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Abstract

Purpose of review: Many studies have reported that individuals with autism spectrum disorder (ASD) have different brain connectivity patterns compared with typically developing individuals. However, the results of more recent studies do not unanimously support the traditional view in which individuals with ASD have lower connectivity between distant brain regions and increased connectivity within local brain regions. In this review, we discuss different methods for measuring brain connectivity and how the use of different metrics may contribute to the lack of convergence of investigations of connectivity in ASD. Recent findings: The discrepancy in brain connectivity results across studies may be due to important methodological factors, such as the connectivity measure applied, the age of patients studied, the brain region(s) examined, and the time interval and frequency band(s) in which connectivity was analyzed. Summary: We conclude that more sophisticated electroencephalography analytic approaches should be utilized to more accurately infer causation and directionality of information transfer between brain regions, which may show dynamic changes of functional connectivity in the brain. Moreover, further investigations of connectivity with respect to behavior and clinical phenotype are needed to probe underlying brain networks implicated in core deficits of ASD.

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... Functional connectivity and graph theoretic analyses were performed with the BioNeCt Toolbox, a custom MATLAB toolbox for brain connectivity analysis (Casanova, El-Baz, & Suri, 2017). Functional brain connectivity between brain regions were estimated by computing the cross-correlation in the frequency domain between EEG signals (Bowyer, 2016;Mohammad-Rezazadeh et al., 2016;Nolte et al., 2004). Among the various metrics of functional connectivity, imaginary coherence (iCoh) exclusively detects 'true' interactions between EEG signals occurring within a certain time delay, thus ignoring instantaneous interaction between neighboring electrodes likely produced by volume conduction of electrical activity from a common brain source (Bullmore & Sporns, 2009;Fallani et al., 2014;Mohammad-Rezazadeh et al., 2016). ...
... Functional brain connectivity between brain regions were estimated by computing the cross-correlation in the frequency domain between EEG signals (Bowyer, 2016;Mohammad-Rezazadeh et al., 2016;Nolte et al., 2004). Among the various metrics of functional connectivity, imaginary coherence (iCoh) exclusively detects 'true' interactions between EEG signals occurring within a certain time delay, thus ignoring instantaneous interaction between neighboring electrodes likely produced by volume conduction of electrical activity from a common brain source (Bullmore & Sporns, 2009;Fallani et al., 2014;Mohammad-Rezazadeh et al., 2016). iCoh values were computed for all possible electrode pairs in the following frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), spindle (12-15 Hz), beta (16-30 Hz), and low gamma (40-50 Hz). ...
... The high multi-dimensionality of the iCoh measures was disentangled with a graph-theoretic approach. Graph theoretic metrics provide information on the degree of network segregation (i.e., the tendency of brain regions to form local clusters with dense functional interconnections) and network integration and efficiency (i.e., the capacity of the network to become interconnected and efficiently exchange information between brain regions; Bullmore & Sporns, 2009;Fallani et al., 2014;Mohammad-Rezazadeh et al., 2016). The following commonly-used graph measures were calculated for all of the above mentioned frequency bands in the pre-and post-stimulation periods: Average Clustering Coefficient (the probability of neighboring nodes being connected to each other, reflecting local connectedness); Global Efficiency (how efficient the network is in transferring information); characteristic Path Length, Radius, and Diameter (the average number of edges along the shortest paths, the minimum possible distance, and the largest possible distance, between all possible pairs of nodes, respectively); Modularity (the degree to which the brain network is segregated into subnetworks or modules); Assortativity (the proportion of nodes that are attached to other nodes with similar degrees vs. dissimilar degrees); Density (the number of edges divided by the number of nodes in the graph); and Mean Coherence (the average coherence between all pairs of nodes). ...
Article
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Meta-memory involves the ability to correctly judge the accuracy of our memories. The retrieval of memories can be improved using transcranial electrical brain stimulation (tES) during sleep, but evidence for improvements to meta-memory sensitivity is limited. Applying tES can enhance sleepdependent memory consolidation, which along with meta-memory requires the coordination of activity across distributed neural systems, suggesting that examining functional connectivity is important for understanding these processes. Nevertheless, little research has examined how functional connectivity modulations relate to overnight changes in meta-memory sensitivity. Here, we developed a closed-loop short-duration tES method, time-locked to up-states of ongoing slow-wave oscillations, to cue specific memory replays in humans. We measured electroencephalographic (EEG) coherence changes following stimulation pulses, and characterized network alterations with graph theoretic metrics. Using machine learning techniques, we show that pulsed tES elicited network changes in multiple frequency bands, including increased connectivity in the Theta band and increased efficiency in the Spindle band. Additionally, stimulation-induced changes in Beta band path length were predictive of overnight changes in meta-memory sensitivity. These findings add new insights into the growing literature investigating increases in memory performance through brain stimulation during sleep, and highlight the importance of examining functional connectivity to explain its effects.
... ASDs seem to be the result of an imbalance between excitatory and inhibitory synapses and of a dysfunction in the synaptic plasticity of the brain, which leads to a disrupted connectivity during the brain development [21][22][23][24][25]. Cholesterol homeostasis was demonstrated to be involved in correct synaptic functioning and consequently this molecule was hypothesized to play a role in the etiology of ASDs [26,27]. ...
... The most accredited hypothesis claims that there is a correspondence between low plasma cholesterol levels and its reduction in the central nervous system, with a consequent alteration in the lipid constitution of neuron membranes [52]. This abnormality would lead to an imbalance of excitatory and inhibitory synapses and to a dysfunction in the synaptic plasticity of the brain [22,24,74]. Another interesting hypothesis is that the alteration of cholesterol and its metabolites, such as OHC, can be involved in the pathogenesis of ASDs by inducing oxidative stress and consequent glutamate toxicity at neuronal level [75]. ...
... Another interesting hypothesis is that the alteration of cholesterol and its metabolites, such as OHC, can be involved in the pathogenesis of ASDs by inducing oxidative stress and consequent glutamate toxicity at neuronal level [75]. The consequence would be a dysfunctional brain connectivity, an altered neurodevelopment and the onset of ASDs [24]. It should be also emphasized that the role of cholesterol in psychiatric disorders has not been fully understood and that the association with its plasma levels varies according to the disorder or psychiatric symptoms. ...
Article
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Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders whose pathogenesis seems to be related to an imbalance of excitatory and inhibitory synapses, which leads to disrupted connectivity during brain development. Among the various biomarkers that have been evaluated in the last years, metabolic factors represent a bridge between genetic vulnerability and environmental aspects. In particular, cholesterol homeostasis and circulating fatty acids seem to be involved in the pathogenesis of ASDs, both through the contribute in the stabilization of cell membranes and the modulation of inflammatory factors. The purpose of the present review is to summarize the available data about the role of cholesterol and fatty acids, mainly long-chain ones, in the onset of ASDs. A bibliographic research on the main databases was performed and 36 studies were included in our review. Most of the studies document a correlation between ASDs and hypocholesterolemia, while the results concerning circulating fatty acids are less univocal. Even though further studies are necessary to confirm the available data, the metabolic biomarkers open to new treatment options such as the modulation of the lipid pattern through the diet.
... Recently, with the rise of DL, interesting alternatives have appeared and new generative DL-based models were proposed to obtain synthetic data with characteristics spanning the original data manifold [10]. Therefore, in this study we refer to generative models as a subclass of DL frameworks able to generate complex data data structure, including the recent modeling approach used to characterize brain networks by means of graph theory [11,12,13]. Given the great capability of graphs to represent complex relations among different areas of the brain, such relational data structure started to be widely employed in many contexts, including social behavioral studies. ...
... Classical approaches, like ROS and the more efficient SMOTE [26] are used for comparisons as well as the more recent adversarial model ARAE [27]. The performance metrics used for the evaluation are 1 , Precision and Accuracy which are defined in Eq. (11,12,13) respectively. ...
Article
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Background and objective:Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced. To overcome this challenge, generative models can be used to generate new data. Methods: In this study, a framework based on generative adversarial network is proposed to create synthetic structural brain networks in Multiple Sclerosis (MS). The dataset consists of 29 relapsing-remitting and 19 secondary-progressive MS patients. T1 and diffusion tensor imaging (DTI) acquisitions were used to obtain the structural brain network for each subject. Evaluation of the quality of newly generated brain networks is performed by (i) analysing their structural properties and (ii) studying their impact on classification performance. Results: We demonstrate that advanced generative models could be directly applied to the structural brain networks. We quantitatively and qualitatively show that newly generated data do not present significant differences compared to the real ones. In addition, augmenting the existing dataset with generated samples leads to an improvement of the classification performance (F1score 81%) with respect to the baseline approach (F1score 66%). Conclusions: Our approach defines a new tool for biomedical application when connectome-based data augmentation is needed, providing a valid alternative to usual image-based data augmentation techniques.
... Furthermore, converging evidence suggests altered functional connectivity of the brain involving the PFC in both resting and task states in ASD, although it is still controversial whether these alterations should be best characterized as global underconnectivity and/or local overconnectivity (Hull et al., 2017;Just, Keller, Malave, Kana, & Varma, 2012;Mohammad-Rezazadeh, Frohlich, Loo, & Jeste, 2016). Since functional coupling between and within regions in the brain, especially the PFC, is important for the execution of WM tasks, altered connectivity of the brain may underlie the WM deficits exhibited by individuals with ASD. ...
... There is accumulated evidence that altered neural connectivity is a hallmark characteristic of ASD. While early studies conceptualized these alterations as local overconnectivity and global underconnectivity (Just et al., 2012), this view has been challenged by more recent evidence that individuals with ASD sometimes exhibit local underconnectivity (Mohammad-Rezazadeh et al., 2016). Our direct comparison between TD and high-functioning ASD children revealed that altered functional connectivity patterns in ASD are mediated by WM load within the right medial and lateral PFC, yet functional connectivity strength within these two ROIs were neither over-nor under-connected. ...
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Background Individuals with autism spectrum disorder (ASD) perform poorly in working memory (WM) tasks, with some literature suggesting that their impaired performance is modulated by WM load. While some neuroimaging and neurophysiological studies have reported altered functional connectivity during WM processing in these individuals, it remains largely unclear whether such alterations are moderated by WM load. The present study aimed to examine the effect of WM load on functional connectivity within the prefrontal cortex (PFC) in ASD using functional near-infrared spectroscopy (fNIRS). Method Twenty-two children with high-functioning ASD aged 8–12 years and 24 age-, intelligent quotient (IQ)-, sex- and handedness-matched typically developing (TD) children performed a number n-back task with three WM loads (0-back, 1-back, and 2-back). Hemodynamic changes in the bilateral lateral and medial PFC during task performance were monitored using a multichannel NIRS device. Results Children with ASD demonstrated slower reaction times, specifically during the “low load” condition, than TD children. In addition, the ASD and TD groups exhibited differential load-dependent functional connectivity changes in the lateral and medial PFC of the right but not the left hemisphere. Conclusion These findings indicate that WM impairment in high-functioning ASD is paralleled by load-dependent alterations in right, but not left, intrahemispheric connectivity during WM processing in children with ASD. A disruption of functional neural connections that support different cognitive processes may underlie poor performance in WM tasks in ASD.
