miércoles, 21 de junio de 2023

lunes, 8 de mayo de 2023

PRESENTACIÓN DEL INSTITUTO- FAMILIA (LA VOZ DE GALICIA)

Presentación del INSTITUTO- FAMILIA



 


ACCEDER A LA INFORMACIÓN DEL DIARIO: LA VOZ DE GALICIA



TRÍPTICO EXPLICATIVO DE LO QUE HACEMOS EN EL INSTITUTO

 ASÓCIATE




Comparative differential study of comorbid symptomatic groups associated with Autism Spectrum

 Comparative differential study of comorbid symptomatic groups associated with Autism Spectrum Disorder diagnosis


Ojea Rúa Manuel
Lydia Castro Núñez

Lourdes Rivas Otero

Tania Justo Román



Abstract

Individuals with autism spectrum disorders (ASD) make up a diagnosis characterized by a multifunctional neurocognitive disorder, based on a limited structure to perform nodal-synaptic interrelationships between the contents of learning. Likewise, this disorder may be associated with a set of comorbid symptom groups, which, regarding their intensity, may border with ASD main diagnosis and lead to basic errors that affect subsequent social- educational treatment. This study analyses most recurrent associated comorbid groups, as well as, if the presence of symptomatic comorbid groups is differential regarding group shape: normotypical and ASD groups. A total of 390 children participated in this study, 128 belonged to normotypical group and 262 did it to experimental group, subdivided into three levels of ASD. Results found through multivariate- test indicate that the whole dimension significantly affects group way intersection, age and sex (sig: .00). The post-hoc test analysis indicates this influence was      differential regarding to the group type for the following dimensions: cognition, behaviour, psychoaffectivity, language and psychomotor disorder, while relative differences were not observed in specific- clinical dimension, where only epilepsy showed a differential result: no differences were found in general- clinic dimension.

Lay abstract

ASD´ diagnosis and treatment shows, to date, many weak points that need to be improved. Previous studies have shown how important is the psycho-educational component regarding ASD treatment, therefore it is necessary to understand the specific characteristics of the nuclear ASD diagnosis, in order to work out a specific therapy according to every single case. 

In the current study, we examined and analysed ASD patients as well as participants showing comorbid symptoms such as epilepsy, in order to show how these comorbidities can reach a very high level, leading to a confused and wrong ASD nuclear diagnosis. 

Therefore, it is essential to gain more insight into the specific diagnosis process, defining the ASD symptoms very precisely in order to develop more accurate and specific educational programs. 

This study contributes to the improvement in ASD diagnosis, providing a large number of participants in order to study the relation between several comorbid symptoms and its reliability as ASD indicative factors or not.


Keywords:

Autism Spectrum Disorder, Comorbidity, diagnosis, synapse- neural- nodes.



References

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders- DSM-5 (5th ed.). American Psychiatric Publishin. https://www.psychiatry.org/psychiatrists/practice/dsm

Amiet, C., Gourfinkel-An, I., Laurent, C., Carayol, J. R., Génin, B. R., Leguern, E., Tordjman, S., & Cohen, D. (2013). Epilepsy in simplex autism pedigrees is much lower than the rate in multiplex autism pedigrees. Biological Psychiatry, 74(3). e3-e4. DOI: 10.1016/j.biopsych.2013.01.037 DOI: https://doi.org/10.1016/j.biopsych.2013.01.037

Anukirthiga, B., Mishra, D., Pandey, S., Juneja, M., & Sharma, N. (2019). Prevalence of epilepsy and interictal epileptiform discharges in children with autism and attention-deficit hyperactivity disorder. Indian Journal of Pediatrics, 86, 897- 902. https://doi.org/10.1007/s12098-019-02977-6 DOI: https://doi.org/10.1007/s12098-019-02977-6

Bauman, M. L. (2010). Medical comorbidities in autism: Challenges to diagnosis and treatment. Neurotherapeutics, 7(3), 320- 327. https://doi.org/10.1016/j.nurt.2010.06.001 DOI: https://doi.org/10.1016/j.nurt.2010.06.001

