The Relationship Between Social Memory Disorders and Sleep Spindles in Children With Autism Spectrum Disorder

Autism Spectrum Disorder Clinical Trial

Official title:

The Involvement of Sleep Spindle Waves in the Auxiliary Diagnosis of Social Memory Disorders in Children With Autism Spectrum Disorders

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06306378
• Other study ID #: XJTU1AF2023LSK-481
• Status: Recruiting
• Start date: December 4, 2023
• Est. completion date: April 30, 2024

Study information

• Verified date: November 2023
• Source: First Affiliated Hospital Xi'an Jiaotong University
• Contact: Dongqi Cui, PhD
• Phone: 18501059233
• Email: 18501059233[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Research background and project basis Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder characterized by social disorders and repetitive stereotypical behavior. Social memory impairment is a significant feature of ASD patients, and the specific pathogenesis of social memory impairment in ASD patients is currently unclear, and there are no objective indicators to measure social memory levels. Sleep spindle wave is a special brain wave in sleep that is closely related to memory consolidation. However, no one has yet studied the impact of sleep spindles on social memory. Research purpose Exploring the correlation between sleep spindles and social memory in the population, providing reference for the auxiliary diagnosis of social memory disorders in children with ASD.

Description:

The goal of this observational study is to exploring the correlation between sleep spindles and social memory in the population, providing reference for the auxiliary diagnosis of social memory disorders in children with autism spectrum disorders(ASD). The main question it aims to answer is the effect of sleep spindles on social memory. The study clinically recruited 30 children with ASD and 30 normal children. Participants will be asked to take face and car recognition memory tests which car recognition memory test as a control. After the two tasks, nighttime EEG recordings and subsequent spindle analysis will be recorded and performed.Then the correlation analysis between social memory levels and spindle levels would be conducted by machine learning model, so that researchers can infer the individual's social memory level through the level of spindles in the EEG.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 60
• Est. completion date: April 30, 2024
• Est. primary completion date: March 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 6 Years to 18 Years

Eligibility

Inclusion Criteria:

• Children with ASD diagnosed through DSM-V (Healthy controls do not have this requirement)
• IQ score = 75(WISC-IV,Wechsler Intelligence Scale for Children)
• Age: 6-18
• Not receiving psychotropic medication (Or stopping medication for at least 2 weeks before the experiment)

Exclusion Criteria:

• In addition to ASD, other mental illnesses are also combined
• Presence of a sleep disorder, sleep apnea, periodic leg movements during sleep, or atypical EEG patterns
• Left handed

Related Conditions & MeSH terms

• Autism Spectrum Disorder
• Autistic Disorder
• Child Development Disorders, Pervasive
• Memory Disorders

Intervention

Diagnostic Test:
• social memory levels and spindle levels
Take a face recognition memory test, and a car recognition memory test as a control . Then record nighttime EEG recordings after the two tasks and performed subsequent spindle analysis.So conduct the correlation between social memory levels and the level of spindles in the EEG by using machine learning to model.

Locations

Country	Name	City	State
China	First Afflicated Hospital Xian Jiaotong University	Xi'an	Shaanxi

Sponsors (2)

Lead Sponsor	Collaborator
First Affiliated Hospital Xi'an Jiaotong University	Xi'an TCM Hospital of Encephalopathy

Country where clinical trial is conducted

China, 

References & Publications (3)

Das S, Zomorrodi R, Mirjalili M, Kirkovski M, Blumberger DM, Rajji TK, Desarkar P. Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry. 2023 Apr 20;123:110705. doi: 10.1016/j.pnpbp.2022.110705. Epub 2022 Dec 24. — View Citation

Georgescu AL, Koehler JC, Weiske J, Vogeley K, Koutsouleris N, Falter-Wagner C. Machine Learning to Study Social Interaction Difficulties in ASD. Front Robot AI. 2019 Nov 29;6:132. doi: 10.3389/frobt.2019.00132. eCollection 2019. — View Citation

Lai M, Lee J, Chiu S, Charm J, So WY, Yuen FP, Kwok C, Tsoi J, Lin Y, Zee B. A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. EClinicalMedicine. 2020 Nov 5;28:100588. doi: 10.1016/j.eclinm.2020.100588. eCollection 2020 Nov. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Recognition accuracy	Recognition accuracy as an evaluation indicator for cars and facial recognition.; The car and face recognition task included a learning phase on the first night (approximately 30 minutes before going to bed) and a recognition test phase on the second morning (approximately 30 minutes after waking up). The learning phase included 11 pictures of adult faces (319 × 432 pixel). During the learning phase, pictures were randomly presented for 3s with an inter-stimulus interval of 2s. During the test phase, two pictures were presented simultaneously, with the picture from the study list (called "old") paired with an unseen picture (called "new"), in random left-right order. Participants were asked to select a picture they had seen previously by pressing the left and right buttons. And the next stimulus was presented immediately after the participant answered. Recognition accuracy was computed as the number of correct responses (hits).	Through face & car recognition task completion, an average of 2-4 days.
Primary	Response delay time	Reaction time is commonly used to evaluate cognitive abilities. Mean reaction times (ms) were calculated for correct responses (hits), which is the response delay time.	Through face & car recognition task completion, an average of 2-4 days.
Primary	Sleep spindle density	Sleep spindle wave recognition and data processing use the YASA (Yet Another Spindle Algorithm) toolbox based on Python to stage EEG sleep automatic recognition of sleep spindle waves. Calculate the density (N/min) of sleep spindles.	Through the 12 hour EEG recording completion, an average of 5-12 days.
Primary	Sleep spindle average duration	Calculate the average duration (s) of single spindle.	Through the 12 hour EEG recording completion, an average of 5-12 days.
Primary	Sleep spindle amplitude	Amplitude (µV) refers to the maximum energy value possessed by the spindle wave.	Through the 12 hour EEG recording completion, an average of 5-12 days.
Primary	Sleep spindle frequency	Frequency (Hz) refers to the number of times the spindle wave vibrates repeatedly per second.	Through the 12 hour EEG recording completion, an average of 5-12 days.