Autism and other disorders of social interaction: where we are and where to go from here

Identifying iNOS and glycogen as biomarkers for degenerated cerebellar purkinje cells in autism spectrum disorder: Protective effects of erythropoietin and zinc sulfate

Autism spectrum disorder (ASD) is a collective neurodevelopmental disorder affecting young children and accounting for 1% of the world's population. The cerebellum is the major part of the human brain affected by ASD and is associated with a substantial reduction in the number of Purkinje cells. An association between ASD and the expression of the nitrosative stress biomarker inducible nitric oxide synthase (iNOS), as well as glycogen deposition in damaged Purkinje cells, has not been previously reported in the medical literature. To explore this correlation, young rats were injected with propionic acid (PPA) (500 mg/kg) for 5 days (model group), while the protection groups were treated with either erythropoietin (EPO, 5,000 U/kg) or 2 mg/kg zinc sulfate immediately after the PPA injections. ASD-like features were developed in the model group, as evidenced by cerebellum damage (degeneration of Purkinje cells) and cerebellar dysfunction (behavioral impairment). This study documented the exclusive expression of iNOS in the degenerated Purkinje cells, along with glycogen deposition in these cells. Additionally, PPA significantly (p < 0.001) modulated cerebellar tissue levels of mammalian target of rapamycin (mTOR), gamma-aminobutyric acid (GABA), GABAA receptor, serotonin, the marker of neuronal loss (calbindin D28K), and social interaction deficit. Some of these parameters were differentially protected by EPO and zinc sulfate, with the former providing greater protection than zinc sulfate. Furthermore, a significant correlation between the iNOS score and these parameters associated with ASD was observed. These findings demonstrate the colocalization of iNOS and glycogen in the damaged Purkinje cells induced by ASD, along with the modulation of ASD parameters, which were protected by EPO and zinc sulfate treatments. Thus, these potential novel biomarkers may offer possible therapeutic targets for the treatment of ASD.

Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity

There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.