Neurodevelopmental sequelae associated with gray and white matter changes and their cellular basis: A comparison between Autism Spectrum Disorder, ADHD and dyslexia

Biallelic ELMO3 mutations and loss of function for DOCK-mediated RAC1 activation result in intellectual disability

The engulfment and cell motility 3 (ELMO3) protein belongs to the ELMO-family of proteins. ELMO proteins form a tight complex with the DOCK1-5 guanine nucleotide exchange factors that regulate RAC1 spatiotemporal activation and signalling. DOCK proteins and RAC1 are known to have fundamental roles in central nervous system development. Here, we searched for homozygous or compound heterozygous mutations in the ELMO3 gene in 390 whole exomes sequenced in trio in individuals with neurodevelopmental disorders compatible with a genetic origin. We found a compound heterozygous mutation in ELMO3 (c.1153A>T, p.Ser385Cys and c.1009 G > A, p.Val337Ile) in a 5 year old male child with autism spectrum disorder (ASD) and developmental delay. These mutations did not interfere with the formation of an ELMO3/DOCK1 complex, but markedly impaired the ability of the complex to promote RAC1-GTP-loading. Consequently, cells expressing DOCK1 and either of the ELMO3 mutants displayed impaired migration and invasion. Collectively, our results suggest that biallelic loss-of-function mutations in ELMO3 may cause a developmental delay and provide new insight into the role of ELMO3 in neurodevelopmental as well as the pathological consequences of ELMO3 mutations.

Air Pollution-Related Brain Metal Dyshomeostasis as a Potential Risk Factor for Neurodevelopmental Disorders and Neurodegenerative Diseases

Increasing evidence links air pollution (AP) exposure to effects on the central nervous system structure and function. Particulate matter AP, especially the ultrafine (nanoparticle) components, can carry numerous metal and trace element contaminants that can reach the brain in utero and after birth. Excess brain exposure to either essential or non-essential elements can result in brain dyshomeostasis, which has been implicated in both neurodevelopmental disorders (NDDs; autism spectrum disorder, schizophrenia, and attention deficit hyperactivity disorder) and neurodegenerative diseases (NDGDs; Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis). This review summarizes the current understanding of the extent to which the inhalational or intranasal instillation of metals reproduces in vivo the shared features of NDDs and NDGDs, including enlarged lateral ventricles, alterations in myelination, glutamatergic dysfunction, neuronal cell death, inflammation, microglial activation, oxidative stress, mitochondrial dysfunction, altered social behaviors, cognitive dysfunction, and impulsivity. Although evidence is limited to date, neuronal cell death, oxidative stress, and mitochondrial dysfunction are reproduced by numerous metals. Understanding the specific contribution of metals/trace elements to this neurotoxicity can guide the development of more realistic animal exposure models of human AP exposure and consequently lead to a more meaningful approach to mechanistic studies, potential intervention strategies, and regulatory requirements.

Relationship between white matter glucose metabolism and fractional anisotropy in healthy and schizophrenia subjects

Decreased fractional anisotropy and increased glucose utilization in the white matter have been reported in schizophrenia. These findings may be indicative of an inverse relationship between these measures of white matter integrity and metabolism. We used ¹⁸F-fluorodeoxyglucose positron emission tomography and diffusion-tensor imaging in 19 healthy and 25 schizophrenia subjects to assess and compare coterritorial correlation patterns between glucose utilization and fractional anisotropy on a voxel-by-voxel basis and across a range of automatically placed representative white matter regions of interest. We found a pattern of predominantly negative correlations between white matter metabolism and fractional anisotropy in both healthy and schizophrenia subjects. The overall strength of the relationship was attenuated in subjects with schizophrenia, who displayed significantly fewer and weaker correlations in all regions assessed with the exception of the corpus callosum. This attenuation was most prominent in the left prefrontal white matter and this region also best predicted the diagnosis of schizophrenia. There exists an inverse relationship between the measures of white matter integrity and metabolism, which may therefore be physiologically linked. In subjects with schizophrenia, hypermetabolism in the white matter may be a function of lower white matter integrity, with lower efficiency and increased energetic cost of task-related computations.

