Altered corpus callosum morphology associated with autism over the first 2 years of life

Adaptive Functioning Development in Infants With Agenesis of the Corpus Callosum

BACKGROUND AND OBJECTIVES

Agenesis of the corpus callosum (ACC) is a common congenital brain malformation. Early development in ACC remains unexamined, despite the increased likelihood for developmental delays and autistic behaviors. This study compares adaptive functioning in infants/toddlers with isolated ACC to children with other neurodevelopmental conditions and typical development.

METHODS

Parents of children with ACC completed the Vineland Adaptive Behavior Scale Interview at 6, 12, 18, and 24 months as part of a prospective longitudinal study. Comparison groups included children with fragile X, Down syndrome, high familial likelihood of autism spectrum disorder (both with and without autism spectrum diagnosis), and typical development (total n = 957; total assessments = 2676).

RESULTS

By 24 months, 29% of children with ACC were delayed in at least 1 domain. Linear mixed effect models showed significant group × time point interactions in all domains. Post-hoc comparisons revealed the ACC group had poorer performance in communication by 6, motor by 12, and daily living by 18 months but equivalent socialization compared with typically developing children; stronger skills across most domains and time points compared with genetic groups; and equivalent communication, stronger socialization, and weaker motor skills compared with the autism group.

CONCLUSIONS

Although there is significant variability, on average, ACC compromises communication skills by 6 months, with reduced motor and daily living skills by 12 and 18 months, respectively. Multipronged intervention programs are needed for ACC beginning early in the first year of life, possibly leveraging early strengths in social skills.

Sensory-movement underpinnings of lifelong neurodivergence: getting a grip on autism

While the autism diagnosis emphasizes "deficits" in social communication, the article advances that sensory-movement differences underpin autism through a review of the following sources of evidence. This account critically challenges "autistic regression", with evidence that sensory-movement features appear by birth as the earliest signs of autism and underlie the behavioral differences used for diagnosis, which may reflect adaptations to inherent differences and misunderstandings from others. Sensory and motor differences are salient to autistic people, but they often go underrecognized by others. They cause cascading effects in infancy on behavior and communication through differences in sensorimotor learning, automatic imitation, eye contact, sensory perception, and interests. The article then explains how sensory processing differences may influence reduced perceptual narrowing, which involves a bottom-up information processing style grounded in the surrounding environment. Furthermore, this bottom-up processing may grow from reduced sensory integration in feedback loops potentially involving the cerebellum of the brain. The article then moves into implications for the widespread consequences of these inherent differences on quality of life. The article closes with implications for autism as a construct (including underestimated empathy and pain), testing the theory, providing sensory-sensitive support and acceptance of autistic people, and applications to diverse autistic people. The theory may apply particularly well to autistic women and girls, autistic people with speech divergence, autistic people with ADHD, and autistic people with co-occurring sensory and motor-related neurodivergences. Throughout the article, the theory also provides clinical, neurological, and experiential evidence for sensory and motor differences as lifelong, challenging the notion of "losing" (an) autism (diagnosis) as instead reflecting (risky and not necessarily "successful") camouflaging.

Missense mutation in the activation segment of the kinase CK2 models Okur-Chung neurodevelopmental disorder and alters the hippocampal glutamatergic synapse

Exome sequencing has enabled the identification of causative genes of monogenic forms of autism, amongst them, in 2016, CSNK2A1, the gene encoding the catalytic subunit of the kinase CK2, linking this kinase to Okur-Chung Neurodevelopmental Syndrome (OCNDS), a newly described neurodevelopmental condition with many symptoms resembling those of autism spectrum disorder. Thus far, no preclinical model of this condition exists. Here we describe a knock-in mouse model that harbors the K198R mutation in the activation segment of the α subunit of CK2. This region is a mutational hotspot, representing one-third of patients. These mice exhibit behavioral phenotypes that mirror patient symptoms. Homozygous knock-in mice die mid-gestation while heterozygous knock-in mice are born at half of the expected mendelian ratio and are smaller in weight and size than wildtype littermates. Heterozygous knock-in mice showed alterations in cognition and memory-assessing paradigms, enhanced stereotypies, altered circadian activity patterns, and nesting behavior. Phosphoproteome analysis from brain tissue revealed alterations in the phosphorylation status of major pre- and postsynaptic proteins of heterozygous knock-in mice. In congruence, we detect reduced synaptic maturation in hippocampal neurons and attenuated long-term potentiation in the hippocampus of knock-in mice. Taken together, heterozygous knock-in mice (CK2αK198R/+) exhibit significant face validity, presenting ASD-relevant phenotypes, synaptic deficits, and alterations in synaptic plasticity, all of which strongly validate this line as a mouse model of OCNDS.

Revealing morphological fingerprints in perinatal brains using quasi-conformal mapping: occurrence and neurodevelopmental implications

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of neonates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 461 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 41 infants who had longitudinal scans. The model was validated on a fold of 20 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 85.90% and 92.20% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, morphological fingerprints successfully predicted the long-term development of cognition and behavior. Furthermore, the folding morphology demonstrated greater discriminative capability than the cortical thickness. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of individual uniqueness, and predicting long-term neurodevelopmental risks in the brain during early development.

White matter microstructure in school-age children with down syndrome

Down syndrome (DS) is the most common genetic cause of intellectual disability, but our understanding of white matter microstructure in children with DS remains limited. Previous studies have reported reductions in white matter integrity, but nearly all studies to date have been conducted in adults or relied solely on diffusion tensor imaging (DTI), which lacks the ability to disentangle underlying properties of white matter organization. This study examined white matter microstructural differences in 7- to 12-year-old children with DS (n = 23), autism (n = 27), and typical development (n = 50) using DTI as well as High Angular Resolution Diffusion Imaging, and Neurite Orientation and Dispersion Imaging. There was a spatially specific pattern of results that showed a dissociation between intra- and inter-hemispheric pathways. Intra-hemispheric pathways (e.g., inferior fronto-occipital fasciculus, superior longitudinal fasciculus) exhibited reduced organization and structural integrity. Inter-hemispheric pathways (e.g., corpus callosum projections) and motor pathways (e.g., corticospinal tract) showed denser neurite packing and lower neurite dispersion. The current findings provide early insight into white matter development in school-aged children with DS and have the potential to further elucidate microstructural differences and inform more targeted clinical trials than what has previously been observed through DTI models alone.