Brookman-Frazee, L., Stadnick, N., Chlebowski, C., Baker- Ericzén, M. J., & Ganger, W. (2017). Characterizing psychiatric comorbidity in children with autism spectrum disorder receiving publicly funded mental health services. Autism, 22(8), 938- 952. https://doi.org/10.1177/1362361317712650 DOI: https://doi.org/10.1177/1362361317712650

Casseus, M. (2022). Prevalence of co-occurring autism spectrum disorder and attention deficit/hyperactivity disorder among children in the United States. Autism, 26(6) 159-1597. DOI: 10.1177/13623613221083279 DOI: https://doi.org/10.1177/13623613221083279

Chiarotti, F., & Venerosi, A. (2020). Epidemiology of autism spectrum disorders: A review of worldwide prevalence estimates since 2014. Brain Sciences, 10(5). E274. https://doi.org/10.3390/brainsci10050274 DOI: https://doi.org/10.3390/brainsci10050274

Danielson, M. L., Bitsko, R. H., Ghandour, R. M., Holbrook, J. R., Kogan, M. D., & Blumberg, S. J. (2018). Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents, 2016. Journal of Clinical Child and Adolescent Psychology, 47(2), 199- 212. https://doi.org/10.1080/15374416.2017.1417860 DOI: https://doi.org/10.1080/15374416.2017.1417860

Danielsson, S., Gillberg, I. C., Billstedt, E., Gillberg, C., & Olsson, I. (2005). Epilepsy in young adults with autism: A prospective population-based follow-up study of 120 individuals diagnosed in childhood. Epilepsia, 46(6), 918- 923. https://doi.org/10.1111/j.1528-1167.2005.57504.x DOI: https://doi.org/10.1111/j.1528-1167.2005.57504.x

Dickson, K. S., Galligan, M. L., & Lok, H. (2021). Short report: A quantitative methodological review of participant characteristics in the literature testing mental health interventions for youth with autism spectrum disorder. Autism, 26(4) 995-1000 DOI: 10.1177/13623613211056408 DOI: https://doi.org/10.1177/13623613211056408

Golan, O., Haruvi-Lamdan, N., Laor, N., & Horesh, D. (2021). The comorbidity between autism spectrum disorder and post-traumatic stress disorder is mediated by brooding rumination. Autism, 26(2) 538-544. DOI: 10.1177/13623613211035240 DOI: https://doi.org/10.1177/13623613211035240

Hellquist, A., & Tammimies, K. (2021). Access, utilization, and awareness for clinical genetic testing in autism spectrum disorder in Sweden: A survey study. Autism, 26(7) 1795- 1804. DOI: 10.1177/13623613211066130

Holingue, C., Poku, O., Pfeiffer, D., Murray, S., & Fallin, D. (2021). Gastrointestinal concerns in children with autism spectrum disorder: A qualitative study of family experiences. Autism, 26(7) 1698- 1711. DOI: 10.1177/13623613211062667

Kado, Y., Sanada, S., Oono, S., Ogino, T., & Nouno, S. (2020). Children with autism spectrum disorder comorbid with attention-deficit/hyperactivity disorder examined by the Wisconsin card sorting test: Analysis by age-related differences. Brain Development, 42, 113- 120 DOI: https://doi.org/10.1016/j.braindev.2019.07.011

Kerns, C. M., Berkowitz, S. J., Moskowitz, L. J., Drahota, A., Lerner, M. D., & Newschaffer, C. J. (2020). Screening and treatment of trauma-related symptoms in youth with autism spectrum disorder among community providers in the United States. Autism, 24(2), 515- 525. https://doi. org/10.1177/1362361319847908 DOI: https://doi.org/10.1177/1362361319847908

Kildahl, A. N., Bakken, T. L., & Iversen, T. E. (2019). Identification of post-traumatic stress disorder in individuals with autism spectrum disorder and intellectual disability: A systematic review identification of post-traumatic stress disorder in individuals with autism spectrum disorder and intellectual. Journal of Mental Health Research in Intellectual Disabilities, 12(1- 2), 1- 25. https://doi.org/10.1 080/19315864.2019.1595233 DOI: https://doi.org/10.1080/19315864.2019.1595233

