Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled "Autism: Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.

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Identification and Evaluation of Children With Autism Spectrum Disorders

Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues unabated and has occurred without any top-down design, yet the topology of its hyperlink structure exhibits characteristic statistical patterns (Pastor-Satorras and Vespig­ nani, 2004). Other technological networks such as the power grid, global transportation networks, or mobile communication networks continue to grow and evolve, each displaying characteristic patterns of expansion and elaboration. Growth and change in social and organizational

Networks of the Brain

Autism Diagnostic Interview—Revised

In a striking display of trans-kingdom symbiosis, gut bacteria cooperate with their animal hosts to regulate the development and function of the immune, metabolic and nervous systems through dynamic bidirectional communication along the ‘gut–brain axis’. These processes may affect human health, as certain animal behaviours appear to correlate with the composition of gut bacteria, and disruptions in microbial communities have been implicated in several neurological disorders. Most insights about host–microbiota interactions come from animal models, which represent crucial tools for studying the various pathways linking the gut and the brain. However, there are complexities and manifest limitations inherent in translating complex human disease to reductionist animal models. In this Review, we discuss emerging and exciting evidence of intricate and crucial connections between the gut microbiota and the brain involving multiple biological systems, and possible contributions by the gut microbiota to neurological disorders. Continued advances from this frontier of biomedicine may lead to tangible impacts on human health. In this Review, Morais, Schreiber and Mazmanian discuss emerging and exciting evidence of intricate and potentially important connections between the gut microbiota and the brain involving multiple biological systems, and possible contributions by the gut microbiota to complex behaviours.

The gut microbiota-brain axis in behaviour and brain disorders.

Phase synchronisation in multichannel electroencephalography (EEG) is known as the manifestation of functional brain connectivity. Traditional phase synchronisation studies are mostly based on time average synchrony measures, and hence do not preserve the temporal evolution of the phase difference. Here we propose a new method to show the existence of a small set of unique phase synchronised patterns or 'states' in multi-channel EEG recordings, each 'state' being stable of the order of ms, from typical and pathological subjects during face perception tasks. The proposed methodology bridges the concepts of EEG microstates and phase synchronisation in the time and frequency domain, respectively. The analysis is reported for four groups of children including typical, autism spectrum disorder, low and high anxiety subjects—a total of 44. In all cases, we observe consistent existence of these states—termed as synchrostates—within specific cognition-related frequency bands (beta and gamma bands), though the topographies of these synchrostates differ for different subject groups with different pathological conditions. The inter-synchrostate switching follows a well-defined sequence capturing the underlying inter-electrode phase relation dynamics in a stimulus- and person-centric manner. Our study is motivated by the well-known EEG microstate exhibiting stable potential maps over the scalp. However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG. The existence of the synchrostates coupled with their unique switching sequence characteristics could be considered as a potentially new field over contemporary EEG phase synchronisation studies.

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On the existence of synchrostates in multichannel EEG signals during face-perception tasks

In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data.

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Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder

Psychodynamic psychotherapy for separation anxiety disorders in children.

Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

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Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

Delays in language are a hallmark feature of Autism Spectrum Disorder (ASD). However, little is known about the predictive role of language developmental trajectories on ASD. The present study aimed at identifying early different language developmental profiles of infants at high familial risk for ASD (HR-ASD) and testing their predictive role on ASD symptoms at 2 years. The role of gestures on socio-communicative skills has also been explored. Trajectories of expressive vocabulary were investigated in 137 HR-ASD infants at 12, 18, and, 24 months of age. Parents were requested to complete the Italian version of the MacArthur-Bates Communicative Development Inventory and ASD symptoms were measured by ADOS-2. Latent class growth analysis defined four trajectories: above average language development group (above-average LD, 18.2%), normal language development group (NLD, 38.7%), late-onset language development group (late-onset LD, 11.7%), and a group of children with stable language delay (SLD, 31.4%). Results showed that the SLD group obtained higher communicative difficulties and restricted/repetitive behavior compared to the other groups. Examining early increase of produced gestures in the different language classes, we found fewer produced gestures between 12 and 18 months in the SLD group compared to the late-onset LD group. The results identified clusters of HR infants who follow similar estimated trajectories based on individual differences in language development. These patterns of early language acquisition, together with produced gestures, may be predictive of later ASD symptoms and useful for planning prompt intervention. LAY SUMMARY: Language/gesture deficits are hallmark features of Autism Spectrum Disorder (ASD), but the predictive role of communicative trajectories on ASD remains unclear. In a longitudinal Italian sample of infants at high familial risk for ASD (HR-ASD), we tested if language trajectories and their link with gestures can predict ASD symptoms. We found four trajectories and HR infants with a stable language delay (SLD) trajectory showed more ASD symptoms later on. SLD infants produced fewer gestures compared to late-onset language development group that show more typical communicative skills.

Early developmental trajectories of expressive vocabulary and gesture production in a longitudinal cohort of Italian infants at high-risk for Autism Spectrum Disorder.

Fabio Apicella

Papers

On the existence of synchrostates in multichannel EEG signals during face-perception tasks

Using brain connectivity measure of EEG synchrostates for discriminating typical and Autism Spectrum Disorder

Psychodynamic psychotherapy for separation anxiety disorders in children.

Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

Early developmental trajectories of expressive vocabulary and gesture production in a longitudinal cohort of Italian infants at high-risk for Autism Spectrum Disorder.