Decreased structural connectivity and resting-state brain activity in the lateral occipital cortex is associated with social communication deficits in boys with autism spectrum disorder

Sex differences in trajectories of cortical development in autistic children from 2-13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2-13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.

Cortical responses to social stimuli in infants at elevated likelihood of ASD and/or ADHD: A prospective cross-condition fNIRS study

Autism spectrum disorders (ASD) and attention-deficit hyperactivity disorder (ADHD) are highly prevalent neurodevelopmental conditions that often co-occur and present both common and distinct neurodevelopmental profiles. Studying the developmental pathways leading to the emergence of ASD and/or ADHD symptomatology is crucial in understanding neurodiversity and discovering the mechanisms that underpin it. This study used functional near-infrared spectroscopy (fNIRS) to investigate differences in cortical specialization to social stimuli between 4- to 6-month-old infants at typical and elevated likelihood of ASD and/or ADHD. Results showed that infants at both elevated likelihood of ASD and ADHD had reduced selectivity to vocal sounds in left middle and superior temporal gyrus. Furthermore, infants at elevated likelihood of ASD showed attenuated responses to visual social stimuli in several cortical regions compared to infants at typical likelihood. Individual brain responses to visual social stimuli were associated with later autism traits, but not ADHD traits. These outcomes support our previous observations showing atypical social brain responses in infants at elevated likelihood of ASD and align with later atypical brain responses to social stimuli observed in children and adults with ASD. These findings highlight the importance of characterizing antecedent biomarkers of atypicalities in processing socially relevant information that might contribute to both phenotypic overlap and divergence across ASD and ADHD conditions and their association with the later emergence of behavioural symptoms.

Vasotocin receptor gene genotypes moderate the relationship between cortical thickness and sensory processing

Sensory processing is the process by which the central nervous system gathers, interprets, and regulates sensory stimuli in response to environmental cues. However, our understanding of the genetic factors and neuroanatomical correlations that influence sensory processing is limited. The vasotocin system modulates sensory input responsiveness, making it a potential candidate for further investigation. Additionally, human neuroimaging studies have demonstrated that the ability to modulate sensory stimuli is related to neuroanatomical features such as cortical thickness. Therefore, this study aimed to examine the relationship between functional polymorphisms in vasotocin receptor (VTR) genes, sensory profiles, and neuroanatomical correlations. We used structural magnetic resonance imaging (MRI) and the Adolescent/Adult Sensory Profile (AASP) questionnaire in 98 healthy adult participants to assess sensory processing and identified seven single nucleotide polymorphisms. We found that A-allele carriers of rs1042615 in VTR had higher scores for "sensory sensitivity" and "sensation avoiding". Moreover, higher scores for three AASP subscales were associated with decreased cortical thickness in various regions, including the right precentral, paracentral, and fusiform gyri, as well as bilateral inferior temporal gyri. This study sheds light on the potential role of genetic variations in the VTR in modulating sensory processing and correlation with cortical thickness which has future implications for better understanding sensory abnormalities in neurodevelopmental disorders.

Autism Is Associated With Interindividual Variations of Gray and White Matter Morphology

BACKGROUND

Although many studies have explored atypicalities in gray matter (GM) and white matter (WM) morphology of autism, most of them relied on unimodal analyses that did not benefit from the likelihood that different imaging modalities may reflect common neurobiology. We aimed to establish brain patterns of modalities that differentiate between individuals with and without autism and explore associations between these brain patterns and clinical measures in the autism group.

METHODS

We studied 183 individuals with autism and 157 nonautistic individuals (age range, 6-30 years) in a large, deeply phenotyped autism dataset (EU-AIMS LEAP [European Autism Interventions-A Multicentre Study for Developing New Medications Longitudinal European Autism Project]). Linked independent component analysis was used to link all participants' GM volume and WM diffusion tensor images, and group comparisons of modality shared variances were examined. Subsequently, we performed univariate and multivariate brain-behavior correlation analyses to separately explore the relationships between brain patterns and clinical profiles.

RESULTS

One multimodal pattern was significantly related to autism. This pattern was primarily associated with GM volume in bilateral insula and frontal, precentral and postcentral, cingulate, and caudate areas and co-occurred with altered WM features in the superior longitudinal fasciculus. The brain-behavior correlation analyses showed a significant multivariate association primarily between brain patterns that involved variation of WM and symptoms of restricted and repetitive behavior in the autism group.

CONCLUSIONS

Our findings demonstrate the assets of integrated analyses of GM and WM alterations to study the brain mechanisms that underpin autism and show that the complex clinical autism phenotype can be interpreted by brain covariation patterns that are spread across the brain involving both cortical and subcortical areas.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma

BACKGROUND

The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear.

PURPOSE

To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG).

STUDY TYPE

Retrospective.

SUBJECTS

Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53).

FIELD STRENGTH/SEQUENCE

3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence.

ASSESSMENT

In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software.

STATISTICAL TESTS

General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant.

RESULTS

For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively).

