Petersson, Linda (2015). “It seems a bit like a lottery” A study about the school situation for pupils with Autism spectrum Condition (ASC). Master degree in Special Education, Faculty of Education and Society, Malmo UniversityReports from the Swedish government have since 2009, stated that the education for children with ASC is somewhat troublesome, for example: pupils with ASC seem often have quite low grades. Similar signals, often regarding the curriculum and it´s inflexibility has also been risen from teachers as well as headmasters.The aim of this study was to contribute to the understanding of the school situation for pupils with ASC with a placement in self –contained settings; a school for pupils with ASC and in small groups placed at general schools. The focus in the study has been academic achievement, organization and placement plus positive- and challenging factors for the school in their mission to educate this group of pupils.The study was conducted by using Mixed Methods, the data was collected both from interviews and a questionnaire. Two teachers, two headmasters, two special needs advisors from the National Agency for Special Needs Education and Schools, SPSM and three students with ASC was interviewed. The questionnaire was directed to teachers for students with ASC.The results of the study is presented in a parallel mixed design/ convergent design. The data from the questionnaire and interviews is both presented and then integrated. In the work of analyze and understanding the results has general system theory and the ecology of human development, as well as earlier research been used.Implication of the results confirms the reports from the Swedish government, there seem to be a risk that pupils with ASC gets lower grades. The implication is that this could due to that the curriculum isn’t adjusted to this pupils, the lack of “autism-friendly” teaching and lack of collaboration within the school system. Some positive factors was found that could facilitate schools, amongst this: Collaboration, knowledge and understanding ASC and the consequences for the individual.

Yes / Background: The Diagnostic and Statistical Manual of Mental Disorders (DSM-V) issued new diagnostic criteria for autism spectrum disorders (ASD) which resulted in missing the diagnosis of some cases of Asperger’s syndrome (AS). This negatively affected the support received by those affected. In this study, we explored if AS could be biologically stratified from the broader spectrum through a gene co-expression network preservation analysis. Methods: We analysed the GEO microarray data of 24 individuals with Asperger’s syndrome and 72 individuals with autism. Then, we used a weighted gene co-expression network (WGCNA) pipeline to construct gene co-expression networks. We explored whether these modules share the same co-expression patterns between autism and Asperger’s syndrome using network preservation analysis. Results: Our results showed that all co-expression modules of autism are preserved into the Asperger’s syndrome. However, three modules of Asperger’s syndrome out of 30 modules were not preserved in autism. Gene enrichment analysis revealed that these modules were involved in chromatin remodelling, immune and neuroinflammatory response, synaptic and neuronal development. Brain enrichment analysis showed significant downregulation of neurodevelopment genes in different brain regions associated with impaired social recognition in Asperger’s syndrome. Conclusions: The identified genetic and molecular profiles suggest that Asperger’s syndrome, despite sharing numerous similarities with autism, possesses a distinct genetic profile that makes it a distinct subtype of autism. This distinction could have significant implications for the management and treatment strategies tailored to individuals with Asperger’s syndrome.

The purpose of the study is to examine how social workers and psychologists work with adolescents who have autism spectrum disorders and suffer from mental illness. This includes examining what working methods social workers and psychologists work with and partly understanding what they consider to be the reason why adolescents with autism spectrum disorders experience anxiety and depression. The study's empirical material was collected through three semi-structured interviews with social workers and psychologists, then analyzed with previous research and theoretical perspectives and literature. A social worker and two psychologists, one of whom is from child and adolescent psychiatry and the other two are from habilitation. The study's theoretical frame of reference consists of three interconnected theories that social workers and psychologists can work with. The first is theory of mind, the second is central coherence and the third is executive functions. The result shows that social workers and psychologists identify information about adolescents with autism spectrum disorders who suffer from mental illness. It also emerges that BUP works with the treatment and medication of mental illness while the habilitation works with autism spectrum disorders and adolescents' behavioral problems by providing support efforts. In summary, the study's findings show that autism spectrum disorders are strongly related to mental illness. The study can conclude that the result has a good connection to previous research and theoretical perspectives. / Syftet med studien är att studera hur socialarbetare och psykologer arbetar med ungdomar som har autismspektrumtillstånd och lider av psykisk ohälsa. Med detta innefattas att dels undersöka vilka arbetsmetoder socialarbetare och psykologer arbetar med dels förstå vad de anser vara orsaken till att ungdomar med autismspektrumtillstånd upplever ångest och depression. Studiens empiriska material har samlats in genom tre semistrukturerade intervjuer med socialarbetare och psykologer, därefter analyserats med tidigare forskning och teoretiska perspektiv samt litteratur. En socialarbetare och två psykologer varav den ena är från barnoch ungdomspsykiatrin och de två andra är från habiliteringen. Studiens teoretiska referensram består av tre sammankopplade teorier som socialarbetare och psykologer kan arbeta med. Den första är theory of mind, den andra är central koherens och den tredje är exekutiva funktioner. I resultatet framkommer det att socialarbetare och psykologer identifierar information om ungdomar med autismspektrumtillstånd som lider av psykisk ohälsa. Det framkommer även att BUP arbetar med behandling och medicinering av psykisk ohälsa medan habiliteringen arbetar med autismspektrumtillstånd och ungdomars beteendeproblematik genom att ge stödinsatser. Sammanfattningsvis visar studiens resultat att autismspektrumtillstånd är starkt relaterat till psykisk ohälsa. Studien kan dra slutsatsen av att resultatet har en god koppling till tidigare forskning och teoretiska perspektiv.