... This image of brain connectivity can be used to quantify the relationships between functionally and physically linked objects as well as to predict changes in the brain's resting state. Although genetic profiles may reveal a predisposition to microscopic changes in brain structure, connectome mapping models the brain as a network of interconnected regions to display the macroscopic manifestations of these changes (Mohammad-Rezazadeh, Frohlich, Loo, & Jeste, 2016). ...
Chapter
Studies have indicated that data generated by healthcare industries can be harnessed utilizing artificial intelligence (AI) to improve medical practice such as diagnosis, evaluation, and treatment strategies. The neural network system can generate signals during physiological or pathophysiological mechanisms, which may be adapted for the prediction of complex neurological outcomes. The future direction of AI in clinical practice covers many disciplines such as physiology, histology, pathology, radiology, and neurology. Data from these areas can be utilized to generate high-resolution images and biomarkers for fast automated disease prediction, interpretation, and treatment strategies. Clinicians can have better decision-making options and more accurate diagnostic predictions when AI is incorporated into medical practice. Thus, this chapter provides a detailed account of advances made in the adoption of AI applications to enhance neurological techniques in clinical settings like medical imaging, translational bioinformatics, cheminformatics, pervasive sensing, public health, and medical informatics. The chapter also provides a critical analysis of potential pitfalls of these techniques.
... This image of brain connectivity can be used to quantify the relationships between functionally and physically linked objects, as well as to predict changes in the brain's resting state. Although genetic profiles may suggest a tendency to microscopic changes in brain structure, connectome mapping depicts the macroscopic manifestations of these changes by modeling the brain as a network of interconnected regions (Mohammad-Rezazadeh, Frohlich, Loo, & Jeste, 2016). Connectome mapping has been limited to a macroscopic level of brain wiring, with 1010 neurons interconnected by 1014 connections, due to current imaging and throughput capabilities. ...
Chapter
Several studies have been carried out that predict there will be a global surge in the number of patients with neurological diseases by 2050. Many of these neurological diseases such as Alzheimer's disease, acute spinal cord injury, amyotrophic lateral sclerosis, ataxia, Bell's palsy, brain tumors, cerebral aneurysm, epilepsy, and seizures are often diagnosed late resulting in several complications and irreversible effects. Recently, physiological signals, patient data, artificial intelligence and machine learning techniques, and medical images are being utilized for advanced signal processing and analysis in clinical decisions and diagnosis of neurological diseases. Several speech monitoring and recording devices are aimed at rapidly detecting and analyzing difficulties in speaking to enhance early diagnosis. These techniques are noninvasive approaches used for analysis and interpretation of complex pathways and biomarkers of neurological diseases. Therefore, application of smart algorithms could be utilized to diagnose, detect, and analyze early dysfunctions in a patient's neurological status. Thus, artificial intelligence-based, noninvasive approaches to speech analysis could serve as inexpensive, easy, and quick methods for overcoming the challenge of neurological diseases. This chapter describes the latest noninvasive techniques such as emotion recognition/intelligence, virtual environments, and behavioral analysis in the diagnosis, screening, evaluation, and early detection of common neurological diseases.
... Although there is overall consensus that FC in ASD is atypical, the direction of FC changes remains under debate (Mohammad-Rezazadeh et al., 2016). Some studies suggest underconnectivity (e.g., Cheng et al., 2017;Jao Keehn et al., 2021), some predominant overconnectivity (e.g., Cerliani et al., 2015;Supekar et al., 2013), some both under and overconnectivity (e.g., Lynch et al., 2013;Monk et al., 2009), and some have failed to detect differences (e.g., Nomi & Uddin, 2015;Tyszka et al., 2014). ...
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Autism spectrum disorder (ASD) is highly heterogeneous in etiology and clinical presentation. Findings on intrinsic functional connectivity (FC) or task-induced FC in ASD have been inconsistent including both over- and underconnectivity and diverse regional patterns. As FC patterns change across different cognitive demands, a novel and more comprehensive approach to network architecture in ASD is to examine the change in FC patterns between rest and task states, referred to as reconfiguration. This approach is suitable for investigating inefficient network connectivity that may underlie impaired behavioral functioning in clinical disorders. We used functional magnetic resonance imaging (fMRI) to examine FC reconfiguration during lexical processing, which is often affected in ASD, with additional focus on interindividual variability. Thirty adolescents with ASD and a matched group of 23 typically developing (TD) participants completed a lexicosemantic decision task during fMRI, using multiecho-multiband pulse sequences with advanced BOLD signal sensitivity and artifact removal. Regions of interest (ROIs) were selected based on task-related activation across both groups, and FC and reconfiguration were compared between groups. The ASD group showed increased interindividual variability and overall greater reconfiguration than the TD group. An ASD subgroup with typical performance accuracy (at the level of TD participants) showed reduced similarity and typicality of FC during the task. In this ASD subgroup, greater FC reconfiguration was associated with increased language skills. Findings suggest that intrinsic functional networks in ASD may be inefficiently organized for lexicosemantic decisions and may require greater reconfiguration during task processing, with high performance levels in some individuals being achieved through idiosyncratic mechanisms. Highlights FC reconfiguration is a comprehensive approach to examining network architecture Functional networks are inefficiently organized for lexicosemantic decisions in ASD ASD may require greater reconfiguration during task processing Some ASD individuals achieve high performance through idiosyncratic mechanisms
... The establishment of axon/dendrite polarity is a critical step in neuronal differentiation (Barnes and Polleux, 2009;Yogev and Shen, 2017). Neurodevelopmental disorders, including autism spectrum disorders, are characterized at cellular levels by abnormal establishment of neuronal connectivity during development (Gilbert and Man, 2017;Mohammad-Rezazadeh et al., 2016). Subcellular signaling, involving protein kinases, plays a significant role in the establishment and regulation of neuronal connectivity at synapses. ...
Article
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The small noncoding vault RNA (vtRNA) is a component of the vault complex, a ribonucleoprotein complex found in most eukaryotes. Emerging evidence suggests that vtRNAs may be involved in the regulation of a variety of cellular functions when unassociated with the vault complex. Here, we demonstrate a novel role for vtRNA in synaptogenesis. Using an in vitro synapse formation model, we show that murine vtRNA (mvtRNA) promotes synapse formation by modulating the MAPK signaling pathway. mvtRNA is transported to the distal region of neurites as part of the vault complex. Interestingly, mvtRNA is released from the vault complex in the neurite by a mitotic kinase Aurora-A–dependent phosphorylation of MVP, a major protein component of the vault complex. mvtRNA binds to and activates MEK1 and thereby enhances MEK1-mediated ERK activation in neurites. These results suggest the existence of a regulatory mechanism of the MAPK signaling pathway by vtRNAs as a new molecular basis for synapse formation.
... Establishment of axon/dendrite polarity is an important step in neuronal differentiation (Barnes and Polleux, 2009;Yogev and Shen, 2017). Autism spectrum disorders (ASD), a group of high-prevalence neurodevelopmental disorders, are known to share common cellular/molecular characteristics, including abnormal morphology of synaptic connections, which result in synaptic dysfunction (Mohammad-Rezazadeh et al., 2016). Intracellular signaling, including protein kinases, play a pivotal role in synapse formation and regulation. ...
Article
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Small non-coding vault RNAs (vtRNAs) have been described as a component of the vault complex, a hollow-and-barrel-shaped ribonucleoprotein complex found in most eukaryotes. It has been suggested that the function of vtRNAs might not be limited to simply maintaining the structure of the vault complex. Despite the increasing research on vtRNAs, little is known about their physiological functions. Recently, we have shown that murine vtRNA (mvtRNA) up-regulates synaptogenesis by activating the mitogen activated protein kinase (MAPK) signaling pathway. mvtRNA binds to and activates mitogen activated protein kinase 1 (MEK1), and thereby enhances MEK1-mediated extracellular signal-regulated kinase activation. Here, we introduce the regulatory mechanism of MAPK signaling in synaptogenesis by vtRNAs and discuss the possibility as a novel molecular basis for synapse formation.
... A critical aspect of early diagnosis is to identify the underlying biomarkers to assess the risk even before the emergence of atypical behavioral symptoms (Zwaigenbaum & Penner, 2018). Characterization of task-specific functional brain connectivity has been considered a promising approach for identifying ASD biomarker as it reveals the information exchange patterns among different areas in the brain (Mohammad-Rezazadeh et al., 2016). Functional brain connectivity is commonly measured by neuroimaging techniques such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI). ...
Article
Autism is a psychiatric condition that is typically diagnosed with behavioral assessment methods. Recent years have seen a rise in the number of children with autism. Since this could have serious health and socioeconomic consequences, it is imperative to investigate how to develop strategies for an early diagnosis that might pave the way to an adequate intervention. In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.
... Resting-state functional magnetic resonance imaging (rsfMRI) has been a widely used tool in assessing cofluctuations (or functional connectivity; FC) across brain regions in the absence of task-related cognitive demand 31,32 . The task-free nature of rsfMRI has several key benefits, including higher participant compliance and the ability to aggregate data across multiple sites, that enable its widespread use in investigating thalamocortical FC in individuals with ASD. ...
Article
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Alterations in sensorimotor functions are common in individuals with autism spectrum disorder (ASD). Such aberrations suggest the involvement of the thalamus due to its key role in modulating sensorimotor signaling in the cortex. Although previous research has linked atypical thalamocortical connectivity with ASD, investigations of this association in high-functioning adults with autism spectrum disorder (HFASD) are lacking. Here, for the first time, we investigated the resting-state functional connectivity of the thalamus, medial prefrontal, posterior cingulate, and left dorsolateral prefrontal cortices and its association with symptom severity in two matched cohorts of HFASD. The principal cohort consisted of 23 HFASD (mean[SD] 27.1[8.9] years, 39.1% female) and 20 age- and sex-matched typically developing controls (25.1[7.2] years, 30.0% female). The secondary cohort was a subset of the ABIDE database consisting of 58 HFASD (25.4[7.8] years, 37.9% female) and 51 typically developing controls (24.4[6.7] years, 39.2% female). Using seed-based connectivity analysis, between-group differences were revealed as hyperconnectivity in HFASD in the principal cohort between the right thalamus and bilateral precentral/postcentral gyri and between the right thalamus and the right superior parietal lobule. The former was associated with autism-spectrum quotient in a sex-specific manner, and was further validated in the secondary ABIDE cohort. Altogether, we present converging evidence for thalamocortical hyperconnectivity in HFASD that is associated with symptom severity. Our results fill an important knowledge gap regarding atypical thalamocortical connectivity in HFASD, previously only reported in younger cohorts.