DATA CONCLUSION

Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Brain structure underlying the empathizing-systemizing difference in children with autism spectrum disorder

BACKGROUND

Behavioral research has shown that children with autism spectrum disorder (ASD) have a higher empathizing-systemizing difference (D score) than normal children. However, there is no research about the neuroanatomical mechanisms of the empathizing-systemizing difference in children with ASD.

METHODS

Participants comprised 41 children with ASD and 39 typically developing (TD) children aged 6‒12 years. Empathizing-systemizing difference was estimated using the D score from the Chinese version of Children's Empathy Quotient and Systemizing Quotient. We quantified brain morphometry, including global and regional brain volumes and surface-based cortical measures (cortical thickness, surface area, and gyrification) via structural magnetic resonance imaging.

RESULTS

We found that the D score was significantly negatively associated with amygdala gray matter volume [β =  -0.16; 95% confidence interval (CI): -0.30,  -0.02; P value = 0.030] in children with ASD. There was a significantly negative association between D score and gyrification in the left lateral occipital cortex (LOC) in children with ASD (B =  -0.10; SE = 0.03; cluster-wise P value = 0.006) and a significantly positive association between D score and gyrification in the right fusiform in TD children (B = 0.10; SE = 0.03; cluster-wise P value = 0.022). Moderation analyses demonstrated significant interactions between D score and diagnosed group in amygdala gray matter volume (β = 0.19; 95% CI 0.04, 0.35; P value = 0.013) and left LOC gyrification (β = 0.11; 95% CI 0.05, 0.17; P value = 0.001) but not in right fusiform gyrification (β = 0.08; 95% CI -0.02, 0.17; P value = 0.105).

CONCLUSIONS

Neuroanatomical variation in amygdala volume and gyrification of LOC could be potential biomarkers for the empathizing-systemizing difference in children with ASD but not in TD children. Large-scale neuroimaging studies are necessary to test the replicability of our findings.

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

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

A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers

Autism spectrum disorder (ASD) refers to a neurodevelopmental condition whose detection still remains challenging in young females due to the heterogeneity of the behavioral phenotype and the capacity of camouflage. The availability of quantitative biomarkers to assess brain function may support in the assessment of ASD. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive and flexible tool that quantifies cortical hemodynamic responses (HDR) that can be easily employed to describe brain activity. Since the study of the visual phenotype is a paradigmatic model to evaluate cerebral processing in many neurodevelopmental conditions, we hypothesized that visually-evoked HDR (vHDR) might represent a potential biomarker in ASD females. We performed a case-control study comparing vHDR in a cohort of high-functioning preschooler females with ASD (fASD) and sex/age matched peers. We demonstrated the feasibility of visual fNIRS measurements in fASD, and the possibility to discriminate between fASD and typical subjects using different signal features, such as the amplitude and lateralization of vHDR. Moreover, the level of response lateralization was correlated to the severity of autistic traits. These results corroborate the cruciality of sensory symptoms in ASD, paving the way for the validation of the fNIRS analytical tool for diagnosis and treatment outcome monitoring in the ASD population.

Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data

Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.

Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Background

Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients.

Methods

We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes.

Results

Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN.

Conclusion

Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.

Dynamic changes in spatiotemporal transcriptome reveal maternal immune dysregulation of autism spectrum disorder

Maternal immune activation (MIA) during pregnancy is known to be an environmental risk factor for neurodevelopment and autism spectrum disorder (ASD). However, it is unclear at which fetal brain developmental windows and regions MIA induces ASD-related neurodevelopmental transcriptional abnormalities. The non-chasm differentially expressed genes (DEGs) involved in MIA inducing ASD during fetal brain developmental windows were identified by performing the differential expression analysis and comparing the common DEGs among MIA at four different gestational development windows, ASD with multiple brain regions from human patients and mouse models, and human and mouse embryonic brain developmental trajectory. The gene set and functional enrichment analyses were performing to identify MIA dysregulated ASD-related the fetal neurodevelopmental windows and brain regions and function annotations. Additionally, the networks were constructed using Cytoscape for visualization. MIA at E12.5 and E14.5 increased the risk of distinct brain regions for ASD. MIA-driven transcriptional alterations of non-chasm DEGs, during the coincidence brain developmental windows between human and mice, involving ASD-relevant synaptic components, as well as immune- and metabolism-related functions and pathways. Furthermore, a great number of non-chasm brain development-, immune-, and metabolism-related DEGs were overlapped in at least two existing ASD-associated databases, suggesting that the others could be considered as the candidate targets to construct the model mice for explaining the pathological changes of ASD when environmental factors (MIA) and gene mutation effects co-occur. Overall, our search supported that transcriptome-based MIA dysregulated the brain development-, immune-, and metabolism-related non-chasm DEGs at specific embryonic brain developmental window and region, leading to abnormal embryonic neurodevelopment, to induce the increasing risk of ASD.

Atypical structural connectivity of language networks in autism spectrum disorder: A meta-analysis of diffusion tensor imaging studies

Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere.

The effect of learning to drum on behavior and brain function in autistic adolescents

SignificanceThere is an acknowledged need for improved service provision in the context of autism spectrum disorders. Previous studies have demonstrated the positive role drum training can play in improving behavioral outcomes for children and adolescents with emotional and behavioral difficulties. However, to date, none of these studies has explored how these behavioral changes translate at the neural level. Our study provides strong evidence that drumming not only reduces hyperactivity and inattention in autistic adolescents but also strengthens functional connectivity in brain regions responsible for inhibitory control and action outcome monitoring.

