2018
DOI: 10.1007/s10548-018-0650-y
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Optical Mapping of Brain Activation and Connectivity in Occipitotemporal Cortex During Chinese Character Recognition

Abstract: In this study, functional near-infrared spectroscopy (fNIRS) was used to examine the brain activation and connectivity in occipitotemporal cortex during Chinese character recognition (CCR). Eighteen healthy participants were recruited to perform a well-designed task with three categories of stimuli (real characters, pseudo characters, and checkerboards). By inspecting the brain activation difference and its relationship with behavioral data, the left laterality during CCR was clearly identified in the Brodmann… Show more

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Cited by 15 publications
(24 citation statements)
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“…Although the brain activation and FC were, respectively, examined in a single study, [28][29][30][31] the relationship between these two perspectives has not been extensively explored. For example, Mennes et al 32 inspected the relationship between restingstate FC and brain activation by using the flanker task, in which they discovered that the resting-state FC within default mode networks showed a negative correlation with the task-evoked brain activation, whereas the task-based FC positively correlated with the brain activation.…”
Section: Introductionmentioning
confidence: 99%
“…Although the brain activation and FC were, respectively, examined in a single study, [28][29][30][31] the relationship between these two perspectives has not been extensively explored. For example, Mennes et al 32 inspected the relationship between restingstate FC and brain activation by using the flanker task, in which they discovered that the resting-state FC within default mode networks showed a negative correlation with the task-evoked brain activation, whereas the task-based FC positively correlated with the brain activation.…”
Section: Introductionmentioning
confidence: 99%
“…Data processing was performed using the MATLAB toolbox HomER2 ( 59 ). Firstly, optical density change was generated from the data on raw intensity ( 60 ), and the motion artifacts were then corrected using the Spline interpolation algorithm ( 61 ). Secondly, a band-pass filter of 0.01–0.2 Hz was used to further process the optical density changes ( 62 ).…”
Section: Methodsmentioning
confidence: 99%
“…The data were further band-pass filtered by a low cutoff filter frequency of 0.1 Hz and a high cut-off filter frequency of 0.01 Hz in order to minimize the physiological noise due to heart pulsation (1∼1.5 Hz), respiration (0.2∼0.5 Hz), and blood pressure (Mayer) waves (∼0.1 Hz) as well as produce the data with the best signal-to-noise ratio. Finally, HbO concentration changes were generated using filtered OD values after normalized to zero mean and unit variance (z-scores; Zhang et al, 2010;Ding et al, 2013Ding et al, , 2014Hu et al, 2018). The relative concentration changes of HbO were calculated according to the modified Beer-Lambert law (MBLL) (Lu et al, 2016) as follows: [HbO] [HbR] = 1 SD ε HbO (λ 1 )DPF(λ 1 ) ε HbR (λ 1 )DPF(λ 1 ) ε HbO (λ 2 )DPF(λ 2 ) ε HbR (λ 2 )DPF(λ 2 )…”
Section: Fnirs Recordings and Processing Fnirs Systemsmentioning
confidence: 99%