Autism common variants associated with white matter alterations at birth: cross-sectional fixel-based analyses of 221 European term-born neonates from the developing human connectome project

Increasing lines of evidence suggest white matter (WM) structural changes associated with autism can be detected in the first year of life. Despite the condition having high heritability, the relationship between autism common genetic variants and WM changes during this period remains unclear. By employing advanced regional and whole-brain fixel-based analysis, the current study investigated the association between autism polygenic scores (PS) and WM microscopic fibre density and macrostructural morphology in 221 term-born infants of European ancestry from the developing Human Connectome Project. The results suggest greater tract mean fibre-bundle cross-section of the left superior corona radiata is associated with higher autism PS. Subsequent exploratory enrichment analysis revealed that the autism risk single nucleotide polymorphisms most associated with the imaging phenotype may have roles in neuronal cellular components. Together, these findings suggest a possible link between autism common variants and early WM development.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

Objectives

Brain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet ( B aby and I nfant B rain S egmentation Neural Net work), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images.

Experimental Design

Included in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes.

Principal Observations

BIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics.

Conclusions

BIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

Social and Communication Development in Infants with Isolated Agenesis of the Corpus Callosum

Objective

To evaluate the development of social behavior, communication, emotion regulation, and repetitive behaviors in infants with congenital malformation of the corpus callosum, specifically those with isolated agenesis of the corpus callosum (ACC), in comparison with infants who are developing typically.

Study design

This case-control longitudinal observation study examined parent report of social behavior, social-communication, emotion regulation, and repetitive behavior development in community-ascertained infants (n = 156) and infants with isolated ACC (n = 57) between 12 and 24 months of age.

Results

Infants with isolated ACC produced fewer words at 12 (P = .003) 18 (P < .001), and 24 months of age (P = .003) and fewer gestures at 12 (P < .001), 18 (P < .001), and 24 months of age (P < .001). In addition, the ACC group demonstrated delays in reciprocal social behavior at 18 months (P = .01) and social competence at 12 (P < .001) and 18 months (P = .01). No concerns were noted in emotion regulation or restricted and repetitive behavior, and social behavior appeared to normalize at 24 months.

Conclusions

Existing data suggest heterogeneity in developmental outcomes among individuals with isolated ACC. The current findings fill a gap in knowledge about development in the second year of life. Surveillance of social and communication ability in infants with ACC may be warranted. The role of the corpus callosum in facilitating rapid interhemispheric information processing affects skills beyond the motor system. More work is needed to identify intervention targets for infants and toddlers with ACC.

Vocal communication in asocial BTBR mice is more malleable by a ketogenic diet in juveniles than adults

Deficits in social communication and language development are a hallmark of autism spectrum disorder currently with no effective approaches to reduce the negative impact. Interventional studies using animal models have been very limited in demonstrating improved vocal communication. Autism has been proposed to involve metabolic dysregulation. Ketogenic diet (KD) is a metabolism-based therapy for medically intractable epilepsy, and its applications in other neurological conditions have been increasingly tested. However, how KD would affect vocal communication has not been explored. The BTBR mouse strain is widely used to model asocial phenotypes. They display robust and pronounced deficits in vocalization during social interaction, and have metabolic changes implicated in autism. We investigated the effects of KD on ultrasonic vocalizations (USVs) in juvenile and adult BTBR mice during male-female social encounters. After a brief treatment with KD, the number, spectral bandwidth, and much of the temporal structure of USVs were robustly closer to control levels in both juvenile and adult BTBR mice. Composition of call categories and transitioning between individual call subtypes were more effectively altered to more closely align with the control group in juvenile BTBR mice. Together, our data provide further support to the hypothesis that metabolism-based dietary intervention could modify disease expression, including core symptoms, in autism. Future studies should tease apart the molecular mechanisms of KD's effects on vocalization.

Twenty-four-month effortful control predicts emerging autism characteristics

Longitudinal research can assess how diverging development of multiple cognitive skills during infancy, as well as familial background, are related to the emergence of neurodevelopmental conditions. Sensorimotor and effortful control difficulties are seen in infants later diagnosed with autism; this study explored the relationships between these skills and autism characteristics in 340 infants (240 with elevated familial autism likelihood) assessed at 4-7, 8-10, 12-15, 24, and 36 months. We tested: (1) the relationship between parent-reported effortful control (Rothbart's temperament questionnaires) and sensorimotor skills (Mullen Scales of Early Learning), using random intercept cross-lagged panel modelling; (2) whether household income and maternal education predicted stable individual differences in cognition; (3) sensorimotor and effortful control skills as individual and interactive predictors of parent-reported autism characteristics (Social Responsiveness Scale) at 3 years, using multiple regression; and (4) moderation of interactions by familial likelihood. Sensorimotor skills were longitudinally associated with effortful control at the subsequent measurement point from 12-15 months. Socioeconomic status indicators did not predict stable between-infant differences in sensorimotor or effortful control skills. Effortful control skills were longitudinally related to 3-year autism characteristics from the first year of life, with evidence for an interaction with sensorimotor skills at 24 months. Effects of effortful control increased with age and were particularly important for infants with family histories of autism. Results are discussed in relation to different theoretical frameworks: Developmental Cascades and Anterior Modifiers in the Emergence of Neurodevelopmental Disorders. We suggest a role for 24-month effortful control in explaining the emergent autism phenotype. RESEARCH HIGHLIGHTS: Sensorimotor skills longitudinally predicted effortful control from 12-15 months onward but effortful control did not longitudinally predict sensorimotor skills during infancy. Measures of effortful control skills taken before the age of 1 predicted continuous variation in autism characteristics at 36 months, with associations increasing in strength with age. Effortful control (measured at 12-15 and 24 months) was a stronger predictor of 36-month autism characteristics in infants with elevated familial likelihood for autism. The relationship between 24-month sensorimotor skills and 36-month autism characteristics was stronger in infants with weaker effortful control skills.

Autism Spectrum Disorder in Children: Early Signs and Therapeutic Interventions

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in communication, social interaction difficulties, and repetitive behaviors that can hinder a child's development. The growing prevalence of autism necessitates early detection and effective intervention strategies. This review summarizes the current knowledge of early indicators of ASD, including brain development markers and behavioral signs visible in infants. It investigates diagnostic processes, emphasizing the importance of timely detection at 18 to 24 months using established screening tools. We discuss a variety of therapeutic approaches, including behavioral interventions, educational strategies such as music therapy, and technological advancements such as speech-generating devices. Furthermore, we investigate pharmacological options for treating associated symptoms, emphasizing the lack of targeted medications for core ASD symptoms. Finally, we present evidence highlighting the positive effects of early intervention on developmental outcomes, advocating for individualized treatment plans to enhance the well-being of children with ASD. This comprehensive overview aims to inform ongoing ASD research and clinical practices.

