Altered modular organization of structural cortical networks in children with autism

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

Background

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

Methods

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

Results

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

Conclusions

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

Clinical factors associated with microstructural connectome related brain dysmaturation in term neonates with congenital heart disease

Objective

Term congenital heart disease (CHD) neonates display abnormalities of brain structure and maturation, which are possibly related to underlying patient factors, abnormal physiology and perioperative insults. Our primary goal was to delineate associations between clinical factors and postnatal brain microstructure in term CHD neonates using diffusion tensor imaging (DTI) magnetic resonance (MR) acquisition combined with complementary data-driven connectome and seed-based tractography quantitative analyses. Our secondary goal was to delineate associations between mild dysplastic structural brain abnormalities and connectome and seed-base tractography quantitative analyses. These mild dysplastic structural abnormalities have been derived from prior human infant CHD MR studies and neonatal mouse models of CHD that were collectively used to calculate to calculate a brain dysplasia score (BDS) that included assessment of subcortical structures including the olfactory bulb, the cerebellum and the hippocampus.

Methods

Neonates undergoing cardiac surgery for CHD were prospectively recruited from two large centers. Both pre- and postoperative MR brain scans were obtained. DTI in 42 directions was segmented into 90 regions using a neonatal brain template and three weighted methods. Clinical data collection included 18 patient-specific and 9 preoperative variables associated with preoperative scan and 6 intraoperative (e.g., cardiopulmonary bypass and deep hypothermic circulatory arrest times) and 12 postoperative variables associated with postoperative scan. We compared patient specific and preoperative clinical factors to network topology and tractography alterations on a preoperative neonatal brain MRI, and intra and postoperative clinical factors to network topology alterations on postoperative neonatal brain MRI. A composite BDS was created to score abnormal findings involving the cerebellar hemispheres and vermis, supratentorial extra-axial fluid, olfactory bulbs and sulci, hippocampus, choroid plexus, corpus callosum, and brainstem. The neuroimaging outcomes of this study included (1) connectome metrics: cost (number of connections) and global/nodal efficiency (network integration); (2) seed based tractography methods of fractional anisotropy (FA), radial diffusivity, and axial diffusivity. Statistics consisted of multiple regression with false discovery rate correction (FDR) comparing the clinical risk factors and BDS (including subcortical components) as predictors/exposures and the global connectome metrics, nodal efficiency, and seed based- tractography (FA, radial diffusivity, and axial diffusivity) as neuroimaging outcome measures.

Results

A total of 133 term neonates with complex CHD were prospectively enrolled and 110 had analyzable DTI. Multiple patient-specific factors including d-transposition of the great arteries (d-TGA) physiology and severity of impairment of fetal cerebral substrate delivery (i.e., how much the CHD lesion alters typical fetal circulation such that the highest oxygen and nutrient rich blood from the placenta are not directed toward the fetal brain) were predictive of preoperative reduced cost (p < 0.0073) and reduced global/nodal efficiency (p < 0.03). Cardiopulmonary bypass time predicted postoperative reduced cost (p < 0.04) and multiple postoperative factors [extracorporeal membrane oxygenation (ECMO), seizures and cardiopulmonary resuscitation (CPR)] were predictive of postoperative reduced cost and reduced global/nodal efficiency (p < 0.05). Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. Total BDS was not predictive of brain network topology. However, key subcortical components of the BDS score did predict key global and nodal network topology: abnormalities of the cerebellum predicted reduced cost (p < 0.0417) and of the hippocampus predicted reduced global efficiency (p < 0.0126). All three subcortical structures predicted unique alterations of nodal efficiency (p < 0.05), including hippocampal abnormalities predicting widespread reduced nodal efficiency in all lobes of the brain, cerebellar abnormalities predicting increased prefrontal nodal efficiency, and olfactory bulb abnormalities predicting posterior parietal-occipital nodal efficiency.

