Evidence map›Paper›PMID 32726664›Full record

ArticleJournal of neuroscience methods2020

Temporal embedding and spatiotemporal feature selection boost multi-voxel pattern analysis decoding accuracy.

Jeiran Choupan, Pamela K Douglas, Yaniv Gal, Mark S Cohen, David C Reutens, Zhengyi Yang

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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

6 authors at 5 institutions in 3 countries.

Jeiran ChoupanCentre for Advanced Imaging, The University of Queensland, Brisbane, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, Australia; Department of Psychology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, USA; Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: choupan@usc.edu.
Pamela K DouglasCenter for Cognitive Neuroscience, University of California, Los Angeles, CA, USA; Modeling & Simulation, and Computer Science Departments, UCF, Florida, USA.
Yaniv GalSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
Mark S CohenNeuropsychiatric Institute, University of California, Los Angeles, CA, USA; Departments of Psychiatry and Behavioral Sciences, Neurology, Radiological Sciences, Biomedical Physics, Psychology, and Bioengineering, University of California, Los Angeles, CA, USA; California Nanosystems Institute UCLA School of Medicine, Los Angeles, CA, USA.
David C ReutensCentre for Advanced Imaging, The University of Queensland, Brisbane, Australia.
Zhengyi YangCentre for Advanced Imaging, The University of Queensland, Brisbane, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Queensland University of Technology · AUCalifornia NanoSystems Institute · USThe University of Queensland · AUUniversity of California, Los Angeles · USUniversity of Southern California · US

Funding

Comprehensive Training in Neuroimaging Fundamentals and ApplicationsR90DA023422 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.7M
Comprehensive Training in Neuroimaging Fundamentals and ApplicationsT90DA022768 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COHEN, MARK STEVEN · 2006 to 2015
$1.5M
Structural and Functional Biomarkers of Attention-Deficit/Hyperactivity DisorderK01MH110645 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DOUGLAS, PK · 2017 to 2020
$632k
NIDA NIH HHS R90 DA023422NIDA NIH HHS T90 DA022768NIMH NIH HHS K01 MH110645
6 · The paper itself

Abstract

backgroundIn fMRI decoding, temporal embedding of spatial features of the brain allows the incorporation of brain activity dynamics into the multivariate pattern classification process, and provides enriched information about stimulus-specific response patterns and potentially improved prediction accuracy. NEW

methodThis study investigates the possibility of enhancing the classification performance by exploring temporal embedding, to identify the optimum combination of spatiotemporal features based on their classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby study (Haxby et al., 2001). Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 s. COMPARISON WITH EXISTING

methodsA wide range of spatiotemporal observations were created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers prediction accuracies for these combinations were then compared with the single spatial multivariate pattern approach that uses only a single temporal observation.

resultsOur findings showed that, on average, spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent.

conclusionsAs expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until -4 s after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design optimal approaches that incorporate spatiotemporal dependencies into feature selection for decoding.

Indexed as

AlgorithmsPattern Recognition, AutomatedBrain MappingMagnetic Resonance ImagingSupport Vector MachinefMRIMultiband EPIMulti-variate pattern analysisRandom forestSpatiotemporal feature selectionSupport vector machine

Identifiers

PMID32726664
PMCPMC9419668
OpenAlexW3044741720

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.