Bakgrund Autismspektrumtillstånd [AST] har ökat i prevalens över tid. Tidigare har inte vuxna diagnostiserats med AST. Idag diagnostiseras allt fler i vuxen ålder. Att diagnostiseras med AST först i vuxen ålder medför en ökad risk för komorbida tillstånd. I och med att allt fler diagnostiseras med AST i vuxen ålder är det av intresse att förstå hur sen diagnostisering av AST kan upplevas. Syfte Syftet var att belysa personers upplevelser av att diagnostiseras med autismspektrumtillstånd i vuxen ålder. Metod En icke systematisk litteraturöversikt baserad på 16 vetenskapliga artiklar. Artikelsökningar utfördes i databaserna PubMed och CINAHL. Samtliga inkluderade artiklar genomgick en kvalitetsgranskning. De inkluderade artiklarna analyserades utifrån en integrerad analys. Resultat Datan resulterade i fyra huvudkategorier; Erfarenheter från livet före diagnosen, Den diagnostiska processen, Den känslomässiga upplevelsen av att få diagnosen samtDiagnosens påverkan på livet. Resultatet påvisade en komplex emotionell upplevelse med flertalet upplevda barriärer till diagnostisering av AST hos personer i vuxen ålder. Upplevelsen av diagnosens betydelse varierar. Flera studiedeltagare fann stort värde i sin diagnos och upplevde att diagnosen har stor påverkan på livet samt ger ett nytt perspektiv. Andra studiedeltagare upplevde att diagnosen inte förändrat deras liv särskilt mycket. Slutsats I föreliggande studie framkommer en ökad känsla av sammanhang för en majoritet av personer som diagnostiseras med AST i vuxen ålder. Vidare belyser denna litteraturöversikt att den komplexa upplevelse som en sen diagnostisering innebär kräver individuellt anpassade stödåtgärder som i dagsläget upplevs som bristfälliga. / Background Autism spectrum condition [ASC] or Autism spectrum disorder [ASD] is increasing in prevalence. Historically adults were not diagnosed with ASC. Today it is getting more common to receive the diagnosis as an adult. Comorbidities are common within the autism spectrum. Diagnosis of ASD in adulthood increases the risk of comorbidities. Since ASDs are getting more common world wide and more people are being diagnosed in adulthood it is of relevance to highlight the experiences of getting the diagnosis in early, mid and late adulthood.  Aim This study aimed to highlight experiences of being diagnosed with ASD in adulthood.  Method Non-systematic literature review based on 16 scientific articles with both qualitative and quantitative approaches. The articles were retrieved from the databases PubMed and CINAHL. All included scientific articles underwent a quality review. The included articles were analyzed through an integrated analysis. Results The data resulted in four main categories; Experiences from before the diagnosis, The diagnostic process, The emotional experience of receiving the diagnosis and The diagnosis’ impact on life. The results indicate a complex emotional experience. The results indicated a complex emotional experience with several perceived barriers to diagnosing ASC in adults. The experience of the significance of the diagnosis varies. Several study participants find great value in their diagnosis and feel that the diagnosis has a major impact on life and gives a new perspective. Other study participants felt that the diagnosis did not change their lives and their perspective.  Conclusions In the present study, an increased sense of coherence emerged for most individuals diagnosed with ASD in adulthood. Furthermore, this literature review highlights that the complex experience that a late diagnosis entails requires individually adapted support measures, of which are currently perceived as deficient.