Neurodevelopmental Outcomes of Prenatal Preeclampsia Exposure

Preeclampsia is a dangerous hypertensive disorder of pregnancy with known links to negative child health outcomes. Here, we review epidemiological and basic neuroscience work from the past several decades linking prenatal preeclampsia to altered neurodevelopment. This work demonstrates increased rates of neuropsychiatric disorders [e.g., increased autism spectrum disorder, attention deficit hyperactivity disorder (ADHD)] in children of preeclamptic pregnancies, as well as increased rates of cognitive impairments [e.g., decreased intelligence quotient (IQ), academic performance] and neurological disease (e.g., stroke and epilepsy). We also review findings from multiple animal models of preeclampsia. Manipulation of key clinical preeclampsia processes in these models (e.g., placental hypoxia, immune dysfunction, angiogenesis, oxidative stress) causes various disruptions in offspring, including ones in white matter/glia, glucocorticoid receptors, neuroimmune outcomes, cerebrovascular structure, and cognition/behavior. This animal work implicates potentially high-yield targets that may be leveraged in the future for clinical application.

Oxytocin and Sensory Network Plasticity

An essential characteristic of nervous systems is their capacity to reshape functional connectivity in response to physiological and environmental cues. Endogenous signals, including neuropeptides, governs nervous system plasticity. Particularly, oxytocin has been recognized for its role in mediating activity-dependent circuit changes. These oxytocin-dependent changes occur at the synaptic level and consequently shape the cellular composition of circuits. Here we discuss recent advances that illustrate how oxytocin functions to reshape neural circuitry in response to environmental changes. Excitingly, recent findings pave the way for promising therapeutic applications of oxytocin to treat neurodevelopmental and neuropsychiatric diseases.

Increased white matter metabolic rates in autism spectrum disorder and schizophrenia

Both autism spectrum disorder (ASD) and schizophrenia are often characterized as disorders of white matter integrity. Multimodal investigations have reported elevated metabolic rates, cerebral perfusion and basal activity in various white matter regions in schizophrenia, but none of these functions has previously been studied in ASD. We used ¹⁸fluorodeoxyglucose positron emission tomography to compare white matter metabolic rates in subjects with ASD (n = 25) to those with schizophrenia (n = 41) and healthy controls (n = 55) across a wide range of stereotaxically placed regions-of-interest. Both subjects with ASD and schizophrenia showed increased metabolic rates across the white matter regions assessed, including internal capsule, corpus callosum, and white matter in the frontal and temporal lobes. These increases were more pronounced, more widespread and more asymmetrical in subjects with ASD than in those with schizophrenia. The highest metabolic increases in both disorders were seen in the prefrontal white matter and anterior limb of the internal capsule. Compared to normal controls, differences in gray matter metabolism were less prominent and differences in adjacent white matter metabolism were more prominent in subjects with ASD than in those with schizophrenia. Autism spectrum disorder and schizophrenia are associated with heightened metabolic activity throughout the white matter. Unlike in the gray matter, the vector of white matter metabolic abnormalities appears to be similar in ASD and schizophrenia, may reflect inefficient functional connectivity with compensatory hypermetabolism, and may be a common feature of neurodevelopmental disorders.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Behavioral Phenotyping for Autism Spectrum Disorders in Mice

Autism spectrum disorder (ASD) represents a heterogeneous group of disorders characterized by alterations in three behavioral symptom domains: Social interactions, verbal and nonverbal communication, and repetitive behaviors. Increasing prevalence of ASD in recent years suggests that exposure to environmental toxicants may be critical in modulating etiology of this disease. As clinical diagnosis of autism still relies on behavioral evaluation, it is important to be able to assess similar behavioral traits in animal models, to provide biological plausibility of associations between environmental exposures and ASD. Rodents naturally exhibit a large number of behaviors that can be linked to similar behaviors in human. In this unit, behavioral tests are described that are relevant to the domains affected in ASD. For the repetitive domain, the T-maze spontaneous alternation test and marble burying test are described. For the communication domain, neonatal ultrasonic vocalization and olfactory habituation test toward social and non-social odor are described. Finally, for the sociability domain, the three-chambered social preference test and the reciprocal interaction test are presented. © 2017 by John Wiley & Sons, Inc.

