Insistence on sameness relates to increased covariance of gray matter structure in autism spectrum disorder

Rightward brain structural asymmetry in young children with autism

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

A non-systematic overview review of self-focused emotion regulation in autistic individuals through the lens of the extended process model

LAY ABSTRACT

What do we already know about emotion regulation in autism?We know that many autistic children, youth, and adults experience difficulties regulating emotions. Existing research has focused mainly on the differences in emotion regulation capabilities between autistic and non-autistic individuals, the relationships between autistic traits and emotion regulation, and how emotion regulation relates to other outcomes, such as social skills and mental health.What does this paper add?We want to take a new approach to review the existing emotion regulation research through the lens of a specific theoretical model: the extended process model of emotion regulation. Professor James Gross developed this model. It consists of four emotion regulation phases: identification, selection, implementation, and monitoring.Our review revealed specific areas within these emotion regulation phases that could significantly impact the emotion regulation experiences of autistic individuals. We also outline the gaps in the research and propose avenues for future investigation.Implications for practiceBy deepening our understanding of emotion regulation in autistic individuals through the proposed future research, researchers and clinicians can pave the way for the development of tailored support programs. These programs will directly target specific emotion regulation mechanisms, offering a much-needed individualized support approach.

Diagnosing autism severity associated with physical fitness and gray matter volume in children with autism spectrum disorder: Explainable machine learning method

PURPOSE

This study aimed to investigate the relationship between physical fitness, gray matter volume (GMV), and autism severity in children with autism spectrum disorder (ASD). Besides, we sought to diagnose autism severity associated with physical fitness and GMV using machine learning methods.

METHODS

Ninety children diagnosed with ASD underwent physical fitness tests, magnetic resonance imaging scans, and autism severity assessments. Diagnosis models were established using extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), and decision tree (DT) algorithms. Hyperparameters were optimized through the grid search cross-validation method. The shapley additive explanation (SHAP) method was employed to explain the diagnosis results.

RESULTS

Our study revealed associations between muscular strength in physical fitness and GMV in specific brain regions (left paracentral lobule, bilateral thalamus, left inferior temporal gyrus, and cerebellar vermis I-II) with autism severity in children with ASD. The accuracy (95 % confidence interval) of the XGB, RF, SVM, and DT models were 77.9 % (77.3, 78.6 %), 72.4 % (71.7, 73.2 %), 71.9 % (71.1, 72.6 %), and 66.9 % (66.2, 67.7 %), respectively. SHAP analysis revealed that muscular strength and thalamic GMV significantly influenced the decision-making process of the XGB model.

CONCLUSION

Machine learning methods can effectively diagnose autism severity associated with physical fitness and GMV in children with ASD. In this respect, the XGB model demonstrated excellent performance across various indicators, suggesting its potential for diagnosing autism severity.

Cortico-basal ganglia white matter microstructure is linked to restricted repetitive behavior in autism spectrum disorder

BACKGROUND

Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB.

METHODS

We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB.

RESULTS

Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FAT) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FAT and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes.

LIMITATIONS

The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated.

CONCLUSIONS

These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FAT was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.

Margin-Maximized Norm-Mixed Representation Learning for Autism Spectrum Disorder Diagnosis With Multi-Level Flux Features

Early diagnosis and timely intervention are significantly beneficial to patients with autism spectrum disorder (ASD). Although structural magnetic resonance imaging (sMRI) has become an essential tool to facilitate the diagnosis of ASD, these sMRI-based approaches still have the following issues. The heterogeneity and subtle anatomical changes place higher demands for effective feature descriptors. Additionally, the original features are usually high-dimensional, while most existing methods prefer to select feature subsets in the original space, in which noises and outliers may hinder the discriminative ability of selected features. In this article, we propose a margin-maximized norm-mixed representation learning framework for ASD diagnosis with multi-level flux features extracted from sMRI. Specifically, a flux feature descriptor is devised to quantify comprehensive gradient information of brain structures on both local and global levels. For the multi-level flux features, we learn latent representations in an assumed low-dimensional space, in which a self-representation term is incorporated to characterize the relationships among features. We also introduce mixed norms to finely select original flux features for the construction of latent representations while preserving the low-rankness of latent representations. Furthermore, a margin maximization strategy is applied to enlarge the inter-class distance of samples, thereby increasing the discriminative ability of latent representations. The extensive experiments on several datasets show that our proposed method can achieve promising classification performance (the average area under curve, accuracy, specificity, and sensitivity on the studied ASD datasets are 0.907, 0.896, 0.892, and 0.908, respectively) and also find potential biomarkers for ASD diagnosis.

