Alterations of White Matter Connectivity in Preschool Children with Autism Spectrum Disorder

Disrupted white matter structural networks in patients with acute ischemic stroke in the right basal ganglia

Widespread structural changes have been observed in patients with stroke in previous diffusion tensor imaging studies. However, the topological organization of white matter structural networks after acute ischemic stroke (AIS) in the right basal ganglia (BG) remains unknown. The aim of our study is to investigate whether the topological structure of the white matter structural network is altered in patients with AIS in the right BG, and its relationship with cognition. Graph theoretical analysis was employed to investigate the topological architecture of whole-brain white matter structural networks in 40 AIS patients in the right BG and 40 healthy controls (HC), and network-based statistics (NBS) were applied to examine structural connectivity alterations. Compared to HC, AIS patients exhibited altered global network properties characterized by increased small-worldness, normalized clustering coefficient, and shortest path length, as well as decreased clustering coefficient, local efficiency, and global efficiency. The nodes with significantly decreased nodal properties in AIS patients were primarily located in the default mode network, limbic system, sensorimotor system, salience network, and central executive network. Reduced structural connectivity detected by NBS in AIS patients were primarily located in the lesional hemisphere. Furthermore, altered nodal properties were correlated with cognitive scores. Documenting the alterations in the topological patterns of white matter structural networks will help to promote the understanding of the neural mechanisms of cognitive impairment after AIS in the right BG.

Abnormal Alterations of the White Matter Structural Network in Patients with Herpes Zoster and Postherpetic Neuralgia

PHN is one of the most common clinical complications of herpes zoster (HZ), the pathogenesis of which is unclear and poorly treated clinically, and many studies now suggest that postherpetic neuralgia (PHN) pain may be related to central neurologic mechanisms. This study aimed to investigate the white matter structural networks and changes in the organization of the rich-club in HZ and PHN. Diffusion imaging (DTI) data from 89 PHN patients, 76 HZ patients, and 66 healthy controls (HCs) were used to construct corresponding structural networks. Using graph-theoretic analysis, changes in the overall and local characteristics of the structural networks and rich-club organization were analyzed, and their correlations with clinical scales were analyzed. Compared with HCs, PHN patients had reduced global efficiency (Eg), reduced local efficiency (Eloc), a reduced clustering coefficient (Cp), a longer characteristic path length (Lp), and reduced nodal efficiency (Ne) in several brain regions, including the right posterior cingulate gyrus, the right supraoccipital gyrus, the bilateral postcentral gyrus, and the right precuneus; HZ patients had reduced Eg, a longer Lp, and reduced right orbital frontalis suprachiasmatic Ne. Moreover, HZ and PHN patients showed a significant reduction in the strength of rich-club connections. Compared with HZ patients, the intensities of the rich-club and feeder connections were lower in the PHN patients. Moreover, the changes in the structural networks and rich-club organization topology indices of the patients in the HZ and PHN patients were significantly correlated with disease duration, pain scores, and emotional changes. The structural networks of HZ and PHN patients exhibited reduced network transmission efficiency and rich-club connectivity, possibly due to structural damage to the white matter, and this was more obvious in PHN patients. The rich-club connectivity of HZ patients showed incomplete compensation in the acute pain stage.

Limbic/paralimbic connection weakening in preschool autism-spectrum disorder based on diffusion basis spectrum imaging

