Neurodevelopmental Disorders
Conditions
Keywords
Neurodevelopmental disorder, EEG, Multi-domain data, Machine Learning
Brief summary
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.
Interventions
DIAGNOSTIC_TESTEEG
resting state EEG
DIAGNOSTIC_TESTDNA samples
genotypings of twelve CNTNAP2 SNPs from DNA obtained by saliva samples
DIAGNOSTIC_TESTclinical data
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).
OTHERenvironmental data
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
Sponsors
Ministry of Health, Italy
IRCCS Eugenio Medea
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
5 Months to 7 Months
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| EEG power spectral density (PSD) | one year after the end of the recruitment | Power spectral density (PSD) estimated in the delta, theta, alpha, beta and gamma frequency bands of the EEG |
| EEG signal coherence | 18 months after the end of the recruitment | EEG signal coherence |
| EEG signal entropy | 18 months after the end of the recruitment | EEG signal entropy |
Countries
Italy
Outcome results
None listed