approach can be helpful when working with children who have autism spectrum disorder (ASD).

DISCUSSION

This method acknowledges and values the various ways that people with ASD interact with one another and experience the world in order to embrace the neurodiversity approach when working with children with ASD.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Using Organoids to Model Sex Differences in the Human Brain

Sex differences are widespread during neurodevelopment and play a role in neuropsychiatric conditions such as autism, which is more prevalent in males than females. In humans, males have been shown to have larger brain volumes than females with development of the hippocampus and amygdala showing prominent sex differences. Mechanistically, sex steroids and sex chromosomes drive these differences in brain development, which seem to peak during prenatal and pubertal stages. Animal models have played a crucial role in understanding sex differences, but the study of human sex differences requires an experimental model that can recapitulate complex genetic traits. To fill this gap, human induced pluripotent stem cell-derived brain organoids are now being used to study how complex genetic traits influence prenatal brain development. For example, brain organoids from individuals with autism and individuals with X chromosome-linked Rett syndrome and fragile X syndrome have revealed prenatal differences in cell proliferation, a measure of brain volume differences, and excitatory-inhibitory imbalances. Brain organoids have also revealed increased neurogenesis of excitatory neurons due to androgens. However, despite growing interest in using brain organoids, several key challenges remain that affect its validity as a model system. In this review, we discuss how sex steroids and the sex chromosomes each contribute to sex differences in brain development. Then, we examine the role of X chromosome inactivation as a factor that drives sex differences. Finally, we discuss the combined challenges of modeling X chromosome inactivation and limitations of brain organoids that need to be taken into consideration when studying sex differences.

Implications of altered pyramidal cell morphology on clinical symptoms of neurodevelopmental disorders

The prevalence of pyramidal cells (PCs) in the mammalian cerebral cortex underscore their value as they play a crucial role in various brain functions, ranging from cognition, sensory processing, to motor output. PC morphology significantly influences brain connectivity and plays a critical role in maintaining normal brain function. Pathological alterations to PC morphology are thought to contribute to the aetiology of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. This review explores the relationship between abnormalities in PC morphology in key cortical areas and the clinical manifestations in schizophrenia and ASD. We focus largely on human postmortem studies and provide evidence that dendritic segment length, complexity and spine density are differentially affected in these disorders. These morphological alterations can lead to disruptions in cortical connectivity, potentially contributing to the cognitive and behavioural deficits observed in these disorders. Furthermore, we highlight the importance of investigating the functional and structural characteristics of PCs in these disorders to illuminate the underlying pathogenesis and stimulate further research in this area.

Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects : Author name

With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method.

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

Seeing beyond words: Visualizing autism spectrum disorder biomarker insights

Objective

This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment.

Methods

A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords.

Results

ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included "DNA methylation," "eye tracking," "metabolomics," and "resting-state fMRI."

Conclusion

The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.

Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

Deficiency of FRMD5 results in neurodevelopmental dysfunction and autistic-like behavior in mice

The pathophysiology of autism spectrum disorders (ASDs) is causally linked to postsynaptic scaffolding proteins, as evidenced by numerous large-scale genomic studies [1, 2] and in vitro and in vivo neurobiological studies of mutations in animal models [3, 4]. However, due to the distinct phenotypic and genetic heterogeneity observed in ASD patients, individual mutation genes account for only a small proportion (<2%) of cases [1, 5]. Recently, a human genetic study revealed a correlation between de novo variants in FERM domain-containing-5 (FRMD5) and neurodevelopmental abnormalities [6]. In this study, we demonstrate that deficiency of the scaffolding protein FRMD5 leads to neurodevelopmental dysfunction and ASD-like behavior in mice. FRMD5 deficiency results in morphological abnormalities in neurons and synaptic dysfunction in mice. Frmd5-deficient mice display learning and memory dysfunction, impaired social function, and increased repetitive stereotyped behavior. Mechanistically, tandem mass tag (TMT)-labeled quantitative proteomics revealed that FRMD5 deletion affects the distribution of synaptic proteins involved in the pathological process of ASD. Collectively, our findings delineate the critical role of FRMD5 in neurodevelopment and ASD pathophysiology, suggesting potential therapeutic implications for the treatment of ASD.