Neurobiological Correlates of Change in Adaptive Behavior in Autism

OBJECTIVE

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition that is associated with significant difficulties in adaptive behavior and variation in clinical outcomes across the life span. Some individuals with ASD improve, whereas others may not change significantly, or regress. Hence, the development of "personalized medicine" approaches is essential. However, this requires an understanding of the biological processes underpinning differences in clinical outcome, at both the individual and subgroup levels, across the lifespan.

METHODS

The authors conducted a longitudinal follow-up study of 483 individuals (204 with ASD and 279 neurotypical individuals, ages 6-30 years), with assessment time points separated by ∼12-24 months. Data collected included behavioral data (Vineland Adaptive Behavior Scale-II), neuroanatomical data (structural MRI), and genetic data (DNA). Individuals with ASD were grouped into clinically meaningful "increasers," "no-changers," and "decreasers" in adaptive behavior. First, the authors compared neuroanatomy between outcome groups. Next, they examined whether deviations from the neurotypical neuroanatomical profile were associated with outcome at the individual level. Finally, they explored the observed neuroanatomical differences' potential genetic underpinnings.

RESULTS

Outcome groups differed in neuroanatomical features (cortical volume and thickness, surface area), including in "social brain" regions previously implicated in ASD. Also, deviations of neuroanatomical features from the neurotypical profile predicted outcome at the individual level. Moreover, neuroanatomical differences were associated with genetic processes relevant to neuroanatomical phenotypes (e.g., synaptic development).

CONCLUSIONS

This study demonstrates, for the first time, that variation in clinical (adaptive) outcome is associated with both group- and individual-level variation in anatomy of brain regions enriched for genes relevant to ASD. This may facilitate the move toward better targeted/precision medicine approaches.

Sex and strategy effects on brain activation during a 3D-navigation task

Sex differences in navigation have often been attributed to the use of different navigation strategies in men and women. However, no study so far has investigated sex differences in the brain networks supporting different navigation strategies. To address this issue, we employed a 3D-navigation task during functional MRI in 36 men and 36 women, all scanned thrice, and modeled navigation strategies by instructions requiring an allocentric vs. egocentric reference frame on the one hand, as well as landmark-based vs. Euclidian strategies on the other hand. We found distinct brain networks supporting different perspectives/strategies. Men showed stronger activation of frontal areas, whereas women showed stronger activation of posterior brain regions. The left inferior frontal gyrus was more strongly recruited during landmark-based navigation in men. The hippocampus showed stronger connectivity with left-lateralized frontal areas in women and stronger connectivity with superior parietal areas in men. We discuss these findings in the light of a stronger recruitment of verbal networks supporting a more verbal strategy in women compared to a stronger recruitment of spatial networks supporting a more spatial strategy use in men. In summary, this study provides evidence that different navigation strategies activate different brain areas in men and women.

Frequency-specific age-related changes in the amplitude of spontaneous fluctuations in autism

BACKGROUND

Autism spectrum disorder is characterized by atypical developmental changes during brain maturation, but regional brain functional changes that occur with age and across different frequency bands are unknown. Therefore, the current study aimed to explore potential age and frequency band-related changes in the regional brain activities in autism.

METHODS

A total of 65 participants who met the DSM-IV criteria for autistic disorder and 55 typically developed (TD) participants (both age 6-30 years) were recruited in the current study. The two groups were matched in age (t=-1.314, P=0.191) and gender (χ²=2.760, P=0.097). The amplitude of low-frequency fluctuations (ALFF) was employed to explore the effect of development on spontaneous brain activity in individuals with autism and in TD participants across slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.1 Hz) frequency bands. The diagnosis-by-age interaction effect in the whole brain voxels in autism and TD groups was investigated.

RESULTS

Autism individuals showed significantly higher ALFF in the dorsal striatum in childhood (Caudate cluster: t=3.626, P=0.001; Putamen cluster: t=2.839, P=0.007) and remarkably lower ALFF in the dorsal striatum in adulthood (Caudate cluster: t=-2.198, P=0.038; Putamen cluster: t=-2.314, P=0.030) relative to TD, while no significant differences were observed in adolescence (all P>0.05). In addition, abnormal ALFF amplitudes were specific to the slow-4 (0.027-0.073 Hz) frequency band in the clusters above.

CONCLUSIONS

The current study indicated abnormal development patterns in the spontaneous activity of the dorsal striatum in autism and highlighted the potential role of the slow-4 frequency band in the pathology of autism. Also, the potential brain mechanism of autism was revealed, suggesting that autism-related variations should be investigated in a specific frequency.

