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Laterality Index Calculations in a Control Study of Functional Near Infrared Spectroscopy
by
Borrell, Jordan A
, Zuniga, Jorge M
, Fraser, Kaitlin
, Manattu, Arun Karumattu
in
Dominance
/ Hemispheric laterality
/ Infrared spectroscopy
/ Neuroimaging
/ Paralysis
/ Sensorimotor system
/ Spectrum analysis
2023
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Laterality Index Calculations in a Control Study of Functional Near Infrared Spectroscopy
by
Borrell, Jordan A
, Zuniga, Jorge M
, Fraser, Kaitlin
, Manattu, Arun Karumattu
in
Dominance
/ Hemispheric laterality
/ Infrared spectroscopy
/ Neuroimaging
/ Paralysis
/ Sensorimotor system
/ Spectrum analysis
2023
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Do you wish to request the book?
Laterality Index Calculations in a Control Study of Functional Near Infrared Spectroscopy
by
Borrell, Jordan A
, Zuniga, Jorge M
, Fraser, Kaitlin
, Manattu, Arun Karumattu
in
Dominance
/ Hemispheric laterality
/ Infrared spectroscopy
/ Neuroimaging
/ Paralysis
/ Sensorimotor system
/ Spectrum analysis
2023
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Laterality Index Calculations in a Control Study of Functional Near Infrared Spectroscopy
Journal Article
Laterality Index Calculations in a Control Study of Functional Near Infrared Spectroscopy
2023
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Overview
Hemispheric dominance has been used to understand the influence of central and peripheral neural damage on the motor function of individuals with stroke, cerebral palsy, and limb loss. It has been well established that greater activation occurs in the contralateral hemisphere to the side of the body used to perform the task. However, there is currently a large variability in calculation procedures for brain laterality when using functional near-infrared spectroscopy (fNIRS) as a non-invasive neuroimaging tool. In this study, we used fNIRS to measure brain activity over the left and right sensorimotor cortices while participants (n = 20, healthy and uninjured) performed left and right-hand movement tasks. Then, we analyzed the fNIRS data using two different processing pipelines (block averaging or general linear model [GLM]), two different criteria of processing for negative values (include all beta values or include only positive beta values), and three different laterality index (LI) formulas. The LI values produced using the block averaging analysis indicated an expected contralateral dominance with some instances of bilateral dominance, which agreed with the expected contralateral activation. However, the inclusion criteria nor the LI formulas altered the outcome. The LI values produced using the GLM analysis displayed a robust left hemisphere dominance regardless of the hand performing the task, which disagreed with the expected contralateral activation but did provide instances of correctly identifying brain laterality. In conclusion, both analysis pipelines were able to correctly determine brain laterality, but processes to account for negative beta values were recommended especially when utilizing the GLM analysis to determine brain laterality.
Publisher
Springer Nature B.V
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