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine

Introduction

Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities.

Methods

A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine.

Results

Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD.

Conclusion

This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.

Subcortical structural covariance predicts symptoms in children with different subtypes of ADHD

Attention-deficit/hyperactivity disorder has increasingly been conceptualized as a disorder of abnormal brain connectivity. However, far less is known about the structural covariance in different subtypes of this disorder and how those differences may contribute to the symptomology of these subtypes. In this study, we used a combined volumetric-based methodology and structural covariance approach to investigate structural covariance of subcortical brain volume in attention-deficit/hyperactivity disorder-combined and attention-deficit/hyperactivity disorder-inattentive patients. In addition, a linear support vector machine was used to predict patient's attention-deficit/hyperactivity disorder symptoms. Results showed that compared with TD children, those with attention-deficit/hyperactivity disorder-combined exhibited decreased volume of both the left and right pallidum. Moreover, we found increased right hippocampal volume in attention-deficit/hyperactivity disorder-inattentive children. Furthermore and when compared with the TD group, both attention-deficit/hyperactivity disorder-combined and attention-deficit/hyperactivity disorder-inattentive groups showed greater nonhomologous inter-regional correlations. The abnormal structural covariance network in the attention-deficit/hyperactivity disorder-combined group was located in the left amygdala-left putamen/left pallidum/right pallidum and right pallidum-left pallidum; in the attention-deficit/hyperactivity disorder-inattentive group, this difference was noted in the left hippocampus-left amygdala/left putamen/right putamen and right hippocampus-left amygdala. Additionally, different combinations of abnormalities in subcortical structural covariance were predictive of symptom severity in different attention-deficit/hyperactivity disorder subtypes. Collectively, our findings demonstrated that structural covariance provided valuable diagnostic markers for attention-deficit/hyperactivity disorder subtypes.

Increased cortical structural covariance correlates with anhedonia in schizophrenia

Anhedonia is a common symptom in schizophrenia and is closely related to poor functional outcomes. Several lines of evidence reveal that the orbitofrontal cortex plays an important role in anhedonia. In the present study, we aimed to investigate abnormalities in structural covariance within the orbitofrontal subregions, and to further study their role in anticipatory and consummatory anhedonia in schizophrenia. T1 images of 35 schizophrenia patients and 45 healthy controls were obtained. The cortical thickness of 68 cerebral regions parcellated by the Desikan-Killiany (DK) atlas was calculated. The structural covariance within the orbitofrontal subregions was calculated in both schizophrenia and healthy control groups. Stepwise linear regression was performed to examine the relationship between structural covariance and anhedonia in schizophrenia patients. Patients with schizophrenia exhibited higher structural covariance between the left and right medial orbitofrontal thickness, the left lateral orbitofrontal thickness and left pars orbitalis thickness compared to healthy controls (p < 0.05, FDR corrected). This results imply that the increased structural covariance in orbitofrontal thickness may be involved in the process of developing anhedonia in schizophrenia. The result indicated that the increased structural covariance between the left and right medial orbitofrontal thickness might be a protective factor for anticipatory pleasure (B' = 0.420, p = 0.012).

Olfactory functional covariance connectivity in Parkinson's disease: Evidence from a Chinese population

Introduction

Central anosmia is a potential marker of the prodrome and progression of Parkinson's disease (PD). Resting-state functional magnetic resonance imaging studies have shown that olfactory dysfunction is related to abnormal changes in central olfactory-related structures in patients with early PD.

Methods

This study, which was conducted at Guanyun People's Hospital, analyzed the resting-state functional magnetic resonance data using the functional covariance connection strength method to decode the functional connectivity between the white-gray matter in a Chinese population comprising 14 patients with PD and 13 controls.