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Stronger Mentalizing Network Connectivity in Expectant Fathers Predicts Postpartum Father-Infant Bonding and Parenting Behavior

Fathers play a critical role in parenting and in shaping child outcomes. However, the neurobiological underpinnings of successful adjustment to fatherhood have not been well-specified. Empathy and mentalizing abilities may characterize more effective fathering. These abilities may be supported by the functional connectivity (FC) of brain regions associated with social cognition and executive control. We used a seed-region-based approach to assess resting-state FC (rsFC) of the medial prefrontal cortex (mPFC) in 40 expectant fathers. We tested associations between mPFC whole-brain rsFC and fathers' self-report measures of empathy during pregnancy, as well as their ratings of father-infant bonding and fathering behaviors at six months postpartum. Stronger prenatal rsFC between the mPFC and precuneus, frontal pole, planum polare, and orbitofrontal cortex (OFC) was negatively associated with self-reported empathic concern and perspective-taking, whereas mPFC rsFC with the lateral occipital cortex (LOC) was positively associated with self-reported perspective-taking. Additionally, stronger prenatal connectivity between the mPFC rsFC and the superior parietal lobule and LOC regions predicted father reports of postpartum bonding with infants, and stronger prenatal mPFC rsFC with the LOC predicted more effective postpartum parenting. This study is the first to measure rsFC in expectant fathers as a predictor of subsequent adjustment to fathering.

Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years

Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.

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

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

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

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

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

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

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

Associations between the pupil light reflex and the broader autism phenotype in children and adults

The pupil light reflex (PLR), a marker of neuronal response to light, is a well-studied index of autonomic functioning. Studies have found that autistic children and adults have slower and weaker PLR responses compared to non-autistic peers, suggesting lower autonomic control. Altered autonomic control has also been associated with increased sensory difficulties in autistic children. With autistic traits varying in the general population, recent studies have begun to examine similar questions in non-autistic individuals. The current study looked at the PLR in relation to individual differences in autistic traits in non-autistic children and adults, asking how differences in the PLR could lead to variation in autistic traits, and how this might change across development. Children and adults completed a PLR task as a measure of sensitivity to light and autonomic response. Results showed that, in adults, increased levels of restricted and repetitive behaviors (RRB) were associated with a weaker and slower PLR. However, in children, PLR responses were not associated with autistic traits. Differences in PLR were also found across age groups, with adults showing smaller baseline pupil diameter and stronger PLR constriction as compared with children. The current study expanded on past work to examine the PLR and autistic traits in non-autistic children and adults, and the relevance of these findings to sensory processing difficulties is discussed. Future studies should continue to examine the neural pathways that might underlie the links between sensory processing and challenging behaviors.

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

The article introduces a method to combine common practices in network psychometrics and network neuroimaging. Namely, we use the unique variance in behavioural measures as regressors to identify unique neural correlates. This enables the description of brain-level and behavioural-level data into a joint network while keeping the dimensionality of the results manageable and interpretable. We illustrate this approach by showing the network of autistic traits and their correlates in resting-state functional connectivity.

Relationship between sensory characteristics and cortical thickness/volume in autism spectrum disorders

Individuals with autism spectrum disorders (ASDs) exhibit atypical sensory characteristics, impaired social skills, deficits in verbal and nonverbal communication, and restricted and repetitive behaviors. The relationship between sensory characteristics and brain morphological changes in ASD remains unclear. In this study, we investigated the association between brain morphological changes and sensory characteristics in individuals with ASD using brain image analysis and a sensory profile test. Forty-three adults with ASD and 84 adults with typical development underwent brain image analysis using FreeSurfer. The brain cortex was divided into 64 regions, and the cortical thickness and volume of the limbic system were calculated. The sensory characteristics of the participants were evaluated using the Adolescent/Adult Sensory Profile (AASP). Correlation analysis was performed for cortical thickness, limbic area volume, and AASP scores. In the ASD group, there was a significant positive correlation between visual sensory sensitivity scores and the right lingual cortical thickness (r = 0.500). There were also significant negative correlations between visual sensation avoiding scores and the right lateral orbitofrontal cortical thickness (r = -0.513), taste/smell sensation avoiding scores and the right hippocampal volume (r = -0.510), and taste/smell sensation avoiding scores and the left hippocampal volume (r = -0.540). The study identified associations among the lingual cortical thickness, lateral orbitofrontal cortical thickness, and hippocampal volume and sensory characteristics. These findings suggest that brain morphological changes may trigger sensory symptoms in adults with ASD.

Application of Intelligent Nursing Information System in Emergency Nursing Management

This paper is combined with the intelligent nursing information system to build the emergency nursing platform architecture, from the system emergency procedures, system functionality, network environment deployment, and database design aspects of the discussion. Based on hospital information security, the nursing monitoring system of the intelligent nursing information system is constructed to realize network communication, which is clear and intuitive. The intelligent information system is applied to safety control, medical order information, condition information, and information inquiry, which can save working time and complete the rapid transmission and accurate execution of medical order, making the network communication of medical care more quick and convenient and maximizing the overall efficiency. Based on the disordered phenomenon of registration triage, the Relief algorithm is used to classify the aetiology and triage, and the combination of medical advice, information query, and IT technology is optimized, so as to eliminate the phenomenon of round diagnosis, insert number, and improve the medical environment of waiting for diagnosis, taking medicine, examination, and testing. Finally, through the testing of system information security, information traceability, and rapid information query, the problems in nursing management have been basically solved.

Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study

Autism spectrum disorders (ASD) are associated with widespread brain alterations. Previous research in our group linked autistic traits with altered gyrification, but without pronounced differences in cortical thickness. Herein, we aim to replicate and extend these findings using a larger and older sample. Additionally, we examined whether (a) brain correlates of autistic traits were associated with polygenic risk scores (PRS) for ASD, and (b) autistic traits are related with brain morphological changes over time in a subset of children with longitudinal data available. The sample included 2400 children from the Generation R cohort. Autistic traits were measured using the Social Responsiveness Scale (SRS) at age 6 years. Gyrification, cortical thickness, surface area, and global morphological measures were obtained from high-resolution structural MRI scans at ages 9-to-12 years. We performed multiple linear regression analyses on a vertex-wise level. Corresponding regions of interest were tested for association with PRS. Results showed that autistic traits were related to (a) lower gyrification in the lateral occipital and the superior and inferior parietal lobes, (b) lower cortical thickness in the superior frontal region, and (c) lower surface area in inferior temporal and rostral middle frontal regions. PRS for ASD and longitudinal analyses showed significant associations that did not survive correction for multiple testing. Our findings support stability in the relationship between higher autistic symptoms and lower gyrification and smaller surface areas in school-aged children. These relationships remained when excluding ASD cases, providing neurobiological evidence for the extension of autistic traits into the general population. LAY SUMMARY: We found that school-aged children with higher levels of autistic traits had smaller total brain volume, cerebellum, cortical thickness, and surface area. Further, we also found differences in the folding patterns of the brain (gyrification). Overall, genetic susceptibility for autism spectrum disorders was not related to these brain regions suggesting that other factors could be involved in their origin. These results remained significant when excluding children with a diagnosis of ASD, providing support for the extension of the relationship between autistic traits and brain findings into the general population.

Altered Gray-White Matter Boundary Contrast in Toddlers at Risk for Autism Relates to Later Diagnosis of Autism Spectrum Disorder

BACKGROUND

Recent neuroimaging studies have highlighted differences in cerebral maturation in individuals with autism spectrum disorder (ASD) in comparison to typical development. For instance, the contrast of the gray-white matter boundary is decreased in adults with ASD. To determine how gray-white matter boundary integrity relates to early ASD phenotypes, we used a regional structural MRI index of gray-white matter contrast (GWC) on a sample of toddlers with a hereditary high risk for ASD.

MATERIALS AND METHODS

We used a surface-based approach to compute vertex-wise GWC in a longitudinal cohort of toddlers at high-risk for ASD imaged twice between 12 and 24 months (n = 20). A full clinical assessment of ASD-related symptoms was performed in conjunction with imaging and again at 3 years of age for diagnostic outcome. Three outcome groups were defined (ASD, n = 9; typical development, n = 8; non-typical development, n = 3).

RESULTS

ASD diagnostic outcome at age 3 was associated with widespread increases in GWC between age 12 and 24 months. Many cortical regions were affected, including regions implicated in social processing and language acquisition. In parallel, we found that early onset of ASD symptoms (i.e., prior to 18-months) was specifically associated with slower GWC rates of change during the second year of life. These alterations were found in areas mainly belonging to the central executive network.

LIMITATIONS

Our study is the first to measure maturational changes in GWC in toddlers who developed autism, but given the limited size of our sample results should be considered exploratory and warrant further replication in independent and larger samples.

CONCLUSION

These preliminary results suggest that ASD is linked to early alterations of the gray-white matter boundary in widespread brain regions. Early onset of ASD diagnosis constitutes an independent clinical parameter associated with a specific corresponding neurobiological developmental trajectory. Altered neural migration and/or altered myelination processes potentially explain these findings.

Changes in Electroencephalogram Coherence in Adolescents With Autism Spectrum Disorder After a Social Skills Intervention

Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavior. There is consensus that neurological differences are present in ASD. Further, theories emphasize the mixture of hypo- and hyper-connectivity as a neuropathologies in ASD [O'Reilly, Lewis, & Elsabbagh, 2017]; however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram [EEG], coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention, using a randomized controlled trial of two groups: an Experimental ASD (EXP) Group and a Waitlist Control ASD (WL) Group. The study had two purposes. First, the study aimed to determine whether changes in EEG coherence differed for adolescents that received PEERS® versus those that did not receive PEERS®. Results revealed a significant increase in connectivity in the occipital left to temporal left pair for the EXP group after intervention. Second, the study aimed to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills measured by questionnaires. At post-intervention, results indicated: (a) positive change in frontal right to parietal right coherence was linked to an increase in social skills scores; and (b) positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get-togethers. Results of this study support utilizing neurobehavioral domains as indicators of treatment outcome. Lay Summary: This study examined how well various areas of the brain communicate in adolescents with ASD before and after a social skills intervention. Results revealed increased connectivity in the adolescents that received the intervention. Secondly, the study aimed to determine if changes in connectivity of brain areas related to changes in behavior, friendships, and social skills. Results indicated that changes in connectivity were also linked to increased social skills. Autism Res 2021, 14: 787-803. © 2021 International Society for Autism Research and Wiley Periodicals LLC.