Kohane, I. S., McMurry, A., Weber, G., MacFadden, D., Rappaport, L., Kunkel, L., Bickel, J., Wattanasin, N., Spence, S., & Murphy, S. (2012). The co-morbidity burden of children and young adults with autism spectrum disorders. PLOS ONE, 7(4). e33224. https://doi. org/10.1371/journal.pone.0033224 DOI: https://doi.org/10.1371/journal.pone.0033224

Lai, M.-C., Kassee, C., Besney, R., Bonato, S., Hull, L., Mandy, W., Szatmari, P., & Ameis, S. H. (2019). Prevalence of co-occurring mental health diagnoses in the autism population: A systematic review and meta-analysis. The Lancet Psychiatry, 6(10), 819- 829. https://doi.org/10.1016/s2215-0366(19)30289-5 DOI: https://doi.org/10.1016/S2215-0366(19)30289-5

Liu, X., Sun, X, Sun, C., Zou, M., Chen, Y., Huang, J., Wu, L., & Chen, W. (2021). Prevalence of epilepsy in autism spectrum disorders: A systematic review and meta-analysis. Autism, 26(1) 33- 50. DOI: 10.1177/13623613211045029 DOI: https://doi.org/10.1177/13623613211045029

Lord, C., Elsabbagh, M., Baird, G., & Veenstra- vanderweele, J. (2018). Autism spectrum disorder. The Lancet, 392, 508- 520. https://doi.org/10.1016/S0140-6736(18)31129-2 DOI: https://doi.org/10.1016/S0140-6736(18)31129-2

Miller, D. T., Adam, M. P., Aradhya, S., Biesecker, L. G., Brothman, A. R., Carter, N. … & Ledbetter, D. H. (2010). Consensus statement: Chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. American Journal of Human Genetics, 86(5), 749- 764. https://doi.org/10.1016/j.ajhg.2010.04.006 DOI: https://doi.org/10.1016/j.ajhg.2010.04.006

Moeschler, J. B., Shevell, M., & Committee on Genetics. (2014). Comprehensive evaluation of the child with intellectual disability or global developmental delays. Pediatrics, 134(3), e903- e918. https://doi.org/10.1542/peds.2014-1839 DOI: https://doi.org/10.1542/peds.2014-1839

Neumeyer, A. M., Anixt, J., Chan, J., Perrin, J. M., Murray, D., Coury, D. L., Bennett, A., Farmer, J., & Parker, R. A. (2019). Identifying associations among co-occurring medical conditions in children with autism spectrum disorders. Academic Pediatrics, 19(3), 300- 306. https://doi. org/10.1016/j.acap.2018.06.014 DOI: https://doi.org/10.1016/j.acap.2018.06.014

Ojea, M. (2021). Classification of the comorbid symptomatic groups on Autism Spectrum Disorder diagnosis. International Journal for Innovation Education and Research, 9(6), 196- 208. ISSN: 2411- 293301. https://scholarsjournal.net/index.php/ijier/issue/view/94 DOI: https://doi.org/10.31686/ijier.vol9.iss6.3167

Ojea, M. (2022). Integrated Scale for Diagnosis of Autism Spectrum Disorder (ISD-ASD). International Journal for Innovation, Education and Research, 10(9), 202-274. https://scholarsjournal.net/index.php/ijier/article/view/3906/2659 DOI: https://doi.org/10.31686/ijier.vol10.iss9.3906

Rosello, R., Martinez-Raga, J., Mira, A., Pastor, J. C., Solmi, M., & Cortese, S. (2021). Cognitive, social, and behavioral manifestations of the co-occurrence of autism spectrum disorder and attention-deficit/hyperactivity disorder: A systematic review. Autism, 26(4) 743- 760. DOI: 10.1177/13623613211065545 DOI: https://doi.org/10.1177/13623613211065545

Rumball, F., Happé, F., & Grey, N. (2020). Experience of trauma and PTSD symptoms in autistic adults: Risk of PTSD development following DSM-5 and non-DSM-5 traumatic life events. Autism Research, 13(12), 2122- 2132. https://doi. org/10.1002/aur.2306 DOI: https://doi.org/10.1002/aur.2306