This study aims to investigate the value of basal ganglia and limbic/paralimbic networks alteration in identifying preschool children with ASD and normal controls using diffusion basis spectrum imaging (DBSI). DBSI data from 31 patients with ASD and 30 NC were collected in Hunan Children's Hospital. All data were imported into the post-processing server. The most discriminative features were extracted from the connection, global and nodal metrics separately using the two-sample t-test. To effectively integrate the multimodal information, we employed the multi-kernel learning support vector machine (MKL-SVM). In ASD group, the value of global efficiency, local efficiency, clustering coefficient and synchronization were lower than NC group, while modularity score, hierarchy, normalized clustering coefficient, normalized characteristic path length, small-world, characteristic path length and assortativity were higher. Significant weaker connections are mainly distributed in the limbic/paralimbic networks. The model combining consensus connection, global and nodal graph metrics features can achieve the best performance in identifying ASD patients, with an accuracy of 96.72%.The most specific brain regions connection weakening associated with preschool ASD are predominantly located in limbic/paralimbic networks, suggesting their involvement in abnormal brain development processes. The effective combination of connection, global and nodal metrics information by MKL-SVM can effectively distinguish patients with ASD.

Decreased Functional and Structural Connectivity is Associated with Core Symptom Improvement in Children with Autism Spectrum Disorder After Mini-basketball Training Program

Exercise intervention has been proven helpful to ameliorate core symptoms of Autism Spectrum Disorder (ASD). However, the underlying mechanisms are not fully understood. In this study, we carried out a 12-week mini-basketball training program (MBTP) on ASD children and examined the changes of brain functional and structural networks before and after exercise intervention. We applied individual-based method to construct functional network and structural morphological network, and investigated their alterations following MBTP as well as their associations with the change in core symptom. Structural MRI and resting-state functional MRI data were obtained from 58 ASD children aged 3-12 years (experiment group: n = 32, control group: n = 26). ASD children who received MBTP intervention showed several distinguishable alternations compared to the control without special intervention. These included decreased functional connectivity within the sensorimotor network (SM) and between SM and the salience network, decreased morphological connectivity strength in a cortical-cortical network centered on the left inferior temporal gyrus, and a subcortical-cortical network centered on the left caudate. Particularly, the aforementioned functional and structural changes induced by MBTP were associated with core symptoms of ASD. Our findings suggested that MBTP intervention could be an effective approach to improve core symptoms in ASD children, decrease connectivity in both structure and function networks, and may drive the brain change towards normal-like neuroanatomy.

Graph Convolutional Network With Self-Supervised Learning for Brain Disease Classification

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.

Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI

OBJECTIVE

To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.

METHODS

We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants' brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.

RESULTS

The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.

CONCLUSION

The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.

Personalized functional network mapping for autism spectrum disorder and attention-deficit/hyperactivity disorder

Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are two typical neurodevelopmental disorders that have a long-term impact on physical and mental health. ASD is usually comorbid with ADHD and thus shares highly overlapping clinical symptoms. Delineating the shared and distinct neurophysiological profiles is important to uncover the neurobiological mechanisms to guide better therapy. In this study, we aimed to establish the behaviors, functional connectome, and network properties differences between ASD, ADHD-Combined, and ADHD-Inattentive using resting-state functional magnetic resonance imaging. We used the non-negative matrix fraction method to define personalized large-scale functional networks for each participant. The individual large-scale functional network connectivity (FNC) and graph-theory-based complex network analyses were executed and identified shared and disorder-specific differences in FNCs and network attributes. In addition, edge-wise functional connectivity analysis revealed abnormal edge co-fluctuation amplitude and number of transitions among different groups. Taken together, our study revealed disorder-specific and -shared regional and edge-wise functional connectivity and network differences for ASD and ADHD using an individual-level functional network mapping approach, which provides new evidence for the brain functional abnormalities in ASD and ADHD and facilitates understanding the neurobiological basis for both disorders.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood

Background

Autism spectrum disorder (ASD) is associated with altered brain development, but it is unclear which specific structural changes may serve as potential diagnostic markers, particularly in young children at the age when symptoms become fully established. Furthermore, such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level.

Objective

This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children.

Methods

A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) together with symptom severity and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets.

Results

Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development. The majority (29/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification achieved very high accuracy of 96.77% in the discovery dataset, and 91.67% and 88.89% in the two independent validation datasets.

Conclusions

Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.