Conclusion

Patient-specific (d-TGA anatomy, preoperative impairment of fetal cerebral substrate delivery) and postoperative (e.g., seizures, need for ECMO, or CPR) clinical factors were most predictive of diffuse postnatal microstructural dysmaturation in term CHD neonates. Anthropometric measurements (weight, length, and head size) predicted tractography outcomes. In contrast, subcortical components (cerebellum, hippocampus, olfactory) of a structurally based BDS (derived from CHD mouse mutants), predicted more localized and regional postnatal microstructural differences. Collectively, these findings suggest that brain DTI connectome and seed-based tractography are complementary techniques which may facilitate deciphering the mechanistic relative contribution of clinical and genetic risk factors related to poor neurodevelopmental outcomes in CHD.

Developmental pattern of the cortical topology in high-functioning individuals with autism spectrum disorder

A number of studies have indicated alterations of brain morphology in individuals with autism spectrum disorder (ASD); however, how ASD influences the topological organization of the brain cortex at different developmental stages is not yet well characterized. In this study, we used structural images of 492 high‐functioning participants in the Autism Brain Imaging Data Exchange database acquired from 17 international imaging centers, including 75 autistic children (age 7–11 years), 91 adolescents with ASD (age 12–17 years), and 80 young adults with ASD (age 18–29 years), and 246 typically developing controls (TDCs) that were age, gender, handedness, and full‐scale IQ matched. Cortical thickness (CT) and surface area (SA) were extracted and the covariance between cortical regions across participants were treated as a network to examine developmental patterns of the cortical topological organization at different stages. A center‐paired resampling strategy was developed to control the center bias during the permutation test. Compared with the TDCs, network of SA (but not CT) of individuals with ASD showed reduced small‐worldness in childhood, and the network hubs were reorganized in the adulthood such that hubs inclined to connect with nonhub nodes and demonstrated more dispersed spatial distribution. Furthermore, the SA network of the ASD cohort exhibited increased segregation of the inferior parietal lobule and prefrontal cortex, and insular‐opercular cortex in all three age groups, resulting in the emergence of two unique modules in the autistic brain. Our findings suggested that individuals with ASD may undergo remarkable remodeling of the cortical topology from childhood to adulthood, which may be associated with the altered social and cognitive functions in ASD.

Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder

BACKGROUND

Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI-based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach.

METHODS

Twenty-two AUD and 18 controls (CON) underwent anatomic and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract-Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a coclassification index matrix (an indicator of community association strength) for each subject. Differences in coclassification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox.

RESULTS

AUD had higher fractional anisotropy (FA) values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula (p_TFCE  < 0.05). Mean diffusivity was generally lower in the AUD group (p_TFCE  < 0.05). AUD had lower coclassification of nodes between ventral attention and default mode networks and higher coclassification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network.

CONCLUSIONS

Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared with CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AUD.

Hemispheric Module-Specific Influence of the X Chromosome on White Matter Connectivity: Evidence from Girls with Turner Syndrome

Turner syndrome (TS) is caused by the congenital absence of all or part of one of the X chromosomes in females, offering a valuable human “knockout model” to study the functioning patterns of the X chromosome in the human brain. Little is known about whether and how the loss of the X chromosome influences the brain structural wiring patterns in human. We acquired a multimodal MRI dataset and cognitive assessments from 22 girls with TS and 21 age-matched control girls to address these questions. Hemispheric white matter (WM) networks and modules were derived using refined diffusion MRI tractography. Statistical comparisons revealed a reduced topological efficiency of both hemispheric networks and bilateral parietal modules in TS girls. Specifically, the efficiency of right parietal module significantly mediated the effect of the X chromosome on working memory performance, indicating that X chromosome loss impairs working memory performance by disrupting this module. Additionally, TS girls showed structural and functional connectivity decoupling across specific within- and between-modular connections, predominantly in the right hemisphere. These findings provide novel insights into the functional pathways in the brain that are regulated by the X chromosome and highlight a module-specific genetic contribution to WM connectivity in the human brain.

