ArticleFrontiers in neurology2019
Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction.
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.
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7 citing papers in PubMed.
- Response to letter regarding "hemispheric imbalance in mild cognitive impairment: a graph-theoretical analysis of multimodal brain networks".Annals of medicine · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- A Rapid fMRI Paradigm for Localisation of the Language Network.The European journal of neuroscience · 2026Article
- 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 · 2024Article
- Bilateral and asymmetrical localization of language function identified by the superselective infusion of propofol in an epilepsy patient with a mild malformation of cortical development: illustrative case.Journal of neurosurgery. Case lessons · 2023Article
- Effects of left and right medial temporal lobe resections on hemodynamic correlates of negative and neutral scene processing.Human brain mapping · 2022Article
- Developing fMRI protocol for clinical useNeurosciences (Riyadh, Saudi Arabia) · 2021Article
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5 authors.
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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.
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