... The fact that concatenating different features reaches a better accuracy than considering single features at once, hints to a disruption of a whole network instead of focal alterations in brain structure. This is in accordance to the view of ASD as a connectivity disorder, which seems to be confirmed by several studies employing functional MRI; current literature evidence suggests a broad disruption in local and diffuse connectivity between networks in ASD, resulting in both under-and overconnectivity patterns (Hull et al., 2017;Mohammad-Rezazadeh et al., 2016). It has also to be noted that the frontal regions seem to be crucial in the disorder: the frontal gyrus is both the region with the highest accuracy and selected by the greedy-forward feature selection process. ...
Article
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Objective Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. Methods A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a “learning by example” procedure; the features with best performance was then selected by “greedy forward-feature selection.” Finally, this model underwent a leave-one-out cross-validation approach. Results From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. Conclusion We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
... Reduced glucose uptake might parallel disturbed connectivity observed in patients with ASD. Individuals with ASD have mostly lower connectivity (or hypo-connectivity) between distant brain regions (such as the frontal and parietal lobes) and increased connectivity (or hyper-connectivity) between local brain regions (such as within the frontal lobe) compared with typically developing individuals 45 . Study of brain glucose metabolism by 18 F-FDG-PET and functional magnetic resonance imaging scans found in these subjects decreased metabolic rates of glucose metabolism in the parietal lobe, frontal premotor, and eye-fields areas, and amygdala; and increased in the posterior cingulate, occipital cortex, hippocampus, and basal ganglia, with metabolic abnormalities also in the social brain 46 . ...
Article
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The autism spectrum disorder (ASD) is an etiologically heterogeneous disorder. Dysfunctions of the intermediate metabolism have been described in some patients. We speculate these metabolic abnormalities are associated with brain insulin resistance (IR), i.e., the reduced glucose metabolism at the level of the nervous central system. The Homeostasis model assessment of insulin resistance (HOMA-IR) is very often used in population studies as estimate of peripheral IR and it has been recently recognized as proxy of brain IR. We investigated HOMA-IR in 60 ASD patients aged 4–18 years and 240 healthy controls, also aged 4–18 years, but unmatched for age, sex, body weight, or body mass index (BMI). At multivariable linear regression model, the HOMA-IR was 0.31 unit higher in ASD individuals than in controls, after having adjusted for sex, age, BMI z-score category, and lipids that are factors known to influence HOMA-IR. Findings of this preliminary study suggest it is worth investigating brain glucose metabolism in larger population of patients with ASD by using gold standard technique. The recognition of a reduced glucose metabolism in some areas of the brain as marker of autism might have tremendous impact on our understanding of the pathogenic mechanisms of the disease and in terms of public health.
... Relative alpha power was calculated by dividing the spectrum power of the alpha-band (8-13Hz) by the summation power of all four frequency bands (delta, theta, alpha, and beta bands). Coherence is defined by the synchronization in a fixed frequency band between two leads with the quantification of the extent to which they share a constant oscillating frequency and phase difference [7]. The coherence is a number between 0 to 1 and a higher number indicates more commonality between two channels in a frequency band. ...
... Three types of neural connectivity may be measured: structural, functional, and effective connectivity (Bowyer, 2016). Structural connectivity focuses on the physical fibre tracts that connect different parts of the brain; Functional connectivity identifies neural connections based on various frequencies, amplitudes, and phases (Bowyer, 2016;Mohammad-Rezazadeh et al., 2016;Sporns, 2014); and Effective connectivity builds on functional connectivity by classifying the influence and direction in which information is flowing between brain regions (Bowyer, 2016;Friston, 2011;Sporns, 2014). Because functional and effective connectivity are typically characterised by statistical dependencies (i.e., they may influence each other, leaving open the possibility of a measurement confound), they both require mathematical processes to evaluate linear and non-linear connectivity between cortical areas (Bronzino, 2000;Sporns, 2014). ...
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Investigation of the neurological underpinnings of the diagnostic symptoms for Autism Spectrum Disorder (ASD) represents a potential pathway towards a biomarker for this disorder. One of the key symptoms of ASD is Sensory Features (SF), which refers to the difficulties that autistic people experience with particular kinds of environmental stimuli. Studies using eeg measures of neural connectivity across various regions of the brain hold promise in identifying how the autistic brain reacts to its environment. This commentary identifies several ‘participant’ and ‘measurement’ methodological issues that need to be adequately addressed in SF-eeg connectivity studies, and applies these comments to a sample of five previous studies. Recommendations are made for future research procedures.
... Emerging neuroimaging findings in the last decade suggest alterations in neuroanatomical features and brain connectivity in ASD (Joshi et al., 2017). Structural connectivity is characterized by anatomical connections between brain regions through white matter tracts (Mohammad-Rezazadeh et al., 2016), whereas functional connectivity refers to the synchronization of neuronal activity across regions (Friston, 2011). The disrupted connectivity model, derived from both resting state and task-dependent functional MRI (Hull et al., 2017;Just et al., 2004;Kana and Just, 2012;Kana et al., 2011;Kana et al., 2006), postulates that individuals with ASD show stronger functional connectivity between brain regions that are closer together and weaker functional connectivity between regions that are farther apart (Kana et al., 2011;Maximo et al., 2014;Vasa et al., 2016). ...
Article
By examining how morphology of the corpus callosum (CC) in autism spectrum disorder (ASD) may affect functional communication across hemispheres, we hope to provide new insights into the structure-function relationship in the brain. We used a sample of 94 participants from the Autism Brain Imaging Data Exchange (ABIDE) database (55 typically-developing (TD) and 39 with ASD). The CC was segmented into five sub-regions (anterior, mid-anterior, central, mid-posterior, posterior) using FreeSurfer software, which were further examined for group differences. The total volume and specific sub-region volumes of the CC, and interhemispheric (homotopic) functional connectivity were calculated, along with the relationship between volume and connectivity. These measures were correlated with social ability assessed by the Social Responsiveness Scale (SRS). The central sub-region of CC was significantly smaller in ASD, although there was no group difference in total CC volume. ASD participants also showed stronger homotopic connectivity in the superior frontal gyrus. SRS scores were negatively correlated with the CC central sub-region volumes in ASD. The findings of this study add to the body of research showing morphological differences in the CC in ASD as well as connectivity differences. The absence of a significant relationship between structure and homotopic functional connectivity aligns with previous findings.
... Many of these findings are, however, controversial and have proven difficult to replicate (Haar et al. 2016;Lefebvre et al. 2015;Traut et al. 2018;Picci et al. 2016;Mohammad-Rezazadeh et al. 2016). Most studies have relied on sample sizes far too small to reach reliable conclusionssometimes just a few dozen subjects, and up to a few hundreds at most. ...
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MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 – far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.
... The establishment of axon/dendrite polarity is a critical step in neuronal differentiation [1,2]. Neurodevelopmental disorders, including autism spectrum disorders, are characterized at cellular levels by abnormal establishment of neuronal connectivity during development [3,4]. Subcellular signaling, involving protein kinases, plays a significant role in the establishment and regulation of neuronal connectivity at synapses. ...
Article
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The small non-coding vault RNA (vtRNA) is a component of the vault complex, a ribonucleoprotein complex found in most eukaryotes. vtRNAs regulate a variety of cellular functions when unassociated with the vault complex. Human has four vtRNA paralogs (hvtRNA1-1, hvtRNA1-2, hvtRNA1-3, hvtRNA2-1), which are highly similar and differ only slightly in primary and secondary structure. Despite the increasing research on vtRNAs, a feature that distinguishes one hvtRNA from the others has not been recognized. Recently, we demonstrated that murine vtRNA (mvtRNA) promotes synapse formation by modulating the MAPK signaling pathway. Here we showed that expression ofhvtRNA1-1, but not hvtRNA2-1 increases the expression of synaptic marker proteins, ERK phosphorylation and the number of PSD95 and Synapsin I double positive puncta to an extent similar to that of mvtRNA, suggesting that hvtRNA1-1 may enhance synapse formation. This finding opens new perspectives to uncover the function of the different vtRNA paralogs.
... Furthermore, converging evidence suggests altered functional connectivity of the brain involving the PFC in both resting and task states in ASD, although it is still controversial whether these alterations should be best characterized as global underconnectivity and/or local overconnectivity [29][30][31] . Since functional coupling between and within regions in the brain, especially the PFC, is important for the execution of WM tasks, altered connectivity of the brain may underlie the WM deficits exhibited by individuals with ASD. ...
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Individuals with autism spectrum disorder (ASD) perform poorly in working memory (WM) tasks, with some literature suggesting that their impaired performance is modulated by WM load. While some neuroimaging and neurophysiological studies have reported altered functional connectivity during WM processing in individuals with autism, it remains largely unclear whether such alterations are moderated by WM load. The present study aimed to examine the effect of WM load on functional connectivity within the prefrontal cortex (PFC) in ASD using functional near-infrared spectroscopy (fNIRS). Twenty-two children with high-functioning ASD aged 8–12 years and 24 age-, intelligent quotient (IQ)-, sex- and handedness-matched typically developing (TD) children performed a number n -back task with three WM loads (0-back, 1-back, and 2-back). Hemodynamic changes in the bilateral lateral and medial PFC during task performance were monitored using a multichannel NIRS device. Children with ASD demonstrated slower reaction times, specifically during the “low load” condition, than TD children. In addition, the ASD and TD groups exhibited differential load-dependent functional connectivity changes in the lateral and medial PFC of the right but not the left hemisphere. These findings indicate that WM impairment in high-functioning ASD is paralleled by load-dependent alterations in right, but not left, intrahemispheric connectivity during WM processing in children with ASD. A disruption of functional neural connections that support different cognitive processes may underlie poor performance in WM tasks in ASD.
... ASC is understood to be a whole brain disorder, whose neural correlates range from alterations in synapses and neural transmission to brain volume and structure. Alterations in functional connectivity have long been implicated in the pathophysiology of ASC since differences in functional connectivity have been found in an estimated 90% of studies (Lau et al., 2013;Chen et al., 2015;Mohammad-Rezazadeh et al., 2016) although the direction of this difference, implicated brain areas, brain state, and age are disputed (Müller et al., 2011;King et al., 2019). A dominant but challenged theory (Picci et al., 2016) is the ''Cortical Underconnectivity Theory'' which states that there is long range underconnectivity particularly between hemispheres and frontal-posterior brain areas in ASC individuals which may explain symptomatology (Just et al., 2012). ...
