Investigating the Microstructural Correlation of White Matter in Autism Spectrum Disorder

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

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

Tract- and gray matter- based spatial statistics show white matter and gray matter microstructural differences in autistic males

Background

Autism spectrum disorder (ASD) is a neurodevelopmental condition commonly studied in the context of early childhood. As ASD is a life-long condition, understanding the characteristics of brain microstructure from adolescence into adulthood and associations to clinical features is critical for improving outcomes across the lifespan. In the current work, we utilized Tract Based Spatial Statistics (TBSS) and Gray Matter Based Spatial Statistics (GBSS) to examine the white matter (WM) and gray matter (GM) microstructure in neurotypical (NT) and autistic males.

Methods

Multi-shell diffusion MRI was acquired from 78 autistic and 81 NT males (12-to-46-years) and fit to the DTI and NODDI diffusion models. TBSS and GBSS were performed to analyze WM and GM microstructure, respectively. General linear models were used to investigate group and age-related group differences. Within the ASD group, relationships between WM and GM microstructure and measures of autistic symptoms were investigated.

Results

All dMRI measures were significantly associated with age across WM and GM. Significant group differences were observed across WM and GM. No significant age-by-group interactions were detected. Within the ASD group, positive relationships with WM microstructure were observed with ADOS-2 Calibrated Severity Scores.

Conclusion

Using TBSS and GBSS our findings provide new insights into group differences of WM and GM microstructure in autistic males from adolescence into adulthood. Detection of microstructural differences across the lifespan as well as their relationship to the level of autistic symptoms will deepen to our understanding of brain-behavior relationships of ASD and may aid in the improvement of intervention options for autistic adults.

Sustained release of sulforaphane by bioactive extracellular vesicles for neuroprotective effect on chick model

Novel studies have shown neurological treatment possibilities with extracellular vesicles (EVs) as natural particles with a special composition that are produced by different cell types. Their stability, natural structure, composition, and bioavailability make them good candidates as drug vehicles. Here, EVs were isolated from amniotic fluid (AF) through differential centrifugation, and characterized for size (<200 nm), structure, and composition, their effectiveness on the human PC12 cell line, and brain of chick embryos exposed to sodium valproate (animal autistic model). Sulforaphane (SFN) was employed as a bioactive compound and then encapsulated into Evs using three methods including passive (incubation), active (sonication), and active-passive (sonication-incubation). Further, the loading and in vitro releases of SFN fitted the Korsmeyer-Peppas (R²  = 0.99) kinetic model by non-Fickian diffusion case II (n = 0.44, passive loading) and Fickian diffusion case I (n = 0.41, active and active-passive loading). SFN-loaded EVs (SFN@EVs; 11 μM: 103 nM) stimulated hPC-12 cell proliferation. The gene expression analysis revealed that SFN@EVs could upregulate Nrf2 and reduce IL-6 expression. Eventually, histopathological results of the coronal cross-section of the chick embryos brain showed treatment with SFN@EVs. This treatment illustrated normality in the gray and white matter and the orientation of the bipolar neurons. Our findings showed EVs' potentially acting as a gene expression regulator in autism spectrum disorder.

Transcriptomic analysis of isolated and pooled human postmortem cerebellar Purkinje cells in autism spectrum disorders

At present, the neuronal mechanisms underlying the diagnosis of autism spectrum disorder (ASD) have not been established. However, studies from human postmortem ASD brains have consistently revealed disruptions in cerebellar circuitry, specifically reductions in Purkinje cell (PC) number and size. Alterations in cerebellar circuitry would have important implications for information processing within the cerebellum and affect a wide range of human motor and non-motor behaviors. Laser capture microdissection was performed to obtain pure PC populations from a cohort of postmortem control and ASD cases and transcriptional profiles were compared. The 427 differentially expressed genes were enriched for gene ontology biological processes related to developmental organization/connectivity, extracellular matrix organization, calcium ion response, immune function and PC signaling alterations. Given the complexity of PCs and their far-ranging roles in response to sensory stimuli and motor function regulation, understanding transcriptional differences in this subset of cerebellar cells in ASD may inform on convergent pathways that impact neuronal function.