Altered Forebrain Functional Connectivity and Neurotransmission in a Kinase-Inactive Met Mouse Model of Autism

MET, the gene encoding the tyrosine kinase receptor for hepatocyte growth factor, is a susceptibility gene for autism spectrum disorder (ASD). Genetically altered mice with a kinase-inactive Met offer a potential model for understanding neural circuit organization changes in autism. Here, we focus on the somatosensory thalamocortical circuitry because distinct somatosensory sensitivity phenotypes accompany ASD, and this system plays a major role in sensorimotor and social behaviors in mice. We employed resting-state functional magnetic resonance imaging and in vivo high-resolution proton MR spectroscopy to examine neuronal connectivity and neurotransmission of wild-type, heterozygous Met-Emx1, and fully inactive homozygous Met-Emx1 mice. Met-Emx1 brains showed impaired maturation of large-scale somatosensory network connectivity when compared with wild-type controls. Significant sex × genotype interaction in both network features and glutamate/gamma-aminobutyric acid (GABA) balance was observed. Female Met-Emx1 brains showed significant connectivity and glutamate/GABA balance changes in the somatosensory thalamocortical system when compared with wild-type brains. The glutamate/GABA ratio in the thalamus was correlated with the connectivity between the somatosensory cortex and the thalamus in heterozygous Met-Emx1 female brains. The findings support the hypothesis that aberrant functioning of the somatosensory thalamocortical system is at the core of the conspicuous somatosensory behavioral phenotypes observed in Met-Emx1 mice.

Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer's Disease Revealed by Individual Morphological Brain Network

Alzheimer’s disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28–30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.

Disrupted functional connectome in antisocial personality disorder

Studies on antisocial personality disorder (ASPD) subjects focus on brain functional alterations in relation to antisocial behaviors. Neuroimaging research has identified a number of focal brain regions with abnormal structures or functions in ASPD. However, little is known about the connections among brain regions in terms of inter-regional whole-brain networks in ASPD patients, as well as possible alterations of brain functional topological organization. In this study, we employ resting-state functional magnetic resonance imaging (R-fMRI) to examine functional connectome of 32 ASPD patients and 35 normal controls by using a variety of network properties, including small-worldness, modularity, and connectivity. The small-world analysis reveals that ASPD patients have increased path length and decreased network efficiency, which implies a reduced ability of global integration of whole-brain functions. Modularity analysis suggests ASPD patients have decreased overall modularity, merged network modules, and reduced intra- and inter-module connectivities related to frontal regions. Also, network-based statistics show that an internal sub-network, composed of 16 nodes and 16 edges, is significantly affected in ASPD patients, where brain regions are mostly located in the fronto-parietal control network. These results suggest that ASPD is associated with both reduced brain integration and segregation in topological organization of functional brain networks, particularly in the fronto-parietal control network. These disruptions may contribute to disturbances in behavior and cognition in patients with ASPD. Our findings may provide insights into a deeper understanding of functional brain networks of ASPD.

