Natural visibility graphs for diagnosing attention deficit hyperactivity disorder (ADHD)

Mira‐Iglesias, Ainara; Conejero, J. Alberto; Navarro‐Pardo, Esperanza

doi:10.1016/j.endm.2016.09.058

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…In addition to this, we analyzed the NVG degree distribution of extreme cases when the number of commission errors were out of the expected range. A brief preliminary description of this approach was presented in reference [42].…”

Section: Outline Of the Workmentioning

confidence: 99%

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

2019

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In this study, we analyze the response times of students to yes/no decision tasks from the perspective of network science. We analyze the properties of the natural visibility graphs (NVG) associated with their reaction time series. We observe that the degree distribution of these graphs usually fits a power-law distribution px = x-α. We study the range in which parameter α occurs and the changes of this exponent with respect to the age and gender of the students. In addition to this, we also study the links between the parameter α and the parameters of the ex-Gaussian distribution that best fit the response times for each subject.

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Section: Outline Of the Workmentioning

confidence: 99%

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

2019

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“…One characteristic of a graph that can be obtained with networkX is the so called degree of the nodes, i.e., the number of nodes to which a specific node is connected. In [9] node degree distributions were used to identify sessions (actually: participants) with 'exceptional' node degree distributions, to see if outlier distributions predict attention disorders (i.c., ADHD). We applied a similar analysis to the node degree distributions of all (sub-)sessions in the Cognates and BALDEY databases.…”

Section: Natural Visibility Graphsmentioning

confidence: 99%

“…One factor that almost certainly affects RTs is attention. NVGs have been used to infer attention fluctuations in the diagnosis of ADHD and dementiarelated conditions [9].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Reaction Time and Error Sequences in Lexical Decision Experiments

Bosch¹,

Boves²,

Mulder³

2019

Interspeech 2019

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Reaction times (RTs) are used widely in psychological and psycholinguistic research as inexpensive measures of underlying cognitive processes. However, inferring cognitive processes from RTs is hampered by the fact that actual responses are the result of multiple factors, many of which may not be related to the process of interest. In lexical decision experiments, the use of RTs is further complicated by the fact that the response to some stimuli is missing, and the fact that part of the responses are 'incorrect'. In this paper we investigate the distribution of missing and incorrect responses in the RT sequences of two large lexical decision experiments. It appears that a substantial part of incorrect responses cluster together. Then, we investigate the effect of clusters of incorrect responses on surrounding RTs. Also, we extend previous research on methods for discovering and removing so-called local speed effects from RT sequences. For this purpose, we show that a recently introduced graphbased RT analysis method can help to better understand and analyze RT sequences.

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“…In this latter context, the application to neuroscience is on its infancy, and has essentially limited so far to the 16 analysis of electroencephalogram (EEG) data (see [1][2][3][4]34] for a few examples). The study of fmRI recordings 17 under these lens has been scarce, and in this work we would like to motivate and justify why we think this is a 18 promising endeavor, both from a univariate and -perhaps more interestingly-from a multivariate time series 19 perspective [24].…”

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confidence: 99%

“…The volumes were corrected for 33 motion, after which slice timing correction was applied to correct for temporal alignment. All voxels were 34 spatially smoothed with a 6mm FWHM isotropic Gaussian kernel and after intensity normalization, a band pass 35 filter was applied between 0.01 and 0.08 Hz. In addition, linear and quadratic trends were removed.…”

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confidence: 99%

Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks

Sannino

Stramaglia

Lacasa

et al. 2017

Preprint

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Visibility algorithms are a family of methods that map time series into graphs, such that the tools of graph 1 theory and network science can be used for the characterization of time series. This approach has proved a 2 convenient tool and visibility graphs have found applications across several disciplines. Recently, an approach has 3 been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective 4 dynamics. Here we test their application to fMRI time series, following two main motivations, namely that (i) 5 this approach allows to simultaneously capture and process relevant aspects of both local and global dynamics in 6 an easy and intuitive way, and (ii) this provides a suggestive bridge between time series and network theory 7 which nicely fits the consolidating field of network neuroscience. Our application to a large open dataset reveals 8 differences in the similarities of temporal networks (and thus in correlated dynamics) across resting state 9 networks, and gives indications that some differences in brain activity connected to psychiatric disorders could be 10 picked up by this approach.

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Natural visibility graphs for diagnosing attention deficit hyperactivity disorder (ADHD)

Cited by 8 publications

References 9 publications

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

Power-Law Distribution of Natural Visibility Graphs from Reaction Times Series

Analyzing Reaction Time and Error Sequences in Lexical Decision Experiments

Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks

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