Placental transcriptional signatures associated with cerebral white matter damage in the neonate

Cerebral white matter is the most common anatomic location of neonatal brain injury in preterm newborns. Factors that predispose preterm newborns to white matter damage are understudied. In relation to studies of the placenta-brain-axis, dysregulated placental gene expression may play a role in preterm brain damage given its implication in programming early life origins of disease, including neurological disorders. There is a critical need to investigate the relationships between the placental transcriptome and white matter damage in the neonate. In a cohort of extremely low gestational age newborns (ELGANs), we aimed to investigate the relationship between the placental transcriptome and white matter damage as assessed by neonatal cranial ultrasound studies (echolucency and/or ventriculomegaly). We hypothesized that genes involved in inflammatory processes would be more highly expressed in placentas of ELGANs who developed ultrasound-defined indicators of white matter damage. Relative to either form of white matter damage, 659 placental genes displayed altered transcriptional profiles. Of these white matter damage-associated genes, largely distinct patterns of gene expression were observed in the study (n = 415/659 genes). Specifically, 381 genes were unique to echolucency and 34 genes were unique to ventriculomegaly. Pathways involved in hormone disruption and metabolism were identified among the unique echolucency or ventriculomegaly genes. Interestingly, a common set of 244 genes or 37% of all genes was similarly dysregulated in the placenta relative to both echolucency and ventriculomegaly. For this common set of white matter damage-related genes, pathways involved in inflammation, immune response and apoptosis, were enriched. Among the white matter damage-associated genes are genes known to be involved in Autism Spectrum Disorder (ASD) and endocrine system disorders. These data highlight differential mRNA expression patterning in the placenta and provide insight into potential etiologic factors that may predispose preterm newborns to white matter damage. Future studies will build upon this work to include functional measures of neurodevelopment as well as measures of brain volume later in life.

Diffusion tensor imaging of fetal brain: principles, potential and limitations of promising technique

Human brain development is a complex process that begins in the third week of gestation. During early development, the fetal brain undergoes dynamic morphological changes. These changes result from events such as neurogenesis, neuronal migration, synapse formation, axonal growth and myelination. Disruption of any of these processes is thought to be responsible for a wide array of different pathologies. Recent advances in magnetic resonance imaging, especially diffusion-weighted imaging and diffusion tensor imaging (DTI), have enabled characterization and evaluation of brain development in utero. In this review, aimed at practitioners involved in fetal medicine and high-risk pregnancies, we provide a comprehensive overview of fetal DTI studies focusing on characterization of early normal brain development as well as evaluation of brain pathology in utero. We also discuss the reliability and limitations of fetal brain DTI. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Longitudinal assessment of early-life white matter development with quantitative relaxometry in nonhuman primates

Alterations in white matter (WM) development are associated with many neuropsychiatric and neurodevelopmental disorders. Most MRI studies examining WM development employ diffusion tensor imaging (DTI), which relies on estimating diffusion patterns of water molecules as a reflection of WM microstructure. Quantitative relaxometry, an alternative method for characterizing WM microstructural changes, is based on molecular interactions associated with the magnetic relaxation of protons. In a longitudinal study of 34 infant non-human primates (NHP) (Macaca mulatta) across the first year of life, we implement a novel, high-resolution, T1-weighted MPnRAGE sequence to examine WM trajectories of the longitudinal relaxation rate (qR1) in relation to DTI metrics and gestational age at scan. To the best of our knowledge, this is the first study to assess developmental WM trajectories in NHPs using quantitative relaxometry and the first to directly compare DTI and relaxometry metrics during infancy. We demonstrate that qR1 exhibits robust logarithmic growth, unfolding in a posterior-anterior and medial-lateral fashion, similar to DTI metrics. On a within-subject level, DTI metrics and qR1 are highly correlated, but are largely unrelated on a between-subject level. Unlike DTI metrics, gestational age at birth (time in utero) is a strong predictor of early postnatal qR1 levels. Whereas individual differences in DTI metrics are maintained across the first year of life, this is not the case for qR1. These results point to the similarities and differences in using quantitative relaxometry and DTI in developmental studies, providing a basis for future studies to characterize the unique processes that these measures reflect at the cellular and molecular level.

