Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder

Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir

doi:10.1016/j.physa.2012.04.025

Cited by 172 publications

(90 citation statements)

References 23 publications

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“…The goal is to help physicians indicate different sleep stages and the occurrence of respiratory, cardiac and muscular events in the sleep scoring process [27]. Combinations of wavelet signal processing technique, chaos theory/nonlinear science and neural network pattern recognition techniques have been reported in recent years for the EEG-based diagnosis of varying disorders such as epilepsy [28,29,30,31,32,33], Alzheimer's disease [34,35], attention deficit hyperactivity disorder [36,37], autism spectrum disorder [38,39,40], major depressive disorder [29] and alcoholism [41]. …”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…1 Visibility graph analysis has proved successful for processing EEG data with the goal of identifying patients with Alzheimer's Disease, ADHD and Autism. [2][3][4] The reader is referred to…”

Section: 4961mentioning

confidence: 99%

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity

Fletcher

Wennekers

2018

Int. J. Neur. Syst.

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It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory-inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

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“…Synchronization measurements are classified in two general categories: linear methods and nonlinear methods. such as synchronization likelihood (introduced by Stam and van Dijk (2002), fuzzy synchronization likelihood (introduced by Ahmadlou and Adeli (2011b), and visibility graph similarity (introduced by Ahmadlou and Adeli (2012)) for diagnosis of attention deficit/hyperactivity disorder (ADHD) Adeli, 2010b, 2012;Ahmadlou and Adeli, 2011 a,b;Ahmadlou et al, 2012a), Autism Spectrum Disorder (ASD) (Ahmadlou et al, 2012b), and Major Depressive Disorder (MDD) (Ahmadlou et al, 2012c). A large number of sequential sample times is needed for nonlinear synchronization measurements (usually more than 10,000 sample times).…”

Section: Functional Connectivitymentioning

confidence: 99%

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

Ahmadlou

Adeli

Bajo

et al. 2014

Clinical Neurophysiology

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HIGHLIGHTS• We use magneto-encephalograms in patients with mild cognitive impairment (MCI).• We investigate complexity of functional connectivity network of MCI patients using two different measures: Graph Index Complexity and Efficiency Complexity.• Efficiency Complexity is superior to Graph Index Complexity and its value at theta band can be used for diagnosis of MCI. ABSTRACT Objectives:The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. Methods: MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks' complexities are measured by Graph Index Complexity (C r ) and Efficiency Complexity (Q). Results:The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. Conclusions: C e would be a better measurement for showing the global differences between normal and MCI brains compared with C r . Significance: The high accuracy of the classification shows C e at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MCI.

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Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder

Cited by 172 publications

References 23 publications

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

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