Evidence map›Paper›PMID 31275236›Full record

ArticleFrontiers in neurology2019

Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction.

Martin Wegrzyn, Markus Mertens, Christian G Bien, Friedrich G Woermann, Kirsten Labudda

Abstract read
In one paragraph

Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. A Rapid fMRI Paradigm for Localisation of the Language Network.The European journal of neuroscience · 2026
    Article
  4. The Relationship between White Matter Architecture and Language Lateralization in the Healthy Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  5. Article
  6. Article
  7. Developing fMRI protocol for clinical useNeurosciences (Riyadh, Saudi Arabia) · 2021
    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

5 authors.

Martin WegrzynDepartment of Psychology, Bielefeld University, Bielefeld, Germany.
Markus MertensBethel Epilepsy Center, Mara Hospital, Bielefeld, Germany.
Christian G BienBethel Epilepsy Center, Mara Hospital, Bielefeld, Germany.
Friedrich G WoermannBethel Epilepsy Center, Mara Hospital, Bielefeld, Germany.
Kirsten LabuddaDepartment of Psychology, Bielefeld University, Bielefeld, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In epilepsy patients, language lateralisation is an important part of the presurgical diagnostic process. Using task-based fMRI, language lateralisation can be determined by visual inspection of activity patterns or by quantifying the difference in left- and right-hemisphere activity using variations of a basic formula [(L-R)/(L+R)]. However, the values of this laterality index (LI) depend on the choice of activity thresholds and regions of interest. The diagnostic utility of the LI also depends on how its continuous values are translated into categorical decisions about a patient's language lateralisation. Here, we analysed fMRI data from 712 epilepsy patients who performed a verbal fluency task. Each fMRI data set was evaluated by a trained human rater as depicting left-sided, right-sided, or bilateral lateralisation or as being inconclusive. We used data-driven methods to define the activity thresholds and regions of interest used for LI computation and to define a classification scheme that allowed us to translate the LI values into categorical decisions. By deconstructing the LI into measures of laterality (L-R) and strength (L+R), we also modelled the relationship between activation strength and conclusiveness of a data set. In a held-out data set, predictions reached 91% correct when using only conclusive data and 82% when inconclusive data were included. Although only trained on human evaluations of fMRIs, the approach generalised to the prediction of language Wada test results, allowing for significant above-chance accuracies. Compared against different existing methods of LI-computation, our approach improved the identification and exclusion of inconclusive cases and ensured that decisions for the remaining data could be made with consistently high accuracies. We discuss how this approach can support clinicians in assessing fMRI data on a single-case level, deciding whether lateralisation can be determined with sufficient certainty or whether additional information is needed.

Indexed as

cerebral dominanceepilepsy surgeryfunctional MRIlanguagelateralisation

Identifiers

PMID31275236
PMCPMC6594217

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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.