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Autism Spectrum Conditions (ASC) are a group of neurodevelopmental disorders characterized by deficits in social communication and interaction as well as repetitive behaviors and restricted range of interests. ASC are complex genetic disorders with moderate to high heritability, and associated with atypical patterns of neural connectivity. Many of the genes implicated in ASC are involved in dendritic spine pruning and spine development, both of which can be mediated by the mammalian target of rapamycin (mTOR) signaling pathway. Consistent with this idea, human postmortem studies have shown increased spine density in ASC compared to controls suggesting that the balance between autophagy and spinogenesis is altered in ASC. However, murine models of ASC have shown inconsistent results for spine morphology, which may underlie functional connectivity. This review seeks to establish the relevance of changes in dendritic spines in ASC using data gathered from rodent models. Using a literature survey, we identify 20 genes that are linked to dendritic spine pruning or development in rodents that are also strongly implicated in ASC in humans. Furthermore, we show that all 20 genes are linked to the mTOR pathway and propose that the mTOR pathway regulating spine dynamics is a potential mechanism underlying the ASC signaling pathway in ASC. We show here that the direction of change in spine density was mostly correlated to the upstream positive or negative regulation of the mTOR pathway and most rodent models of mutant mTOR regulators show increases in immature spines, based on morphological analyses. We further explore the idea that these mutations in these genes result in aberrant social behavior in rodent models that is due to these altered spine dynamics. This review should therefore pave the way for further research on the specific genes outlined, their effect on spine morphology or density with an emphasis on understanding the functional role of these changes in ASC.
... This study focused on EEG alpha activity during the process of generating creative ideas, which might limit the acquisition of results consistent with previous studies because time-related neural responses during the process of creative ideation are different (Ellamil et al., 2012;Schwab et al., 2014;Rominger et al., 2019). Second, although specific functional coupling may provide information about the interaction between different cortical regions during creative ideation, it is limited by the finite information it provides about nonlinear relationships, and volume conduction may lead to inaccurate results (Mohammad-Rezazadeh et al., 2016). The current study focused on the responses of frontal regions to strong inhibition. ...
Article
Everyday creativity is the basic ability of human survival and penetrates every aspect of life. Nevertheless, the neural mechanisms underlying everyday creativity was largely unexplored. In this study, seventy-five participants completed the creative behaviour inventory, a tool for assessing creative behaviour in daily life. The participants also completed the alternate uses task (AUT) during an electroencephalography (EEG) assessment to evaluate creative thinking. Alpha power was used to quantify neural oscillations during the creative process, while alpha coherence was used to quantify information communication between frontal regions and other sites during creative ideation. Moreover, these two task-related quantitative measures were combined to investigate the relationship between individual differences in everyday creativity and EEG alpha activity during creative idea generation. Compared with the reference period, increased alpha power was observed in the frontal cortex of the right hemisphere and increased functional coupling was observed between frontal and parietal/temporal regions during the activation period. Interestingly, individual differences in everyday creativity were associated with distinct patterns of EEG alpha activity. Specifically, individuals with higher everyday creativity had increased alpha power in the frontal cortex, and increased changes in coherence in frontal-temporal regions of the right hemisphere while performing the AUT. It might indicate that individuals with higher everyday creativity had an enhanced ability to focus on internal information processing and control bottom-up stimuli, as well as better selection of novel semantic information when performing creative ideation tasks.
... ASD are heterogeneous disorders with multisystem and multigenic origin, where even identical genetic variations may lead to divergent phenotypic characteristics [2]. Neuroimaging studies suggested widespread abnormalities involving distributed brain networks [3][4][5][6][7], but convincing evidences of systematic differences in brain network dynamics underlying the cognitive and behavioral symptoms of ASD are still lacking. On the other hand, an accumulating body of evidence indicates a tight relationship between the modulatory functions of the endocrine system and typical and atypical social behavior [8][9][10][11][12]. ...
Article
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An accumulating body of evidence indicates a tight relationship between the endocrine system and abnormal social behavior. Two evolutionarily conserved hypothalamic peptides, oxytocin and arginine-vasopressin, because of their extensively documented function in supporting and regulating affiliative and socio-emotional responses, have attracted great interest for their critical implications for autism spectrum disorders (ASD). A large number of controlled trials demonstrated that exogenous oxytocin or arginine-vasopressin administration can mitigate social behavior impairment in ASD. Furthermore, there exists long-standing evidence of severe socioemotional dysfunctions after hypothalamic lesions in animals and humans. However, despite the major role of the hypothalamus for the synthesis and release of oxytocin and vasopressin, and the evident hypothalamic implication in affiliative behavior in animals and humans, a rather small number of neuroimaging studies showed an association between this region and socioemotional responses in ASD. This review aims to provide a critical synthesis of evidences linking alterations of the hypothalamus with impaired social cognition and behavior in ASD by integrating results of both anatomical and functional studies in individuals with ASD as well as in healthy carriers of oxytocin receptor (OXTR) genetic risk variant for ASD. Current findings, although limited, indicate that morphofunctional anomalies are implicated in the pathophysiology of ASD and call for further investigations aiming to elucidate anatomical and functional properties of hypothalamic nuclei underlying atypical socioemotional behavior in ASD.
... Indeed, many recent studies have reported that individuals within the autism spectrum disorder (ASD) exhibit altered brain connectivity compared to typically developing individuals. However, literature reports are often inconsistent [see review papers by Maximo et al. (2014), Mohammad-Rezazadeh et al. (2016), Carroll et al. (2021)]. The traditional point of view, predominantly supported by studies using structural and functional MRI, hypothesizes that autism is characterized by long-range underconnectivity, potentially combined with local overconnectivity (Just et al., 2012;Abrams et al., 2013;Delbruck et al., 2019). ...
Article
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Brain connectivity is often altered in autism spectrum disorder (ASD). However, there is little consensus on the nature of these alterations, with studies pointing to either increased or decreased connectivity strength across the broad autism spectrum. An important confound in the interpretation of these contradictory results is the lack of information about the directionality of the tested connections. Here, we aimed at disambiguating these confounds by measuring differences in directed connectivity using EEG resting-state recordings in individuals with low and high autistic traits. Brain connectivity was estimated using temporal Granger Causality applied to cortical signals reconstructed from EEG. Between-group differences were summarized using centrality indices taken from graph theory ( in degree , out degree , authority , and hubness ). Results demonstrate that individuals with higher autistic traits exhibited a significant increase in authority and in degree in frontal regions involved in high-level mechanisms (emotional regulation, decision-making, and social cognition), suggesting that anterior areas mostly receive information from more posterior areas. Moreover, the same individuals exhibited a significant increase in the hubness and out degree over occipital regions (especially the left and right pericalcarine regions, where the primary visual cortex is located), suggesting that these areas mostly send information to more anterior regions. Hubness and authority appeared to be more sensitive indices than the in degree and out degree . The observed brain connectivity differences suggest that, in individual with higher autistic traits, bottom-up signaling overcomes top-down channeled flow. This imbalance may contribute to some behavioral alterations observed in ASD.
... Hz, beta = 13-30 Hz) and increased connectivity in the gamma (30-80 Hz) frequency band, compared to neurotypical control participants (O'Reilly et al., 2017;Schwartz et al., 2017;Wang et al., 2013). Additionally, several studies have found reduced long-range (> 90 mm) connectivity and increased short-range (< 30 mm) connectivity in autistic individuals (Catarino et al., 2013;Coben et al., 2008;Isler et al., 2010;Lazarev et al., 2015;van den Heuvel et al., 2012;Wang et al., 2013), although there have been some contradictory findings (Duffy & Als, 2012;Elhabashy et al., 2015;Mohammad-Rezazadeh et al., 2016;Murias et al., 2007;O'Reilly et al., 2017). However, much of this research has been focused on global connectivity, whereas examination of specific neural pathways that have been associated with anxiety, and their links with SF, have yet to be clearly reported. ...
Article
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Objectives Because atypical global neural connectivity has been documented in autistic youth, but only limited data are available regarding the association between generalized anxiety disorder (GAD), sensory features (SF), and neural connectivity between frontal and parietal brain regions, these links were investigated in a sample of male autistic children and adolescents. Methods Forty-one autistic males aged between 6 and 18 years and their mothers were recruited as volunteer participants from Queensland, Australia. Participants underwent 3 min of eyes-closed and 3 min of eyes-opened electroencephalography (EEG) under resting conditions. EEG connectivity was investigated using Granger causality between frontal and parietal regions in alpha (8–13 Hz) and beta (13–30 Hz) bands. Results There was a significant ( p < .01) positive correlation between SF and GAD. GAD was associated with some characteristics of SF in the sample population. Additionally, there was a significant ( p < .01) inverse correlation between directional frontoparietal connectivity and SF during the eyes-closed condition, specifically in relation to avoiding stimuli and sensitivity to the environment. Conclusions Reduced frontoparietal connectivity in association with higher anxiety and SF may demonstrate reduced relaxation due to greater sensitivity to sensory input.
... However, the clinical scores are ambiguous; therefore, there is a need to identify reliable brain biomarkers and automate the process for the diagnosis of ASD (Yerys and Pennington, 2011). Studies reveal that the microscopic and macroscopic synaptic connectivity and structural or Functional Connectivity (FC) patterns can be substantial biomarker for ASD (Sharda et al., 2016;Mohammad-Rezazadeh et al., 2016). Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) is an efficient tool to examine brain network connections. ...
Article
Background Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with altered connectivity in brain networks. New method In this study, brain connections in Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) of ASD and Typical Developing (TD) are analyzed by partial and full correlation methods such as Gaussian Graphical Least Absolute Shrinkage and Selection Operator (GLASSO), Max-Det Matrix Completion (MDMC), and Pearson Correlation Co-Efficient (PCCE). We investigated Functional Connectivity (FC) of ASD and TD brain from 238 functionally defined regions of interest. Furthermore, we constructed a series of feature sets by applying conditional random forests and conditional permutation importance. We built classifier models by Random Forest (RF), Oblique RF (ORF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) for each feature set. FC features are ranked based on p-value and we analyzed the top 20 FC features. Results We achieved a single-trial test accuracy of 72.5%, though MDMC-SVM and PCCE-CNN pipelines. Further, PCCE-CNN pipeline gives better average test accuracy (70.31%) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD. Comparison with existing methods and Conclusions The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
... EEG is a clinically available and relatively inexpensive neuroimaging technique that allows for the capturing of a wide range of brain processes [2], and brain connectivity techniques treat the pathophysiology of neuropsychiatric disorders from a system perspective. Characterizing the atypical patterns of whole-brain system would be helpful for better understanding the neurodevelopmental disorders, since a coherent interaction between the brain regions has a critical role in the human's cognition and behavior [3,4]. ...
