Autism - Cause Factors, Early Diagnosis and Therapies

Rev. Neurosci. 2014; 25(6): 841850

Shreya Bhat, U. Rajendra Acharya, Hojjat Adeli*, G. Muralidhar Bairy and Amir Adeli

Autism: cause factors, early diagnosis and therapiesAbstract: Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsycholog-ical and behavioral deficits. Cognitive impairment, lack of social skills, and stereotyped behavior are the major autistic symptoms, visible after a certain age. It is one of the fastest growing disabilities. Its current prevalence rate in the U.S. estimated by the Centers for Disease Control and Prevention is 1 in 68 births. The genetic and physiological structure of the brain is studied to determine the pathology of autism, but diagnosis of autism at an early age is challenging due to the existing phenotypic and etiological heterogeneity among ASD individuals. Volumetric and neuroimaging techniques are explored to elucidate the neuroanatomy of the ASD brain. Nuroanatomical, neurochemical, and neu-roimaging biomarkers can help in the early diagnosis and treatment of ASD. This paper presents a review of the types of autism, etiologies, early detection, and treatment of ASD.

Keywords: autism spectrum disorders; CHD8; GABA; neural connectivity; virtual reality.

DOI 10.1515/revneuro-2014-0056Received August 8, 2014; accepted August 11, 2014; previously pub-lished online September 12, 2014

IntroductionThe human brain is one of the most composite organs of the body because of its complex genetic structure

and compound neural connectivity. A synaptic connec-tion between neurons is termed as scale-free network as it changes with development. The more information collected by the brain, the more will be the synaptic connections and its study becomes more complex. The relationship between the functional brain wiring and cog-nitive development enhances the understanding of neu-rodevelopmental disorders (Bosl etal., 2011).

Autism is one of the psychological and heterogene-ous developmental disorders due to the abnormal wiring between the different brain regions (Figure 1) (Matson et al., 2012). It is a neuropsychiatric syndrome, derived from the Greek word autos, meaning an isolated self, in which a person keeps himself/herself isolated from the surrounding interactions. The Centers for Disease Control and Prevention (CDC) estimated that the prevalence rate of autism in 2006 was 1 in 110 children (Kotagal and Broomall, 2012) and increased to 1 in 88 births by 2012 (CDC, 2012). Its current prevalence rate estimated by the CDC is 1 in 68 births, or 14.7 children per 1000 (Falco, 2014). Around 1 in 175 children in Alabama and 1 in 45 children in New Jersey are identified as having an autism spectrum disorder (ASD). It is more common in White chil-dren compared with African-American or Hispanic chil-dren, and boys are five times more prone to this disorder compared with girls (Falco, 2014) because of mutations in the X-chromosome patched-related (PTCHD1) gene. The microdeletion of the PTCHD1 gene as shown in Figure 2A is maternally inherited and is dominant in males as they possess XY chromosomes whereas females have XX chro-mosomes. The microdeletion of the PTCHD1 gene becomes a recessive character in females (Noor etal., 2010). Around 5% of male ASD cases are due to the compound heterozy-gous, rare inherited functional loss of homozygous, and X-chromosome hemizygous mutations (Stein etal., 2013).

Autism is believed to affect various systems of the body and appears to have numerous etiologies (Matson etal., 2012). Clinical symptoms are observed in children above 1.52years of age due to irregularity in the physical and computational connectivity of neurons. It can mani-fest in the form of disturbed sleep, depression, decreased sleep duration, anxiety, and increased sleep onset delay (Belmonte et al., 2004). The prevalence of sleep prob-lems in autistic children is more when compared with

*Corresponding author: Hojjat Adeli, Departments of Neuroscience, Biomedical Engineering, Biomedical Informatics, Electrical and Computer Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA, e-mail: [email protected] Bhat and G. Muralidhar Bairy: Manipal Institute of Technology, Department of Biomedical Engineering, Manipal, Karnataka 576104, IndiaU. Rajendra Acharya: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; and Faculty of Engineering, Department of Biomedical Engineering, University of Malaya, 50603, MalaysiaAmir Adeli: Department of Neurology, The Ohio State University, Columbus, OH 43210, USA

Brought to you by | New York University Bobst Library Technical ServicesAuthenticated

Download Date | 5/22/15 7:59 PM

842S. Bhat etal.: Autism: cause factors, early diagnosis, and therapies

A

B

Figure 2Chromosomal variations.(A) Microdeletion of the chromosome leading to neurodevelopmen-tal disorder. (B) De novo copy number variation in ASDs.

A

B

Figure 1The brain wiring between different brain regions.(A) Normal brain and (B) autistic brain.

those with developmental delay. Researchers portray that aggression, hyperactivity, and stereotyped behaviors are common in autistic males, whereas autistic females show anxiety, depression, and greater intellectual impairment (Jeste and Geschwind, 2014). Other features are macrocephaly (Herbert, 2005), where the growth of head circumference speeds up in the first 2 years fol-lowed by deceleration in later childhood (Aylward etal., 2002), repetitive behavior, developmental delay, cogni-tive impairment (Happe etal., 2006; Yates and Couteur, 2013), and lack of communication and interactive skills (Narain, 2006). Early behavioral characteristics observed in infants are delay in babbling and improper sleep and eating habits (DiCicco-Bloom et al., 2006). Ongoing research indicates that in identical twins, if one child is autistic, there is a 3695% chance for the other child to be autistic, whereas in nonidentical twins, it is in the 030%

range. Siblings of an affected individual have 218% chances of being autistic (CDC, 2014).

