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Query: UMLS:C0004352 (autism)
 32,579 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Autism is a developmental disorder with possibly multiple pathophysiologies. It has been theorized that cortical feature maps in individuals with autism are inadequate for forming abstract codes and representations. Cortical feature maps make it possible to classify stimuli, such as phonemes of speech, disregarding incidental detail. Hierarchies of such maps are instrumental in creating abstract codes and representations of objects and events. Self-Organizing Maps (SOMs) are artificial neural networks that offer insights into the development of cortical feature maps. Attentional impairment is prevalent in autism, but whether it is caused by attention-shift impairment or strong familiarity preference or negative response to novelty is a matter of debate. We model attention shift during self-organization by presenting a SOM with stimuli from two sources in four different modes, namely, novelty seeking (regarded as normal learning), attention-shift impairment (shifts are made with a low probability), familiarity preference (shifts made with a lower probability to the source that is the less familiar to the SOM of the two sources), and familiarity preference in conjunction with attention-shift impairment. The resulting feature maps from learning with novelty seeking and with attention-shift impairment are much the same except that learning with attention-shift impairment often yields maps with a somewhat better discrimination capacity than learning with novelty seeking. In contrast, the resulting maps from learning with strong familiarity preference are adapted to one of the sources at the expense of the other, and if one of the sources has a set of stimuli with smaller variability, the resulting maps are adapted to stimuli from that source. When familiarity preference is less pronounced, the resulting maps may become normal or fully restricted to one of the sources, and in that case, always the source with smaller variability if such a source is present. Such learning, in a system with many different maps, will result in very uneven capacities. Learning with familiarity preference in conjunction with attention-shift impairment surprisingly has higher probability for the development of normal maps than learning with familiarity preference alone.
J Autism Dev Disord 2004 Apr
PMID:Self-organization of an artificial neural network subjected to attention shift impairments and familiarity preference, characteristics studied in autism. 1516 37

A series of recent empirical observations demonstrate structured activity patterns that exist during passive task states. One observation is that a network of regions, referred to as the default network, shows preferentially greater activity during passive task states as compared to a wide range of active tasks. The second observation is that distributed regions spontaneously increase and decrease their activity together within functional-anatomic networks, even under anesthesia. We believe these rest activity patterns may reflect neural functions that consolidate the past, stabilize brain ensembles, and prepare us for the future. Accumulating data further suggest that differences in rest activity may be relevant to understanding clinical conditions such as Alzheimer's disease and autism. Maps of spontaneous network correlations also provide tools for functional localization and study of comparative anatomy between primate species. For all of these reasons, we advocate the systematic exploration of rest activity.
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PMID:Unrest at rest: default activity and spontaneous network correlations. 1768 17

Brain overgrowth in early developmental stages of children with autism is well documented. This paper explores the possibility that increases in propagation delays of stimuli and the signals triggered by them, resulting from this overgrowth, may be conducive to the development of poorly structured cortical maps, which may in turn be associated with autistic characteristics. We use a framework based on Self-Organizing Maps (SOMs). Unlike the conventional SOM model that assumes that all neurons in the neighborhood of the neuron closest to a stimulus instantaneously react to it and adjust their weights, we propose a more biologically realistic model that acknowledges delays inherent in the propagation of signals. We show that propagation delays can significantly affect the performance of SOMs. Coverage of stimuli is negatively affected by either an increase in the dilution factor (a parameter in the proposed model that controls the adjustment of responses to overlapping stimuli), or a decrease in propagation speed. For large dilution factors the topological structure of the maps is also compromised. We also demonstrate the model's robustness to different input stimuli layouts and distributions.
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PMID:Modeling propagation delays in the development of SOMs--a parallel with abnormal brain growth in autism. 1819 81