Disrupted Topological Organization of White Matter Network in Angelman Syndrome

BACKGROUND

Angelman syndrome (AS) is a genetic disorder that affects neurodevelopment. The investigation of changes in the brain white matter network, which would contribute to a better understanding of the pathogenesis of AS brain, was lacking.

PURPOSE

To investigate both local and global alterations of white matter in patients with AS.

STUDY TYPE

Prospective.

SUBJECTS

A total of 29 AS patients (6.6 ± 1.4 years, 15 [52%] females) and 19 age-matched healthy controls (HC) (7.0 ± 1.5 years, 10 [53%] females).

FIELD STRENGTH/SEQUENCE

A 3-T, three-dimensional (3D) T1-weighted imaging by using gradient-echo-based sequence, single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging.

ASSESSMENT

Network metrics including global efficiency (E_g ), local efficiency (E_loc ), small world coefficient (Swc), rich-club coefficient (Φ), and nodal degree (ND) were estimated from diffusion MR (dMR) data. Connections among highly connected (hub) regions and less connected (peripheral) regions were also assessed. Correlation between the topological parameters and age for each group was also calculated to assess the development of the brain.

STATISTICAL TESTS

Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction.

RESULTS

AS patients showed significantly lower E_g and higher swc compared to HC. Φ_n significantly increased at higher k-levels in AS patients. In addition, the connections among hub regions and peripheral regions were significantly interrupted in AS patients.

DATA CONCLUSION

The AS brain showed diminished connectivity, reflected by reduced network efficiency compared to HC. Compared to densely connected regions, less connected regions were more vulnerable in AS.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

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

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

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

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

Altered topological properties of white matter structural network in adults with autism spectrum disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex developmental disability and is currently viewed as a disorder of brain connectivity in which white matter abnormalities. However, the majority of the research to date has focused on children with ASD. Understanding the topological organization of the white matter structural network in adults may help uncover the nature of ASD pathology in adulthood.

METHOD

This study investigated the topological properties of white matter structural network using diffusion tensor imaging and graph theory analysis in a sample of 32 adults with ASD compared to 35 matched typically developing (TD) controls. Group differences in global and nodal topological metrics were compared. The relationships between the altered network metrics and the severity of clinical symptoms were calculated.

RESULTS

Compared to TD controls, ASD patients exhibited decreased small-worldness and increased global efficiency. In addition, the reduced nodal efficiency and increased nodal degree were found in the frontal (e.g., the inferior frontal gyrus) and parietal (e.g., postcentral gyrus) regions. Furthermore, the altered topological metrics (e.g., increased global efficiency and reduced nodal efficiency) were correlated with the severity of ASD symptoms.

CONCLUSION

These results indicated that the complicatedly topological organization of the white matter structural network was abnormal and may play an essential role in the underlying pathological mechanism of ASD in adults.

Altered white matter structural connectivity in primary Sjögren's syndrome: a link-based analysis

PURPOSE

Cognitive impairment has been revealed in primary Sjögren's syndrome (pSS). However, the underlying white matter structural connectivity (SC) changes have not been studied. This study aimed to investigate the altered white matter brain network in patients with pSS using diffusion tensor imaging (DTI).

METHODS

Forty-one pSS patients and sixty matched healthy controls (HCs) underwent neuropsychological tests and the subsequent MRI examinations. The clinical data were gathered from the medical record. The structural brain network was established using DTI, and a link-based comparison was performed between patients with pSS and HCs (false discovery rate correction, P < 0.05). Furthermore, the mean fractional anisotropy (FA) of the altered SCs was correlated with the neuropsychological tests and clinical data in patients with pSS (Bonferroni correction, P < 0.05).

RESULTS

Compared with HCs, patients with pSS mainly exhibited decreased SC in the frontal and parietal lobes and some parts of the temporal and occipital lobes. In addition, increased SC was found between the right caudate nucleus and right median cingulate/paracingulate gyri. Specifically, the reduced SC between the left middle temporal gyrus and left middle occipital gyrus was negatively correlated with white matter high signal intensity (WMH).