Transient oxytocin signaling primes the development and function of excitatory hippocampal neurons

Beyond its role in parturition and lactation, oxytocin influences higher brain processes that control social behavior of mammals, and perturbed oxytocin signaling has been linked to the pathogenesis of several psychiatric disorders. However, it is still largely unknown how oxytocin exactly regulates neuronal function. We show that early, transient oxytocin exposure in vitro inhibits the development of hippocampal glutamatergic neurons, leading to reduced dendrite complexity, synapse density, and excitatory transmission, while sparing GABAergic neurons. Conversely, genetic elimination of oxytocin receptors increases the expression of protein components of excitatory synapses and excitatory synaptic transmission in vitro. In vivo, oxytocin-receptor-deficient hippocampal pyramidal neurons develop more complex dendrites, which leads to increased spine number and reduced γ-oscillations. These results indicate that oxytocin controls the development of hippocampal excitatory neurons and contributes to the maintenance of a physiological excitation/inhibition balance, whose disruption can cause neurobehavioral disturbances.

DOI:http://dx.doi.org/10.7554/eLife.22466.001

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.

SLC9A9 Co-expression modules in autism-associated brain regions

SLC9A9 is a sodium hydrogen exchanger present in the recycling endosome and highly expressed in the brain. It is implicated in neuropsychiatric disorders, including autism spectrum disorders (ASDs). Little research concerning its gene expression patterns and biological pathways has been conducted. We sought to investigate its possible biological roles in autism-associated brain regions throughout development. We conducted a weighted gene co-expression network analysis on RNA-seq data downloaded from Brainspan. We compared prenatal and postnatal gene expression networks for three ASD-associated brain regions known to have high SLC9A9 gene expression. We also performed an ASD-associated single nucleotide polymorphism enrichment analysis and a cell signature enrichment analysis. The modules showed differences in gene constituents (membership), gene number, and connectivity throughout time. SLC9A9 was highly associated with immune system functions, metabolism, apoptosis, endocytosis, and signaling cascades. Gene list comparison with co-immunoprecipitation data was significant for multiple modules. We found a disproportionately high autism risk signal among genes constituting the prenatal hippocampal module. The modules were enriched with astrocyte and oligodendrocyte markers. SLC9A9 is potentially involved in the pathophysiology of ASDs. Our investigation confirmed proposed functions for SLC9A9, such as endocytosis and immune regulation, while also revealing potential roles in mTOR signaling and cell survival.. By providing a concise molecular map and interactions, evidence of cell type and implicated brain regions we hope this will guide future research on SLC9A9. Autism Res 2017, 10: 414-429. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Sleep Spindle Characteristics in Children with Neurodevelopmental Disorders and Their Relation to Cognition

Empirical evidence indicates that sleep spindles facilitate neuroplasticity and “off-line” processing during sleep, which supports learning, memory consolidation, and intellectual performance. Children with neurodevelopmental disorders (NDDs) exhibit characteristics that may increase both the risk for and vulnerability to abnormal spindle generation. Despite the high prevalence of sleep problems and cognitive deficits in children with NDD, only a few studies have examined the putative association between spindle characteristics and cognitive function. This paper reviews the literature regarding sleep spindle characteristics in children with NDD and their relation to cognition in light of what is known in typically developing children and based on the available evidence regarding children with NDD. We integrate available data, identify gaps in understanding, and recommend future research directions. Collectively, studies are limited by small sample sizes, heterogeneous populations with multiple comorbidities, and nonstandardized methods for collecting and analyzing findings. These limitations notwithstanding, the evidence suggests that future studies should examine associations between sleep spindle characteristics and cognitive function in children with and without NDD, and preliminary findings raise the intriguing question of whether enhancement or manipulation of sleep spindles could improve sleep-dependent memory and other aspects of cognitive function in this population.