Lateralization of Receptive Language Function Using near Infrared Spectroscopy

Paquette, Natacha; González-Frankenberger, Berta; Vannasing, Phetsamone; Tremblay, Julie; Florea, Olivia; Beland, Renée; Leporé, Franco; Lassonde, Maryse

doi:10.4236/nm.2010.12010

Cited by 10 publications

(6 citation statements)

References 24 publications

(32 reference statements)

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“…In order to evaluate hemispheric dominance using fNIRS, a few attempts have been reported. 20,31 Chaudhary et al calculated LI using frequency domain NIRS from the HbT concentration. 31 But for a CW NIRS system, the signals are relative concentration changes of HbO and HbR.…”

Section: Discussionmentioning

confidence: 99%

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Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas

et al. 2017

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Purpose Understanding language dominance is crucial in pre-surgical evaluation of patients with epilepsy and in patients having a tumour close to the language area. Functional magnetic resonance imaging (fMRI) studies are well established in evaluating language dominance. Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging modality that offers a convenient and affordable technique to image language-related cortical areas. This study investigates the agreement between results from task-based fMRI and fNIRS in determining language lateralisation. Methods Language laterality indices LIs were calculated from both fMRI and fNIRS measurements of the same individual volunteers by using an identical paradigm. Statistical measures of percentage agreement and kappa value have been calculated for testing agreement and reliability. Results A correlation analysis of the LI values shows a good correlation with r = 0.677 at p < 0.05. Statistical comparison of both fMRI and fNIRS methods for language lateralisation yielded a percentage agreement of 90% and a moderate kappa value of κ = 0.621. Conclusion Our study suggests that fNIRS is in moderate accordance with fMRI in determining lateralisation of the frontal language areas. It implies that the optical imaging technique can provide additional information on functional lateralisation of frontal language areas.

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Section: Discussionmentioning

confidence: 99%

“…Though there are a few studies that have tried to understand the usefulness of fNIRS in language lateralisation, 20,21 a comparison on the agreement of results from fMRI and fNIRS in determining language dominance is lacking. We hypothesised that the results of both studies will have good agreement in assessment of language lateralisation.…”

Section: Introductionmentioning

confidence: 99%

Functional near-infrared spectroscopy is in moderate accordance with functional MRI in determining lateralisation of frontal language areas

et al. 2017

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“…Hemodynamic fluctuations were recorded while the subject listened to a story presented in their native language. The story was presented in 18 individual blocks of 20 seconds each (Paquette et al, 2010). Note that the description used in B is also used here to illustrate HbO and HbR concentrations (solid and dotted lines, respectively), the duration of the task (horizontal blue line) and the expected HRF model (black line).…”

Section: Task-related Hemodynamic Responsementioning

confidence: 99%

LIONirs: flexible Matlab toolbox for fNIRS data analysis

Tremblay

Martı́nez-Montes

Hüsser

et al. 2020

Preprint

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BackgroundFunctional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses.New methodWe introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System.ResultsWithin the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures.Comparison with existing methodsSeveral good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format.ConclusionsLIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples.HighlightsThe LIONirs toolbox is designed for fNIRS data inspection and visualization.Methods are integrated for isolation of relevant activity and correction of artifacts.Multimodal auxiliary, EEG or audio-video are visualized alongside the fNIRS data.Task-based and functional connectivity measure analysis tools are available.The code structure allows to automated and standardized analysis of large data set.Graphical abstract

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“…The cortical responses were assessed using the average of Hob concentrations in a 3-s sample, 15 from 17 s to 20 s after the stimulus appeared. In both, the orienting and control conditions the mean HbO concentrations were obtained for each participant.…”

Section: Wwwmedigraphicorgmxmentioning

confidence: 99%