Results

The following correlations were observed in patients with PD: specific gray matter areas related to smell (i.e., the brainstem, right cerebellum, right temporal fusiform cortex, bilateral superior temporal gyrus, right Insula, left frontal pole and right superior parietal lobule) had abnormal connections with white matter fiber bundles (i.e., the left posterior thalamic radiation, bilateral posterior corona radiata, bilateral superior corona radiata and right superior longitudinal fasciculus); the connection between the brainstem [region of interest (ROI) 1] and right cerebellum (ROI2) showed a strong correlation. Right posterior corona radiation (ROI11) showed a strong correlation with part 2 of the Unified Parkinson's Disease Rating Scale, and right superior longitudinal fasciculus (ROI14) showed a strong correlation with parts 1, 2, and 3 of the Unified Parkinson's Disease Rating Scale and Hoehn and Yahr Scale.

Discussion

The characteristics of olfactory-related brain networks can be potentially used as neuroimaging biomarkers for characterizing PD states. In the future, dynamic testing of olfactory function may help improve the accuracy and specificity of olfactory dysfunction in the diagnosis of neurodegenerative diseases.

Gray matter volume alteration is associated with insistence on sameness and cognitive flexibility in autistic youth

Restricted and repetitive behaviors (RRBs) are hallmark characteristics of autism spectrum disorder (ASD). Previous studies suggest that insistence on sameness (IS) characterized as higher-order RRBs may be a promising subgrouping variable for ASD. Cognitive inflexibility may underpin IS behaviors. However, the neuroanatomical correlates of IS and associated cognitive functions remain unclear. We analyzed data from 140 autistic youth and 124 typically developing (TD) youth (mean age = 15.8 years). Autistic youth were stratified by median-split based on three current IS items in the autism diagnostic interview-revised into two groups (high, HIS, n = 70, and low, LIS, n = 70). Differences in cognitive flexibility were assessed by the Cambridge neuropsychological test automated battery (CANTAB). T1-weighted brain structural images were analyzed using voxel-based morphometry (VBM) to identify differences in gray matter (GM) volume among the three groups. GM volume of regions showing group differences was then correlated with cognitive flexibility. The HIS group showed decreased GM volumes in the left supramarginal gyrus compared to the LIS group and increased GM volumes in the vermis VIII and left cerebellar lobule VIII compared to TD individuals. We did not find significant correlations between regional GM volumes and extra-dimensional shift errors. IS may be a unique RRB component and a potentially valuable stratifier of ASD. However, the neurocognitive underpinnings require further clarification. LAY SUMMARY: The present study found parietal, temporal and cerebellar gray matter volume alterations in autistic youth with greater insistence on sameness. The findings suggest that insistence on sameness may be a useful feature to parse the heterogeneity of the autism spectrum yet further research investigating the underlying neurocognitive mechanism is warranted.

Functional Covariance Connectivity of Gray and White Matter in Olfactory-Related Brain Regions in Parkinson’s Disease

Before the onset of motor symptoms, Parkinson’s disease (PD) involves dysfunction of the anterior olfactory nucleus and olfactory bulb, causing olfactory disturbance, commonly resulting in hyposmia in the early stages of PD. Accumulating evidence has shown that blood oxygen level dependent (BOLD) signals in white matter are altered by olfactory disorders and related stimuli, and the signal changes in brain white matter pathways show a certain degree of specificity, which can reflect changes of early olfactory dysfunction in Parkinson’s disease. In this study, we apply the functional covariance connectivity (FCC) method to decode FCC of gray and white matter in olfactory-related brain regions in Parkinson’s disease. Our results show that the dorsolateral prefrontal, anterior entorhinal cortex and fronto-orbital cortices in the gray matter have abnormal connectivity with the posterior corona radiata and superior corona radiata in white matter in patients with Parkinson’s hyposmia. The functional covariance connection strength (FCS) of the right dorsolateral prefrontal cortex and white matter, and the covariance connection strength of the left superior corona radiata and gray matter function have potential diagnostic value. These results demonstrate that alterations in FCC of gray and white matter in olfactory-related brain regions can reflect the change of olfactory function in the early stages of Parkinson’s disease, indicating that it could be a potential neuroimaging marker for early diagnosis.

