Evidence map›Paper›PMID 28842385›Full record

ArticleNeuroImage2017

Effects of spatial fMRI resolution on the classification of naturalistic movies.

H Mandelkow, J A de Zwart, J H Duyn

Abstract read
In one paragraph

Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Layer-dependent functional connectivity methods.Progress in neurobiology · 2021
    Review
  10. Review
  11. Article
  12. Article
  13. 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

3 authors.

H MandelkowAdvanced MRI, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA. Electronic address: Hendrik.Mandelkow@nih.gov.
J A de ZwartAdvanced MRI, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA.
J H DuynAdvanced MRI, LFMI, NINDS, National Institutes of Health, Bethesda, MD, USA.

Funding

Magnetic Resonance Imaging of Human Brain Anatomy and FunctionZIANS003027 · NINDS · NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE · PI DUYN, JEFF · 2009 to 2025
$31.2M
Intramural NIH HHS Z99 NS999999
6 · The paper itself

Abstract

Studies involving multivariate pattern analysis (MVPA) of BOLD fMRI data generally attribute the success of the information-theoretic approach to BOLD signal contrast on the fine spatial scale of millimeters facilitating the classification or decoding of perceptual stimuli. However, to date MVPA studies that have actually explored fMRI resolutions at less than 2 mm voxel size are rare and limited to small sets of unnatural stimuli (like visual gratings) as well as specific sub-regions of the brain, notably the primary somatosensory cortices. To investigate what spatial scale best supports high information extraction under more general conditions this study combined naturalistic movie stimuli with high-resolution fMRI at 7 T and linear discriminant analysis (LDA) of global and local BOLD signal patterns. Contrary to predictions, LDA and similar classifiers reached a maximum in classification accuracy (CA) at a smoothed resolution close to 3 mm, well above the 1.2 mm voxel size of the fMRI acquisition. Maximal CAs around 90% were contingent upon global fMRI signal patterns comprising 4 k-16 k of the most reactive voxels distributed sparsely throughout the occipital and ventro-temporal cortices. A Searchlight analysis of local fMRI patterns largely confirmed the global results, but also revealed a small subset of brain regions in early visual cortex showing limited increases in CA with higher resolution. Principal component analysis of the global and local fMRI signal patterns suggested that reproducible neuronal contributions were spatially auto-correlated and smooth, while other components of higher spatial frequency were likely related to physiological noise and responsible for the reduced CA at higher resolution. Systematic differences between experiments and subjects suggested that higher CA was significantly correlated with more consistent behavior revealed by eye tracking. Thus, the optimal resolution of fMRI data for MVPA was mainly limited by physiological noise of high spatial frequency as well as behavioral (in-)consistency.

Indexed as

Machine LearningBrainBrain MappingEye MovementsFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMalePattern Recognition, AutomatedPrincipal Component AnalysisVisual PerceptionYoung Adult7TEye trackingfMRIHigh-fieldHigh-resolutionMoviesMultivariate pattern analysisMVPANaturalistic stimuli

Identifiers

PMID28842385
PMCPMC9881349

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Registered trials

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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.