Disruption of structural covariance networks for language in autism is modulated by verbal ability

Assessing cortical features in early stage ASD children

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental disorder largely investigated in the neurologic field. Recently, neuroimaging studies have been conducted in order to investigate cerebral morphologic alterations in ASD patients, demonstrating an atypical brain development before the clinical manifestations of the disorder. Cortical Thickness (CT) and Local Gyrification Index (LGI) distribution for ASD children were investigated in this study, with the aim to evaluate possible relationship between brain measures and individual characteristics (i.e., IQ and verbal ability). 3D T1-w sequences from 129 ASD and 58 age-matched Healthy Controls (HC) were acquired and processed in order to assess CT and LGI for each subject. Intergroup differences between ASD and HC were investigated, including analyses of 2 ASD subgroups, split according to patient verbal ability and IQ. When compared to HC, ASD showed increased CT and LGI within several brain areas, both as an overall group and as verbal ability an IQ subgroups. Moreover, when comparing language characteristics of the ASD subjects, those patients with verbal ability exhibit significant CT and LGI increase was found within the occipital lobe of right hemisphere. No significant results occurred when comparing ASD patients according to their IQ value. These results support the hypothesis of abnormal brain maturation in ASD since early childhood with differences among clinical subgroups suggesting different anatomical substrates underlying an aberrant connectivity.

Disrupted brain gray matter connectome in social anxiety disorder: a novel individualized structural covariance network analysis

Phenotyping approaches grounded in structural network science can offer insights into the neurobiological substrates of psychiatric diseases, but this remains to be clarified at the individual level in social anxiety disorder (SAD). Using a recently developed approach combining probability density estimation and Kullback-Leibler divergence, we constructed single-subject structural covariance networks (SCNs) based on multivariate morphometry (cortical thickness, surface area, curvature, and volume) and quantified their global/nodal network properties using graph-theoretical analysis. We compared network metrics between SAD patients and healthy controls (HC) and analyzed the relationship to clinical characteristics. We also used support vector machine analysis to explore the ability of graph-theoretical metrics to discriminate SAD patients from HC. Globally, SAD patients showed higher global efficiency, shorter characteristic path length, and stronger small-worldness. Locally, SAD patients showed abnormal nodal centrality mainly involving left superior frontal gyrus, right superior parietal lobe, left amygdala, right paracentral gyrus, right lingual, and right pericalcarine cortex. Altered topological metrics were associated with the symptom severity and duration. Graph-based metrics allowed single-subject classification of SAD versus HC with total accuracy of 78.7%. This finding, that the topological organization of SCNs in SAD patients is altered toward more randomized configurations, adds to our understanding of network-level neuropathology in SAD.

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.

Important Preliminary Insights for Designing Successful Communication between a Robotic Learning Assistant and Children with Autism Spectrum Disorder in Germany

Early therapeutic intervention programs help children diagnosed with Autism Spectrum Disorder (ASD) to improve their socio-emotional and functional skills. To relieve the children’s caregivers while ensuring that the children are adequately supported in their training exercises, new technologies may offer suitable solutions. This study investigates the potential of a robotic learning assistant which is planned to monitor the children’s state of engagement and to intervene with appropriate motivational nudges when necessary. To analyze stakeholder requirements, interviews with parents as well as therapists of children with ASD were conducted. Besides a general positive attitude towards the usage of new technologies, we received some important insights for the design of the robot and its interaction with the children. One strongly accentuated aspect was the robot’s adequate and context-specific communication behavior, which we plan to address via an AI-based engagement detection system. Further aspects comprise for instance customizability, adaptability, and variability of the robot’s behavior, which should further be not too distracting while still being highly predictable.

Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging

Background

Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified.

Results

The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas.

Conclusions

Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.

Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism

The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.