A recurrent SHANK1 mutation implicated in autism spectrum disorder causes autistic-like core behaviors in mice via downregulation of mGluR1-IP3R1-calcium signaling

The genetic etiology and underlying mechanism of autism spectrum disorder (ASD) remain elusive. SHANK family genes (SHANK1/2/3) are well known ASD-related genes. However, little is known about how SHANK missense mutations contribute to ASD. Here, we aimed to clarify the molecular mechanism of and the multilevel neuropathological features induced by Shank1 mutations in knock-in (KI) mice. In this study, by sequencing the SHANK1 gene in a cohort of 615 ASD patients and 503 controls, we identified an ASD-specific recurrent missense mutation, c.2621 G > A (p.R874H). This mutation demonstrated strong pathogenic potential in in vitro experiments, and we generated the corresponding Shank1 R882H-KI mice. Shank1 R882H-KI mice displayed core symptoms of ASD, namely, social disability and repetitive behaviors, without confounding comorbidities of abnormal motor function and heightened anxiety. Brain structural changes in the frontal cortex, hippocampus and cerebellar cortex were observed in Shank1 R882H-KI mice via structural magnetic resonance imaging. These key brain regions also showed severe and consistent downregulation of mGluR1-IP3R1-calcium signaling, which subsequently affected the release of intracellular calcium. Corresponding cellular structural and functional changes were present in Shank1 R882H-KI mice, including decreased spine size, reduced spine density, abnormal morphology of postsynaptic densities, and impaired hippocampal long-term potentiation and basal excitatory transmission. These findings demonstrate the causative role of SHANK1 in ASD and elucidate the underlying biological mechanism of core symptoms of ASD. We also provide a reliable model of ASD with core symptoms for future studies, such as biomarker identification and therapeutic intervention studies.

Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adult participants (25 ASD and 66 neurotypicals; age: 18-60 years). We examined the relationship between regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging) and autistic traits (using Autism Spectrum Quotient). Our findings show grey matter differences in regions including the orbitofrontal cortex and lingual gyrus, and suggestive evidence for white matter microstructure differences in tracts including the superior longitudinal fasciculus being related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.

Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored.

METHODS

An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD.

RESULTS

Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations.

CONCLUSION

These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.

Atypical Functional Covariance Connectivity Between Gray and White Matter in Children With Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with atypical gray matter (GM) and white matter (WM) functional developmental course. However, the functional co-developmental pattern between GM and WM in ASD is unclear. Here, we utilized a functional covariance connectivity method to explore the concordance pattern between GM and WM function in individuals with ASD. A multi-center resting-state fMRI dataset composed of 105 male children with ASD and 102 well-matched healthy controls (HCs) from six sites of the ABIDE dataset was utilized. GM and WM ALFF maps were calculated for each subject. Voxel by voxel functional covariance connectivity of the ALFF values across subjects was calculated between GM and WM for children with ASD and HCs. A Z-test combining FDR multi-comparison correction was then employed to determine whether the functional covariance is significantly different between the two groups. A "bundling" strategy was utilized to ensure that the GM/WM clusters showing atypical functional covariance were larger than 5 voxels. Finally, canonical correlation analysis was conducted to explore whether the atypical GM/WM functional covariance is related to ASD symptoms. Results showed atypical functional covariance connections between specific GM and WM regions, whereas the ALFF values of these regions indicated no significant difference between the two groups. Canonical correlation analysis revealed a significant relationship between the atypical functional covariance and stereotyped behaviors of ASD. The results indicated an altered functional co-developmental pattern between WM and GM in ASD. LAY SUMMARY: White matter (WM) and gray matter (GM) are two major human brain organs supporting brain function. WM and GM functions show a specific co-developmental pattern in typical developed individuals. This study showed that this GM/WM co-developmental pattern was altered in children with ASD, while this altered GM/WM co-developmental pattern was related to stereotyped behaviors. These findings may help understand the GM/WM functional development of ASD.

Digitally-Mediated Social Stories Support Children on the Autism Spectrum Adapting to a Change in a 'Real-World' Context

Social Stories™ (SS) is a widely used intervention for children on the autism spectrum. A preliminary survey of 103 practitioners highlighted that SS are often used to support adapting to a change. This study investigated the use of digitally-mediated SS to support ten children on the autism spectrum attending a school summer camp. Teacher perceptions of anxiety, understanding and closeness to the goal of the SS were assessed before and after the intervention (prior to the event). The pre- post-intervention comparisons highlighted significant improvements in child understanding, anxiety, and closeness to goal with medium-large effect sizes. The child’s understanding and closeness to SS goal post-intervention related to their difficulties with the SS goal and their anxiety during the event.

