Abstract
Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsychological 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 neuroimaging 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.
References
Acharya, 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, 1250002.10.1142/S0129065712500025Search in Google Scholar PubMed
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, 274–282.10.1016/j.knosys.2013.02.014Search in Google Scholar
Adeli, H. and Ghosh-Dastidar S. (2010). Automated EEG-Based Diagnosis of Neurological Disorders—Inventing the Future of Neurology (Boca Raton, FL: CRC Press, Taylor & Francis).10.1201/9781439815328Search in Google Scholar
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, 328–333.10.1097/WNP.0b013e3181f40dc8Search in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Improved visibility graph fractality with application for diagnosis of autism spectrum disorder. Physica A 391, 4720–4726.10.1016/j.physa.2012.04.025Search in Google Scholar
Ahmadlou, M., Adeli, H., and Adeli, A. (2012b). Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J. Neurosci. Methods 211, 203–209.10.1016/j.jneumeth.2012.08.020Search in Google Scholar PubMed
Alexandridis, A. (2013). Evolving RBF neural networks for adaptive soft-sensor design. Int. J. Neural Syst. 23, 1350029.10.1142/S0129065713500299Search in Google Scholar PubMed
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., et al. (2011). Decreased interhemispheric functional connectivity in autism. Cereb. Cortex 21, 1134–1146.10.1093/cercor/bhq190Search in Google Scholar PubMed PubMed Central
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, 175–183.10.1212/WNL.59.2.175Search in Google Scholar
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, 711–720.10.1109/TVCG.2013.42Search in Google Scholar PubMed PubMed Central
Belmonte, M.K., Allen, G., Beckel-Mitchener, A., Boulanger, L.M., Carper, R.A., Webb, S.J. (2004). Autism and abnormal development of brain connectivity. J. Neurosci. 24, 9228–9231.10.1523/JNEUROSCI.3340-04.2004Search in Google Scholar PubMed PubMed Central
Bernier, R., Golzio, C., Xiong, B., Stressman, H.A., Coe, B.P., Osnat, P., Kali, W., Jennifer, G., Carl B., Anneke T.V., et al. (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.Search in Google Scholar
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, 813–823.10.1515/revneuro-2014-0036Search in Google Scholar PubMed
Bohil, C.J., Alicea, B., and Biocca, F.A. (2011). Virtual reality in neuroscience research and therapy. Nat. Rev. Neurosci. 12, 752–762.10.1038/nrn3122Search in Google Scholar PubMed
Bosl, W., Tierney, A., Flusberg, H.T., and Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med. 9, 1–16.10.1186/1741-7015-9-18Search in Google Scholar PubMed PubMed Central
Brandwein, A.B., Foxe, J.J., Butler, J.S., Russo, N.N., Altschuler, T.S., Gomes, H., Molholm, S. (2013). The development of multisensory integration in high-functioning autism: high-density electrical mapping and psychophysical measures reveal impairments in the processing of audiovisual inputs. Cereb. Cortex 23, 1329–1341.10.1093/cercor/bhs109Search in Google Scholar PubMed PubMed Central
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, 208–217.10.1109/TNSRE.2013.2240700Search in Google Scholar PubMed
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, 2–17.10.1111/j.1467-8667.2012.00798.xSearch in Google Scholar
Celikoglu, H.B. (2013). An approach to dynamic classification of traffic flow patterns by neural networks. Comput. Aided Civ. Infrastruct. Eng. 28, 273–288.10.1111/j.1467-8667.2012.00792.xSearch in Google Scholar
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, 1350025.10.1142/S0129065713500251Search in Google Scholar PubMed
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.Search in Google Scholar
Center for Disease Control and Prevention (2014). Facts about ASD. Available at: http://www.cdc.gov/ncbddd/autism/facts.html. Accessed February 20, 2014.Search in Google Scholar
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, 1224–1229.10.1126/science.1153252Search in Google Scholar PubMed PubMed Central
Cong, F., Phan, A.H., Astikainen, P., Zhao, Q., Wu, Q., Hietanen, J.K., Ristaniemi, T., Cichocki, A. (2013). Multi-domain feature extraction for event-related potential through nonnegative multi-way array decomposition from low dense array EEG. Int. J. Neural Syst. 23, 1350006.10.1142/S0129065713500068Search in Google Scholar PubMed
Dawson, G. (2008). Early behavioral intervention, brain plasticity and the prevention of autism spectrum disorder. Dev. Psychopathol. 20, 775–803.10.1017/S0954579408000370Search in Google Scholar PubMed