Connectivity Alterations in Vascular Parkinsonism: A Structural Covariance Study

This study aimed to investigate the structural covariance between the striatum and large-scale brain regions in patients with vascular parkinsonism (VP) compared to Parkinson’s disease (PD) and control subjects, and then explore the relationship between brain connectivity and the clinical features of our patients. Forty subjects (13 VP, 15 PD, and 12 age-and-sex-matched healthy controls) were enrolled in this study. They each underwent a careful clinical and neuropsychological evaluation, DAT-SPECT scintigraphy and 3T MRI scan. While there were no differences between PD and VP in the disease duration and severity, nor in terms of the DAT-SPECT evaluations, VP patients had a reduction in structural covariance between the bilateral corpus striatum (both putamen and caudate) and several brain regions, including the insula, thalamus, hippocampus, anterior cingulate cortex and orbito-frontal cortex compared to PD and controls. VP patients also showed lower scores on several neuropsychological tests. Interestingly, in the VP group, structural connectivity alterations were significantly related to cognitive evaluations exploring executive functions, memory, anxiety and depression. This compelling evidence suggests that structural disconnection in the basal ganglia circuits spreading in critical cortical regions may be involved in the pathophysiology of cognitive impairment in VP.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium's ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Region-specific associations between gamma-aminobutyric acid A receptor binding and cortical thickness in high-functioning autistic adults

The neurobiology of autism has been shown to involve alterations in cortical morphology and gamma-aminobutyric acid A (GABA_A ) receptor density. We hypothesized that GABA_A receptor binding potential (GABA_A R BP_ND ) would correlate with cortical thickness, but their correlations would differ between autistic adults and typically developing (TD) controls. We studied 50 adults (23 autism, 27 TD, mean age of 27 years) using magnetic resonance imaging to measure cortical thickness, and [¹⁸ F]flumazenil positron emission tomography imaging to measure GABA_A R BP_ND . We determined the correlations between cortical thickness and GABA_A R BP_ND by cortical lobe, region-of-interest, and diagnosis of autism spectrum disorder (ASD). We also explored potential sex differences in the relationship between cortical thickness and autism characteristics, as measured by autism spectrum quotient (AQ) scores. Comparing autism and TD groups, no significant differences were found in cortical thickness or GABA_A R BP_ND . In both autism and TD groups, a negative relationship between cortical thickness and GABA_A R BP_ND was observed in the frontal and occipital cortices, but no relationship was found in the temporal or limbic cortices. A positive correlation was seen in the parietal cortex that was only significant for the autism group. Interestingly, in an exploratory analysis, we found sex differences in the relationships between cortical thickness and GABA_A R BP_ND , and cortical thickness and AQ scores in the left postcentral gyrus. LAY SUMMARY: The thickness of the brain cortex and the density of the receptors associated with inhibitory neurotransmitter GABA have been hypothesized to underlie the neurobiology of autism. In this study, we found that these biomarkers correlate positively in the parietal cortex, but negatively in the frontal and occipital cortical regions of the brain. Furthermore, we collected preliminary evidence that the correlations between cortical thickness and GABA receptor density are sexdependent in a brain region where sensory inputs are registered.

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

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

Abnormal Degree Centrality in Children with Low-Function Autism Spectrum Disorders: A Sleeping-State Functional Magnetic Resonance Imaging Study

PURPOSE

This study used the graph-theory approach, degree centrality (DC) to analyze whole-brain functional networks at the voxel level in children with ASD, and investigated whether DC changes were correlated with any clinical variables in ASD children.

METHODS

The current study included 86 children with ASD and 54 matched healthy subjects Aged 2-5.5 years. Next, chloral hydrate induced sleeping-state functional magnetic resonance imaging (ss-fMRI) datasets were acquired from these ASD and healthy subjects. For a given voxel, the DC was calculated by calculating the number of functional connections with significantly positive correlations at the individual level. Group differences were tested using two-sample t-tests (p < 0.01, AlphaSim corrected). Finally, relationships between abnormal DCs and clinical variables were investigated via Pearson's correlation analysis.

RESULTS

Children with ASD exhibited low DC values in the right middle frontal gyrus (MFG) (p < 0.01, AlphaSim corrected). Furthermore, significantly negative correlations were established between the decreased average DC values within the right MFG in ASD children and the total ABC scores, as well as with two ABC subscales measuring highly relevant impairments in ASD (ie, stereotypes and object-use behaviors and difficulties in language).

CONCLUSION

Taken together, the results of our ss-fMRI study suggest that abnormal DC may represent an important contribution to elucidation of the neuropathophysiological mechanisms of preschoolers with ASD.

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.

Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects.

METHODS

A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach.

RESULTS

From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process.

CONCLUSION

We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.