Immune Dysregulation in Autism Spectrum Disorder: What Do We Know about It?

Autism spectrum disorder (ASD) is a group of complex multifactorial neurodevelopmental disorders characterized by a wide and variable set of neuropsychiatric symptoms, including deficits in social communication, narrow and restricted interests, and repetitive behavior. The immune hypothesis is considered to be a major factor contributing to autism pathogenesis, as well as a way to explain the differences of the clinical phenotypes and comorbidities influencing disease course and severity. Evidence highlights a link between immune dysfunction and behavioral traits in autism from several types of evidence found in both cerebrospinal fluid and peripheral blood and their utility to identify autistic subgroups with specific immunophenotypes; underlying behavioral symptoms are also shown. This review summarizes current insights into immune dysfunction in ASD, with particular reference to the impact of immunological factors related to the maternal influence of autism development; comorbidities influencing autism disease course and severity; and others factors with particular relevance, including obesity. Finally, we described main elements of similarities between immunopathology overlapping neurodevelopmental and neurodegenerative disorders, taking as examples autism and Parkinson Disease, respectively.

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

BACKGROUND

Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking.

METHODS

Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants.

RESULTS

LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value.

CONCLUSION

These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

Third-trimester in utero fetal brain diffusion tensor imaging fiber tractography: a prospective longitudinal characterization of normal white matter tract development

BACKGROUND

White matter is responsible for inter-neuronal connections throughout the brain that are a driving force in cognitive development. Diffusion tensor imaging (DTI) fiber tractography has been used to evaluate white matter development in the fetal brain; however, longitudinal studies of DTI fiber tractography to assess white matter development in the third trimester are lacking.

OBJECTIVE

To characterize in utero longitudinal changes in the fetal brain DTI fiber tracts of normal third-trimester fetuses.

MATERIALS AND METHODS

For this single-center prospective longitudinal observational pilot study, we recruited 28 pregnant females with normal third-trimester pregnancies who had routine prenatal ultrasound. MRI of the in utero fetal brain was performed with a Siemens 1.5-tesla (T) Espree scanner at 31 weeks, 33 weeks and 36 weeks of gestation, with 14 DTI tractography parameters quantified in 7 brain regions using DTI-studio version 2.4 (Johns Hopkins University, Baltimore, MD; n=98 measurements). We used multilevel mixed models to examine the relationship between longitudinal changes in DTI measurements and between 98 DTI measurements at 31 weeks and 4 routine fetal brain anatomical biometrics (n=392 assessments).

RESULTS

We observed statistically significant decreases in radial diffusivity and apparent diffusion coefficient in 13 of 14 brain regions from 31 weeks to 36 weeks of gestation (P<0.001 for all regions except the genu of the corpus callosum). Significant decreases in radial diffusivity from weeks 33 to 36 and weeks 31 to 36 were seen in the corticospinal tracts, centrum semiovale, posterior limb of the internal capsule, and crus cerebri (P<0.001 for all). When considering all possible combinations of DTI fiber tract measurements and the routine morphological fetal brain biometrics, only 6% (24/392) had a significant association (P<0.05), indicating relative independence of the DTI fiber tract measurements from anatomical biometrics.

CONCLUSION

In utero longitudinal changes in fetal brain DTI fiber tractography are quantifiable in normal third-trimester fetuses and are largely independent of morphological brain changes.

White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.

Neuroinflammation in preterm babies and autism spectrum disorders

Genetic anomalies have a role in autism spectrum disorders (ASD). Each genetic factor is responsible for a small fraction of cases. Environment factors, like preterm delivery, have an important role in ASD. Preterm infants have a 10-fold higher risk of developing ASD. Preterm birth is often associated with maternal/fetal inflammation, leading to a fetal/neonatal inflammatory syndrome. There are demonstrated experimental links between fetal inflammation and the later development of behavioral symptoms consistent with ASD. Preterm infants have deficits in connectivity. Most ASD genes encode synaptic proteins, suggesting that ASD are connectivity pathologies. Microglia are essential for normal synaptogenesis. Microglia are diverted from homeostatic functions towards inflammatory phenotypes during perinatal inflammation, impairing synaptogenesis. Preterm infants with ASD have a different phenotype from term born peers. Our original hypothesis is that exposure to inflammation in preterm infants, combined with at risk genetic background, deregulates brain development leading to ASD.