Different forms of autismAutism is expanded to ASD representing a range of dis-orders affecting an individuals communication, behav-ior, and social interaction. Even though the three major areas, communication, behavior, and social interaction, are affected in autism, autistic individuals have enhanced discrimination ability where they can observe minute var-iations in feature and visual search tasks (Figure 3). This unique characteristic acts as an anomaly in autistic indi-viduals as they are biased to variations in the surrounding (Brandwein etal., 2013). These variations, distracting the normal population, help in stimulus information process-ing (Elsabbagh et al., 2013). Around 10% of the autistic population has special skills called savant skills. These



S. Bhat etal.: Autism: cause factors, early diagnosis, and therapies843

people are brilliant in mathematical calculations, possess high memory power, and have extraordinary artistic and musical abilities. For example, an autistic systems admin-istrator named Gary McKinnon hacked most of the US government computers in 2002 (Kushner, 2011) and dis-covered multiple errors in their system. Table 1 describes three major types of ASD. Table 2 presents autism catego-rization according to the latest clinical research.

In addition to the three major types of autism described in Table 1, there are several other less common types, termed as pervasive developmental disorders: regressive autistic spectrum disorder, where a child is

normal until 1824months and then regresses to autistic symptoms; childhood disintegrative disorder, a rare dis-order affecting social, motor, and language skills (NIMH, 2014); and Rett syndrome, where mutations are linked to the X-chromosome and are generally seen in girls (Chah-rour etal., 2008).

Seizure in epileptic patients hampers their neurologi-cal function, which in turn affects the social functioning of the brain (Mammone et al., 2012; Martis et al., 2013; Hearld, 2014; Strzelecka, 2014). Around 25% of children with autism develop seizures (Scassellati, 2005). Accord-ing to Gabis et al. (2005), the frequency of epilepsy in

Figure 3Illustration of the enhanced discrimination ability among autistic individuals.The response of a normal subject to the surrounding stimuli is also shown.

Table 1Different types of ASD (CDC, 2014).

Types Also termed as Clinical features Percentage affected

Autistic disorder Classic autism Impairment in interactive, cognitive, communication and language skills

20% of the population

Self-injurious and unusual behavior

Aspergers syndrome High functioning autism

Normal language and cognitive ability

Majority of the population

Unusual behavior, social impairment

Pervasive developmental disordernot otherwise specified

Atypical autism Challenges in social interaction and communication

Below 5%7% of the population




autistic children is higher. Autistic girls have a higher rate of epilepsy compared with boys, thus explaining the cause of lower analyzing ability in autistic girls.

Factors causing autismVarious studies and experiment-based analyses have attempted to provide probable causes of autism, summa-rized in Figure 4.

The gene expression is varied due to copy number var-iations or environmental toxins. Welburg (2011) reviewed the role of genetic variants and copy number variations in ASDs. Few cases of autism are due to de novo mutations (Iossifov etal., 2012). The de novo genes are responsible for neuron motility, axon guidance, and synaptic devel-opment (Gilman etal., 2011). Studies reveal that de novo copy number variations (structural changes) are more common in autistic children compared with normal chil-dren, as illustrated in Figure 2B (Levy etal., 2011; Sanders et al., 2011). According to the latest research on genet-ics, the mutation of CHD8 (chromodomain helicase DNA binding protein 8) gene is linked to autism, resulting in macrocephaly and wide set eyes (Bernier et al., 2014).

Figure 4Possible causes of ASD.

Table 2Autism categorization according to the latest clinical research (Venker etal., 2013).

Types Clinical features

Persistently severe

Difficulty with daily living activities, self-injurious behavior, severe cognitive disability

Persistently moderate

Impairment in social interaction and communication

Improving Improvement in development due to behavioral therapies

Worsening Intensive and self-injurious behavior

Stereotyped behavior, impaired social interaction, and weak synaptic transmission are associated with the irreg-ular microglia-mediated synaptic pruning (Zhan et al., 2014). The different types of brain cells are present in the six distinct layers of cortex responsible for learning and memory. Changes in genetic structure vary the formation of cortex layers, leading to patches of disorganization in the cortex (Hamilton, 2014).

Apart from genetic factors contributing to autism, environmental factors including mercury, radiation, and diesel exhaust have been implicated. Further, mater-nal viral infections, valproic acid and thalidomide used during pregnancy, and exposure to pesticides have been reported to affect the central nervous system of the fetal brain (CDC, 2014). Krakowiak et al. (2012) associated a mothers metabolic conditions during pregnancy to ASD, developmental delay, and cognitive impairment in the off-spring. It has also been found that gestational maternal hypothyroxinemia is linked to ASD (Roman etal., 2013).

Autism is a neurobiological abnormality affecting the size of the corpus callosum (He etal., 2010), a collection of nerve fibers connecting the two hemispheres (left and right) of the brain and playing a major role in the trans-mission of sensory, motor, and cognitive information. Agenesis of the corpus callosum contributes to develop-ing autism, depicted in Figure 5 (Paul etal., 2014). Zielin-ski etal. (2014) reported increased cortical thinning in the frontal lobe, parietal lobe, occipital lobe, and the entire cortex of the ASD subjects.

