Autism Spectrum Disorders (ASD), grouping besides autism, a wider spectrum of symptoms, refer to a series of abnormal manifestations of cognitive development that can emerge in babies between 6 and 24 months, for example: deficiency in verbal and non-verbal communication, deficiency in social interactions, repetitive and stereotyped behaviours. Autism is classified as neurological pathology and it has been detected for the first time in the early years of 1900. Since then the disease has been object of study, because what triggers it has not been identified yet, even if undeniably a genetic component seems to play an essential role. Notably, researches focused on early diagnosis, because it has been observed that it is fundamental to detect the pathology as soon as possible, in order to tailor appropriate interventions, for the benefit of present and future baby’s life.
A recent study by Carolina Institute for Developmental Disabilities from University of North Carolina applies neuroimaging techniques with the aim of identifying babies having the highest risk to develop the pathology, in the frame of early diagnosis (http://stm.sciencemag.org/content/9/393/eaag2882). They used functional magnetic resonance on 6 months old babies to detect brain areas that show abnormalities and that therefore could be an indicator of future development of ASD. The study involves also the implementation of machine learning techniques for correct automatic detection of neural connections.
However, this is not the only possible approach: here in Ab.Acus we focused on ASD early diagnosis, but exploiting a behavioural criterion. It has been observed that there is a correlation between involuntary movements of limbs typical in newborn babies (so called general movements) and the possibility to develop some neurological deficits: if abnormalities are detected in these movements, an atypical neuromotor development could later appear (https://www.ncbi.nlm.nih.gov/pubmed/18403821). This is the starting point of our work, aiming at assessing a correlation between general movements and ASD. So far, general movements have been observed mainly qualitatively, involving an operator’s contribution in terms of a “by eye” analysis, so we thought to improve this approach, making the analysis process easier and quantitative.