ArticleCognitive neurodynamics2013
Down syndrome's brain dynamics: analysis of fractality in resting state.
Article in Cognitive neurodynamics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Brain health in diverse settings: How age, demographics and cognition shape brain function.NeuroImage · 2024Article
- Brain signal complexity in adults with Down syndrome: Potential application in the detection of mild cognitive impairment.Frontiers in aging neuroscience · 2022Article
- Everyday Memory in People with Down Syndrome.Brain sciences · 2021Article
- Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study.Network neuroscience (Cambridge, Mass.) · 2021Article
- Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.Cognitive neurodynamics · 2017Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To the best knowledge of the authors there is no study on nonlinear brain dynamics of down syndrome (DS) patients, whereas brain is a highly complex and nonlinear system. In this study, fractal dimension of EEG, as a key characteristic of brain dynamics, showing irregularity and complexity of brain dynamics, was used for evaluation of the dynamical changes in the DS brain. The results showed higher fractality of the DS brain in almost all regions compared to the normal brain, which indicates less centrality and higher irregular or random functioning of the DS brain regions. Also, laterality analysis of the frontal lobe showed that the normal brain had a right frontal laterality of complexity whereas the DS brain had an inverse pattern (left frontal laterality). Furthermore, the high accuracy of 95.8 % obtained by enhanced probabilistic neural network classifier showed the potential of nonlinear dynamic analysis of the brain for diagnosis of DS patients. Moreover, the results showed that the higher EEG fractality in DS is associated with the higher fractality in the low frequencies (delta and theta), in broad regions of the brain, and the high frequencies (beta and gamma), majorly in the frontal regions.
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