Neuroimaging techniques have shown that children suffering from ASD possess anomalous brain connectiv-ity. The intrinsic wiring potential of a brain region cor-responds to lower wiring costs associated with shorter geodesic distances. The geodesic distances capture the complex surface of the brain. It has been observed that the brains intrinsic connectivity differs in ASD subjects compared with normal subjects (Figure 6), and hence, the wiring costs in autistic subjects are significantly reduced (Ecker and Murphy, 2014). It has been found that func-tional connectivity with other brain regions is decreased within the frontal and temporal cortical regions of the ASD brain (Tyszka etal., 2014). The transfer of informa-tion is reduced due to less specialized autistic brain, i.e., overconnectivity between neural assemblies (Misic etal., 2014) and underconnectivity of the functional brain regions (Mostofsky and Ewen, 2011; Just et al., 2012), resulting in language impairment (Verly etal., 2013) and reduced learning rate (Schipul etal., 2012).

Dinstein etal. (2011) reported weak interhemispheric neural synchronization (Anderson et al., 2011) in tod-dlers with autism. The disrupted neural synchronization




is evident in naturally sleeping autistic toddlers, and the strength of cortical synchronization is negatively corre-lated in autistic subjects, whereas it is positively corre-lated in subjects with verbal ability. Atypical autonomic processing resulting in low skin conductance (Eilam-Stock et al., 2014) and decreased neuropsychological functioning (Nair et al., 2013) results in ASD symptom severity.

Early diagnosisThe diagnosis of autism is based on the standards explained in Diagnostic and Statistical Manual of Mental

A

B

Figure 5Corpus callosum: (A) normal and (B) autistic (agenesis of the corpus callosum).

Disorders, 5th edition (DSM, 2013) and Autism Diagnostic Observation Schedule (Lord etal., 2012).

Early diagnosis of autism helps to provide behavio-ral therapies to the affected individuals. Toddlers with developing autism concentrate more on the mouth region of the face compared with the eye region (Rutishauser etal., 2013; Shic etal., 2014) and have weak judgmental ability. Hence, detection of gaze and position can help in the diagnosis of autism (Lahiri etal., 2011; Guillon etal., 2014). Bekele et al. (2013) used a virtual-reality-based (Bohil etal., 2011; Carozza etal., 2014) facial expression intervention system that monitors eye gaze and physi-ological signals for ten ASD adolescents and ten typically developing adolescents in emotion recognition tasks. The differences between the ASD and typically developing groups were determined using eye tracking indices and performance data. Weigelt et al. (2012) found quantita-tive difference in facial discrimination between autistic and normal subjects as autistic subjects possess impaired facial identity recognition and eye discrimination. Studies reveal that the increased response to direct gaze triggering unprompted mental state attributions is reduced in autis-tic subjects, and they show increased response to averted gaze than the direct gaze (Hagen etal., 2014).

Takarae et al. (2014) reported on the correlation of neurons in visual motion processing using the functional magnetic resonance imaging technique. The brain area V5 responsible for visual perception and pursuit was con-sidered. The ASD and the typically developing groups were subjected to passive viewing of visual movement and visual pursuit tracking. Passive viewing is related to static images, and pursuit tracking, to moving images. They reported increased V5 activation during passive viewing and decreased V5 activation during visual pursuit in the autistic subjects. The increased activation during passive viewing implied connectivity alterations in the V5 area, followed by reduced GABAergic tone (-amino butyric acid) and inhibi-tory modulation. The study also suggested that high abnor-malities at the network level are related to visual processing in autism. The cortical response to the dynamic social stimuli is disrupted in ASD adolescents, indicating disordered con-nectivity between the different brain regions and the lateral region of fusiform gyrus (Weisburg etal., 2014).

Chromosomal microarray analysis, exome sequenc-ing (Yu et al., 2013), and genetic testing are appropriate tools in the identification of de novo mutations and ASD risk genes (Jeste and Geschwind, 2014). Keehn et al. (2013) proposed a hypothesis that links abnormal atten-tion networks including alerting, orienting, and executive control network to autism. Autistic individuals lack com-munication skills, speech perception (Kujala etal., 2013),




Intrinsic wiring ofthe brain

Normal brain

Shorter geodesicdistance Significantly lower

geodesic distance dueto the abnormal

connectivity

Normal behavior Repetitive behavior

Autistic brain

Minimum distance between the two different points along the cortical surface(Geodesic distance)

Figure 6Intrinsic wiring of the normal and autistic brain.

and comprehension (Jones etal., 2014). The left temporal cortex activity is responsible for social language compre-hension in typically developing children, but it is reduced in autistic children and the activity rate decreases with age (Eyler etal., 2012). The early diagnosis of lateralized abnormalities of temporal cortex processing can lead to early neurodevelopmental pathology in autism.

Stevenson etal. (2014) reported on the link between multisensory temporal function and speech processing in ASD individuals. The ASD and typically developing par-ticipants underwent three tasks: (a) an audiovisual sim-ultaneity judgment task that includes single stimulus per run, audio, and visual-leading stimuli, (b) a McGurk task including audio only, visual only, and audiovisual presen-tations, and (c) auditory and visual temporal-order judg-ment tasks including run with auditory and visual stimuli. The sensory representations are the building blocks of higher order domain of speech perception. They observed weak binding between the multisensory temporal func-tion and audiovisual speech processing using the McBurk effect that in turn causes communication impairment in ASD individuals.

