Evidence map›Paper›PMID 24339802›Full record

ArticleFrontiers in neuroscience2013

ICA model order selection of task co-activation networks.

Kimberly L Ray, D Reese McKay, Peter M Fox, Michael C Riedel, Angela M Uecker, Christian F Beckmann, Stephen M Smith, Peter T Fox, Angela R Laird

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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

  1. Pooled it
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  12. Enhanced motor network engagement during reward gain anticipation in fibromyalgia.Cortex; a journal devoted to the study of the nervous system and behavior · 2024
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  16. Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
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11 more citing papers are in PubMed but not listed here.

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

9 authors.

Kimberly L RayResearch Imaging Institute, University of Texas Health Science Center, San Antonio TX, USA.
D Reese McKay
Peter M Fox
Michael C Riedel
Angela M Uecker
Christian F Beckmann
Stephen M Smith
Peter T Fox
Angela R Laird

Funding

Meta-anaylsis in human brain mappingR01MH074457 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON · 2006 to 2024
$10.5M
BrainMap Tracker: Automated Annotation of Brain Mapping ExperimentsR56MH097870 · NIMH · THE MIND RESEARCH NETWORK · PI LAIRD, ANGELA R, TURNER, JESS · 2012 to 2013
$1.1M
Development of a Cognitive Paradigm Ontology: BrainMap and BIRN IntergrationR01MH084812 · NIMH · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI FOX, PETER THORNTON, TURNER, JESS · 2009 to 2011
$869k
NIMH NIH HHS R01 MH074457NIMH NIH HHS R01 MH084812NIMH NIH HHS R56 MH097870
6 · The paper itself

Abstract

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

Indexed as

BrainMapco-activationsfunctional brain networksfunctional connectivityindependent component analysisintrinsic connectivity networksmeta-analysisresting state networks

Identifiers

PMID24339802
PMCPMC3857551

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.