CNTN4 modulates neural elongation through interplay with APP

The neuronal cell adhesion molecule contactin-4 (CNTN4) is genetically associated with autism spectrum disorder (ASD) and other psychiatric disorders. Cntn4-deficient mouse models have previously shown that CNTN4 plays important roles in axon guidance and synaptic plasticity in the hippocampus. However, the pathogenesis and functional role of CNTN4 in the cortex has not yet been investigated. Our study found a reduction in cortical thickness in the motor cortex of Cntn4 -/- mice, but cortical cell migration and differentiation were unaffected. Significant morphological changes were observed in neurons in the M1 region of the motor cortex, indicating that CNTN4 is also involved in the morphology and spine density of neurons in the motor cortex. Furthermore, mass spectrometry analysis identified an interaction partner for CNTN4, confirming an interaction between CNTN4 and amyloid-precursor protein (APP). Knockout human cells for CNTN4 and/or APP revealed a relationship between CNTN4 and APP. This study demonstrates that CNTN4 contributes to cortical development and that binding and interplay with APP controls neural elongation. This is an important finding for understanding the physiological function of APP, a key protein for Alzheimer's disease. The binding between CNTN4 and APP, which is involved in neurodevelopment, is essential for healthy nerve outgrowth.

Volumetric analysis of age- and sex-related changes in the corpus striatum and thalamus in the 1-18 age group: a retrospective magnetic resonance imaging study

Studies of the development and asymmetry of the corpus striatum and thalamus in early childhood are rare. Studies investigating these structures across the lifespan have not presented their changes during childhood and adolescence in detail. For these reasons, this study investigated the effect of age and sex factors on the development and asymmetry of the corpus striatum and thalamus in the 1-18 age group. In this retrospective study, we included 652 individuals [362 (56%) males] aged 1-18 years with normal brain MRI between 2012 and 2021. Absolute and relative volumes of the corpus striatum and thalamus were obtained by segmentation of three-dimensional T1-weighted MRIs with volBrain1.0. We created age-specific volume data and month-based development models with the help of SPSS (ver.28). The corpus striatum and thalamus had cubic absolute volumetric developmental models. The relative volume of the caudate and thalamus (only males) is consistent with the decreasing "growth" model, the others with the decreasing cubic model. The absolute volumes of the males' bilateral corpus striatum and thalamus and the relative volumes of the caudate and thalamus of the females were significantly larger (P < 0.05). The caudate showed right > left lateralization; putamen, globus pallidus, and thalamus showed left > right lateralization.

Retinal Thinning in Adults with Autism Spectrum Disorder

Since the retina shares its embryological origin with the central nervous system, optical coherence tomography (OCT), an imaging technique frequently employed in ophthalmology to analyze the macula and intraretinal layer thicknesses and volumes, has recently become increasingly important in psychiatric research. We examined 34 autistic and 31 neurotypical adults (NT) using OCT. Autistic adults had reduced overall macular and outer nuclear layer (ONL) thickness and volume compared to NT. Both macular and ONL thickness showed significant inverse associations with the severity of autistic symptoms measured with the Social Responsiveness Scale 2 (SRS-2). Longitudinal studies across different age groups are required to clarify whether retinal changes may represent a possible trait marker.

Vulnerability of the Hippocampus to Insults: Links to Blood-Brain Barrier Dysfunction