Repeated Sessions of Transcranial Direct Current Stimulation on Adolescents With Autism Spectrum Disorder: Study Protocol for a Randomized, Double-Blind, and Sham-Controlled Clinical Trial

Background: Social-emotional difficulties are a core symptom of autism spectrum disorder (ASD). Accordingly, individuals with ASD have problems with social cognition such as recognizing emotions from other peoples' faces. Various results from functional magnetic resonance imaging and electroencephalography studies as well as eye-tracking data reveal a neurophysiological basis of these deficits by linking them to abnormal brain activity. Thus, an intervention targeting the neural origin of ASD impairments seems warranted. A safe method able to influence neural activity is transcranial direct current stimulation (tDCS). This non-invasive brain stimulation method has already demonstrated promising results in several neuropsychiatric disorders in adults and children. The aim of this project is to investigate the effects of tDCS on ASD symptoms and their neural correlates in children and adolescents with ASD. Method: This study is designed as a double-blind, randomized, and sham-controlled trial with a target sample size of 20 male participants (aged 12-17 years) diagnosed with ASD. Before randomization, the participants will be stratified into comorbid depression, comorbid ADHS/conduct disorder, or no-comorbidity groups. The intervention phase comprises 10 sessions of anodal or sham tDCS applied over the left prefrontal cortex within 2 consecutive weeks. To engage the targeted brain regions, participants will perform a social cognition training during the stimulation. TDCS-induced effects on ASD symptoms and involved neural circuits will be investigated through psychological, neurophysiological, imaging, and behavioral data at pre- and post-measurements. Tolerability will be evaluated using a standardized questionnaire. Follow-up assessments 1 and 6 months after the intervention will examine long-lasting effects. Discussion: The results of this study will provide insights into the changeability of social impairments in ASD by investigating social and emotional abilities on different modalities following repeated sessions of anodal tDCS with an intra-simulation training. Furthermore, this trial will elucidate the tolerability and the potential of tDCS as a new treatment approach for ASD in adolescents. Clinical Trial Registration: The study is ongoing and has been registered in the German Registry of Clinical Trials (DRKS00017505) on 02/07/2019.

Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements

An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.

Age-Dependent Statistical Changes of Involuntary Head Motion Signatures Across Autism and Controls of the ABIDE Repository

The DSM-5 definition of autism spectrum disorders includes sensory issues and part of the sensory information that the brain continuously receives comes from kinesthetic reafference, in the form of self-generated motions, including those that the nervous systems produce at rest. Some of the movements that we self-generate are deliberate, while some occur spontaneously, consequentially following those that we can control. Yet, some motions occur involuntarily, largely beneath our awareness. We do not know much about involuntary motions across development, but these motions typically manifest during resting state in fMRI studies. Here we ask in a large data set from the Autism Brain Imaging Exchange repository, whether the stochastic signatures of variability in the involuntary motions of the head typically shift with age. We further ask if those motions registered from individuals with autism show a significant departure from the normative data as we examine different age groups selected at random from cross-sections of the population. We find significant shifts in statistical features of typical levels of involuntary head motions for different age groups. Further, we find that in autism these changes also manifest in non-uniform ways, and that they significantly differ from their age-matched groups. The results suggest that the levels of random involuntary motor noise are elevated in autism across age groups. This calls for the use of different age-appropriate statistical models in research that involves dynamically changing signals self-generated by the nervous systems.

DNA Methylation Associated with Mitochondrial Dysfunction in a South African Autism Spectrum Disorder Cohort

Autism spectrum disorder (ASD) is characterized by phenotypic heterogeneity and a complex genetic architecture which includes distinctive epigenetic patterns. We report differential DNA methylation patterns associated with ASD in South African children. An exploratory whole‐epigenome methylation screen using the Illumina 450 K MethylationArray identified differentially methylated CpG sites between ASD and controls that mapped to 898 genes (P ≤ 0.05) which were enriched for nine canonical pathways converging on mitochondrial metabolism and protein ubiquitination. Targeted Next Generation Bisulfite Sequencing of 27 genes confirmed differential methylation between ASD and control in our cohort. DNA pyrosequencing of two of these genes, the mitochondrial enzyme Propionyl‐CoA Carboxylase subunit Beta (PCCB) and Protocadherin Alpha 12 (PCDHA12), revealed a wide range of methylation levels (9–49% and 0–54%, respectively) in both ASD and controls. Three CpG loci were differentially methylated in PCCB (P ≤ 0.05), while PCDHA12, previously linked to ASD, had two significantly different CpG sites (P ≤ 0.001) between ASD and control. Differentially methylated CpGs were hypomethylated in ASD. Metabolomic analysis of urinary organic acids revealed that three metabolites, 3‐hydroxy‐3‐methylglutaric acid (P = 0.008), 3‐methyglutaconic acid (P = 0.018), and ethylmalonic acid (P = 0.043) were significantly elevated in individuals with ASD. These metabolites are directly linked to mitochondrial respiratory chain disorders, with a putative link to PCCB, consistent with impaired mitochondrial function. Our data support an association between DNA methylation and mitochondrial dysfunction in the etiology of ASD. Autism Res 2020, 13: 1079‐1093. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals, Inc.