BCCT: A GUI Toolkit for Brain Structural Covariance Connectivity Analysis on MATLAB

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Executive Function in High-Functioning Autism Spectrum Disorder: A Meta-analysis of fMRI Studies

Abnormalities in executive function (EF) are clinical markers for autism spectrum disorder (ASD). However, the neural mechanisms underlying abnormal EF in ASD remain unclear. This meta-analysis investigated the construct, abnormalities, and age-related changes of EF in ASD. Thirty-three fMRI studies of inhibition, updating, and switching in individuals with high-functioning ASD were included (n = 1114; age range 7-57 years). The results revealed that the EF construct in ASD could be unitary (i.e., common EF) in children/adolescents, but unitary and diverse (i.e., common EF and inhibition) in adults. Abnormalities in this EF construct were found across development in individuals with ASD in comparison with typically developing individuals. Implications and recommendations are discussed for EF theory and for practice in ASD.

Cortico-striato-thalamo-cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic-clonic seizures

Generalized tonic-clonic seizures (GTCS) are the severest and most remarkable clinical expressions of human epilepsy. Cortical, subcortical, and cerebellar structures, organized with different network patterns, underlying the pathophysiological substrates of genetic associated epilepsy with GTCS (GE-GTCS) and focal epilepsy associated with focal to bilateral tonic-clonic seizure (FE-FBTS). Structural covariance analysis can delineate the features of epilepsy network related with long-term effects from seizure. Morphometric MRI data of 111 patients with GE-GTCS, 111 patients with FE-FBTS and 111 healthy controls were studied. Cortico-striato-thalao-cerebellar networks of structural covariance within the gray matter were constructed using a Winner-take-all strategy with five cortical parcellations. Comparisons of structural covariance networks were conducted using permutation tests, and module effects of disease duration on networks were conducted using GLM model. Both patient groups showed increased connectivity of structural covariance relative to controls, mainly within the striatum and thalamus, and mostly correlated with the frontal, motor, and somatosensory cortices. Connectivity changes increased as a function of epilepsy durations. FE-FBTS showed more intensive and extensive gray matter changes with volumetric loss and connectivity increment than GE-GTCS. Our findings implicated cortico-striato-thalamo-cerebellar network changes at a large temporal scale in GTCS, with FE-FBTS showing more severe network disruption. The study contributed novel imaging evidence for understanding the different epilepsy syndromes associated with generalized seizures.

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.

Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation

BACKGROUND

Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with altered connectivity in brain networks.

NEW METHOD

In this study, brain connections in Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) of ASD and Typical Developing (TD) are analyzed by partial and full correlation methods such as Gaussian Graphical Least Absolute Shrinkage and Selection Operator (GLASSO), Max-Det Matrix Completion (MDMC), and Pearson Correlation Co-Efficient (PCCE). We investigated Functional Connectivity (FC) of ASD and TD brain from 238 functionally defined regions of interest. Furthermore, we constructed a series of feature sets by applying conditional random forests and conditional permutation importance. We built classifier models by Random Forest (RF), Oblique RF (ORF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) for each feature set. FC features are ranked based on p-value and we analyzed the top 20 FC features.

RESULTS

We achieved a single-trial test accuracy of 72.5 %, though MDMC-SVM and PCCE-CNN pipelines. Further, PCCE-CNN pipeline gives better average test accuracy (70.31 %) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.

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.

Exploring Individual Brain Variability during Development based on Patterns of Maturational Coupling of Cortical Thickness: A Longitudinal MRI Study

Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbor cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.

Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls

Dancers and musicians differ in brain structure from untrained individuals. Structural covariance (SC) analysis can provide further insight into training-associated brain plasticity by evaluating interregional relationships in gray matter (GM) structure. The objectives of the present study were to compare SC of cortical thickness (CT) between expert dancers, expert musicians and untrained controls, as well as to examine the relationship between SC and performance on dance- and music-related tasks. A reduced correlation between CT in the left dorsolateral prefrontal cortex (DLPFC) and mean CT across the whole brain was found in the dancers compared to the controls, and a reduced correlation between these two CT measures was associated with higher performance on a dance video game task. This suggests that the left DLPFC is structurally decoupled in dancers and may be more strongly affected by local training-related factors than global factors in this group. This work provides a better understanding of structural brain connectivity and training-induced brain plasticity, as well as their interaction with behavior in dance and music.

Structural Covariance Networks in Children with Autism or ADHD

Background

While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics.