The hippocampus is a critical brain substrate for learning and memory; events that harm the hippocampus can seriously impair mental and behavioral functioning. Hippocampal pathophysiologies have been identified as potential causes and effects of a remarkably diverse array of medical diseases, psychological disorders, and environmental sources of damage. It may be that the hippocampus is more vulnerable than other brain areas to insults that are related to these conditions. One purpose of this review is to assess the vulnerability of the hippocampus to the most prevalent types of insults in multiple biomedical domains (i.e., neuroactive pathogens, neurotoxins, neurological conditions, trauma, aging, neurodegenerative disease, acquired brain injury, mental health conditions, endocrine disorders, developmental disabilities, nutrition) and to evaluate whether these insults affect the hippocampus first and more prominently compared to other brain loci. A second purpose is to consider the role of hippocampal blood-brain barrier (BBB) breakdown in either causing or worsening the harmful effects of each insult. Recent research suggests that the hippocampal BBB is more fragile compared to other brain areas and may also be more prone to the disruption of the transport mechanisms that act to maintain the internal milieu. Moreover, a compromised BBB could be a factor that is common to many different types of insults. Our analysis indicates that the hippocampus is more vulnerable to insults compared to other parts of the brain, and that developing interventions that protect the hippocampal BBB may help to prevent or ameliorate the harmful effects of many insults on memory and cognition.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Cranio-Facial Characteristics in Autism Spectrum Disorder: A Scoping Review

Autism spectrum disorders (ASD) consist of a complex group of neurodevelopmental disorders characterised by qualitative impairments of social interactions, communication abilities, and a limited, stereotyped, and repetitive selection of interests and activities. In light of the imperative to identify a possible biomarker for ASD, it has been determined that craniofacial anomalies serve as significant risk factors for neurodevelopmental disorders. The aim of this scoping review is to deepen the knowledge of the scientific literature related to cranio-facial characteristics in individuals with ASD, with a particular focus on recent research advancements. The review was performed by employing the search strings (("Autism Spectrum Disorder" OR autism OR ASD OR "Autism Spectrum") AND ("facial morphology" OR "facial phenotype")) on the databases PubMed/MEDLINE, Scopus, and ERIC as of March 9, 2023. The review comprised seven studies whose findings were obtained through quantitative analysis of Euclidean distances between anatomical landmarks. The examination of facial abnormalities represents a possible reliable diagnostic biomarker that could aid in the timely identification of ASD. Phenotypic characteristics that may serve as predictive indicators of the severity of autistic symptoms can be observed in certain individuals with ASD by applying anthropometric and instrumental measurements. The presence of a phenotype characterised by an increased intercanthal distance and a reduced facial midline height appears to be associated with a higher degree of severity in autistic symptoms. In addition, it is worth noting that facial asymmetry and facial masculinity can be considered reliable indicators for predicting a more severe manifestation of symptoms.

Peripheral oxytocin levels are linked to hypothalamic gray matter volume in autistic adults: a cross-sectional secondary data analysis

Oxytocin (OXT) is known to modulate social behavior and cognition and has been discussed as pathophysiological and therapeutic factor for autism spectrum disorder (ASD). An accumulating body of evidence indicates the hypothalamus to be of particular importance with regard to the underlying neurobiology. Here we used a region of interest voxel-based morphometry (VBM) approach to investigate hypothalamic gray matter volume (GMV) in autistic (n = 29, age 36.03 ± 11.0) and non-autistic adults (n = 27, age 30.96 ± 11.2). Peripheral plasma OXT levels and the autism spectrum quotient (AQ) were used for correlation analyses. Results showed no differences in hypothalamic GMV in autistic compared to non-autistic adults but suggested a differential association between hypothalamic GMV and OXT levels, such that a positive association was found for the ASD group. In addition, hypothalamic GMV showed a positive association with autistic traits in the ASD group. Bearing in mind the limitations such as a relatively small sample size, a wide age range and a high rate of psychopharmacological treatment in the ASD sample, these results provide new preliminary evidence for a potentially important role of the HTH in ASD and its relationship to the OXT system, but also point towards the importance of interindividual differences.

Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

A growing number of evidence supports a continued distribution of autistic traits in the general population. However, brain maturation trajectories of autistic traits as well as the influence of sex on these trajectories remain largely unknown. We investigated the association of autistic traits in the general population, with longitudinal gray matter (GM) maturation trajectories during the critical period of adolescence. We assessed 709 community-based adolescents (54.7% women) at age 14 and 22. After testing the effect of sex, we used whole-brain voxel-based morphometry to measure longitudinal GM volumes changes associated with autistic traits measured by the Social Responsiveness Scale (SRS) total and sub-scores. In women, we observed that the SRS was associated with slower GM volume decrease globally and in the left parahippocampus and middle temporal gyrus. The social communication sub-score correlated with slower GM volume decrease in the left parahippocampal, superior temporal gyrus, and pallidum; and the social cognition sub-score correlated with slower GM volume decrease in the left middle temporal gyrus, the right ventromedial prefrontal and orbitofrontal cortex. No longitudinal association was found in men. Autistic traits in young women were found to be associated with specific brain trajectories in regions of the social brain and the reward circuit known to be involved in Autism Spectrum Disorder. These findings support both the hypothesis of an earlier GM maturation associated with autistic traits in adolescence and of protective mechanisms in women. They advocate for further studies on brain trajectories associated with autistic traits in women.

Taurine Improved Autism-Like Behaviours and Defective Neurogenesis of the Hippocampus in BTBR Mice through the PTEN/mTOR/AKT Signalling Pathway

Effective treatment of patients with autism spectrum disorder (ASD) is still absent so far. Taurine exhibits therapeutic effects towards the autism-like behaviour in ASD model animals. Here, we determined the mechanism of taurine effect on hippocampal neurogenesis in genetically inbred BTBR T+ tf/J (BTBR) mice, a proposed model of ASD. In this ASD mouse model, we explored the effect of oral taurine supplementation on ASD-like behaviours in an open field test, elevated plus maze, marble burying test, self-grooming test, and three-chamber test. The mice were divided into four groups of normal controls (WT) and models (BTBR), who did or did not receive 6-week taurine supplementation in water (WT, WT+ Taurine, BTBR, and BTBR+Taurine). Neurogenesis-related effects were determined by Ki67 immunofluorescence staining. Western blot analysis was performed to detect the expression of phosphatase and tensin homologue deleted from chromosome 10 (PTEN)/mTOR/AKT pathway-associated proteins. Our results showed that taurine improved the autism-like behaviour, increased the proliferation of hippocampal cells, promoted PTEN expression, and reduced phosphorylation of mTOR and AKT in hippocampal tissue of the BTBR mice. In conclusion, taurine reduced the autism-like behaviour in partially inherited autism model mice, which may be associa-ted with improving the defective neural precursor cell proliferation and enhancing the PTEN-associated pathway in hippocampal tissue.

Cortical thickness abnormalities in autism spectrum disorder

The pathological mechanism of autism spectrum disorder (ASD) remains unclear. Nowadays, surface-based morphometry (SBM) based on structural magnetic resonance imaging (sMRI) techniques have reported cortical thickness (CT) variations in ASD. However, the findings were inconsistent and heterogeneous. This current meta-analysis conducted a whole-brain vertex-wise coordinate-based meta-analysis (CBMA) on CT studies to explore the most noticeable and robust CT changes in ASD individuals by applying the seed-based d mapping (SDM) program. A total of 26 investigations comprised 27 datasets were included, containing 1,635 subjects with ASD and 1470 HC, along with 94 coordinates. Individuals with ASD exhibited significantly altered CT in several regions compared to HC, including four clusters with thicker CT in the right superior temporal gyrus (STG.R), the left middle temporal gyrus (MTG.L), the left anterior cingulate/paracingulate gyri, the right superior frontal gyrus (SFG.R, medial orbital parts), as well as three clusters with cortical thinning including the left parahippocampal gyrus (PHG.L), the right precentral gyrus (PCG.R) and the left middle frontal gyrus (MFG.L). Adults with ASD only demonstrated CT thinning in the right parahippocampal gyrus (PHG.R), revealed by subgroup meta-analyses. Meta-regression analyses found that CT in STG.R was positively correlated with age. Meanwhile, CT in MFG.L and PHG.L had negative correlations with the age of ASD individuals. These results suggested a complicated and atypical cortical development trajectory in ASD, and would provide a deeper understanding of the neural mechanism underlying the cortical morphology in ASD.