Lay Summary

Epigenetic changes are chemical modifications of DNA which can change gene function. DNA methylation, a type of epigenetic modification, is linked to autism. We examined DNA methylation in South African children with autism and identified mitochondrial genes associated with autism. Mitochondria are power‐suppliers in cells and mitochondrial genes are essential to metabolism and energy production, which are important for brain cells during development. Our findings suggest that some individuals with ASD also have mitochondrial dysfunction.

Potential Locations for Noninvasive Brain Stimulation in Treating Autism Spectrum Disorders-A Functional Connectivity Study

OBJECTIVES

Noninvasive brain stimulation (NIBS) is an emerging tool for treating autism spectrum disorder (ASD). Exploring new stimulation targets may improve the efficacy of NIBS for ASD.

MATERIALS AND METHODS

We first conducted a meta-analysis on 170 functional magnetic resonance imaging studies to identify ASD-associated brain regions. We then performed resting state functional connectivity analysis on 70 individuals with ASD to investigate brain surface regions correlated with these ASD-associated regions and identify potential NIBS targets for ASD.

RESULTS

We found that the medial prefrontal cortex, angular gyrus, dorsal lateral prefrontal cortex, inferior frontal gyrus, superior parietal lobe, postcentral gyrus, precentral gyrus, middle temporal gyrus, superior temporal sulcus, lateral occipital cortex, and supplementary motor area/paracentral gyrus are potential locations for NIBS in ASD.

CONCLUSION

Our findings may shed light on the development of new NIBS targets for ASD.

Thalamic Volume Is Related to Increased Anterior Thalamic Radiations in Children with Reactive Attachment Disorder

Reactive attachment disorder (RAD) is associated with childhood maltreatment and affects approximately 1% of the general population. Recent data suggest that childhood maltreatment is associated with brain alterations in white and gray matter. However, the neural mechanisms of RAD-related brain alterations remain unknown. Herein, we evaluated the white matter pathways and gray matter volumes in 31 and 41 age-matched children with RAD and typical development (TD), respectively, by analyzing T1- and diffusion-weighted images. An increased fractional anisotropy (FA) and axial diffusivity in the anterior thalamic radiations (ATR) and an increased volume in the bilateral pallidum and right thalamus were observed in children with RAD compared with those with TD. Moreover, the volume of the thalamus was associated with increased ATR FA in children with RAD. Our study confirmed the existence of atypical neurodevelopment processes in the thalamus, pallidum, and ATR in children with RAD and highlighted an interdependent relationship between the alterations in the thalamus and ATR. These findings may help to improve our understanding of the comprehensive neural mechanisms of RAD.

Evidence Implicating Non-Dioxin-Like Congeners as the Key Mediators of Polychlorinated Biphenyl (PCB) Developmental Neurotoxicity

Despite being banned from production for decades, polychlorinated biphenyls (PCBs) continue to pose a significant risk to human health. This is due to not only the continued release of legacy PCBs from PCB-containing equipment and materials manufactured prior to the ban on PCB production, but also the inadvertent production of PCBs as byproducts of contemporary pigment and dye production. Evidence from human and animal studies clearly identifies developmental neurotoxicity as a primary endpoint of concern associated with PCB exposures. However, the relative role(s) of specific PCB congeners in mediating the adverse effects of PCBs on the developing nervous system, and the mechanism(s) by which PCBs disrupt typical neurodevelopment remain outstanding questions. New questions are also emerging regarding the potential developmental neurotoxicity of lower chlorinated PCBs that were not present in the legacy commercial PCB mixtures, but constitute a significant proportion of contemporary human PCB exposures. Here, we review behavioral and mechanistic data obtained from experimental models as well as recent epidemiological studies that suggest the non-dioxin-like (NDL) PCBs are primarily responsible for the developmental neurotoxicity associated with PCBs. We also discuss emerging data demonstrating the potential for non-legacy, lower chlorinated PCBs to cause adverse neurodevelopmental outcomes. Molecular targets, the relevance of PCB interactions with these targets to neurodevelopmental disorders, and critical data gaps are addressed as well.

Sex Differences in White Matter Pathways Related to Language Ability

Evidence from functional imaging studies points to a role for gender in language ability. However, recent studies suggest that sex differences in the neural basis of language are still unclear, reflecting a complex interaction between sex and language ability. We used diffusion weighted magnetic resonance imaging and global probabilistic tractography to investigate white matter (WM) pathways between 32 male and 35 age- and IQ-matched female adult participants in relation to their verbal abilities. Males showed higher fractional anisotropy (FA) in the left anterior thalamic radiations (ATR), right cingulum-angular bundle, right corticospinal tract, bilateral superior longitudinal fasciculus-temporal terminations, bilateral uncinate fasciculus (UNC), and corpus callosum-forceps minor when compared with the female group. In contrast, females showed higher radial diffusivity (RD) in the left ATR and left UNC when compared to the male group. The relationship between WM metrics and verbal ability also differed across the two groups: a negative correlation between verbal comprehension index (VCI) and FA as well as axial diffusivity (AD) in left cingulum-cingulate gyrus (CCG) supracallosal bundle in males but not in females; a negative correlation between verbal IQ (VIQ) and FA in the right corticospinal tract (CST), and a positive correlation between VCI and RD in corpus callosum-forceps minor in the female but not in the male group. A direct comparison of these correlation coefficients yielded significant differences between the groups for the VCI-AD and VIQ -FA associations. The findings may reflect sex differences in WM related to language ability.