A recent development is automated electroencepha-logram-based diagnosis (Adeli and Ghosh-Dastidar, 2010; Cong et al., 2013; Kimiskidis et al., 2013; Herrera et al., 2013) of ASD (Ahmadlou etal., 2010, 2012a,b) using three different computational paradigms of signal processing such as wavelets (Tao etal., 2012; Xiang and Liang, 2012; Kodogiannis et al., 2013), neural networks (Graf et al., 2012; Alexandridis, 2013; Celikoglu, 2013; Zhang and Ge, 2013), and nonlinear analysis (Acharya etal., 2012, 2013) and chaos theory (Cen etal., 2013; Hsu, 2013). This is the subject of another review article by the authors (Bhat etal., 2014).

TherapiesIntervention methods can enhance social engagement and reciprocity in autistic children. Infants and toddlers at risk for ASD are introduced to learning therapies, and parent-child interactions are enhanced to develop inter-active and communicative skills in toddlers at high risk




(Dawson, 2008). Multisensory speech integration ability is enhanced when ASD children enter adolescence due to plausible causes such as hormonal changes after puberty, differential myelination patterns along the white matter tracts, and potential increases in social interaction (Foxe etal., 2013).

Rodriguez and Kern (2011) suggest that therapies addressing neuroinflammation can be introduced to control microglial activation and enhance neuronal con-nection. The concentration of methionine, cystine, and glutathione in ASD children is less, and oxidized glu-tathione concentration is high. The lower concentration of cystine in autistic children results in high oxidative stress. Thus, zonisamide, an antiepileptic drug, can be admin-istered that enhances the influx of cystine to reduce the oxidative stress (Ghanizadeh, 2011).

Lai etal. (2012) reported that the left inferior frontal gyrus in ASD subjects is highly activated during song stim-ulation but not speech stimulation. Since musical abilities are preserved due to increased neural connectivity and sensitivity for song, musical therapies can be introduced to improve verbal communication in the ASD population. Few ASD individuals possess extraordinary cognitive strengths in different domains such as problem solving, art, music, and innovative skills.

Table 3Summary of psychological therapies in the treatment of ASD.

Applied therapies Areas of improvement Authors

Neurofeedback training and speech therapy

Enhancement in cognitive skills

Karimi etal., 2011

Reduction in aggressive behavior Attention control

Psychoeducation therapy

Increase in learning rate

Zdravkovic etal., 2010

Attention control Applied behavioral analysis

Skill developmentCommunication

Matson etal., 2012

Interactive three-dimensional technology and graphics

Attention controlSocial interactionEnhancing activity involvement quality

Dorsey and Howard, 2011

Dolphin-assisted therapy

Increase in nonverbal communication

Cai etal., 2013

Virtual assessment tools (entertainment technology)

Speech developmentCommunicationInteractive skills

Munson and Pasquel, 2012

Assistive reading tool Developing reading and comprehending skills

Pavlov, 2014

Iuculano et al. (2014) studied the brain activity pat-terns in the ASD and typically developing children while solving complex numerical problems. ASD children show different multivariate activation patterns in cortical regions involved in perceptual skills and prove that they have better problem-solving ability. This ability can act as a boon to the autistic population and improve the quality of life.

A summary of the current psychological therapies used in the treatment of ASD is presented in Table 3.

ConclusionAutism is a neurodevelopmental disorder that cannot be cured, but measures can be taken to convert this disabil-ity to ability. Studies have revealed that alterations in the chromosome structure due to environmental factors, vari-ations in the neural connectivity, and different parts of the brain converge to autistic symptoms. Atypical behavior in children arises after 1824 months, but the identification of phenotypic, behavioral, and neurophysiological risk indices with the help of neuroimaging techniques can determine the early signs of the disorder. Advances in sci-entific understanding of ASDs help in the innovation of several pharmacotherapies. The increase in parent-child interactions, applied behavioral analysis, developmental psychopathology, cognitive neuroscience, and neurobi-ology has led to the development of effective treatments after the early diagnosis of autistic symptoms.

ReferencesAcharya, U.R., Sree, S.V., Alvin, A.P.C., Yanti, R., and Suri, J. (2012).

Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22, 1250002.

Acharya, U.R., Sree, S.V., Swapna, G., Martis, R.J., and Suri, J. (2013). Automated EEG analysis of epilepsy: a review. Knowl. Based Syst. 37, 274282.

Adeli, H. and Ghosh-Dastidar S. (2010). Automated EEG-Based Diagnosis of Neurological DisordersInventing the Future of Neurology (Boca Raton, FL: CRC Press, Taylor & Francis).

Ahmadlou, M., Adeli, H., and Adeli, A. (2010). Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J. Clin. Neurophysiol. 27, 328333.

Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Improved visibility graph fractality with application for diagnosis of autism spec-trum disorder. Physica A 391, 47204726.

Ahmadlou, M., Adeli, H., and Adeli, A. (2012b). Fuzzy synchroniza-tion likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J. Neurosci. Methods 211, 203209.




