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Mechanisms of Voice Processing: Evidence from Autism Spectrum DisorderSchelinski, Stefanie 06 April 2018 (has links)
Die korrekte Wahrnehmung stimmlicher Information ist eine Grundvoraussetzung erfolgreicher zwischenmenschlicher Kommunikation. Die Stimme einer anderen Person liefert Information darüber wer spricht (Sprechererkennung), was gesagt wird (stimmliche Spracherkennung) und über den emotionalen Zustand einer Person (stimmliche Emotionserkennung). Autismus Spektrum Störungen (ASS) sind mit Einschränkungen in der Sprechererkennung und der stimmlichen Emotionserkennung assoziiert, während die Wahrnehmung stimmlicher Sprache relativ intakt ist. Die zugrunde liegenden Mechanismen dieser Einschränkungen sind bisher jedoch unklar. Es ist beispielsweise unklar, auf welcher Verarbeitungsstufe diese Einschränkungen in der Stimmenwahrnehmung entstehen oder ob sie mit einer Dysfunktion stimmensensitiver Hirnregionen in Verbindung stehen. Im Rahmen meiner Dissertation haben wir systematisch Stimmenverarbeitung und dessen Einschränkungen bei Erwachsenen mit hochfunktionalem ASS und typisch entwickelten Kontrollprobanden (vergleichbar in Alter, Geschlecht und intellektuellen Fähigkeiten) untersucht. In den ersten beiden Studien charakterisierten wir Sprechererkennung bei ASS mittels einer umfassenden verhaltensbezogenen Testbatterie und zweier funktionaler Magnet Resonanz Tomographie (fMRT) Experimente. In der dritten Studie untersuchten wir Mechanismen eingeschränkter stimmlicher Emotionserkennung bei ASS. Unsere Ergebnisse bringen neue Kenntnisse für Modelle zwischenmenschlicher Kommunikation und erhöhen unser Verständnis elementarer Mechanismen, die den Kernsymptomen in ASS wie Schwierigkeiten in der Kommunikation, zugrunde liegen könnten. Beispielsweise unterstützen unsere Ergebnisse die Annahme, dass Einschränkungen in der Wahrnehmung und Integration basaler sensorischer Merkmale (i.S. akustischer Merkmale der Stimme) entscheidend zu Einschränkungen in sozialer Kognition (i.S. Sprechererkennung und stimmliche Emotionserkennung) beitragen. / The correct perception of information carried by the voice is a key requirement for successful human communication. Hearing another person’s voice provides information about who is speaking (voice identity), what is said (vocal speech) and the emotional state of a person (vocal emotion). Autism spectrum disorder (ASD) is associated with impaired voice identity and vocal emotion perception while the perception of vocal speech is relatively intact. However, the underlying mechanisms of these voice perception impairments are unclear. For example, it is unclear at which processing stage voice perception difficulties occur, i.e. whether they are rather of apperceptive or associative nature or whether impairments in voice identity processing in ASD are associated with dysfunction of voice-sensitive brain regions. Within the scope of my dissertation we systematically investigated voice perception and its impairments in adults with high-functioning ASD and typically developing matched controls (matched pairwise on age, gender, and intellectual abilities). In the first two studies we characterised the behavioural and neuronal profile of voice identity recognition in ASD using two functional magnetic resonance imaging (fMRI) experiments and a comprehensive behavioural test battery. In the third study we investigated the underlying behavioural mechanisms of impaired vocal emotion recognition in ASD. Our results inform models on human communication and advance our understanding for basic mechanisms which might contribute to core symptoms in ASD, such as difficulties in communication. For example, our results converge to support the view that in ASD difficulties in perceiving and integrating lower-level sensory features, i.e. acoustic characteristics of the voice might critically contribute to difficulties in higher-level social cognition, i.e. voice identity and vocal emotion recognition.
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Abilities and Disabilities—Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum DisordersWolff, Nicole, Eberlein, Matthias, Stroth, Sanna, Poustka, Luise, Roepke, Stefan, Kamp-Becker, Inge, Roessner, Veit 22 April 2024 (has links)
Objective: Although autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level.
Methods: The study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module.
Results: In DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms.
Conclusions: Using ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.
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