CONCLUSIONS

Patients with pSS showed diffusely decreased SC mainly in the frontoparietal network and exhibited a negative correlation between the reduced SC and WMH. SC represents a potential biomarker for preclinical brain impairment in patients with pSS.

Altered cortical structure network in children with obstructive sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is characterized by recurrent airway collapse during sleep, resulting in intermittent hypoxia and sleep fragmentation that may contribute to alternations in brain structure and function. We hypothesized that OSA in children reorganizes and alters cortical structure, which can cause changes in cortical thickness correlation between brain regions across subjects.

METHODS

We constructed cortical structure networks based on cortical thickness measurements from 41 controls (age 15.54 ± 1.66 years, male 19) and 50 children with OSA (age 15.32 ± 1.65 years, male 29). The global (clustering coefficient [CC], path length, and small-worldness) and regional (nodal betweenness centrality, NBC) network properties and hub region distributions were examined between groups.

RESULTS

We found increased CCs in OSA compared to controls across a wide range of network densities (p-value < .05) and lower NBC area under the curve in left caudal anterior cingulate, left caudal middle frontal, left fusiform, left transverse temporal, right pars opercularis, and right precentral gyri (p-value < .05). In addition, while most of the hub regions were the same between groups, the OSA group had fewer hub regions and a different hub distribution compared to controls.

CONCLUSIONS

Our findings suggest that children with OSA exhibit altered global and regional network characteristics compared to healthy controls. Our approach to the investigation of cortical structure in children with OSA could prove useful in understanding the etiology of OSA-related brain functional disorders.

Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia

Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Subcortical structures play a significant role in cognitive and social-emotional behaviors and their abnormalities are associated with neuropsychiatric conditions. This exploratory study utilized ABIDE II/COBRE MRI and corresponding phenotypic datasets to compare subcortical volumes of adults with ASD (n = 29), SZ (n = 51) and age and gender matched neurotypicals (NT). We examined the association between subcortical volumes and select behavioral measures to determine whether core symptomatology of disorders could be explained by subcortical association patterns. We observed volume differences in ASD (viz., left pallidum, left thalamus, left accumbens, right amygdala) but not in SZ compared to their respective NT controls, reflecting morphometric changes specific to one of the disorder groups. However, left hippocampus and amygdala volumes were implicated in both disorders. A disorder-specific negative correlation (r = −0.39, p = 0.038) was found between left-amygdala and scores on the Social Responsiveness Scale (SRS) Social-Cognition in ASD, and a positive association (r = 0.29, p = 0.039) between full scale IQ (FIQ) and right caudate in SZ. Significant correlations between behavior measures and subcortical volumes were observed in NT groups (ASD-NT range; r = −0.53 to −0.52, p = 0.002 to 0.004, SZ-NT range; r = −0.41 to −0.32, p = 0.007 to 0.021) that were non-significant in the disorder groups. The overlap of subcortical volumes implicated in ASD and SZ may reflect common neurological mechanisms. Furthermore, the difference in correlation patterns between disorder and NT groups may suggest dysfunctional connectivity with cascading effects unique to each disorder and a potential role for IQ in mediating behavior and brain circuits.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

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

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

Overconnectivity of the right Heschl's and inferior temporal gyrus correlates with symptom severity in preschoolers with autism spectrum disorder