Article
Background Analysis of effective connectivity among brain regions is an important key to decipher the mechanisms underlying neural disorders such as Attention Deficit Hyperactivity Disorder (ADHD). We previously introduced a new method, called nCREANN (nonlinear Causal Relationship Estimation by Artificial Neural Network), for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism. Using the nCREANN method in the present study, we assessed effective connectivity patterns of ADHD children based on their EEG signals recorded during a visual attention task, and compared them with the aged-matched Typically Developing (TD) subjects. Method In addition to the nCREANN method for estimating linear and nonlinear aspects of effective connectivity, the direct Directed Transfer Function (dDTF) was utilized to extract the spectral information of connectivity patterns. Results The dDTF results did not suggest a specific frequency band for distinguishing between the two groups, and different patterns of effective connectivity were observed in all bands. Both nCREANN and dDTF methods showed decreased connectivity between temporal/frontal, and temporal/occipital regions, and increased connection between frontal/parietal regions in ADHDs than TDs. Furthermore, the nCREANN results showed more left-lateralized connections in ADHDs compared to the symmetric bilateral inter-hemispheric interactions in TDs. In addition, by fusion of linear and nonlinear connectivity measures of nCREANN method, we achieved an accuracy of 99% in classification of the two groups. Conclusion These findings emphasize the capability of nCREANN method to investigate the brain functioning of neural disorders and its strength in preciously distinguish between healthy and disordered subjects.
... The human brain is a complex system consisting of regions that are functionally and structurally connected to process information during either a behavioural /cognitive task or in a resting-state (Mohammad-Rezazadeh et al. 2016). This concept is called brain connectivity, and it was first addressed in the literature in the early 1960s (Adey et al. 1961). ...
Thesis
This dissertation aims to identify the neurological biomarkers that could assist in providing reliable, automated and objective prediction of neurodevelopmental disorders (NDDs) in early infancy. Quantitative electroencephalography analysis (qEEG), mainly phase synchronisation-based functional brain connectivity estimated using phase locking value (PLV) and weighted phase lag index (WPLI), were investigated to deduce whether it can be used for the early prediction of such disorders. The resulting connectivity network was quantitatively characterised using complex graph-theoretical features, namely transitivity, global efficiency, radius, diameter, and characteristic path length. These features were then fed into the machine learning algorithms such as linear discriminant analysis (LDA), support vector machine (SVM), decision tree and k-nearest neighbour to examine their discriminant capability in classifying /predicting NDDs. The proposed framework has gained initial validation in classifying autism spectrum disorders (ASD) from an experimentally obtained EEG data set of 24 children. Then, the framework was utilised to predict the appearance of cerebral palsy (CP) at two years of age. The EEG data were recorded within the first week after birth from a cohort of infants born with hypoxic-ischaemic encephalopathy (HIE). The exploration results revealed that the proposed analytical methodology successfully predicted the infants that would develop CP with a performance of 84.6% accuracy, 83% sensitivity, 85% specificity, 84% balanced accuracy and 0.85 area under the curve (AUC) in the delta band, with a close result also obtained in the theta and alpha bands. The WPLI and graph parameters were then used to predict the cognitive scores of infants born with HIE by developing the regression framework correlating these EEG features and a cognitive profile completed in a follow-up assessment at two years of age. The regression analysis showed that the radius feature yielded the best performance (root mean square error (RMSE)= 16.78, mean absolute error (MAE)= 12.07 and R-squared= 0.24). Although this study has successfully demonstrated that the qEEG features could be considered potential biomarkers for identifying the brain deficits causing the NDDs, it has a certain limitation due to the size of the data set. It needs to be validated on large trials with a statistically significant population.
... In this regard, brain connectivity studies have shown a promising starting and is reflected in the volume of connectivity studies in ASD for the last 30 years. Aetiology of ASD has its neurobiological underpinning in synaptic pathways (Mohammad-Rezazadeh et al., 2016) resulting in abnormal integration between different cortical regions resulting in diminished cortical integration. ...
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Over the last two decades, there has been a tremendous increase in research activity on brain connectivity studies and its application in different neurological disorders. Studies have been focused on different connectivity patterns generated and potential biomarkers that could be derived to find the etymology of the disorder. In this review, the focus is on the utilization of wireless electroencephalogram monitoring system for functional connectivity analysis and its capacity for deciphering neurological disorders. The paper reviews different methods adopted to estimate connectivity and the possible convergence of connectivity patterns in four neurological disorders: epilepsy, autism spectrum disorder, Alzheimer and Parkinson's disease. The paper reviews the current status of connectivity research in the aforementioned neurological disorders and its potential in developing a smart e‐health service.
... Many of these findings are, however, controversial and have proven difficult to replicate (Haar et al. 2016;Lefebvre et al. 2015;Traut et al. 2018;Picci et al. 2016;Mohammad-Rezazadeh et al. 2016). Most studies have relied on sample sizes far too small to reach reliable conclusionssometimes just a few dozen subjects, and up to a few hundreds at most. ...
Article
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MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 – far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.
... Although MRI analysis has provided information about selective neurodevelopmental characteristics and behavioral criteria for ASD diagnosis, they still do not completely validate whether neuroanatomical biomarkers can predict ASD in the entire population. Based on this, we will focus on brain abnormalities correlated to reward defects in the mesocorticolimbic structures identified by functional MRI (fMRI) analysis to confirm failure in connectivity patterns (Esteban et al. 2019;Mohammad-Rezazadeh et al. 2016), and also, in structural MRI reports focused on volumetric and morphometric analyses to give evidence of abnormal brain anatomy in ASD people (Lerch et al. 2011). By functionality, fMRI is a reliable and efficient experimental approach based on time series of blood oxygenation level dependent signals coupled to statistical modeling able to measure functional connectivity between two brain areas (Jiang and Zuo 2016;Herold et al. 2020). ...
Article
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Autism spectrum disorder (ASD) is a complex neurodevelopmental disease which involves functional and structural defects in selective central nervous system (CNS) regions that harm function and individual ability to process and respond to external stimuli. Individuals with ASD spend less time engaging in social interaction compared to non‐affected subjects. Studies employing structural and functional magnetic resonance imaging reported morphological and functional abnormalities in the connectivity of the mesocorticolimbic reward pathway between the nucleus accumbens (NAc) and the ventral tegmental area (VTA) in response to social stimuli, as well as diminished medial prefrontal cortex (mPFC) in response to visual cues, whereas stronger reward system responses for the non‐social realm (e.g., video games) than social rewards (e.g., approval), associated with caudate nucleus responsiveness in ASD children. Defects in the mesocorticolimbic reward pathway have been modulated in transgenic murine models using D2 dopamine receptor heterozygous (D2+/‐) or dopamine transporter (DAT) knockout mice, which exhibit sociability deficits and repetitive behaviors observed in ASD phenotypes. Notably, the mesocorticolimbic reward pathway is modulated by systemic and central inflammation, such as primed microglia, which occurs during obesity or maternal overnutrition. Therefore, we propose that a positive energy balance during obesity/maternal overnutrition coordinates a systemic and central inflammatory crosstalk that modulates the dopaminergic neurotransmission in selective brain areas of the mesocorticolimbic reward pathway. Here, we will describe how obesity/maternal overnutrition may prime microglia, causing abnormalities in dopamine neurotransmission of the mesocorticolimbic reward pathway, postulating a possible immune role in the development of ASD.
... Mohammad-Rezazadeh, Frohlich, Loo, & Jeste, 2016) as reflecting important markers of variation in severity (or even type) of ASD. In general, improved understanding of etiologyincluding especially the recent identification of a myriad of risk genes linked to discrete syndromic forms of ASD(Chahrour et al., 2016) will aid in improving understanding of the wide variety of ways ASD may manifest. ...
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The membrane‐associated mucin (MAM) domain containing glycosylphosphatidylinositol anchor 2 protein single knock‐out mice (MDGA2+/‐) are models of ASD. We examined the behavioural phenotypes of male and female MDGA2+/‐ and wildtype mice on C57BL6/NJ and C57BL6/N backgrounds at 2 months of age and measured MDGA2, neuroligin 1 and neuroligin 2 levels at 7 months. Mice on the C57BL6/NJ background performed better than those on the C57BL6/N background in visual ability and in learning and memory performance in the Morris water maze and differed in measures of motor behavior and anxiety. Mice with the MDGA2+/‐ genotype differed from WT mice in motor, social and repetitive behaviour and anxiety, but most of these effects involved interactions between MDGA2+/‐ genotype and background strain. The background strain also influenced MDGA2 levels and NLGN2 association in MDGA2+/‐ mice. Our findings emphasize the importance of the background strain used in studies of genetically modified mice. This article is protected by copyright. All rights reserved.
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Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6–30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.
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Tuberous sclerosis complex (TSC) is a neurocutaneous disorder caused by mutations of either the TSC1 or TSC2 gene. Various neuropsychiatric features, including autism, are prevalent in TSC. Recently, significant progress has been possible with the prospective calculation of the prevalence of autism in TSC, identification of early clinical and neurophysiological biomarkers to predict autism, and investigation of different therapies to prevent autism in this high-risk population. The author provides a narrative review of recent findings related to biomarkers for diagnosis of autism in TSC, as well as recent studies related to the management of TSC-associated autism. Further sophisticated modeling and analysis are required to understand the role of different models—tuber models, seizures and related neurophysiological factors models, genotype models, and brain connectivity models—to unravel the neurobiological basis of autism in TSC. Early neuropsychologic assessments may be beneficial in this high-risk group. Targeted intervention to improve visual skill, cognition, and fine motor skills with later addition of social skill training can be helpful. Multicenter, prospective studies are ongoing to identify if presymptomatic treatment with vigabatrin in patients with TSC can improve outcomes, including autism. Several studies indicated reasonable safety of everolimus in young children, and its potential application in high-risk infants with TSC, before the closure of the temporal window of permanent changes, maybe undertaken shortly.
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During the recent decade, there is a growing interest in the use of neuroimaging methods and different data analysis approaches to recognize and understand neuropsychiatric disorders. In this study, we investigated resting-state Electroencephalography (EEG) data of children with autism and healthy children. The direct Directed Transfer Function (dDTF) method was used to estimate the effective connectivity. We introduced and applied the directed temporal network measures for quantifying the effective brain connections in frequency bands of Alpha, Beta1, Beta2, Delta, Theta, and Gamma. Our results showed that each of the global measures was able to demonstrate a significant distinction at least in one frequency band, between the healthy and Autistic Spectrum Disorder (ASD) groups. The burstiness properties of edges and the directed temporal centrality properties of nodes were different in all the frequency bands in both groups. Also, the significant edges and nodes were determined in each group. The number of significant bursty edges in ASD was less than the healthy group, in Alpha, Delta, Beta1, and Theta bands. Finally, we could show how autism changes the pattern of the brain network across time.