Alexandridis, A. (2013). Evolving RBF neural networks for adaptive soft-sensor design. Int. J. Neural Syst. 23, 1350029.

Anderson, J.S., Druzgal, T.J., Froehlich, A., DuBray, M.B., Lange,N., Alexander, A.L., Abildskov, T., Nielsen, J.A., Cariello, A.N., Cooperrider, J.R., etal. (2011). Decreased interhemispheric functional connectivity in autism. Cereb. Cortex 21, 11341146.

Aylward, E.H., Minshew, N.J., Field, K., Sparks, B.F., and Singh, N. (2002). Effects of age on brain volume and head circumference in autism. Neurology 59, 175183.

Bekele, E., Zheng, Z., Swanson, A., Crittendon, J., Warren, Z., Sarkar, N. (2013). Understanding how adolescents with autism respond to facial expressions in virtual reality environments. IEEE Trans. Visual. Comput. Graphics 19, 711720.

Belmonte, M.K., Allen, G., Beckel-Mitchener, A., Boulanger, L.M., Carper, R.A., Webb, S.J. (2004). Autism and abnormal develop-ment of brain connectivity. J. Neurosci. 24, 92289231.

Bernier, R., Golzio, C., Xiong, B., Stressman, H.A., Coe, B.P., Osnat,P., Kali, W., Jennifer, G., Carl B., AnnekeT.V., etal. (2014). Disruptive CHD8 mutations define a subtype of autism early in development. Cell. To be published. Available at: http://www.sciencedaily.com/releases/2014/07/140703125851.htm. Accessed July 7, 2014.

Bhat, S., Acharya, U.R., Adeli, A., Bairy, G.M., and Adeli, A. (2014). Automated diagnosis of autism: in search of a mathematical marker. Rev. Neurosci. 25, 813823.

Bohil, C.J., Alicea, B., and Biocca, F.A. (2011). Virtual reality in neuro-science research and therapy. Nat. Rev. Neurosci. 12, 752762.

Bosl, W., Tierney, A., Flusberg, H.T., and Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med. 9, 116.

Brandwein, A.B., Foxe, J.J., Butler, J.S., Russo, N.N., Altschuler, T.S., Gomes, H., Molholm, S. (2013). The development of multi-sensory integration in high-functioning autism: high-density electrical mapping and psychophysical measures reveal impairments in the processing of audiovisual inputs. Cereb. Cortex 23, 13291341.

Cai, Y., Chia, N.K.H., Thalmann, D., Kee, N.K.N., Zheng, J., Thalmann,N.M. (2013). Design and development of a virtual dolphinarium for children with autism. IEEE Trans. Neural Syst. Rehabil. Eng. 21, 208217.

Carozza, L., Tingdahl, D., Bosche, F., and Van Gool, L. (2014). Markerless vision-based augmented reality for urban planning. Comput. Aided Civ. Infrastruct. Eng. 29, 217.

Celikoglu, H.B. (2013). An approach to dynamic classification of traffic flow patterns by neural networks. Comput. Aided Civ. Infrastruct. Eng. 28, 273288.

Cen, Z., Wei, J., and Jiang, R. (2013). A grey-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems. Int. J. Neural Syst. 23, 1350025.

Center for Disease Control and Prevention (2012). New data on autism spectrum disorders. Available at: http://www.cdc.gov/features/countingautism/. Accessed July 15, 2014.

Center for Disease Control and Prevention (2014). Facts about ASD. Available at: http://www.cdc.gov/ncbddd/autism/facts.html. Accessed February 20, 2014.

Chahrour, M., Jung, S.Y., Shaw, C., Zhou, X., Wong, S.T.C., Qin, J., Zoghbi, H.Y. (2008). MeCP2, a key contributor to neurological disease, activates and represses transcription. Science 320, 12241229.

Cong, F., Phan, A.H., Astikainen, P., Zhao, Q., Wu, Q., Hietanen, J.K., Ristaniemi, T., Cichocki, A. (2013). Multi-domain feature extrac-

tion for event-related potential through nonnegative multi-way array decomposition from low dense array EEG. Int. J. Neural Syst. 23, 1350006.

Dawson, G. (2008). Early behavioral intervention, brain plasticity and the prevention of autism spectrum disorder. Dev. Psycho-pathol. 20, 775803.

DiCicco-Bloom, E., Lord, C., Zwaigenbaum, L., Courchesne, E., Dager, S.R., Schmitz, C., Schultz, R.T., Crawley, J., Young, L.J. (2006). The developmental neurobiology of autism spectrum disorder. J. Neurosci. 26, 68976906.

Dinstein, I., Pierce, K., Eyler, L., Solso, S., Malach, R., Behrmann,M., Courchesne, E. (2011). Disrupted neural synchronization in tod-dlers with autism. Neuron 70, 12181225.

Dorsey, R. and Howard, A.M. (2011). Examining the effects of technology-based learning on children with autism: a case study. 2011 11th IEEE International Conference on Advanced Learning Technologies, University of Georgia, Athens, GA, USA, July 68, 2011, 260261.

DSM (2013). American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (Arlington, VA: American Psychiatric Association). Available at: http://dsm.psychiatryonline.org/book.aspx?bookid=556. Accessed April 3, 2014.

