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Flur im Fokus: Nutzungsanalyse der Verkehrsfläche einer intensivpädagogischen WohngruppeHeise, Delia 07 August 2024 (has links)
In diesem Artikel sollen Potenziale im Flurbereich einer Wohngruppe für Menschen mit Autismus-Spektrum-Störung (ASS) betrachtet werden. Die Bewohner:innen dieser Wohngruppe haben einen äußerst hohen Bedarf an Unterstützung und können zum gegenwärtigen Zeitpunkt keine Sozialkontakte eingehen bzw. sind nicht gruppenfähig.
Die Erkenntnisse sollen ermöglichen, dass Flure nicht nur die Funktion eines Durchgangsbereiches einnehmen, sondern auch als Orte der Begegnung, der Orientierung und der Förderung von Selbstständigkeit dienen. Und auch als solche konzeptionell berücksichtigt werden.
Um sich der Frage zu nähern, ob es diese Potenziale gibt und wie diese durch architektonische Elemente unterstützt werden können, wurde ein ortszentriertes Behavior Mapping im Flur der intensivpädagogischen Wohngruppe durchgeführt. Ziel der Beobachtungen war das Nutzungsverhalten von Bewohner:innen und Personal der Wohngruppe zu analysieren, um daraus Gestaltungsempfehlungen abzuleiten.
Folgende Elemente der Flurgestaltung ließen sich aus den Beobachtungen und der Literaturrecherche ableiten: Reizarmut der Umgebung zur Vermeidung von sensorischen Reizen. Ausreichend Platz für funktionelle Verkehrsfläche und Durchwegung sowie Arbeitsabläufe. Orientierungs- und Leitsysteme, um die Funktion von Bereichen zu kennzeichnen und die Orientierung zu erleichtern. Zonierung nach Funktionen zur Stärkung bestimmter Aktivitäten wie Kommunikation. Rückzugsmöglichkeiten tragen durch eine kontrollierbare Umwelt zur Stressreduktion bei. [... aus dem Text]
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Responses in left inferior frontal gyrus are altered for speech-in-noise processing, but not for clear speech in autismSchelinski, Stefanie, Kriegstein, Katharina von 04 June 2024 (has links)
Introduction
Autistic individuals often have difficulties with recognizing what another person is saying in noisy conditions such as in a crowded classroom or a restaurant. The underlying neural mechanisms of this speech perception difficulty are unclear. In typically developed individuals, three cerebral cortex regions are particularly related to speech-in-noise perception: the left inferior frontal gyrus (IFG), the right insula, and the left inferior parietal lobule (IPL). Here, we tested whether responses in these cerebral cortex regions are altered in speech-in-noise perception in autism.
Methods
Seventeen autistic adults and 17 typically developed controls (matched pairwise on age, sex, and IQ) performed an auditory-only speech recognition task during functional magnetic resonance imaging (fMRI). Speech was presented either with noise (noise condition) or without noise (no noise condition, i.e., clear speech).
Results
In the left IFG, blood-oxygenation-level-dependent (BOLD) responses were higher in the control compared to the autism group for recognizing speech-in-noise compared to clear speech. For this contrast, both groups had similar response magnitudes in the right insula and left IPL. Additionally, we replicated previous findings that BOLD responses in speech-related and auditory brain regions (including bilateral superior temporal sulcus and Heschl's gyrus) for clear speech were similar in both groups and that voice identity recognition was impaired for clear and noisy speech in autism.
Discussion
Our findings show that in autism, the processing of speech is particularly reduced under noisy conditions in the left IFG—a dysfunction that might be important in explaining restricted speech comprehension in noisy environments.
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A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autismWolff, Nicole, Kohls, Gregor, Mack, Judith T., Vahid, Amirali, Elster, Erik M., Stroth, Sanna, Poustka, Luise, Kuepper, Charlotte, Roepke, Stefan, Kamp-Becker, Inge, Roessner, Veit 22 April 2024 (has links)
Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two frequently co-occurring neurodevelopmental conditions that share certain symptomatology, including social difficulties. This presents practitioners with challenging (differential) diagnostic considerations, particularly in clinically more complex cases with co-occurring ASD and ADHD. Therefore, the primary aim of the current study was to apply a data-driven machine learning approach (support vector machine) to determine whether and which items from the best-practice clinical instruments for diagnosing ASD (ADOS, ADI-R) would best differentiate between four groups of individuals referred to specialized ASD clinics (i.e., ASD, ADHD, ASD + ADHD, ND = no diagnosis). We found that a subset of five features from both ADOS (clinical observation) and ADI-R (parental interview) reliably differentiated between ASD groups (ASD & ASD + ADHD) and non-ASD groups (ADHD & ND), and these features corresponded to the social-communication but also restrictive and repetitive behavior domains. In conclusion, the results of the current study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms (i.e., 92% item reduction) while preserving relatively high diagnostic accuracy. Clinical implications and study limitations are discussed.
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