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

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

Purpose To investigate the topologic architecture of white matter connectivity networks in preschool-aged children with a diagnosis of autism spectrum disorder (ASD) versus typical development (TD). Materials and Methods Forty-two participants were enrolled, including 21 preschool children with ASD (14 male children and seven female children; mean age, 4.56 years ± 0.97 [standard deviation]) and 21 children with TD (11 males and 10 females; mean age, 5.13 years ± 0.82). The diagnosis of ASD was determined according to the Diagnostic and Statistical Manual of Mental Disorders Global Assessment of Functioning scores (mean score, 8.00 ± 0.50). All participants underwent diffusion-tensor imaging (DTI) and T2-weighted imaging on a 3-T magnetic resonance system. A graph theoretical analysis was applied to investigate the topologic organization of the brain network including global and local topologic parameters. Statistical analysis was then performed for the comparison between the groups. Results Compared with the TD group, children with ASD demonstrated shortened characteristic path length (t₁ = 0.536, t₂ = 0.534, t₃ = 0.523, t₄ = 0.510, and t₅ = 0.501; P < .05) and increased global efficiency (t₁ = 0.499, t₂ = 0.497, t₃ = 0.486, t₄ = 0.473, and t₅ = 0.465; P < .05) and clustering coefficient (t₁ = 0.673, t₂ = 0.750, t₃ = 0.757, t₄ = 0.738, and t₅ = 0.741; P < .05). Significant increases in nodal efficiency were mainly found in left pallidum (0.037 vs 0.032, respectively; P < .01) and right caudate nucleus (0.037 vs 0.032, respectively; P < .01) of the basal ganglia network. Conclusion Significantly altered patterns of global and local brain network topography may underlie the abnormal brain development in preschool children with ASD compared with those who have TD. The identification of altered structural connectivity in basal ganglia and paralimbic-limbic networks may point toward potential imaging biomarkers for preschool-age patients with ASD. ^© RSNA, 2018.

Aberrant brain functional connectivity in newborns with congenital heart disease before cardiac surgery

Newborns with congenital heart disease (CHD) requiring open heart surgery are at increased risk for neurodevelopmental disabilities. Recent quantitative MRI studies have reported disrupted growth, microstructure, and metabolism in fetuses and newborns with complex CHD. To date, no study has examined whether functional brain connectivity is altered in this high-risk population after birth, before surgery. Our objective was to compare whole-brain functional connectivity of resting state networks in healthy, term newborns (n = 82) and in term neonates with CHD before surgery (n = 30) using graph theory and network-based statistics. We report for the first time intact global network topology – efficient and economic small world networks – but reduced regional functional connectivity involving critical brain regions (i.e. network hubs and/or rich club nodes) in newborns with CHD before surgery. These findings suggest the presence of early-life brain dysfunction in CHD which may be associated with neurodevelopmental impairments in the years following cardiac surgery. Additional studies are needed to evaluate the prognostic, diagnostic and surveillance potential of these findings.

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We examined network topology of CHD resting state networks before cardiac surgery.

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Small world architecture was preserved in newborns with CHD.

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The density of connections among rich club nodes in CHD was diminished.

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Connectivity among some critical brain hubs was reduced in CHD.

Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study

Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi-modality multi-center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task-task and modality-modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081-3097, 2017. © 2017 Wiley Periodicals, Inc.

Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test-retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.

Structural Brain Network Reorganization in Patients with Neuropsychiatric Systemic Lupus Erythematosus

BACKGROUND AND PURPOSE

Patients with neuropsychiatric systemic lupus erythematosus have worse outcomes compared with those with systemic lupus erythematosus. A better understanding of the mechanisms of neuropsychiatric systemic lupus erythematosus could potentially improve diagnosis and management. The goal of this study was to investigate the differences in the structural brain network of patients with neuropsychiatric systemic lupus erythematosus compared with patients with systemic lupus erythematosus by using brain connectivity analysis.

MATERIALS AND METHODS

We recruited 20 subjects for each patient cohort and age-matched healthy controls. The topology and efficiency of the network and the characteristics of various brain hubs were investigated by using brain connectivity analysis of diffusion MR imaging data.

RESULTS

There were more extensive reorganizations in the structural brain network of patients with neuropsychiatric systemic lupus erythematosus than in patients with systemic lupus erythematosus. For example, the network of the former had significantly decreased clustering coefficient and local efficiency. They also had significantly lower nodal efficiency in the superior temporal gyrus (P = .046) and middle temporal gyrus (P = .041).

CONCLUSIONS

Our results hint at a plausible relationship between the neuropsychiatric symptoms and reorganization of the structural brain network of patients with systemic lupus erythematosus. Brain connectivity analysis may be a potential tool to subtype these patients.