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, 6897–6906.10.1523/JNEUROSCI.1712-06.2006Search in Google Scholar PubMed PubMed Central
Dinstein, I., Pierce, K., Eyler, L., Solso, S., Malach, R., Behrmann, M., Courchesne, E. (2011). Disrupted neural synchronization in toddlers with autism. Neuron 70, 1218–1225.10.1016/j.neuron.2011.04.018Search in Google Scholar PubMed PubMed Central
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 6–8, 2011, 260–261.10.1109/ICALT.2011.81Search in Google Scholar
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.Search in Google Scholar
Ecker, C. and Murphy, D. (2014). Neuroimaging in autism – from basic science to translational research. Nat. Rev. Neurol. 10, 82–91.10.1038/nrneurol.2013.276Search in Google Scholar PubMed
Eilam-Stock, T., Xu, P., Cao, M., Gu, X., Dam, N.T.V., Anagnostou, E., Kolevzon, A., Soorya, L., Park, Y., Siller, M., et al. (2014). Abnormal autonomic and associated brain activities during rest in autism spectrum disorder. Brain 137, 153–171.10.1093/brain/awt294Search in Google Scholar PubMed PubMed Central
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 associated with emerging autism in toddlerhood. Biol. Psychiatry 74, 189–194.10.1016/j.biopsych.2012.11.030Search in Google Scholar PubMed PubMed Central
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, 949–960.10.1093/brain/awr364Search in Google Scholar PubMed PubMed Central
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.Search in Google Scholar
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, 19–29.Search in Google Scholar
Gabis, L., Pomeroy, J., and Andriola, M.R. (2005). Autism and epilepsy: cause, consequence, comorbidity, or coincidence?. Epilepsy Behav. 7, 652–656.10.1016/j.yebeh.2005.08.008Search in Google Scholar PubMed
Ghanizadeh, A. (2011). A novel hypothesized clinical implication of zonisamide for autism. Ann. Neurol. 69, 426.10.1002/ana.22153Search in Google Scholar PubMed
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 formation and function of synapses. Neuron 70, 898–907.10.1016/j.neuron.2011.05.021Search in Google Scholar PubMed PubMed Central
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, 640–654.Search in Google Scholar
Guillon, Q., Hadjikhani, N., Baduel, S., and Rogé, B. (2014). Visual social attention in autism spectrum disorder: Insights from eye tracking studies. Neurosci. Biobehav. Rev. 42, 279–297.10.1016/j.neubiorev.2014.03.013Search in Google Scholar PubMed
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, 1485–1492.10.1093/cercor/bht003Search in Google Scholar PubMed PubMed Central
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.Search in Google Scholar
Happe, F., Ronald, A., and Plomin, R. (2006). Time to give up on a single explanation for autism. Nat. Neurosci. 9, 1218–1220.10.1038/nn1770Search in Google Scholar PubMed
He, Q., Duan, Y., Karsch, K., and Miles, J. (2010). Detecting corpus callosum abnormalities in autism based on anatomical landmarks. Psychiatry Res. 183, 126–132.10.1016/j.pscychresns.2010.05.006Search in Google Scholar PubMed PubMed Central
Hearld (2014). The Hearld Scotland. Available at: http://www.heraldscotland.com/news/health/study-links-epilepsy-and-autism.21079369. Accessed February 21, 2014.Search in Google Scholar
Herbert, M.R. (2005). Large brains in autism: the challenge of pervasive abnormality. Neuroscientist 11, 417–440.10.1177/0091270005278866Search in Google Scholar PubMed
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, 1350012.10.1142/S0129065713500123Search in Google Scholar PubMed
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, 1350007.10.1142/S012906571350007XSearch in Google Scholar PubMed
Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.H., Narzisi, G., Leotta, A., et al. (2012). De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299.10.1016/j.neuron.2012.04.009Search in Google Scholar PubMed PubMed Central
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, 223–230.10.1016/j.biopsych.2013.06.018Search in Google Scholar PubMed PubMed Central
Jeste, S.S. and Geschwind, D.H. (2014). Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat. Rev. Neurol. 10, 74–81.10.1038/nrneurol.2013.278Search in Google Scholar PubMed PubMed Central
Jones, E.J.H., Gliga, T., Bedford, R., Charman, T., and Johnson, M.H. (2014). Developmental pathways to autism: a review of prospective studies of infants at risk. Neurosci. Biobehav. Rev. 39, 1–33.10.1016/j.neubiorev.2013.12.001Search in Google Scholar PubMed PubMed Central
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, 1292–1313.10.1016/j.neubiorev.2012.02.007Search in Google Scholar PubMed PubMed Central