Article
Anxiety is exceedingly prevalent among individuals with an autism spectrum condition (ASC). While recent literature postulates anxiety as a mechanism encompassing an underlying amygdala-related elevated baseline level of arousal even to nonthreatening cues, whether this same mechanism contributes to anxiety in those with an ASC and supports the transdiagnostic nature of anxiety remains elusive. In this case–control study of 51 youths (26 ASC), we assessed autism and anxiety via the Autism-Spectrum Quotient and the State–Trait Anxiety Inventory, respectively. Hemodynamic responses, including amygdala reactivity, to explicit and implicit (backwardly masked) perception of threatening faces were acquired using functional Magnetic Resonance Imaging (fMRI). For explicit fear, ASC individuals showed significantly greater negative correlations between the amygdala and the attentional deployment-parietal network. For implicit fear, ASC individuals showed significantly stronger correlations of the amygdala with the prefrontal networks, temporal pole, and hippocampus. Additionally, an fMRI-based neurologic signature for anxiety in ASCs was identified via the LibSVM machine learning model using amygdala-centered functional connectivity during the emotional processing of explicit and implicit stimuli. Hypervigilance to implicit threat in ASCs comorbid with anxiety might exacerbate explicit threat reactivity; hence the use of attentional avoidance patterns to restrict affective hyperarousal for explicitly perceived socioemotional stimuli. Consequently, developing an attention-independent behavioral/neural marker identifying anxiety in ASCs is highly warranted. Lay Summary This study identifies a dissociation of amygdala reactivity dependent on explicit and implicit threat processing. Implicit anxiety in individuals with an autism spectrum condition (ASC) could outweigh explicitly induced threat. When explicitly perceiving socioemotional stimuli, ASC individuals with anxiety might use attentional avoidance patterns to restrict affective hyperarousal.
Article
Twins provide a valuable perspective for exploring the pathological mechanism of autism spectrum disorder (ASD). We aim to analyze differences in the topological properties of the white matter (WM) network between monozygotic twins with ASD (MZCo-ASD) and children with typical development (TD). We enrolled 67 subjects aged 2-9 years. Twenty-three pairs of MZCo-ASD and 21 singleton children with TD completed clinical assessments and diffusion tensor imaging (DTI). Graph theory was used to compare the topological properties of the WM network between the two groups, and analyzed their correlations with the severity of clinical symptoms. We found that the global efficiency (Eg) of MZCo-ASD is weaker than that of TD children, while the shortest path length (Lp) of MZCo-ASD is longer than that of TD children, and MZCo-ASD have three unique hubs (the bilateral dorsolateral superior frontal gyrus and right insula). Eg and Lp were both correlated with the repetitive behavior scores of the Autism Diagnostic Interview-Revised (ADI-R) in the MZCo-ASD group, and the nodal efficiency of the dorsal superior frontal gyrus (SFGdor) was correlated with the ADI-R scores of repetitive behaviors. Left SFGdor nodal efficiency was correlated with Repetitive Behavior and Communication, two core symptoms of autism. The results implicated that MZCo-ASD had atypical brain structural network attributes and node distributions. Using MZCo-ASD, we found that the WM topological properties that correlate with the severity of ASD core symptoms were Eg, Lp, and the nodal efficiency of the SFGdor.
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The etiology of psychiatric disorders remains largely unknown. The exploration of the neurobiological mechanisms of mental illness helps improve diagnostic efficacy and develop new therapies. This review focuses on the application of concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in various mental diseases, including major depressive disorder, bipolar disorder, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, substance use disorder, and insomnia. First, we summarize the commonly used protocols and output measures of TMS-EEG; then, we review the literature exploring the alterations in neural patterns, particularly cortical excitability, plasticity, and connectivity alterations, and studies that predict treatment responses and clinical states in mental disorders using TMS-EEG. Finally, we discuss the potential mechanisms underlying TMS-EEG in establishing biomarkers for psychiatric disorders and future research directions. This article is part of the special Issue on ‘Stress, Addiction and Plasticity'.
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Studying brain connectivity has shed light on understanding brain functions. Electroencephalogram signals recorded from the scalp surface comprise inter-dependent multi-channel signals each of which is a linear combination of simultaneously active brain sources as well as adjacent non-brain sources whose activity is widely volume conducted to the scalp through overlapping patterns. Evaluation of brain connectivity based on multivariate autoregressive (MVAR) model identification from neurological time series can be a proper tool for brain signal analysis. However, the MVAR model only considers the lagged influences between time series while ignoring the instantaneous effects (zero-lagged interactions) among simultaneously recorded neurological signals. Hence predicting instant interactions may result in fake connectivity, which may lead to misinterpreting in results. In this study, we aim to find instantaneous effects from coefficients of the MVAR model acquired using an ADALINE neural network and investigate the efficiency of the proposed algorithm by applying it to a simulated signal. We show that our coefficients are estimated accurately from channels of the simulated signal. Moreover, we apply the proposed method on a dataset of a group of 18 healthy children and 10 children with autism by comparing their effective connectivity estimated by direct directed transfer function method using new and old coefficients. Finally, to show the efficiency of the algorithm we exploit the support vector machine method for classifying the dataset. We show that there is a significant improvement in the results obtained from the proposed method.
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This research monograph describes desvtibes the mathernal pathologing and the clinical studywith of infant hyperactivity and attention deficit disorder ADHD, the sungroup pf ADHD, the child deficit of learning and memory and aggresivity-In addition we have reported the cliivical study of Autism spectrum disorder (ASD), the brain-gut relationship in ASD, and the reported Neuroscience Theories, Hypothesis and Approaches on Autism Spectrum Disorder (ASD).
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Pain can be ignited by noxious chemical (e.g., acid), mechanical (e.g., pressure), and thermal (e.g., heat) stimuli and generated by the activation of sensory neurons and their axonal terminals called nociceptors in the periphery. Nociceptive information transmitted from the periphery is projected to the central nervous system (thalamus, somatosensory cortex, insular, anterior cingulate cortex, amygdala, periaqueductal grey, prefrontal cortex, etc.) to generate a unified experience of pain. Local field potential (LFP) recording is one of the neurophysiological tools to investigate the combined neuronal activity, ranging from several hundred micrometers to a few millimeters (radius), located around the embedded electrode. The advantage of recording LFP is that it provides stable simultaneous activities in various brain regions in response to external stimuli. In this study, differential LFP activities from the contralateral anterior cingulate cortex (ACC), ventral tegmental area (VTA), and bilateral amygdala in response to peripheral noxious formalin injection were recorded in anesthetized male rats. The results indicated increased power of delta, theta, alpha, beta, and gamma bands in the ACC and amygdala but no change of gamma-band in the right amygdala. Within the VTA, intensities of the delta, theta, and beta bands were only enhanced significantly after formalin injection. It was found that the connectivity (i.t. the coherence) among these brain regions reduced significantly under the formalin-induced nociception, which suggests a significant interruption within the brain. With further study, it will sort out the key combination of structures that will serve as the signature for pain state.
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Background Autism spectrum disorder (ASD) is a complex developmental disability and is currently viewed as a disorder of brain connectivity in which white matter abnormalities. However, the majority of the research to date has focused on children with ASD. Understanding the topological organization of the white matter structural network in adults may help uncover the nature of ASD pathology in adulthood. Method This study investigated the topological properties of white matter structural network using diffusion tensor imaging and graph theory analysis in a sample of 32 adults with ASD compared to 35 matched typically developing (TD) controls. Group differences in global and nodal topological metrics were compared. The relationships between the altered network metrics and the severity of clinical symptoms were calculated. Results Compared to TD controls, ASD patients exhibited decreased small-worldness and increased global efficiency. In addition, the reduced nodal efficiency and increased nodal degree were found in the frontal (e.g., the inferior frontal gyrus) and parietal (e.g., postcentral gyrus) regions. Furthermore, the altered topological metrics (e.g., increased global efficiency and reduced nodal efficiency) were correlated with the severity of ASD symptoms. Conclusion These results indicated that the complicatedly topological organization of the white matter structural network was abnormal and may play an essential role in the underlying pathological mechanism of ASD in adults.
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Patients with autism spectrum disorder (ASD) often show pervasive and complex language impairments that are closely associated with aberrant structural connectivity of language networks. However, the characteristics of white matter connectivity in ASD have remained inconclusive in previous diffusion tensor imaging (DTI) studies. The current meta‐analysis aimed to comprehensively elucidate the abnormality in language‐related white matter connectivity in individuals with ASD. We searched PubMed, Web of Science, Scopus, and Medline databases to identify relevant studies. The standardized mean difference was calculated to measure the pooled difference in DTI metrics in each tract between the ASD and typically developing (TD) groups. The moderating effects of age, sex, language ability, and symptom severity were investigated using subgroup and meta‐regression analysis. Thirty‐three DTI studies involving 831 individuals with ASD and 836 TD controls were included in the meta‐analysis. ASD subjects showed significantly lower fractional anisotropy or higher mean diffusivity across language‐associated tracts than TD controls. These abnormalities tended to be more prominent in the left language networks than in the right. In addition, children with ASD exhibit more pronounced and pervasive disturbances in white matter connectivity than adults. These results support the under‐connectivity hypothesis and demonstrate the widespread abnormal microstructure of language‐related tracts in patients with ASD. Otherwise, white matter abnormalities in the autistic brain could vary depending on the developmental stage and hemisphere. This meta‐analysis explored abnormalities in white matter connectivity in language networks of individuals with ASD. Significantly reduced white matter integrity was found in all language‐associated tracts in subjects with ASD compared with TD controls. In addition, structural disturbances of language networks in the autistic brain exhibit a leftward tendency, and more prominent abnormalities are observed in younger people with ASD than in adults.
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The graph convolution neural network uses topological graph to portray inter-node relationships and update node features. However, the traditional topological graph can only describe the certain relationship between nodes (that is, the weight of the connecting edge is a fixed value), while ignoring the uncertainty widely existing in the real world. These uncertainties not only affect the relationship between nodes, but also affect the final classification performance of the model. In order to overcome this defect, a graph convolution neural network algorithm based on rough graph is proposed in this paper. Specifically, the algorithm first constructs a rough graph using a combination of the upper and lower approximation theory of the rough set and the edge theory of the topological graph, the paired maximum-minimum relationship values are used to characterize the uncertain relationship between nodes. Then, this paper designs an end-to-end training neural network architecture based on rough graph, the trained rough graph is fed to this neural network to update node features with these uncertain relationship. Finally, nodes are classified according to these learned node features. The experimental results on real data show that the proposed algorithm can significantly improve the accuracy of node classification compared with the traditional graph convolution neural network.