Sex Differences in Human Brain Structure at Birth

BACKGROUND

Sex differences in human brain anatomy have been well-documented, though remain significantly underexplored during early development. The neonatal period is a critical stage for brain development and can provide key insights into the role that prenatal and early postnatal factors play in shaping sex differences in the brain.

METHODS

Here, we assessed on-average sex differences in global and regional brain volumes in 514 newborns aged 0-28 days (236 birth-assigned females and 278 birth-assigned males) using data from the developing Human Connectome Project. We also assessed sex-by-age interactions to investigate sex differences in early postnatal brain development.

RESULTS

On average, males had significantly larger intracranial and total brain volumes, even after controlling for birth weight. After controlling for total brain volume, females showed significantly greater total cortical gray matter volumes, whilst males showed greater total white matter volumes. After controlling for total brain volume in regional comparisons, females had significantly increased white matter volumes in the corpus callosum and increased gray matter volumes in the bilateral parahippocampal gyri (posterior parts), left anterior cingulate gyrus, bilateral parietal lobes, and left caudate nucleus. Males had significantly increased gray matter volumes in the right medial and inferior temporal gyrus (posterior part) and right subthalamic nucleus. Effect sizes ranged from small for regional comparisons to large for global comparisons. Significant sex-by-age interactions were noted in the left anterior cingulate gyrus and left superior temporal gyrus (posterior parts).

CONCLUSIONS

Our findings demonstrate that sex differences in brain structure are already present at birth and remain comparatively stable during early postnatal development, highlighting an important role of prenatal factors in shaping sex differences in the brain.

Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature

Background

A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities.

Methods

Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023.

Results

In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis.

Conclusions

Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.

Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder

BACKGROUND

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder associated with alterations in structural and functional coupling in gray matter. However, despite the detectability and modulation of brain signals in white matter, the structure-function coupling in white matter in autism remains less explored.

METHODS

In this study, we investigated structural-functional coupling in white matter (WM) regions, by integrating diffusion tensor data that contain fiber orientation information from WM tracts, with functional connectivity tensor data that reflect local functional anisotropy information. Using functional and diffusion magnetic resonance images, we analyzed a cohort of 89 ASD and 63 typically developing (TD) individuals from the Autism Brain Imaging Data Exchange II (ABIDE-II). Subsequently, the associations between structural-functional coupling in WM regions and ASD severity symptoms assessed by Autism Diagnostic Observation Schedule-2 were examined via supervised machine learning in an independent test cohort of 29 ASD individuals. Furthermore, we also compared the performance of multi-model features (i.e. structural-functional coupling) with single-model features (i.e. functional or structural models alone).

RESULTS

In the discovery cohort (ABIDE-II), individuals with ASD exhibited widespread reductions in structural-functional coupling in WM regions compared to TD individuals, particularly in commissural tracts (e.g. corpus callosum), association tracts (sagittal stratum), and projection tracts (e.g. internal capsule). Notably, supervised machine learning analysis in the independent test cohort revealed a significant correlation between these alterations in structural-functional coupling and ASD severity scores. Furthermore, compared to single-model features, the integration of multi-model features (i.e., structural-functional coupling) performed best in predicting ASD severity scores.

CONCLUSION

This work provides novel evidence for atypical structural-functional coupling in ASD in white matter regions, further refining our understanding of the critical role of WM networks in the pathophysiology of ASD.

Locomotion and Postural Control in Young Adults with Autism Spectrum Disorders: A Novel Kinesiological Assessment

Background/Objectives: The purposes of the present study were to assess gait by using a novel approach that plots two adjacent joint angles and the postural control in individuals with autism (ASD) and individuals with typical neurodevelopmental (TD). Methods: The surface electromyography (sEMG) activity was measured synchronously with the other variables. Twenty young adult men, 10 with TD and 10 with a diagnosis of ASD, took part in this study. Results: There was a significant difference between ASD and TD groups in the area described by the knee-ankle diagram (p < 0.05). The sEMG activity recorded from the lateral gastrocnemius (LG) during the contact phase of gait was significantly lower in the ASD group compared with the TD group (p < 0.05). The sEMG activity recorded in the different postural conditions showed differences in LG and tibialis anterior (TA) between the ASD and TD groups (p < 0.05). Conclusions: The knee-ankle diagram provided a sensitive and specific movement descriptor to differentiate individuals with ASD from individuals with TD. The reduced LG activation is responsible for the reduced area in the knee-ankle diagram and 'toe-walking' in individuals with ASD and represents the common denominator of an altered ankle strategy during locomotion and postural control.

SCN2A-linked myelination deficits and synaptic plasticity alterations drive auditory processing disorders in ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by complex sensory processing deficits. A key unresolved question is how alterations in neural connectivity and communication translate into the behavioral manifestations seen in ASD. Here, we investigate how oligodendrocyte dysfunction alters myelin plasticity and neuronal activity, leading to auditory processing disorder associated with ASD. We focus on the SCN2A gene, an ASD-risk factor, to understand its role in myelination and neural processing within the auditory nervous system. Through transcriptional profiling, we identified alterations in the expression of myelin-associated genes in Scn2a conditional knockout mice, highlighting the cellular consequences engendered by Scn2a deletion in oligodendrocytes. The results reveal a nuanced interplay between oligodendrocytes and axons, where Scn2a deletion causes alterations in the intricate process of myelination. This disruption instigates changes in axonal properties, presynaptic excitability, and synaptic plasticity at the single cell level. Furthermore, oligodendrocyte-specific Scn2a deletion compromises the integrity of neural circuitry within auditory pathways, leading to auditory hypersensitivity. Our findings reveal a novel pathway linking myelin deficits to synaptic activity and sensory abnormalities in ASD.

Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders

Autism spectrum disorder (ASD) is identified by a set of neurodevelopmental divergences that typically affect the social communication domain. ASD is also characterized by heterogeneous cognitive impairments and is associated with cooccurring physical and medical conditions. As behaviors emerge as the brain matures, it is particularly essential to identify any gaps in neurodevelopmental trajectories during early perinatal life. Here, we introduce the potential of light-sheet imaging for studying developmental biology and cross-scale interactions among genetic, cellular, molecular and macroscale levels of circuitry and connectivity. We first report the core principles of light-sheet imaging and the recent progress in studying brain development in preclinical animal models and human organoids. We also present studies using light-sheet imaging to understand the development and function of other organs, such as the skin and gastrointestinal tract. We also provide information on the potential of light-sheet imaging in preclinical drug development. Finally, we speculate on the translational benefits of light-sheet imaging for studying individual brain-body interactions in advancing ASD research and creating personalized interventions.

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

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

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis

RATIONALE AND OBJECTIVES

This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC).

METHODS AND MATERIALS

Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions.

RESULTS

TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropy were notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions.

CONCLUSION

This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.

Scn2a deletion disrupts oligodendroglia function: Implication for myelination, neural circuitry, and auditory hypersensitivity in ASD

Autism spectrum disorder (ASD) is characterized by a complex etiology, with genetic determinants significantly influencing its manifestation. Among these, the Scn2a gene emerges as a pivotal player, crucially involved in both glial and neuronal functionality. This study elucidates the underexplored roles of Scn2a in oligodendrocytes, and its subsequent impact on myelination and auditory neural processes. The results reveal a nuanced interplay between oligodendrocytes and axons, where Scn2a deletion causes alterations in the intricate process of myelination. This disruption, in turn, instigates changes in axonal properties and neuronal activities at the single cell level. Furthermore, oligodendrocyte-specific Scn2a deletion compromises the integrity of neural circuitry within auditory pathways, leading to auditory hypersensitivity-a common sensory abnormality observed in ASD. Through transcriptional profiling, we identified alterations in the expression of myelin-associated genes, highlighting the cellular consequences engendered by Scn2a deletion. In summary, the findings provide unprecedented insights into the pathway from Scn2a deletion in oligodendrocytes to sensory abnormalities in ASD, underscoring the integral role of Scn2a -mediated myelination in auditory responses. This research thereby provides novel insights into the intricate tapestry of genetic and cellular interactions inherent in ASD.

Functional myelin in cognition and neurodevelopmental disorders

In vertebrates, oligodendrocytes (OLs) are glial cells of the central nervous system (CNS) responsible for the formation of the myelin sheath that surrounds the axons of neurons. The myelin sheath plays a crucial role in the transmission of neuronal information by promoting the rapid saltatory conduction of action potentials and providing neurons with structural and metabolic support. Saltatory conduction, first described in the peripheral nervous system (PNS), is now generally recognized as a universal evolutionary innovation to respond quickly to the environment: myelin helps us think and act fast. Nevertheless, the role of myelin in the central nervous system, especially in the brain, may not be primarily focused on accelerating conduction speed but rather on ensuring precision. Its principal function could be to coordinate various neuronal networks, promoting their synchronization through oscillations (or rhythms) relevant for specific information processing tasks. Interestingly, myelin has been directly involved in different types of cognitive processes relying on brain oscillations, and myelin plasticity is currently considered to be part of the fundamental mechanisms for memory formation and maintenance. However, despite ample evidence showing the involvement of myelin in cognition and neurodevelopmental disorders characterized by cognitive impairments, the link between myelin, brain oscillations, cognition and disease is not yet fully understood. In this review, we aim to highlight what is known and what remains to be explored to understand the role of myelin in high order brain processes.

Developmental dopaminergic signaling modulates neural circuit formation and contributes to autism spectrum disorder (ASD)-related phenotypes

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with a complex etiology. Recent evidence suggests that dopamine plays a crucial role in neural development. However, it remains unclear whether and how disrupted dopaminergic signaling during development contributes to ASD. In this study, human brain RNA-seq transcriptome analysis revealed a significant correlation between changes in dopaminergic signaling pathways and neural developmental signaling in ASD patients. In the zebrafish model, disrupted developmental dopaminergic signaling led to neural circuit abnormalities and behavior reminiscent of autism. Dopaminergic signaling may impact neuronal specification by potentially modulating integrins. These findings shed light on the mechanisms underlying the link between disrupted developmental dopamine signaling and ASD, and they point to the possibility of targeting dopaminergic signaling in early development for ASD treatment.

Cortical mapping of callosal connections in healthy young adults

The corpus callosum (CC) is the principal white matter bundle supporting communication between the two brain hemispheres. Despite its importance, a comprehensive mapping of callosal connections is still lacking. Here, we constructed the first bidirectional population-based callosal connectional atlas between the midsagittal section of the CC and the cerebral cortex of the human brain by means of diffusion-weighted imaging tractography. The estimated connectional topographic maps within this atlas have the most fine-grained spatial resolution, demonstrate histological validity, and were reproducible in two independent samples. This new resource, a complete and comprehensive atlas, will facilitate the investigation of interhemispheric communication and come with a user-friendly companion online tool (CCmapping) for easy access and visualization of the atlas.

Three Decades of Valproate: A Current Model for Studying Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increased prevalence and incidence in recent decades. Its etiology remains largely unclear, but it seems to involve a strong genetic component and environmental factors that, in turn, induce epigenetic changes during embryonic and postnatal brain development. In recent decades, clinical studies have shown that inutero exposure to valproic acid (VPA), a commonly prescribed antiepileptic drug, is an environmental factor associated with an increased risk of ASD. Subsequently, prenatal VPA exposure in rodents has been established as a reliable translational model to study the pathophysiology of ASD, which has helped demonstrate neurobiological changes in rodents, non-human primates, and brain organoids from human pluripotent stem cells. This evidence supports the notion that prenatal VPA exposure is a valid and current model to replicate an idiopathic ASD-like disorder in experimental animals. This review summarizes and describes the current features reported with this animal model of autism and the main neurobiological findings and correlates that help elucidate the pathophysiology of ASD. Finally, we discuss the general framework of the VPA model in comparison to other environmental and genetic ASD models.

John T, Estes AM, Schultz RT, Hazlett HC, Dager SR, Botteron KN, Styner M, Piven J, Shen MD. Enlarged Perivascular Spaces in Infancy and Autism Diagnosis, Cerebrospinal Fluid Volume, and Later Sleep Problems

Importance

Perivascular spaces (PVS) and cerebrospinal fluid (CSF) are essential components of the glymphatic system, regulating brain homeostasis and clearing neural waste throughout the lifespan. Enlarged PVS have been implicated in neurological disorders and sleep problems in adults, and excessive CSF volume has been reported in infants who develop autism. Enlarged PVS have not been sufficiently studied longitudinally in infancy or in relation to autism outcomes or CSF volume.

