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1.
Front Cell Neurosci ; 14: 585395, 2020.
Article in English | MEDLINE | ID: mdl-33262691

ABSTRACT

Maternal exposure to the valproate short-chain fatty acid (SCFA) during pregnancy is known to possibly induce autism spectrum disorders (ASDs) in the offspring. By contrast, case studies have evidenced positive outcomes of this anticonvulsant drug in children with severe autism. Interestingly, the same paradoxical pattern applies to the IL-17a inflammatory cytokine involved in the immune system regulation. Such joint apparent contradictions can be overcome by pointing out that, among their respective signaling pathways, valproate and IL-17a share an enhancement of the "type A monoamine oxidase" (MAOA) enzyme carried by the X chromosome. In the Guided Propagation (GP) model of autism, such enzymatic rise triggers a prenatal epigenetic downregulation, which, without possible X-inactivation, and when coinciding with genetic expression variants of other brain enzymes, results in the delayed onset of autistic symptoms. The underlying imbalance of synaptic monoamines, serotonin in the first place, would reflect a mismatch between the environment to which the brain metabolism was prepared during gestation and the postnatal actual surroundings. Following a prenatal exposure to molecules that significantly elicit the MAOA gene expression, a daily treatment with the same metabolic impact would tend to recreate the fetal environment and contribute to rebalance monoamines, thus allowing proper neural circuits to gradually develop, provided behavioral re-education. Given the multifaceted other players than MAOA that are involved in the regulation of serotonin levels, potential compensatory effects are surveyed, which may underlie the autism heterogeneity. This explanatory framework opens up prospects regarding autism prevention and treatment, strikingly in line with current advances along the gut microbiome-brain axis.

2.
Artif Intell Med ; 14(3): 237-58, 1998 Nov.
Article in English | MEDLINE | ID: mdl-9821516

ABSTRACT

This paper presents a computational model of Parkinson's Disease (PD) symptoms. Based on psychophysiological data, the underlying system (Guided Propagation Network) implements coincidence detection between internal flows and stimuli, and can be dynamically controlled for representing the action of neuromodulators such as dopamine (DA). By modelling the DA deficit involved in PD through a decrease of response thresholds in the production modules of a GPN, four symptoms are observed in experiments carried out on a computer simulation, and then attributed to a lack of synchrony between 'proprioceptive stimuli' and internal flows: reduced intensity, increased rate, saccades and spontaneous repetitions.


Subject(s)
Computer Simulation , Models, Biological , Neural Networks, Computer , Parkinson Disease/physiopathology , Humans , Neurotransmitter Agents
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