ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging
Power spectral analysis can determine language laterality from resting-state functional MRI data in healthy controls.
Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- The evolution of phonemic verbal fluency test: bridging tradition with contemporary insights.Frontiers in psychology · 2026Review
- Evolutionary Strategies Enable Systematic and Reliable Uncertainty Quantification: A Proof-of-Concept Pilot Study on Resting-State Functional MRI Language Lateralization.Journal of imaging informatics in medicine · 2025Article
- Application value of resting-state fMRI in preoperative lateralization of language areas in epilepsy with left-sided epileptogenic foci.Frontiers in neurologyArticle
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
background and purposeResting-state functional magnetic resonance imaging (rsfMRI) has been proposed as an alternative to task-based fMRI including clinical situations such as preoperative brain tumor planning, due to advantages including ease of performance and time savings. However, one of its drawbacks is the limited ability to accurately lateralize language function.
methodsUsing the rsfMRI data of healthy controls, we carried out a power spectra analysis on three regions of interest (ROIs): Broca's area (BA) in the frontal cortex for language, hand motor (HM) area in the primary motor cortex, and the primary visual cortex (V1). Spike removal, motion correction, linear trend removal, and spatial smoothing were applied. Spontaneous low-frequency fluctuations (0.01-0.1 Hz) were filtered to enable functional integration.
resultsBA showed greater power on the left hemisphere relative to the right (p = .0055), while HM (p = .1563) and V1 (p = .4681) were not statistically significant. A novel index, termed the power laterality index (PLI), computed to estimate the degree of power lateralization for each brain region, revealed a statistically significant difference between BA and V1 (p < .00001), where V1 was used as a control since the primary visual cortex does not lateralize. Validation studies used to compare PLI to a laterality index computed using phonemic fluency, a task-based, language fMRI paradigm, demonstrated good correlation.
conclusionsThe power spectra for BA revealed left language lateralization, which was not replicated in HM or V1. This work demonstrates the feasibility and validity of an ROI-based power spectra analysis on rsfMRI data for language lateralization.
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