Ecker, C. and Murphy, D. (2014). Neuroimaging in autism from basic science to translational research. Nat. Rev. Neurol. 10, 8291.

Eilam-Stock, T., Xu, P., Cao, M., Gu, X., Dam, N.T.V., Anagnostou, E., Kolevzon, A., Soorya, L., Park, Y., Siller, M., etal. (2014). Abnor-mal autonomic and associated brain activities during rest in autism spectrum disorder. Brain 137, 153171.

Elsabbagh, M., Fernandes, J., Webb, S.J., Dawson, G., Charman, T., Johnson, M.H.; British Autism Study of Infant Siblings Team. (2013). Disengagement of visual attention in infancy is associ-ated with emerging autism in toddlerhood. Biol. Psychiatry 74, 189194.

Eyler, L.T., Pierce, K., and Courchesne, E. (2012). A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135, 949960.

Falco, M. (2014). Autism rates now in 1 in 68 U.S. children: CDC. Available at: http://www.cnn.com/2014/03/27/health/cdc-autism/index.html?iref=allsearch. Accessed April 3, 2014.

Foxe, J.J., Molholm, S., Bene, V.A.D., Frey, H.P., Russo, N.N., Blanco,D., Saint-Amour, D., and Ross, L.A. (2013). Severe multisensory speech integration deficits in high-functioning school-aged children with autism spectrum disorder (ASD) and their resolution during early adolescence. Cereb. Cortex, bht213, 1929.

Gabis, L., Pomeroy, J., and Andriola, M.R. (2005). Autism and epilepsy: cause, consequence, comorbidity, or coincidence?. Epilepsy Behav. 7, 652656.

Ghanizadeh, A. (2011). A novel hypothesized clinical implication of zonisamide for autism. Ann. Neurol. 69, 426.

Gilman, S.R., Iossifov, I., Levy, D., Ronemus, M., Wigler, M., Vitkup,D. (2011). Rare de novo variants associated with autism implicate a large functional network of genes involved in for-mation and function of synapses. Neuron 70, 898907.

Graf, W., Freitag, S., Sickert, J.U., and Kaliske, M. (2012), Structural analysis with fuzzy data and neural network-based material description. Comput. Aided Civ. Infrastruct. Eng. 27, 640654.

Guillon, Q., Hadjikhani, N., Baduel, S., and Rog, B. (2014). Visual social attention in autism spectrum disorder: Insights from eye tracking studies. Neurosci. Biobehav. Rev. 42, 279297.




Hagen, E.A.H., Stoyanova, R.S., Rowe, J.B., Cohen, S.B., and Calder,A.J. (2014). Direct gaze elicits atypical activation of the theory-of-mind network in autism spectrum conditions. Cereb. Cortex 24, 14851492.

Hamilton, J. (2014). Brain changes suggest autism starts in the womb. Available at: http://www.npr.org/blogs/health/2014/03/26/294446735/brain-changes-suggest-autism-starts-in-the-womb?ft=1&f=1007. Accessed April 3, 2014.

Happe, F., Ronald, A., and Plomin, R. (2006). Time to give up on a single explanation for autism. Nat. Neurosci. 9, 12181220.

He, Q., Duan, Y., Karsch, K., and Miles, J. (2010). Detecting corpus callosum abnormalities in autism based on anatomical land-marks. Psychiatry Res. 183, 126132.

Hearld (2014). The Hearld Scotland. Available at: http://www.heraldscotland.com/news/health/study-links-epilepsy-and-autism.21079369. Accessed February 21, 2014.

Herbert, M.R. (2005). Large brains in autism: the challenge of perva-sive abnormality. Neuroscientist 11, 417440.

Herrera, L.J., Fernandes, C., Mora, A.M., Migotina, D., Largo,R., Guillen, A., Rosa, A.C. (2013). Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification. Int. J. Neural Syst. 23, 1350012.

Hsu, W.Y. (2013). Single-trial motor imagery classification using asymmetry ratio, phase relation and wavelet-based fractal features, and their selected combination. Int. J. Neural Syst. 23, 1350007.

Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum,J., Yamrom, B., Lee, Y.H., Narzisi, G., Leotta, A., etal. (2012). De novo gene disruptions in children on the autis-tic spectrum. Neuron 74, 285299.

Iuculano, T., Rosenberg-Lee, M., Supekar, K., Lynch, C.J., Khouzam,A., Phillips, J., Uddin, L.Q., Menon, V. (2014). Brain organization underlying superior mathematical abilities in children with autism. Biol. Psychiatry 75, 223230.

Jeste, S.S. and Geschwind, D.H. (2014). Disentangling the hetero-geneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 7481.

Jones, E.J.H., Gliga, T., Bedford, R., Charman, T., and Johnson,M.H. (2014). Developmental pathways to autism: a review of prospec-tive studies of infants at risk. Neurosci. Biobehav. Rev. 39, 133.

Just, M.A., Keller, T.A., Malave, V.L., Kana, R.K., and Varma, S. (2012). Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neurosci. Biobehav. Rev. 36, 12921313.

Karimi, M., Haghshenas, S., and Rostami, R. (2011). Neurofeedback and autism spectrum: a case study. Procedia Soc. Behav. Sci. 30, 14721475.