Method

Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness.

Results

We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups.

Conclusions

Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence.

Neural Correlates of Sensory Abnormalities Across Developmental Disabilities

Abnormalities in sensory processing are a common feature of many developmental disabilities (DDs). Sensory dysfunction can contribute to deficits in brain maturation, as well as many vital functions. Unfortunately, while some patients with DD benefit from the currently available treatments for sensory dysfunction, many do not. Deficiencies in clinical practice surrounding sensory dysfunction may be related to lack of understanding of the neural mechanisms that underlie sensory abnormalities. Evidence of overlap in sensory symptoms between diagnoses suggests that there may be common neural mechanisms that mediate many aspects of sensory dysfunction. Thus, the current manuscript aims to review the extant literature regarding the neural correlates of sensory dysfunction across DD in order to identify patterns of abnormality that span diagnostic categories. Such anomalies in brain structure, function, and connectivity may eventually serve as targets for treatment.

Aberrant development of the asymmetry between hemispheric brain white matter networks in autism spectrum disorder

Atypical brain asymmetry/lateralization has long been hypothesized for autism spectrum disorder (ASD), and this model has been repeatedly supported by various neuroimaging studies. Recently, hemispheric network topologies have been found to be asymmetric, thereby providing a new avenue for investigating brain asymmetries under various conditions. To date, however, how network topological asymmetries are altered in ASD remains largely unexplored. To clarify this, the present study included ASD individuals from the newly released Autism Brain Imaging Data Exchange II database (58 right-handed male ASD individuals aged 5 to 26 years and 70 age- and IQ-matched typically developing (TD) individuals). Diffusion and structural magnetic resonance imaging were used to construct hemispheric white matter networks, and graph-theory approaches were applied to quantify topological efficiencies for hemispheric networks. Statistical analyses revealed a decreased rightward asymmetry of network efficiencies with increasing age in the TD group, but not in the ASD group. More specifically, the TD group did not exhibit an age-related increase in network efficiency in the right hemisphere, but the ASD group did. For the left hemisphere, no difference between the groups was observed for the developmental trajectory of network efficiencies. Intriguingly, within the ASD group, more severe restricted and repetitive behavior in ASD was found to be correlated with less rightward asymmetry of network local efficiency. These findings provide suggestive evidence of atypical network topological asymmetries and offer important insights into the abnormal development of ASD brains.

Language Ability Predicts Cortical Structure and Covariance in Boys with Autism Spectrum Disorder

There is significant clinical heterogeneity in language and communication abilities of individuals with Autism Spectrum Disorders (ASD). However, no consistent pathology regarding the relationship of these abilities to brain structure has emerged. Recent developments in anatomical correlation-based approaches to map structural covariance networks (SCNs), combined with detailed behavioral characterization, offer an alternative for studying these relationships. In this study, such an approach was used to study the integrity of SCNs of cortical thickness and surface area associated with language and communication, in 46 high-functioning, school-age children with ASD compared with 50 matched, typically developing controls (all males) with IQ > 75. Findings showed that there was alteration of cortical structure and disruption of fronto-temporal cortical covariance in ASD compared with controls. Furthermore, in an analysis of a subset of ASD participants, alterations in both cortical structure and covariance were modulated by structural language ability of the participants, but not communicative function. These findings indicate that structural language abilities are related to altered fronto-temporal cortical covariance in ASD, much more than symptom severity or cognitive ability. They also support the importance of better characterizing ASD samples while studying brain structure and for better understanding individual differences in language and communication abilities in ASD.

White Matter Integrity and Treatment-Based Change in Speech Performance in Minimally Verbal Children with Autism Spectrum Disorder