Altered brain network integrity after childhood maltreatment: A structural connectomic DTI-study

Childhood maltreatment is associated with alterations in neural architecture that potentially put these children at increased risk for psychopathology. Alterations in white matter (WM) tracts have been reported, however no study to date has investigated WM connectivity in brain networks in maltreated children to quantify global and local abnormalities through graph theoretical analyses of DTI data. We aimed for a multilevel investigation examining the DTI-based structural connectome and its associations with basal cortisol levels of 25 children with documented maltreatment experiences before age 3, and 24 matched controls (age: 10.6 ± 1.75 years). On the global and lobar level, maltreated children showed significant reductions in global connectivity strength, local connectivity and increased path length, suggesting deviations from the small-world network architecture previously associated with psychopathology. Reductions in global connectivity were associated with placement instability, attenuated cortisol secretion and higher levels of internalizing and externalizing behaviours. Regional measures revealed lower connectivity strength especially in regions within the ventromedial prefrontal cortex (vMPFC) in maltreated children. These findings show that childhood maltreatment is associated with systemic global neurodevelopmental alterations in WM networks next to regional alterations in areas involved in the regulation of affect. These alterations in WM organization could underlie global functional deficits and multi-symptom patterns frequently observed in children with maltreatment experiences. Hum Brain Mapp 38:855-868, 2017. © 2016 Wiley Periodicals, Inc.

Eye Tracking Reveals Abnormal Visual Preference for Geometric Images as an Early Biomarker of an Autism Spectrum Disorder Subtype Associated With Increased Symptom Severity

BACKGROUND

Clinically and biologically, autism spectrum disorder (ASD) is heterogeneous. Unusual patterns of visual preference as indexed by eye tracking are hallmarks; however, whether they can be used to define an early biomarker of ASD as a whole or leveraged to define a subtype is unclear. To begin to examine this issue, large cohorts are required.

METHODS

A sample of 334 toddlers from six distinct groups (115 toddlers with ASD, 20 toddlers with ASD features, 57 toddlers with developmental delay, 53 toddlers with other conditions [e.g., premature birth, prenatal drug exposure], 64 toddlers with typical development, and 25 unaffected toddlers with siblings with ASD) was studied. Toddlers watched a movie containing geometric and social images. Fixation duration and number of saccades within each area of interest and validation statistics for this independent sample were computed. Next, to maximize power, data from our previous study (n = 110) were added for a total of 444 subjects. A subset of toddlers repeated the eye-tracking procedure.

RESULTS

As in the original study, a subset of toddlers with ASD fixated on geometric images >69% of the time. Using this cutoff, sensitivity for ASD was 21%, specificity was 98%, and positive predictive value was 86%. Toddlers with ASD who strongly preferred geometric images had 1) worse cognitive, language, and social skills relative to toddlers with ASD who strongly preferred social images and 2) fewer saccades when viewing geometric images. Unaffected siblings of ASD probands did not show evidence of heightened preference for geometric images. Test-retest reliability was good. Examination of age effects suggested that this test may not be appropriate with children >4 years old.

CONCLUSIONS

Enhanced visual preference for geometric repetition may be an early developmental biomarker of an ASD subtype with more severe symptoms.

Are Autistic Traits in the General Population Related to Global and Regional Brain Differences?

There is accumulating evidence that autistic-related traits in the general population lie on a continuum, with autism spectrum disorders representing the extreme end of this distribution. Here, we tested the hypothesis of a possible relationship between autistic traits and brain morphometry in the general population. Participants completed the short autism-spectrum quotient-questionnaire (AQ); T1-anatomical and DWI-scans were acquired. Associations between autistic traits and gray matter, and white matter microstructural-integrity were performed on the exploration-group (N = 204; 105 males, M-age = 22.85), and validated in the validation-group (N = 304; 155 males, M-age = 22.82). No significant associations were found between AQ-scores and brain morphometry in the exploration-group, or after pooling the data. This questions the assumption that autistic traits and their morphological associations do lie on a continuum in the general population.

Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis

BACKGROUND

Several neuroimaging studies support the model of abnormal development of brain connectivity in patients with autism-spectrum disorders (ASD). In this study, we aimed to test the hypothesis of reduced functional network segregation in autistic patients compared with controls.

METHODS

Functional MRI data from children acquired under a resting-state protocol (Autism Brain Imaging Data Exchange [ABIDE]) were submitted to both fuzzy spectral clustering (FSC) with entropy analysis and graph modularity analysis.

RESULTS

We included data from 814 children in our analysis. We identified 5 regions of interest comprising the motor, temporal and occipitotemporal cortices with increased entropy (p < 0.05) in the clustering structure (i.e., more segregation in the controls). Moreover, we noticed a statistically reduced modularity (p < 0.001) in the autistic patients compared with the controls. Significantly reduced eigenvector centrality values (p < 0.05) in the patients were observed in the same regions that were identified in the FSC analysis.

LIMITATIONS

There is considerable heterogeneity in the fMRI acquisition protocols among the sites that contributed to the ABIDE data set (e.g., scanner type, pulse sequence, duration of scan and resting-state protocol). Moreover, the sites differed in many variables related to sample characterization (e.g., age, IQ and ASD diagnostic criteria). Therefore, we cannot rule out the possibility that additional differences in functional network organization would be found in a more homogeneous data sample of individuals with ASD.

CONCLUSION

Our results suggest that the organization of the whole-brain functional network in patients with ASD is different from that observed in controls, which implies a reduced modularity of the brain functional networks involved in sensorimotor, social, affective and cognitive processing.

Reorganization of functionally connected brain subnetworks in high-functioning autism

Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Twenty-six subjects (13 with ASD) watched a 68-min movie during functional magnetic resonance imaging. For each subject, we computed Pearson's correlation between haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the whole-brain functional networks, we derived individual and group-level subnetworks using graph theory. Scaled inclusivity was then calculated between all subject pairs to estimate intersubject similarity of connectivity structure of each subnetwork. Additional 54 individuals (27 with ASD) from the ABIDE resting-state database were included to test the reproducibility of the results. Between-group differences were observed in the composition of default-mode and ventro-temporal-limbic (VTL) subnetworks. The VTL subnetwork included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior temporal gyri. Further, VTL subnetwork similarity between subject pairs correlated significantly with similarity of symptom gravity measured with autism quotient. This correlation was observed also within the controls, and in the reproducibility dataset with ADI-R and ADOS scores. Our results highlight how the reorganization of functional subnetworks in individuals with ASD clarifies the mixture of hypo- and hyper-connectivity findings. Importantly, only the functional organization of the VTL subnetwork emerges as a marker of inter-individual similarities that co-vary with behavioral measures across all participants. These findings suggest a pivotal role of ventro-temporal and limbic systems in autism.

Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.

Sex differences in brain plasticity: a new hypothesis for sex ratio bias in autism

Several observations support the hypothesis that differences in synaptic and regional cerebral plasticity between the sexes account for the high ratio of males to females in autism. First, males are more susceptible than females to perturbations in genes involved in synaptic plasticity. Second, sex-related differences in non-autistic brain structure and function are observed in highly variable regions, namely, the heteromodal associative cortices, and overlap with structural particularities and enhanced activity of perceptual associative regions in autistic individuals. Finally, functional cortical reallocations following brain lesions in non-autistic adults (for example, traumatic brain injury, multiple sclerosis) are sex-dependent. Interactions between genetic sex and hormones may therefore result in higher synaptic and consecutively regional plasticity in perceptual brain areas in males than in females. The onset of autism may largely involve mutations altering synaptic plasticity that create a plastic reaction affecting the most variable and sexually dimorphic brain regions. The sex ratio bias in autism may arise because males have a lower threshold than females for the development of this plastic reaction following a genetic or environmental event.

Altered brain network modules induce helplessness in major depressive disorder

OBJECTIVE

The abnormal brain functional connectivity (FC) has been assumed to be a pathophysiological aspect of major depressive disorder (MDD). However, it is poorly understood, regarding the underlying patterns of global FC network and their relationships with the clinical characteristics of MDD.