Elevated protein concentrations in newborn blood and the risks of autism spectrum disorder, and of social impairment, at age 10 years among infants born before the 28th week of gestation

Among the 1 of 10 children who are born preterm annually in the United States, 6% are born before the third trimester. Among children who survive birth before the 28th week of gestation, the risks of autism spectrum disorder (ASD) and non-autistic social impairment are severalfold higher than in the general population. We examined the relationship between top quartile inflammation-related protein concentrations among children born extremely preterm and ASD or, separately, a high score on the Social Responsiveness Scale (SRS total score ≥65) among those who did not meet ASD criteria, using information only from the subset of children whose DAS-II verbal or non-verbal IQ was ≥70, who were assessed for ASD, and who had proteins measured in blood collected on ≥2 days (N = 763). ASD (N = 36) assessed at age 10 years is associated with recurrent top quartile concentrations of inflammation-related proteins during the first post-natal month (e.g., SAA odds ratio (OR); 95% confidence interval (CI): 2.5; 1.2-5.3) and IL-6 (OR; 95% CI: 2.6; 1.03-6.4)). Top quartile concentrations of neurotrophic proteins appear to moderate the increased risk of ASD associated with repeated top quartile concentrations of inflammation-related proteins. High (top quartile) concentrations of SAA are associated with elevated risk of ASD (2.8; 1.2-6.7) when Ang-1 concentrations are below the top quartile, but not when Ang-1 concentrations are high (1.3; 0.3-5.8). Similarly, high concentrations of TNF-α are associated with heightened risk of SRS-defined social impairment (N = 130) (2.0; 1.1-3.8) when ANG-1 concentrations are not high, but not when ANG-1 concentrations are elevated (0.5; 0.1-4.2).

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.

Longitudinal development of thalamic and internal capsule microstructure in autism spectrum disorder

The thalamus is a key sensorimotor relay area that is implicated in autism spectrum disorder (ASD). However, it is unknown how the thalamus and white-matter structures that contain thalamo-cortical fiber connections (e.g., the internal capsule) develop from childhood into adulthood and whether this microstructure relates to basic motor challenges in ASD. We used diffusion weighted imaging in a cohort-sequential design to assess longitudinal development of the thalamus, and posterior- and anterior-limbs of the internal capsule (PLIC and ALIC, respectively) in 89 males with ASD and 56 males with typical development (3-41 years; all verbal). Our results showed that the group with ASD exhibited different developmental trajectories of microstructure in all regions, demonstrating childhood group differences that appeared to approach and, in some cases, surpass the typically developing group in adolescence and adulthood. The PLIC (but not ALIC nor thalamus) mediated the relation between age and finger-tapping speed in both groups. Yet, the gap in finger-tapping speed appeared to widen at the same time that the between-group gap in the PLIC appeared to narrow. Overall, these results suggest that childhood group differences in microstructure of the thalamus and PLIC become less robust in adolescence and adulthood. Further, finger-tapping speed appears to be mediated by the PLIC in both groups, but group differences in motor speed that widen during adolescence and adulthood suggest that factors beyond the microstructure of the thalamus and internal capsule may contribute to atypical motor profiles in ASD. Autism Res 2018, 11: 450-462. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

Microstructure of the thalamus, a key sensory and motor brain area, appears to develop differently in individuals with autism spectrum disorder (ASD). Microstructure is important because it informs us of the density and organization of different brain tissues. During childhood, thalamic microstructure was distinct in the ASD group compared to the typically developing group. However, these group differences appeared to narrow with age, suggesting that the thalamus continues to dynamically change in ASD into adulthood.

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.