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p>It is commonly known that different brain waves are associated with different types of activities that we perform. This paper attempts to compare the activation states for Alpha, Beta, Delta, Gamma, Theta waves of three subjects, two of which are typical brains, and the third subject has Autistic Spectrum Disorder (ASD). The intent is to observe the brain patterns as the subjects perform conditions of normal and sports activity. We use Mind Monitor, MATLAB, EEGLAB to capture, analyze and plot the brain waves. We expected to observe differences in brain wave activation between typical and autistic brain. We also expected more activation in Alpha and Beta spectrums as those are associated more with the conditions we tested. The results were quite exciting as we saw differences in activation levels at the expected spectrum frequencies between the two types of brain. While this is more of a qualitative experiment than a quantitative one, we did observe a significant skew in Front Right Electrode activation for the autistic brain across all spectrums. </p
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Since the initial psychological report by Leo Kanner in 1943, relatively little formal biochemical/neurological research on the cause of autism, other than peripheral searches for genomic mutations, had been carried until the end of the 20th century. As a result of studies on twin sets and the conclusion that autism was largely a hereditary defect, numerous investigations have sought various genetic faults in particular. However, such studies were able to reveal a plausible etiology for this malady in only a small percentage of instances. Key bio-molecular characteristics of this syndrome have been uncovered when the potential roles of the glia were studied in depth. Findings related to biochemical deficiencies appearing early in the newborn, such as depressed IGF-1 (insulin-like growth factor #1) in neurogenesis/myelination, are becoming emphasized in many laboratories. Progress leading to timely diagnoses and subsequent prevention of central nervous system dysconnectivity now seems plausible. The tendency for an infant to develop autism may currently be determinable and preventable before irreversible psychosocial disturbances become established. These discussions about glial function will be inter-spersed with comments about their apparent relevance to autism. The concluding portion of this presentation will be a detailed review and summation of this diagnosis and prevention proposition.
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Background: While there is increasing evidence of altered brain connectivity in autism, the degree and direction of these alterations in connectivity and their uniqueness to autism has not been established. The aim of the present study was to compare connectivity in children with autism to that of typically developing controls and children with developmental delay without autism. Methods: We assessed EEG spectral power, coherence, phase lag, Pearson and partial correlations, and epileptiform activity during the awake, slow wave sleep, and REM sleep states in 137 children aged 2 to 6 years with autism (n = 87), developmental delay without autism (n = 21), or typical development (n = 29). Findings: We found that brain connectivity, as measured by coherence, phase lag, and Pearson and partial correlations distinguished children with autism from both neurotypical and developmentally delayed children. In general, children with autism had increased coherence which was most prominent during slow wave sleep. Interpretation: Functional connectivity is distinctly different in children with autism compared to samples with typical development and developmental delay without autism. Differences in connectivity in autism are state and region related. In this study, children with autism were characterized by a dynamically evolving pattern of altered connectivity.
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This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = −0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.
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Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.
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It has been previously reported that structural and functional brain connectivity in individuals with autism spectrum disorders (ASD) is atypical and may vary with age. However, to date, no measures of functional connectivity measured within the first 2 years have specifically associated with a later ASD diagnosis. In the present study, we analyzed functional brain connectivity in 14-month-old infants at high and low familial risk for ASD using electroencephalography (EEG). EEG was recorded while infants attended to videos. Connectivity was assessed using debiased weighted phase lag index (dbWPLI). At 36 months, the high-risk infants were assessed for symptoms of ASD. As a group, high-risk infants who were later diagnosed with ASD demonstrated elevated phase-lagged alpha-range connectivity as compared to both low-risk infants and high-risk infants who did not go on to ASD. Hyper-connectivity was most prominent over frontal and central areas. The degree of hyper-connectivity at 14 months strongly correlated with the severity of restricted and repetitive behaviors in participants with ASD at 3 years. These effects were not attributable to differences in behavior during the EEG session or to differences in spectral power. The results suggest that early hyper-connectivity in the alpha frequency range is an important feature of the ASD neurophysiological phenotype.
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Functional connectivity (FC) and graph measures provide powerful means to analyze complex networks. The current study determines the inter-subject-variability using the coefficient of variation (CoV) and long-term test-retest-reliability (TRT) using the intra-class correlation coefficient (ICC) in 44 healthy subjects with 35 having a follow-up at years 1 and 2. FC was estimated from 256-channel-EEG by the phase-lag-index (PLI) and weighted PLI (wPLI) during an eyes-closed resting state condition. PLI quantifies the asymmetry of the distribution of instantaneous phase differences of two time-series and signifies, whether a consistent non-zero phase lag exists. WPLI extends the PLI by additionally accounting for the magnitude of the phase difference. Signal-space global and regional PLI/wPLI and weighted first-order graph measures, i.e. normalized clustering coefficient (gamma), normalized average path length (lambda), and the small-world-index (SWI) were calculated for theta-, alpha1-, alpha2- and beta-frequency bands. Inter-subject variability of global PLI was low to moderate over frequency bands (0.12<CoV<0.28), higher for wPLI (0.25<CoV<0.55) and very low for gamma, lambda and SWI (CoV<0.048). TRT was good to excellent for global PLI/wPLI (0.68<ICC<0.80), regional PLI/wPLI (0.58<ICC<0.77), and fair to good for graph measures (0.32<ICC<0.73) except wPLI-based lambda in alpha1 (ICC = 0.12). Inter-electrode distance correlated very weakly with inter-electrode PLI (-0.06<rho<0) and weakly with inter-electrode wPLI (-0.22<rho<-0.18). Global PLI/wPLI and topographic connectivity patterns differed between frequency bands, and all individual networks showed a small-world-configuration. PLI/wPLI based network characterization derived from high-resolution EEG has apparently good reliability, which is one important requirement for longitudinal studies exploring the effects of chronic brain diseases over several years.
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Neuroimaging technologies and research has shown that autism is largely a disorder of neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and other forms of structural and functional connectivity analyses, the use of EEG for these purposes is of additional great utility. Cantor et al. (1986) were the first to examine the utility of pairwise coherence measures for depicting connectivity impairments in autism. Since that time research has shown a combination of mixed over and under-connectivity that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless, there is reason to believe that these simplistic pairwise measurements under represent the true and quite complicated picture of connectivity anomalies in these persons. We have presented three different forms of multivariate connectivity analysis with increasing levels of sophistication (including one based on principle components analysis, sLORETA source coherence, and Granger causality) to present a hypothesis that more advanced statistical approaches to EEG coherence analysis may provide more detailed and accurate information than pairwise measurements. A single case study is examined with findings from MR-DTI, pairwise and coherence and these three forms of multivariate coherence analysis. In this case pairwise coherences did not resemble structural connectivity, whereas multivariate measures did. The possible advantages and disadvantages of different techniques are discussed. Future work in this area will be important to determine the validity and utility of these techniques.
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Autism is a neurodevelopmental disorder that has been associated with atypical brain functioning. Functional connectivity MRI (fcMRI) studies examining neural networks in autism have seen an exponential rise over the last decade. Such investigations have led to the characterization of autism as a distributed neural systems disorder. Studies have found widespread cortical underconnectivity, local overconnectivity, and mixed results suggesting disrupted brain connectivity as a potential neural signature of autism. In this review, we summarize the findings of previous fcMRI studies in autism with a detailed examination of their methodology, in order to better understand its potential and to delineate the pitfalls. We also address how a multimodal neuroimaging approach (incorporating different measures of brain connectivity) may help characterize the complex neurobiology of autism at a global level. Finally, we also address the potential of neuroimaging-based markers in assisting neuropsychological assessment of autism. The quest for a neural marker for autism is still ongoing, yet new findings suggest that aberrant brain connectivity may be a promising candidate.
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Long-range cortical functional connectivity is often reduced in autism spectrum disorders (ASD), but the nature of local cortical functional connectivity in ASD has remained elusive. We used magnetoencephalography to measure task-related local functional connectivity, as manifested by coupling between the phase of alpha oscillations and the amplitude of gamma oscillations, in the fusiform face area (FFA) of individuals diagnosed with ASD and typically developing individuals while they viewed neutral faces, emotional faces, and houses. We also measured task-related long-range functional connectivity between the FFA and the rest of the cortex during the same paradigm. In agreement with earlier studies, long-range functional connectivity between the FFA and three distant cortical regions was reduced in the ASD group. However, contrary to the prevailing hypothesis in the field, we found that local functional connectivity within the FFA was also reduced in individuals with ASD when viewing faces. Furthermore, the strength of long-range functional connectivity was directly correlated to the strength of local functional connectivity in both groups; thus, long-range and local connectivity were reduced proportionally in the ASD group. Finally, the magnitude of local functional connectivity correlated with ASD severity, and statistical classification using local and long-range functional connectivity data identified ASD diagnosis with 90% accuracy. These results suggest that failure to entrain neuronal assemblies fully both within and across cortical regions may be characteristic of ASD.
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Using microarrays, we identified de novo copy number variations in the SHANK2 synaptic scaffolding gene in two unrelated individuals with autism-spectrum disorder (ASD) and mental retardation. DNA sequencing of SHANK2 in 396 individuals with ASD, 184 individuals with mental retardation and 659 unaffected individuals (controls) revealed additional variants that were specific to ASD and mental retardation cases, including a de novo nonsense mutation and seven rare inherited changes. Our findings further link common genes between ASD and intellectual disability.
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Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.
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It is well established that autism spectrum disorders (ASD) have a strong genetic component; however, for at least 70% of cases, the underlying genetic cause is unknown. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes--so-called sporadic or simplex families--we sequenced all coding regions of the genome (the exome) for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 that were previously reported. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19), for a total of 677 individual exomes from 209 families. Here we show that de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD. Moreover, 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected β-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes: CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3 and SCN1A. Combined with copy number variant (CNV) data, these results indicate extreme locus heterogeneity but also provide a target for future discovery, diagnostics and therapeutics.
