A Multi-domain Approach for the Characterization and Early Diagnosis of Neurodevelopmental Disorders

Study Overview
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Brief Summary
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Diagnosis and characterization of neurodevelopmental disorders are considered challenging processes because of their complexity, multi-factoriality and heterogeneity. The present project will consider two of the most common neurodevelopmental disorders (i.e. autism spectrum disorders (ASD) and language disorders (LD)), with the aim to overcome these difficulties, by: a) deeply investigating their neuronal correlates; b) identifying multi-domain biomarkers (electrophysiological, genetic, environmental and clinical); c) developing a machine learning algorithm for early diagnosis. To achieve the above mentioned aims a multi-domain dataset will be used, comprising data collected from typically developing infants, infants at high risk for ASD and infants at high risk for LD. The data that will be used have been already collected within other trials performed at the Scientific Institute E. Medea.

Official Title
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A Multi-domain Approach for the Characterization and Early Diagnosis of Neurodevelopmental Disorders

Conditions
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Neurodevelopmental Disorders

Intervention / Treatment
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* Diagnostic Test: EEG
* Diagnostic Test: DNA samples
* Diagnostic Test: clinical data
* Other: environmental data


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: APGAR scores at birth at 1' and 5' > 7; newborn screening otoacoustic emissions in the norm; absence of neurological disorders; Italian as the language mainly spoken in the family nucleus. Exclusion Criteria: APGAR scores at birth at 1' and 5' <= 7; newborn screening otoacoustic emissions not in the norm; presence of neurological disorders; non-Italian native speaker

Ages Eligible for Study
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Minimum Age: 5 Months
Maximum Age: 7 Months

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: TD (typically developing) infants<br>Typically developing infants, infants without familiar risk for ASD or LD. | Diagnostic Test: EEG<br>* resting state EEG<br>Diagnostic Test: DNA samples<br>* genotypings of twelve CNTNAP2 SNPs from DNA obtained by saliva samples<br>Diagnostic Test: clinical data<br>* standardized scales measuring the infants' cognitive, motor and linguistic development administered at 6, 12 and 18. Measures available at 6 and 12 months: a) Bayley Scales of Infant and Toddler Development- Third Edition; b) Griffiths mental development scales (0-2) (GMDS-R); Measures available at 18 months: c) Language Development Survey (LDS); d) Il PRIMO VOCABOLARIO DEL BAMBINO (PVB); e) CHILD BEHAVIOR CHECKLIST (CBCL); (f) Modified Checklist for Autism in Toddlers (M-CHAT).<br>Other: environmental data<br>* questionnaires administered to parents related to the following environmental factors: a) information about the presence of a clinical diagnosis of neurodevelopmental disorders within first-degree relatives; b) gestational age; c) birth weight; d) parental age and education; e) socio-economic status<br>|
| Experimental: HR (high risk) for LD infants<br>Infants at high risk for language disorders | Diagnostic Test: EEG<br>* resting state EEG<br>Diagnostic Test: DNA samples<br>* genotypings of twelve CNTNAP2 SNPs from DNA obtained by saliva samples<br>Diagnostic Test: clinical data<br>* standardized scales measuring the infants' cognitive, motor and linguistic development administered at 6, 12 and 18. Measures available at 6 and 12 months: a) Bayley Scales of Infant and Toddler Development- Third Edition; b) Griffiths mental development scales (0-2) (GMDS-R); Measures available at 18 months: c) Language Development Survey (LDS); d) Il PRIMO VOCABOLARIO DEL BAMBINO (PVB); e) CHILD BEHAVIOR CHECKLIST (CBCL); (f) Modified Checklist for Autism in Toddlers (M-CHAT).<br>Other: environmental data<br>* questionnaires administered to parents related to the following environmental factors: a) information about the presence of a clinical diagnosis of neurodevelopmental disorders within first-degree relatives; b) gestational age; c) birth weight; d) parental age and education; e) socio-economic status<br>|
| Experimental: HR for ASD infants<br>Infants at high risk for autism spectrum disorder | Diagnostic Test: EEG<br>* resting state EEG<br>Diagnostic Test: DNA samples<br>* genotypings of twelve CNTNAP2 SNPs from DNA obtained by saliva samples<br>Diagnostic Test: clinical data<br>* standardized scales measuring the infants' cognitive, motor and linguistic development administered at 6, 12 and 18. Measures available at 6 and 12 months: a) Bayley Scales of Infant and Toddler Development- Third Edition; b) Griffiths mental development scales (0-2) (GMDS-R); Measures available at 18 months: c) Language Development Survey (LDS); d) Il PRIMO VOCABOLARIO DEL BAMBINO (PVB); e) CHILD BEHAVIOR CHECKLIST (CBCL); (f) Modified Checklist for Autism in Toddlers (M-CHAT).<br>Other: environmental data<br>* questionnaires administered to parents related to the following environmental factors: a) information about the presence of a clinical diagnosis of neurodevelopmental disorders within first-degree relatives; b) gestational age; c) birth weight; d) parental age and education; e) socio-economic status<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| EEG power spectral density (PSD) | Power spectral density (PSD) estimated in the delta, theta, alpha, beta and gamma frequency bands of the EEG | one year after the end of the recruitment | 
| EEG signal coherence | EEG signal coherence | 18 months after the end of the recruitment | 
| EEG signal entropy | EEG signal entropy | 18 months after the end of the recruitment | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Neurodevelopmental disorder, EEG, Multi-domain data, Machine Learning