Paper Info
Title
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 (\(t(15); p<.001\), \(p<.01\), and \(p<.001\), respectively) during BS modification while performing the hand-tracing task.
Year
Venue
Keywords
2017
Brain Informatics
Body schema, Near-infrared spectroscopy, General linear model, Statistical parametric mapping, Hand-tracing task, Motion artifacts
Field
DocType
Volume
Akaike information criterion,Pattern recognition,Convolution,General linear model,Computer science,Statistical parametric mapping,Gaussian,Artificial intelligence,Kernel (image processing),Gaussian function,Tracing
Journal
4
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
9
1
Name
Order
Citations
PageRank
Satoshi Suzuki19915.23