Please, check the publication: https://ejtas.com/index.php/journal/article/view/1751
VALIDATION OF PRECISON SCALE PERCEPTIVE-COGNITVE TO PEOPLE WIHT ASD DIAGNOSIS 'PS-PC-ASD'
INTRODUCTION
Ojea, M. (2026). Validation of Precision Sale Perceptive-Cognitive to Peple with ASD diagnosis PS-PC-ASD'. European Journal of Theoretical and Applied Sciences, 4(1), 147-168.
Ojea (2023) has published the Precision Scale Perceptivo- Cognitive (PS-PC-ASD), with the aim of determining the diagnosis of people with autism spectrum disorder (ASD), which consists of three main weighted domains: 1) the perceptual-cognitive domain, 2) the social domain, and 3) the practical domain, in order to incorporate the measurement of cognitive neural values, which involve the level of neuropsychological and biological processing of information, from the input of information through perceptual sensory memory to semantic memory and its related episodic memory, as well as the development of interconceptual relationships that develop throughout the processes of information coding through working memory.
In this study, the PS-PC-ASD scale has been validated for a total of N: 346 participants, which is a significantly broad sample, being a highly specific group, of which 112 don’t have any specific diagnosis, 140 have a level 1 autism diagnosis, 67 have level 2, and 27 have level 3, as the International Classification of the American Psychiatric Association (APA, 2013).
The comparative data, obtained using a one-way ANOVA test, as well as the subsequent transformation of all direct scores (DS) found in the observation questionnaire into typical scores (Z), were used to construct the three categorical dimensions with typified scores: 1) processing category, 2) social category, and 3) behavioural category, whose typical sum provides a highly accurate analysis of explanatory variance, analysed using stepwise linear regression analysis, in which the three dimensions exhibit significant critical levels explaining the diagnostic data within the three categories (sig: .00).
Finally, the correspondence of the total sum of typical scores found in accordance with the corresponding percentile, in intervals of five, the 50th percentile has corresponded to the typical average sum of -1.38, from which point a diagnosis compatible with autism can be definitively considered. From this percentile onwards, an increase in intensity implies greater severity in the diagnostic group for this disorder.
In essence, the initial data found for the construction of the Scale has been corroborated, concluding that the Diagnostic Precision scale is a highly effective and positive instrument for the specific diagnostic precision of individuals with ASD.
Keywords: autism spectrum disorder, diagnostic test, perception, cognition, semantic memory, source memory, episodic memory.
REFERENCES
Abell, F., Krams, M., Ashburner, J., Passingham, R., Friston, K., Frackowiak, R., ... Frith, U. (1999). The neuroanatomy of autism: A voxel-based whole brain analysis of structural scans. NeuroReport, 10(8), 1647–1651. https://pubmed.ncbi.nlm.nih.gov/10501551/
Adorjan, I., Ahmed, B., Feher, V., Torso, M., Krug, K., Esiri, M., … Szele, F. G. (2017). Calretinin interneuron density in the caudate nucleus is lower in autism spectrum disorder. Brain, 140(7), 2028–2040. https://doi.org/10.1093/brain/awx131
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. https://doi.org/10.1176/appi.books.9780890425596
Amina, S., Falcone, C., Hong, T., Wolf-Ochoa, M. W., Vakilzadeh, G., Allen, E., … Martinez-Cerdeno, V. (2021). Chandelier cartridge density is reduced in the prefrontal cortex in autism. Cerebral Cortex, 31(6), 2944–2951. https://doi.org/10.1093/cercor/bhaa402
Ariza, J., Rogers, H., Hashemi, E., Noctor, S. C., & Martinez-Cerdeno, V. (2018). The number of chandelier and basket cells are differentially decreased in prefrontal cortex in autism. Cerebral Cortex, 28(2), 411–420. https://doi.org/10.1093/cercor/bhw349
Beer, J. S., John, O. P., Scabini, D., & Knight, R. T. (2006). Orbitofrontal cortex and social behavior: Integrating selfmonitoring and emotion-cognition interactions. Journal of Cognitive Neuroscience, 18(6), 871–879. https://doi.org/10.1162/jocn.2006.18.6.871
