Evidence map›Paper›PMID 28782871›Full record

ArticleHuman brain mapping2017

Combining task-related activation and connectivity analysis of fMRI data reveals complex modulation of brain networks.

Martin Fungisai Gerchen, Peter Kirsch

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In one paragraph

Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Martin Fungisai GerchenDepartment of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Germany.ORCID 0000-0003-3071-5296
Peter KirschDepartment of Clinical Psychology, Central Institute of Mental Health, University of Heidelberg/Medical Faculty Mannheim, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Task-related effects in functional magnetic resonance imaging (fMRI) data are usually analyzed with local activation approaches or integrative connectivity approaches, for example, by psychophysiological interaction (PPI) analysis. While both approaches are often applied to the same data set, a systematic combination of the results with a whole-brain (WB) perspective is rarely conducted and the relationship between task-dependent activation and connectivity effects is relatively unexplored. Here, we combined brain activation and graph theoretical analysis of WB-PPI results in an exemplary episodic memory data set of N = 136 healthy human participants and found regions with congruent as well as incongruent activation and connectivity changes between task and control conditions. A comparison with large-scale resting state networks showed that in congruent as well as incongruent regions task-positively modulated connections were mainly between-network connections, especially with the default mode network, while task-negatively modulated connections were mainly found within resting state networks. Over all regions, the strength of absolute activation effects was associated with the tendency to exhibit task-positive connectivity changes, mainly driven by a strong relationship in negatively activated regions. These results demonstrate that task demands lead to a complex modulation of brain networks and provide evidence that task-evoked activation and connectivity effects reflect separable and complementary information on the macroscale brain level assessed by fMRI. Hum Brain Mapp 38:5726-5739, 2017. © 2017 Wiley Periodicals, Inc.

Indexed as

Neuropsychological TestsAdolescentAdultBrainBrain MappingCerebrovascular CirculationCluster AnalysisFemaleHumansMagnetic Resonance ImagingMaleMental ProcessesMiddle AgedOxygenReproducibility of ResultsRestOxygendefault mode networkfunctional magnetic resonance imaginggraph theoryhippocampuspsychophysiological interactionresting statetask

Identifiers

PMID28782871
PMCPMC6866920

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.