Le trouble du spectre de l'autisme (TSA) est un trouble neurodéveloppemental caractérisé par des interactions sociales atypiques. L’hyperscanning est une technique émergente permettant l'enregistrement simultané de l'activité cérébrale de plusieurs individus lors d'interactions sociales. Dans cette étude, des données d'EEG hyperscanning issues de participants autistes et neurotypiques seront traitées par des techniques d’apprentissage profond (AP), améliorées par l'apprentissage auto-supervisé (AAS) pour analyser et discerner des schémas indicatifs de TSA. L'AP est utilisé pour extraire des schémas à partir des données brutes de l'EEG, réduisant la dépendance à l'ingénierie de caractéristiques manuelles, puis l’AAS est appliqué aux des données EEG non étiquetées. Cependant, malgré le potentiel des techniques d’AP, leur application au TSA reste largement inexplorée, notamment en hyperscanning. Afin de combler cette lacune, nous avons adapté et personnalisé des techniques d'AAS proposée par Banville et al., (2020), en incorporant deux encodeurs AP distincts entraînés pour extraire des caractéristiques significatives à partir de données EEG individuelles, et affinés dans un modèle d’AP de classificateur binaire. Des comparaisons ont été réalisées avec des encodeurs initialement aléatoires et des caractéristiques extraites manuellement des données EEG utilisées comme entrées pour un modèle de régression logistique. Le classificateur binaire entraîné sur des caractéristiques apprises par AAS surpasse systématiquement le classificateur de régression logistique et les encodeurs initialisés aléatoirement, atteignant une précision de 78 %, comparable à la performance la plus élevée rapportée par Banville et al. (2020) de 79,4 %. Nos résultats soulignent l'importance des représentations acquises à partir de signaux EEG individuels dans l'architecture multi-cerveaux adaptée à la classification d’EEG hyperscanning. Cette étude encourage ainsi l’utilisation des modèles d’AP dans les analyses d’EEG hyperscanning, notamment pour le développement d'outils de diagnostic et d'interventions plus précis et efficaces pour les personnes autistes, et ce même avec un nombre limité d'échantillons de données. / Autism spectrum condition (ASC) is a neurodevelopmental condition characterized by atypical social interactions. Traditional research on ASC has primarily focused on individual brain signals, but the emerging technique of hyperscanning enables simultaneous recording of multiple individuals' brain activity during social interactions. In this study, we leverage hyperscanning EEG data and employ Deep Learning (DL) techniques, augmented by self-supervised learning (SSL), to analyze and discern patterns indicative of ASC. DL is utilized to extract patterns from raw EEG data, reducing the reliance on manual feature engineering. SSL further enhances DL's efficacy by training on unlabeled EEG data, particularly useful when labeled datasets are limited. Despite the potential of DL techniques, their application in ASC diagnosis and treatment, particularly in hyperscanning, remains largely unexplored. This project aimed to bridge this gap by analyzing hyperscanning EEG data from autistic and neurotypical participants. Specifically, we adapted and customized SSL techniques proposed by Banville et al., incorporating two distinct DL embedders. These embedders are trained to extract meaningful features from single-brain EEG data and finetuned within a binary classifier DL model using hyperscanning EEG data from autistic and control dyads. Baseline comparisons were conducted with supervised, randomly initialized embedders, and hand-engineered features extracted from hyperscanning EEG using as inputs to a logistic regression model. Notably, the binary classifier trained on SSL-learned features consistently outperforms the logistic regression classifier and randomly initialized embedders, achieving an accuracy of 78%. This accuracy is comparable to Banville et al.'s highest reported performance of 79.4%. Our results underscore the significance of representations acquired from individual EEG signals within the multi-brain architecture tailored for hyperscanning EEG classification. Moreover, they hold promise for broader utilization of DL models in hyperscanning EEG analyses, especially for developing more accurate and efficient diagnostic tools and interventions for autistic individuals, even with limited data samples available.