Behrens, T. E. J., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., WheelerKingshott, C. A. M., Boulby, P. A., … Mathews, P. M. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6(7), 750–757. https://pubmed.ncbi.nlm.nih.gov/12808459/
www.ejtas.com E u ropean Journal of Theoretical and Applied S(IcSiSeNn c2e7s8-76447 ) 2 026 | Volum4e | Numbe1r
19
Boucher, J., Cowell, P., Howard, M., Broks, P., Farrant, A., Roberts, N., & Mayes, A. (2005). A combined clinical, neuropsychological, and neuroanatomical study of adults with high functioning autism. Cognitive Neuropsychiatry, 10(3), 165–213. https://pubmed.ncbi.nlm.nih.gov/16571459/
Bowler, D. M., Gardiner, J. M., & Gaigg, S. B. (2007). Factors affecting conscious awareness in the recollective experience of adults with Asperger’s syndrome. Consciousness and Cognition, 16(1), 124–143. https://doi.org/10.1016/j.concog.2005.12.001
Bowler, D. M., Gardiner, J. M., & Grice, S. (2000). Episodic memory and remembering in adults with Asperger’s syndrome. Journal of Autism and Developmental Disorders, 30(4), 305–316. https://doi.org/10.1023/a:1005575216176
Carter, A. S., Volkmar, F. R., Sparrow, S. S., Wang, J. J., Lord, C., Dawson, G., … & Schopler, E. (1998). The Vineland Adaptive Behavior Scales: Supplementary norms for individuals with autism. Journal of Autism and Developmental Disorders, 28(4). 287-302. https://doi.org/10.1023/A:1026056518470
Casanova, M. F., van Kooten, I. A. J., Switala, A. E., van Engeland, H., Heinsen, H., Steinbusch, H. W. M., ... Schmitz, Ch. (2006). Abnormalities of cortical minicolumnar abnormalities in autism. Acta Neuropathologica, 112(3), 287–303. https://pubmed.ncbi.nlm.nih.gov/16819561/
Cohen, I. L. (2007). A neural network model of autism: Implications for theory and treatment. In D. Mareschal, S. Sirois, & G. Westermann (Eds.), Neuroconstructivism, Vol. II: Perspectives and prospects (pp. 231–264). Oxford, UK: Oxford University Press. https://www.researchgate.net/publication/232799076_A_neural_network_model_of_autism_Implications_for_theory_and_treatment
D’Angelo, E., & Casali, S. (2012). Seeking a unified framework for cerebellar function and dysfunction: From circuit operations to cognition. Frontiers in Neural Circuits, 6, 116. https://doi.org/10.3389/fncir.2012.00116
DeFelipe, J., Lopez-Cruz, P. L., Benavides-Piccione, R., Bielza, C., Larranaga, P., Anderson, S., … Ascoli, G. A. (2013). New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Reviews Neuroscience, 14(3), 202–216. https://doi.org/10.1038/nrn3444
Fatemi, S. H., Folsom, T. D., Reutiman, T. J., & Thuras, P. D. (2009). Expression of GABA(B) receptors is altered in brains of subjects with autism. Cerebellum, 8(1), 64–69. https://doi.org/10.1007/s12311-008-0075-3
Fields, R. D. (2008). White matter in learning, cognition and psychiatric disorders. Trends in Neurosciences, 31(7), 361–370. https://pubmed.ncbi.nlm.nih.gov/18538868/
Friston, K. (2009). Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biology, 7(2), e1000033. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1000033
Gaigg, S. B., Gardiner, J. M., & Bowler, D. M. (2008). Free recall in autism spectrum disorder: the role of relational and item-specific encoding. Neuropsychologia, 46(4), 993–999. https://doi.org/10.1016/j.neuropsychologia.2007.11.011
Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology, 21(4), 424–430. https://pubmed.ncbi.nlm.nih.gov/18607202/
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Van Wedeen, J., & Spoms, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6(7), 1479–1493. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0060159
Hagmann, P., Thiran, J. P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., & Meuli, R. (2003). DTI mapping of human brain connectivity: Statistical fibre tracking and virtual dissection. NeuroImage, 19(3), 545–554. https://pubmed.ncbi.nlm.nih.gov/12880786/
Happè, F. (1999). Autism: Cognitive deficit or cognitive style? Trends in Cognitive Sciences, 3(6), 216–222. https://pubmed.ncbi.nlm.nih.gov/10354574/
