Identification of autism-spectrum disorder traits

Recruiting

Conditions: ICD-10 F-Diagnoses
F00-F99
Mental and behavioural disorders

Registration Number: DRKS00026020

Lead Sponsor: Klinik der Psychiatrie, Psychotherapie und Psychosomatik der Uniklinik RWTH Aachen.

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: Recruiting

Sex: All

Target Recruitment: 150

Inclusion Criteria

Psychiatric patients:
* formal diagnosis of a psychiatric illness (ICD-10, F-diagnosis)
* Legal age of majority
* Capable of business and able to follow staff instructions
* Written informed consent to participate in this research project.

Healthy participants:
* Age of majority (age: 18-75 years).
* Legally competent and able to follow the instructions of the staff
* Written consent to participate in this research project.

Exclusion Criteria

Individuals who are incapable of giving consent and/or are unable to understand the nature, significance, and scope of the research project and the provision of their written consent.

Acute suicidality

For healthy control subjects, additionally the existence of a severe psychiatric or neurological disease.

Study & Design

Study Type: observational

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
We expect a linear association between autistic traits as measured by the Autism Quotient and the performance in four behavioural tasks. In specific, we hypothesize participants who score higher on the AQ (higher autistic traits) to:<br>• Exhibit more autism-related voice characteristics with higher variability in the vocal pitch spectrum (Lehmann et al., 2022)<br>• Exhibit higher switching costs when global follow local stimuli (Iglesias-Fuster et al., 2015).<br>• Exhibit slower responses when identifying the emotions of anger, happiness and neutral (Klasen et al., 2011).<br>• Exhibit larger proportion of fixation times on the non-social compared to the social half of the complex-scene images (Frost-Karlsson et al., 2019).<br>

Secondary Outcome Measures

Name	Time	Method
• Determine detailed behavioral profile in the tasks by looking at additional task parameters such as error rates, reaction times, articulations, facial expressions, and eye movements).<br>• Association between the different clinical findings and the psychological markers in the questionnaires<br>• Combination of behavioral profiles to using classical statistics and machine learning, such as training neural networks predicting autism spectrum and other psychological characteristics. <br><br>Here, we will also evaluate if the inclusion of additional data sources results in improved classification accuracy for diagnoses.<br>