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 16 first episode, medication-naïve MDD patients and 16 healthy control subjects. The global FC network was constructed using 90 brain regions. The global topological patterns, e.g., small-worldness and modularity, and their relationships with depressive characteristics were investigated. Furthermore, the participant coefficient and module degree of MDD patients were measured to reflect the regional roles in module network, and the impairment of FC was examined by network based statistic.

RESULTS

Small-world property was not altered in MDD. However, MDD patients exhibited 5 atypically reorganized modules compared to the controls. A positive relationship was also found among MDD patients between the intra-module I and helplessness factor evaluated via the Hamilton Depression Scale. Specifically, eight regions exhibited the abnormal participant coefficient or module degree, e.g., left superior orbital frontal cortex and right amygdala. The decreased FC was identified among the sub-network of 24 brain regions, e.g., frontal cortex, supplementary motor area, amygdala, thalamus, and hippocampus.

LIMITATION

The limited size of MDD samples precluded meaningful study of distinct clinical characteristics in relation to aberrant FC.

CONCLUSIONS

The results revealed altered patterns of brain module network at the global level in MDD patients, which might contribute to the feelings of helplessness.

Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.

Structural MRI connectome in development: challenges of the changing brain

MRI connectomics is an emerging approach to study the brain as a network of interconnected brain regions. Understanding and mapping the development of the MRI connectome may offer new insights into the development of brain connectivity and plasticity, ultimately leading to improved understanding of normal development and to more effective diagnosis and treatment of developmental disorders. In this review, we describe the attempts made to date to map the whole-brain structural MRI connectome in the developing brain and pay a special attention to the challenges associated with the rapid changes that the brain is undergoing during maturation. The two main steps in constructing a structural brain network are (i) choosing connectivity measures that will serve as the network "edges" and (ii) finding an appropriate way to divide the brain into regions that will serve as the network "nodes". We will discuss how these two steps are usually performed in developmental studies and the rationale behind different strategies. Changes in local and global network properties that have been described during maturation in neonates and children will be reviewed, along with differences in network topology between typically and atypically developing subjects, for example, owing to pre-mature birth or hypoxic ischaemic encephalopathy. Finally, future directions of connectomics will be discussed, addressing important steps necessary to advance the study of the structural MRI connectome in development.

Longitudinal development of cortical thickness, folding, and fiber density networks in the first 2 years of life

Quantitatively characterizing the development of cortical anatomical networks during the early stage of life plays an important role in revealing the relationship between cortical structural connection and high-level functional development. The development of correlation networks of cortical-thickness, cortical folding, and fiber-density is systematically analyzed in this article to study the relationship between different anatomical properties during the first 2 years of life. Specifically, longitudinal MR images of 73 healthy subjects from birth to 2 year old are used. For each subject at each time point, its measures of cortical thickness, cortical folding, and fiber density are projected to its cortical surface that has been partitioned into 78 cortical regions. Then, the correlation matrices for cortical thickness, cortical folding, and fiber density at each time point can be constructed, respectively, by computing the inter-regional Pearson correlation coefficient (of any pair of ROIs) across all 73 subjects. Finally, the presence/absence pattern (i.e., binary pattern) of the connection network is constructed from each inter-regional correlation matrix, and its statistical and anatomical properties are adopted to analyze the longitudinal development of anatomical networks. The results show that the development of anatomical network could be characterized differently by using different anatomical properties (i.e., using cortical thickness, cortical folding, or fiber density).

A comparison of neuroimaging findings in childhood onset schizophrenia and autism spectrum disorder: a review of the literature

BACKGROUND

Autism spectrum disorder (ASD) and childhood onset schizophrenia (COS) are pediatric neurodevelopmental disorders associated with significant morbidity. Both conditions are thought to share an underlying genetic architecture. A comparison of neuroimaging findings across ASD and COS with a focus on altered neurodevelopmental trajectories can shed light on potential clinical biomarkers and may highlight an underlying etiopathogenesis.