Keehn, B., Muller, R., and Townsend, J. (2013). Atypical attentional networks and the emergence of autism. Neurosci. Biobehav. Rev. 37, 164183.

Kimiskidis, V.K., Kugiumtzis, D., Papagiannopoulos, S., and Vlaikidis, N. (2013). Transcranial magnetic stimulation (TMS) modulates epileptiform discharges in patients with partial epilepsy: a combined EEG-TMS study. Int. J. Neural Syst. 23, 1250035.

Kodogiannis, V.S., Amina, M., and Petrounias, I. (2013). A cluster-ing-based fuzzy-wavelet neural network model for short-term load forecasting. Int. J. Neural Syst. 23, 1350024.

Kotagal, S. and Broomall, E. (2012). Sleep in children with autism spectrum disorder. Pediatr. Neurol. 47, 242251.

Krakowiak, P., Walker, C.K., Bremer, A.A., Baker, A.S., Ozonoff, S., Hansen, R.L., Hertz-Picciotto, I. (2012). Maternal metabolic conditions and risk for autism and other neurodevelopmental disorders. Pediatrics 129, 11211128.

Kujala, T., Lepisto, T., and Naatanen, R. (2013). The neural basis of aberrant speech and audition in autism spectrum disorders. Neurosci. Biobehav. Rev. 37, 697704.

Kushner, D. (2011). The autism defense. IEEE Spectr. 48, 3337.Lai, G., Pantazatos, S.P., Schneider, H., and Hirsch, J. (2012). Neural

systems for speech and song in autism. Brain 135, 961975.Lahiri, U., Warren, Z., and Sarkar, N. (2011). Design of a gaze-sensi-

tive virtual social interactive system for children with autism. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 443452.

Levy, D., Ronemus, M., Yamrom, B., Lee, Y., Leotta, A., Kendall, J., Marks, S., Lakshmi, B., Pai, D., Ye, K., etal. (2011). Rare de novo and transmitted copy-number variation in autistic spec-trum disorders. Neuron 70, 886897.

Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., and Bishop,S.L. (2012). Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) (Torrance, CA: Western Psychological Services). Available at: http://www.hogrefe.co.uk/autism-diagnostic-observation-schedule-2nd-edition-ados-2.html. Accessed April 3, 2014.

Mammone, N., Labate, D., Lay-Ekuakille, A., and Morabito, F.C. (2012). Analysis of absence seizure generation using EEG spatial-temporal regularity measures. Int. J. Neural Syst. 22, 125002417.

Martis, R.J., Acharya, U.R., Tan, J.H., Petznick, A., Chua, C.K., and Ng, E.Y.K. (2013). Application of intrinsic time-scale decomposi-tion (ITD) to EEG signals for automated seizure prediction. Int. J. Neural Syst. 23, 1350023.

Matson, J.L., Turygin, N.C., Beighley, J., Rieske, R., Tureck, K., and Matson, M. L. (2012). Applied behavior analysis in autism spectrum disorders: recent developments, strengths, and pitfalls. Res. Autism Spectr. Disord. 6, 144150.

Misic, B., Doesburg, S.M., Fatima, Z., Vidal, J., Vakorin, V.A., Taylor,M.J., and McIntosh, A.R. (2014). Coordinated informa-tion generation and mental flexibility: large-scale network disruption in children with autism. Cereb. Cortex. in press. DOI:10.1093/cercor/bhu082.

Mostofsky, S.H. and Ewen, J.B. (2011). Altered connectivity and action model formation in autism is autism. Neuroscientist 17, 437448.

Munson, J. and Pasquel, P. (2012). Using technology in autism research: the promise and the perils. Entertainment Comput. 45, 8991.

Nair, A., Treiber, J.M., Shukla, D.K., Shih, P., and Muller, R. (2013). Impaired thalamocortical connectivity in autism spectrum dis-order: a study of functional and anatomical connectivity. Brain 136, 19421955.

Narain, C. (2006). Childhood developmental disorders. Nat. Neuro-sci. 9, 1209.

NIMH (2014). Autism spectrum disorder. Available at: http://www.nimh.nih.gov/health/topics/autism-spectrum-disorders-asd/index.shtml. Accessed February 22, 2014.

Noor, A., Whibley, A., Marshall, C.R., Gianakopoulos, P.J., Piton, A., Carson, A.R., Orlic-Milacic, M., Lionel, A.C., Sato, D., Pinto, D., etal. (2010). Disruption at the PTCHD1 locus on Xp22.11 in autism spectrum disorder and intellectual disability. Sci. Transl. Med. 2, 19.




Paul, L.K., Corsello, C., Kennedy, D.P., and Adolphs, R. (2014). Agenesis of the corpus callosum and autism: a comprehensive comparison. Brain 137, 18131829.

Pavlov, N. (2014). User interface for people with autism spectrum disorders. J. Software Eng. Appl. 7, 128134.

Rodriguez, J.I. and Kern, J.K. (2011). Evidence of microglial activa-tion in autism and its possible role in brain underconnectivity. Neuron Glia Biol. 7, 205213.

Roman, G.C., Ghassabian, A., Bongers-Schokking, J.J., Jaddoe,V.W.V., Hofman, A., de Rijke, Y.B., Verhulst, F.C., Tiemeier, H. (2013). Association of gestational maternal hypothyroxinemia and increased autism risk. Ann. Neurol. 74, 733742.