Schaefer, G. B., Mendelsohn, N. J., & Professional Practice Guidelines Committee. (2013). Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013 guideline revisions. Genetics in Medicine, 15(5), 399- 407. https://doi.org/10.1038/gim.2013.32 DOI: https://doi.org/10.1038/gim.2013.32

Srivastava, S., Love-Nichols, J. A., Dies, K. A., Ledbetter, D. H., Martin, C. L., Chung, W. K., Firth, H. V., Frazier, T., Hansen, R. L., Prock, L., Brunner, H., Hoang, N., Scherer, S. W., Sahin, M., Miller, D. T., & NDD Exome Scoping Review Work Group. (2019). Meta-analysis and multidisciplinary consensus statement: Exome sequencing is a firsttier clinical diagnostic test for individuals with neurodevelopmental disorders. Genetics in Medicine, 21(11), 2413- 2421. https://doi.org/10.1038/s41436-019-0554-6 DOI: https://doi.org/10.1038/s41436-019-0554-6

Tammimies, K., Marshall, C. R., Walker, S., Kaur, G., Thiruvahindrapuram, B., Lionel, A. C. . . . & Fernandez, B. A. (2015). Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with autism spectrum disorder. JAMA, 314(9), 895- 903. https://doi.org/10.1001/jama.2015.10078 DOI: https://doi.org/10.1001/jama.2015.10078


Most read articles by the same author(s)



Predictive hypotheses of the conceptual category of Autism Spectrum Disorder

Predictive hypotheses of the conceptual category ofAutism Spectrum Disorder





Manuel Ojea Rúa (PhD University of Vigo).

Tania Justo Román (Doctoral Student University of Vigo).

Elsa M. Castañeda Mikrukova (PgDipAutonomousUniversitat of Barcelona).

Alba Pereiras Martínez (PgDip University of Vigo).

Social Institute for Scientific Research (CIF: 44568509). Faculty of Education Sciences, University of Vigo (32004- Ourense). Manuel Ojea Rúa. ORCID-ID: https://orcid.org/my-orcid?orcid=0000-0002-9787-2520


Abstract
People with Autism Spectrum Disorder (ASD) are characterized by presenting a neurodevelopmental disorder of fundamentally genetic etiology with consequences in the global cognitive process, affecting the psychoneurological processing of interrelational information processing as a systemic whole. For this reason, the International Classification DSM-5 (American Psychiatric Association (APA), 2013), which includes only behavioral criteria, is very reduced in the face of a disorder that affects the global developmental system, both in the perceptual-cognitive area, as well as in the motor area and other clinical components related to health. In this study, the following general objectives are set out: 1) to analyze the most important predictor variables that make up the explanatory hypotheses for the diagnosis of autism, 2) to analyze whether these predictor variables differ according to the type of ASD group, age and sex of the participants in the sample, and 3) to elaborate the implications of the predictive analysis for the application of adapted programs. A total of 262 children belonging to the three ASD groups (ASD-1: 124, ASD-2: 83, and ASD-3: 84) participated in this study, which have been distributed according to five age intervals and two groups according to the sex of the participants. The results found using linear stepwise regression analysis indicate that there are four predictor variables that accumulate to explain the hypotheses explaining the disorder: 1) SocialCommunication, which represents an explanatory R for autism of .477 (47.7%), R2: .228 (22.8%), adjusted: .225 (22.5%), 2) in the second phase of the model, the Cognition variable is incorporated, whose interaction explains an R: .520, R2: .270, adjusted: .265, 3) the third step is configured sum of the Visual-Motor variable, which justifies an R: .53, R2: .284, adjusted: .275, and 4) the fourth and last step is collected the Rigidity-Motor variable, with a total explanatory sum of R: .54, R2: 29.7% (adjusted: 28.6%). 

Lay abstract
People with ASD are characterized by presenting a neurodevelopmental disorder of basically genetic etiology with consequences in the global cognitive process, which affects the psycho-neurological processing of interrelational information processing, influencing the global set of the neurocognitive system, both in the perceptual-cognitive, motor, and/or clinical level. This global systemic position requires the application of programs based on the development of processing modes, which can generate holistic development and reduce the cognitive consequences derived from the exposure to stimuli perceived as negative by people with ASD, as they would increase the types and levels of associated comorbidities. For this reason, programs have to design learning contexts that provide for positive responses, elaborated according to previously acquired competencies, which will progressively increase the level of difficulty according to the skills of elaboration of relationships between previous learning and new acquisitions. The subsequent presentation of a wide range of variety of learning contexts will facilitate the processes of generalization of the learned contents to new situations.