Stress and the brain: Perceived stress mediates the impact of the superior frontal gyrus spontaneous activity on depressive symptoms in late adolescence

Identifying factors for the prediction of depression is a long-standing research topic in psychiatry and psychology. Perceived stress, which reflects the tendency to appraise one's life situations as stressful and overwhelming, has emerged as a stable predictor for depressive symptoms. However, the neurobiological bases of perceived stress and how perceived stress influences depressive symptoms in the healthy brain remain largely unknown. Here, we investigated these issues in 217 healthy adolescents by estimating the fractional amplitude of low-frequency fluctuations (fALFFs) via resting-state functional magnetic resonance imaging. A whole-brain correlation analysis showed that higher levels of perceived stress were associated with greater fALFF in the left superior frontal gyrus (SFG), which is a core brain region for cognitive control and emotion regulation-related processes. Mediation analysis further indicated that perceived stress mediated the link between the fALFF in the left SFG and depressive symptoms. Importantly, our results remained significant even when excluding the influences of head motion, anxiety, SFG gray matter structure, and school environment. Altogether, our findings suggested that the fALFF in the left SFG is a neurofunctional marker of perceived stress in adolescents and revealed a potential indirect effect of perceived stress on the association between the SFG spontaneous activity and depressive symptoms.

Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder

BACKGROUND

Both attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental disorders with a high prevalence. They are often comorbid and both exhibit abnormalities in sustained attention, yet common and distinct neural patterns of ASD and ADHD remain unidentified.AimsTo investigate shared and distinct functional connectivity patterns in a relatively large sample of boys (7- to 15-year-olds) with ADHD, ASD and typical development matched by age, gender and IQ.

METHOD

We applied machine learning techniques to investigate patterns of surface-based brain resting-state connectivity in 86 boys with ASD, 83 boys with ADHD and 125 boys with typical development.

RESULTS

We observed increased functional connectivity within the limbic and somatomotor networks in boys with ASD compared with boys with typical development. We also observed increased functional connectivity within the limbic, visual, default mode, somatomotor, dorsal attention, frontoparietal and ventral attention networks in boys with ADHD compared with boys with ASD. In addition, using a machine learning approach, we were able to discriminate typical development from ASD, typical development from ADHD and ASD from ADHD with accuracy rates of 76.3%, 84.1%, and 79.3%, respectively.

CONCLUSIONS

Our results may shed new light on the underlying mechanisms of ASD and ADHD and facilitate the development of new diagnostic methods for these disorders.Declaration of interestJ.K. holds equity in a startup company, MNT.

Alpha Waves as a Neuromarker of Autism Spectrum Disorder: The Challenge of Reproducibility and Heterogeneity

Background: There is no consensus in the literature concerning the presence of abnormal alpha wave profiles in patients with autism spectrum disorder (ASD). This may be due to phenotypic heterogeneity among patients as well as the limited sample sizes utilized. Here we present our results of alpha wave profile analysis based on a sample larger than most of those in the field, performed using a robust processing pipeline. Methods: We compared the alpha waves profiles at rest in children with ASD to those of age-, sex-, and IQ-matched control individuals. We used linear regression and non-parametric normative models using age as covariate forparsing the clinical heterogeneity. We explored the correlation between EEG profiles and the patient's brain volumes, obtained from structural MRI. We automatized the detection of the alpha peak and visually quality controled our MRI measurements. We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0.05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0.05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines. Discussions: Our study did not find evidence for abnormal alpha wave profiles in ASD. We propose, however, an analysis pipeline to perform standardized and automatized EEG analyses on large cohorts. These should help the community to address the challenge of clinical heterogeneity of ASD and to tackle the problems of reproducibility.

Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis

Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective intervention may have passed when the disorder is detected. Therefore, it is clinically urgent to identify imaging-based biomarkers for early diagnosis and intervention. In this paper, for the first time, we proposed a volume-based analysis of infant subjects with risk of ASD at very early age, i.e., as early as at 6 months of age. A critical part of volume-based analysis is to accurately segment 6-month-old infant brain MRI scans into different regions of interest, e.g., white matter, gray matter, and cerebrospinal fluid. This is actually very challenging since the tissue contrast at 6-month-old is extremely low, caused by inherent ongoing myelination and maturation. To address this challenge, we propose an anatomy-guided, densely-connected network for accurate tissue segmentation. Based on tissue segmentations, we further perform brain parcellation and statistical analysis to identify those significantly different regions between autistic and normal subjects. Experimental results on National Database for Autism Research (NDAR) show the advantages of our proposed method in terms of both segmentation accuracy and diagnosis accuracy over state-of-the-art results.