www.ejtas.com E u ropean Journal of Theoretical and Applied S(IcSiSeNn c2e7s8-76447 ) 2 026 | Volum4e | Numbe1r
20
Happè, F., & Frith, U. (2006). The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(1), 5–25. https://pubmed.ncbi.nlm.nih.gov/16450045/
Hashemi, E., Ariza, J., Rogers, H., Noctor, S. C., & Martinez-Cerdeno, V. (2017). The number of parvalbumin-expressing interneurons is decreased in the prefrontal cortex in autism. Cerebral Cortex, 27(3), 1931–1943. https://doi.org/10.1093/cercor/bhw021
Herbert, M. R. (2005). Large brains in autism: The challenge of pervasive abnormality. Neuroscientist, 11(5), 417–440. https://pubmed.ncbi.nlm.nih.gov/16151044/
Hertrich, I., Dietrich, S., Blum, C., & Ackermann, H. (2021). The role of the dorsolateral prefrontal cortex for speech and language processing. Frontiers in Human Neuroscience, 17(15), Article 645209. https://doi.org/10.3389/fnhum.2021.645209
Hof, P. R., Glezer, I. I., Conde, F., Flagg, R. A., Rubin, M. B., Nimchinsky, E. A., & Vogt Weisenhorn, D. M. (1999). Cellular distribution of the calcium-binding proteins parvalbumin, calbindin, and calretinin in the neocortex of mammals: Phylogenetic and developmental patterns. Journal of Chemical Neuroanatomy, 16(2), 77–116. https://doi.org/10.1016/s0891-0618(98)00065-9
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meulic, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040. https://pubmed.ncbi.nlm.nih.gov/19188601/
Horwitz, B., & Glabus, M. F. (2005). Neural modeling and functional brain imaging: The interplay between the data-fitting and simulation approaches. International Review of Neurobiology, 267–290. https://pubmed.ncbi.nlm.nih.gov/16387207/
Howlin, P., Moss, P., Savage, S., & Rutter, M. (2013). Social outcomes in mid- to later adulthood among individuals diagnosed with autism and average nonverbal IQ as children. Journal of the American Academy of Child and Adolescent Psychiatry, 52(6), 572-581. https://doi.org/10.1016/j.jaac.2013.02.017
Igelström, K. M., Webb, T. W., & Graziano, M. S. A. (2017). Functional connectivity between the temporoparietal cortex and cerebellum in autism spectrum disorder. Cerebral Cortex, 27, 2617– 2627. https://doi.org/10.1093/cercor/bhw079
Just, M. A., Cherkassky, V. L., Keller, T. A., & Minshew, N. J. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127(8), 1811–1821. https://pubmed.ncbi.nlm.nih.gov/15215213/
Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., & Minshew, N. J. (2007). Functional and anatomical cortical underconnectivity in autism: Evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cerebral Cortex, 17(4), 951–961. https://pubmed.ncbi.nlm.nih.gov/16772313/
Kana, R. K., Libero, L. E., & Moore, M. S. (2011). Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Physics of Life Reviews, 8, 410–437. https://doi.org/10.1016/j.plrev.2011.10.001
Kanne, S. M., Gerber, A. J., Quirmbach, L. M., Sparrow, S. S., Cicchetti, D. V., & Saulnier, C. A. (2011). The role of adaptive behavior in autism spectrum disorders: Implications for functional outcome. Journal of Autism and Developmental Disorders, 41(8), 1007-1018. https://doi.org/10.1007/s10803-010-1126-4
Karadottir, R., Hamilton, N. B., Bakiri, Y., & Attwell, D. (2008). Spiking and nonspiking classes of oligodendrocyte precursor glia in CNS white matter. Nature Neuroscience, 11(4), 450–456. https://pubmed.ncbi.nlm.nih.gov/18311136/
Karlsgodt, K. H., Sun, D., Jimenez, A. M., Lutkenhoff, E. S., Willhite, R., Van erp, T G. M., … Cannon, T. D. (2008). Developmental disruptions in neural connectivity in the pathophysiology of schizophrenia. Development
www.ejtas.com E u ropean Journal of Theoretical and Applied S(IcSiSeNn c2e7s8-76447 ) 2 026 | Volum4e | Numbe1r
21
and Psychopathology, 20(4), 1297–1327. https://pubmed.ncbi.nlm.nih.gov/18838043/
Khan, A. J., Nair, A., Keown, C. L., Datko, M. C., Lincoln, A. J., & Müller, R. A. (2015). Cerebro-cerebellar resting-state functional connectivity in children and adolescents with autism spectrum disorder. Biological Psychiatry, 78, 625–634. https://doi.org/10.1016/j.biopsych.2015.03.024 .