Keywords: Autism Spectrum Disorder, Perception- Cognition, Semantic- Encoding, Visual- Motor, Behaviour.

References

[1]. Alexander, A. L., Lee, J. E., Lazar, M., Boudos, R., DuBray, M. B., Oakes, T. R., ... &Lainhart, J. E. (2007). Diffusion tensor imaging of the corpus callosum in autism. Neuroimage, 34, 61- 73. DOI: 10.1016/j.neuroimage.2006.08.032 
[2]. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders- DSM-5 (5th ed.). American Psychiatric Publishin. https://www.psychiatry.org/psychiatrists/practice/dsm 
[3]. Barnea-Goraly, N., Lotspeich, L. J., & Reiss, A. L. (2010). Similar white matter aberrations in children with autism and their unaffected siblings: a diffusion tensor imaging study using tract-based spatial statistics. Archives of General Psychiatry, 67, 1052- 1060. DOI: 10.1001/archgenpsychiatry.2010.123 
[4]. Barttfeld, P, Wicker, B., Cukier, S., Navarta, S., Lew, S, Leiguarda, R., &Sigman, M. (2012). State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder. Neuropsychologia, 50, 3653-3662. journal homepage: www.elsevier.com/locate/neuropsychologia 
[5]. Barttfeld, P., Wicker, B., Cukier, S., Navarta, S., Lew, S., &Sigman, M. (2011). A big-world network in ASD: dynamical connectivity analysis reflects a deficit in long-range connections and an excess of short-range connections. Neuropsychologia, 49, 254-26. DOI: 10.1016/j.neuropsychologia.2010.11.024 
[6]. Bertone, A., Hanck, J., Kogan, C., Chaudhuri, A., & Cornish, K. (2010). Associating neural alterations and genotype in Autism and Fragile X Syndrome: incorporating perceptual phenotypes in causal modeling. J Autism Dev Disord, 40, 1541-1548. DOI: 10.1007/s10803-010-1110-z 
[7]. Carmo, J. C., Duarte, E., Souza, C., Pinho, S., & Filipe, C. N. (2017). Brief Report: testing the impairment of initiation processes hypothesis in Autism Spectrum Disorder. J Autism Dev Disord, 47, 1256-1260. DOI 10.1007/s10803-017-3031-6 
[8]. Carole, P., da Fonseca, D., Santos, A., Moore, D. G., Monfardini, E., &Deruelle, C. (2008). Recognition of biological motion in children with autistic spectrum disorders. Autism, 12(3) 261-274. DOI: 10.1177/1362361307089520 
[9]. Cohen, I.L. (1994). An artificial neural network analogue of learning in autism. Biological Psychiatry, 36, 5-20. DOI:10.1016/0006- 3223(94)90057-4 
[10]. Cohen, I.L. (1998). Neural network analysis of learning in autism. In D.J. Stein & J. Ludik (Eds.), Neural networks and psychopathology (pp. 274-315). New York: Cambridge University Press. DOI: 10.1017/CBO9780511547195.012 
[11]. Courchesne, E., Redcay, E., Morgan, J. T., & Kennedy, D. P. (2005). Autism at the beginning: microstructural and growth abnormalities underlying the cognitive and behavioral phenotype of autism. Development and Psychopathology, 17, 577-597. DOI: 10.1017/S0954579405050285
[12]. Fletcher, P. T., Whitaker, R. T., Tao, R., DuBray, M. B., Froehlich, A., Ravichandran, C., … &Lagen, N. (2010). Microstructural connectivity of the arcuate fasciculus in adolescents with high-functioning autism. Neuroimage, 51, 1117-1125. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2966943/ 
[13]. Gotham, K. O, Marvin, A. R., Taylor, J. L., Warren, Z., Anderson, C. M., Law, P. A., … & Lipkin, P. H. (2015). Characterizing the daily life, needs, and priorities of adults with autism spectrum disorder from interactive autism network data. Autism, 19(7), 794- 804. DOI: 10.1177/1362361315583818 