Previous studies have reported varying findings regarding the association of brain connectivity in autism spectrum disorder (ASD) with overconnectivity, underconnectivity, or both. Despite the emerging understanding that ASD is a developmental disconnection syndrome, very little is known about structural brain networks in preschool‐aged children with low‐functioning ASD. We aimed to investigate the structural brain connectivity of low‐functioning ASD using diffusion magnetic resonance imaging and graph theory to examine alterations in different brain network topologies and identify any correlations with the clinical severity of ASD in preschool‐aged children. Fifty‐two preschool‐aged children (28 with ASD and 24 with typical development) were included in the analysis. Graph‐based network analysis was performed to examine the global and local structural brain networks. Nodal network measures exhibited increased nodal strength in the right Heschl's gyrus, which was positively associated with all autistic clinical symptoms (Autism Diagnostic Observation Schedule and Childhood Autism Rating Scale [CARS]). The nodal strength of the right inferior temporal gyrus showed a moderate correlation with the CARS score. Using network‐based statistics, we identified a subnetwork with increased connections encompassing the right Heschl's gyrus and the right inferior temporal gyrus in preschool‐aged children with ASD. The asymmetric value in the inferior temporal gyrus exhibited right dominance of nodal strength in children with ASD compared to that in typically developing children. Our findings support the theory of aberrant brain growth and overconnectivity as the underlying mechanism of ASD and provides new insights into potential regional biomarkers that can detect low‐functioning ASD in preschool‐aged children.

Shared and Distinct Topologically Structural Connectivity Patterns in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Background

Previous neuroimaging studies have described shared and distinct neurobiological mechanisms between autism spectrum disorders (ASDs) and attention-deficit/hyperactivity disorder (ADHD). However, little is known about the similarities and differences in topologically structural connectivity patterns between the two disorders.

Methods

Diffusion tensor imaging (DTI) and deterministic tractography were used to construct the brain white matter (WM) structural networks of children and adolescents (age range, 6–16 years); 31 had ASD, 34 had ADHD, and 30 were age- and sex-matched typically developing (TD) individuals. Then, graph theoretical analysis was performed to investigate the alterations in the global and node-based properties of the WM structural networks in these groups. Next, measures of ASD traits [Social Responsiveness Scale (SRS)] and ADHD traits (Swanson, Nolan, and Pelham, version IV scale, SNAP-IV) were correlated with the alterations to determine the functional significance of such changes.

Results

First, there were no significant differences in the global network properties among the three groups; moreover, compared with that of the TD group, nodal degree (Ki) of the right amygdala (AMYG.R) and right parahippocampal gyrus (PHG.R) were found in both the ASD and ADHD groups. Also, the ASD and ADHD groups shared four additional hubs, including the left middle temporal gyrus (MTG.L), left superior temporal gyrus (STG.L), left postcentral gyrus (PoCG.L), and right middle frontal gyrus (MFG.R) compared with the TD group. Moreover, the ASD and ADHD groups exhibited no significant differences regarding regional connectivity characteristics. Second, the ADHD group showed significantly increased nodal betweenness centrality (Bi) of the left hippocampus (HIP.L) compared with the ASD group; also, compared with the ADHD group, the ASD group lacked the left anterior cingulate gyrus (ACG.L) as a hub. Last, decreased nodal efficiency (Enodal) of the AMYG.R, Ki of the AMYG.R, and Ki of the PHG.R were associated with higher SRS scores in the ASD group. Decreased Ki of the PHG.R was associated with higher SRS scores in the full sample, whereas decreased Bi of the PHG.R was associated with lower oppositional defiance subscale scores of the SNAP-IV in the ADHD group, and decreased Bi of the HIP.L was associated with lower inattention subscale scores of the SNAP-IV in the full sample.

Conclusion

From the perspective of the topological properties of brain WM structural networks, ADHD and ASD have both shared and distinct features. More interestingly, some shared and distinct topological properties of WM structures are related to the core symptoms of these disorders.