Objective

To examine whether enlarged PVS are more prevalent in infants who develop autism compared with controls and whether they are associated with trajectories of extra-axial CSF volume (EA-CSF) and sleep problems in later childhood.

Design, Setting, and Participants

This prospective, longitudinal cohort study used data from the Infant Brain Imaging Study. Magnetic resonance images were acquired at ages 6, 12, and 24 months (2007-2017), with sleep questionnaires performed between ages 7 and 12 years (starting in 2018). Data were collected at 4 sites in North Carolina, Missouri, Pennsylvania, and Washington. Data were analyzed from March 2021 through August 2022.

Exposure

PVS (ie, fluid-filled channels that surround blood vessels in the brain) that are enlarged (ie, visible on magnetic resonance imaging).

Main Outcomes and Measures

Outcomes of interest were enlarged PVS and EA-CSF volume from 6 to 24 months, autism diagnosis at 24 months, sleep problems between ages 7 and 12 years.

Results

A total of 311 infants (197 [63.3%] male) were included: 47 infants at high familial likelihood for autism (ie, having an older sibling with autism) who were diagnosed with autism at age 24 months, 180 high likelihood infants not diagnosed with autism, and 84 low likelihood control infants not diagnosed with autism. Sleep measures at school-age were available for 109 participants. Of infants who developed autism, 21 (44.7%) had enlarged PVS at 24 months compared with 48 infants (26.7%) in the high likelihood but no autism diagnosis group (P = .02) and 22 infants in the control group (26.2%) (P = .03). Across all groups, enlarged PVS at 24 months was associated with greater EA-CSF volume from ages 6 to 24 months (β = 4.64; 95% CI, 0.58-8.72; P = .002) and more frequent night wakings at school-age (F = 7.76; η2 = 0.08; P = .006).

Conclusions and Relevance

These findings suggest that enlarged PVS emerged between ages 12 and 24 months in infants who developed autism. These results add to a growing body of evidence that, along with excessive CSF volume and sleep dysfunction, the glymphatic system could be dysregulated in infants who develop autism.

A meta-analysis of differentially expressed and regulatory genes with their functional enrichment analysis for brain transcriptome data in autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent challenges in social interactions and repetitive behavioral patterns. It is a significant problem emerging worldwide, as one in 100 children is affected by this disorder globally. In this study, a meta-analysis was performed for the identification of differentially expressed genes (DEGs) along with the expression analysis of regulatory genes. Functional enrichment analysis was an integral part of current findings to notify the significant pathways of this complex disorder. The study was conducted with two RNA-Seq datasets, viz., GSE64018 and GSE62098, for ASD patients and control samples from the GEO database. The identification of up-regulatory and down-regulatory genes was performed by the interaction analysis of transcription factors (TF) and DEGs. As an outcome of the meta-analysis, 2543 DEGs were identified as common across both of the datasets in which 1402 DEGs exhibited upregulation and 1130 genes have shown downregulation. In network analysis, upregulatory genes have shown strong interaction while downregulatory genes exhibit weak or null interaction. Further, in the enrichment analysis of screened upregulatory DEGs, three major significant pathways were identified namely the ATP synthesis pathway, FAS signaling pathway, and the Huntington's disease pathway. The common expression of CYC 1 gene in all the identified pathways has indicated that it is an important key regulator gene for the majorly associated pathways. The study concludes that all the potential DEGs were found to show their related high expression in neurobiological regulations specifically with ASD.Communicated by Ramaswamy S. Sarma.

Corpus callosum in children with neurodevelopmental delay: MRI standard qualitative assessment versus automatic quantitative analysis

BACKGROUND

The corpus callosum (CC) is a key brain structure. In children with neurodevelopmental delay, we compared standard qualitative radiological assessments with an automatic quantitative tool.

METHODS

We prospectively enrolled 73 children (46 males, 63.0%) with neurodevelopmental delay at single university hospital between September 2020 and September 2022. All of them underwent 1.5-T brain magnetic resonance imaging (MRI) including a magnetization-prepared 2 rapid acquisition gradient echoes - MP2RAGE sequence. Two radiologists blindly reviewed the images to classify qualitatively the CC into normal, hypoplasic, hyperplasic, and/or dysgenetic classes. An automatic tool (QuantiFIRE) was used to provide brain volumetry and T1 relaxometry automatically as well as deviations of those parameters compared with a healthy age-matched cohort. The MRI reference standard for CC volumetry was based on the Garel et al. study. Cohen κ statistics was used for interrater agreement. The radiologists and QuantiFIRE's diagnostic accuracy were compared with the reference standard using the Delong test.

RESULTS

The CC was normal in 42 cases (57.5%), hypoplastic in 20 cases (27.4%), and hypertrophic in 11 cases (15.1%). T1 relaxometry values were abnormal in 26 children (35.6%); either abnormally high (18 cases, 24.6%) or low (8 cases, 11.0%). The interrater Cohen κ coefficient was 0.91. The diagnostic accuracy of the QuantiFIRE prototype was higher than that of the radiologists for hypoplastic and normal CC (p = 0.003 for both subgroups, Delong test).

CONCLUSIONS

An automated volumetric and relaxometric assessment can assist the evaluation of brain structure such as the CC, particularly in the case of subtle abnormalities.

RELEVANCE STATEMENT

Automated brain MRI segmentation combined with statistical comparison to normal volume and T1 relaxometry values can be a useful diagnostic support tool for radiologists.

KEY POINTS

• Corpus callosum abnormality detection is challenging but clinically relevant. • Automated quantitative volumetric analysis had a higher diagnostic accuracy than that of visual appreciation of radiologists. • Quantitative T1 relaxometric analysis might help characterizing corpus callosum better.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

Corpus Callosum Volumes in Children with Autism Spectrum Disorders: Sex-Associated Differences

This study aimed to analyze the relationship between sex and corpus callosum (CC) volume in children with autism spectrum disorders (ASD) aged 2-4 years. This prospective study included 50 children with ASD and 50 typically developing (TD) children aged 2-4 years. Midsagittal slices of the CCs of the participants were divided into five subregions using FreeSurfer software. The PMCC, AMCC and TCC volumes were significantly higher in ASD participants than in TD participants, and results were significant in females with ASD rather than in males with ASD (all P < 0.05). In toddlers with ASD, the CC volumes were increased and more pronounced in females than in males. This could be due to overgrowth of axons or/and axonal pruning disorders.