Kraijer, D. W. (2000). Review of adaptive behavior studies in men-tally retarded persons with autism/pervasive developmental disorder. Journal of Autism and Developmental Disorders, 30(1), 39-47. https://doi.org/10.1023/A:1005460027636
Lawrence, Y. A., Kemper, T. L., Bauman, M. L., & Blatt, G. J. (2010). Parvalbumin-, calbindin, and calretinin-immunoreactive hippocampal interneuron density in autism. Acta Neurologica Scandinavica, 121(2), 99–108. https://doi.org/10.1111/j.1600-0404.2009.01234.x
Lewis, J. D., & Elman, J. L. (2008). Growth-related neural reorganization and the autism phenotype: A test of the hypothesis that altered brain growth leads to altered connectivity. Developmental Science, 11(1), 135–155. https://pmc.ncbi.nlm.nih.gov/articles/PMC2706588/
Lewis, J. D., Theilmann, R. J., Sereno, M. I., & Townsend, J. (2009). The relation between connection length and degree of connectivity in young adults: A DTI analysis. Cerebral Cortex, 19(3), 554–562. https://pmc.ncbi.nlm.nih.gov/articles/PMC2638815/
Lind, S. E., & Bowler, D. M. (2010). Episodic memory and episodic future thinking in adults with autism. Journal of Abnormal Psychology, 119(4), 896–905. https://doi.org/10.1037/a0020631
Ma, D. Q., Whitehead, P. L., Menold, M. M., Martin, E. R., AshleyKoch, A. E., Mei, H., ... Gilbert, J. R. (2005). Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. American Journal of Human Genetics, 77(3), 377– 388. https://doi.org/10.1086/433195
Matson, J. L., & Shoemaker, M. (2009). Intellectual disability and its relationship to autism spectrum disorders. Research in Developmental Disabilities, 30(6), 1107-1114. https://doi.org/10.1016/j.ridd.2009.06.003
McClelland, J. L. (2000). The basis of hyperspecificity in autism: A preliminary suggestion based on properties of neural nets. Journal of Autism and Developmental Disorders, 30(5), 497–502. https://pubmed.ncbi.nlm.nih.gov/11098891/
McQuaid, G. A., Pelphrey, K. A., Bookheimer, S. Y, Dapretto, D., Webb, S. J., Bernier, R. A., … & Wallace, G. L. (2021). The gap between IQ and adaptive functioning in autism spectrum disorder: Disentangling diagnostic and sex differences. Autism, 25(6), 1565-1579. DOI: 10.1177/1362361321995620. journals.sagepub.com/home/aut
Menon, V. (2011). Large-scale brain networks and psychopathology: A unifying triple network model. Trends in Cognitive Sciences, 15, 483–506. https://doi.org/10.1016/j.tics.2011.08.003.
