Down syndrome’s brain dynamics: analysis of fractality in resting state
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...
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Published in | Cognitive neurodynamics Vol. 7; no. 4; pp. 333 - 340 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Dordrecht
Springer Netherlands
01.08.2013
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1871-4080 1871-4099 |
DOI | 10.1007/s11571-013-9248-y |
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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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AbstractList | 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. 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. 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.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. |
Author | Vameghi, Roshanak Ahmadlou, Mehran Gharib, Masoud Sajedi, Firoozeh Hemmati, Sahel |
Author_xml | – sequence: 1 givenname: Sahel surname: Hemmati fullname: Hemmati, Sahel organization: Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences – sequence: 2 givenname: Mehran surname: Ahmadlou fullname: Ahmadlou, Mehran organization: Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Dynamic Brain Research Group, Netherlands Institute for Neuroscience – sequence: 3 givenname: Masoud surname: Gharib fullname: Gharib, Masoud email: pediatricnrc@yahoo.com organization: Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences – sequence: 4 givenname: Roshanak surname: Vameghi fullname: Vameghi, Roshanak organization: Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences – sequence: 5 givenname: Firoozeh surname: Sajedi fullname: Sajedi, Firoozeh organization: Pediatric Neurorehabilitation Research Center, University of Social Welfare and Rehabilitation Sciences, Dynamic Brain Research Group |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24427209$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_3389_fnagi_2022_988540 crossref_primary_10_1016_j_lmot_2020_101685 crossref_primary_10_1007_s11571_016_9418_9 crossref_primary_10_3390_brainsci11050551 crossref_primary_10_1162_netn_a_00177 crossref_primary_10_1016_j_neuroimage_2024_120636 |
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SubjectTerms | Algorithms Artificial Intelligence Autism Biochemistry Biomedical and Life Sciences Biomedicine Birth weight Brain Cognitive Psychology Complexity Computer Science Down syndrome Down's syndrome Dynamical systems EEG Electrodes Fractal geometry Fractals Frontal lobe Handedness Neural networks Neurosciences Nonlinear dynamics Nonlinear systems Research Article Time series Variance analysis |
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Title | Down syndrome’s brain dynamics: analysis of fractality in resting state |
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