[14]. Gotham, K. O., Siegle, G. J., Han, G. T., Tomarken, A. J., Crist, R. N., Simon, D. M., &Bodfish, J. W. (2018). Pupil response to social-emotional material is associated with rumination and depressive symptoms in adults with autism spectrum disorder. PLOS ONE, 13(8). https://doi.org/10.1371/journal.pone.0200340 
[15]. Hubert, B., Wicker, B., Moore, D. G., Monfardini, E., Duverger, H., da Fonseca, D., &Deruelle, C. (2007). Recognition of emotional and non-emotional biological motion in individuals with Autistic Spectrum Disorders. Journal of Autism and Developmental Disorders 37, 1386-92. DOI: 10.1007/s10803-006-0275-y 
[16]. James W. Tanaka, J. W., & Andrew Sung, A. (2016). The ‘‘Eye Avoidance’’ hypothesis of Autism face processing. J Autism Dev Disord, 46, 1538- 1552. DOI: 10.1007/s10803-013-1976-7 
[17]. Keenan, E. G., Gotham, K., & Lerner, M. D. (2018). Hooked on a feeling: repetitive cognition and internalizing symptomatology in relation to autism spectrum symptomatology. Depression, 22(7), 814-824. https://doi.org/10. 1177/1362361317709603 
[18]. Keenan, E. G., Gotham, K., & Lerner, M. D. (2018). Hooked on a feeling: Repetitive cognition and internalizing symptomatology in relation to Autism Spectrum symptomatology. Depression, 22(7), 814–824. https://doi.org/10. 1177/1362361317709603 
[19]. Kulp, M. T. (1999). Relationship between visual motor integration skill and academic performance in kindergarten through third grade. Optometry & Vision Science, 76(3), 159-163. DOI: 10.1097/00006324-199903000-00015 
[20]. Marchena, A. de, Eigsti, I. M., Worek, A., Ono, K. E., &SnedekerJ. B. (2011). Mutual exclusivity in autism spectrum disorders: testing the pragmatic hypothesis. Cognition, 119, 96-113. journal homepage: www.elsevier.com/locate/COGNIT 
[21]. Markman, E. (1990). Constraints children place on word learning. Cognitive Science, 14, 154- 173. https://doi.org/10.1207/s15516709cog1401_4 
[22]. Markman, E. M., & Wachtel, G. F. (1988). Children´s use of mutual exclusivity to constrain the meanings of words. Cognitive Psychology, 20, 121-157. https://doi.org/10.1016/0010-0285(88)90017-5 [23]. Mazefsky, C. A., Collier, A., Golt, J., &Siegle, G. J. (2020). Neural features of sustained emotional information processing in autism spectrum disorder. Autism, 24(4), 941-953. sagepub.com/journals-permissions DOI: 10.1177/1362361320903137 
[24]. Mazefsky, C. A., Pelphrey, K. A., & Dahl, R. E. (2012). The need for a broader approach to emotion regulation research in autism. Child Development Perspectives, 6(1), 92-97. https://doi.org/10.1111/j.1750-8606.2011.00229.x 
[25]. McHale, K., & Cermak, S. A. (1992). Fine motor activities in elementary school: preliminary findings and provisional implications for children with fine motor problems. American Journal of Occupational Therapy, 46(10), 898-903. DOI: 10.5014/ajot.46.10.898
[26]. Moore, D.G., Hobson, R. P., & Lee, A. (1997). Components of Person Perception: an investigation with Autistic, Non-Autistic retarded and typically developing children and adolescents. British Journal of Developmental Psychology 15, 401- 23.https://doi.org/10.1111/j.2044-835X.1997.tb00738.x 
[27]. Ojea, M. (2018).Developmentalof conceptual categories in studentswithautismspectrumdisorder (Desarrollo de categorías conceptuales en estudiantes con trastorno del espectro autista). Madrid: Pirámide. PROGRAMA RELATEA. Desarrollo de categorías conceptuales en estudiantes con trastornos del espectro autista | Ediciones Pirámide (edicionespiramide.es) 
