Diagnosis of autism through EEG processed by advanced computational algorithms: A pilot study.
BACKGROUND
Multi-Scale Ranked Organizing Map coupled with Implicit Function as Squashing Time algorithm(MS-ROM/I-FAST) is a new, complex system based on Artificial Neural networks (ANNs) able to extract features of interest in computerized EEG through the analysis of few minutes of their EEG without any preliminary pre-processing. A proof of concept study previously published showed accuracy values ranging from 94%-98% in discerning subjects with Mild Cognitive Impairment and/or Alzheimer's Disease from healthy elderly people. The presence of deviant patterns in simple resting state EEG recordings in autism, consistent with the atypical organization of the cerebral cortex present, prompted us in applying this potent analytical systems in search of a EEG signature of the disease.


OBJECTIVE
The aim of the study is to assess how effectively this methodology distinguishes subjects with autism from typically developing ones.


METHODS
Fifteen definite ASD subjects (13 males; 2 females; age range 7-14; mean value = 10.4) and ten typically developing subjects (4 males; 6 females; age range 7-12; mean value 9.2) were included in the study. Patients received Autism diagnoses according to DSM-V criteria, subsequently confirmed by the ADOS scale. A segment of artefact-free EEG lasting 60 seconds was used to compute input values for subsequent analyses. MS-ROM/I-FAST coupled with a well-documented evolutionary system able to select predictive features (TWIST) created an invariant features vector input of EEG on which supervised machine learning systems acted as blind classifiers.


RESULTS
The overall predictive capability of machine learning system in sorting out autistic cases from normal control amounted consistently to 100% with all kind of systems employed using training-testing protocol and to 84% - 92.8% using Leave One Out protocol. The similarities among the ANN weight matrixes measured with apposite algorithms were not affected by the age of the subjects. This suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature.


CONCLUSIONS
This pilot study seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD.
Autism Research Unit, Villa Santa Maria Institute, Italy, Via IV Novembre 22038 Tavernerio (CO). Electronic address: enzo.grossi@bracco.com.
Enzo
E
Grossi
Autism Research Unit, Villa Santa Maria Institute, Italy, Via IV Novembre 22038 Tavernerio (CO). Electronic address: chiara.olivieri.co@gmail.com.
Chiara
C
Olivieri
Semeion Research Centre of Sciences of Communication, Via Sersale 117, Rome, 00128, Italy. Electronic address: m.buscema@semeion.it.
Massimo
M
Buscema
D000293:Adolescent; D000465:Algorithms; D016477:Artifacts; D000067877:Autism Spectrum Disorder / Q000175:diagnosis / Q000503:physiopathology; D001321:Autistic Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D015415:Biomarkers / Q000378:metabolism; D002540:Cerebral Cortex / Q000473:pathology; D002648:Child; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D016571:Neural Networks, Computer; D010865:Pilot Projects; D012984:Software
D015415:Biomarkers
Artificial neural networks; Autism spectrum disorder; Diagnosis; EEG