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Multiple studies have confirmed the contribution of rare de novo copy number variations to the risk for autism spectrum disorders. But whereas de novo single nucleotide variants have been identified in affected individuals, their contribution to risk has yet to be clarified. Specifically, the frequency and distribution of these mutations have not been well characterized in matched unaffected controls, and such data are vital to the interpretation of de novo coding mutations observed in probands. Here we show, using whole-exome sequencing of 928 individuals, including 200 phenotypically discordant sibling pairs, that highly disruptive (nonsense and splice-site) de novo mutations in brain-expressed genes are associated with autism spectrum disorders and carry large effects. On the basis of mutation rates in unaffected individuals, we demonstrate that multiple independent de novo single nucleotide variants in the same gene among unrelated probands reliably identifies risk alleles, providing a clear path forward for gene discovery. Among a total of 279 identified de novo coding mutations, there is a single instance in probands, and none in siblings, in which two independent nonsense variants disrupt the same gene, SCN2A (sodium channel, voltage-gated, type II, α subunit), a result that is highly unlikely by chance.
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Here we review findings from studies investigating functional and structural brain connectivity in high functioning individuals with autism spectrum disorders (ASDs). The dominant theory regarding brain connectivity in people with ASD is that there is long distance under-connectivity and local over-connectivity of the frontal cortex. Consistent with this theory, long-range cortico-cortical functional and structural connectivity appears to be weaker in people with ASD than in controls. However, in contrast to the theory, there is less evidence for local over-connectivity of the frontal cortex. Moreover, some patterns of abnormal functional connectivity in ASD are not captured by current theoretical models. Taken together, empirical findings measuring different forms of connectivity demonstrate complex patterns of abnormal connectivity in people with ASD. The frequently suggested pattern of long-range under-connectivity and local over-connectivity is in need of refinement.
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In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for each subject individually and then group templates were estimated by averaging all matrices in each group. Furthermore, we performed classification of the subjects using support vector machines and Fisher's criterion to rank the features and identify the best subset for maximum separation of the groups. Our results show that a combined model based on connectivity matrices and graph theory measures can provide 87.5% accuracy in separating the two groups. These findings suggest that analysis of functional connectivity patterns may provide a valuable method for the early detection of autism.
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Functional interregional neural coupling was measured as EEG coherence during REM sleep, a state of endogenous cortical activation, in 9 adult autistic individuals (21.1±4.0 years) and 13 typically developed controls (21.5±4.3 years) monitored for two consecutive nights in a sleep laboratory. Spectral analysis was performed on 60 s of artefact-free EEG samples distributed equally throughout the first four REM sleep periods of the second night. EEG coherence was calculated for six frequency bands (delta, theta, alpha, sigma, beta, and total spectrum) using a 22-electrode montage. The magnitude of coherence function was computed for intra- and interhemispheric pairs of recording sites. Results were compared by Multivariate Analysis of Variance (MANOVA). Each time the autistic group showed a greater EEG coherence than the controls; it involved intrahemispheric communication among the left visual cortex (O1) and other regions either close to or distant from the occipital cortex. In contrast, lower coherence values involved frontal electrodes in the right hemisphere. No significant differences between groups were found for interhemispheric EEG coherence. These results show that the analysis of EEG coherence during REM sleep can disclose patterns of cortical connectivity that can be reduced or increased in adults with autism compared to typically developed individuals, depending of the cortical areas studied. Superior coherence involving visual perceptual areas in autism is consistent with an enhanced role of perception in autistic brain organization.
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Results of comparative genomic hybridization studies have suggested that rare copy number variations (CNVs) at numerous loci are involved in the cause of mental retardation, autism spectrum disorders, and schizophrenia. To provide an estimate of the collective frequency of a set of recurrent or overlapping CNVs in 3 different groups of cases compared with healthy control subjects and to assess whether each CNV is present in more than 1 clinical category. Case-control study. Academic research. We investigated 28 candidate loci previously identified by comparative genomic hybridization studies for gene dosage alteration in 247 cases with mental retardation, in 260 cases with autism spectrum disorders, in 236 cases with schizophrenia or schizoaffective disorder, and in 236 controls. Collective and individual frequencies of the analyzed CNVs in cases compared with controls. Recurrent or overlapping CNVs were found in cases at 39.3% of the selected loci. The collective frequency of CNVs at these loci is significantly increased in cases with autism, in cases with schizophrenia, and in cases with mental retardation compared with controls (P < .001, P = .01, and P = .001, respectively, Fisher exact test). Individual significance (P = .02 without correction for multiple testing) was reached for the association between autism and a 350-kilobase deletion located at 22q11 and spanning the PRODH and DGCR6 genes. Weakly to moderately recurrent CNVs (transmitted or occurring de novo) seem to be causative or contributory factors for these diseases. Most of these CNVs (which contain genes involved in neurotransmission or in synapse formation and maintenance) are present in the 3 pathologic conditions (schizophrenia, autism, and mental retardation), supporting the existence of shared biologic pathways in these neurodevelopmental disorders.
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The potential benefits of functional magnetic resonance imaging (MRI) for the investigation of normal development have been limited by difficulties in its use with children. We describe the practical aspects, including failure rates, involved in conducting large-scale functional MRI studies with normal children. Two hundred and nine healthy children between the ages of 5 and 18 years participated in a functional MRI study of language development. Reliable activation maps were obtained across the age range. Younger children had significantly higher failure rates than older children and adolescents. It is concluded that it is feasible to conduct large-scale functional MRI studies of children as young as 5 years old. These findings can be used by other research groups to guide study design and plans for recruitment of young subjects.
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For human working memory the neural correlates of the phonological loop and the visuospatial sketch pad are well explored. In contrast, less is known about central executive processes. Neuroimaging studies suggest that central executive processes are related to a complex fronto-parietal network. In the present study we investigate the question whether varying demands on central executive processes are reflected by differences in coherent activity between and within a fronto-parietal network. We calculated coherence during a visuospatial working memory task. Under an easy executive condition subjects had to mentally imagine previously studied abstract patterns, whereas in the difficult condition, subjects had to mentally manipulate these patterns. The results indicate the involvement of prefrontal areas in executive functions reflected by a decrease of anterior upper alpha short-range connectivity and a parallel increase of fronto-parietal long-range coherence mirroring activation of a fronto-parietal network.
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Current MEG instruments derive the whole-head coverage by utilizing a helmet-shaped opening at the bottom of the dewar. These helmets, however, are quite a bit larger than most people's heads so subjects commonly lean against the back wall of the helmet in order to maintain a steady position. In such cases the anterior brain sources may be too distant to be picked up by the sensors reliably. Potential "invisibility" of the frontal and anterior temporal sources may be particularly troublesome for the studies of cognition and language, as they are subserved significantly by these areas. We examined the sensitivity of the distributed anatomically-constrained MEG (aMEG) approach to the head position ("front" vs. "back") secured within a helmet with custom-tailored bite-bars during a lexical decision task. The anterior head position indeed resulted in much greater sensitivity to language-related activity in frontal and anterior temporal locations. These results emphasize the need to adjust the head position in the helmet in order to maximize the "visibility" of the sources in the anterior brain regions in cognitive and language tasks.
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Cognitive processing requires integration of information processed simultaneously in spatially distinct areas of the brain. The influence that two brain areas exert on each others activity is usually governed by an unknown function, which is likely to have nonlinear terms. If the functional relationship between activities in different areas is dominated by the nonlinear terms, linear measures of correlation may not detect the statistical interdependency satisfactorily. Therefore, algorithms for detecting nonlinear dependencies may prove invaluable for characterizing the functional coupling in certain neuronal systems, conditions or pathologies. Synchronization likelihood (SL) is a method based on the concept of generalized synchronization and detects nonlinear and linear dependencies between two signals (Stam, C.J., van Dijk, B.W., 2002. Synchronization likelihood: An unbiased measure of generalized synchronization in multivariate data sets. Physica D, 163: 236-241.). SL relies on the detection of simultaneously occurring patterns, which can be complex and widely different in the two signals. Clinical studies applying SL to electro- or magnetoencephalography (EEG/MEG) signals have shown promising results. In previous implementations of the algorithm, however, a number of parameters have lacked a rigorous definition with respect to the time-frequency characteristics of the underlying physiological processes. Here we introduce a rationale for choosing these parameters as a function of the time-frequency content of the patterns of interest. The number of parameters that can be arbitrarily chosen by the user of the SL algorithm is thereby decreased from six to two. Empirical evidence for the advantages of our proposal is given by an application to EEG data of an epileptic seizure and simulations of two unidirectionally coupled Hénon systems.
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SHANK3 (also known as ProSAP2) regulates the structural organization of dendritic spines and is a binding partner of neuroligins; genes encoding neuroligins are mutated in autism and Asperger syndrome. Here, we report that a mutation of a single copy of SHANK3 on chromosome 22q13 can result in language and/or social communication disorders. These mutations concern only a small number of individuals, but they shed light on one gene dosage-sensitive synaptic pathway that is involved in autism spectrum disorders.
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We tested the hypothesis that de novo copy number variation (CNV) is associated with autism spectrum disorders (ASDs). We performed comparative genomic hybridization (CGH) on the genomic DNA of patients and unaffected subjects to detect copy number variants not present in their respective parents. Candidate genomic regions were validated by higher-resolution CGH, paternity testing, cytogenetics, fluorescence in situ hybridization, and microsatellite genotyping. Confirmed de novo CNVs were significantly associated with autism (P = 0.0005). Such CNVs were identified in 12 out of 118 (10%) of patients with sporadic autism, in 2 out of 77 (3%) of patients with an affected first-degree relative, and in 2 out of 196 (1%) of controls. Most de novo CNVs were smaller than microscopic resolution. Affected genomic regions were highly heterogeneous and included mutations of single genes. These findings establish de novo germline mutation as a more significant risk factor for ASD than previously recognized.
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After participating in this activity, learners should be better able to:Assess the resting state and diffusion tensor imaging connectivity literature regarding subjects with autism spectrum disorder. Autism spectrum disorder (ASD) affects 1 in 50 children between the ages of 6 and 17 years. The etiology of ASD is not precisely known. ASD is an umbrella term, which includes both low- (IQ < 70) and high-functioning (IQ > 70) individuals. A better understanding of the disorder and how it manifests in individual subjects can lead to more effective intervention plans to fulfill the individual's treatment needs.Magnetic resonance imaging (MRI) is a non-invasive investigational tool that can be used to study the ways in which the brain develops or deviates from the typical developmental trajectory. MRI offers insights into the structure, function, and metabolism of the brain. In this article, we review published studies on brain connectivity changes in ASD using either resting state functional MRI or diffusion tensor imaging.The general findings of decreases in white matter integrity and in long-range neural coherence are well known in the ASD literature. Nevertheless, the detailed localization of these findings remains uncertain, and few studies link these changes in connectivity with the behavioral phenotype of the disorder. With the help of data sharing and large-scale analytic efforts, however, the field is advancing toward several convergent themes, including the reduced functional coherence of long-range intra-hemispheric cortico-cortical default mode circuitry, impaired inter-hemispheric regulation, and an associated, perhaps compensatory, increase in local and short-range cortico-subcortical coherence.