Elp2 mutations perturb the epitranscriptome and lead to a complex neurodevelopmental phenotype

Intellectual disability (ID) and autism spectrum disorder (ASD) are the most common neurodevelopmental disorders and are characterized by substantial impairment in intellectual and adaptive functioning, with their genetic and molecular basis remaining largely unknown. Here, we identify biallelic variants in the gene encoding one of the Elongator complex subunits, ELP2, in patients with ID and ASD. Modelling the variants in mice recapitulates the patient features, with brain imaging and tractography analysis revealing microcephaly, loss of white matter tract integrity and an aberrant functional connectome. We show that the Elp2 mutations negatively impact the activity of the complex and its function in translation via tRNA modification. Further, we elucidate that the mutations perturb protein homeostasis leading to impaired neurogenesis, myelin loss and neurodegeneration. Collectively, our data demonstrate an unexpected role for tRNA modification in the pathogenesis of monogenic ID and ASD and define Elp2 as a key regulator of brain development.

The relationships between the topological properties of the whole-brain white matter network and the severity of autism spectrum disorder: A study from monozygotic twins

Twins provide a valuable perspective for exploring the pathological mechanism of autism spectrum disorder (ASD). We aim to analyze differences in the topological properties of the white matter (WM) network between monozygotic twins with ASD (MZCo-ASD) and children with typical development (TD). We enrolled 67 subjects aged 2-9 years. Twenty-three pairs of MZCo-ASD and 21 singleton children with TD completed clinical assessments and diffusion tensor imaging (DTI). Graph theory was used to compare the topological properties of the WM network between the two groups, and analyzed their correlations with the severity of clinical symptoms. We found that the global efficiency (Eg) of MZCo-ASD is weaker than that of TD children, while the shortest path length (Lp) of MZCo-ASD is longer than that of TD children, and MZCo-ASD have three unique hubs (the bilateral dorsolateral superior frontal gyrus and right insula). Eg and Lp were both correlated with the repetitive behavior scores of the Autism Diagnostic Interview-Revised (ADI-R) in the MZCo-ASD group, and the nodal efficiency of the dorsal superior frontal gyrus (SFGdor) was correlated with the ADI-R scores of repetitive behaviors. Left SFGdor nodal efficiency was correlated with Repetitive Behavior and Communication, two core symptoms of autism. The results implicated that MZCo-ASD had atypical brain structural network attributes and node distributions. Using MZCo-ASD, we found that the WM topological properties that correlate with the severity of ASD core symptoms were Eg, Lp, and the nodal efficiency of the SFGdor.

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Association of Reduced Tract Integrity with Social Communication Deficits in Preschool Autism Children: A Tract-Based Spatial Statistics Study

PURPOSE

To analyze the changes in white matter tracts in preschool children with autism spectrum disorder (ASD), and the correlation between these changes and social communication deficits.

METHODS

Diffuse tensor imaging was used to assess white matter integrity using tract-based spatial statistics in a sample of 50 right-handed children with ASD aged 2-6 years vis a reference sample of 46 typically developing children aged 2-6 years. We then correlated these significant different fiber tracts between groups with communication and social interaction scores using the Autism Diagnostic Interview-Revised Assessment (ADI-R) Scale.

RESULTS

We observed decreased fractional anisotropy (FA) in tracts including the left superior longitudinal fasciculus (SLF), the splenium of the corpus callosum (splCC), the left corticospinal tracts, and the left inferior longitudinal fasciculus (ILF) in children with ASD. Specifically, there was reduced white matter integrity of these tracts in the left cerebral hemisphere. In addition, we found that the decreased FA of left SLF and ILF was negatively associated with the ADI-R scores in children with ASD.

CONCLUSION

The structural integrity of some white matter tracts in the five-level anatomical model for the social communication was reduced. The reduced integrity white matter that we observed primarily in the left cerebral hemisphere may be a neural substrate of social communication deficits in preschool children with ASD, and we speculate that the reduction is associated with the severity of social interaction. The reduced FA of the splCC might be a substantial biomarker for children with ASD.

An assessment of the autism neuroimaging literature for the prospects of re-executability

Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if the technical aspects of our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available.  Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.

An assessment of the autism neuroimaging literature for the prospects of re-executability

Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available.  Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.