Karimi, M., Haghshenas, S., and Rostami, R. (2011). Neurofeedback and autism spectrum: a case study. Procedia Soc. Behav. Sci. 30, 1472–1475.10.1016/j.sbspro.2011.10.285Search in Google Scholar
Keehn, B., Muller, R., and Townsend, J. (2013). Atypical attentional networks and the emergence of autism. Neurosci. Biobehav. Rev. 37, 164–183.10.1016/j.neubiorev.2012.11.014Search in Google Scholar PubMed PubMed Central
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, 1250035.10.1142/S0129065712500359Search in Google Scholar PubMed
Kodogiannis, V.S., Amina, M., and Petrounias, I. (2013). A clustering-based fuzzy-wavelet neural network model for short-term load forecasting. Int. J. Neural Syst. 23, 1350024.10.1142/S012906571350024XSearch in Google Scholar PubMed
Kotagal, S. and Broomall, E. (2012). Sleep in children with autism spectrum disorder. Pediatr. Neurol. 47, 242–251.10.1016/j.pediatrneurol.2012.05.007Search in Google Scholar PubMed
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, 1121–1128.10.1542/peds.2011-2583Search in Google Scholar PubMed PubMed Central
Kujala, T., Lepisto, T., and Naatanen, R. (2013). The neural basis of aberrant speech and audition in autism spectrum disorders. Neurosci. Biobehav. Rev. 37, 697–704.10.1016/j.neubiorev.2013.01.006Search in Google Scholar PubMed
Kushner, D. (2011). The autism defense. IEEE Spectr. 48, 33–37.10.1109/MSPEC.2011.5910445Search in Google Scholar
Lai, G., Pantazatos, S.P., Schneider, H., and Hirsch, J. (2012). Neural systems for speech and song in autism. Brain 135, 961–975.10.1093/brain/awr335Search in Google Scholar PubMed PubMed Central
Lahiri, U., Warren, Z., and Sarkar, N. (2011). Design of a gaze-sensitive virtual social interactive system for children with autism. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 443–452.10.1109/TNSRE.2011.2153874Search in Google Scholar PubMed PubMed Central
Levy, D., Ronemus, M., Yamrom, B., Lee, Y., Leotta, A., Kendall, J., Marks, S., Lakshmi, B., Pai, D., Ye, K., et al. (2011). Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70, 886–897.10.1016/j.neuron.2011.05.015Search in Google Scholar PubMed
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.Search in Google Scholar
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, 1250024–17.10.1142/S0129065712500244Search in Google Scholar PubMed
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 decomposition (ITD) to EEG signals for automated seizure prediction. Int. J. Neural Syst. 23, 1350023.10.1142/S0129065713500238Search in Google Scholar PubMed
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, 144–150.10.1016/j.rasd.2011.03.014Search in Google Scholar
Misic, B., Doesburg, S.M., Fatima, Z., Vidal, J., Vakorin, V.A., Taylor, M.J., and McIntosh, A.R. (2014). Coordinated information generation and mental flexibility: large-scale network disruption in children with autism. Cereb. Cortex. in press. doi: 10.1093/cercor/bhu082.10.1093/cercor/bhu082Search in Google Scholar PubMed PubMed Central
Mostofsky, S.H. and Ewen, J.B. (2011). Altered connectivity and action model formation in autism is autism. Neuroscientist 17, 437–448.10.1177/1073858410392381Search in Google Scholar PubMed PubMed Central
Munson, J. and Pasquel, P. (2012). Using technology in autism research: the promise and the perils. Entertainment Comput. 45, 89–91.10.1109/MC.2012.220Search in Google Scholar
Nair, A., Treiber, J.M., Shukla, D.K., Shih, P., and Muller, R. (2013). Impaired thalamocortical connectivity in autism spectrum disorder: a study of functional and anatomical connectivity. Brain 136, 1942–1955.10.1093/brain/awt079Search in Google Scholar PubMed PubMed Central
Narain, C. (2006). Childhood developmental disorders. Nat. Neurosci. 9, 1209.10.1038/nn1006-1209Search in Google Scholar
NIMH (2014). Autism spectrum disorder. Available at: http://www.nimh.nih.gov/health/topics/autism-spectrum-disorders-asd/index.shtml. Accessed February 22, 2014.Search in Google Scholar
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., et al. (2010). Disruption at the PTCHD1 locus on Xp22.11 in autism spectrum disorder and intellectual disability. Sci. Transl. Med. 2, 1–9.10.1126/scitranslmed.3001267Search in Google Scholar PubMed PubMed Central
Paul, L.K., Corsello, C., Kennedy, D.P., and Adolphs, R. (2014). Agenesis of the corpus callosum and autism: a comprehensive comparison. Brain 137, 1813–1829.10.1093/brain/awu070Search in Google Scholar PubMed PubMed Central
Pavlov, N. (2014). User interface for people with autism spectrum disorders. J. Software Eng. Appl. 7, 128–134.10.4236/jsea.2014.72014Search in Google Scholar
Rodriguez, J.I. and Kern, J.K. (2011). Evidence of microglial activation in autism and its possible role in brain underconnectivity. Neuron Glia Biol. 7, 205–213.10.1017/S1740925X12000142Search in Google Scholar PubMed PubMed Central
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, 733–742.10.1002/ana.23976Search in Google Scholar PubMed
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, 887–899.10.1016/j.neuron.2013.08.029Search in Google Scholar PubMed PubMed Central
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., et al. (2011). Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885.10.1016/j.neuron.2011.05.002Search in Google Scholar PubMed PubMed Central
Scassellati, B. (2005). Quantitative metrics of social response for autism diagnosis. IEEE International Workshop on Robots and Human Interactive Communication, Nashville, TN, USA, August 13–15, 2005, 585–590.10.1109/ROMAN.2005.1513843Search in Google Scholar
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, 937–950.10.1093/cercor/bhr162Search in Google Scholar PubMed PubMed Central
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, 231–237.10.1016/j.biopsych.2013.07.009Search in Google Scholar PubMed PubMed Central
Stein, J.L., Parikshak, N.N., and Geschwind, D.H. (2013). Rare inherited variation in autism: beginning to see the forest and a few trees. Neuron 77, 209–211.10.1016/j.neuron.2013.01.010Search in Google Scholar PubMed PubMed Central
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, 691–697.10.1523/JNEUROSCI.3615-13.2014Search in Google Scholar PubMed PubMed Central
Strzelecka, J. (2014). Electroencephalographic studies in children with autism spectrum disorders. Res. Autism Spectr. Disord. 8, 317–323.10.1016/j.rasd.2013.11.010Search in Google Scholar
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, 113–122.10.1017/S1355617713001203Search in Google Scholar PubMed PubMed Central
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, 81–91.10.3233/ICA-2012-0392Search in Google Scholar
Tyszka, J.M., Kennedy, D.P., Paul, L.K., and Adolphs, R. (2014). Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. Cereb. Cortex. 24, 1894–1905.10.1093/cercor/bht040Search in Google Scholar PubMed PubMed Central
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, 546–563.10.1007/s10803-013-1903-ySearch in Google Scholar PubMed PubMed Central
Verly, M., Verhoeven, J., Zink, I., Mantini, D., Oudenhove, L.V., Lagae, L., Sunaert, S., Romme, N. (2013). Structural and functional underconnectivity as a negative predictor for language in autism. Hum. Brain Map. 35, 3602–3615.10.1002/hbm.22424Search in Google Scholar PubMed PubMed Central
Weigelt, S., Koldewyn, K., and Kanwisher, N. (2012). Face identity recognition in autism spectrum disorders: a review of behavioral studies. Neurosci. Biobehav. Rev. 36, 1060–1084.10.1016/j.neubiorev.2011.12.008Search in Google Scholar PubMed
Weisburg, J., Milleville, S.C., Kenworthy, L., Wallace, G.L., Gotts, S.J., Beauchamp, M.S., Martin, A. (2014). Social perception in autism spectrum disorders: impaired category selectivity for dynamic but not static images in ventral temporal cortex. Cereb. Cortex 24, 37–48.10.1093/cercor/bhs276Search in Google Scholar PubMed PubMed Central
Welburg, L. (2011). Autism – the importance of getting the dose right. Nat. Rev. Neurosci. 12, 429.10.1038/nrn3083Search in Google Scholar PubMed
Xiang, J. and Liang, M. (2012). Wavelet-based detection of beam cracks using modal shape and frequency measurements. Comput. Aided Civ. Infrastruct. Eng. 27, 439–454.10.1111/j.1467-8667.2012.00760.xSearch in Google Scholar
Yates, K. and Couteur, A.L. (2013). Diagnosing autism. Paediatr. Child Health 23, 5–10.10.1016/j.paed.2012.09.008Search in Google Scholar
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., et al. (2013). Using whole-exome sequencing to identify inherited causes of autism. Neuron 77, 259–273.10.1016/j.neuron.2012.11.002Search in Google Scholar PubMed PubMed Central
Zdravkovic, A.D., Milisavljevic, M.J., and Petrovic, D.M. (2010). Attention in children with intellectual disabilities. Procedia Soc. Behav. Sci. 5, 1601–1606.10.1016/j.sbspro.2010.07.332Search in Google Scholar
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, 400–406.10.1038/nn.3641Search in Google Scholar PubMed
Zhang, Y. and Ge, H. (2013). Freeway travel time prediction using Takagi-Sugeno-Kang fuzzy neural network. Comput. Aided Civ. Infrastruct. Eng. 28, 594–603.10.1111/mice.12014Search in Google Scholar
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., et al. (2014). Longitudinal changes in cortical thickness in autism and typical development. Brain 137, 1799–1812.10.1093/brain/awu083Search in Google Scholar PubMed PubMed Central
©2014 by De Gruyter