Subcortical structural covariance in young children with autism spectrum disorder

Abnormalities in the structure of subcortical regions are central to numerous behaviors affected by autism spectrum disorder (ASD), and these regions may undergo atypical coordinated neurodevelopment. However, relatively little is known about morphological correlations among subcortical structures in young children with ASD. In this study, using volumetric-based methodology and structural covariance approach, we investigated structural covariance of subcortical brain volume in 40 young children with ASD (<7.5 years old) and 38 age-, gender-, and handedness-matched typically developing (TD) children. Results showed that compared with TD children, children with ASD exhibited decreased structural covariation between the left and right cerebral hemispheres, specifically between the left and right thalami, right globus pallidus and left nucleus accumbens, and left globus pallidus and right nucleus accumbens. Compared with TD children, children with ASD exhibited increased structural covariation between adjacent regions, such as between the right globus pallidus and right putamen. Additionally, abnormalities in subcortical structural covariance can predict social communication and repetitive and stereotypic behavior in young children with ASD. Overall, these results suggest decreased long-range structural covariation and enhanced local covariation in subcortical structures in children with ASD, highlighting aberrant developmental coordination or synchronized maturation between subcortical regions that play crucial roles in social cognition and behavior in ASD.

The cerebellum is associated with 2-year prognosis in patients with high-frequency migraine

Background

The increase of headache frequency is associated with higher headache related disability and lower quality of life in patients with migraine. However, the pathophysiology of migraine progression, persistence, or remission is elusive. The purpose of this study is to identify the brain signatures that are predictive of the long-term outcomes among patients with high-frequency migraine (HFM: 10–30 headache days/month).

Methods

We prospectively enrolled patients with HFM and healthy controls and collected their baseline clinical profiles and brain-MRI data at first visit. We longitudinally followed the patients and determined their outcomes at 2-year follow-up. Good outcome was defined as ≥50% reduction of baseline headache days and poor outcome was defined as reduction < 50% or frequency increase. Voxel-based morphometry was used to study gray matter volume (GMV), and structural covariance was used to investigate structural connectivity.

Results

Among 56 patients with HFM, 37 had good outcome and 19 poor outcome. Compared to the healthy controls (n = 37), patients with poor outcome had decreased GMV over the left posterior cingulate gyrus, and increased GMV over the bilateral cerebellum and the right precentral gyrus. Further, patients with poor outcome had greater GMV over the right and the left cerebella compared to patients with good outcome, and the GMVs of the cerebella were correlated to 2-year headache frequencies (right: r = 0.38, P = 0.005; left: r = 0.35, P = 0.009). Structural connectivity were increased between the cerebellum and the cuneus, the calcarine cortex, and the temporal lobe, respectively, in patients with poor outcome, and was decreased between the cerebellum and the prefrontal cortex in patients with poor outcome. The structural covariance integrities between the right cerebellum and the right cuneus were correlated to 2-year headache frequencies (r = 0.36, P = 0.008).

Conclusions

Structural volume and connectivity changes of the cerebellum may underlie headache persistence in patients with HFM.

Measurement of subcategories of repetitive behaviors in autistic adolescents and adults

Background

Restricted and repetitive behaviors (RRBs) are core features of autism. Factor-analytic studies comprised primarily of children have provided evidence for two domains of RRBs: Repetitive Sensory Motor (RSM) and Insistence on Sameness (IS) behaviors. The present study explores the validity of the Autism Diagnostic Interview-Revised (ADI-R) and the Repetitive Behavior Scale-Revised (RBS-R) for assessing these RRB subtypes in autistic adolescents and adults.

Methods

The sample included 293 participants (M_age=19.89, SD=4.88 years) whose RRBs were assessed via ADI-R or RBS-R Caregiver-report or RBS-R Self-Report. Confirmatory factor analysis (CFA) was conducted to assess the validity of the two-factor structure for each instrument. Cronbach's alpha was computed to assess subscale reliability. Correlations were examined between instrument subscales, NVIQ and age.

Results

Exploratory correlations were modest and provided weak evidence in favor of the utility of a CFA for the ADI-R. The RBS-R Caregiver and Self-Report CFA and internal consistencies supported the two-factor RSM and IS model tested. Consistent with previous literature, NVIQ was negatively correlated with the RBS-R Caregiver RSM subscale, but not meaningfully associated with IS. Neither RBS-R Self-Report subscale were meaningfully correlated with NVIQ. Across instruments, RSM subscales were correlated, but associations between IS were minimal.

