1. Lazarev VV, Pontes A, Mitrofanov AA, Deazevedo LC. {{Reduced Interhemispheric Connectivity in Childhood Autism Detected by Electroencephalographic Photic Driving Coherence}}. {J Autism Dev Disord}. 2013 Oct 6.
The EEG coherence among 14 scalp points during intermittent photic stimulation at 11 fixed frequencies of 3-24 Hz was studied in 14 boys with autism, aged 6-14 years, with relatively intact verbal and intellectual functions, and 19 normally developing boys. The number of interhemispheric coherent connections pertaining to the 20 highest connections of each individual was significantly lower in autistic patients than in the control group at all the EEG beta frequencies corresponding to those of stimulation. The coefficient of coherence values between homologous occipital, parietal and central areas at the same frequencies were also lower in the autistic group in both mono- and bipolar montages due to a deficit in reactive photic driving increase. No differences between the groups were observed in the spontaneous EEG.
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2. Ruggeri B, Sarkans U, Schumann G, Persico AM. {{Biomarkers in autism spectrum disorder: the old and the new}}. {Psychopharmacology}. 2013 Oct 6.
RATIONALE: Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, ‘multiple-hit’, oligogenic/polygenic underpinnings involving several loci and possibly gene-environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs. OBJECTIVES: The present paper reviews (a) similarities and differences between the concepts of ‘biomarker’ and ‘endophenotype’, (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism. RESULTS: Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels. CONCLUSIONS: Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.
