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Preprocessing strategy influences graph-based exploration of altered functional networks in major depression

Overview of attention for article published in Human Brain Mapping, February 2016
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Title
Preprocessing strategy influences graph-based exploration of altered functional networks in major depression
Published in
Human Brain Mapping, February 2016
DOI 10.1002/hbm.23111
Pubmed ID
Authors

Viola Borchardt, Anton Richard Lord, Meng Li, Johan van der Meer, Hans-Jochen Heinze, Bernhard Bogerts, Michael Breakspear, Martin Walter

Abstract

Resting-state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease-related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting-state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph-theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean-signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
United Kingdom 1 2%
Brazil 1 2%
Hong Kong 1 2%
Unknown 59 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 12 19%
Student > Master 11 17%
Unspecified 10 16%
Professor > Associate Professor 7 11%
Other 8 13%
Readers by discipline Count As %
Neuroscience 19 30%
Unspecified 16 25%
Medicine and Dentistry 9 14%
Psychology 8 13%
Agricultural and Biological Sciences 4 6%
Other 8 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2016.
All research outputs
#9,735,711
of 12,177,962 outputs
Outputs from Human Brain Mapping
#2,169
of 2,591 outputs
Outputs of similar age
#206,943
of 292,545 outputs
Outputs of similar age from Human Brain Mapping
#129
of 167 outputs
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We're also able to compare this research output to 167 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.