The relevance of heterotopic callosal fibers to interhemispheric connectivity of the mammalian brain

The corpus callosum (CC) is the largest white matter structure and the primary pathway for interhemispheric brain communication. Investigating callosal connectivity is crucial to unraveling the brain's anatomical and functional organization in health and disease. Classical anatomical studies have characterized the bulk of callosal axonal fibers as connecting primarily homotopic cortical areas. Whenever detected, heterotopic callosal fibers were ascribed to altered sprouting and pruning mechanisms in neurodevelopmental diseases such as CC dysgenesis (CCD). We hypothesized that these heterotopic connections had been grossly underestimated due to their complex nature and methodological limitations. We used the Allen Mouse Brain Connectivity Atlas and high-resolution diffusion-weighted imaging to identify and quantify homotopic and heterotopic callosal connections in mice, marmosets, and humans. In all 3 species, we show that ~75% of interhemispheric callosal connections are heterotopic and comprise the central core of the CC, whereas the homotopic fibers lay along its periphery. We also demonstrate that heterotopic connections have an essential role in determining the global properties of brain networks. These findings reshape our view of the corpus callosum's role as the primary hub for interhemispheric brain communication, directly impacting multiple neuroscience fields investigating cortical connectivity, neurodevelopment, and neurodevelopmental disorders.

Prediction of autism in infants: progress and challenges

Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.

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

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

Differential impacts of Cntnap2 heterozygosity and Cntnap2 null homozygosity on axon and myelinated fiber development in mouse

Over the last decade, a large variety of alterations of the Contactin Associated Protein 2 (CNTNAP2) gene, encoding Caspr2, have been identified in several neuronal disorders, including neurodevelopmental disorders and peripheral neuropathies. Some of these alterations are homozygous but most are heterozygous, and one of the current challenges is to estimate to what extent they could affect the functions of Caspr2 and contribute to the development of these pathologies. Notably, it is not known whether the disruption of a single CNTNAP2 allele could be sufficient to perturb the functions of Caspr2. To get insights into this issue, we questioned whether Cntnap2 heterozygosity and Cntnap2 null homozygosity in mice could both impact, either similarly or differentially, some specific functions of Caspr2 during development and in adulthood. We focused on yet poorly explored functions of Caspr2 in axon development and myelination, and performed a morphological study from embryonic day E17.5 to adulthood of two major brain interhemispheric myelinated tracts, the anterior commissure (AC) and the corpus callosum (CC), comparing wild-type (WT), Cntnap2 ^-/- and Cntnap2 ^+/- mice. We also looked for myelinated fiber abnormalities in the sciatic nerves of mutant mice. Our work revealed that Caspr2 controls the morphology of the CC and AC throughout development, axon diameter at early developmental stages, cortical neuron intrinsic excitability at the onset of myelination, and axon diameter and myelin thickness at later developmental stages. Changes in axon diameter, myelin thickness and node of Ranvier morphology were also detected in the sciatic nerves of the mutant mice. Importantly, most of the parameters analyzed were affected in Cntnap2 ^+/- mice, either specifically, more severely, or oppositely as compared to Cntnap2 ^-/- mice. In addition, Cntnap2 ^+/- mice, but not Cntnap2 ^-/- mice, showed motor/coordination deficits in the grid-walking test. Thus, our observations show that both Cntnap2 heterozygosity and Cntnap2 null homozygosity impact axon and central and peripheral myelinated fiber development, but in a differential manner. This is a first step indicating that CNTNAP2 alterations could lead to a multiplicity of phenotypes in humans, and raising the need to evaluate the impact of Cntnap2 heterozygosity on the other neurodevelopmental functions of Caspr2.

Gabrb3 is required for the functional integration of pyramidal neuron subtypes in the somatosensory cortex

Dysfunction of gamma-aminobutyric acid (GABA)ergic circuits is strongly associated with neurodevelopmental disorders. However, it is unclear how genetic predispositions impact circuit assembly. Using in vivo two-photon and widefield calcium imaging in developing mice, we show that Gabrb3, a gene strongly associated with autism spectrum disorder (ASD) and Angelman syndrome (AS), is enriched in contralaterally projecting pyramidal neurons and is required for inhibitory function. We report that Gabrb3 ablation leads to a developmental decrease in GABAergic synapses, increased local network synchrony, and long-lasting enhancement in functional connectivity of contralateral-but not ipsilateral-pyramidal neuron subtypes. In addition, Gabrb3 deletion leads to increased cortical response to tactile stimulation at neonatal stages. Using human transcriptomics and neuroimaging datasets from ASD subjects, we show that the spatial distribution of GABRB3 expression correlates with atypical connectivity in these subjects. Our studies reveal a requirement for Gabrb3 during the emergence of interhemispheric circuits for sensory processing.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection.

Methods

Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections.

Results

Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections.

Conclusions

We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Neurodevelopmental toxicity induced by airborne particulate matter

Airborne particulate matter (PM), the primary component associated with health risks in air pollution, can negatively impact human health. Studies have shown that PM can enter the brain by inhalation, but data on the exact quantity of particles that reach the brain are unknown. Particulate matter exposure can result in neurotoxicity. Exposure to PM poses a greater health risk to infants and children because their nervous systems are not fully developed. This review paper highlights the association between PM and neurodevelopmental toxicity (NDT). Exposure to PM can induce oxidative stress and inflammation, potentially resulting in blood-brain barrier damage and increased susceptibility to development of neurodevelopmental disorders (NDD), such as autism spectrum disorders and attention deficit disorders. In addition, human and animal exposure to PM can induce microglia activation and epigenetic alterations and alter the neurotransmitter levels, which may increase risks for development of NDD. However, the systematic comparisons of the effects of PM on NDD at different ages of exposure are deficient. The elucidation of PM exposure risks and NDT in children during the early developmental stages are of great importance. The synthesis of current research may help to identify markers and mechanisms of PM-induced neurodevelopmental toxicity, allowing for the development of strategies to prevent permanent damage of developing brain.

Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder

BACKGROUND

Histogram analysis is a texture analysis method that can be used in medical images. Quantitative values of the intensity of images can be obtained with histogram analysis. It aimed to evaluate corpus callosum in magnetic resonance images (MRIs) using histogram analysis of pediatric patients with autism spectrum disorder (ASD) to compare them with healthy controls.

METHODS

This study included 29 children with ASD and 29 healthy children with normal brain MRI. High-resolution three-dimensional turbo field echo images were obtained with a 1.5 T scanner device for brain magnetic resonance imaging. On the corpus callosum in the sagittal T1-weighted images obtained, mean gray level density (mean), the standard deviation, median, minimum, maximum, entropy, variance, skewness, kurtosis, uniformity, size % L, size % M, size % U, and percentile parameters were measured.

RESULTS

In ASD patients, mean, standard deviation, maximum, median, variance, entropy, 25%, 75%, 90%, 97%, and 99% values were found to be lower than the control group, and size % U value was higher. In addition, the corpus callosum area was significantly lower in the ASD compared to the controls.

CONCLUSION

According to our study, corpus callosum of patients with ASD showed differences compared to healthy controls by histogram analysis, even though they were seen as normal in brain MRI. We think that histogram analysis can be used to evaluate possibly affected areas of brain in ASD patients.

Infant Visual Brain Development and Inherited Genetic Liability in Autism

OBJECTIVE

Autism spectrum disorder (ASD) is heritable, and younger siblings of ASD probands are at higher likelihood of developing ASD themselves. Prospective MRI studies of siblings report that atypical brain development precedes ASD diagnosis, although the link between brain maturation and genetic factors is unclear. Given that familial recurrence of ASD is predicted by higher levels of ASD traits in the proband, the authors investigated associations between proband ASD traits and brain development among younger siblings.

METHODS

In a sample of 384 proband-sibling pairs (89 pairs concordant for ASD), the authors examined associations between proband ASD traits and sibling brain development at 6, 12, and 24 months in key MRI phenotypes: total cerebral volume, cortical surface area, extra-axial cerebrospinal fluid, occipital cortical surface area, and splenium white matter microstructure. Results from primary analyses led the authors to implement a data-driven approach using functional connectivity MRI at 6 months.

RESULTS

Greater levels of proband ASD traits were associated with larger total cerebral volume and surface area and larger surface area and reduced white matter integrity in components of the visual system in siblings who developed ASD. This aligned with weaker functional connectivity between several networks and the visual system among all siblings during infancy.

CONCLUSIONS

The findings provide evidence that specific early brain MRI phenotypes of ASD reflect quantitative variation in familial ASD traits. Multimodal anatomical and functional convergence on cortical regions, fiber pathways, and functional networks involved in visual processing suggest that inherited liability has a role in shaping the prodromal development of visual circuitry in ASD.

Subcortical Brain Development in Autism and Fragile X Syndrome: Evidence for Dynamic, Age- and Disorder-Specific Trajectories in Infancy

OBJECTIVE

Previous research has demonstrated that the amygdala is enlarged in children with autism spectrum disorder (ASD). However, the precise onset of this enlargement during infancy, how it relates to later diagnostic behaviors, whether the timing of enlargement in infancy is specific to the amygdala, and whether it is specific to ASD (or present in other neurodevelopmental disorders, such as fragile X syndrome) are all unknown.

METHODS

Longitudinal MRIs were acquired at 6-24 months of age in 29 infants with fragile X syndrome, 58 infants at high likelihood for ASD who were later diagnosed with ASD, 212 high-likelihood infants not diagnosed with ASD, and 109 control infants (1,099 total scans).

RESULTS

Infants who developed ASD had typically sized amygdala volumes at 6 months, but exhibited significantly faster amygdala growth between 6 and 24 months, such that by 12 months the ASD group had significantly larger amygdala volume (Cohen's d=0.56) compared with all other groups. Amygdala growth rate between 6 and 12 months was significantly associated with greater social deficits at 24 months when the infants were diagnosed with ASD. Infants with fragile X syndrome had a persistent and significantly enlarged caudate volume at all ages between 6 and 24 months (d=2.12), compared with all other groups, which was significantly associated with greater repetitive behaviors.

CONCLUSIONS

This is the first MRI study comparing fragile X syndrome and ASD in infancy, demonstrating strikingly different patterns of brain and behavior development. Fragile X syndrome-related changes were present from 6 months of age, whereas ASD-related changes unfolded over the first 2 years of life, starting with no detectable group differences at 6 months. Increased amygdala growth rate between 6 and 12 months occurs prior to social deficits and well before diagnosis. This gradual onset of brain and behavior changes in ASD, but not fragile X syndrome, suggests an age- and disorder-specific pattern of cascading brain changes preceding autism diagnosis.

SHANK3 deficiency leads to myelin defects in the central and peripheral nervous system

Mutations or deletions of the SHANK3 gene are causative for Phelan–McDermid syndrome (PMDS), a syndromic form of autism spectrum disorders (ASDs). We analyzed Shank3Δ11(−/−) mice and organoids from PMDS individuals to study effects on myelin. SHANK3 was found to be expressed in oligodendrocytes and Schwann cells, and MRI analysis of Shank3Δ11(−/−) mice revealed a reduced volume of the corpus callosum as seen in PMDS patients. Myelin proteins including myelin basic protein showed significant temporal and regional differences with lower levels in the CNS but increased amounts in the PNS of Shank3Δ11(−/−) animals. Node, as well as paranode, lengths were increased and ultrastructural analysis revealed region-specific alterations of the myelin sheaths. In PMDS hiPSC-derived cerebral organoids we observed an altered number and delayed maturation of myelinating cells. These findings provide evidence that, in addition to a synaptic deregulation, impairment of myelin might profoundly contribute to the clinical manifestation of SHANK3 deficiency.

Neuroimaging Techniques as Descriptive and Diagnostic Tools for Infants at Risk for Autism Spectrum Disorder: A Systematic Review

Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 - far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis

The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.

Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder

Early interventions for autism spectrum disorder (ASD) are increasingly available, while only 42-50% of ASD children are diagnosed before 3 years old (YO). To identify neuroimaging biomarkers for early ASD diagnosis, we evaluated surface- and voxel-based brain morphometry in participants under 3YO who were later diagnosed with ASD. Magnetic resonance imaging data were retrospectively obtained from patients later diagnosed with ASD at Boston Children's Hospital. The ASD participants with comorbidities such as congenital disorder, epilepsy, and global developmental delay/intellectual disability were excluded from statistical analyses. Eighty-five structural brain magnetic resonance imaging images were collected from 81 participants under 3YO and compared with 45 images from 45 gender- and age-matched nonautistic controls (non-ASD). Using an Infant FreeSurfer pipeline, 236 regionally distributed measurements were extracted from each scan. By t-tests and linear mixed models, the smaller nucleus accumbens and larger bilateral lateral, third, and fourth ventricles were identified in the ASD group. Vertex-wise t-statistical maps showed decreased thickness in the caudal anterior cingulate cortex and increased thickness in the right medial orbitofrontal cortex in ASD. The smaller bilateral accumbens nuclei and larger cerebral ventricles were independent of age, gender, or gestational age at birth, suggesting that there are MRI-based biomarkers in prospective ASD patients before they receive the diagnosis and that the volume of the nucleus accumbens and cerebral ventricles can be key MRI-based early biomarkers to predict the emergence of ASD.

Atypical Intrinsic Hemispheric Interaction Associated with Autism Spectrum Disorder Is Present within the First Year of Life

Autism spectrum disorder (ASD) is characterized by atypical connectivity lateralization of functional networks. However, previous studies have not directly investigated if differences in specialization between ASD and typically developing (TD) peers are present in infancy, leaving the timing of onset of these differences relatively unknown. We studied the hemispheric asymmetries of connectivity in children with ASD and infants later meeting the diagnostic criteria for ASD. Analyses were performed in 733 children with ASD and TD peers and in 71 infants at high risk (HR) or normal risk (NR) for ASD, with data collected at 1 month and 9 months of age. Comparing children with ASD (n = 301) to TDs (n = 432), four regions demonstrated group differences in connectivity: posterior cingulate cortex (PCC), posterior superior temporal gyrus, extrastriate cortex, and anterior prefrontal cortex. At 1 month, none of these regions exhibited group differences between ASD (n = 10), HR-nonASD (n = 15), or NR (n = 18) infants. However, by 9 months, the PCC and extrastriate exhibited atypical connectivity in ASD (n = 11) and HR-nonASD infants (n = 24) compared to NR infants (n = 22). Connectivity did not correlate with symptoms in either sample. Our results demonstrate that differences in network asymmetries associated with ASD risk are observable prior to the age of a reliable clinical diagnosis.

Association and epistatic analysis of white matter related genes across the continuum schizophrenia and autism spectrum disorders: The joint effect of NRG1-ErbB genes

BACKGROUND

Schizophrenia-spectrum disorders (SSD) and Autism spectrum disorders (ASD) are neurodevelopmental disorders that share clinical, cognitive, and genetic characteristics, as well as particular white matter (WM) abnormalities. In this study, we aimed to investigate the role of a set of oligodendrocyte/myelin-related (OMR) genes and their epistatic effect on the risk for SSD and ASD.

METHODS

We examined 108 SNPs in a set of 22 OMR genes in 1749 subjects divided into three independent samples (187 SSD trios, 915 SSD cases/control, and 91 ASD trios). Genetic association and gene-gene interaction analyses were conducted with PLINK and MB-MDR, and permutation procedures were implemented in both.

RESULTS

Some OMR genes showed an association trend with SSD, while after correction, the ones that remained significantly associated were MBP, ERBB3, and AKT1. Significant gene-gene interactions were found between (i) NRG1*MBP (perm p-value = 0.002) in the SSD trios sample, (ii) ERBB3*AKT1 (perm p-value = 0.001) in the SSD case-control sample, and (iii) ERBB3*QKI (perm p-value = 0.0006) in the ASD trios sample.

DISCUSSION

Our results suggest the implication of OMR genes in the risk for both SSD and ASD and highlight the role of NRG1 and ERBB genes. These findings are in line with the previous evidence and may suggest pathophysiological mechanisms related to NRG1/ERBBs signalling in these disorders.

The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review

Given the significance of validating reliable tests for the early detection of autism spectrum disorder (ASD), this systematic review aims to summarize available evidence of neuroimaging and neurophysiological changes in high-risk infants to improve ASD early diagnosis. We included peer-reviewed, primary research in English published before May 21, 2021, involving the use of magnetic resonance imaging (MRI), electroencephalogram (EEG), or functional near-infrared spectroscopy (fNIRS) in children with high risk for ASD under 24 months of age. The main exclusion criteria includes diagnosis of a genetic disorder and gestation age of less the 36 weeks. Online research was performed on PubMed, Web of Science, PsycINFO, and CINAHL. Article selection was conducted by two reviewers to minimize bias. This research was funded by Massachusetts General Hospital Sundry funding. IRB approval was not submitted as it was deemed unnecessary. We included 75 primary research articles. Studies showed that high-risk infants had divergent developmental trajectories for fractional anisotropy and regional brain volumes, increased CSF volume, and global connectivity abnormalities on MRI, decreased sensitivity for familiar faces, atypical lateralization during facial and auditory processing, and different spectral powers across multiple band frequencies on EEG, and distinct developmental trajectories in functional connectivity and regional oxyhemoglobin concentrations in fNIRS. These findings in infants were found to be correlated with the core ASD symptoms and diagnosis at toddler age. Despite the lack of quantitative analysis of the research database, neuroimaging and electrophysiological biomarkers have promising value for the screening of ASD as early as infancy with high accuracy, which warrants further investigation.

Looking Back at the Next 40 Years of ASD Neuroscience Research

During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.

Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.

Pre-symptomatic intervention for autism spectrum disorder (ASD): defining a research agenda

Autism spectrum disorder (ASD) impacts an individual's ability to socialize, communicate, and interact with, and adapt to, the environment. Over the last two decades, research has focused on early identification of ASD with significant progress being made in understanding the early behavioral and biological markers that precede a diagnosis, providing a catalyst for pre-symptomatic identification and intervention. Evidence from preclinical trials suggest that intervention prior to the onset of ASD symptoms may yield more improved developmental outcomes, and clinical studies suggest that the earlier intervention is administered, the better the outcomes. This article brings together a multidisciplinary group of experts to develop a conceptual framework for behavioral intervention, during the pre-symptomatic period prior to the consolidation of symptoms into diagnosis, in infants at very-high-likelihood for developing ASD (VHL-ASD). The overarching goals of this paper are to promote the development of new intervention approaches, empirical research, and policy efforts aimed at VHL-ASD infants during the pre-symptomatic period (i.e., prior to the consolidation of the defining features of ASD).