Biology subjects

Thompson, W. H.

Publications and source records attributed to Thompson, W. H..

4 recordsLinked to original sources

bioRxiv2018

Brain network segregation and integration during an epoch-related working memory fMRI experiment

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, many subnetworks, including the default mode, visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

neuroscience↗

bioRxiv2017

A common framework for the problem of derivingestimates of dynamic functional brain connectivity.

The research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions. In order to understand in which way the results from different techniques can be compared to each other, it is useful to be able to formulate them within a common theoretical framework. In this study, we describe such a framework that is suitable for many of the dynamic functional connectivity methods that have been proposed. Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same framework. At the same time, care was given to the fact that key features of each technique could be easily illustrated within the framework and thus highlighting critical assumptions that are made. We aimed to create a common framework which should serve to assist comparisons between different analytical methods for dynamic functional brain connectivity and promote an understanding of their methodological advantages as well as potential drawbacks.\n\nHighlightsDifferent approaches to compute dynamic functional brain connectivity have been proposed, each with their own assumptions.\n\nWe present a theoretical framework that encompasses a large majority of proposed methods.\n\nOur common framework facilitates comparisons between different methods and illustrates their underlying assumptions.

neuroscience↗

bioRxiv2017

A simulation and comparison of dynamic functional connectivity methods

There is a current interest in quantifying brain dynamic functional connectivity (DFC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brains dynamics. However, given that the ground truth for DFC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exists many DFC methods it is difficult to assess differences in dynamic brain connectivity between studies. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating DFC (sliding window, tapered sliding window, temporal derivative, spatial distance and jackknife correlation). In particular, we were interested in each methods ability to track changes in covariance over time, which is a key property in DFC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future DFC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. In this paper, we present dfcbenchmarker, which is a Python package where researchers can easily submit and compare their own DFC methods to evaluate its performance.

neuroscience↗

bioRxiv2017

The Readability Of Scientific Texts Is DecreasingOver Time

Clarity and accuracy of reporting are fundamental to the scientific process. The understandability of written language can be estimated using readability formulae. Here, in a corpus consisting of 707 452 scientific abstracts published between 1881 and 2015 from 122 influential biomedical journals, we show that the readability of science is steadily decreasing. Further, we demonstrate that this trend is indicative of a growing usage of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.

scientific communication and education↗