Literature DB >> 21745580

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study.

Joseph D Ramsey1, Stephen José Hanson, Clark Glymour.   

Abstract

Smith et al. report a large study of the accuracy of 38 search procedures for recovering effective connections in simulations of DCM models under 28 different conditions. Their results are disappointing: no method reliably finds and directs connections without large false negatives, large false positives, or both. Using multiple subject inputs, we apply a previously published search algorithm, IMaGES, and novel orientation algorithms, LOFS, in tandem to all of the simulations of DCM models described by Smith et al. (2011). We find that the procedures accurately identify effective connections in almost all of the conditions that Smith et al. simulated and, in most conditions, direct causal connections with precision greater than 90% and recall greater than 80%.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21745580     DOI: 10.1016/j.neuroimage.2011.06.068

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  44 in total

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5.  Latent variable GIMME using model implied instrumental variables (MIIVs).

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6.  Modeling Causal Relationship Between Brain Regions Within the Drug-Cue Processing Network in Chronic Cocaine Smokers.

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Journal:  Neuropsychopharmacology       Date:  2015-06-03       Impact factor: 7.853

7.  Inferring consistent functional interaction patterns from natural stimulus FMRI data.

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Journal:  Neuroimage       Date:  2012-03-14       Impact factor: 6.556

8.  Reversing the Standard Neural Signature of the Word-Nonword Distinction.

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9.  Investigation of Information Flow During a Novel Working Memory Task in Individuals with Traumatic Brain Injury.

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Journal:  Brain Connect       Date:  2015-01-28

10.  Intrinsic and task-evoked network architectures of the human brain.

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