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

NCT06306378 | Unknown

• 1 other identifier: XJTU1AF2023LSK-481
• Study Type: observational
• Target: 60
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Autism Spectrum Disorder

Keywords

Sleep spindles; Social Memory Deficits; Diagnosis

Outcome Measures

Primary Outcomes (6)

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

Study Arms (2)

ASD group

Diagnosed as ASD based on DSM-V diagnostic criteria and combined with clinical manifestations.

Diagnostic Test: social memory levels and spindle levels

Control group

Healthy children

Diagnostic Test: social memory levels and spindle levels

Interventions

social memory levels and spindle levels DIAGNOSTIC_TEST

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.

ASD group; Control group

Eligibility Criteria

• Age: 6 Years - 18 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

It is expected to recruit 60 participants aged 6-18, of which 30 in the case group are all inpatient and outpatient cases from Xi'an Traditional Chinese Medicine Brain Disease Hospital, and are designated as ASD according to unified diagnostic standards; The healthy control group consists of 30 individuals from nearby community kindergartens and primary schools at Xi'an Traditional Chinese Medicine Brain Disease Hospital, who have not suffered from ASD or other diseases related to ASD research factors.

You may qualify if:

• 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)

You may not qualify if:

• 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

Sponsors & Collaborators

• First Affiliated Hospital Xi'an Jiaotong University: lead
• Xi'an TCM Hospital of Encephalopathy: collaborator

Study Sites (1)

First Afflicated Hospital Xian Jiaotong University

Xi'an, Shaanxi, 710061, China

RECRUITING

Related Publications (3)

• 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.

  PMID: 33294809 BACKGROUND

• 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.

  PMID: 33501147 BACKGROUND

• 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.

  PMID: 36574922 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

The observational study does not involve biological sample collection, only collects and analyzes participants' memory test data and EEG recording data.

MeSH Terms

Conditions

Autism Spectrum Disorder; Disease

Condition Hierarchy (Ancestors)

Child Development Disorders, Pervasive; Neurodevelopmental Disorders; Mental Disorders; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Yan Li, PhD

  First Afflicated Hospital of Xian Jiaotong University

  STUDY DIRECTOR

Central Study Contacts

Dongqi Cui, PhD

CONTACT

18501059233; 18501059233@163.com

Xiaodan Wang, PhD

CONTACT

13720418610; m19834513386@163.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 27, 2024
• First Posted: March 12, 2024
• Study Start: December 4, 2023
• Primary Completion: March 30, 2024
• Study Completion: April 30, 2024
• Last Updated: March 12, 2024

Record last verified: 2023-11

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)