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

Borchardt, Viola, Lord, Anton Richard, Li, Meng, van der Meer, Johan, Heinze, Hans-Jochen, Bogerts, Bernhard, Breakspear, Michael and Walter, Martin (2016) Preprocessing strategy influences graph-based exploration of altered functional networks in major depression. Human Brain Mapping, 37 4: 1422-1442. doi:10.1002/hbm.23111


Author Borchardt, Viola
Lord, Anton Richard
Li, Meng
van der Meer, Johan
Heinze, Hans-Jochen
Bogerts, Bernhard
Breakspear, Michael
Walter, Martin
Title Preprocessing strategy influences graph-based exploration of altered functional networks in major depression
Journal name Human Brain Mapping   Check publisher's open access policy
ISSN 1065-9471
1097-0193
Publication date 2016-04
Sub-type Article (original research)
DOI 10.1002/hbm.23111
Open Access Status Not Open Access
Volume 37
Issue 4
Start page 1422
End page 1442
Total pages 21
Place of publication Hoboken, NJ, United States
Publisher John Wiley and Sons
Collection year 2017
Language eng
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.
Keyword Resting-state fMRI
Graph-theory
Functional connectivity
Major depressive disorder
Functional network analysis
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
 
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