| Abstrakt: |
Background. Autism is a disorder characterized by deficits in communication, social interaction, a limited range of interests, and repetitive stereotypical behavior. Although it is believed that changes in the brain leading to Autism occur early on in prenatal and early postnatal development, there is no definitive test for a diagnosis of Autism. The diagnosis is made on the basis of behavioral signs and symptoms alone and is usually not made until age 2 or later. There have been numerous neuroanatomical abnormalities noted in Autism, some of which can be linked to neuropsychological dysfunction. Recently a new theory has become prominent which suggests the disorder may be due to aberrant neural connectivity patterns. Evidence in support of this theory has come from anatomical studies of white matter as well as functional neuroimaging studies.Methods. Most studies have employed functional magnetic resonance imaging to investigate connectivity, or electroencephalography (EEG) coherence studies. The high temporal resolution of EEG lends itself well to the investigation of cerebral connectivity. Research suggests there may be patterns of both hyperand hypoconnectivity between various brain regions. Seven different patterns of abnormal connectivity which can be analyzed with EEG are proposed.Results. Patterns of hyperconnectivity may be found in frontotemporal and left hemispheric regions, whereas patterns of hypoconnectivity are often seen in frontal (orbitofrontal), right posterior (occipital/parietal-temporal), frontal-posterior, and left hemispheric regions. In addition to these patterns of hypoand hyperconnectivity, a mu rhythm complex has been identified. Treatment goals may be based on coherence anomalies identified by quantitative EEG analysis. Increased coherence between brain regions may be downtrained, whereas decreased coherence between brain regions may be uptrained. Clinical examples of each pattern and a discussion of their neurofeedback treatment are provided.Conclusion. A theory of autistic disorders is presented that has at its' core neural connectivity disturbances. Multivariate EEG connectivity indices are utilized to formulate a typology of connectivity anomalies or patterns that have been observed over a series of autistic patients. These represent phenotypic expressions of the underlying pathology that leads to autistic symptoms. Examples demonstrate how these connectivity metrics can be used to understand autistic disturbances and formulate neurofeedback strategies for remedying these difficulties. [ABSTRACT FROM AUTHOR] |