Conclusions

The present study provides initial support for the use of the RBS-R Caregiver and Self-Report to measure dimensions of RSM and IS behaviors in autistic adolescents and adults. The present data did not support the use of the ADI-R to assess these RRB subtypes in older individuals. Conclusions must be interpreted cautiously in light of the present study's sample limitations. Additional research is needed to understand differences in caregiver and self-reported RRBs. Further research on RRBs in autistic adolescents and adults, particularly in samples of greater gender and racial/ethnic diversity, is critical to inform community understanding and knowledge of autism in adulthood.

The neural circuitry of restricted repetitive behavior: Magnetic resonance imaging in neurodevelopmental disorders and animal models

Restricted, repetitive behaviors (RRBs) are patterns of behavior that exhibit little variation in form and have no obvious function. RRBs although transdiagonstic are a particularly prominent feature of certain neurodevelopmental disorders, yet relatively little is known about the neural circuitry of RRBs. Past work in this area has focused on isolated brain regions and neurotransmitter systems, but implementing a neural circuit approach has the potential to greatly improve understanding of RRBs. Magnetic resonance imaging (MRI) is well-suited to studying the structural and functional connectivity of the nervous system, and is a highly translational research tool. In this review, we synthesize MRI research from both neurodevelopmental disorders and relevant animal models that informs the neural circuitry of RRB. Together, these studies implicate distributed neural circuits between the cortex, basal ganglia, and cerebellum. Despite progress in neuroimaging of RRB, there are many opportunities for conceptual and methodological improvement. We conclude by suggesting future directions for MRI research in RRB, and how such studies can benefit from complementary approaches in neuroscience.

Interpreting and Utilising Intersubject Variability in Brain Function

We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.

Indices of repetitive behaviour are correlated with patterns of intrinsic functional connectivity in youth with autism spectrum disorder

The purpose of the current study was to examine how repetitive behaviour in Autism Spectrum Disorder (ASD) is related to intrinsic functional connectivity patterns in a number of large-scale, neural networks. Resting-state fMRI scans from thirty subjects with ASD and thirty-two age-matched, typically developing control subjects were analysed. Seed-to-voxel and ROI-to-ROI functional connectivity analyses were used to examine resting-state connectivity in a number of cortical and subcortical neural networks. Bivariate correlation analysis was performed to examine the relationship between repetitive behaviour scores from the Repetitive Behaviour Scale - Revised and intrinsic functional connectivity in ASD subjects. Compared to control subjects, ASD subjects displayed marked over-connectivity of the thalamus with several cortical sensory processing areas, as well as over-connectivity of the basal ganglia with somatosensory and motor cortices. Within the ASD group, significant correlations were found between functional connectivity patterns and total RBS-R scores as well as one principal component analysis-derived score from the RBS-R. These results suggest that thalamocortical resting-state connectivity is altered in individuals with ASD, and that resting-state functional connectivity is associated with ASD symptomatology.

Altered reward system reactivity for personalized circumscribed interests in autism

Background

Neurobiological research in autism spectrum disorders (ASD) has paid little attention on brain mechanisms that cause and maintain restricted and repetitive behaviors and interests (RRBIs). Evidence indicates an imbalance in the brain’s reward system responsiveness to social and non-social stimuli may contribute to both social deficits and RRBIs. Thus, this study’s central aim was to compare brain responsiveness to individual RRBI (i.e., circumscribed interests), with social rewards (i.e., social approval), in youth with ASD relative to typically developing controls (TDCs).

Methods

We conducted a 3T functional magnetic resonance imaging (fMRI) study to investigate the blood-oxygenation-level-dependent effect of personalized circumscribed interest rewards versus social rewards in 39 youth with ASD relative to 22 TDC. To probe the reward system, we employed short video clips as reinforcement in an instrumental incentive delay task. This optimization increased the task’s ecological validity compared to still pictures that are often used in this line of research.

Results

Compared to TDCs, youth with ASD had stronger reward system responses for CIs mostly within the non-social realm (e.g., video games) than social rewards (e.g., approval). Additionally, this imbalance within the caudate nucleus’ responsiveness was related to greater social impairment.