[28]. Oliver, K. (2013). Visual, motor, and visual- motor integration difficulties in students with Autism Spectrum Disorders. Georgia State University Digital Archive @ GSU. https://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1095&context=cps_diss 
[29]. Sanghavi, R., & Kelkar, R. (2005). Visual-motor integration and learning- disabled children. Journal of Indian Occupational Therapy, 27(2), 33-38. https://aiota.org/temp/ijotpdf/ibat05i2p33.pdf [30]. Sheehan, K., Lowe, N., Kirley A, Mullins, C., Fitzgerald, M., Gill, M., &Hawi, Z. (2005). Tryptophan hydroxylase 2 (TPH2) gene variants associated with ADHD. Molecular Psychiatry 10, 944-949. DOI: 10.1038/sj.mp.4001698 
[31]. Siegle, G. J., D´Andrea, W., Jones, N., Hallquist, M. N., Stepp, S. D., Fortunato, A.,… &Pilkonis, P. A. (2015). Prolonged physiological reactivity and loss: Association of pupillary reactivity with negative thinking and feelings. International Journal of Psychophysiology, 98(2), 310-320. https://doi. org/10.1016/j.ijpsycho.2015.05.009 
[32]. Siegle, G. J., Steinhauer, S. R., Thase, M. E., Stenger, V. A., & Carter, C. S. (2002). Can’t shake that feeling: Eventrelated fMRI assessment of sustained amygdala activity in response to emotional information in depressed individuals. Biological Psychiatry, 51(9), 693-707. https://doi.org/10.1016/S0006-3223(02)01314-8 
[33]. Simmons, D.R., McKay, L., McAleer, P., Toal, E., & Robertson, A., &Pollick, F. E. (2007). Neural noise and autism spectrum disorders [ECVP Abstract]. Perception, 36 (Suppl.), 119-120. http://eprints.gla.ac.uk/29863/ 
[34]. Sizoo, B. B., van derGaag, R. J., & van den Brink, W. (2025). Temperament and character as endophenotype in adults with autism spectrum disorders or attention deficit/hyperactivity disorder. Autism, 19(4) 400-408. DOI: 10.1177/1362361314522352 
[35]. Sizoo, B., van den Brink, W., Franke, B., Arias, A., van Wijngaarden- Cremers, P., & van derGaag, R. J. (2010). Do candidate genes discriminate patients with an autism spectrum disorder from those with attention deficit/hyperactivity disorder and is there an effect of lifetime substance use disorders? The World Journal of Biological Psychiatry, 11, 699- 708.DOI: 10.3109/15622975.2010.480985 
[36]. Tang, Y. Y., Rothbart, M.., & Posner, M. I. (2012). Neural correlates of establishing, maintaining, and switching brain states. Trends in Cognitive Sciences, 16, 330-337. DOI: 10.1016/j.tics.2012.05.001 [37]. Thomas, M. S. C., Davis, R., Karmiloff-Smith, A. Knowland, V. C. P., & Charman, T. (2016). The over-pruning hypothesis of Autism. Developmental Science, 19(2), 284-305. DOI: 10.1111/desc.12303 [38]. Tottenham, N., Hertzig, M. E., Gillespie-Lynch, K., Gilhooly, T., Millner, A. J., & Casey, B. J. (2014). Elevated amygdala response to faces and gaze aversion in autism spectrum disorder. Social Cognitive and Affective Neuroscience, 9, 106-117. https://doi.org/10.1093/scan/nst050 
[39]. Zikopoulos, B., &Barbas, H. (2010). Changes in prefrontal axons may disrupt the network in autism. Journal of Neuroscience, 30, 14595-14609. https://www.jneurosci.org/content/30/44/14595


ANÁLISIS DIFERENCIAL DE DIVERSOS TIPOS DE ENSEÑANZA- APRENDIZAJE PARA LA EDUCACIÓN DE LOS ESTUDIANTES CON AUTISMO

DIFFERENTIAL ANALYSIS OF FOUR TEACHING- LEARNING MODELS APPLYING TO STUDENTS WITH AUTISM SPECTRUM DISORDER International Journal of Humaniti...