Rutishauser, U., Tudusciuc, O., Wang, S., Mamelak, A.N., Ross, I.B., Adolphs, R. (2013). Single-neuron correlates of atypical face processing in autism. Neuron 80, 887899.

Sanders, S.J., Ercan-Sencicek, A.G., Hus, V., Luo, R., Murtha, M.T., Moreno-De-Luca, D., Chu, S.H., Moreau, M.P., Gupta, A.R., Thomson, S.A., etal. (2011). Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863885.

Scassellati, B. (2005). Quantitative metrics of social response for autism diagnosis. IEEE International Workshop on Robots and Human Interactive Communication, Nashville, TN, USA, August 1315, 2005, 585590.

Schipul, S.E., Williams, D.L., Keller, T.A., Minshew, N.J., and Just, M.A. (2012). Distinctive neural processes during learning in autism. Cereb. Cortex 22, 937950.

Shic, F., Macari, S., and Chawarska, K. (2014). Speech disturbs face scanning in 6-month-old infants who develop autism spectrum disorder. Biol. Psychiatry 75, 231237.

Stein, J.L., Parikshak, N.N., and Geschwind, D.H. (2013). Rare inher-ited variation in autism: beginning to see the forest and a few trees. Neuron 77, 209211.

Stevenson, R.A., Siemann, J.K., Schneider, B.C., Eberly, H.E., Woynaroski, T.G., Camarata, S.M., Wallace, M.T. (2014). Multisensory temporal integration in autism spectrum disorders. J.Neurosci. 34, 691697.

Strzelecka, J. (2014). Electroencephalographic studies in children with autism spectrum disorders. Res. Autism Spectr. Disord. 8, 317323.

Takarae, Y., Luna, B., Minshew, N.J., and Sweeney, J.A. (2014). Visual motion processing and visual sensorimotor control in autism. J. Int. Neuropsychol. Soc. 20, 113122.

Tao, H., Zain, J.M., Ahmed, M.M., Abdalla, A.N., and Jing, W. (2012). A wavelet-based particle swarm optimization algorithm for digital image watermarking. Integr. Comput. Aided Eng. 19, 8191.

Tyszka, J.M., Kennedy, D.P., Paul, L.K., and Adolphs, R. (2014). Largely typical patterns of resting-state functional connectiv-ity in high-functioning adults with autism. Cereb. Cortex. 24, 18941905.

Venker, C.E., Ray-Subramanian, C.E., Bolt, D.M., and Weismer,S.E. (2013). Trajectories of autism severity in early childhood. J.Autism Dev. Disord. 44, 546563.

Verly, M., Verhoeven, J., Zink, I., Mantini, D., Oudenhove, L.V., Lagae, L., Sunaert, S., Romme, N. (2013). Structural and func-tional underconnectivity as a negative predictor for language in autism. Hum. Brain Map. 35, 36023615.

Weigelt, S., Koldewyn, K., and Kanwisher, N. (2012). Face identity recognition in autism spectrum disorders: a review of behavio-ral studies. Neurosci. Biobehav. Rev. 36, 10601084.

Weisburg, J., Milleville, S.C., Kenworthy, L., Wallace, G.L., Gotts,S.J., Beauchamp, M.S., Martin, A. (2014). Social percep-tion in autism spectrum disorders: impaired category selectiv-ity for dynamic but not static images in ventral temporal cortex. Cereb. Cortex 24, 3748.

Welburg, L. (2011). Autism the importance of getting the dose right. Nat. Rev. Neurosci. 12, 429.

Xiang, J. and Liang, M. (2012). Wavelet-based detection of beam cracks using modal shape and frequency measurements. Comput. Aided Civ. Infrastruct. Eng. 27, 439454.

Yates, K. and Couteur, A.L. (2013). Diagnosing autism. Paediatr. Child Health 23, 510.

Yu, T.W., Chahrour, M.H., Coulter, M.E., Jiralerspong, S., Okamura-Ikeda, K., Ataman, B., Schmitz-Abe, K., Harmin,D.A., Adli, M., Malik, A.N., etal. (2013). Using whole-exome sequencing to identify inherited causes of autism. Neuron 77, 259273.

Zdravkovic, A.D., Milisavljevic, M.J., and Petrovic, D.M. (2010). Attention in children with intellectual disabilities. Procedia Soc. Behav. Sci. 5, 16011606.

Zhan, Y., Paolicelli, R.C., Sforazzini, F., Weinhard, L., and Bolasco,G. (2014). Deficient neuron-microglia signalling results in impaired functional brain connectivity and social behavior. Nat. Neurosci. 17, 400406.

Zhang, Y. and Ge, H. (2013). Freeway travel time prediction using Takagi-Sugeno-Kang fuzzy neural network. Comput. Aided Civ. Infrastruct. Eng. 28, 594603.

Zielinski, B.A., Prigge, M.B.D., Nielsen, J.A., Froehlich, A.L., Abildskov, T.J., Anderson, J.S., Fletcher, P.T., Zygmunt, K.M., Travers, B.G., Lange, N., etal. (2014). Longitudinal changes in cortical thickness in autism and typical development. Brain 137, 17991812.



Documents

Autism - Cause Factors, Early Diagnosis and Therapies