Conclusions

The current data support the idea of reward system dysfunction that may contribute to enhanced motivation for RRBIs in ASD, accompanied by diminished motivation for social engagement. If a dysregulated reward system indeed supports the emergence and maintenance of social and non-social symptoms of ASD, then strategically targeting the reward system in future treatment endeavors may allow for more efficacious treatment practices that help improve outcomes for individuals with ASD and their families.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0195-7) contains supplementary material, which is available to authorized users.

Structural Covariance of Sensory Networks, the Cerebellum, and Amygdala in Autism Spectrum Disorder

Sensory dysfunction is a core symptom of autism spectrum disorder (ASD), and abnormalities with sensory responsivity and processing can be extremely debilitating to ASD patients and their families. However, relatively little is known about the underlying neuroanatomical and neurophysiological factors that lead to sensory abnormalities in ASD. Investigation into these aspects of ASD could lead to significant advancements in our general knowledge about ASD, as well as provide targets for treatment and inform diagnostic procedures. Thus, the current study aimed to measure the covariation of volumes of brain structures (i.e., structural magnetic resonance imaging) that may be involved in abnormal sensory processing, in order to infer connectivity of these brain regions. Specifically, we quantified the structural covariation of sensory-related cerebral cortical structures, in addition to the cerebellum and amygdala by computing partial correlations between the structural volumes of these structures. These analyses were performed in participants with ASD (n = 36), as well as typically developing peers (n = 32). Results showed decreased structural covariation between sensory-related cortical structures, especially between the left and right cerebral hemispheres, in participants with ASD. In contrast, these same participants presented with increased structural covariation of structures in the right cerebral hemisphere. Additionally, sensory-related cerebral structures exhibited decreased structural covariation with functionally identified cerebellar networks. Also, the left amygdala showed significantly increased structural covariation with cerebral structures related to visual processing. Taken together, these results may suggest several patterns of altered connectivity both within and between cerebral cortices and other brain structures that may be related to sensory processing.

Does Prenatal Valproate Interact with a Genetic Reduction in the Serotonin Transporter? A Rat Study on Anxiety and Cognition

There is ample evidence that prenatal exposure to valproate (or valproic acid, VPA) enhances the risk of developing Autism Spectrum Disorders (ASD). In line with this, a single injection of VPA induces a multitude of ASD-like symptoms in animals, such as rats and mice. However, there is equally strong evidence that genetic factors contribute significantly to the risk of ASD and indeed, like most other psychiatric disorders, ASD is now generally thought to results from an interaction between genetic and environmental factors. Given that VPA significantly impacts on the serotonergic system, and serotonin has strong biochemical and genetic links to ASD, we aimed to investigate the interaction between genetic reduction in the serotonin transporter and prenatal valproate administration. More specifically, we exposed both wildtype (SERT^+/+) rats and rats heterozygous for the serotonin transporter deletion (SERT^+/-) to a single injection of 400 mg/kg VPA at gestational day (GD) 12. The offspring, in adulthood, was assessed in four different tests: Elevated Plus Maze and Novelty Suppressed Feeding as measures for anxiety and prepulse inhibition (PPI) and latent inhibition as measures for cognition and information processing. The results show that prenatal VPA significantly increased anxiety in both paradigm, reduced PPI and reduced conditioning in the latent inhibition paradigm. However, we failed to find a significant gene-environment interaction. We propose that this may be related to the timing of the VPA injection and suggest that whereas GD12 might be optimal for affecting normal rat, rats with a genetically compromised serotonergic system may be more sensitive to VPA at earlier time points during gestation. Overall our data are the first to investigate gene ^* environmental interactions in a genetic rat model for ASD and suggest that timing may be of crucial importance to the long-term outcome.

ASD Validity

ASD research is at an important crossroads. The ASD diagnosis is important for assigning a child to early behavioral intervention and explaining a child’s condition. But ASD research has not provided a diagnosis-specific medical treatment, or a consistent early predictor, or a unified life course. If the ASD diagnosis also lacks biological and construct validity, a shift away from studying ASD-defined samples would be warranted. Consequently, this paper reviews recent findings for the neurobiological validity of ASD, the construct validity of ASD diagnostic criteria, and the construct validity of ASD spectrum features. The findings reviewed indicate that the ASD diagnosis lacks biological and construct validity. The paper concludes with proposals for research going forward.