METHODS

A comprehensive review of the medical literature was conducted to summarize neuroimaging data with respect to both conditions in terms of structural imaging (including volumetric analysis, cortical thickness and morphology, and region of interest studies), white matter analysis (include volumetric analysis and diffusion tensor imaging) and functional connectivity.

RESULTS

In ASD, a pattern of early brain overgrowth in the first few years of life is followed by dysmaturation in adolescence. Functional analyses have suggested impaired long-range connectivity as well as increased local and/or subcortical connectivity in this condition. In COS, deficits in cerebral volume, cortical thickness, and white matter maturation seem most pronounced in childhood and adolescence, and may level off in adulthood. Deficits in local connectivity, with increased long-range connectivity have been proposed, in keeping with exaggerated cortical thinning.

CONCLUSION

The neuroimaging literature supports a neurodevelopmental origin of both ASD and COS and provides evidence for dynamic changes in both conditions that vary across space and time in the developing brain. Looking forward, imaging studies which capture the early post natal period, which are longitudinal and prospective, and which maximize the signal to noise ratio across heterogeneous conditions will be required to translate research findings into a clinical environment.

Preferential detachment during human brain development: age- and sex-specific structural connectivity in diffusion tensor imaging (DTI) data

Human brain maturation is characterized by the prolonged development of structural and functional properties of large-scale networks that extends into adulthood. However, it is not clearly understood which features change and which remain stable over time. Here, we examined structural connectivity based on diffusion tensor imaging (DTI) in 121 participants between 4 and 40 years of age. DTI data were analyzed for small-world parameters, modularity, and the number of fiber tracts at the level of streamlines. First, our findings showed that the number of fiber tracts, small-world topology, and modular organization remained largely stable despite a substantial overall decrease in the number of streamlines with age. Second, this decrease mainly affected fiber tracts that had a large number of streamlines, were short, within modules and within hemispheres; such connections were affected significantly more often than would be expected given their number of occurrences in the network. Third, streamline loss occurred earlier in females than in males. In summary, our findings suggest that core properties of structural brain connectivity, such as the small-world and modular organization, remain stable during brain maturation by focusing streamline loss to specific types of fiber tracts.

Reduced Theta Connectivity during Set-Shifting in Children with Autism

Autism spectrum disorder (ASD) is a characterized by deficits in social cognition and executive function. An area of particular difficulty for children with ASD is cognitive flexibility, such as the ability to shift between attentional or response sets. The biological basis of such deficits remains poorly understood, although atypical development of structural and functional brain connectivity have been reported in ASD, suggesting that disruptions of normal patterns of inter-regional communication may contribute to cognitive problems in this group. The present magnetoencephalography study measured inter-regional phase synchronization while children with ASD and typically developing matched controls (6–14 years of age) performed a set-shifting task. Reduced theta-band phase synchronization was observed in children with ASD during extradimensional set-shifting. This reduction in task-dependent inter-regional connectivity encompassed numerous areas including multiple frontal lobe regions, and indicates that problems with communication among brain areas may contribute to difficulties with executive function in ASD.

Connecting signaling pathways underlying communication to ASD vulnerability

Language is a human-specific trait that likely facilitated the rapid increase in higher cognitive function in our species. A consequence of the selective pressures that have permitted language and cognition to flourish in humans is the unique vulnerability of humans to developing cognitive disorders such as autism. Therefore, progress in understanding the genetic and molecular mechanisms of language evolution should provide insight into such disorders. Here, we discuss the few genes that have been identified in both autism-related pathways and language. We also detail the use of animal models to uncover the function of these genes at a mechanistic and circuit level. Finally, we present the use of comparative genomics to identify novel genes and gene networks involved in autism. Together, all of these approaches will allow for a broader and deeper view of the molecular brain mechanisms involved in the evolution of language and the gene disruptions associated with autism.