Dyslexia on a continuum: A complex network approach

Edwards, Erica S.; Burke, Kali; Booth, James R.; McNorgan, Chris

doi:10.1371/journal.pone.0208923

Cited by 16 publications

(12 citation statements)

References 73 publications

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“…Here we describe the public neuroimaging and behavioral dataset entitled “Longitudinal Brain Correlates of Multisensory Lexical Processing in Children” available on the OpenNeuro project (https://openneuro.org), and organized in compliance with the Brain Imaging Data Structure (BIDS). This dataset has been used, in part, in previous publications 17–28 . Our hopes in making this raw data publically accessible is to aid in openness, reproducibility, and reliability in neuroimaging research.…”

Section: Background and Summarymentioning

confidence: 99%

A longitudinal neuroimaging dataset on multisensory lexical processing in school-aged children

2019

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Here we describe the open access dataset entitled “Longitudinal Brain Correlates of Multisensory Lexical Processing in Children” hosted on OpenNeuro.org. This dataset examines reading development through a longitudinal multimodal neuroimaging and behavioral approach, including diffusion-weighted and T1-weighted structural magnetic resonance imaging (MRI), task based functional MRI, and a battery of psycho-educational assessments and parental questionnaires. Neuroimaging, psycho-educational testing, and functional task behavioral data were collected from 188 typically developing children when they were approximately 10.5 years old (session T1). Seventy children returned approximately 2.5 years later (session T2), of which all completed longitudinal follow-ups of psycho-educational testing, and 49 completed neuroimaging and functional tasks. At session T1 participants completed auditory, visual, and audio-visual word and pseudo-word rhyming judgment tasks in the scanner. At session T2 participants completed visual word and pseudo-word rhyming judgement tasks in the scanner.

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Section: Background and Summarymentioning

confidence: 99%

A longitudinal neuroimaging dataset on multisensory lexical processing in school-aged children

2019

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“…Network analysis has now been biologically applied on multiple levels, including sub-cellular (e.g., gene expression and protein dynamics) (Arodz and Bonchev, 2015;Kontou et al, 2016;Joehanes, 2018), cellular (e.g., neural networks) (Rothkegel and Lehnertz, 2014;Fernandes et al, 2015;Edwards et al, 2018) and tissue level signaling (Oliveira et al, 2014;Taroni et al, 2017). It is now also being applied to organ system analysis at a functional level.…”

Section: Introductionmentioning

confidence: 99%

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

2020

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Background: A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis. Methods: 201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups. Results: There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up. Conclusion: This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a

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“…Importantly, both within (f)MRI-based techniques as well as between (f)MRI and other neurophysiological techniques, there is a high convergence in the findings on neurocognitive underpinnings of both typical reading processes and failures thereof in dyslexic readers (see [8] for an overview). Another misconception we would like to correct is that the evidence on dyslexia is not only based on between-group comparisons but also on within-group associations between neurocognitive findings and reading problems (e.g., [9,10], see also [11] demonstrating on a cognitive level the presence of phonologically-based deficits in the large majority of individuals with a reading disability).…”

Section: Replymentioning

confidence: 99%