We investigated the relationship between imaging variables for two language/speech-motor tracts and speech fluency variables in 10 minimally verbal (MV) children with autism. Specifically, we tested whether measures of white matter integrity—fractional anisotropy (FA) of the arcuate fasciculus (AF) and frontal aslant tract (FAT)—were related to change in percent syllable-initial consonants correct, percent items responded to, and percent syllable insertion errors (from best baseline to post 25 treatment sessions). Twenty-three MV children with autism spectrum disorder (ASD) received Auditory-Motor Mapping Training (AMMT), an intonation-based treatment to improve fluency in spoken output, and we report on seven who received a matched control treatment. Ten of the AMMT participants were able to undergo a magnetic resonance imaging study at baseline; their performance on baseline speech production measures is compared to that of the other two groups. No baseline differences were found between groups. A canonical correlation analysis (CCA) relating FA values for left- and right-hemisphere AF and FAT to speech production measures showed that FA of the left AF and right FAT were the largest contributors to the synthetic independent imaging-related variable. Change in percent syllable-initial consonants correct and percent syllable-insertion errors were the largest contributors to the synthetic dependent fluency-related variable. Regression analyses showed that FA values in left AF significantly predicted change in percent syllable-initial consonants correct, no FA variables significantly predicted change in percent items responded to, and FA of right FAT significantly predicted change in percent syllable-insertion errors. Results are consistent with previously identified roles for the AF in mediating bidirectional mapping between articulation and acoustics, and the FAT in its relationship to speech initiation and fluency. They further suggest a division of labor between the hemispheres, implicating the left hemisphere in accuracy of speech production and the right hemisphere in fluency in this population. Changes in response rate are interpreted as stemming from factors other than the integrity of these two fiber tracts. This study is the first to document the existence of a subgroup of MV children who experience increases in syllable- insertion errors as their speech develops in response to therapy.

Longitudinal trajectory of clinical insight and covariation with cortical thickness in first-episode psychosis

Among people with a first-episode of psychosis, those with poorer clinical insight show neuroanatomical abnormalities in frontal, temporal and parietal cortices compared to those with better clinical insight. Whether changes in clinical insight are associated with progressive structural brain changes is unknown. We aimed to evaluate 1) associations between clinical insight and cortical thickness at a baseline assessment, 2) covariation between clinical insight and cortical thickness across baseline, one-year and two-year follow-up assessments, and 3) the predictive value of clinical insight for cortical thickness at one-year and two-year follow-ups. Scale for the assessment of Unawareness of Mental Disorder ratings and magnetic resonance imaging scans were acquired at baseline, one-year, and two-year follow-ups in 128, 74, and 44 individuals with a first-episode psychosis, respectively. Cortical thickness metrics were then computed at baseline, one-year and two-year follow-ups and analyzed with linear mixed models. At baseline, clinical insight was not significantly associated with cortical thickness in any region. Longitudinal mixed effects models showed that a worsening in clinical insight between the one-year and two-year assessments was significantly associated with cortical thinning in dorsal pre-central and post-central gyri. Cortical thinning in left fusiform gyrus at two-years was predicted by poorer clinical insight at baseline. Results suggest that poor clinical insight soon after the onset of a first-episode psychosis may lead to progressive cortical changes in temporal lobe, while changes in clinical insight during the second year covary with cortical thinning in circumscribed dorsal frontal and parietal cortices.

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

Background

Autism spectrum disorder (ASD) is characterized by atypical development of cortical and subcortical gray matter volume. Subcortical structural changes have been associated with restricted and repetitive behavior (RRB), a core component of ASD. Behavioral studies have identified insistence on sameness (IS) as a separable RRB dimension prominent in high-functioning ASD, though no simple brain-behavior relationship has emerged. Structural covariance, a measure of morphological coupling among brain regions using magnetic resonance imaging (MRI), has proven an informative measure of anatomical relationships in typical development and neurodevelopmental disorders. In this study, we use this measure to characterize the relationship between brain structure and IS.

Methods

We quantified the structural covariance of cortical and subcortical gray matter volume in 55 individuals with high-functioning ASD using 3T MRI. We then related these structural metrics to individual IS scores, as assessed by the Repetitive Behavior Scale-Revised (RBS-R).

Results

We found that increased coupling among subcortical regions and between subcortical and cortical regions related to greater IS symptom severity. Most pronounced, the striatum and amygdala participated in a plurality of identified relationships, indicating a central role for these structures in IS symptomatology. These structural associations were specific to IS and did not relate to any of the other RRB subcomponents measured by the RBS-R.

Conclusions

This study indicates that behavioral dimensions in ASD can relate to the coordination of development across multiple brain regions, which might be otherwise obscured using typical brain-behavior correlations. It also expands the structures traditionally related to RRB in ASD and provides neuroanatomical evidence supportive of IS as a separate RRB dimension.

Trial registration

ClinicalTrials.gov NCT01031407

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0047-7) contains supplementary material, which is available to authorized users.