Mottron, L., Dawson, M., Soulieres, I., Hubert, B., & Burack, J. (2006). Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 36(1), 27–43. https://pubmed.ncbi.nlm.nih.gov/16453071/
Oishi, K., Zilles, K., Amunts, K., Faria, A., Jiang, H., Li, X., … Mori, S. (2008). Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter. NeuroImage, 43(3), 447–457. https://pubmed.ncbi.nlm.nih.gov/18692144/
Ojea, M. (2023). The Precicion Scale Cognitive-Perceptive to people with ASD ‘PS-PC-ASD’. Lima (Perú): Ed. Barcelona. https://libreriaites.com/producto/escala-de-precision-perceptivo-conductual-ep-pc-tea/
Oldehinkel, M., Mennes, M., Marquand, A., Charman, T., Tillmann, J., Ecker, C., … Zwiers, M. P. (2019). Altered connectivity between cerebellum, visual, and sensory-motor networks in autism spectrum disorder: Results from the
www.ejtas.com E u ropean Journal of Theoretical and Applied S(IcSiSeNn c2e7s8-76447 ) 2 026 | Volum4e | Numbe1r
22
EU-AIMS Longitudinal European Autism Project. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4, 260–270. https://doi.org/10.1016/j.bpsc.2018.11.010
Olivito, G., Clausi, S., Laghi, F., Tedesco, A. M., Baiocco, R., Mastropasqua, C., … Leggio, M. (2016). Resting-state functional connectivity changes between dentate nucleus and cortical social brain regions in autism spectrum disorders. Cerebellum. https://doi.org/10.1007/s1231 1-016-0795-8
Olivito, G., Lupo, M., Laghi, F., Clausi, S., Baiocco, R., Cercignani, M., … Leggio, M. (2017). Lobular patterns of cerebellar resting-state connectivity in adults with Autism Spectrum Disorder. European Journal of Neuroscience. https://doi.org/10.1111/ejn.13752
Pardo, C. A., & Eberhart, C. G. (2007). The neurobiology of autism. Brain Pathology, 17(4), 434–447. https://onlinelibrary.wiley.com/doi/10.1111/j.1750-3639.2007.00102.x
Plaisted, K., O’Riordan, M., & Baron-Cohen, S. (1998). Enhanced visual search for a conjunctive target in autism: A research note. Journal of Child Psychology and Psychiatry and Allied Disciplines, 39(5), 777–783. https://pubmed.ncbi.nlm.nih.gov/9690940/
Redcay, E., & Courchesne, E. (2005). When is the brain enlarged in autism? A metaanalysis of all brain size reports. Biological Psychiatry, 58(1), 1–9. https://pubmed.ncbi.nlm.nih.gov/15935993/
Schummers, J., Yu, H., & Sur, M. (2008). Tuned responses of astrocytes and their influence on hemodynamic signals in the visual cortex. Science, 320(5883), 1638–1643. https://pubmed.ncbi.nlm.nih.gov/18566287/
Shah, A., & Frith, U. (1983). An islet of ability in autistic children: A research note. Journal of Child Psychology and Psychiatry and Allied Disciplines, 24(4), 613–620. Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. Journal of Neuroscience, 28(14), 3586–3594. https://pubmed.ncbi.nlm.nih.gov/6630333/
Snow, P. J. (2016). The structural and functional organization of cognition. Frontiers in Human Neuroscience, 10, Article 501. https://doi.org/10.3389/fnhum.2016.00501
Tanji, J., & Hoshi, E. (2008). Role of the lateral prefrontal cortex in executive behavioral control. Physiological Reviews, 88(1), 37–57. https://doi.org/10.1152/physrev.00014.2007
Van Overwalle, F., & Mariën, P. (2016). Functional connectivity between the cerebrum and cerebellum in social cognition: A multi-study analysis. NeuroImage, 124, 248–255. https//doi.org/10.1016/j.neuroimage.2015.09.001
Wang, Y., Zhang, P., & Wyskiel, D. R. (2016). Chandelier cells in functional and dysfunctional neural circuits. Frontiers in Neural Circuits, 10(33), 1–8. https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2016.00033/full
Wedeen, V. J., Wang, R. P., Schmahmann, J. D., Benner, T., Tseng, W. Y. I., Dai, G., ... de Crespigny, A. J. (2008). Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. NeuroImage, 41(4), 1267–1277. https://pubmed.ncbi.nlm.nih.gov/18495497/
Zaitsev, A. V., Gonzalez-Burgos, G., Povysheva, N. V., Kroner, S., Lewis, D. A., & Krimer, L. S. (2005). Localization of calcium-binding proteins in physiologically and morphologically characterized interneurons of monkey dorsolateral prefrontal cortex. Cerebral Cortex, 15(8), 1178–1186. https://doi.org/10.1093/cercor/bhh218
Ziskin, J. L., Nishiyama, A., Rubio, M., Fukaya, M., & Bergles, D. E. (2007). Vesicular release of glutamate from unmyelinated axons in white matter. Nature Neuroscience, 10(3), 321–330. https://pubmed.ncbi.nlm.nih.gov/17293857/