Big data approaches to develop a comprehensive and accurate tool aimed at improving autism spectrum disorder diagnosis and subtype stratification

Purpose

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches.

Design/methodology/approach

Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data.

Findings

By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool.

Originality/value

This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.

Heterozygosity for Nuclear Factor One X in mice models features of Malan syndrome

BACKGROUND

Nuclear Factor One X (NFIX) haploinsufficiency in humans results in Malan syndrome, a disorder characterized by overgrowth, macrocephaly and intellectual disability. Although clinical assessments have determined the underlying symptomology of Malan syndrome, the fundamental mechanisms contributing to the enlarged head circumference and intellectual disability in these patients remains undefined.

METHODS

Here, we used Nfix heterozygous mice as a model to investigate these aspects of Malan syndrome. Volumetric magnetic resonance imaging (MRI) was used to calculate the volumes of 20 brain sub regions. Diffusion tensor MRI was used to perform tractography-based analyses of the corpus callosum, hippocampal commissure, and anterior commissure, as well as structural connectome mapping of the whole brain. Immunohistochemistry examined the neocortical cellular populations. Two behavioral assays were performed, including the active place avoidance task to assess spatial navigation and learning and memory function, and the 3-chambered sociability task to examine social behaviour.

FINDINGS

Adult Nfix+/- mice exhibit significantly increased brain volume (megalencephaly) compared to wildtypes, with the cerebral cortex showing the highest increase. Moreover, all three forebrain commissures, in particular the anterior commissure, revealed significantly reduced fractional anisotropy, axial and radial diffusivity, and tract density intensity. Structural connectome analyses revealed aberrant connectivity between many crucial brain regions. Finally, Nfix+/- mice exhibit behavioral deficits that model intellectual disability.

INTERPRETATION

Collectively, these data provide a significant conceptual advance in our understanding of Malan syndrome by suggesting that megalencephaly underlies the enlarged head size of these patients, and that disrupted cortical connectivity may contribute to the intellectual disability these patients exhibit. FUND: Australian Research Council (ARC) Discovery Project Grants, ARC Fellowship, NYSTEM and Australian Postgraduate Fellowships.

Aberrant functional connectivity of neural circuits associated with social and sensorimotor deficits in young children with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical functional integration of brain regions. The vast majority of neuroimaging studies of ASD have focused on older children, adolescents, and adults with the disorder. Very little work has explored whole-brain functional connectivity of young children with ASD. Here, we collected resting-state functional magnetic resonance imaging data from 58 young children (mean age 4.98 years; 29 with ASD; 29 matched healthy controls [HC]). All children were under sedation during scanning. A functional "connectedness" method was first used to seek for brain regions showing atypical functional connectivity (FC) in children with ASD. Then, a recurrent-seek strategy was applied to reveal atypical FC circuits in ASD children. FC matrices between regions-of-interest (ROIs) were compared between ASD and HC. Finally, a support vector regression (SVR) method was used to assess the relationship between the FC circuits and ASD symptom severity. Two atypical FC circuits comprising 23 ROIs in ASD were revealed: one predominantly comprised brain regions involved with social cognition showing under-connectivity in ASD; the other predominantly comprised sensory-motor and visual brain regions showing over-connectivity in ASD. The SVR analysis showed that the two FC circuits were separately related to social deficits and restricted behavior scores. These findings indicate disrupted FC of neural circuits involved in the social and sensorimotor processes in young children with ASD. The finding of the atypical FC patterns in young children with ASD underscores the utility of studying younger children with the disorder, and highlights nuanced patterns of brain connectivity underlying behavior closer to disorder onset. Autism Research 2018, 11: 1643-1652. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder. Understanding brain functional alterations at early ages is important for understanding biological mechanisms of ASD. Here, we found two atypical brain functional circuits in young children with ASD that were related to social and sensorimotor function. These results show how atypical patterns of brain functional connectivity in young children with of ASD may underlie core symptoms of the disorder.