Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts

Abstract

This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7–BA40–BA21 in the right hemisphere became significantly activated (, , and , respectively) during BS modification while performing the hand-tracing task.

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

“…FNIRS uses near-infrared light to measure local concentration changes of deoxygenated hemoglobin (HbR) and oxygenated hemoglobin (HbO) from cortical brain areas which are seen as correlates of functional brain activity (Villringer et al, 1993; Sassaroli and Fantini, 2004). In comparison to HbO, HbR signals are considered to be less influenced by systemic physiological artifacts like cardiac pulsation, respiration, or Mayer wave fluctuations than HbO (Obrig et al, 2000; Zhang et al, 2005, 2009; Huppert et al, 2009; Suzuki, 2017). Other studies additionally reported that HbR tends to correlate stronger with blood oxygenation level dependent (BOLD) response than HbO (MacIntosh et al, 2003; Huppert et al, 2006; Schroeter et al, 2006; Foy et al, 2016).…”

Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situations

“…The following fNIRS analysis was based on HbR signal, as HbR signals are considered to be less influenced by systemic physiological artifacts like cardiac pulsation, respiration, or Mayer wave fluctuations than HbO (Obrig et al, 2000 ; Zhang et al, 2005 , 2009 ; Huppert et al, 2009 ; Suzuki, 2017 ). Moreover, other studies reported that HbR tends to correlate stronger with the blood oxygenated level dependent (BOLD) response than HbO (MacIntosh et al, 2003 ; Huppert et al, 2006 ; Schroeter et al, 2006 ; Foy et al, 2016 ).…”

Driver's turning intent recognition model based on brain activation and contextual information

“…In this article, the fNIRS analyses were based only on HbR signals, as these are considered to be less susceptible to systemic physiological artifacts like cardiac pulsation, respiration, or Mayer wave fluctuations than HbO (Obrig et al, 2000 ; Zhang et al, 2005 , 2009 ; Huppert et al, 2009 ; Suzuki, 2017 ). Moreover, other studies reported that HbR tends to correlate stronger with the blood oxygenated level dependent (BOLD) response than HbO (MacIntosh et al, 2003 ; Huppert et al, 2006 ; Schroeter et al, 2006 ; Foy et al, 2016 ).…”