Altered brain network dynamics in youths with autism spectrum disorder
| Autor: | Evguenia Malaia, Katherine R. Coppess, Benjamin A. Seitzman, Erik Bates |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Male
medicine.medical_specialty Neurology Time Factors Adolescent Autism Spectrum Disorder Neuroscience(all) Models Neurological Electroencephalography Development behavioral disciplines and activities 050105 experimental psychology Developmental psychology 03 medical and health sciences 0302 clinical medicine Ministate Neural Pathways medicine Humans 0501 psychology and cognitive sciences Resting state Child Evoked Potentials Analysis of Variance medicine.diagnostic_test Resting state fMRI General Neuroscience 05 social sciences Brain medicine.disease Nonlinear Dynamics Autism spectrum disorder Autism spectrum Autism Edit distance Network analysis Female Psychology Neuroscience 030217 neurology & neurosurgery Research Article |
| Zdroj: | Experimental Brain Research |
| ISSN: | 1432-1106 0014-4819 |
| Popis: | The heterogeneity of behavioral manifestation of autism spectrum disorders (ASDs) requires a model which incorporates understanding of dynamic differences in neural processing between ASD and typically developing (TD) populations. We use network approach to characterization of spatiotemporal dynamics of EEG data in TD and ASD youths. EEG recorded during both wakeful rest (resting state) and a social–visual task was analyzed using cross-correlation analysis of the 32-channel time series to produce weighted, undirected graphs corresponding to functional brain networks. The stability of these networks was assessed by novel use of the L1-norm for matrix entries (edit distance). There were a significantly larger number of stable networks observed in the resting condition compared to the task condition in both populations. In resting state, stable networks persisted for a significantly longer time in children with ASD than in TD children; networks in ASD children also had larger diameter, indicative of long-range connectivity. The resulting analysis combines key features of microstate and network analyses of EEG. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink